| TestfortrainingZip | | | | | 83.28 1 | 90.91 7 | 58.80 11 | 87.61 74 | 91.34 11 | 56.28 332 | 88.36 1 | 95.55 1 | 65.41 5 | 96.39 4 | | 88.20 15 | 94.63 3 |
|
| DeepPCF-MVS | | 69.37 1 | 80.65 14 | 81.56 11 | 77.94 111 | 85.46 74 | 49.56 260 | 90.99 21 | 86.66 102 | 70.58 40 | 80.07 34 | 95.30 2 | 56.18 30 | 90.97 105 | 82.57 37 | 86.22 37 | 93.28 15 |
|
| fmvsm_l_mol_unc0.5_1 | | | 78.65 29 | 79.09 25 | 77.33 128 | 78.55 280 | 53.79 131 | 88.87 51 | 71.62 426 | 74.12 8 | 81.93 16 | 95.02 3 | 57.79 22 | 86.96 282 | 80.83 52 | 83.10 65 | 91.23 92 |
|
| fmvsm_s_conf0.5_n_9 | | | 76.66 70 | 76.94 56 | 75.85 184 | 79.54 248 | 48.30 306 | 82.63 273 | 71.84 419 | 70.25 44 | 80.63 31 | 94.53 4 | 50.78 71 | 87.42 266 | 88.32 5 | 73.92 197 | 91.82 62 |
|
| DPM-MVS | | | 82.39 4 | 82.36 7 | 82.49 6 | 80.12 234 | 59.50 5 | 92.24 8 | 90.72 19 | 69.37 60 | 83.22 9 | 94.47 5 | 63.81 6 | 93.18 39 | 74.02 117 | 93.25 2 | 94.80 1 |
|
| fmvsm_l_conf0.5_n_9 | | | 77.10 56 | 77.48 45 | 75.98 181 | 77.54 303 | 47.77 331 | 86.35 119 | 73.46 410 | 68.69 67 | 81.07 26 | 94.40 6 | 49.06 88 | 88.89 192 | 87.39 8 | 79.32 108 | 91.27 91 |
|
| fmvsm_s_conf0.5_n_8 | | | 76.50 74 | 76.68 64 | 75.94 182 | 78.67 274 | 47.92 324 | 85.18 173 | 74.71 389 | 68.09 74 | 80.67 30 | 94.26 7 | 47.09 113 | 89.26 171 | 86.62 10 | 74.85 187 | 90.65 119 |
|
| fmvsm_s_conf0.5_n_10 | | | 76.80 65 | 76.81 59 | 76.78 155 | 78.91 269 | 47.85 326 | 83.44 243 | 74.66 390 | 68.93 66 | 81.31 24 | 94.12 8 | 47.44 108 | 90.82 108 | 83.43 29 | 79.06 113 | 91.66 67 |
|
| SED-MVS | | | 81.92 8 | 81.75 9 | 82.44 8 | 89.48 18 | 56.89 32 | 92.48 3 | 88.94 38 | 57.50 300 | 84.61 5 | 94.09 9 | 58.81 15 | 96.37 7 | 82.28 38 | 87.60 19 | 94.06 4 |
|
| test_241102_TWO | | | | | | | | | 88.76 47 | 57.50 300 | 83.60 7 | 94.09 9 | 56.14 31 | 96.37 7 | 82.28 38 | 87.43 21 | 92.55 34 |
|
| OPU-MVS | | | | | 81.71 15 | 92.05 3 | 55.97 53 | 92.48 3 | | | | 94.01 11 | 67.21 2 | 95.10 16 | 89.82 3 | 92.55 3 | 94.06 4 |
|
| test0726 | | | | | | 89.40 21 | 57.45 22 | 92.32 7 | 88.63 51 | 57.71 294 | 83.14 10 | 93.96 12 | 55.17 35 | | | | |
|
| fmvsm_s_conf0.5_n_11 | | | 76.28 79 | 76.81 59 | 74.71 231 | 79.21 258 | 46.90 346 | 85.03 183 | 73.96 399 | 69.00 65 | 79.70 38 | 93.88 13 | 48.07 94 | 87.71 252 | 84.26 22 | 78.15 124 | 89.50 167 |
|
| CNVR-MVS | | | 81.76 9 | 81.90 8 | 81.33 21 | 90.04 11 | 57.70 17 | 91.71 11 | 88.87 42 | 70.31 42 | 77.64 52 | 93.87 14 | 52.58 54 | 93.91 30 | 84.17 23 | 87.92 17 | 92.39 37 |
|
| MM | | | 82.69 2 | 83.29 3 | 80.89 25 | 84.38 95 | 55.40 64 | 92.16 10 | 89.85 26 | 75.28 4 | 82.41 12 | 93.86 15 | 54.30 41 | 93.98 27 | 90.29 1 | 87.13 22 | 93.30 14 |
|
| fmvsm_l_conf0.5_n_3 | | | 75.73 103 | 75.78 78 | 75.61 192 | 76.03 337 | 48.33 304 | 85.34 163 | 72.92 413 | 67.16 92 | 78.55 46 | 93.85 16 | 46.22 130 | 87.53 262 | 85.61 14 | 76.30 155 | 90.98 108 |
|
| MGCNet | | | 82.10 7 | 82.64 4 | 80.47 30 | 86.63 54 | 54.69 108 | 92.20 9 | 86.66 102 | 74.48 5 | 82.63 11 | 93.80 17 | 50.83 70 | 93.70 34 | 90.11 2 | 86.44 34 | 93.01 23 |
|
| fmvsm_s_conf0.5_n_3 | | | 74.97 118 | 75.42 89 | 73.62 269 | 76.99 317 | 46.67 351 | 83.13 258 | 71.14 429 | 66.20 114 | 82.13 14 | 93.76 18 | 47.49 106 | 84.00 358 | 81.95 41 | 76.02 159 | 90.19 140 |
|
| PC_three_1452 | | | | | | | | | | 66.58 104 | 87.27 3 | 93.70 19 | 66.82 4 | 94.95 18 | 89.74 4 | 91.98 4 | 93.98 6 |
|
| DPE-MVS |  | | 79.82 21 | 79.66 19 | 80.29 34 | 89.27 25 | 55.08 80 | 88.70 53 | 87.92 72 | 55.55 342 | 81.21 25 | 93.69 20 | 56.51 28 | 94.27 26 | 78.36 72 | 85.70 43 | 91.51 77 |
| Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025 |
| DVP-MVS |  | | 81.30 10 | 81.00 13 | 82.20 9 | 89.40 21 | 57.45 22 | 92.34 5 | 89.99 24 | 57.71 294 | 81.91 17 | 93.64 21 | 55.17 35 | 96.44 2 | 81.68 42 | 87.13 22 | 92.72 31 |
| Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025 |
| test_0728_THIRD | | | | | | | | | | 58.00 286 | 81.91 17 | 93.64 21 | 56.54 27 | 96.44 2 | 81.64 44 | 86.86 27 | 92.23 42 |
|
| fmvsm_l_conf0.5_n_a | | | 75.88 93 | 76.07 74 | 75.31 209 | 76.08 334 | 48.34 302 | 85.24 169 | 70.62 433 | 63.13 181 | 81.45 23 | 93.62 23 | 49.98 79 | 87.40 268 | 87.76 7 | 76.77 145 | 90.20 138 |
|
| fmvsm_l_conf0.5_n | | | 75.95 90 | 76.16 72 | 75.31 209 | 76.01 339 | 48.44 299 | 84.98 186 | 71.08 430 | 63.50 173 | 81.70 22 | 93.52 24 | 50.00 77 | 87.18 275 | 87.80 6 | 76.87 143 | 90.32 133 |
|
| fmvsm_s_conf0.5_n | | | 74.48 125 | 74.12 118 | 75.56 195 | 76.96 318 | 47.85 326 | 85.32 167 | 69.80 440 | 64.16 153 | 78.74 43 | 93.48 25 | 45.51 157 | 89.29 170 | 86.48 11 | 66.62 280 | 89.55 162 |
|
| test_fmvsm_n_1920 | | | 75.56 105 | 75.54 85 | 75.61 192 | 74.60 364 | 49.51 265 | 81.82 298 | 74.08 396 | 66.52 107 | 80.40 32 | 93.46 26 | 46.95 114 | 89.72 150 | 86.69 9 | 75.30 175 | 87.61 227 |
|
| fmvsm_s_conf0.5_n_2 | | | 72.02 184 | 71.72 167 | 72.92 285 | 76.79 321 | 45.90 371 | 84.48 207 | 66.11 453 | 64.26 149 | 76.12 60 | 93.40 27 | 36.26 304 | 86.04 321 | 81.47 46 | 66.54 283 | 86.82 252 |
|
| DVP-MVS++ | | | 82.44 3 | 82.38 6 | 82.62 5 | 91.77 4 | 57.49 20 | 84.98 186 | 88.88 40 | 58.00 286 | 83.60 7 | 93.39 28 | 67.21 2 | 96.39 4 | 81.64 44 | 91.98 4 | 93.98 6 |
|
| test_one_0601 | | | | | | 89.39 23 | 57.29 25 | | 88.09 69 | 57.21 308 | 82.06 15 | 93.39 28 | 54.94 40 | | | | |
|
| fmvsm_s_conf0.5_n_a | | | 73.68 148 | 73.15 135 | 75.29 212 | 75.45 348 | 48.05 316 | 83.88 229 | 68.84 445 | 63.43 175 | 78.60 44 | 93.37 30 | 45.32 160 | 88.92 191 | 85.39 15 | 64.04 307 | 88.89 186 |
|
| PHI-MVS | | | 77.49 50 | 77.00 54 | 78.95 62 | 85.33 77 | 50.69 225 | 88.57 56 | 88.59 56 | 58.14 283 | 73.60 78 | 93.31 31 | 43.14 202 | 93.79 31 | 73.81 121 | 88.53 13 | 92.37 38 |
|
| fmvsm_s_conf0.1_n | | | 73.80 143 | 73.26 134 | 75.43 202 | 73.28 380 | 47.80 329 | 84.57 206 | 69.43 442 | 63.34 176 | 78.40 47 | 93.29 32 | 44.73 176 | 89.22 174 | 85.99 12 | 66.28 289 | 89.26 174 |
|
| test_241102_ONE | | | | | | 89.48 18 | 56.89 32 | | 88.94 38 | 57.53 298 | 84.61 5 | 93.29 32 | 58.81 15 | 96.45 1 | | | |
|
| PS-MVSNAJ | | | 80.06 18 | 79.52 20 | 81.68 16 | 85.58 71 | 60.97 3 | 91.69 12 | 87.02 92 | 70.62 38 | 80.75 28 | 93.22 34 | 37.77 267 | 92.50 55 | 82.75 34 | 86.25 36 | 91.57 72 |
|
| SMA-MVS |  | | 79.10 27 | 78.76 29 | 80.12 41 | 84.42 93 | 55.87 54 | 87.58 82 | 86.76 99 | 61.48 217 | 80.26 33 | 93.10 35 | 46.53 125 | 92.41 57 | 79.97 59 | 88.77 11 | 92.08 47 |
| Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology |
| CANet | | | 80.90 11 | 81.17 12 | 80.09 43 | 87.62 45 | 54.21 123 | 91.60 14 | 86.47 107 | 73.13 10 | 79.89 35 | 93.10 35 | 49.88 81 | 92.98 40 | 84.09 25 | 84.75 56 | 93.08 21 |
|
| MCST-MVS | | | 83.01 1 | 83.30 2 | 82.15 11 | 92.84 2 | 57.58 19 | 93.77 1 | 91.10 14 | 75.95 3 | 77.10 53 | 93.09 37 | 54.15 44 | 95.57 13 | 85.80 13 | 85.87 41 | 93.31 13 |
|
| fmvsm_s_conf0.1_n_a | | | 72.82 163 | 72.05 163 | 75.12 218 | 70.95 411 | 47.97 319 | 82.72 270 | 68.43 447 | 62.52 197 | 78.17 48 | 93.08 38 | 44.21 182 | 88.86 193 | 84.82 17 | 63.54 314 | 88.54 202 |
|
| xiu_mvs_v2_base | | | 79.86 20 | 79.31 22 | 81.53 18 | 85.03 83 | 60.73 4 | 91.65 13 | 86.86 95 | 70.30 43 | 80.77 27 | 93.07 39 | 37.63 273 | 92.28 62 | 82.73 35 | 85.71 42 | 91.57 72 |
|
| fmvsm_s_conf0.1_n_2 | | | 71.45 198 | 71.01 182 | 72.78 291 | 75.37 351 | 45.82 375 | 84.18 217 | 64.59 461 | 64.02 155 | 75.67 61 | 93.02 40 | 34.99 328 | 85.99 324 | 81.18 50 | 66.04 292 | 86.52 259 |
|
| HPM-MVS++ |  | | 80.50 15 | 80.71 14 | 79.88 46 | 87.34 48 | 55.20 75 | 89.93 29 | 87.55 82 | 66.04 122 | 79.46 39 | 93.00 41 | 53.10 51 | 91.76 73 | 80.40 54 | 89.56 9 | 92.68 33 |
|
| MED-MVS | | | 79.56 23 | 79.39 21 | 80.06 44 | 84.34 96 | 54.93 88 | 87.61 74 | 87.22 86 | 56.22 333 | 81.85 19 | 92.98 42 | 58.11 21 | 93.75 32 | 80.19 55 | 85.96 38 | 91.52 75 |
|
| TestfortrainingZip a | | | 77.64 48 | 76.79 61 | 80.20 36 | 84.34 96 | 54.79 101 | 87.61 74 | 87.03 91 | 56.22 333 | 78.78 42 | 92.98 42 | 50.45 73 | 94.28 24 | 74.37 111 | 79.31 109 | 91.52 75 |
|
| fmvsm_s_conf0.5_n_6 | | | 76.17 83 | 76.84 58 | 74.15 249 | 77.42 306 | 46.46 357 | 85.53 159 | 77.86 347 | 69.78 54 | 79.78 37 | 92.90 44 | 46.80 118 | 84.81 349 | 84.67 19 | 76.86 144 | 91.17 97 |
|
| aaatest | | | | | 80.14 40 | 84.34 96 | 54.93 88 | 87.61 74 | 87.22 86 | 57.43 302 | 81.85 19 | 92.88 45 | | 93.75 32 | 80.19 55 | 85.13 51 | 91.76 64 |
|
| aaEdge-Enhanced | | | 79.48 24 | 79.20 24 | 80.35 33 | 88.96 27 | 54.93 88 | 88.65 54 | 88.50 59 | 56.62 322 | 79.87 36 | 92.88 45 | 51.96 58 | 94.36 23 | 80.19 55 | 85.13 51 | 91.76 64 |
|
| test_fmvsmconf_n | | | 74.41 128 | 74.05 120 | 75.49 201 | 74.16 372 | 48.38 300 | 82.66 271 | 72.57 414 | 67.05 98 | 75.11 64 | 92.88 45 | 46.35 129 | 87.81 242 | 83.93 26 | 71.71 227 | 90.28 134 |
|
| MSP-MVS | | | 82.30 6 | 83.47 1 | 78.80 68 | 82.99 135 | 52.71 169 | 85.04 182 | 88.63 51 | 66.08 119 | 86.77 4 | 92.75 48 | 72.05 1 | 91.46 81 | 83.35 30 | 93.53 1 | 92.23 42 |
| Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025 |
| NCCC | | | 79.57 22 | 79.23 23 | 80.59 27 | 89.50 16 | 56.99 29 | 91.38 16 | 88.17 67 | 67.71 84 | 73.81 77 | 92.75 48 | 46.88 115 | 93.28 36 | 78.79 68 | 84.07 61 | 91.50 78 |
|
| DeepC-MVS_fast | | 67.50 3 | 78.00 42 | 77.63 41 | 79.13 58 | 88.52 29 | 55.12 77 | 89.95 28 | 85.98 118 | 68.31 69 | 71.33 122 | 92.75 48 | 45.52 156 | 90.37 126 | 71.15 148 | 85.14 50 | 91.91 56 |
| Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
| 9.14 | | | | 78.19 33 | | 85.67 68 | | 88.32 58 | 88.84 44 | 59.89 244 | 74.58 70 | 92.62 51 | 46.80 118 | 92.66 49 | 81.40 49 | 85.62 44 | |
|
| fmvsm_s_conf0.5_n_4 | | | 74.92 119 | 74.88 103 | 75.03 221 | 75.96 340 | 47.53 334 | 85.84 137 | 73.19 412 | 67.07 96 | 79.43 40 | 92.60 52 | 46.12 132 | 88.03 233 | 84.70 18 | 69.01 258 | 89.53 164 |
|
| test_fmvsmconf0.1_n | | | 73.69 147 | 73.15 135 | 75.34 207 | 70.71 413 | 48.26 307 | 82.15 287 | 71.83 420 | 66.75 103 | 74.47 72 | 92.59 53 | 44.89 170 | 87.78 249 | 83.59 28 | 71.35 234 | 89.97 150 |
|
| APDe-MVS |  | | 78.44 32 | 78.20 32 | 79.19 54 | 88.56 28 | 54.55 114 | 89.76 33 | 87.77 76 | 55.91 337 | 78.56 45 | 92.49 54 | 48.20 93 | 92.65 50 | 79.49 60 | 83.04 67 | 90.39 129 |
| Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition |
| fmvsm_s_conf0.5_n_5 | | | 75.02 116 | 75.07 97 | 74.88 226 | 74.33 369 | 47.83 328 | 83.99 224 | 73.54 405 | 67.10 94 | 76.32 59 | 92.43 55 | 45.42 159 | 86.35 309 | 82.98 32 | 79.50 107 | 90.47 128 |
|
| SF-MVS | | | 77.64 48 | 77.42 46 | 78.32 101 | 83.75 112 | 52.47 174 | 86.63 115 | 87.80 73 | 58.78 274 | 74.63 68 | 92.38 56 | 47.75 102 | 91.35 84 | 78.18 75 | 86.85 28 | 91.15 98 |
|
| MAR-MVS | | | 76.76 67 | 75.60 83 | 80.21 35 | 90.87 8 | 54.68 109 | 89.14 46 | 89.11 35 | 62.95 184 | 70.54 144 | 92.33 57 | 41.05 227 | 94.95 18 | 57.90 278 | 86.55 33 | 91.00 107 |
| Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020 |
| MSLP-MVS++ | | | 74.21 133 | 72.25 156 | 80.11 42 | 81.45 191 | 56.47 42 | 86.32 120 | 79.65 301 | 58.19 282 | 66.36 185 | 92.29 58 | 36.11 309 | 90.66 115 | 67.39 178 | 82.49 71 | 93.18 19 |
|
| DELS-MVS | | | 82.32 5 | 82.50 5 | 81.79 14 | 86.80 52 | 56.89 32 | 92.77 2 | 86.30 111 | 77.83 1 | 77.88 49 | 92.13 59 | 60.24 9 | 94.78 20 | 78.97 65 | 89.61 8 | 93.69 9 |
| Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023 |
| 1112_ss | | | 70.05 229 | 69.37 215 | 72.10 314 | 80.77 212 | 42.78 412 | 85.12 179 | 76.75 367 | 59.69 249 | 61.19 270 | 92.12 60 | 47.48 107 | 83.84 360 | 53.04 324 | 68.21 267 | 89.66 158 |
|
| ab-mvs-re | | | 7.68 485 | 10.24 486 | 0.00 543 | 0.00 566 | 0.00 569 | 0.00 555 | 0.00 567 | 0.00 559 | 0.00 563 | 92.12 60 | 0.00 563 | 0.00 561 | 0.00 562 | 0.00 560 | 0.00 559 |
|
| sasdasda | | | 78.17 39 | 77.86 38 | 79.12 59 | 84.30 99 | 54.22 121 | 87.71 70 | 84.57 185 | 67.70 85 | 77.70 50 | 92.11 62 | 50.90 66 | 89.95 141 | 78.18 75 | 77.54 131 | 93.20 17 |
|
| canonicalmvs | | | 78.17 39 | 77.86 38 | 79.12 59 | 84.30 99 | 54.22 121 | 87.71 70 | 84.57 185 | 67.70 85 | 77.70 50 | 92.11 62 | 50.90 66 | 89.95 141 | 78.18 75 | 77.54 131 | 93.20 17 |
|
| cdsmvs_eth3d_5k | | | 18.33 477 | 24.44 469 | 0.00 543 | 0.00 566 | 0.00 569 | 0.00 555 | 89.40 30 | 0.00 559 | 0.00 563 | 92.02 64 | 38.55 259 | 0.00 561 | 0.00 562 | 0.00 560 | 0.00 559 |
|
| lupinMVS | | | 78.38 34 | 78.11 34 | 79.19 54 | 83.02 133 | 55.24 69 | 91.57 15 | 84.82 170 | 69.12 63 | 76.67 56 | 92.02 64 | 44.82 173 | 90.23 133 | 80.83 52 | 80.09 96 | 92.08 47 |
|
| test_fmvsmvis_n_1920 | | | 71.29 200 | 70.38 196 | 74.00 254 | 71.04 410 | 48.79 286 | 79.19 358 | 64.62 459 | 62.75 191 | 66.73 177 | 91.99 66 | 40.94 229 | 88.35 218 | 83.00 31 | 73.18 207 | 84.85 293 |
|
| alignmvs | | | 78.08 41 | 77.98 35 | 78.39 98 | 83.53 115 | 53.22 150 | 89.77 32 | 85.45 134 | 66.11 117 | 76.59 58 | 91.99 66 | 54.07 45 | 89.05 180 | 77.34 82 | 77.00 139 | 92.89 25 |
|
| SPE-MVS-test | | | 77.20 54 | 77.25 48 | 77.05 139 | 84.60 90 | 49.04 277 | 89.42 38 | 85.83 122 | 65.90 123 | 72.85 92 | 91.98 68 | 45.10 163 | 91.27 87 | 75.02 104 | 84.56 57 | 90.84 113 |
|
| PRO-TEST | | | 79.94 19 | 79.98 16 | 79.81 47 | 87.63 44 | 55.24 69 | 87.59 79 | 88.40 62 | 71.10 31 | 76.93 55 | 91.92 69 | 46.57 124 | 91.41 82 | 84.32 21 | 85.41 47 | 92.79 29 |
|
| MGCFI-Net | | | 74.07 136 | 74.64 112 | 72.34 309 | 82.90 139 | 43.33 406 | 80.04 343 | 79.96 289 | 65.61 125 | 74.93 65 | 91.85 70 | 48.01 98 | 80.86 388 | 71.41 146 | 77.10 136 | 92.84 26 |
|
| SD-MVS | | | 76.18 82 | 74.85 104 | 80.18 37 | 85.39 75 | 56.90 31 | 85.75 143 | 82.45 234 | 56.79 318 | 74.48 71 | 91.81 71 | 43.72 190 | 90.75 111 | 74.61 106 | 78.65 117 | 92.91 24 |
| Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024 |
| MP-MVS-pluss | | | 75.54 106 | 75.03 99 | 77.04 140 | 81.37 193 | 52.65 171 | 84.34 212 | 84.46 187 | 61.16 221 | 69.14 158 | 91.76 72 | 39.98 246 | 88.99 185 | 78.19 73 | 84.89 55 | 89.48 169 |
| MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
| fmvsm_s_conf0.5_n_7 | | | 73.10 157 | 73.89 126 | 70.72 345 | 74.17 371 | 46.03 370 | 83.28 252 | 74.19 394 | 67.10 94 | 73.94 76 | 91.73 73 | 43.42 197 | 77.61 428 | 83.92 27 | 73.26 206 | 88.53 203 |
|
| EPNet | | | 78.36 35 | 78.49 30 | 77.97 108 | 85.49 73 | 52.04 185 | 89.36 41 | 84.07 199 | 73.22 9 | 77.03 54 | 91.72 74 | 49.32 87 | 90.17 135 | 73.46 127 | 82.77 68 | 91.69 66 |
| Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
| WTY-MVS | | | 77.47 51 | 77.52 44 | 77.30 131 | 88.33 32 | 46.25 365 | 88.46 57 | 90.32 22 | 71.40 27 | 72.32 101 | 91.72 74 | 53.44 48 | 92.37 59 | 66.28 187 | 75.42 174 | 93.28 15 |
|
| test_fmvsmconf0.01_n | | | 71.97 186 | 70.95 184 | 75.04 220 | 66.21 449 | 47.87 325 | 80.35 337 | 70.08 437 | 65.85 124 | 72.69 94 | 91.68 76 | 39.99 245 | 87.67 254 | 82.03 40 | 69.66 253 | 89.58 161 |
|
| APD-MVS |  | | 76.15 84 | 75.68 79 | 77.54 122 | 88.52 29 | 53.44 141 | 87.26 92 | 85.03 161 | 53.79 364 | 74.91 66 | 91.68 76 | 43.80 186 | 90.31 129 | 74.36 112 | 81.82 77 | 88.87 187 |
| Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
| CS-MVS | | | 76.77 66 | 76.70 63 | 76.99 144 | 83.55 114 | 48.75 287 | 88.60 55 | 85.18 148 | 66.38 110 | 72.47 99 | 91.62 78 | 45.53 155 | 90.99 104 | 74.48 109 | 82.51 70 | 91.23 92 |
|
| TSAR-MVS + GP. | | | 77.82 44 | 77.59 42 | 78.49 91 | 85.25 79 | 50.27 245 | 90.02 26 | 90.57 20 | 56.58 325 | 74.26 73 | 91.60 79 | 54.26 42 | 92.16 65 | 75.87 94 | 79.91 100 | 93.05 22 |
|
| SteuartSystems-ACMMP | | | 77.08 58 | 76.33 68 | 79.34 51 | 80.98 202 | 55.31 67 | 89.76 33 | 86.91 94 | 62.94 185 | 71.65 112 | 91.56 80 | 42.33 210 | 92.56 54 | 77.14 85 | 83.69 63 | 90.15 141 |
| Skip Steuart: Steuart Systems R&D Blog. |
| ACMMP_NAP | | | 76.43 75 | 75.66 82 | 78.73 70 | 81.92 167 | 54.67 110 | 84.06 222 | 85.35 138 | 61.10 224 | 72.99 89 | 91.50 81 | 40.25 239 | 91.00 100 | 76.84 87 | 86.98 26 | 90.51 127 |
|
| MVS | | | 76.91 60 | 75.48 86 | 81.23 22 | 84.56 91 | 55.21 72 | 80.23 340 | 91.64 5 | 58.65 276 | 65.37 199 | 91.48 82 | 45.72 150 | 95.05 17 | 72.11 144 | 89.52 10 | 93.44 11 |
|
| test_prior2 | | | | | | | | 89.04 48 | | 61.88 209 | 73.55 79 | 91.46 83 | 48.01 98 | | 74.73 105 | 85.46 45 | |
|
| patch_mono-2 | | | 80.84 12 | 81.59 10 | 78.62 80 | 90.34 10 | 53.77 132 | 88.08 62 | 88.36 63 | 76.17 2 | 79.40 41 | 91.09 84 | 55.43 33 | 90.09 137 | 85.01 16 | 80.40 92 | 91.99 55 |
|
| NormalMVS | | | 77.09 57 | 77.02 53 | 77.32 130 | 81.66 178 | 52.32 178 | 89.31 42 | 82.11 238 | 72.20 17 | 73.23 85 | 91.05 85 | 46.52 126 | 91.00 100 | 76.23 89 | 80.83 85 | 88.64 194 |
|
| SymmetryMVS | | | 77.43 52 | 77.09 52 | 78.44 96 | 82.56 151 | 52.32 178 | 89.31 42 | 84.15 197 | 72.20 17 | 73.23 85 | 91.05 85 | 46.52 126 | 91.00 100 | 76.23 89 | 78.55 119 | 92.00 54 |
|
| ZD-MVS | | | | | | 89.55 15 | 53.46 138 | | 84.38 188 | 57.02 310 | 73.97 75 | 91.03 87 | 44.57 178 | 91.17 92 | 75.41 101 | 81.78 79 | |
|
| test_8 | | | | | | 85.72 65 | 55.31 67 | 87.60 78 | 83.88 203 | 57.84 291 | 72.84 93 | 90.99 88 | 44.99 166 | 88.34 219 | | | |
|
| TEST9 | | | | | | 85.68 66 | 55.42 61 | 87.59 79 | 84.00 200 | 57.72 293 | 72.99 89 | 90.98 89 | 44.87 171 | 88.58 204 | | | |
|
| train_agg | | | 76.91 60 | 76.40 67 | 78.45 95 | 85.68 66 | 55.42 61 | 87.59 79 | 84.00 200 | 57.84 291 | 72.99 89 | 90.98 89 | 44.99 166 | 88.58 204 | 78.19 73 | 85.32 48 | 91.34 86 |
|
| reproduce-ours | | | 71.77 193 | 70.43 193 | 75.78 186 | 81.96 165 | 49.54 263 | 82.54 278 | 81.01 265 | 48.77 404 | 69.21 156 | 90.96 91 | 37.13 288 | 89.40 166 | 66.28 187 | 76.01 160 | 88.39 208 |
|
| our_new_method | | | 71.77 193 | 70.43 193 | 75.78 186 | 81.96 165 | 49.54 263 | 82.54 278 | 81.01 265 | 48.77 404 | 69.21 156 | 90.96 91 | 37.13 288 | 89.40 166 | 66.28 187 | 76.01 160 | 88.39 208 |
|
| MTAPA | | | 72.73 166 | 71.22 176 | 77.27 133 | 81.54 187 | 53.57 136 | 67.06 446 | 81.31 258 | 59.41 255 | 68.39 165 | 90.96 91 | 36.07 311 | 89.01 182 | 73.80 122 | 82.45 72 | 89.23 176 |
|
| lecture | | | 74.14 135 | 73.05 140 | 77.44 126 | 81.66 178 | 50.39 236 | 87.43 83 | 84.22 196 | 51.38 385 | 72.10 104 | 90.95 94 | 38.31 262 | 93.23 38 | 70.51 151 | 80.83 85 | 88.69 192 |
|
| MVSFormer | | | 73.53 150 | 72.19 158 | 77.57 120 | 83.02 133 | 55.24 69 | 81.63 306 | 81.44 256 | 50.28 392 | 76.67 56 | 90.91 95 | 44.82 173 | 86.11 314 | 60.83 241 | 80.09 96 | 91.36 83 |
|
| jason | | | 77.01 59 | 76.45 66 | 78.69 72 | 79.69 244 | 54.74 103 | 90.56 24 | 83.99 202 | 68.26 70 | 74.10 74 | 90.91 95 | 42.14 214 | 89.99 139 | 79.30 62 | 79.12 110 | 91.36 83 |
| jason: jason. |
| CDPH-MVS | | | 76.05 87 | 75.19 93 | 78.62 80 | 86.51 55 | 54.98 85 | 87.32 87 | 84.59 184 | 58.62 277 | 70.75 138 | 90.85 97 | 43.10 204 | 90.63 118 | 70.50 152 | 84.51 59 | 90.24 135 |
|
| LFMVS | | | 78.52 30 | 77.14 51 | 82.67 4 | 89.58 14 | 58.90 10 | 91.27 19 | 88.05 70 | 63.22 179 | 74.63 68 | 90.83 98 | 41.38 226 | 94.40 22 | 75.42 100 | 79.90 101 | 94.72 2 |
|
| reproduce_model | | | 71.07 206 | 69.67 211 | 75.28 214 | 81.51 190 | 48.82 285 | 81.73 302 | 80.57 275 | 47.81 410 | 68.26 166 | 90.78 99 | 36.49 302 | 88.60 203 | 65.12 203 | 74.76 188 | 88.42 207 |
|
| PAPR | | | 75.20 113 | 74.13 117 | 78.41 97 | 88.31 34 | 55.10 79 | 84.31 213 | 85.66 126 | 63.76 165 | 67.55 173 | 90.73 100 | 43.48 195 | 89.40 166 | 66.36 186 | 77.03 138 | 90.73 117 |
|
| HFP-MVS | | | 74.37 129 | 73.13 139 | 78.10 106 | 84.30 99 | 53.68 134 | 85.58 154 | 84.36 189 | 56.82 316 | 65.78 193 | 90.56 101 | 40.70 236 | 90.90 106 | 69.18 165 | 80.88 83 | 89.71 156 |
|
| ZNCC-MVS | | | 75.82 97 | 75.02 100 | 78.23 102 | 83.88 110 | 53.80 130 | 86.91 104 | 86.05 117 | 59.71 248 | 67.85 172 | 90.55 102 | 42.23 212 | 91.02 98 | 72.66 135 | 85.29 49 | 89.87 154 |
|
| EIA-MVS | | | 75.92 91 | 75.18 94 | 78.13 105 | 85.14 80 | 51.60 203 | 87.17 94 | 85.32 140 | 64.69 143 | 68.56 164 | 90.53 103 | 45.79 149 | 91.58 78 | 67.21 180 | 82.18 74 | 91.20 95 |
|
| ETV-MVS | | | 77.17 55 | 76.74 62 | 78.48 92 | 81.80 170 | 54.55 114 | 86.13 126 | 85.33 139 | 68.20 72 | 73.10 87 | 90.52 104 | 45.23 162 | 90.66 115 | 79.37 61 | 80.95 82 | 90.22 136 |
|
| SR-MVS | | | 70.92 211 | 69.73 210 | 74.50 235 | 83.38 121 | 50.48 233 | 84.27 214 | 79.35 311 | 48.96 402 | 66.57 183 | 90.45 105 | 33.65 345 | 87.11 277 | 66.42 184 | 74.56 190 | 85.91 272 |
|
| region2R | | | 73.75 145 | 72.55 147 | 77.33 128 | 83.90 109 | 52.98 160 | 85.54 158 | 84.09 198 | 56.83 315 | 65.10 203 | 90.45 105 | 37.34 282 | 90.24 132 | 68.89 167 | 80.83 85 | 88.77 191 |
|
| ACMMPR | | | 73.76 144 | 72.61 145 | 77.24 136 | 83.92 108 | 52.96 161 | 85.58 154 | 84.29 190 | 56.82 316 | 65.12 202 | 90.45 105 | 37.24 285 | 90.18 134 | 69.18 165 | 80.84 84 | 88.58 198 |
|
| CP-MVS | | | 72.59 170 | 71.46 171 | 76.00 180 | 82.93 138 | 52.32 178 | 86.93 103 | 82.48 233 | 55.15 349 | 63.65 239 | 90.44 108 | 35.03 327 | 88.53 210 | 68.69 170 | 77.83 129 | 87.15 239 |
|
| GDP-MVS | | | 75.27 109 | 74.38 114 | 77.95 110 | 79.04 264 | 52.86 165 | 85.22 170 | 86.19 114 | 62.43 200 | 70.66 141 | 90.40 109 | 53.51 47 | 91.60 77 | 69.25 163 | 72.68 215 | 89.39 171 |
|
| PMMVS | | | 72.98 159 | 72.05 163 | 75.78 186 | 83.57 113 | 48.60 291 | 84.08 220 | 82.85 228 | 61.62 213 | 68.24 167 | 90.33 110 | 28.35 389 | 87.78 249 | 72.71 133 | 76.69 148 | 90.95 110 |
|
| BP-MVS1 | | | 76.09 85 | 75.55 84 | 77.71 117 | 79.49 249 | 52.27 182 | 84.70 198 | 90.49 21 | 64.44 145 | 69.86 152 | 90.31 111 | 55.05 38 | 91.35 84 | 70.07 156 | 75.58 173 | 89.53 164 |
|
| dcpmvs_2 | | | 79.33 25 | 78.94 26 | 80.49 28 | 89.75 13 | 56.54 40 | 84.83 194 | 83.68 207 | 67.85 81 | 69.36 155 | 90.24 112 | 60.20 10 | 92.10 68 | 84.14 24 | 80.40 92 | 92.82 27 |
|
| MP-MVS |  | | 74.99 117 | 74.33 115 | 76.95 146 | 82.89 140 | 53.05 158 | 85.63 153 | 83.50 213 | 57.86 290 | 67.25 175 | 90.24 112 | 43.38 198 | 88.85 196 | 76.03 91 | 82.23 73 | 88.96 184 |
| Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
| ET-MVSNet_ETH3D | | | 75.23 112 | 74.08 119 | 78.67 74 | 84.52 92 | 55.59 56 | 88.92 49 | 89.21 34 | 68.06 78 | 53.13 384 | 90.22 114 | 49.71 82 | 87.62 258 | 72.12 143 | 70.82 239 | 92.82 27 |
|
| xiu_mvs_v1_base_debu | | | 71.60 195 | 70.29 199 | 75.55 196 | 77.26 311 | 53.15 152 | 85.34 163 | 79.37 307 | 55.83 338 | 72.54 95 | 90.19 115 | 22.38 436 | 86.66 297 | 73.28 129 | 76.39 150 | 86.85 248 |
|
| xiu_mvs_v1_base | | | 71.60 195 | 70.29 199 | 75.55 196 | 77.26 311 | 53.15 152 | 85.34 163 | 79.37 307 | 55.83 338 | 72.54 95 | 90.19 115 | 22.38 436 | 86.66 297 | 73.28 129 | 76.39 150 | 86.85 248 |
|
| xiu_mvs_v1_base_debi | | | 71.60 195 | 70.29 199 | 75.55 196 | 77.26 311 | 53.15 152 | 85.34 163 | 79.37 307 | 55.83 338 | 72.54 95 | 90.19 115 | 22.38 436 | 86.66 297 | 73.28 129 | 76.39 150 | 86.85 248 |
|
| VNet | | | 77.99 43 | 77.92 37 | 78.19 104 | 87.43 47 | 50.12 246 | 90.93 22 | 91.41 9 | 67.48 88 | 75.12 63 | 90.15 118 | 46.77 120 | 91.00 100 | 73.52 125 | 78.46 120 | 93.44 11 |
|
| EC-MVSNet | | | 75.30 107 | 75.20 92 | 75.62 191 | 80.98 202 | 49.00 278 | 87.43 83 | 84.68 182 | 63.49 174 | 70.97 130 | 90.15 118 | 42.86 207 | 91.14 94 | 74.33 113 | 81.90 76 | 86.71 255 |
|
| CSCG | | | 80.41 16 | 79.72 18 | 82.49 6 | 89.12 26 | 57.67 18 | 89.29 45 | 91.54 6 | 59.19 262 | 71.82 110 | 90.05 120 | 59.72 12 | 96.04 11 | 78.37 71 | 88.40 14 | 93.75 8 |
|
| CANet_DTU | | | 73.71 146 | 73.14 137 | 75.40 203 | 82.61 150 | 50.05 247 | 84.67 202 | 79.36 310 | 69.72 56 | 75.39 62 | 90.03 121 | 29.41 385 | 85.93 329 | 67.99 176 | 79.11 111 | 90.22 136 |
|
| DeepC-MVS | | 67.15 4 | 76.90 62 | 76.27 69 | 78.80 68 | 80.70 213 | 55.02 82 | 86.39 117 | 86.71 100 | 66.96 101 | 67.91 171 | 89.97 122 | 48.03 96 | 91.41 82 | 75.60 97 | 84.14 60 | 89.96 151 |
| Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
| MVS_111021_HR | | | 76.39 76 | 75.38 91 | 79.42 50 | 85.33 77 | 56.47 42 | 88.15 61 | 84.97 163 | 65.15 139 | 66.06 188 | 89.88 123 | 43.79 187 | 92.16 65 | 75.03 103 | 80.03 99 | 89.64 159 |
|
| GST-MVS | | | 74.87 121 | 73.90 124 | 77.77 115 | 83.30 122 | 53.45 140 | 85.75 143 | 85.29 143 | 59.22 261 | 66.50 184 | 89.85 124 | 40.94 229 | 90.76 110 | 70.94 149 | 83.35 64 | 89.10 182 |
|
| PGM-MVS | | | 72.60 168 | 71.20 177 | 76.80 153 | 82.95 136 | 52.82 166 | 83.07 261 | 82.14 236 | 56.51 327 | 63.18 244 | 89.81 125 | 35.68 317 | 89.76 149 | 67.30 179 | 80.19 95 | 87.83 220 |
|
| APD-MVS_3200maxsize | | | 69.62 243 | 68.23 237 | 73.80 262 | 81.58 185 | 48.22 308 | 81.91 294 | 79.50 304 | 48.21 408 | 64.24 224 | 89.75 126 | 31.91 367 | 87.55 261 | 63.08 219 | 73.85 200 | 85.64 278 |
|
| mPP-MVS | | | 71.79 192 | 70.38 196 | 76.04 178 | 82.65 149 | 52.06 184 | 84.45 208 | 81.78 249 | 55.59 341 | 62.05 262 | 89.68 127 | 33.48 346 | 88.28 225 | 65.45 198 | 78.24 123 | 87.77 222 |
|
| testing915 | | | 80.82 13 | 80.48 15 | 81.83 13 | 86.14 61 | 59.23 8 | 88.16 60 | 92.18 1 | 72.63 14 | 73.09 88 | 89.67 128 | 62.49 7 | 92.70 48 | 81.13 51 | 78.86 116 | 93.55 10 |
|
| XVS | | | 72.92 160 | 71.62 168 | 76.81 151 | 83.41 117 | 52.48 172 | 84.88 191 | 83.20 220 | 58.03 284 | 63.91 229 | 89.63 129 | 35.50 320 | 89.78 147 | 65.50 193 | 80.50 90 | 88.16 211 |
|
| HPM-MVS |  | | 72.60 168 | 71.50 170 | 75.89 183 | 82.02 163 | 51.42 208 | 80.70 331 | 83.05 223 | 56.12 336 | 64.03 227 | 89.53 130 | 37.55 276 | 88.37 216 | 70.48 153 | 80.04 98 | 87.88 219 |
| Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
| DP-MVS Recon | | | 71.99 185 | 70.31 198 | 77.01 142 | 90.65 9 | 53.44 141 | 89.37 39 | 82.97 226 | 56.33 330 | 63.56 242 | 89.47 131 | 34.02 340 | 92.15 67 | 54.05 315 | 72.41 218 | 85.43 282 |
|
| SR-MVS-dyc-post | | | 68.27 272 | 66.87 268 | 72.48 303 | 80.96 204 | 48.14 312 | 81.54 312 | 76.98 363 | 46.42 422 | 62.75 251 | 89.42 132 | 31.17 375 | 86.09 318 | 60.52 247 | 72.06 224 | 83.19 334 |
|
| RE-MVS-def | | | | 66.66 275 | | 80.96 204 | 48.14 312 | 81.54 312 | 76.98 363 | 46.42 422 | 62.75 251 | 89.42 132 | 29.28 387 | | 60.52 247 | 72.06 224 | 83.19 334 |
|
| Effi-MVS+ | | | 75.24 111 | 73.61 130 | 80.16 38 | 81.92 167 | 57.42 24 | 85.21 171 | 76.71 370 | 60.68 235 | 73.32 83 | 89.34 134 | 47.30 109 | 91.63 76 | 68.28 173 | 79.72 103 | 91.42 79 |
|
| VDD-MVS | | | 76.08 86 | 74.97 101 | 79.44 49 | 84.27 102 | 53.33 147 | 91.13 20 | 85.88 120 | 65.33 134 | 72.37 100 | 89.34 134 | 32.52 357 | 92.76 47 | 77.90 79 | 75.96 162 | 92.22 44 |
|
| PVSNet_Blended | | | 76.53 73 | 76.54 65 | 76.50 161 | 85.91 63 | 51.83 194 | 88.89 50 | 84.24 194 | 67.82 82 | 69.09 159 | 89.33 136 | 46.70 121 | 88.13 228 | 75.43 98 | 81.48 81 | 89.55 162 |
|
| test_yl | | | 75.85 94 | 74.83 105 | 78.91 63 | 88.08 40 | 51.94 189 | 91.30 17 | 89.28 32 | 57.91 288 | 71.19 124 | 89.20 137 | 42.03 217 | 92.77 45 | 69.41 160 | 75.07 182 | 92.01 52 |
|
| DCV-MVSNet | | | 75.85 94 | 74.83 105 | 78.91 63 | 88.08 40 | 51.94 189 | 91.30 17 | 89.28 32 | 57.91 288 | 71.19 124 | 89.20 137 | 42.03 217 | 92.77 45 | 69.41 160 | 75.07 182 | 92.01 52 |
|
| baseline | | | 76.86 63 | 76.24 70 | 78.71 71 | 80.47 223 | 54.20 125 | 83.90 228 | 84.88 169 | 71.38 28 | 71.51 117 | 89.15 139 | 50.51 72 | 90.55 120 | 75.71 95 | 78.65 117 | 91.39 80 |
|
| EI-MVSNet-Vis-set | | | 73.19 156 | 72.60 146 | 74.99 224 | 82.56 151 | 49.80 255 | 82.55 277 | 89.00 37 | 66.17 115 | 65.89 191 | 88.98 140 | 43.83 185 | 92.29 61 | 65.38 201 | 69.01 258 | 82.87 342 |
|
| CLD-MVS | | | 75.60 104 | 75.39 90 | 76.24 169 | 80.69 214 | 52.40 175 | 90.69 23 | 86.20 113 | 74.40 6 | 65.01 206 | 88.93 141 | 42.05 216 | 90.58 119 | 76.57 88 | 73.96 195 | 85.73 275 |
| Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
| ACMMP |  | | 70.81 213 | 69.29 218 | 75.39 206 | 81.52 189 | 51.92 191 | 83.43 244 | 83.03 224 | 56.67 321 | 58.80 308 | 88.91 142 | 31.92 366 | 88.58 204 | 65.89 192 | 73.39 205 | 85.67 276 |
| Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence |
| 1314 | | | 71.11 205 | 69.41 214 | 76.22 170 | 79.32 254 | 50.49 231 | 80.23 340 | 85.14 157 | 59.44 254 | 58.93 303 | 88.89 143 | 33.83 344 | 89.60 158 | 61.49 236 | 77.42 134 | 88.57 199 |
|
| PAPM | | | 76.76 67 | 76.07 74 | 78.81 67 | 80.20 232 | 59.11 9 | 86.86 106 | 86.23 112 | 68.60 68 | 70.18 150 | 88.84 144 | 51.57 60 | 87.16 276 | 65.48 195 | 86.68 31 | 90.15 141 |
|
| diffmvs |  | | 75.11 115 | 74.65 111 | 76.46 162 | 78.52 281 | 53.35 145 | 83.28 252 | 79.94 290 | 70.51 41 | 71.64 113 | 88.72 145 | 46.02 138 | 86.08 319 | 77.52 80 | 75.75 170 | 89.96 151 |
| Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025 |
| testing222 | | | 77.70 47 | 77.22 50 | 79.14 57 | 86.95 50 | 54.89 97 | 87.18 93 | 91.96 3 | 72.29 16 | 71.17 126 | 88.70 146 | 55.19 34 | 91.24 89 | 65.18 202 | 76.32 154 | 91.29 88 |
|
| 旧先验1 | | | | | | 81.57 186 | 47.48 336 | | 71.83 420 | | | 88.66 147 | 36.94 292 | | | 78.34 122 | 88.67 193 |
|
| PAPM_NR | | | 71.80 191 | 69.98 207 | 77.26 135 | 81.54 187 | 53.34 146 | 78.60 364 | 85.25 146 | 53.46 367 | 60.53 278 | 88.66 147 | 45.69 151 | 89.24 172 | 56.49 293 | 79.62 106 | 89.19 178 |
|
| 3Dnovator | | 64.70 6 | 74.46 126 | 72.48 148 | 80.41 32 | 82.84 143 | 55.40 64 | 83.08 260 | 88.61 54 | 67.61 87 | 59.85 283 | 88.66 147 | 34.57 334 | 93.97 28 | 58.42 267 | 88.70 12 | 91.85 60 |
|
| h-mvs33 | | | 73.95 138 | 72.89 142 | 77.15 138 | 80.17 233 | 50.37 239 | 84.68 200 | 83.33 214 | 68.08 75 | 71.97 106 | 88.65 150 | 42.50 208 | 91.15 93 | 78.82 66 | 57.78 377 | 89.91 153 |
|
| casdiffmvs |  | | 77.36 53 | 76.85 57 | 78.88 65 | 80.40 229 | 54.66 111 | 87.06 96 | 85.88 120 | 72.11 19 | 71.57 114 | 88.63 151 | 50.89 69 | 90.35 127 | 76.00 92 | 79.11 111 | 91.63 69 |
| Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025 |
| E3new | | | 76.85 64 | 76.24 70 | 78.66 75 | 81.62 181 | 55.01 83 | 86.94 101 | 85.10 158 | 71.55 25 | 71.93 108 | 88.61 152 | 48.40 91 | 89.60 158 | 74.50 108 | 77.53 133 | 91.36 83 |
|
| diffmvs_AUTHOR | | | 74.80 123 | 74.30 116 | 76.29 166 | 77.34 307 | 53.19 151 | 83.17 257 | 79.50 304 | 69.93 52 | 71.55 115 | 88.57 153 | 45.85 148 | 86.03 322 | 77.17 84 | 75.64 171 | 89.67 157 |
|
| viewmanbaseed2359cas | | | 76.71 69 | 76.16 72 | 78.37 100 | 81.16 196 | 55.05 81 | 86.96 99 | 85.32 140 | 71.71 22 | 72.25 103 | 88.50 154 | 46.86 116 | 88.96 187 | 74.55 107 | 78.08 125 | 91.08 100 |
|
| viewcassd2359sk11 | | | 76.66 70 | 76.01 76 | 78.62 80 | 81.14 197 | 54.95 86 | 86.88 105 | 85.04 160 | 71.37 29 | 71.76 111 | 88.44 155 | 48.02 97 | 89.57 160 | 74.17 115 | 77.23 135 | 91.33 87 |
|
| UBG | | | 78.86 28 | 78.86 27 | 78.86 66 | 87.80 43 | 55.43 60 | 87.67 72 | 91.21 13 | 72.83 12 | 72.10 104 | 88.40 156 | 58.53 19 | 89.08 178 | 73.21 132 | 77.98 126 | 92.08 47 |
|
| viewdifsd2359ckpt09 | | | 74.92 119 | 73.70 128 | 78.60 84 | 80.28 230 | 54.94 87 | 84.77 196 | 80.56 276 | 69.96 51 | 69.38 154 | 88.38 157 | 46.01 139 | 90.50 122 | 72.44 136 | 71.49 231 | 90.38 130 |
|
| testing11 | | | 79.18 26 | 78.85 28 | 80.16 38 | 88.33 32 | 56.99 29 | 88.31 59 | 92.06 2 | 72.82 13 | 70.62 143 | 88.37 158 | 57.69 23 | 92.30 60 | 75.25 102 | 76.24 156 | 91.20 95 |
|
| test_vis1_n_1920 | | | 68.59 265 | 68.31 234 | 69.44 364 | 69.16 434 | 41.51 426 | 84.63 203 | 68.58 446 | 58.80 273 | 73.26 84 | 88.37 158 | 25.30 414 | 80.60 394 | 79.10 63 | 67.55 273 | 86.23 265 |
|
| Casviewmamba |  | | 76.27 80 | 75.48 86 | 78.63 79 | 79.14 261 | 54.27 120 | 85.81 138 | 83.09 222 | 70.96 34 | 70.41 147 | 88.36 160 | 48.71 90 | 90.81 109 | 75.92 93 | 76.95 140 | 90.80 115 |
|
| casdiffmvs_mvg |  | | 77.75 46 | 77.28 47 | 79.16 56 | 80.42 228 | 54.44 117 | 87.76 69 | 85.46 133 | 71.67 23 | 71.38 121 | 88.35 161 | 51.58 59 | 91.22 90 | 79.02 64 | 79.89 102 | 91.83 61 |
| Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025 |
| testdata | | | | | 67.08 390 | 77.59 298 | 45.46 379 | | 69.20 443 | 44.47 438 | 71.50 120 | 88.34 162 | 31.21 374 | 70.76 466 | 52.20 337 | 75.88 163 | 85.03 287 |
|
| hybridnocas07 | | | 74.65 124 | 74.00 123 | 76.61 159 | 77.58 299 | 52.72 168 | 83.64 234 | 79.72 296 | 69.43 59 | 70.80 137 | 88.33 163 | 45.56 153 | 87.34 270 | 76.88 86 | 74.07 193 | 89.78 155 |
|
| 3Dnovator+ | | 62.71 7 | 72.29 179 | 70.50 191 | 77.65 119 | 83.40 120 | 51.29 212 | 87.32 87 | 86.40 109 | 59.01 269 | 58.49 318 | 88.32 164 | 32.40 358 | 91.27 87 | 57.04 287 | 82.15 75 | 90.38 130 |
|
| EI-MVSNet-UG-set | | | 72.37 175 | 71.73 166 | 74.29 245 | 81.60 183 | 49.29 272 | 81.85 296 | 88.64 50 | 65.29 136 | 65.05 204 | 88.29 165 | 43.18 200 | 91.83 72 | 63.74 216 | 67.97 270 | 81.75 354 |
|
| myMVS_eth3d28 | | | 77.77 45 | 77.94 36 | 77.27 133 | 87.58 46 | 52.89 163 | 86.06 128 | 91.33 12 | 74.15 7 | 68.16 168 | 88.24 166 | 58.17 20 | 88.31 222 | 69.88 158 | 77.87 127 | 90.61 122 |
|
| gm-plane-assit | | | | | | 83.24 124 | 54.21 123 | | | 70.91 35 | | 88.23 167 | | 95.25 15 | 66.37 185 | | |
|
| hybridcas | | | 76.66 70 | 75.99 77 | 78.65 77 | 79.25 257 | 54.46 116 | 86.82 108 | 85.53 130 | 70.88 37 | 70.40 148 | 88.21 168 | 49.55 84 | 90.12 136 | 74.42 110 | 78.88 115 | 91.37 82 |
|
| E2 | | | 76.39 76 | 75.67 80 | 78.56 87 | 80.49 221 | 54.87 98 | 86.80 109 | 84.95 164 | 71.09 32 | 71.51 117 | 88.21 168 | 47.55 104 | 89.53 161 | 73.65 123 | 76.77 145 | 91.29 88 |
|
| E3 | | | 76.39 76 | 75.67 80 | 78.56 87 | 80.49 221 | 54.87 98 | 86.80 109 | 84.95 164 | 71.09 32 | 71.51 117 | 88.21 168 | 47.55 104 | 89.53 161 | 73.65 123 | 76.77 145 | 91.29 88 |
|
| viewdifsd2359ckpt13 | | | 75.96 89 | 75.07 97 | 78.65 77 | 81.14 197 | 55.21 72 | 86.15 125 | 84.95 164 | 69.98 49 | 70.49 146 | 88.16 171 | 46.10 134 | 89.86 143 | 72.39 137 | 76.23 157 | 90.89 112 |
|
| TSAR-MVS + MP. | | | 78.31 37 | 78.26 31 | 78.48 92 | 81.33 194 | 56.31 46 | 81.59 309 | 86.41 108 | 69.61 57 | 81.72 21 | 88.16 171 | 55.09 37 | 88.04 232 | 74.12 116 | 86.31 35 | 91.09 99 |
| Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition |
| hybrid | | | 74.44 127 | 73.79 127 | 76.39 163 | 77.31 309 | 52.89 163 | 83.37 250 | 79.79 294 | 68.21 71 | 71.01 129 | 88.14 173 | 44.93 169 | 86.68 295 | 77.29 83 | 74.11 192 | 89.59 160 |
|
| onestephybrid01 | | | 74.31 131 | 73.65 129 | 76.27 167 | 77.58 299 | 51.99 187 | 82.22 286 | 78.44 336 | 69.26 61 | 70.95 131 | 88.11 174 | 44.46 179 | 87.30 271 | 78.01 78 | 73.86 199 | 89.51 166 |
|
| testing99 | | | 78.45 31 | 77.78 40 | 80.45 31 | 88.28 35 | 56.81 35 | 87.95 67 | 91.49 7 | 71.72 21 | 70.84 136 | 88.09 175 | 57.29 25 | 92.63 53 | 69.24 164 | 75.13 180 | 91.91 56 |
|
| sss | | | 70.49 220 | 70.13 203 | 71.58 332 | 81.59 184 | 39.02 439 | 80.78 329 | 84.71 181 | 59.34 257 | 66.61 181 | 88.09 175 | 37.17 287 | 85.52 334 | 61.82 234 | 71.02 237 | 90.20 138 |
|
| MG-MVS | | | 78.42 33 | 76.99 55 | 82.73 3 | 93.17 1 | 64.46 1 | 89.93 29 | 88.51 58 | 64.83 142 | 73.52 80 | 88.09 175 | 48.07 94 | 92.19 64 | 62.24 229 | 84.53 58 | 91.53 74 |
|
| viewmambaseed2359dif | | | 73.51 151 | 72.78 143 | 75.71 189 | 76.93 319 | 51.89 192 | 82.81 268 | 79.66 299 | 65.46 127 | 70.29 149 | 88.05 178 | 45.55 154 | 85.85 330 | 73.49 126 | 72.76 214 | 89.39 171 |
|
| HPM-MVS_fast | | | 67.86 278 | 66.28 283 | 72.61 298 | 80.67 215 | 48.34 302 | 81.18 320 | 75.95 378 | 50.81 388 | 59.55 290 | 88.05 178 | 27.86 394 | 85.98 325 | 58.83 260 | 73.58 202 | 83.51 327 |
|
| testing91 | | | 78.30 38 | 77.54 43 | 80.61 26 | 88.16 38 | 57.12 28 | 87.94 68 | 91.07 17 | 71.43 26 | 70.75 138 | 88.04 180 | 55.82 32 | 92.65 50 | 69.61 159 | 75.00 185 | 92.05 50 |
|
| viewmamba |  | | 73.92 140 | 73.03 141 | 76.58 160 | 77.56 301 | 52.73 167 | 82.91 266 | 78.77 324 | 69.23 62 | 68.85 161 | 88.01 181 | 44.71 177 | 87.57 260 | 73.86 120 | 73.40 204 | 89.44 170 |
|
| baseline1 | | | 72.51 171 | 72.12 161 | 73.69 266 | 85.05 81 | 44.46 388 | 83.51 240 | 86.13 116 | 71.61 24 | 64.64 214 | 87.97 182 | 55.00 39 | 89.48 163 | 59.07 258 | 56.05 391 | 87.13 240 |
|
| ETVMVS | | | 75.80 98 | 75.44 88 | 76.89 148 | 86.23 60 | 50.38 238 | 85.55 157 | 91.42 8 | 71.30 30 | 68.80 162 | 87.94 183 | 56.42 29 | 89.24 172 | 56.54 292 | 74.75 189 | 91.07 101 |
|
| E4 | | | 75.99 88 | 75.16 95 | 78.48 92 | 79.56 247 | 54.74 103 | 86.66 114 | 84.80 172 | 70.62 38 | 71.16 127 | 87.90 184 | 46.84 117 | 89.47 165 | 72.70 134 | 76.20 158 | 91.23 92 |
|
| viewmacassd2359aftdt | | | 75.91 92 | 75.14 96 | 78.21 103 | 79.40 251 | 54.82 100 | 86.71 112 | 84.98 162 | 70.89 36 | 71.52 116 | 87.89 185 | 45.43 158 | 88.85 196 | 72.35 138 | 77.08 137 | 90.97 109 |
|
| MVS_111021_LR | | | 69.07 250 | 67.91 241 | 72.54 300 | 77.27 310 | 49.56 260 | 79.77 348 | 73.96 399 | 59.33 259 | 60.73 275 | 87.82 186 | 30.19 381 | 81.53 381 | 69.94 157 | 72.19 223 | 86.53 258 |
|
| Vis-MVSNet |  | | 70.61 218 | 69.34 216 | 74.42 238 | 80.95 207 | 48.49 296 | 86.03 130 | 77.51 354 | 58.74 275 | 65.55 198 | 87.78 187 | 34.37 337 | 85.95 328 | 52.53 334 | 80.61 88 | 88.80 189 |
| Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
| API-MVS | | | 74.17 134 | 72.07 162 | 80.49 28 | 90.02 12 | 58.55 12 | 87.30 89 | 84.27 191 | 57.51 299 | 65.77 194 | 87.77 188 | 41.61 223 | 95.97 12 | 51.71 338 | 82.63 69 | 86.94 243 |
|
| E5new | | | 75.74 99 | 74.80 107 | 78.57 85 | 79.85 238 | 54.93 88 | 85.87 133 | 84.72 177 | 70.19 45 | 70.90 132 | 87.74 189 | 45.97 143 | 89.71 151 | 72.15 141 | 75.79 164 | 91.06 102 |
|
| E5 | | | 75.74 99 | 74.80 107 | 78.57 85 | 79.85 238 | 54.93 88 | 85.87 133 | 84.72 177 | 70.19 45 | 70.90 132 | 87.74 189 | 45.97 143 | 89.71 151 | 72.15 141 | 75.79 164 | 91.06 102 |
|
| E6new | | | 75.74 99 | 74.80 107 | 78.56 87 | 79.85 238 | 54.92 93 | 85.87 133 | 84.72 177 | 70.19 45 | 70.90 132 | 87.73 191 | 45.98 140 | 89.71 151 | 72.16 139 | 75.78 167 | 91.06 102 |
|
| E6 | | | 75.74 99 | 74.80 107 | 78.56 87 | 79.85 238 | 54.92 93 | 85.87 133 | 84.72 177 | 70.19 45 | 70.90 132 | 87.73 191 | 45.98 140 | 89.71 151 | 72.16 139 | 75.78 167 | 91.06 102 |
|
| test_cas_vis1_n_1920 | | | 67.10 301 | 66.60 277 | 68.59 377 | 65.17 457 | 43.23 407 | 83.23 254 | 69.84 439 | 55.34 347 | 70.67 140 | 87.71 193 | 24.70 422 | 76.66 437 | 78.57 70 | 64.20 306 | 85.89 273 |
|
| OpenMVS |  | 61.00 11 | 69.99 232 | 67.55 255 | 77.30 131 | 78.37 285 | 54.07 128 | 84.36 210 | 85.76 123 | 57.22 307 | 56.71 349 | 87.67 194 | 30.79 377 | 92.83 43 | 43.04 393 | 84.06 62 | 85.01 288 |
|
| dtuplus | | | 73.09 158 | 72.29 155 | 75.52 200 | 76.27 331 | 51.82 196 | 82.99 264 | 79.98 287 | 65.08 140 | 70.11 151 | 87.66 195 | 44.38 181 | 85.64 332 | 71.56 145 | 72.55 217 | 89.11 181 |
|
| CPTT-MVS | | | 67.15 300 | 65.84 294 | 71.07 340 | 80.96 204 | 50.32 242 | 81.94 293 | 74.10 395 | 46.18 428 | 57.91 325 | 87.64 196 | 29.57 384 | 81.31 383 | 64.10 209 | 70.18 250 | 81.56 358 |
|
| FBQ-MVS | | | 78.34 36 | 77.25 48 | 81.62 17 | 86.35 58 | 59.48 6 | 86.95 100 | 90.95 18 | 72.89 11 | 71.91 109 | 87.60 197 | 53.35 49 | 92.65 50 | 70.19 154 | 75.03 184 | 92.72 31 |
|
| QAPM | | | 71.88 189 | 69.33 217 | 79.52 48 | 82.20 162 | 54.30 119 | 86.30 121 | 88.77 46 | 56.61 323 | 59.72 285 | 87.48 198 | 33.90 342 | 95.36 14 | 47.48 367 | 81.49 80 | 88.90 185 |
|
| GG-mvs-BLEND | | | | | 77.77 115 | 86.68 53 | 50.61 227 | 68.67 438 | 88.45 60 | | 68.73 163 | 87.45 199 | 59.15 13 | 90.67 114 | 54.83 309 | 87.67 18 | 92.03 51 |
|
| test2506 | | | 72.91 161 | 72.43 150 | 74.32 244 | 80.12 234 | 44.18 395 | 83.19 255 | 84.77 174 | 64.02 155 | 65.97 189 | 87.43 200 | 47.67 103 | 88.72 198 | 59.08 257 | 79.66 104 | 90.08 147 |
|
| test1111 | | | 71.06 207 | 70.42 195 | 72.97 284 | 79.48 250 | 41.49 427 | 84.82 195 | 82.74 229 | 64.20 152 | 62.98 247 | 87.43 200 | 35.20 323 | 87.92 235 | 58.54 264 | 78.42 121 | 89.49 168 |
|
| ECVR-MVS |  | | 71.81 190 | 71.00 183 | 74.26 246 | 80.12 234 | 43.49 401 | 84.69 199 | 82.16 235 | 64.02 155 | 64.64 214 | 87.43 200 | 35.04 326 | 89.21 175 | 61.24 238 | 79.66 104 | 90.08 147 |
|
| viewdifsd2359ckpt07 | | | 74.81 122 | 74.01 122 | 77.21 137 | 79.62 245 | 53.13 155 | 85.70 152 | 83.75 205 | 68.12 73 | 68.14 169 | 87.33 203 | 46.51 128 | 87.92 235 | 73.32 128 | 73.63 201 | 90.57 123 |
|
| VDDNet | | | 74.37 129 | 72.13 160 | 81.09 23 | 79.58 246 | 56.52 41 | 90.02 26 | 86.70 101 | 52.61 374 | 71.23 123 | 87.20 204 | 31.75 370 | 93.96 29 | 74.30 114 | 75.77 169 | 92.79 29 |
|
| æ–°å‡ ä½•1 | | | | | 73.30 278 | 83.10 127 | 53.48 137 | | 71.43 427 | 45.55 430 | 66.14 186 | 87.17 205 | 33.88 343 | 80.54 395 | 48.50 360 | 80.33 94 | 85.88 274 |
|
| TR-MVS | | | 69.71 237 | 67.85 249 | 75.27 215 | 82.94 137 | 48.48 297 | 87.40 86 | 80.86 268 | 57.15 309 | 64.61 216 | 87.08 206 | 32.67 356 | 89.64 157 | 46.38 375 | 71.55 230 | 87.68 225 |
|
| 原ACMM1 | | | | | 76.13 175 | 84.89 85 | 54.59 113 | | 85.26 145 | 51.98 378 | 66.70 178 | 87.07 207 | 40.15 242 | 89.70 155 | 51.23 342 | 85.06 54 | 84.10 304 |
|
| EPNet_dtu | | | 66.25 319 | 66.71 273 | 64.87 411 | 78.66 277 | 34.12 459 | 82.80 269 | 75.51 381 | 61.75 210 | 64.47 222 | 86.90 208 | 37.06 290 | 72.46 460 | 43.65 390 | 69.63 255 | 88.02 217 |
| Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
| Anonymous202405211 | | | 70.11 226 | 67.88 245 | 76.79 154 | 87.20 49 | 47.24 343 | 89.49 36 | 77.38 357 | 54.88 354 | 66.14 186 | 86.84 209 | 20.93 445 | 91.54 79 | 56.45 296 | 71.62 228 | 91.59 70 |
|
| BH-RMVSNet | | | 70.08 228 | 68.01 239 | 76.27 167 | 84.21 103 | 51.22 214 | 87.29 90 | 79.33 313 | 58.96 271 | 63.63 240 | 86.77 210 | 33.29 348 | 90.30 131 | 44.63 384 | 73.96 195 | 87.30 235 |
|
| IS-MVSNet | | | 68.80 260 | 67.55 255 | 72.54 300 | 78.50 282 | 43.43 403 | 81.03 322 | 79.35 311 | 59.12 267 | 57.27 341 | 86.71 211 | 46.05 136 | 87.70 253 | 44.32 387 | 75.60 172 | 86.49 260 |
|
| Vis-MVSNet (Re-imp) | | | 65.52 327 | 65.63 299 | 65.17 409 | 77.49 304 | 30.54 474 | 75.49 386 | 77.73 350 | 59.34 257 | 52.26 391 | 86.69 212 | 49.38 86 | 80.53 396 | 37.07 415 | 75.28 176 | 84.42 297 |
|
| BridgeMVS | | | 80.28 17 | 79.73 17 | 81.90 12 | 86.47 56 | 59.34 7 | 80.45 334 | 89.51 29 | 69.76 55 | 71.05 128 | 86.66 213 | 58.68 18 | 93.24 37 | 84.64 20 | 90.40 6 | 93.14 20 |
|
| AdaColmap |  | | 67.86 278 | 65.48 302 | 75.00 223 | 88.15 39 | 54.99 84 | 86.10 127 | 76.63 372 | 49.30 399 | 57.80 327 | 86.65 214 | 29.39 386 | 88.94 190 | 45.10 381 | 70.21 249 | 81.06 372 |
|
| test222 | | | | | | 79.36 252 | 50.97 215 | 77.99 368 | 67.84 448 | 42.54 449 | 62.84 250 | 86.53 215 | 30.26 380 | | | 76.91 141 | 85.23 283 |
|
| TAPA-MVS | | 56.12 14 | 61.82 365 | 60.18 364 | 66.71 394 | 78.48 283 | 37.97 446 | 75.19 388 | 76.41 375 | 46.82 418 | 57.04 344 | 86.52 216 | 27.67 397 | 77.03 432 | 26.50 469 | 67.02 277 | 85.14 286 |
| Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
| PCF-MVS | | 61.03 10 | 70.10 227 | 68.40 233 | 75.22 217 | 77.15 315 | 51.99 187 | 79.30 357 | 82.12 237 | 56.47 328 | 61.88 264 | 86.48 217 | 43.98 183 | 87.24 274 | 55.37 307 | 72.79 213 | 86.43 262 |
| Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
| casdiffseed414692147 | | | 74.22 132 | 72.73 144 | 78.69 72 | 79.85 238 | 54.64 112 | 85.13 175 | 83.67 211 | 69.07 64 | 69.41 153 | 86.47 218 | 43.27 199 | 90.69 112 | 63.77 215 | 73.91 198 | 90.73 117 |
|
| OMC-MVS | | | 65.97 323 | 65.06 313 | 68.71 374 | 72.97 385 | 42.58 416 | 78.61 363 | 75.35 384 | 54.72 355 | 59.31 295 | 86.25 219 | 33.30 347 | 77.88 424 | 57.99 273 | 67.05 276 | 85.66 277 |
|
| icg_test_0407_2 | | | 71.26 201 | 69.99 206 | 75.09 219 | 82.26 156 | 50.87 216 | 79.65 350 | 85.16 151 | 62.91 186 | 63.68 237 | 86.07 220 | 35.56 318 | 84.32 355 | 64.03 210 | 70.55 243 | 90.09 143 |
|
| IMVS_0407 | | | 71.97 186 | 70.10 204 | 77.57 120 | 82.26 156 | 50.87 216 | 80.69 332 | 85.16 151 | 62.91 186 | 63.68 237 | 86.07 220 | 35.56 318 | 91.75 74 | 64.03 210 | 70.55 243 | 90.09 143 |
|
| IMVS_0404 | | | 69.11 249 | 67.25 264 | 74.68 232 | 82.26 156 | 50.87 216 | 76.74 375 | 85.16 151 | 62.91 186 | 50.76 409 | 86.07 220 | 26.76 402 | 83.06 372 | 64.03 210 | 70.55 243 | 90.09 143 |
|
| IMVS_0403 | | | 72.39 173 | 70.59 190 | 77.79 114 | 82.26 156 | 50.87 216 | 81.76 299 | 85.16 151 | 62.91 186 | 64.87 211 | 86.07 220 | 37.71 272 | 92.40 58 | 64.03 210 | 70.55 243 | 90.09 143 |
|
| AUN-MVS | | | 68.20 274 | 66.35 280 | 73.76 263 | 76.37 325 | 47.45 338 | 79.52 354 | 79.52 303 | 60.98 227 | 62.34 254 | 86.02 224 | 36.59 301 | 86.94 284 | 62.32 228 | 53.47 414 | 86.89 244 |
|
| baseline2 | | | 75.15 114 | 74.54 113 | 76.98 145 | 81.67 177 | 51.74 200 | 83.84 230 | 91.94 4 | 69.97 50 | 58.98 301 | 86.02 224 | 59.73 11 | 91.73 75 | 68.37 172 | 70.40 248 | 87.48 229 |
|
| hse-mvs2 | | | 71.44 199 | 70.68 187 | 73.73 265 | 76.34 326 | 47.44 339 | 79.45 355 | 79.47 306 | 68.08 75 | 71.97 106 | 86.01 226 | 42.50 208 | 86.93 285 | 78.82 66 | 53.46 415 | 86.83 251 |
|
| OPM-MVS | | | 70.75 214 | 69.58 212 | 74.26 246 | 75.55 347 | 51.34 210 | 86.05 129 | 83.29 218 | 61.94 208 | 62.95 249 | 85.77 227 | 34.15 339 | 88.44 214 | 65.44 199 | 71.07 236 | 82.99 338 |
| Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS). |
| thisisatest0515 | | | 73.64 149 | 72.20 157 | 77.97 108 | 81.63 180 | 53.01 159 | 86.69 113 | 88.81 45 | 62.53 196 | 64.06 226 | 85.65 228 | 52.15 57 | 92.50 55 | 58.43 265 | 69.84 251 | 88.39 208 |
|
| 114514_t | | | 69.87 235 | 67.88 245 | 75.85 184 | 88.38 31 | 52.35 177 | 86.94 101 | 83.68 207 | 53.70 365 | 55.68 359 | 85.60 229 | 30.07 383 | 91.20 91 | 55.84 301 | 71.02 237 | 83.99 308 |
|
| BH-w/o | | | 70.02 230 | 68.51 231 | 74.56 234 | 82.77 144 | 50.39 236 | 86.60 116 | 78.14 341 | 59.77 247 | 59.65 286 | 85.57 230 | 39.27 253 | 87.30 271 | 49.86 349 | 74.94 186 | 85.99 269 |
|
| CDS-MVSNet | | | 70.48 221 | 69.43 213 | 73.64 267 | 77.56 301 | 48.83 284 | 83.51 240 | 77.45 355 | 63.27 178 | 62.33 255 | 85.54 231 | 43.85 184 | 83.29 370 | 57.38 286 | 74.00 194 | 88.79 190 |
| Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
| viewdifsd2359ckpt11 | | | 70.68 215 | 69.10 223 | 75.40 203 | 75.33 352 | 50.85 220 | 81.57 310 | 78.00 343 | 66.99 99 | 64.96 208 | 85.52 232 | 39.52 249 | 86.81 290 | 68.86 168 | 61.15 338 | 88.56 200 |
|
| viewmsd2359difaftdt | | | 70.68 215 | 69.10 223 | 75.40 203 | 75.33 352 | 50.85 220 | 81.57 310 | 78.00 343 | 66.99 99 | 64.96 208 | 85.52 232 | 39.52 249 | 86.81 290 | 68.86 168 | 61.16 337 | 88.56 200 |
|
| PVSNet_Blended_VisFu | | | 73.40 153 | 72.44 149 | 76.30 165 | 81.32 195 | 54.70 107 | 85.81 138 | 78.82 322 | 63.70 167 | 64.53 218 | 85.38 234 | 47.11 112 | 87.38 269 | 67.75 177 | 77.55 130 | 86.81 253 |
|
| KinetiMVS | | | 71.15 202 | 69.25 220 | 76.82 150 | 77.99 290 | 50.49 231 | 85.05 181 | 86.51 105 | 59.78 246 | 64.10 225 | 85.34 235 | 32.16 361 | 91.33 86 | 58.82 261 | 73.54 203 | 88.64 194 |
|
| balanced_ft_v1 | | | 75.25 110 | 73.90 124 | 79.29 52 | 85.59 70 | 56.72 36 | 74.35 397 | 87.27 85 | 60.24 240 | 59.07 300 | 85.17 236 | 47.76 101 | 90.51 121 | 82.62 36 | 83.06 66 | 90.64 120 |
|
| HQP-MVS | | | 72.34 176 | 71.44 172 | 75.03 221 | 79.02 265 | 51.56 204 | 88.00 63 | 83.68 207 | 65.45 128 | 64.48 219 | 85.13 237 | 37.35 280 | 88.62 201 | 66.70 182 | 73.12 208 | 84.91 291 |
|
| NP-MVS | | | | | | 78.76 271 | 50.43 234 | | | | | 85.12 238 | | | | | |
|
| UWE-MVS | | | 72.17 182 | 72.15 159 | 72.21 311 | 82.26 156 | 44.29 392 | 86.83 107 | 89.58 28 | 65.58 126 | 65.82 192 | 85.06 239 | 45.02 165 | 84.35 354 | 54.07 314 | 75.18 177 | 87.99 218 |
|
| SSM_0407 | | | 69.71 237 | 67.38 260 | 76.69 158 | 80.45 224 | 51.81 197 | 81.36 318 | 80.18 282 | 54.07 362 | 63.82 233 | 85.05 240 | 33.09 350 | 91.01 99 | 59.40 254 | 68.97 260 | 87.25 236 |
|
| SSM_0404 | | | 70.13 224 | 67.87 248 | 76.88 149 | 80.22 231 | 52.00 186 | 81.71 304 | 80.18 282 | 54.07 362 | 65.36 200 | 85.05 240 | 33.09 350 | 91.03 96 | 59.40 254 | 71.80 226 | 87.63 226 |
|
| VPNet | | | 72.07 183 | 71.42 173 | 74.04 252 | 78.64 278 | 47.17 344 | 89.91 31 | 87.97 71 | 72.56 15 | 64.66 213 | 85.04 242 | 41.83 221 | 88.33 220 | 61.17 239 | 60.97 339 | 86.62 256 |
|
| dmvs_re | | | 67.61 284 | 66.00 289 | 72.42 306 | 81.86 169 | 43.45 402 | 64.67 452 | 80.00 286 | 69.56 58 | 60.07 281 | 85.00 243 | 34.71 331 | 87.63 256 | 51.48 340 | 66.68 278 | 86.17 266 |
|
| PVSNet | | 62.49 8 | 69.27 248 | 67.81 250 | 73.64 267 | 84.41 94 | 51.85 193 | 84.63 203 | 77.80 348 | 66.42 109 | 59.80 284 | 84.95 244 | 22.14 440 | 80.44 397 | 55.03 308 | 75.11 181 | 88.62 197 |
|
| EPP-MVSNet | | | 71.14 203 | 70.07 205 | 74.33 243 | 79.18 260 | 46.52 356 | 83.81 231 | 86.49 106 | 56.32 331 | 57.95 324 | 84.90 245 | 54.23 43 | 89.14 177 | 58.14 272 | 69.65 254 | 87.33 233 |
|
| testing3-2 | | | 72.30 178 | 72.35 151 | 72.15 313 | 83.07 130 | 47.64 332 | 85.46 162 | 89.81 27 | 66.17 115 | 61.96 263 | 84.88 246 | 58.93 14 | 82.27 375 | 55.87 299 | 64.97 297 | 86.54 257 |
|
| mamba_0408 | | | 66.33 317 | 62.87 328 | 76.70 157 | 80.45 224 | 51.81 197 | 46.11 489 | 78.90 318 | 55.46 344 | 63.82 233 | 84.54 247 | 31.91 367 | 91.03 96 | 55.68 303 | 68.97 260 | 87.25 236 |
|
| SSM_04072 | | | 64.04 340 | 62.87 328 | 67.56 384 | 80.45 224 | 51.81 197 | 46.11 489 | 78.90 318 | 55.46 344 | 63.82 233 | 84.54 247 | 31.91 367 | 63.62 476 | 55.68 303 | 68.97 260 | 87.25 236 |
|
| AstraMVS | | | 70.12 225 | 68.56 228 | 74.81 228 | 76.48 324 | 47.48 336 | 84.35 211 | 82.58 232 | 63.80 163 | 62.09 261 | 84.54 247 | 31.39 373 | 89.96 140 | 68.24 175 | 63.58 313 | 87.00 242 |
|
| UA-Net | | | 67.32 296 | 66.23 284 | 70.59 347 | 78.85 270 | 41.23 430 | 73.60 402 | 75.45 383 | 61.54 215 | 66.61 181 | 84.53 250 | 38.73 258 | 86.57 302 | 42.48 398 | 74.24 191 | 83.98 310 |
|
| GeoE | | | 69.96 233 | 67.88 245 | 76.22 170 | 81.11 200 | 51.71 201 | 84.15 218 | 76.74 369 | 59.83 245 | 60.91 272 | 84.38 251 | 41.56 224 | 88.10 230 | 51.67 339 | 70.57 242 | 88.84 188 |
|
| nrg030 | | | 72.27 181 | 71.56 169 | 74.42 238 | 75.93 341 | 50.60 228 | 86.97 98 | 83.21 219 | 62.75 191 | 67.15 176 | 84.38 251 | 50.07 76 | 86.66 297 | 71.19 147 | 62.37 331 | 85.99 269 |
|
| SD_0403 | | | 65.51 328 | 65.18 311 | 66.48 398 | 78.37 285 | 29.94 481 | 74.64 394 | 78.55 332 | 66.47 108 | 54.87 366 | 84.35 253 | 38.20 263 | 82.47 374 | 38.90 407 | 72.30 222 | 87.05 241 |
|
| TAMVS | | | 69.51 245 | 68.16 238 | 73.56 271 | 76.30 329 | 48.71 290 | 82.57 275 | 77.17 360 | 62.10 203 | 61.32 269 | 84.23 254 | 41.90 219 | 83.46 367 | 54.80 311 | 73.09 210 | 88.50 205 |
|
| FIs | | | 70.00 231 | 70.24 202 | 69.30 365 | 77.93 293 | 38.55 443 | 83.99 224 | 87.72 78 | 66.86 102 | 57.66 331 | 84.17 255 | 52.28 55 | 85.31 338 | 52.72 331 | 68.80 263 | 84.02 306 |
|
| UWE-MVS-28 | | | 67.43 290 | 67.98 240 | 65.75 402 | 75.66 345 | 34.74 454 | 80.00 346 | 88.17 67 | 64.21 151 | 57.27 341 | 84.14 256 | 45.68 152 | 78.82 412 | 44.33 385 | 72.40 219 | 83.70 322 |
|
| Fast-Effi-MVS+ | | | 72.73 166 | 71.15 178 | 77.48 123 | 82.75 145 | 54.76 102 | 86.77 111 | 80.64 272 | 63.05 183 | 65.93 190 | 84.01 257 | 44.42 180 | 89.03 181 | 56.45 296 | 76.36 153 | 88.64 194 |
|
| CNLPA | | | 60.59 371 | 58.44 375 | 67.05 391 | 79.21 258 | 47.26 342 | 79.75 349 | 64.34 463 | 42.46 450 | 51.90 394 | 83.94 258 | 27.79 396 | 75.41 445 | 37.12 413 | 59.49 353 | 78.47 399 |
|
| dtuonly | | | 62.58 356 | 61.91 343 | 64.58 413 | 66.49 448 | 44.72 386 | 75.64 380 | 65.78 455 | 57.26 306 | 55.48 362 | 83.93 259 | 30.08 382 | 67.36 473 | 56.40 298 | 66.10 291 | 81.67 356 |
|
| HY-MVS | | 67.03 5 | 73.90 141 | 73.14 137 | 76.18 174 | 84.70 87 | 47.36 340 | 75.56 383 | 86.36 110 | 66.27 112 | 70.66 141 | 83.91 260 | 51.05 64 | 89.31 169 | 67.10 181 | 72.61 216 | 91.88 58 |
|
| LPG-MVS_test | | | 66.44 316 | 64.58 317 | 72.02 317 | 74.42 366 | 48.60 291 | 83.07 261 | 80.64 272 | 54.69 356 | 53.75 380 | 83.83 261 | 25.73 412 | 86.98 280 | 60.33 251 | 64.71 301 | 80.48 379 |
|
| LGP-MVS_train | | | | | 72.02 317 | 74.42 366 | 48.60 291 | | 80.64 272 | 54.69 356 | 53.75 380 | 83.83 261 | 25.73 412 | 86.98 280 | 60.33 251 | 64.71 301 | 80.48 379 |
|
| guyue | | | 70.53 219 | 69.12 221 | 74.76 230 | 77.61 296 | 47.53 334 | 84.86 193 | 85.17 149 | 62.70 193 | 62.18 257 | 83.74 263 | 34.72 330 | 89.86 143 | 64.69 206 | 66.38 285 | 86.87 245 |
|
| EI-MVSNet | | | 69.70 241 | 68.70 227 | 72.68 296 | 75.00 358 | 48.90 282 | 79.54 352 | 87.16 89 | 61.05 225 | 63.88 231 | 83.74 263 | 45.87 146 | 90.44 124 | 57.42 285 | 64.68 304 | 78.70 395 |
|
| CVMVSNet | | | 60.85 370 | 60.44 359 | 62.07 429 | 75.00 358 | 32.73 466 | 79.54 352 | 73.49 406 | 36.98 466 | 56.28 355 | 83.74 263 | 29.28 387 | 69.53 469 | 46.48 374 | 63.23 320 | 83.94 313 |
|
| TESTMET0.1,1 | | | 72.86 162 | 72.33 152 | 74.46 236 | 81.98 164 | 50.77 223 | 85.13 175 | 85.47 132 | 66.09 118 | 67.30 174 | 83.69 266 | 37.27 283 | 83.57 365 | 65.06 204 | 78.97 114 | 89.05 183 |
|
| BH-untuned | | | 68.28 271 | 66.40 279 | 73.91 257 | 81.62 181 | 50.01 249 | 85.56 156 | 77.39 356 | 57.63 296 | 57.47 338 | 83.69 266 | 36.36 303 | 87.08 278 | 44.81 382 | 73.08 211 | 84.65 294 |
|
| dmvs_testset | | | 57.65 396 | 58.21 376 | 55.97 456 | 74.62 363 | 9.82 518 | 63.75 455 | 63.34 465 | 67.23 90 | 48.89 417 | 83.68 268 | 39.12 254 | 76.14 440 | 23.43 478 | 59.80 350 | 81.96 351 |
|
| CHOSEN 1792x2688 | | | 76.24 81 | 74.03 121 | 82.88 2 | 83.09 129 | 62.84 2 | 85.73 147 | 85.39 136 | 69.79 53 | 64.87 211 | 83.49 269 | 41.52 225 | 93.69 35 | 70.55 150 | 81.82 77 | 92.12 46 |
|
| thres200 | | | 68.71 262 | 67.27 263 | 73.02 282 | 84.73 86 | 46.76 350 | 85.03 183 | 87.73 77 | 62.34 201 | 59.87 282 | 83.45 270 | 43.15 201 | 88.32 221 | 31.25 448 | 67.91 271 | 83.98 310 |
|
| MVSMamba_PlusPlus | | | 75.28 108 | 73.39 131 | 80.96 24 | 80.85 209 | 58.25 13 | 74.47 395 | 87.61 81 | 50.53 391 | 65.24 201 | 83.41 271 | 57.38 24 | 92.83 43 | 73.92 119 | 87.13 22 | 91.80 63 |
|
| Anonymous20240529 | | | 69.71 237 | 67.28 262 | 77.00 143 | 83.78 111 | 50.36 240 | 88.87 51 | 85.10 158 | 47.22 415 | 64.03 227 | 83.37 272 | 27.93 393 | 92.10 68 | 57.78 281 | 67.44 274 | 88.53 203 |
|
| XVG-OURS-SEG-HR | | | 62.02 363 | 59.54 367 | 69.46 363 | 65.30 455 | 45.88 372 | 65.06 450 | 73.57 404 | 46.45 421 | 57.42 339 | 83.35 273 | 26.95 401 | 78.09 418 | 53.77 317 | 64.03 308 | 84.42 297 |
|
| HQP_MVS | | | 70.96 210 | 69.91 208 | 74.12 250 | 77.95 291 | 49.57 257 | 85.76 141 | 82.59 230 | 63.60 170 | 62.15 259 | 83.28 274 | 36.04 312 | 88.30 223 | 65.46 196 | 72.34 220 | 84.49 295 |
|
| plane_prior4 | | | | | | | | | | | | 83.28 274 | | | | | |
|
| PLC |  | 52.38 18 | 60.89 369 | 58.97 373 | 66.68 396 | 81.77 171 | 45.70 377 | 78.96 360 | 74.04 398 | 43.66 444 | 47.63 425 | 83.19 276 | 23.52 430 | 77.78 427 | 37.47 410 | 60.46 342 | 76.55 426 |
| Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
| FC-MVSNet-test | | | 67.49 288 | 67.91 241 | 66.21 399 | 76.06 335 | 33.06 464 | 80.82 328 | 87.18 88 | 64.44 145 | 54.81 367 | 82.87 277 | 50.40 75 | 82.60 373 | 48.05 364 | 66.55 282 | 82.98 340 |
|
| XVG-OURS | | | 61.88 364 | 59.34 369 | 69.49 362 | 65.37 454 | 46.27 364 | 64.80 451 | 73.49 406 | 47.04 417 | 57.41 340 | 82.85 278 | 25.15 417 | 78.18 416 | 53.00 325 | 64.98 296 | 84.01 307 |
|
| thisisatest0530 | | | 70.47 222 | 68.56 228 | 76.20 172 | 79.78 243 | 51.52 206 | 83.49 242 | 88.58 57 | 57.62 297 | 58.60 314 | 82.79 279 | 51.03 65 | 91.48 80 | 52.84 326 | 62.36 332 | 85.59 280 |
|
| tfpn200view9 | | | 67.57 286 | 66.13 286 | 71.89 327 | 84.05 105 | 45.07 382 | 83.40 246 | 87.71 79 | 60.79 232 | 57.79 328 | 82.76 280 | 43.53 193 | 87.80 245 | 28.80 456 | 66.36 286 | 82.78 344 |
|
| thres400 | | | 67.40 294 | 66.13 286 | 71.19 338 | 84.05 105 | 45.07 382 | 83.40 246 | 87.71 79 | 60.79 232 | 57.79 328 | 82.76 280 | 43.53 193 | 87.80 245 | 28.80 456 | 66.36 286 | 80.71 377 |
|
| MVS_Test | | | 75.85 94 | 74.93 102 | 78.62 80 | 84.08 104 | 55.20 75 | 83.99 224 | 85.17 149 | 68.07 77 | 73.38 82 | 82.76 280 | 50.44 74 | 89.00 183 | 65.90 191 | 80.61 88 | 91.64 68 |
|
| UGNet | | | 68.71 262 | 67.11 266 | 73.50 272 | 80.55 220 | 47.61 333 | 84.08 220 | 78.51 333 | 59.45 253 | 65.68 196 | 82.73 283 | 23.78 427 | 85.08 345 | 52.80 327 | 76.40 149 | 87.80 221 |
| Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022 |
| ACMP | | 61.11 9 | 66.24 320 | 64.33 321 | 72.00 319 | 74.89 360 | 49.12 273 | 83.18 256 | 79.83 293 | 55.41 346 | 52.29 389 | 82.68 284 | 25.83 410 | 86.10 316 | 60.89 240 | 63.94 310 | 80.78 375 |
| Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
| Syy-MVS | | | 61.51 366 | 61.35 350 | 62.00 431 | 81.73 172 | 30.09 478 | 80.97 324 | 81.02 263 | 60.93 229 | 55.06 363 | 82.64 285 | 35.09 325 | 80.81 389 | 16.40 497 | 58.32 363 | 75.10 438 |
|
| myMVS_eth3d | | | 63.52 346 | 63.56 327 | 63.40 422 | 81.73 172 | 34.28 456 | 80.97 324 | 81.02 263 | 60.93 229 | 55.06 363 | 82.64 285 | 48.00 100 | 80.81 389 | 23.42 480 | 58.32 363 | 75.10 438 |
|
| test-LLR | | | 69.65 242 | 69.01 225 | 71.60 330 | 78.67 274 | 48.17 310 | 85.13 175 | 79.72 296 | 59.18 264 | 63.13 245 | 82.58 287 | 36.91 293 | 80.24 399 | 60.56 245 | 75.17 178 | 86.39 263 |
|
| test-mter | | | 68.36 268 | 67.29 261 | 71.60 330 | 78.67 274 | 48.17 310 | 85.13 175 | 79.72 296 | 53.38 368 | 63.13 245 | 82.58 287 | 27.23 399 | 80.24 399 | 60.56 245 | 75.17 178 | 86.39 263 |
|
| test_fmvs1 | | | 53.60 420 | 52.54 414 | 56.78 452 | 58.07 479 | 30.26 476 | 68.95 437 | 42.19 494 | 32.46 479 | 63.59 241 | 82.56 289 | 11.55 480 | 60.81 481 | 58.25 270 | 55.27 398 | 79.28 389 |
|
| UniMVSNet_NR-MVSNet | | | 68.82 258 | 68.29 235 | 70.40 351 | 75.71 344 | 42.59 414 | 84.23 215 | 86.78 98 | 66.31 111 | 58.51 315 | 82.45 290 | 51.57 60 | 84.64 352 | 53.11 322 | 55.96 392 | 83.96 312 |
|
| test0.0.03 1 | | | 62.54 357 | 62.44 334 | 62.86 427 | 72.28 396 | 29.51 484 | 82.93 265 | 78.78 323 | 59.18 264 | 53.07 385 | 82.41 291 | 36.91 293 | 77.39 429 | 37.45 411 | 58.96 357 | 81.66 357 |
|
| Test_1112_low_res | | | 67.18 299 | 66.23 284 | 70.02 359 | 78.75 272 | 41.02 431 | 83.43 244 | 73.69 402 | 57.29 304 | 58.45 320 | 82.39 292 | 45.30 161 | 80.88 387 | 50.50 345 | 66.26 290 | 88.16 211 |
|
| WB-MVSnew | | | 69.36 247 | 68.24 236 | 72.72 293 | 79.26 256 | 49.40 269 | 85.72 148 | 88.85 43 | 61.33 218 | 64.59 217 | 82.38 293 | 34.57 334 | 87.53 262 | 46.82 373 | 70.63 240 | 81.22 371 |
|
| SDMVSNet | | | 71.89 188 | 70.62 189 | 75.70 190 | 81.70 174 | 51.61 202 | 73.89 399 | 88.72 48 | 66.58 104 | 61.64 266 | 82.38 293 | 37.63 273 | 89.48 163 | 77.44 81 | 65.60 294 | 86.01 267 |
|
| sd_testset | | | 67.79 281 | 65.95 291 | 73.32 276 | 81.70 174 | 46.33 362 | 68.99 436 | 80.30 280 | 66.58 104 | 61.64 266 | 82.38 293 | 30.45 379 | 87.63 256 | 55.86 300 | 65.60 294 | 86.01 267 |
|
| RRT-MVS | | | 73.29 154 | 71.37 174 | 79.07 61 | 84.63 89 | 54.16 126 | 78.16 366 | 86.64 104 | 61.67 212 | 60.17 280 | 82.35 296 | 40.63 237 | 92.26 63 | 70.19 154 | 77.87 127 | 90.81 114 |
|
| XXY-MVS | | | 70.18 223 | 69.28 219 | 72.89 288 | 77.64 295 | 42.88 411 | 85.06 180 | 87.50 83 | 62.58 195 | 62.66 253 | 82.34 297 | 43.64 192 | 89.83 146 | 58.42 267 | 63.70 312 | 85.96 271 |
|
| thres600view7 | | | 66.46 315 | 65.12 312 | 70.47 348 | 83.41 117 | 43.80 399 | 82.15 287 | 87.78 74 | 59.37 256 | 56.02 356 | 82.21 298 | 43.73 188 | 86.90 286 | 26.51 468 | 64.94 298 | 80.71 377 |
|
| thres100view900 | | | 66.87 308 | 65.42 306 | 71.24 336 | 83.29 123 | 43.15 408 | 81.67 305 | 87.78 74 | 59.04 268 | 55.92 357 | 82.18 299 | 43.73 188 | 87.80 245 | 28.80 456 | 66.36 286 | 82.78 344 |
|
| DU-MVS | | | 66.84 309 | 65.74 297 | 70.16 354 | 73.27 381 | 42.59 414 | 81.50 314 | 82.92 227 | 63.53 172 | 58.51 315 | 82.11 300 | 40.75 233 | 84.64 352 | 53.11 322 | 55.96 392 | 83.24 332 |
|
| NR-MVSNet | | | 67.25 297 | 65.99 290 | 71.04 341 | 73.27 381 | 43.91 397 | 85.32 167 | 84.75 175 | 66.05 121 | 53.65 382 | 82.11 300 | 45.05 164 | 85.97 327 | 47.55 366 | 56.18 389 | 83.24 332 |
|
| mvsmamba | | | 69.38 246 | 67.52 257 | 74.95 225 | 82.86 141 | 52.22 183 | 67.36 444 | 76.75 367 | 61.14 222 | 49.43 413 | 82.04 302 | 37.26 284 | 84.14 356 | 73.93 118 | 76.91 141 | 88.50 205 |
|
| test_fmvs1_n | | | 52.55 425 | 51.19 419 | 56.65 453 | 51.90 490 | 30.14 477 | 67.66 442 | 42.84 493 | 32.27 480 | 62.30 256 | 82.02 303 | 9.12 489 | 60.84 480 | 57.82 279 | 54.75 404 | 78.99 391 |
|
| TranMVSNet+NR-MVSNet | | | 66.94 307 | 65.61 300 | 70.93 343 | 73.45 377 | 43.38 404 | 83.02 263 | 84.25 192 | 65.31 135 | 58.33 322 | 81.90 304 | 39.92 247 | 85.52 334 | 49.43 352 | 54.89 401 | 83.89 315 |
|
| 0.3-1-1-0.015 | | | 72.75 165 | 71.06 181 | 77.81 113 | 80.58 218 | 50.62 226 | 89.45 37 | 88.60 55 | 63.74 166 | 65.56 197 | 81.82 305 | 46.61 123 | 90.64 117 | 62.86 223 | 60.35 343 | 92.17 45 |
|
| 0.4-1-1-0.2 | | | 72.79 164 | 71.07 180 | 77.94 111 | 80.58 218 | 50.83 222 | 89.59 35 | 88.63 51 | 63.94 161 | 65.74 195 | 81.80 306 | 46.05 136 | 90.68 113 | 62.98 222 | 60.35 343 | 92.31 41 |
|
| IB-MVS | | 68.87 2 | 74.01 137 | 72.03 165 | 79.94 45 | 83.04 132 | 55.50 58 | 90.24 25 | 88.65 49 | 67.14 93 | 61.38 268 | 81.74 307 | 53.21 50 | 94.28 24 | 60.45 249 | 62.41 330 | 90.03 149 |
| Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021 |
| tt0805 | | | 63.39 348 | 61.31 351 | 69.64 361 | 69.36 432 | 38.87 441 | 78.00 367 | 85.48 131 | 48.82 403 | 55.66 361 | 81.66 308 | 24.38 424 | 86.37 307 | 49.04 356 | 59.36 355 | 83.68 323 |
|
| MVSTER | | | 73.25 155 | 72.33 152 | 76.01 179 | 85.54 72 | 53.76 133 | 83.52 236 | 87.16 89 | 67.06 97 | 63.88 231 | 81.66 308 | 52.77 52 | 90.44 124 | 64.66 207 | 64.69 303 | 83.84 316 |
|
| 0.4-1-1-0.1 | | | 72.39 173 | 70.70 186 | 77.46 125 | 80.45 224 | 50.04 248 | 89.09 47 | 88.45 60 | 63.06 182 | 64.91 210 | 81.60 310 | 45.98 140 | 90.46 123 | 62.40 226 | 60.34 345 | 91.88 58 |
|
| VPA-MVSNet | | | 71.12 204 | 70.66 188 | 72.49 302 | 78.75 272 | 44.43 390 | 87.64 73 | 90.02 23 | 63.97 159 | 65.02 205 | 81.58 311 | 42.14 214 | 87.42 266 | 63.42 218 | 63.38 318 | 85.63 279 |
|
| cascas | | | 69.01 254 | 66.13 286 | 77.66 118 | 79.36 252 | 55.41 63 | 86.99 97 | 83.75 205 | 56.69 320 | 58.92 304 | 81.35 312 | 24.31 425 | 92.10 68 | 53.23 321 | 70.61 241 | 85.46 281 |
|
| WR-MVS | | | 67.58 285 | 66.76 272 | 70.04 358 | 75.92 342 | 45.06 385 | 86.23 122 | 85.28 144 | 64.31 148 | 58.50 317 | 81.00 313 | 44.80 175 | 82.00 380 | 49.21 355 | 55.57 397 | 83.06 337 |
|
| UniMVSNet (Re) | | | 67.71 282 | 66.80 271 | 70.45 349 | 74.44 365 | 42.93 410 | 82.42 283 | 84.90 168 | 63.69 168 | 59.63 287 | 80.99 314 | 47.18 110 | 85.23 341 | 51.17 343 | 56.75 383 | 83.19 334 |
|
| ab-mvs | | | 70.65 217 | 69.11 222 | 75.29 212 | 80.87 208 | 46.23 368 | 73.48 404 | 85.24 147 | 59.99 243 | 66.65 179 | 80.94 315 | 43.13 203 | 88.69 199 | 63.58 217 | 68.07 268 | 90.95 110 |
|
| PVSNet_BlendedMVS | | | 73.42 152 | 73.30 133 | 73.76 263 | 85.91 63 | 51.83 194 | 86.18 124 | 84.24 194 | 65.40 131 | 69.09 159 | 80.86 316 | 46.70 121 | 88.13 228 | 75.43 98 | 65.92 293 | 81.33 367 |
|
| tttt0517 | | | 68.33 270 | 66.29 282 | 74.46 236 | 78.08 288 | 49.06 274 | 80.88 327 | 89.08 36 | 54.40 360 | 54.75 369 | 80.77 317 | 51.31 62 | 90.33 128 | 49.35 353 | 58.01 371 | 83.99 308 |
|
| MS-PatchMatch | | | 72.34 176 | 71.26 175 | 75.61 192 | 82.38 154 | 55.55 57 | 88.00 63 | 89.95 25 | 65.38 132 | 56.51 353 | 80.74 318 | 32.28 360 | 92.89 41 | 57.95 276 | 88.10 16 | 78.39 402 |
|
| HyFIR lowres test | | | 69.94 234 | 67.58 253 | 77.04 140 | 77.11 316 | 57.29 25 | 81.49 316 | 79.11 316 | 58.27 281 | 58.86 306 | 80.41 319 | 42.33 210 | 86.96 282 | 61.91 232 | 68.68 265 | 86.87 245 |
|
| usedtu_dtu_shiyan1 | | | 69.05 251 | 67.91 241 | 72.46 304 | 75.40 349 | 46.24 366 | 85.74 145 | 86.80 96 | 65.23 137 | 58.75 310 | 80.31 320 | 40.90 231 | 86.83 288 | 53.29 319 | 64.77 299 | 84.31 299 |
|
| FE-MVSNET3 | | | 69.05 251 | 67.91 241 | 72.46 304 | 75.39 350 | 46.24 366 | 85.74 145 | 86.80 96 | 65.23 137 | 58.75 310 | 80.31 320 | 40.90 231 | 86.83 288 | 53.29 319 | 64.77 299 | 84.31 299 |
|
| WBMVS | | | 73.93 139 | 73.39 131 | 75.55 196 | 87.82 42 | 55.21 72 | 89.37 39 | 87.29 84 | 67.27 89 | 63.70 236 | 80.30 322 | 60.32 8 | 86.47 303 | 61.58 235 | 62.85 327 | 84.97 289 |
|
| nomal-1 | | | 72.45 172 | 71.14 179 | 76.37 164 | 84.65 88 | 56.28 47 | 68.39 440 | 88.28 64 | 67.21 91 | 62.98 247 | 80.23 323 | 49.71 82 | 86.05 320 | 69.36 162 | 69.48 257 | 86.78 254 |
|
| ACMM | | 58.35 12 | 64.35 336 | 62.01 342 | 71.38 334 | 74.21 370 | 48.51 295 | 82.25 285 | 79.66 299 | 47.61 412 | 54.54 371 | 80.11 324 | 25.26 415 | 86.00 323 | 51.26 341 | 63.16 322 | 79.64 388 |
| Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
| testing3 | | | 59.97 373 | 60.19 363 | 59.32 444 | 77.60 297 | 30.01 480 | 81.75 301 | 81.79 248 | 53.54 366 | 50.34 410 | 79.94 325 | 48.99 89 | 76.91 433 | 17.19 495 | 50.59 423 | 71.03 467 |
|
| LS3D | | | 56.40 404 | 53.82 404 | 64.12 415 | 81.12 199 | 45.69 378 | 73.42 405 | 66.14 452 | 35.30 476 | 43.24 448 | 79.88 326 | 22.18 439 | 79.62 408 | 19.10 491 | 64.00 309 | 67.05 473 |
|
| test_vis1_n | | | 51.19 433 | 49.66 428 | 55.76 457 | 51.26 493 | 29.85 482 | 67.20 445 | 38.86 498 | 32.12 481 | 59.50 291 | 79.86 327 | 8.78 490 | 58.23 488 | 56.95 288 | 52.46 418 | 79.19 390 |
|
| PS-MVSNAJss | | | 68.78 261 | 67.17 265 | 73.62 269 | 73.01 384 | 48.33 304 | 84.95 189 | 84.81 171 | 59.30 260 | 58.91 305 | 79.84 328 | 37.77 267 | 88.86 193 | 62.83 224 | 63.12 324 | 83.67 324 |
|
| SSC-MVS3.2 | | | 68.13 275 | 66.89 267 | 71.85 328 | 82.26 156 | 43.97 396 | 82.09 290 | 89.29 31 | 71.74 20 | 61.12 271 | 79.83 329 | 34.60 333 | 87.45 264 | 41.23 400 | 59.85 349 | 84.14 302 |
|
| Elysia | | | 65.59 325 | 62.65 331 | 74.42 238 | 69.85 428 | 49.46 267 | 80.04 343 | 82.11 238 | 46.32 425 | 58.74 312 | 79.64 330 | 20.30 448 | 88.57 207 | 55.48 305 | 71.37 232 | 85.22 284 |
|
| StellarMVS | | | 65.59 325 | 62.65 331 | 74.42 238 | 69.85 428 | 49.46 267 | 80.04 343 | 82.11 238 | 46.32 425 | 58.74 312 | 79.64 330 | 20.30 448 | 88.57 207 | 55.48 305 | 71.37 232 | 85.22 284 |
|
| UniMVSNet_ETH3D | | | 62.51 358 | 60.49 358 | 68.57 378 | 68.30 442 | 40.88 433 | 73.89 399 | 79.93 291 | 51.81 382 | 54.77 368 | 79.61 332 | 24.80 420 | 81.10 384 | 49.93 348 | 61.35 335 | 83.73 317 |
|
| miper_enhance_ethall | | | 69.77 236 | 68.90 226 | 72.38 307 | 78.93 268 | 49.91 251 | 83.29 251 | 78.85 320 | 64.90 141 | 59.37 293 | 79.46 333 | 52.77 52 | 85.16 343 | 63.78 214 | 58.72 359 | 82.08 349 |
|
| F-COLMAP | | | 55.96 408 | 53.65 406 | 62.87 426 | 72.76 388 | 42.77 413 | 74.70 393 | 70.37 435 | 40.03 453 | 41.11 460 | 79.36 334 | 17.77 463 | 73.70 453 | 32.80 442 | 53.96 408 | 72.15 459 |
|
| mvs_anonymous | | | 72.29 179 | 70.74 185 | 76.94 147 | 82.85 142 | 54.72 106 | 78.43 365 | 81.54 254 | 63.77 164 | 61.69 265 | 79.32 335 | 51.11 63 | 85.31 338 | 62.15 231 | 75.79 164 | 90.79 116 |
|
| v2v482 | | | 69.55 244 | 67.64 252 | 75.26 216 | 72.32 394 | 53.83 129 | 84.93 190 | 81.94 243 | 65.37 133 | 60.80 274 | 79.25 336 | 41.62 222 | 88.98 186 | 63.03 221 | 59.51 352 | 82.98 340 |
|
| GA-MVS | | | 69.04 253 | 66.70 274 | 76.06 177 | 75.11 355 | 52.36 176 | 83.12 259 | 80.23 281 | 63.32 177 | 60.65 276 | 79.22 337 | 30.98 376 | 88.37 216 | 61.25 237 | 66.41 284 | 87.46 230 |
|
| FMVSNet3 | | | 68.84 257 | 67.40 259 | 73.19 281 | 85.05 81 | 48.53 294 | 85.71 149 | 85.36 137 | 60.90 231 | 57.58 333 | 79.15 338 | 42.16 213 | 86.77 292 | 47.25 369 | 63.40 315 | 84.27 301 |
|
| Fast-Effi-MVS+-dtu | | | 66.53 314 | 64.10 324 | 73.84 260 | 72.41 392 | 52.30 181 | 84.73 197 | 75.66 379 | 59.51 252 | 56.34 354 | 79.11 339 | 28.11 391 | 85.85 330 | 57.74 282 | 63.29 319 | 83.35 328 |
|
| MVP-Stereo | | | 70.97 209 | 70.44 192 | 72.59 299 | 76.03 337 | 51.36 209 | 85.02 185 | 86.99 93 | 60.31 239 | 56.53 352 | 78.92 340 | 40.11 243 | 90.00 138 | 60.00 253 | 90.01 7 | 76.41 427 |
| Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
| DP-MVS | | | 59.24 378 | 56.12 391 | 68.63 375 | 88.24 36 | 50.35 241 | 82.51 280 | 64.43 462 | 41.10 452 | 46.70 433 | 78.77 341 | 24.75 421 | 88.57 207 | 22.26 482 | 56.29 388 | 66.96 474 |
|
| pmmvs4 | | | 63.34 349 | 61.07 354 | 70.16 354 | 70.14 424 | 50.53 230 | 79.97 347 | 71.41 428 | 55.08 350 | 54.12 376 | 78.58 342 | 32.79 355 | 82.09 379 | 50.33 346 | 57.22 380 | 77.86 409 |
|
| pmmvs5 | | | 62.80 355 | 61.18 352 | 67.66 383 | 69.53 431 | 42.37 419 | 82.65 272 | 75.19 385 | 54.30 361 | 52.03 393 | 78.51 343 | 31.64 371 | 80.67 391 | 48.60 359 | 58.15 367 | 79.95 386 |
|
| FA-MVS(test-final) | | | 69.00 255 | 66.60 277 | 76.19 173 | 83.48 116 | 47.96 321 | 74.73 391 | 82.07 241 | 57.27 305 | 62.18 257 | 78.47 344 | 36.09 310 | 92.89 41 | 53.76 318 | 71.32 235 | 87.73 223 |
|
| LuminaMVS | | | 66.60 313 | 64.37 320 | 73.27 280 | 70.06 427 | 49.57 257 | 80.77 330 | 81.76 251 | 50.81 388 | 60.56 277 | 78.41 345 | 24.50 423 | 87.26 273 | 64.24 208 | 68.25 266 | 82.99 338 |
|
| FMVSNet2 | | | 67.57 286 | 65.79 295 | 72.90 286 | 82.71 146 | 47.97 319 | 85.15 174 | 84.93 167 | 58.55 278 | 56.71 349 | 78.26 346 | 36.72 298 | 86.67 296 | 46.15 377 | 62.94 326 | 84.07 305 |
|
| cl22 | | | 68.85 256 | 67.69 251 | 72.35 308 | 78.07 289 | 49.98 250 | 82.45 282 | 78.48 334 | 62.50 198 | 58.46 319 | 77.95 347 | 49.99 78 | 85.17 342 | 62.55 225 | 58.72 359 | 81.90 352 |
|
| v1144 | | | 68.81 259 | 66.82 270 | 74.80 229 | 72.34 393 | 53.46 138 | 84.68 200 | 81.77 250 | 64.25 150 | 60.28 279 | 77.91 348 | 40.23 240 | 88.95 188 | 60.37 250 | 59.52 351 | 81.97 350 |
|
| miper_ehance_all_eth | | | 68.70 264 | 67.58 253 | 72.08 315 | 76.91 320 | 49.48 266 | 82.47 281 | 78.45 335 | 62.68 194 | 58.28 323 | 77.88 349 | 50.90 66 | 85.01 346 | 61.91 232 | 58.72 359 | 81.75 354 |
|
| pm-mvs1 | | | 64.12 339 | 62.56 333 | 68.78 372 | 71.68 400 | 38.87 441 | 82.89 267 | 81.57 253 | 55.54 343 | 53.89 379 | 77.82 350 | 37.73 270 | 86.74 293 | 48.46 362 | 53.49 413 | 80.72 376 |
|
| jajsoiax | | | 63.21 350 | 60.84 355 | 70.32 352 | 68.33 441 | 44.45 389 | 81.23 319 | 81.05 262 | 53.37 369 | 50.96 404 | 77.81 351 | 17.49 465 | 85.49 336 | 59.31 256 | 58.05 370 | 81.02 373 |
|
| mvs_tets | | | 62.96 353 | 60.55 357 | 70.19 353 | 68.22 444 | 44.24 394 | 80.90 326 | 80.74 270 | 52.99 372 | 50.82 408 | 77.56 352 | 16.74 469 | 85.44 337 | 59.04 259 | 57.94 372 | 80.89 374 |
|
| MSDG | | | 59.44 376 | 55.14 397 | 72.32 310 | 74.69 361 | 50.71 224 | 74.39 396 | 73.58 403 | 44.44 439 | 43.40 446 | 77.52 353 | 19.45 452 | 90.87 107 | 31.31 447 | 57.49 379 | 75.38 433 |
|
| V42 | | | 67.66 283 | 65.60 301 | 73.86 259 | 70.69 416 | 53.63 135 | 81.50 314 | 78.61 330 | 63.85 162 | 59.49 292 | 77.49 354 | 37.98 264 | 87.65 255 | 62.33 227 | 58.43 362 | 80.29 382 |
|
| reproduce_monomvs | | | 69.71 237 | 68.52 230 | 73.29 279 | 86.43 57 | 48.21 309 | 83.91 227 | 86.17 115 | 68.02 79 | 54.91 365 | 77.46 355 | 42.96 205 | 88.86 193 | 68.44 171 | 48.38 434 | 82.80 343 |
|
| v1192 | | | 67.96 277 | 65.74 297 | 74.63 233 | 71.79 398 | 53.43 143 | 84.06 222 | 80.99 267 | 63.19 180 | 59.56 289 | 77.46 355 | 37.50 279 | 88.65 200 | 58.20 271 | 58.93 358 | 81.79 353 |
|
| CHOSEN 280x420 | | | 57.53 398 | 56.38 390 | 60.97 440 | 74.01 373 | 48.10 314 | 46.30 488 | 54.31 481 | 48.18 409 | 50.88 407 | 77.43 357 | 38.37 261 | 59.16 487 | 54.83 309 | 63.14 323 | 75.66 431 |
|
| testgi | | | 54.25 414 | 52.57 413 | 59.29 446 | 62.76 471 | 21.65 502 | 72.21 418 | 70.47 434 | 53.25 370 | 41.94 453 | 77.33 358 | 14.28 475 | 77.95 423 | 29.18 455 | 51.72 421 | 78.28 404 |
|
| v144192 | | | 67.86 278 | 65.76 296 | 74.16 248 | 71.68 400 | 53.09 156 | 84.14 219 | 80.83 269 | 62.85 190 | 59.21 298 | 77.28 359 | 39.30 252 | 88.00 234 | 58.67 263 | 57.88 375 | 81.40 364 |
|
| v1921920 | | | 67.45 289 | 65.23 310 | 74.10 251 | 71.51 403 | 52.90 162 | 83.75 233 | 80.44 277 | 62.48 199 | 59.12 299 | 77.13 360 | 36.98 291 | 87.90 237 | 57.53 283 | 58.14 369 | 81.49 359 |
|
| v1240 | | | 66.99 305 | 64.68 316 | 73.93 256 | 71.38 407 | 52.66 170 | 83.39 248 | 79.98 287 | 61.97 207 | 58.44 321 | 77.11 361 | 35.25 322 | 87.81 242 | 56.46 295 | 58.15 367 | 81.33 367 |
|
| IterMVS-LS | | | 66.63 311 | 65.36 307 | 70.42 350 | 75.10 356 | 48.90 282 | 81.45 317 | 76.69 371 | 61.05 225 | 55.71 358 | 77.10 362 | 45.86 147 | 83.65 364 | 57.44 284 | 57.88 375 | 78.70 395 |
| Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
| VortexMVS | | | 68.49 266 | 66.84 269 | 73.46 273 | 81.10 201 | 48.75 287 | 84.63 203 | 84.73 176 | 62.05 204 | 57.22 343 | 77.08 363 | 34.54 336 | 89.20 176 | 63.08 219 | 57.12 381 | 82.43 346 |
|
| eth_miper_zixun_eth | | | 66.98 306 | 65.28 308 | 72.06 316 | 75.61 346 | 50.40 235 | 81.00 323 | 76.97 366 | 62.00 205 | 56.99 345 | 76.97 364 | 44.84 172 | 85.58 333 | 58.75 262 | 54.42 405 | 80.21 383 |
|
| c3_l | | | 67.97 276 | 66.66 275 | 71.91 326 | 76.20 333 | 49.31 271 | 82.13 289 | 78.00 343 | 61.99 206 | 57.64 332 | 76.94 365 | 49.41 85 | 84.93 347 | 60.62 244 | 57.01 382 | 81.49 359 |
|
| cl____ | | | 67.43 290 | 65.93 292 | 71.95 323 | 76.33 327 | 48.02 317 | 82.58 274 | 79.12 315 | 61.30 220 | 56.72 348 | 76.92 366 | 46.12 132 | 86.44 305 | 57.98 274 | 56.31 386 | 81.38 366 |
|
| DIV-MVS_self_test | | | 67.43 290 | 65.93 292 | 71.94 324 | 76.33 327 | 48.01 318 | 82.57 275 | 79.11 316 | 61.31 219 | 56.73 347 | 76.92 366 | 46.09 135 | 86.43 306 | 57.98 274 | 56.31 386 | 81.39 365 |
|
| Baseline_NR-MVSNet | | | 65.49 329 | 64.27 322 | 69.13 366 | 74.37 368 | 41.65 424 | 83.39 248 | 78.85 320 | 59.56 251 | 59.62 288 | 76.88 368 | 40.75 233 | 87.44 265 | 49.99 347 | 55.05 399 | 78.28 404 |
|
| CostFormer | | | 73.89 142 | 72.30 154 | 78.66 75 | 82.36 155 | 56.58 37 | 75.56 383 | 85.30 142 | 66.06 120 | 70.50 145 | 76.88 368 | 57.02 26 | 89.06 179 | 68.27 174 | 68.74 264 | 90.33 132 |
|
| PEN-MVS | | | 58.35 393 | 57.15 382 | 61.94 432 | 67.55 446 | 34.39 455 | 77.01 372 | 78.35 338 | 51.87 380 | 47.72 424 | 76.73 370 | 33.91 341 | 73.75 452 | 34.03 435 | 47.17 444 | 77.68 412 |
|
| Anonymous20231211 | | | 66.08 322 | 63.67 325 | 73.31 277 | 83.07 130 | 48.75 287 | 86.01 131 | 84.67 183 | 45.27 432 | 56.54 351 | 76.67 371 | 28.06 392 | 88.95 188 | 52.78 328 | 59.95 346 | 82.23 348 |
|
| CP-MVSNet | | | 58.54 392 | 57.57 380 | 61.46 436 | 68.50 439 | 33.96 460 | 76.90 374 | 78.60 331 | 51.67 383 | 47.83 423 | 76.60 372 | 34.99 328 | 72.79 458 | 35.45 425 | 47.58 440 | 77.64 414 |
|
| v148 | | | 68.24 273 | 66.35 280 | 73.88 258 | 71.76 399 | 51.47 207 | 84.23 215 | 81.90 247 | 63.69 168 | 58.94 302 | 76.44 373 | 43.72 190 | 87.78 249 | 60.63 243 | 55.86 394 | 82.39 347 |
|
| TransMVSNet (Re) | | | 62.82 354 | 60.76 356 | 69.02 367 | 73.98 374 | 41.61 425 | 86.36 118 | 79.30 314 | 56.90 311 | 52.53 387 | 76.44 373 | 41.85 220 | 87.60 259 | 38.83 408 | 40.61 465 | 77.86 409 |
|
| DTE-MVSNet | | | 57.03 399 | 55.73 394 | 60.95 441 | 65.94 451 | 32.57 467 | 75.71 379 | 77.09 362 | 51.16 387 | 46.65 434 | 76.34 375 | 32.84 354 | 73.22 457 | 30.94 449 | 44.87 453 | 77.06 417 |
|
| test_djsdf | | | 63.84 342 | 61.56 346 | 70.70 346 | 68.78 436 | 44.69 387 | 81.63 306 | 81.44 256 | 50.28 392 | 52.27 390 | 76.26 376 | 26.72 403 | 86.11 314 | 60.83 241 | 55.84 395 | 81.29 370 |
|
| GBi-Net | | | 67.09 302 | 65.47 303 | 71.96 320 | 82.71 146 | 46.36 359 | 83.52 236 | 83.31 215 | 58.55 278 | 57.58 333 | 76.23 377 | 36.72 298 | 86.20 310 | 47.25 369 | 63.40 315 | 83.32 329 |
|
| test1 | | | 67.09 302 | 65.47 303 | 71.96 320 | 82.71 146 | 46.36 359 | 83.52 236 | 83.31 215 | 58.55 278 | 57.58 333 | 76.23 377 | 36.72 298 | 86.20 310 | 47.25 369 | 63.40 315 | 83.32 329 |
|
| FMVSNet1 | | | 64.57 334 | 62.11 339 | 71.96 320 | 77.32 308 | 46.36 359 | 83.52 236 | 83.31 215 | 52.43 376 | 54.42 372 | 76.23 377 | 27.80 395 | 86.20 310 | 42.59 397 | 61.34 336 | 83.32 329 |
|
| PS-CasMVS | | | 58.12 394 | 57.03 384 | 61.37 437 | 68.24 443 | 33.80 462 | 76.73 376 | 78.01 342 | 51.20 386 | 47.54 427 | 76.20 380 | 32.85 353 | 72.76 459 | 35.17 430 | 47.37 442 | 77.55 415 |
|
| Effi-MVS+-dtu | | | 66.24 320 | 64.96 315 | 70.08 356 | 75.17 354 | 49.64 256 | 82.01 291 | 74.48 392 | 62.15 202 | 57.83 326 | 76.08 381 | 30.59 378 | 83.79 361 | 65.40 200 | 60.93 340 | 76.81 420 |
|
| v8 | | | 67.25 297 | 64.99 314 | 74.04 252 | 72.89 387 | 53.31 148 | 82.37 284 | 80.11 285 | 61.54 215 | 54.29 375 | 76.02 382 | 42.89 206 | 88.41 215 | 58.43 265 | 56.36 384 | 80.39 381 |
|
| RPSCF | | | 45.77 446 | 44.13 448 | 50.68 462 | 57.67 482 | 29.66 483 | 54.92 482 | 45.25 490 | 26.69 489 | 45.92 437 | 75.92 383 | 17.43 466 | 45.70 502 | 27.44 465 | 45.95 451 | 76.67 421 |
|
| v10 | | | 66.61 312 | 64.20 323 | 73.83 261 | 72.59 390 | 53.37 144 | 81.88 295 | 79.91 292 | 61.11 223 | 54.09 377 | 75.60 384 | 40.06 244 | 88.26 226 | 56.47 294 | 56.10 390 | 79.86 387 |
|
| ACMH+ | | 54.58 15 | 58.55 391 | 55.24 395 | 68.50 379 | 74.68 362 | 45.80 376 | 80.27 338 | 70.21 436 | 47.15 416 | 42.77 450 | 75.48 385 | 16.73 470 | 85.98 325 | 35.10 432 | 54.78 402 | 73.72 448 |
|
| tpm2 | | | 70.82 212 | 68.44 232 | 77.98 107 | 80.78 211 | 56.11 49 | 74.21 398 | 81.28 260 | 60.24 240 | 68.04 170 | 75.27 386 | 52.26 56 | 88.50 211 | 55.82 302 | 68.03 269 | 89.33 173 |
|
| ITE_SJBPF | | | | | 51.84 461 | 58.03 480 | 31.94 472 | | 53.57 484 | 36.67 467 | 41.32 458 | 75.23 387 | 11.17 482 | 51.57 496 | 25.81 470 | 48.04 437 | 72.02 461 |
|
| tpm | | | 68.36 268 | 67.48 258 | 70.97 342 | 79.93 237 | 51.34 210 | 76.58 377 | 78.75 326 | 67.73 83 | 63.54 243 | 74.86 388 | 48.33 92 | 72.36 461 | 53.93 316 | 63.71 311 | 89.21 177 |
|
| WR-MVS_H | | | 58.91 385 | 58.04 377 | 61.54 435 | 69.07 435 | 33.83 461 | 76.91 373 | 81.99 242 | 51.40 384 | 48.17 419 | 74.67 389 | 40.23 240 | 74.15 448 | 31.78 445 | 48.10 436 | 76.64 424 |
|
| CMPMVS |  | 40.41 21 | 55.34 409 | 52.64 412 | 63.46 421 | 60.88 476 | 43.84 398 | 61.58 466 | 71.06 431 | 30.43 484 | 36.33 474 | 74.63 390 | 24.14 426 | 75.44 444 | 48.05 364 | 66.62 280 | 71.12 466 |
| M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
| test_fmvs2 | | | 45.89 445 | 44.32 447 | 50.62 463 | 45.85 502 | 24.70 494 | 58.87 474 | 37.84 501 | 25.22 490 | 52.46 388 | 74.56 391 | 7.07 493 | 54.69 492 | 49.28 354 | 47.70 439 | 72.48 457 |
|
| mvsany_test1 | | | 43.38 449 | 42.57 451 | 45.82 470 | 50.96 494 | 26.10 492 | 55.80 478 | 27.74 511 | 27.15 488 | 47.41 429 | 74.39 392 | 18.67 458 | 44.95 503 | 44.66 383 | 36.31 475 | 66.40 476 |
|
| XVG-ACMP-BASELINE | | | 56.03 406 | 52.85 410 | 65.58 404 | 61.91 473 | 40.95 432 | 63.36 456 | 72.43 415 | 45.20 433 | 46.02 436 | 74.09 393 | 9.20 488 | 78.12 417 | 45.13 380 | 58.27 365 | 77.66 413 |
|
| LTVRE_ROB | | 45.45 19 | 52.73 423 | 49.74 427 | 61.69 434 | 69.78 430 | 34.99 452 | 44.52 491 | 67.60 450 | 43.11 447 | 43.79 443 | 74.03 394 | 18.54 459 | 81.45 382 | 28.39 461 | 57.94 372 | 68.62 470 |
| Andreas Kuhn, Heiko Hirschmüller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016 |
| MonoMVSNet | | | 66.80 310 | 64.41 319 | 73.96 255 | 76.21 332 | 48.07 315 | 76.56 378 | 78.26 339 | 64.34 147 | 54.32 374 | 74.02 395 | 37.21 286 | 86.36 308 | 64.85 205 | 53.96 408 | 87.45 231 |
|
| pmmvs6 | | | 59.64 375 | 57.15 382 | 67.09 389 | 66.01 450 | 36.86 450 | 80.50 333 | 78.64 328 | 45.05 434 | 49.05 416 | 73.94 396 | 27.28 398 | 86.10 316 | 43.96 389 | 49.94 425 | 78.31 403 |
|
| FE-MVS | | | 64.15 338 | 60.43 360 | 75.30 211 | 80.85 209 | 49.86 253 | 68.28 441 | 78.37 337 | 50.26 395 | 59.31 295 | 73.79 397 | 26.19 407 | 91.92 71 | 40.19 403 | 66.67 279 | 84.12 303 |
|
| IterMVS-SCA-FT | | | 59.12 380 | 58.81 374 | 60.08 442 | 70.68 417 | 45.07 382 | 80.42 336 | 74.25 393 | 43.54 445 | 50.02 411 | 73.73 398 | 31.97 364 | 56.74 491 | 51.06 344 | 53.60 412 | 78.42 401 |
|
| tpmrst | | | 71.04 208 | 69.77 209 | 74.86 227 | 83.19 126 | 55.86 55 | 75.64 380 | 78.73 327 | 67.88 80 | 64.99 207 | 73.73 398 | 49.96 80 | 79.56 409 | 65.92 190 | 67.85 272 | 89.14 180 |
|
| PatchMatch-RL | | | 56.66 400 | 53.75 405 | 65.37 408 | 77.91 294 | 45.28 380 | 69.78 433 | 60.38 470 | 41.35 451 | 47.57 426 | 73.73 398 | 16.83 468 | 76.91 433 | 36.99 416 | 59.21 356 | 73.92 447 |
|
| IterMVS | | | 63.77 344 | 61.67 344 | 70.08 356 | 72.68 389 | 51.24 213 | 80.44 335 | 75.51 381 | 60.51 237 | 51.41 396 | 73.70 401 | 32.08 363 | 78.91 410 | 54.30 313 | 54.35 406 | 80.08 385 |
| Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
| tfpnnormal | | | 61.47 367 | 59.09 371 | 68.62 376 | 76.29 330 | 41.69 423 | 81.14 321 | 85.16 151 | 54.48 358 | 51.32 397 | 73.63 402 | 32.32 359 | 86.89 287 | 21.78 484 | 55.71 396 | 77.29 416 |
|
| COLMAP_ROB |  | 43.60 20 | 50.90 435 | 48.05 436 | 59.47 443 | 67.81 445 | 40.57 434 | 71.25 426 | 62.72 468 | 36.49 469 | 36.19 475 | 73.51 403 | 13.48 476 | 73.92 451 | 20.71 486 | 50.26 424 | 63.92 482 |
| Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
| EG-PatchMatch MVS | | | 62.40 362 | 59.59 366 | 70.81 344 | 73.29 379 | 49.05 275 | 85.81 138 | 84.78 173 | 51.85 381 | 44.19 441 | 73.48 404 | 15.52 474 | 89.85 145 | 40.16 404 | 67.24 275 | 73.54 450 |
|
| ACMH | | 53.70 16 | 59.78 374 | 55.94 393 | 71.28 335 | 76.59 323 | 48.35 301 | 80.15 342 | 76.11 376 | 49.74 397 | 41.91 454 | 73.45 405 | 16.50 471 | 90.31 129 | 31.42 446 | 57.63 378 | 75.17 436 |
| Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
| v7n | | | 62.50 359 | 59.27 370 | 72.20 312 | 67.25 447 | 49.83 254 | 77.87 369 | 80.12 284 | 52.50 375 | 48.80 418 | 73.07 406 | 32.10 362 | 87.90 237 | 46.83 372 | 54.92 400 | 78.86 393 |
|
| OpenMVS_ROB |  | 53.19 17 | 59.20 379 | 56.00 392 | 68.83 370 | 71.13 409 | 44.30 391 | 83.64 234 | 75.02 386 | 46.42 422 | 46.48 435 | 73.03 407 | 18.69 457 | 88.14 227 | 27.74 464 | 61.80 333 | 74.05 446 |
|
| blend_shiyan4 | | | 67.33 295 | 65.28 308 | 73.45 274 | 70.71 413 | 47.96 321 | 86.21 123 | 85.65 128 | 56.45 329 | 52.18 392 | 72.99 408 | 45.89 145 | 88.50 211 | 56.81 289 | 60.68 341 | 83.90 314 |
|
| AllTest | | | 47.32 443 | 44.66 445 | 55.32 458 | 65.08 458 | 37.50 448 | 62.96 460 | 54.25 482 | 35.45 474 | 33.42 484 | 72.82 409 | 9.98 485 | 59.33 484 | 24.13 474 | 43.84 456 | 69.13 468 |
|
| TestCases | | | | | 55.32 458 | 65.08 458 | 37.50 448 | | 54.25 482 | 35.45 474 | 33.42 484 | 72.82 409 | 9.98 485 | 59.33 484 | 24.13 474 | 43.84 456 | 69.13 468 |
|
| anonymousdsp | | | 60.46 372 | 57.65 378 | 68.88 368 | 63.63 467 | 45.09 381 | 72.93 408 | 78.63 329 | 46.52 420 | 51.12 401 | 72.80 411 | 21.46 443 | 83.07 371 | 57.79 280 | 53.97 407 | 78.47 399 |
|
| CL-MVSNet_self_test | | | 62.98 352 | 61.14 353 | 68.50 379 | 65.86 452 | 42.96 409 | 84.37 209 | 82.98 225 | 60.98 227 | 53.95 378 | 72.70 412 | 40.43 238 | 83.71 363 | 41.10 401 | 47.93 438 | 78.83 394 |
|
| EPMVS | | | 68.45 267 | 65.44 305 | 77.47 124 | 84.91 84 | 56.17 48 | 71.89 424 | 81.91 246 | 61.72 211 | 60.85 273 | 72.49 413 | 36.21 305 | 87.06 279 | 47.32 368 | 71.62 228 | 89.17 179 |
|
| LCM-MVSNet-Re | | | 58.82 386 | 56.54 385 | 65.68 403 | 79.31 255 | 29.09 487 | 61.39 467 | 45.79 488 | 60.73 234 | 37.65 471 | 72.47 414 | 31.42 372 | 81.08 385 | 49.66 350 | 70.41 247 | 86.87 245 |
|
| PVSNet_0 | | 57.04 13 | 61.19 368 | 57.24 381 | 73.02 282 | 77.45 305 | 50.31 243 | 79.43 356 | 77.36 358 | 63.96 160 | 47.51 428 | 72.45 415 | 25.03 418 | 83.78 362 | 52.76 330 | 19.22 504 | 84.96 290 |
|
| miper_lstm_enhance | | | 63.91 341 | 62.30 335 | 68.75 373 | 75.06 357 | 46.78 349 | 69.02 435 | 81.14 261 | 59.68 250 | 52.76 386 | 72.39 416 | 40.71 235 | 77.99 422 | 56.81 289 | 53.09 416 | 81.48 361 |
|
| Anonymous20231206 | | | 59.08 382 | 57.59 379 | 63.55 419 | 68.77 437 | 32.14 470 | 80.26 339 | 79.78 295 | 50.00 396 | 49.39 414 | 72.39 416 | 26.64 404 | 78.36 415 | 33.12 441 | 57.94 372 | 80.14 384 |
|
| test20.03 | | | 55.22 410 | 54.07 403 | 58.68 448 | 63.14 470 | 25.00 493 | 77.69 370 | 74.78 388 | 52.64 373 | 43.43 445 | 72.39 416 | 26.21 406 | 74.76 447 | 29.31 454 | 47.05 446 | 76.28 428 |
|
| wanda-best-256-512 | | | 64.87 330 | 62.23 336 | 72.81 289 | 70.49 418 | 46.85 347 | 85.71 149 | 85.71 124 | 56.85 312 | 51.25 398 | 72.31 419 | 36.16 306 | 87.84 239 | 52.67 332 | 48.90 428 | 83.73 317 |
|
| FE-blended-shiyan7 | | | 64.87 330 | 62.23 336 | 72.81 289 | 70.49 418 | 46.85 347 | 85.71 149 | 85.71 124 | 56.85 312 | 51.25 398 | 72.31 419 | 36.16 306 | 87.84 239 | 52.67 332 | 48.90 428 | 83.73 317 |
|
| usedtu_blend_shiyan5 | | | 63.62 345 | 60.36 361 | 73.40 275 | 70.49 418 | 47.96 321 | 79.13 359 | 80.68 271 | 47.51 414 | 51.25 398 | 72.31 419 | 36.16 306 | 88.50 211 | 56.81 289 | 48.90 428 | 83.73 317 |
|
| gbinet_0.2-2-1-0.02 | | | 64.20 337 | 61.39 348 | 72.63 297 | 70.85 412 | 46.32 363 | 85.92 132 | 85.98 118 | 55.27 348 | 51.88 395 | 72.29 422 | 33.14 349 | 87.82 241 | 48.50 360 | 48.72 432 | 83.73 317 |
|
| blended_shiyan8 | | | 64.70 332 | 62.04 340 | 72.69 294 | 70.33 422 | 46.62 353 | 85.48 160 | 85.66 126 | 56.58 325 | 50.94 405 | 72.18 423 | 35.81 316 | 87.80 245 | 52.47 335 | 48.91 427 | 83.65 326 |
|
| blended_shiyan6 | | | 64.70 332 | 62.04 340 | 72.69 294 | 70.34 421 | 46.60 355 | 85.48 160 | 85.65 128 | 56.59 324 | 50.91 406 | 72.18 423 | 35.82 315 | 87.81 242 | 52.46 336 | 48.90 428 | 83.66 325 |
|
| test_0402 | | | 56.45 403 | 53.03 407 | 66.69 395 | 76.78 322 | 50.31 243 | 81.76 299 | 69.61 441 | 42.79 448 | 43.88 442 | 72.13 425 | 22.82 434 | 86.46 304 | 16.57 496 | 50.94 422 | 63.31 483 |
|
| EU-MVSNet | | | 52.63 424 | 50.72 420 | 58.37 449 | 62.69 472 | 28.13 490 | 72.60 411 | 75.97 377 | 30.94 483 | 40.76 462 | 72.11 426 | 20.16 450 | 70.80 465 | 35.11 431 | 46.11 450 | 76.19 429 |
|
| D2MVS | | | 63.49 347 | 61.39 348 | 69.77 360 | 69.29 433 | 48.93 281 | 78.89 361 | 77.71 351 | 60.64 236 | 49.70 412 | 72.10 427 | 27.08 400 | 83.48 366 | 54.48 312 | 62.65 328 | 76.90 418 |
|
| USDC | | | 54.36 413 | 51.23 418 | 63.76 417 | 64.29 464 | 37.71 447 | 62.84 461 | 73.48 408 | 56.85 312 | 35.47 477 | 71.94 428 | 9.23 487 | 78.43 413 | 38.43 409 | 48.57 433 | 75.13 437 |
|
| OurMVSNet-221017-0 | | | 52.39 427 | 48.73 431 | 63.35 423 | 65.21 456 | 38.42 444 | 68.54 439 | 64.95 457 | 38.19 459 | 39.57 464 | 71.43 429 | 13.23 477 | 79.92 403 | 37.16 412 | 40.32 467 | 71.72 462 |
|
| KD-MVS_2432*1600 | | | 59.04 383 | 56.44 387 | 66.86 392 | 79.07 262 | 45.87 373 | 72.13 420 | 80.42 278 | 55.03 351 | 48.15 420 | 71.01 430 | 36.73 296 | 78.05 420 | 35.21 428 | 30.18 490 | 76.67 421 |
|
| miper_refine_blended | | | 59.04 383 | 56.44 387 | 66.86 392 | 79.07 262 | 45.87 373 | 72.13 420 | 80.42 278 | 55.03 351 | 48.15 420 | 71.01 430 | 36.73 296 | 78.05 420 | 35.21 428 | 30.18 490 | 76.67 421 |
|
| PatchmatchNet |  | | 67.07 304 | 63.63 326 | 77.40 127 | 83.10 127 | 58.03 14 | 72.11 422 | 77.77 349 | 58.85 272 | 59.37 293 | 70.83 432 | 37.84 266 | 84.93 347 | 42.96 394 | 69.83 252 | 89.26 174 |
| Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
| SCA | | | 63.84 342 | 60.01 365 | 75.32 208 | 78.58 279 | 57.92 15 | 61.61 465 | 77.53 353 | 56.71 319 | 57.75 330 | 70.77 433 | 31.97 364 | 79.91 405 | 48.80 357 | 56.36 384 | 88.13 214 |
|
| Patchmatch-test | | | 53.33 422 | 48.17 435 | 68.81 371 | 73.31 378 | 42.38 418 | 42.98 493 | 58.23 474 | 32.53 478 | 38.79 468 | 70.77 433 | 39.66 248 | 73.51 454 | 25.18 471 | 52.06 420 | 90.55 124 |
|
| tpm cat1 | | | 66.28 318 | 62.78 330 | 76.77 156 | 81.40 192 | 57.14 27 | 70.03 431 | 77.19 359 | 53.00 371 | 58.76 309 | 70.73 435 | 46.17 131 | 86.73 294 | 43.27 391 | 64.46 305 | 86.44 261 |
|
| dp | | | 64.41 335 | 61.58 345 | 72.90 286 | 82.40 153 | 54.09 127 | 72.53 412 | 76.59 373 | 60.39 238 | 55.68 359 | 70.39 436 | 35.18 324 | 76.90 435 | 39.34 406 | 61.71 334 | 87.73 223 |
|
| UnsupCasMVSNet_eth | | | 57.56 397 | 55.15 396 | 64.79 412 | 64.57 462 | 33.12 463 | 73.17 407 | 83.87 204 | 58.98 270 | 41.75 455 | 70.03 437 | 22.54 435 | 79.92 403 | 46.12 378 | 35.31 477 | 81.32 369 |
|
| SixPastTwentyTwo | | | 54.37 412 | 50.10 422 | 67.21 388 | 70.70 415 | 41.46 428 | 74.73 391 | 64.69 458 | 47.56 413 | 39.12 466 | 69.49 438 | 18.49 460 | 84.69 351 | 31.87 444 | 34.20 483 | 75.48 432 |
|
| MIMVSNet | | | 63.12 351 | 60.29 362 | 71.61 329 | 75.92 342 | 46.65 352 | 65.15 449 | 81.94 243 | 59.14 266 | 54.65 370 | 69.47 439 | 25.74 411 | 80.63 393 | 41.03 402 | 69.56 256 | 87.55 228 |
|
| ttmdpeth | | | 40.58 452 | 37.50 456 | 49.85 465 | 49.40 496 | 22.71 497 | 56.65 477 | 46.78 486 | 28.35 486 | 40.29 463 | 69.42 440 | 5.35 501 | 61.86 479 | 20.16 488 | 21.06 502 | 64.96 480 |
|
| MDTV_nov1_ep13 | | | | 61.56 346 | | 81.68 176 | 55.12 77 | 72.41 415 | 78.18 340 | 59.19 262 | 58.85 307 | 69.29 441 | 34.69 332 | 86.16 313 | 36.76 420 | 62.96 325 | |
|
| our_test_3 | | | 59.11 381 | 55.08 398 | 71.18 339 | 71.42 405 | 53.29 149 | 81.96 292 | 74.52 391 | 48.32 406 | 42.08 451 | 69.28 442 | 28.14 390 | 82.15 377 | 34.35 434 | 45.68 452 | 78.11 407 |
|
| ppachtmachnet_test | | | 58.56 390 | 54.34 400 | 71.24 336 | 71.42 405 | 54.74 103 | 81.84 297 | 72.27 416 | 49.02 401 | 45.86 438 | 68.99 443 | 26.27 405 | 83.30 369 | 30.12 451 | 43.23 458 | 75.69 430 |
|
| tpmvs | | | 62.45 361 | 59.42 368 | 71.53 333 | 83.93 107 | 54.32 118 | 70.03 431 | 77.61 352 | 51.91 379 | 53.48 383 | 68.29 444 | 37.91 265 | 86.66 297 | 33.36 438 | 58.27 365 | 73.62 449 |
|
| FE-MVSNET2 | | | 58.78 387 | 56.44 387 | 65.82 401 | 63.57 468 | 38.92 440 | 79.59 351 | 81.75 252 | 56.14 335 | 43.06 449 | 68.15 445 | 25.22 416 | 80.64 392 | 42.29 399 | 48.16 435 | 77.91 408 |
|
| FMVSNet5 | | | 58.61 389 | 56.45 386 | 65.10 410 | 77.20 314 | 39.74 435 | 74.77 390 | 77.12 361 | 50.27 394 | 43.28 447 | 67.71 446 | 26.15 408 | 76.90 435 | 36.78 419 | 54.78 402 | 78.65 397 |
|
| pmmvs-eth3d | | | 55.97 407 | 52.78 411 | 65.54 405 | 61.02 475 | 46.44 358 | 75.36 387 | 67.72 449 | 49.61 398 | 43.65 444 | 67.58 447 | 21.63 442 | 77.04 431 | 44.11 388 | 44.33 454 | 73.15 455 |
|
| TDRefinement | | | 40.91 451 | 38.37 455 | 48.55 468 | 50.45 495 | 33.03 465 | 58.98 473 | 50.97 485 | 28.50 485 | 29.89 490 | 67.39 448 | 6.21 500 | 54.51 493 | 17.67 494 | 35.25 478 | 58.11 487 |
|
| TinyColmap | | | 48.15 442 | 44.49 446 | 59.13 447 | 65.73 453 | 38.04 445 | 63.34 457 | 62.86 467 | 38.78 456 | 29.48 491 | 67.23 449 | 6.46 498 | 73.30 456 | 24.59 473 | 41.90 461 | 66.04 477 |
|
| sc_t1 | | | 53.51 421 | 49.92 426 | 64.29 414 | 70.33 422 | 39.55 438 | 72.93 408 | 59.60 473 | 38.74 458 | 47.16 430 | 66.47 450 | 17.59 464 | 76.50 438 | 36.83 418 | 39.62 469 | 76.82 419 |
|
| FE-MVSNET | | | 51.43 432 | 48.22 434 | 61.06 439 | 60.78 477 | 32.48 468 | 73.85 401 | 64.62 459 | 46.30 427 | 37.47 472 | 66.27 451 | 20.80 446 | 77.38 430 | 23.43 478 | 40.48 466 | 73.31 452 |
|
| PM-MVS | | | 46.92 444 | 43.76 450 | 56.41 455 | 52.18 489 | 32.26 469 | 63.21 459 | 38.18 499 | 37.99 461 | 40.78 461 | 66.20 452 | 5.09 502 | 65.42 475 | 48.19 363 | 41.99 460 | 71.54 464 |
|
| CR-MVSNet | | | 62.47 360 | 59.04 372 | 72.77 292 | 73.97 375 | 56.57 38 | 60.52 468 | 71.72 422 | 60.04 242 | 57.49 336 | 65.86 453 | 38.94 255 | 80.31 398 | 42.86 395 | 59.93 347 | 81.42 362 |
|
| Patchmtry | | | 56.56 402 | 52.95 409 | 67.42 386 | 72.53 391 | 50.59 229 | 59.05 472 | 71.72 422 | 37.86 462 | 46.92 431 | 65.86 453 | 38.94 255 | 80.06 402 | 36.94 417 | 46.72 448 | 71.60 463 |
|
| MVStest1 | | | 38.35 454 | 34.53 460 | 49.82 466 | 51.43 492 | 30.41 475 | 50.39 484 | 55.25 478 | 17.56 499 | 26.45 497 | 65.85 455 | 11.72 479 | 57.00 490 | 14.79 498 | 17.31 506 | 62.05 486 |
|
| lessismore_v0 | | | | | 67.98 381 | 64.76 461 | 41.25 429 | | 45.75 489 | | 36.03 476 | 65.63 456 | 19.29 455 | 84.11 357 | 35.67 423 | 21.24 501 | 78.59 398 |
|
| mvs5depth | | | 50.97 434 | 46.98 440 | 62.95 425 | 56.63 483 | 34.23 458 | 62.73 462 | 67.35 451 | 45.03 435 | 48.00 422 | 65.41 457 | 10.40 484 | 79.88 407 | 36.00 421 | 31.27 488 | 74.73 441 |
|
| MIMVSNet1 | | | 50.35 437 | 47.81 437 | 57.96 450 | 61.53 474 | 27.80 491 | 67.40 443 | 74.06 397 | 43.25 446 | 33.31 487 | 65.38 458 | 16.03 472 | 71.34 463 | 21.80 483 | 47.55 441 | 74.75 440 |
|
| K. test v3 | | | 54.04 416 | 49.42 429 | 67.92 382 | 68.55 438 | 42.57 417 | 75.51 385 | 63.07 466 | 52.07 377 | 39.21 465 | 64.59 459 | 19.34 453 | 82.21 376 | 37.11 414 | 25.31 495 | 78.97 392 |
|
| Anonymous20240521 | | | 51.65 430 | 48.42 432 | 61.34 438 | 56.43 484 | 39.65 437 | 73.57 403 | 73.47 409 | 36.64 468 | 36.59 473 | 63.98 460 | 10.75 483 | 72.25 462 | 35.35 426 | 49.01 426 | 72.11 460 |
|
| MDA-MVSNet-bldmvs | | | 51.56 431 | 47.75 439 | 63.00 424 | 71.60 402 | 47.32 341 | 69.70 434 | 72.12 417 | 43.81 443 | 27.65 496 | 63.38 461 | 21.97 441 | 75.96 441 | 27.30 466 | 32.19 485 | 65.70 479 |
|
| MDA-MVSNet_test_wron | | | 53.82 418 | 49.95 425 | 65.43 406 | 70.13 425 | 49.05 275 | 72.30 416 | 71.65 425 | 44.23 442 | 31.85 489 | 63.13 462 | 23.68 429 | 74.01 449 | 33.25 440 | 39.35 471 | 73.23 454 |
|
| YYNet1 | | | 53.82 418 | 49.96 424 | 65.41 407 | 70.09 426 | 48.95 279 | 72.30 416 | 71.66 424 | 44.25 441 | 31.89 488 | 63.07 463 | 23.73 428 | 73.95 450 | 33.26 439 | 39.40 470 | 73.34 451 |
|
| mmtdpeth | | | 57.93 395 | 54.78 399 | 67.39 387 | 72.32 394 | 43.38 404 | 72.72 410 | 68.93 444 | 54.45 359 | 56.85 346 | 62.43 464 | 17.02 467 | 83.46 367 | 57.95 276 | 30.31 489 | 75.31 434 |
|
| LF4IMVS | | | 33.04 463 | 32.55 463 | 34.52 483 | 40.96 503 | 22.03 499 | 44.45 492 | 35.62 503 | 20.42 494 | 28.12 494 | 62.35 465 | 5.03 503 | 31.88 515 | 21.61 485 | 34.42 480 | 49.63 495 |
|
| test_fmvs3 | | | 37.95 456 | 35.75 458 | 44.55 473 | 35.50 508 | 18.92 506 | 48.32 485 | 34.00 506 | 18.36 498 | 41.31 459 | 61.58 466 | 2.29 509 | 48.06 501 | 42.72 396 | 37.71 473 | 66.66 475 |
|
| tt0320-xc | | | 52.22 429 | 48.38 433 | 63.75 418 | 72.19 397 | 42.25 420 | 72.19 419 | 57.59 476 | 37.24 464 | 44.41 440 | 61.56 467 | 17.90 462 | 75.89 442 | 35.60 424 | 36.73 474 | 73.12 456 |
|
| tt0320 | | | 52.45 426 | 48.75 430 | 63.55 419 | 71.47 404 | 41.85 421 | 72.42 414 | 59.73 472 | 36.33 471 | 44.52 439 | 61.55 468 | 19.34 453 | 76.45 439 | 33.53 436 | 39.85 468 | 72.36 458 |
|
| N_pmnet | | | 41.25 450 | 39.77 453 | 45.66 471 | 68.50 439 | 0.82 541 | 72.51 413 | 0.38 539 | 35.61 473 | 35.26 478 | 61.51 469 | 20.07 451 | 67.74 470 | 23.51 476 | 40.63 464 | 68.42 472 |
|
| ADS-MVSNet2 | | | 55.21 411 | 51.44 417 | 66.51 397 | 80.60 216 | 49.56 260 | 55.03 480 | 65.44 456 | 44.72 436 | 51.00 402 | 61.19 470 | 22.83 432 | 75.41 445 | 28.54 459 | 53.63 410 | 74.57 443 |
|
| ADS-MVSNet | | | 56.17 405 | 51.95 416 | 68.84 369 | 80.60 216 | 53.07 157 | 55.03 480 | 70.02 438 | 44.72 436 | 51.00 402 | 61.19 470 | 22.83 432 | 78.88 411 | 28.54 459 | 53.63 410 | 74.57 443 |
|
| kuosan | | | 50.20 438 | 50.09 423 | 50.52 464 | 73.09 383 | 29.09 487 | 65.25 448 | 74.89 387 | 48.27 407 | 41.34 457 | 60.85 472 | 43.45 196 | 67.48 472 | 18.59 493 | 25.07 496 | 55.01 490 |
|
| new-patchmatchnet | | | 48.21 441 | 46.55 442 | 53.18 460 | 57.73 481 | 18.19 510 | 70.24 429 | 71.02 432 | 45.70 429 | 33.70 482 | 60.23 473 | 18.00 461 | 69.86 468 | 27.97 463 | 34.35 481 | 71.49 465 |
|
| dtuonlycased | | | 54.12 415 | 52.39 415 | 59.30 445 | 64.31 463 | 41.80 422 | 78.63 362 | 65.85 454 | 50.56 390 | 42.00 452 | 60.21 474 | 26.14 409 | 73.31 455 | 43.06 392 | 40.73 463 | 62.79 485 |
|
| ambc | | | | | 62.06 430 | 53.98 487 | 29.38 485 | 35.08 501 | 79.65 301 | | 41.37 456 | 59.96 475 | 6.27 499 | 82.15 377 | 35.34 427 | 38.22 472 | 74.65 442 |
|
| patchmatchnet-post | | | | | | | | | | | | 59.74 476 | 38.41 260 | 79.91 405 | | | |
|
| DSMNet-mixed | | | 38.35 454 | 35.36 459 | 47.33 469 | 48.11 500 | 14.91 514 | 37.87 499 | 36.60 502 | 19.18 496 | 34.37 480 | 59.56 477 | 15.53 473 | 53.01 495 | 20.14 489 | 46.89 447 | 74.07 445 |
|
| KD-MVS_self_test | | | 49.24 439 | 46.85 441 | 56.44 454 | 54.32 485 | 22.87 496 | 57.39 475 | 73.36 411 | 44.36 440 | 37.98 470 | 59.30 478 | 18.97 456 | 71.17 464 | 33.48 437 | 42.44 459 | 75.26 435 |
|
| RPMNet | | | 59.29 377 | 54.25 402 | 74.42 238 | 73.97 375 | 56.57 38 | 60.52 468 | 76.98 363 | 35.72 472 | 57.49 336 | 58.87 479 | 37.73 270 | 85.26 340 | 27.01 467 | 59.93 347 | 81.42 362 |
|
| UnsupCasMVSNet_bld | | | 53.86 417 | 50.53 421 | 63.84 416 | 63.52 469 | 34.75 453 | 71.38 425 | 81.92 245 | 46.53 419 | 38.95 467 | 57.93 480 | 20.55 447 | 80.20 401 | 39.91 405 | 34.09 484 | 76.57 425 |
|
| pmmvs3 | | | 45.53 447 | 41.55 452 | 57.44 451 | 48.97 498 | 39.68 436 | 70.06 430 | 57.66 475 | 28.32 487 | 34.06 481 | 57.29 481 | 8.50 491 | 66.85 474 | 34.86 433 | 34.26 482 | 65.80 478 |
|
| PatchT | | | 56.60 401 | 52.97 408 | 67.48 385 | 72.94 386 | 46.16 369 | 57.30 476 | 73.78 401 | 38.77 457 | 54.37 373 | 57.26 482 | 37.52 277 | 78.06 419 | 32.02 443 | 52.79 417 | 78.23 406 |
|
| usedtu_dtu_shiyan2 | | | 50.47 436 | 46.43 443 | 62.61 428 | 51.66 491 | 31.70 473 | 75.62 382 | 75.65 380 | 36.36 470 | 34.89 479 | 56.91 483 | 12.01 478 | 78.40 414 | 30.87 450 | 43.86 455 | 77.72 411 |
|
| WB-MVS | | | 37.41 457 | 36.37 457 | 40.54 478 | 54.23 486 | 10.43 517 | 65.29 447 | 43.75 491 | 34.86 477 | 27.81 495 | 54.63 484 | 24.94 419 | 63.21 477 | 6.81 514 | 15.00 507 | 47.98 497 |
|
| dongtai | | | 43.51 448 | 44.07 449 | 41.82 475 | 63.75 466 | 21.90 500 | 63.80 454 | 72.05 418 | 39.59 454 | 33.35 486 | 54.54 485 | 41.04 228 | 57.30 489 | 10.75 507 | 17.77 505 | 46.26 498 |
|
| Patchmatch-RL test | | | 58.72 388 | 54.32 401 | 71.92 325 | 63.91 465 | 44.25 393 | 61.73 464 | 55.19 479 | 57.38 303 | 49.31 415 | 54.24 486 | 37.60 275 | 80.89 386 | 62.19 230 | 47.28 443 | 90.63 121 |
|
| EGC-MVSNET | | | 33.75 461 | 30.42 465 | 43.75 474 | 64.94 460 | 36.21 451 | 60.47 470 | 40.70 497 | 0.02 558 | 0.10 555 | 53.79 487 | 7.39 492 | 60.26 482 | 11.09 505 | 35.23 479 | 34.79 502 |
|
| FPMVS | | | 35.40 458 | 33.67 462 | 40.57 477 | 46.34 501 | 28.74 489 | 41.05 495 | 57.05 477 | 20.37 495 | 22.27 500 | 53.38 488 | 6.87 495 | 44.94 504 | 8.62 508 | 47.11 445 | 48.01 496 |
|
| mvsany_test3 | | | 28.00 465 | 25.98 467 | 34.05 484 | 28.97 513 | 15.31 512 | 34.54 502 | 18.17 517 | 16.24 500 | 29.30 492 | 53.37 489 | 2.79 507 | 33.38 514 | 30.01 452 | 20.41 503 | 53.45 492 |
|
| SSC-MVS | | | 35.20 459 | 34.30 461 | 37.90 480 | 52.58 488 | 8.65 520 | 61.86 463 | 41.64 495 | 31.81 482 | 25.54 498 | 52.94 490 | 23.39 431 | 59.28 486 | 6.10 516 | 12.86 509 | 45.78 500 |
|
| test_vis1_rt | | | 40.29 453 | 38.64 454 | 45.25 472 | 48.91 499 | 30.09 478 | 59.44 471 | 27.07 512 | 24.52 492 | 38.48 469 | 51.67 491 | 6.71 496 | 49.44 497 | 44.33 385 | 46.59 449 | 56.23 488 |
|
| test_f | | | 27.12 467 | 24.85 468 | 33.93 485 | 26.17 518 | 15.25 513 | 30.24 506 | 22.38 516 | 12.53 505 | 28.23 493 | 49.43 492 | 2.59 508 | 34.34 513 | 25.12 472 | 26.99 493 | 52.20 493 |
|
| new_pmnet | | | 33.56 462 | 31.89 464 | 38.59 479 | 49.01 497 | 20.42 503 | 51.01 483 | 37.92 500 | 20.58 493 | 23.45 499 | 46.79 493 | 6.66 497 | 49.28 499 | 20.00 490 | 31.57 487 | 46.09 499 |
|
| APD_test1 | | | 26.46 469 | 24.41 470 | 32.62 488 | 37.58 505 | 21.74 501 | 40.50 497 | 30.39 508 | 11.45 506 | 16.33 503 | 43.76 494 | 1.63 515 | 41.62 505 | 11.24 504 | 26.82 494 | 34.51 503 |
|
| gg-mvs-nofinetune | | | 67.43 290 | 64.53 318 | 76.13 175 | 85.95 62 | 47.79 330 | 64.38 453 | 88.28 64 | 39.34 455 | 66.62 180 | 41.27 495 | 58.69 17 | 89.00 183 | 49.64 351 | 86.62 32 | 91.59 70 |
|
| ArgMatch-Sym | | | 13.78 479 | 13.16 482 | 15.65 496 | 13.75 521 | 8.38 522 | 21.56 508 | 2.56 524 | 7.09 513 | 14.16 507 | 40.67 496 | 0.28 523 | 11.85 520 | 13.55 502 | 4.84 519 | 26.71 508 |
|
| ArgMatch-SfM | | | 13.59 480 | 12.41 483 | 17.15 495 | 12.50 522 | 7.57 524 | 19.17 510 | 3.21 523 | 5.58 514 | 12.94 509 | 39.91 497 | 0.26 524 | 13.40 517 | 13.23 503 | 4.84 519 | 30.48 505 |
|
| PMMVS2 | | | 26.71 468 | 22.98 473 | 37.87 481 | 36.89 506 | 8.51 521 | 42.51 494 | 29.32 510 | 19.09 497 | 13.01 508 | 37.54 498 | 2.23 510 | 53.11 494 | 14.54 499 | 11.71 510 | 51.99 494 |
|
| JIA-IIPM | | | 52.33 428 | 47.77 438 | 66.03 400 | 71.20 408 | 46.92 345 | 40.00 498 | 76.48 374 | 37.10 465 | 46.73 432 | 37.02 499 | 32.96 352 | 77.88 424 | 35.97 422 | 52.45 419 | 73.29 453 |
|
| test_method | | | 24.09 472 | 21.07 476 | 33.16 486 | 27.67 516 | 8.35 523 | 26.63 507 | 35.11 505 | 3.40 517 | 14.35 506 | 36.98 500 | 3.46 506 | 35.31 510 | 19.08 492 | 22.95 498 | 55.81 489 |
|
| MVS-HIRNet | | | 49.01 440 | 44.71 444 | 61.92 433 | 76.06 335 | 46.61 354 | 63.23 458 | 54.90 480 | 24.77 491 | 33.56 483 | 36.60 501 | 21.28 444 | 75.88 443 | 29.49 453 | 62.54 329 | 63.26 484 |
|
| ANet_high | | | 34.39 460 | 29.59 466 | 48.78 467 | 30.34 512 | 22.28 498 | 55.53 479 | 63.79 464 | 38.11 460 | 15.47 505 | 36.56 502 | 6.94 494 | 59.98 483 | 13.93 500 | 5.64 517 | 64.08 481 |
|
| PMVS |  | 19.57 22 | 25.07 470 | 22.43 475 | 32.99 487 | 23.12 519 | 22.98 495 | 40.98 496 | 35.19 504 | 15.99 501 | 11.95 513 | 35.87 503 | 1.47 517 | 49.29 498 | 5.41 519 | 31.90 486 | 26.70 509 |
| Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
| LCM-MVSNet | | | 28.07 464 | 23.85 472 | 40.71 476 | 27.46 517 | 18.93 505 | 30.82 505 | 46.19 487 | 12.76 504 | 16.40 502 | 34.70 504 | 1.90 512 | 48.69 500 | 20.25 487 | 24.22 497 | 54.51 491 |
|
| testf1 | | | 21.11 473 | 19.08 477 | 27.18 491 | 30.56 510 | 18.28 508 | 33.43 503 | 24.48 513 | 8.02 510 | 12.02 511 | 33.50 505 | 0.75 520 | 35.09 511 | 7.68 510 | 21.32 499 | 28.17 506 |
|
| APD_test2 | | | 21.11 473 | 19.08 477 | 27.18 491 | 30.56 510 | 18.28 508 | 33.43 503 | 24.48 513 | 8.02 510 | 12.02 511 | 33.50 505 | 0.75 520 | 35.09 511 | 7.68 510 | 21.32 499 | 28.17 506 |
|
| VLMVS_CLIP | | | 11.28 481 | 11.90 484 | 9.42 500 | 7.54 525 | 3.26 528 | 13.10 512 | 10.36 521 | 1.51 523 | 15.95 504 | 32.54 507 | 1.51 516 | 12.70 518 | 10.98 506 | 13.62 508 | 12.29 515 |
|
| DeepMVS_CX |  | | | | 13.10 497 | 21.34 520 | 8.99 519 | | 10.02 522 | 10.59 508 | 7.53 518 | 30.55 508 | 1.82 513 | 14.55 516 | 6.83 513 | 7.52 513 | 15.75 512 |
|
| test_vis3_rt | | | 24.79 471 | 22.95 474 | 30.31 489 | 28.59 514 | 18.92 506 | 37.43 500 | 17.27 519 | 12.90 503 | 21.28 501 | 29.92 509 | 1.02 518 | 36.35 508 | 28.28 462 | 29.82 492 | 35.65 501 |
|
| RoMa-SfM | | | 7.02 486 | 6.78 491 | 7.74 501 | 5.47 528 | 3.55 527 | 8.83 516 | 0.67 532 | 3.41 516 | 7.06 519 | 27.85 510 | 0.08 528 | 7.13 522 | 5.86 518 | 1.82 526 | 12.53 513 |
|
| DenseAffine | | | 8.44 484 | 7.90 490 | 10.07 499 | 9.51 523 | 4.71 525 | 11.43 514 | 1.10 527 | 4.32 515 | 8.26 516 | 27.67 511 | 0.09 527 | 8.71 521 | 6.30 515 | 2.41 524 | 16.80 511 |
|
| VLMVS | | | 5.96 489 | 6.29 492 | 4.99 506 | 5.31 529 | 1.01 536 | 4.24 523 | 0.93 529 | 0.06 542 | 8.90 515 | 26.22 512 | 1.69 514 | 1.62 533 | 3.76 526 | 5.49 518 | 12.33 514 |
|
| DKM | | | 5.93 490 | 5.87 493 | 6.10 504 | 5.64 526 | 2.81 529 | 7.85 517 | 0.52 535 | 2.62 518 | 6.30 520 | 23.31 513 | 0.05 533 | 4.93 525 | 5.11 521 | 1.45 528 | 10.57 519 |
|
| RoMa-HiRes | | | 4.68 493 | 4.75 496 | 4.46 507 | 3.18 533 | 1.88 532 | 5.38 521 | 0.37 540 | 2.04 521 | 4.84 524 | 21.68 514 | 0.06 530 | 3.78 528 | 4.17 524 | 1.04 533 | 7.71 523 |
|
| MVE |  | 16.60 23 | 17.34 478 | 13.39 481 | 29.16 490 | 28.43 515 | 19.72 504 | 13.73 511 | 23.63 515 | 7.23 512 | 7.96 517 | 21.41 515 | 0.80 519 | 36.08 509 | 6.97 512 | 10.39 511 | 31.69 504 |
| Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
| PDCNetPlus | | | 5.70 491 | 5.56 494 | 6.14 503 | 8.32 524 | 1.98 531 | 7.37 518 | 0.76 531 | 2.18 520 | 3.69 528 | 20.81 516 | 0.12 526 | 4.60 526 | 4.55 522 | 2.21 525 | 11.83 516 |
|
| tmp_tt | | | 9.44 482 | 10.68 485 | 5.73 505 | 2.49 536 | 4.21 526 | 10.48 515 | 18.04 518 | 0.34 529 | 12.59 510 | 20.49 517 | 11.39 481 | 7.03 523 | 13.84 501 | 6.46 516 | 5.95 524 |
|
| LoFTR | | | 5.36 492 | 5.09 495 | 6.17 502 | 5.52 527 | 2.23 530 | 6.04 519 | 2.15 525 | 1.23 524 | 5.61 522 | 19.15 518 | 0.07 529 | 5.98 524 | 1.61 529 | 4.48 521 | 10.30 520 |
|
| DKM-HiRes | | | 4.42 494 | 4.49 497 | 4.23 508 | 3.85 531 | 1.83 533 | 5.38 521 | 0.33 541 | 1.86 522 | 4.78 525 | 18.85 519 | 0.04 539 | 2.97 530 | 4.34 523 | 0.97 534 | 7.88 522 |
|
| E-PMN | | | 19.16 475 | 18.40 479 | 21.44 493 | 36.19 507 | 13.63 515 | 47.59 486 | 30.89 507 | 10.73 507 | 5.91 521 | 16.59 520 | 3.66 505 | 39.77 506 | 5.95 517 | 8.14 512 | 10.92 517 |
|
| test_post | | | | | | | | | | | | 16.22 521 | 37.52 277 | 84.72 350 | | | |
|
| Gipuma |  | | 27.47 466 | 24.26 471 | 37.12 482 | 60.55 478 | 29.17 486 | 11.68 513 | 60.00 471 | 14.18 502 | 10.52 514 | 15.12 522 | 2.20 511 | 63.01 478 | 8.39 509 | 35.65 476 | 19.18 510 |
| S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
| EMVS | | | 18.42 476 | 17.66 480 | 20.71 494 | 34.13 509 | 12.64 516 | 46.94 487 | 29.94 509 | 10.46 509 | 5.58 523 | 14.93 523 | 4.23 504 | 38.83 507 | 5.24 520 | 7.51 514 | 10.67 518 |
|
| test_post1 | | | | | | | | 70.84 428 | | | | 14.72 524 | 34.33 338 | 83.86 359 | 48.80 357 | | |
|
| MVS_clip | | | 3.10 497 | 3.65 500 | 1.44 513 | 3.78 532 | 1.17 535 | 2.78 524 | 0.19 543 | 0.20 532 | 4.48 527 | 14.54 525 | 0.35 522 | 0.47 539 | 2.92 527 | 3.64 522 | 2.67 529 |
|
| MatchFormer | | | 3.89 495 | 3.84 499 | 4.03 509 | 4.08 530 | 1.73 534 | 5.52 520 | 1.59 526 | 0.67 525 | 4.77 526 | 13.56 526 | 0.04 539 | 4.50 527 | 0.74 533 | 3.60 523 | 5.85 525 |
|
| PMatch-SfM | | | 2.38 499 | 2.41 501 | 2.29 512 | 1.48 539 | 0.76 542 | 2.51 525 | 0.18 545 | 0.59 526 | 2.43 532 | 12.04 527 | 0.01 548 | 1.67 532 | 1.93 528 | 0.55 541 | 4.44 527 |
|
| GLUNet-SfM | | | 2.60 498 | 2.13 502 | 4.01 510 | 1.95 538 | 0.86 539 | 1.72 530 | 0.81 530 | 0.34 529 | 3.35 529 | 9.72 528 | 0.04 539 | 3.15 529 | 0.50 534 | 0.73 537 | 8.02 521 |
|
| MASt3R-SfM | | | 1.80 501 | 2.02 503 | 1.14 515 | 1.03 545 | 0.52 544 | 1.83 528 | 0.53 534 | 0.34 529 | 2.55 531 | 9.61 529 | 0.05 533 | 0.77 536 | 1.06 531 | 1.16 532 | 2.14 530 |
|
| ELoFTR | | | 2.17 500 | 1.90 504 | 2.99 511 | 1.19 542 | 0.63 543 | 1.84 527 | 0.60 533 | 0.46 527 | 2.17 533 | 9.10 530 | 0.02 547 | 2.92 531 | 1.00 532 | 0.72 538 | 5.42 526 |
|
| PMatch-Up-SfM | | | 1.67 502 | 1.74 505 | 1.44 513 | 1.00 546 | 0.50 545 | 1.72 530 | 0.11 551 | 0.40 528 | 1.75 534 | 8.98 531 | 0.00 563 | 1.07 534 | 1.34 530 | 0.35 554 | 2.76 528 |
|
| MVS_baseline | | | 1.13 504 | 1.40 506 | 0.34 525 | 0.74 552 | 0.01 567 | 0.24 552 | 0.03 565 | 0.00 559 | 1.75 534 | 7.74 532 | 0.03 544 | 0.00 561 | 0.31 535 | 1.74 527 | 0.99 532 |
|
| X-MVStestdata | | | 65.85 324 | 62.20 338 | 76.81 151 | 83.41 117 | 52.48 172 | 84.88 191 | 83.20 220 | 58.03 284 | 63.91 229 | 4.82 533 | 35.50 320 | 89.78 147 | 65.50 193 | 80.50 90 | 88.16 211 |
|
| ALIKED-LG | | | 1.21 503 | 1.31 507 | 0.90 516 | 2.88 534 | 0.91 538 | 1.96 526 | 0.48 536 | 0.17 533 | 0.94 536 | 3.75 534 | 0.06 530 | 0.81 535 | 0.10 543 | 1.43 529 | 0.99 532 |
|
| ALIKED-MNN | | | 1.07 505 | 1.15 508 | 0.84 517 | 2.67 535 | 0.92 537 | 1.81 529 | 0.39 537 | 0.12 534 | 0.73 538 | 3.13 535 | 0.05 533 | 0.77 536 | 0.09 544 | 1.34 530 | 0.84 534 |
|
| ALIKED-NN | | | 1.00 506 | 1.09 509 | 0.75 518 | 2.44 537 | 0.84 540 | 1.63 532 | 0.39 537 | 0.12 534 | 0.72 539 | 3.04 536 | 0.05 533 | 0.70 538 | 0.08 545 | 1.32 531 | 0.72 540 |
|
| XFeat-MNN | | | 0.55 507 | 0.60 510 | 0.39 520 | 0.26 563 | 0.16 560 | 0.58 538 | 0.20 542 | 0.08 538 | 0.82 537 | 2.26 537 | 0.03 544 | 0.39 540 | 0.19 537 | 0.95 535 | 0.62 541 |
|
| SP-DiffGlue | | | 0.50 508 | 0.53 511 | 0.38 523 | 0.41 562 | 0.20 552 | 0.62 537 | 0.19 543 | 0.09 536 | 0.64 541 | 1.95 538 | 0.06 530 | 0.17 546 | 0.26 536 | 0.60 539 | 0.77 538 |
|
| XFeat-NN | | | 0.44 512 | 0.49 514 | 0.30 526 | 0.24 564 | 0.12 563 | 0.48 539 | 0.15 550 | 0.06 542 | 0.71 540 | 1.78 539 | 0.03 544 | 0.28 541 | 0.14 538 | 0.83 536 | 0.48 542 |
|
| SP-LightGlue | | | 0.48 509 | 0.50 512 | 0.40 519 | 1.33 540 | 0.19 553 | 0.86 533 | 0.17 546 | 0.08 538 | 0.25 543 | 1.08 540 | 0.05 533 | 0.19 543 | 0.13 539 | 0.57 540 | 0.80 535 |
|
| SP-SuperGlue | | | 0.47 510 | 0.50 512 | 0.39 520 | 1.30 541 | 0.19 553 | 0.86 533 | 0.17 546 | 0.09 536 | 0.26 542 | 1.08 540 | 0.05 533 | 0.18 545 | 0.13 539 | 0.55 541 | 0.79 537 |
|
| SP-MNN | | | 0.45 511 | 0.47 515 | 0.39 520 | 1.18 543 | 0.17 557 | 0.85 535 | 0.16 548 | 0.07 540 | 0.24 544 | 1.05 542 | 0.04 539 | 0.20 542 | 0.12 541 | 0.54 543 | 0.80 535 |
|
| SP-NN | | | 0.43 513 | 0.45 516 | 0.37 524 | 1.13 544 | 0.17 557 | 0.82 536 | 0.16 548 | 0.07 540 | 0.24 544 | 1.00 543 | 0.04 539 | 0.19 543 | 0.12 541 | 0.51 544 | 0.74 539 |
|
| wuyk23d | | | 9.11 483 | 8.77 487 | 10.15 498 | 40.18 504 | 16.76 511 | 20.28 509 | 1.01 528 | 2.58 519 | 2.66 530 | 0.98 544 | 0.23 525 | 12.49 519 | 4.08 525 | 6.90 515 | 1.19 531 |
|
| SIFT-NN | | | 0.30 514 | 0.33 517 | 0.22 527 | 0.96 547 | 0.28 546 | 0.45 540 | 0.08 552 | 0.05 544 | 0.17 546 | 0.72 545 | 0.01 548 | 0.14 547 | 0.02 546 | 0.48 545 | 0.25 543 |
|
| SIFT-MNN | | | 0.28 515 | 0.31 518 | 0.21 528 | 0.89 548 | 0.25 547 | 0.41 541 | 0.08 552 | 0.05 544 | 0.15 547 | 0.70 546 | 0.01 548 | 0.14 547 | 0.02 546 | 0.46 547 | 0.25 543 |
|
| SIFT-NN-UMatch | | | 0.24 519 | 0.26 521 | 0.18 532 | 0.64 557 | 0.18 555 | 0.38 543 | 0.06 555 | 0.05 544 | 0.12 552 | 0.65 547 | 0.01 548 | 0.13 551 | 0.02 546 | 0.43 549 | 0.22 547 |
|
| SIFT-NN-NCMNet | | | 0.27 516 | 0.29 519 | 0.20 529 | 0.81 550 | 0.24 548 | 0.40 542 | 0.08 552 | 0.05 544 | 0.14 549 | 0.65 547 | 0.01 548 | 0.14 547 | 0.02 546 | 0.47 546 | 0.22 547 |
|
| SIFT-NN-CMatch | | | 0.25 518 | 0.26 521 | 0.19 530 | 0.68 555 | 0.21 550 | 0.35 545 | 0.06 555 | 0.05 544 | 0.15 547 | 0.65 547 | 0.01 548 | 0.13 551 | 0.02 546 | 0.41 550 | 0.23 545 |
|
| SIFT-ConvMatch | | | 0.24 519 | 0.26 521 | 0.18 532 | 0.76 551 | 0.21 550 | 0.32 547 | 0.05 558 | 0.05 544 | 0.13 550 | 0.63 550 | 0.01 548 | 0.13 551 | 0.02 546 | 0.38 552 | 0.19 550 |
|
| SIFT-UMatch | | | 0.23 521 | 0.25 524 | 0.16 535 | 0.74 552 | 0.17 557 | 0.33 546 | 0.05 558 | 0.05 544 | 0.11 553 | 0.60 551 | 0.01 548 | 0.13 551 | 0.02 546 | 0.37 553 | 0.18 552 |
|
| SIFT-NCM-Cal | | | 0.26 517 | 0.28 520 | 0.19 530 | 0.84 549 | 0.23 549 | 0.38 543 | 0.06 555 | 0.05 544 | 0.11 553 | 0.59 552 | 0.01 548 | 0.14 547 | 0.02 546 | 0.45 548 | 0.21 549 |
|
| SIFT-NN-PointCN | | | 0.22 522 | 0.24 525 | 0.17 534 | 0.59 558 | 0.14 562 | 0.32 547 | 0.05 558 | 0.04 554 | 0.13 550 | 0.57 553 | 0.01 548 | 0.13 551 | 0.02 546 | 0.39 551 | 0.23 545 |
|
| SIFT-UM-Cal | | | 0.21 523 | 0.23 526 | 0.14 537 | 0.68 555 | 0.15 561 | 0.29 549 | 0.04 562 | 0.05 544 | 0.10 555 | 0.56 554 | 0.01 548 | 0.12 556 | 0.02 546 | 0.34 555 | 0.15 555 |
|
| SIFT-CM-Cal | | | 0.21 523 | 0.23 526 | 0.15 536 | 0.71 554 | 0.18 555 | 0.28 550 | 0.05 558 | 0.05 544 | 0.10 555 | 0.55 555 | 0.01 548 | 0.12 556 | 0.01 558 | 0.33 556 | 0.17 553 |
|
| SIFT-PCN-Cal | | | 0.18 525 | 0.20 528 | 0.13 538 | 0.58 559 | 0.10 565 | 0.23 553 | 0.04 562 | 0.04 554 | 0.08 558 | 0.47 556 | 0.01 548 | 0.10 558 | 0.01 558 | 0.30 557 | 0.19 550 |
|
| SIFT-PointCN | | | 0.18 525 | 0.20 528 | 0.13 538 | 0.58 559 | 0.11 564 | 0.25 551 | 0.04 562 | 0.04 554 | 0.08 558 | 0.45 557 | 0.01 548 | 0.10 558 | 0.01 558 | 0.30 557 | 0.17 553 |
|
| SIFT-NCMNet | | | 0.15 527 | 0.17 530 | 0.10 540 | 0.52 561 | 0.09 566 | 0.19 554 | 0.02 566 | 0.04 554 | 0.07 560 | 0.39 558 | 0.01 548 | 0.08 560 | 0.01 558 | 0.24 559 | 0.11 556 |
|
| testmvs | | | 6.14 487 | 8.18 488 | 0.01 541 | 0.01 565 | 0.00 569 | 73.40 406 | 0.00 567 | 0.00 559 | 0.02 561 | 0.15 559 | 0.00 563 | 0.00 561 | 0.02 546 | 0.00 560 | 0.02 557 |
|
| test123 | | | 6.01 488 | 8.01 489 | 0.01 541 | 0.00 566 | 0.01 567 | 71.93 423 | 0.00 567 | 0.00 559 | 0.02 561 | 0.11 560 | 0.00 563 | 0.00 561 | 0.02 546 | 0.00 560 | 0.02 557 |
|
| mmdepth | | | 0.00 528 | 0.00 531 | 0.00 543 | 0.00 566 | 0.00 569 | 0.00 555 | 0.00 567 | 0.00 559 | 0.00 563 | 0.00 561 | 0.00 563 | 0.00 561 | 0.00 562 | 0.00 560 | 0.00 559 |
|
| monomultidepth | | | 0.00 528 | 0.00 531 | 0.00 543 | 0.00 566 | 0.00 569 | 0.00 555 | 0.00 567 | 0.00 559 | 0.00 563 | 0.00 561 | 0.00 563 | 0.00 561 | 0.00 562 | 0.00 560 | 0.00 559 |
|
| test_blank | | | 0.00 528 | 0.00 531 | 0.00 543 | 0.00 566 | 0.00 569 | 0.00 555 | 0.00 567 | 0.00 559 | 0.00 563 | 0.00 561 | 0.00 563 | 0.00 561 | 0.00 562 | 0.00 560 | 0.00 559 |
|
| uanet_test | | | 0.00 528 | 0.00 531 | 0.00 543 | 0.00 566 | 0.00 569 | 0.00 555 | 0.00 567 | 0.00 559 | 0.00 563 | 0.00 561 | 0.00 563 | 0.00 561 | 0.00 562 | 0.00 560 | 0.00 559 |
|
| DCPMVS | | | 0.00 528 | 0.00 531 | 0.00 543 | 0.00 566 | 0.00 569 | 0.00 555 | 0.00 567 | 0.00 559 | 0.00 563 | 0.00 561 | 0.00 563 | 0.00 561 | 0.00 562 | 0.00 560 | 0.00 559 |
|
| pcd_1.5k_mvsjas | | | 3.15 496 | 4.20 498 | 0.00 543 | 0.00 566 | 0.00 569 | 0.00 555 | 0.00 567 | 0.00 559 | 0.00 563 | 0.00 561 | 37.77 267 | 0.00 561 | 0.00 562 | 0.00 560 | 0.00 559 |
|
| sosnet-low-res | | | 0.00 528 | 0.00 531 | 0.00 543 | 0.00 566 | 0.00 569 | 0.00 555 | 0.00 567 | 0.00 559 | 0.00 563 | 0.00 561 | 0.00 563 | 0.00 561 | 0.00 562 | 0.00 560 | 0.00 559 |
|
| sosnet | | | 0.00 528 | 0.00 531 | 0.00 543 | 0.00 566 | 0.00 569 | 0.00 555 | 0.00 567 | 0.00 559 | 0.00 563 | 0.00 561 | 0.00 563 | 0.00 561 | 0.00 562 | 0.00 560 | 0.00 559 |
|
| uncertanet | | | 0.00 528 | 0.00 531 | 0.00 543 | 0.00 566 | 0.00 569 | 0.00 555 | 0.00 567 | 0.00 559 | 0.00 563 | 0.00 561 | 0.00 563 | 0.00 561 | 0.00 562 | 0.00 560 | 0.00 559 |
|
| Regformer | | | 0.00 528 | 0.00 531 | 0.00 543 | 0.00 566 | 0.00 569 | 0.00 555 | 0.00 567 | 0.00 559 | 0.00 563 | 0.00 561 | 0.00 563 | 0.00 561 | 0.00 562 | 0.00 560 | 0.00 559 |
|
| uanet | | | 0.00 528 | 0.00 531 | 0.00 543 | 0.00 566 | 0.00 569 | 0.00 555 | 0.00 567 | 0.00 559 | 0.00 563 | 0.00 561 | 0.00 563 | 0.00 561 | 0.00 562 | 0.00 560 | 0.00 559 |
|
| Meshroom |  | | | | | | | | | | | | | 0.00 561 | | | |
| : In preparation. |
| AliceVision / Meshro | | | | | | | | | | | | | | 0.00 561 | | | |
| : In preparation. |
| AliceVision_Meshroom |  | | | | | | | | | | | | | 0.00 561 | | | |
| : In preparation. |
| PatchmatchNet2 |  | | | | | 0.00 566 | 32.03 471 | 74.85 389 | 61.13 469 | 37.29 463 | | | | | | | |
| Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021 |
| PatchmatchNet1 |  | | | | | | | | | | | | | | 23.45 477 | 40.77 462 | 68.54 471 |
| Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021 |
| PatchmatchNet3 |  | | | | | | | | | | | | | 67.71 471 | | | |
| Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021 |
| test-260524 | | | | | | 88.20 37 | 55.35 66 | | 88.22 66 | | 80.74 29 | | 53.67 46 | 94.67 21 | 80.11 58 | 85.96 38 | |
|
| WAC-MVS | | | | | | | 34.28 456 | | | | | | | | 22.56 481 | | |
|
| FOURS1 | | | | | | 83.24 124 | 49.90 252 | 84.98 186 | 78.76 325 | 47.71 411 | 73.42 81 | | | | | | |
|
| MSC_two_6792asdad | | | | | 81.53 18 | 91.77 4 | 56.03 51 | | 91.10 14 | | | | | 96.22 9 | 81.46 47 | 86.80 29 | 92.34 39 |
|
| No_MVS | | | | | 81.53 18 | 91.77 4 | 56.03 51 | | 91.10 14 | | | | | 96.22 9 | 81.46 47 | 86.80 29 | 92.34 39 |
|
| eth-test2 | | | | | | 0.00 566 | | | | | | | | | | | |
|
| eth-test | | | | | | 0.00 566 | | | | | | | | | | | |
|
| IU-MVS | | | | | | 89.48 18 | 57.49 20 | | 91.38 10 | 66.22 113 | 88.26 2 | | | | 82.83 33 | 87.60 19 | 92.44 36 |
|
| save fliter | | | | | | 85.35 76 | 56.34 45 | 89.31 42 | 81.46 255 | 61.55 214 | | | | | | | |
|
| test_0728_SECOND | | | | | 82.20 9 | 89.50 16 | 57.73 16 | 92.34 5 | 88.88 40 | | | | | 96.39 4 | 81.68 42 | 87.13 22 | 92.47 35 |
|
| GSMVS | | | | | | | | | | | | | | | | | 88.13 214 |
|
| test_part2 | | | | | | 89.33 24 | 55.48 59 | | | | 82.27 13 | | | | | | |
|
| sam_mvs1 | | | | | | | | | | | | | 38.86 257 | | | | 88.13 214 |
|
| sam_mvs | | | | | | | | | | | | | 35.99 314 | | | | |
|
| MTGPA |  | | | | | | | | 81.31 258 | | | | | | | | |
|
| MTMP | | | | | | | | 87.27 91 | 15.34 520 | | | | | | | | |
|
| test9_res | | | | | | | | | | | | | | | 78.72 69 | 85.44 46 | 91.39 80 |
|
| agg_prior2 | | | | | | | | | | | | | | | 75.65 96 | 85.11 53 | 91.01 106 |
|
| agg_prior | | | | | | 85.64 69 | 54.92 93 | | 83.61 212 | | 72.53 98 | | | 88.10 230 | | | |
|
| test_prior4 | | | | | | | 56.39 44 | 87.15 95 | | | | | | | | | |
|
| test_prior | | | | | 78.39 98 | 86.35 58 | 54.91 96 | | 85.45 134 | | | | | 89.70 155 | | | 90.55 124 |
|
| 旧先验2 | | | | | | | | 81.73 302 | | 45.53 431 | 74.66 67 | | | 70.48 467 | 58.31 269 | | |
|
| æ–°å‡ ä½•2 | | | | | | | | 81.61 308 | | | | | | | | | |
|
| æ— å…ˆéªŒ | | | | | | | | 85.19 172 | 78.00 343 | 49.08 400 | | | | 85.13 344 | 52.78 328 | | 87.45 231 |
|
| 原ACMM2 | | | | | | | | 83.77 232 | | | | | | | | | |
|
| testdata2 | | | | | | | | | | | | | | 77.81 426 | 45.64 379 | | |
|
| segment_acmp | | | | | | | | | | | | | 44.97 168 | | | | |
|
| testdata1 | | | | | | | | 77.55 371 | | 64.14 154 | | | | | | | |
|
| test12 | | | | | 79.24 53 | 86.89 51 | 56.08 50 | | 85.16 151 | | 72.27 102 | | 47.15 111 | 91.10 95 | | 85.93 40 | 90.54 126 |
|
| plane_prior7 | | | | | | 77.95 291 | 48.46 298 | | | | | | | | | | |
|
| plane_prior6 | | | | | | 78.42 284 | 49.39 270 | | | | | | 36.04 312 | | | | |
|
| plane_prior5 | | | | | | | | | 82.59 230 | | | | | 88.30 223 | 65.46 196 | 72.34 220 | 84.49 295 |
|
| plane_prior3 | | | | | | | 48.95 279 | | | 64.01 158 | 62.15 259 | | | | | | |
|
| plane_prior2 | | | | | | | | 85.76 141 | | 63.60 170 | | | | | | | |
|
| plane_prior1 | | | | | | 78.31 287 | | | | | | | | | | | |
|
| plane_prior | | | | | | | 49.57 257 | 87.43 83 | | 64.57 144 | | | | | | 72.84 212 | |
|
| n2 | | | | | | | | | 0.00 567 | | | | | | | | |
|
| nn | | | | | | | | | 0.00 567 | | | | | | | | |
|
| door-mid | | | | | | | | | 41.31 496 | | | | | | | | |
|
| test11 | | | | | | | | | 84.25 192 | | | | | | | | |
|
| door | | | | | | | | | 43.27 492 | | | | | | | | |
|
| HQP5-MVS | | | | | | | 51.56 204 | | | | | | | | | | |
|
| HQP-NCC | | | | | | 79.02 265 | | 88.00 63 | | 65.45 128 | 64.48 219 | | | | | | |
|
| ACMP_Plane | | | | | | 79.02 265 | | 88.00 63 | | 65.45 128 | 64.48 219 | | | | | | |
|
| BP-MVS | | | | | | | | | | | | | | | 66.70 182 | | |
|
| HQP4-MVS | | | | | | | | | | | 64.47 222 | | | 88.61 202 | | | 84.91 291 |
|
| HQP3-MVS | | | | | | | | | 83.68 207 | | | | | | | 73.12 208 | |
|
| HQP2-MVS | | | | | | | | | | | | | 37.35 280 | | | | |
|
| MDTV_nov1_ep13_2view | | | | | | | 43.62 400 | 71.13 427 | | 54.95 353 | 59.29 297 | | 36.76 295 | | 46.33 376 | | 87.32 234 |
|
| ACMMP++_ref | | | | | | | | | | | | | | | | 63.20 321 | |
|
| ACMMP++ | | | | | | | | | | | | | | | | 59.38 354 | |
|
| Test By Simon | | | | | | | | | | | | | 39.38 251 | | | | |
|