| test_fmvsm_n_1920 | | | 97.55 13 | 97.89 3 | 96.53 99 | 98.41 80 | 91.73 125 | 98.01 61 | 99.02 1 | 96.37 10 | 99.30 4 | 98.92 20 | 92.39 41 | 99.79 40 | 99.16 11 | 99.46 41 | 98.08 205 |
|
| PGM-MVS | | | 96.81 52 | 96.53 63 | 97.65 43 | 99.35 22 | 93.53 61 | 97.65 122 | 98.98 2 | 92.22 158 | 97.14 69 | 98.44 57 | 91.17 68 | 99.85 18 | 94.35 142 | 99.46 41 | 99.57 31 |
|
| MVS_111021_HR | | | 96.68 63 | 96.58 62 | 96.99 80 | 98.46 75 | 92.31 106 | 96.20 281 | 98.90 3 | 94.30 83 | 95.86 126 | 97.74 124 | 92.33 42 | 99.38 129 | 96.04 88 | 99.42 51 | 99.28 72 |
|
| test_fmvsmconf_n | | | 97.49 17 | 97.56 12 | 97.29 60 | 97.44 158 | 92.37 103 | 97.91 80 | 98.88 4 | 95.83 16 | 98.92 20 | 99.05 12 | 91.45 58 | 99.80 35 | 99.12 13 | 99.46 41 | 99.69 13 |
|
| lecture | | | 97.58 12 | 97.63 9 | 97.43 54 | 99.37 16 | 92.93 82 | 98.86 7 | 98.85 5 | 95.27 31 | 98.65 30 | 98.90 22 | 91.97 49 | 99.80 35 | 97.63 35 | 99.21 76 | 99.57 31 |
|
| ACMMP |  | | 96.27 80 | 95.93 83 | 97.28 62 | 99.24 30 | 92.62 94 | 98.25 36 | 98.81 6 | 92.99 132 | 94.56 163 | 98.39 61 | 88.96 97 | 99.85 18 | 94.57 140 | 97.63 156 | 99.36 67 |
| 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 |
| MVS_111021_LR | | | 96.24 81 | 96.19 79 | 96.39 117 | 98.23 98 | 91.35 146 | 96.24 279 | 98.79 7 | 93.99 90 | 95.80 128 | 97.65 133 | 89.92 87 | 99.24 142 | 95.87 92 | 99.20 81 | 98.58 152 |
|
| patch_mono-2 | | | 96.83 51 | 97.44 20 | 95.01 205 | 99.05 41 | 85.39 341 | 96.98 205 | 98.77 8 | 94.70 63 | 97.99 44 | 98.66 40 | 93.61 19 | 99.91 1 | 97.67 34 | 99.50 35 | 99.72 12 |
|
| fmvsm_s_conf0.5_n | | | 96.85 48 | 97.13 25 | 96.04 141 | 98.07 113 | 90.28 194 | 97.97 72 | 98.76 9 | 94.93 45 | 98.84 25 | 99.06 11 | 88.80 101 | 99.65 72 | 99.06 15 | 98.63 116 | 98.18 191 |
|
| fmvsm_l_conf0.5_n | | | 97.65 7 | 97.75 6 | 97.34 57 | 98.21 99 | 92.75 88 | 97.83 92 | 98.73 10 | 95.04 42 | 99.30 4 | 98.84 33 | 93.34 22 | 99.78 43 | 99.32 5 | 99.13 91 | 99.50 47 |
|
| fmvsm_s_conf0.5_n_a | | | 96.75 56 | 96.93 40 | 96.20 133 | 97.64 144 | 90.72 176 | 98.00 62 | 98.73 10 | 94.55 70 | 98.91 21 | 99.08 7 | 88.22 113 | 99.63 81 | 98.91 18 | 98.37 129 | 98.25 186 |
|
| FC-MVSNet-test | | | 93.94 163 | 93.57 155 | 95.04 203 | 95.48 293 | 91.45 143 | 98.12 51 | 98.71 12 | 93.37 115 | 90.23 273 | 96.70 199 | 87.66 123 | 97.85 324 | 91.49 204 | 90.39 315 | 95.83 299 |
|
| UniMVSNet (Re) | | | 93.31 188 | 92.55 201 | 95.61 174 | 95.39 299 | 93.34 67 | 97.39 164 | 98.71 12 | 93.14 128 | 90.10 282 | 94.83 301 | 87.71 122 | 98.03 297 | 91.67 202 | 83.99 387 | 95.46 318 |
|
| fmvsm_l_conf0.5_n_a | | | 97.63 9 | 97.76 5 | 97.26 64 | 98.25 93 | 92.59 96 | 97.81 97 | 98.68 14 | 94.93 45 | 99.24 7 | 98.87 28 | 93.52 20 | 99.79 40 | 99.32 5 | 99.21 76 | 99.40 61 |
|
| FIs | | | 94.09 154 | 93.70 151 | 95.27 192 | 95.70 282 | 92.03 118 | 98.10 52 | 98.68 14 | 93.36 117 | 90.39 270 | 96.70 199 | 87.63 126 | 97.94 315 | 92.25 182 | 90.50 314 | 95.84 298 |
|
| WR-MVS_H | | | 92.00 245 | 91.35 242 | 93.95 270 | 95.09 326 | 89.47 223 | 98.04 59 | 98.68 14 | 91.46 187 | 88.34 333 | 94.68 308 | 85.86 158 | 97.56 353 | 85.77 328 | 84.24 385 | 94.82 363 |
|
| fmvsm_s_conf0.5_n_4 | | | 96.75 56 | 97.07 28 | 95.79 161 | 97.76 135 | 89.57 217 | 97.66 121 | 98.66 17 | 95.36 27 | 99.03 13 | 98.90 22 | 88.39 109 | 99.73 54 | 99.17 10 | 98.66 114 | 98.08 205 |
|
| VPA-MVSNet | | | 93.24 190 | 92.48 206 | 95.51 180 | 95.70 282 | 92.39 102 | 97.86 85 | 98.66 17 | 92.30 155 | 92.09 231 | 95.37 276 | 80.49 268 | 98.40 251 | 93.95 148 | 85.86 358 | 95.75 307 |
|
| fmvsm_l_conf0.5_n_3 | | | 97.64 8 | 97.60 10 | 97.79 30 | 98.14 106 | 93.94 52 | 97.93 78 | 98.65 19 | 96.70 5 | 99.38 2 | 99.07 10 | 89.92 87 | 99.81 30 | 99.16 11 | 99.43 48 | 99.61 25 |
|
| fmvsm_s_conf0.5_n_3 | | | 97.15 30 | 97.36 22 | 96.52 101 | 97.98 119 | 91.19 154 | 97.84 89 | 98.65 19 | 97.08 4 | 99.25 6 | 99.10 5 | 87.88 120 | 99.79 40 | 99.32 5 | 99.18 83 | 98.59 151 |
|
| fmvsm_s_conf0.5_n_8 | | | 97.32 24 | 97.48 19 | 96.85 82 | 98.28 89 | 91.07 162 | 97.76 102 | 98.62 21 | 97.53 2 | 99.20 9 | 99.12 4 | 88.24 112 | 99.81 30 | 99.41 3 | 99.17 84 | 99.67 14 |
|
| fmvsm_s_conf0.5_n_2 | | | 96.62 64 | 96.82 49 | 96.02 143 | 97.98 119 | 90.43 186 | 97.50 145 | 98.59 22 | 96.59 7 | 99.31 3 | 99.08 7 | 84.47 184 | 99.75 51 | 99.37 4 | 98.45 126 | 97.88 218 |
|
| UniMVSNet_NR-MVSNet | | | 93.37 186 | 92.67 195 | 95.47 185 | 95.34 305 | 92.83 85 | 97.17 188 | 98.58 23 | 92.98 137 | 90.13 278 | 95.80 252 | 88.37 111 | 97.85 324 | 91.71 199 | 83.93 388 | 95.73 309 |
|
| CSCG | | | 96.05 84 | 95.91 84 | 96.46 111 | 99.24 30 | 90.47 183 | 98.30 29 | 98.57 24 | 89.01 279 | 93.97 180 | 97.57 143 | 92.62 37 | 99.76 47 | 94.66 134 | 99.27 69 | 99.15 82 |
|
| fmvsm_s_conf0.5_n_9 | | | 97.33 23 | 97.57 11 | 96.62 95 | 98.43 78 | 90.32 193 | 97.80 98 | 98.53 25 | 97.24 3 | 99.62 1 | 99.14 1 | 88.65 104 | 99.80 35 | 99.54 1 | 99.15 88 | 99.74 8 |
|
| fmvsm_s_conf0.5_n_6 | | | 97.08 33 | 97.17 24 | 96.81 83 | 97.28 163 | 91.73 125 | 97.75 104 | 98.50 26 | 94.86 49 | 99.22 8 | 98.78 37 | 89.75 90 | 99.76 47 | 99.10 14 | 99.29 67 | 98.94 111 |
|
| MSLP-MVS++ | | | 96.94 42 | 97.06 29 | 96.59 96 | 98.72 60 | 91.86 123 | 97.67 118 | 98.49 27 | 94.66 66 | 97.24 65 | 98.41 60 | 92.31 44 | 98.94 188 | 96.61 64 | 99.46 41 | 98.96 107 |
|
| HyFIR lowres test | | | 93.66 175 | 92.92 183 | 95.87 153 | 98.24 94 | 89.88 208 | 94.58 355 | 98.49 27 | 85.06 376 | 93.78 183 | 95.78 256 | 82.86 219 | 98.67 226 | 91.77 197 | 95.71 213 | 99.07 93 |
|
| CHOSEN 1792x2688 | | | 94.15 149 | 93.51 161 | 96.06 139 | 98.27 90 | 89.38 228 | 95.18 341 | 98.48 29 | 85.60 366 | 93.76 184 | 97.11 174 | 83.15 209 | 99.61 83 | 91.33 207 | 98.72 112 | 99.19 78 |
|
| fmvsm_s_conf0.5_n_7 | | | 96.45 71 | 96.80 51 | 95.37 188 | 97.29 162 | 88.38 260 | 97.23 182 | 98.47 30 | 95.14 36 | 98.43 35 | 99.09 6 | 87.58 127 | 99.72 58 | 98.80 22 | 99.21 76 | 98.02 209 |
|
| fmvsm_s_conf0.5_n_5 | | | 97.00 39 | 96.97 37 | 97.09 75 | 97.58 154 | 92.56 97 | 97.68 117 | 98.47 30 | 94.02 88 | 98.90 22 | 98.89 25 | 88.94 98 | 99.78 43 | 99.18 9 | 99.03 100 | 98.93 115 |
|
| PHI-MVS | | | 96.77 54 | 96.46 70 | 97.71 41 | 98.40 81 | 94.07 48 | 98.21 43 | 98.45 32 | 89.86 251 | 97.11 71 | 98.01 97 | 92.52 39 | 99.69 66 | 96.03 89 | 99.53 29 | 99.36 67 |
|
| fmvsm_s_conf0.1_n | | | 96.58 67 | 96.77 54 | 96.01 146 | 96.67 214 | 90.25 195 | 97.91 80 | 98.38 33 | 94.48 74 | 98.84 25 | 99.14 1 | 88.06 115 | 99.62 82 | 98.82 20 | 98.60 118 | 98.15 195 |
|
| PVSNet_BlendedMVS | | | 94.06 155 | 93.92 145 | 94.47 239 | 98.27 90 | 89.46 225 | 96.73 230 | 98.36 34 | 90.17 243 | 94.36 168 | 95.24 284 | 88.02 116 | 99.58 91 | 93.44 160 | 90.72 310 | 94.36 383 |
|
| PVSNet_Blended | | | 94.87 127 | 94.56 125 | 95.81 159 | 98.27 90 | 89.46 225 | 95.47 324 | 98.36 34 | 88.84 288 | 94.36 168 | 96.09 241 | 88.02 116 | 99.58 91 | 93.44 160 | 98.18 138 | 98.40 173 |
|
| 3Dnovator | | 91.36 5 | 95.19 115 | 94.44 133 | 97.44 53 | 96.56 224 | 93.36 66 | 98.65 12 | 98.36 34 | 94.12 85 | 89.25 312 | 98.06 91 | 82.20 236 | 99.77 46 | 93.41 162 | 99.32 65 | 99.18 79 |
|
| FOURS1 | | | | | | 99.55 1 | 93.34 67 | 99.29 1 | 98.35 37 | 94.98 43 | 98.49 33 | | | | | | |
|
| DPE-MVS |  | | 97.86 4 | 97.65 8 | 98.47 5 | 99.17 34 | 95.78 7 | 97.21 185 | 98.35 37 | 95.16 35 | 98.71 29 | 98.80 35 | 95.05 10 | 99.89 3 | 96.70 62 | 99.73 1 | 99.73 11 |
| Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025 |
| fmvsm_s_conf0.1_n_a | | | 96.40 73 | 96.47 67 | 96.16 135 | 95.48 293 | 90.69 177 | 97.91 80 | 98.33 39 | 94.07 86 | 98.93 17 | 99.14 1 | 87.44 134 | 99.61 83 | 98.63 23 | 98.32 131 | 98.18 191 |
|
| HFP-MVS | | | 97.14 31 | 96.92 41 | 97.83 26 | 99.42 7 | 94.12 46 | 98.52 16 | 98.32 40 | 93.21 120 | 97.18 66 | 98.29 77 | 92.08 46 | 99.83 26 | 95.63 105 | 99.59 19 | 99.54 40 |
|
| ACMMPR | | | 97.07 35 | 96.84 45 | 97.79 30 | 99.44 6 | 93.88 53 | 98.52 16 | 98.31 41 | 93.21 120 | 97.15 68 | 98.33 71 | 91.35 62 | 99.86 9 | 95.63 105 | 99.59 19 | 99.62 22 |
|
| test_fmvsmvis_n_1920 | | | 96.70 59 | 96.84 45 | 96.31 122 | 96.62 216 | 91.73 125 | 97.98 66 | 98.30 42 | 96.19 11 | 96.10 116 | 98.95 18 | 89.42 91 | 99.76 47 | 98.90 19 | 99.08 95 | 97.43 245 |
|
| APDe-MVS |  | | 97.82 5 | 97.73 7 | 98.08 18 | 99.15 35 | 94.82 28 | 98.81 8 | 98.30 42 | 94.76 61 | 98.30 37 | 98.90 22 | 93.77 17 | 99.68 68 | 97.93 26 | 99.69 3 | 99.75 6 |
| Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition |
| test0726 | | | | | | 99.45 3 | 95.36 13 | 98.31 28 | 98.29 44 | 94.92 47 | 98.99 15 | 98.92 20 | 95.08 8 | | | | |
|
| MSP-MVS | | | 97.59 11 | 97.54 13 | 97.73 38 | 99.40 11 | 93.77 57 | 98.53 15 | 98.29 44 | 95.55 24 | 98.56 32 | 97.81 119 | 93.90 15 | 99.65 72 | 96.62 63 | 99.21 76 | 99.77 2 |
| 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 |
| DVP-MVS++ | | | 98.06 1 | 97.99 1 | 98.28 9 | 98.67 63 | 95.39 11 | 99.29 1 | 98.28 46 | 94.78 58 | 98.93 17 | 98.87 28 | 96.04 2 | 99.86 9 | 97.45 43 | 99.58 23 | 99.59 27 |
|
| test_0728_SECOND | | | | | 98.51 4 | 99.45 3 | 95.93 5 | 98.21 43 | 98.28 46 | | | | | 99.86 9 | 97.52 39 | 99.67 6 | 99.75 6 |
|
| CP-MVS | | | 97.02 37 | 96.81 50 | 97.64 45 | 99.33 23 | 93.54 60 | 98.80 9 | 98.28 46 | 92.99 132 | 96.45 103 | 98.30 76 | 91.90 50 | 99.85 18 | 95.61 107 | 99.68 4 | 99.54 40 |
|
| test_fmvsmconf0.1_n | | | 97.09 32 | 97.06 29 | 97.19 69 | 95.67 284 | 92.21 110 | 97.95 75 | 98.27 49 | 95.78 20 | 98.40 36 | 99.00 14 | 89.99 85 | 99.78 43 | 99.06 15 | 99.41 54 | 99.59 27 |
|
| SED-MVS | | | 98.05 2 | 97.99 1 | 98.24 10 | 99.42 7 | 95.30 17 | 98.25 36 | 98.27 49 | 95.13 37 | 99.19 10 | 98.89 25 | 95.54 5 | 99.85 18 | 97.52 39 | 99.66 10 | 99.56 35 |
|
| test_241102_TWO | | | | | | | | | 98.27 49 | 95.13 37 | 98.93 17 | 98.89 25 | 94.99 11 | 99.85 18 | 97.52 39 | 99.65 13 | 99.74 8 |
|
| test_241102_ONE | | | | | | 99.42 7 | 95.30 17 | | 98.27 49 | 95.09 40 | 99.19 10 | 98.81 34 | 95.54 5 | 99.65 72 | | | |
|
| SF-MVS | | | 97.39 20 | 97.13 25 | 98.17 15 | 99.02 44 | 95.28 19 | 98.23 40 | 98.27 49 | 92.37 154 | 98.27 38 | 98.65 42 | 93.33 23 | 99.72 58 | 96.49 68 | 99.52 30 | 99.51 44 |
|
| SteuartSystems-ACMMP | | | 97.62 10 | 97.53 14 | 97.87 24 | 98.39 83 | 94.25 40 | 98.43 23 | 98.27 49 | 95.34 29 | 98.11 40 | 98.56 44 | 94.53 12 | 99.71 60 | 96.57 66 | 99.62 17 | 99.65 19 |
| Skip Steuart: Steuart Systems R&D Blog. |
| test_one_0601 | | | | | | 99.32 24 | 95.20 20 | | 98.25 55 | 95.13 37 | 98.48 34 | 98.87 28 | 95.16 7 | | | | |
|
| PVSNet_Blended_VisFu | | | 95.27 108 | 94.91 114 | 96.38 118 | 98.20 100 | 90.86 170 | 97.27 176 | 98.25 55 | 90.21 242 | 94.18 173 | 97.27 163 | 87.48 133 | 99.73 54 | 93.53 157 | 97.77 154 | 98.55 154 |
|
| region2R | | | 97.07 35 | 96.84 45 | 97.77 34 | 99.46 2 | 93.79 55 | 98.52 16 | 98.24 57 | 93.19 123 | 97.14 69 | 98.34 68 | 91.59 57 | 99.87 7 | 95.46 111 | 99.59 19 | 99.64 20 |
|
| PS-CasMVS | | | 91.55 265 | 90.84 266 | 93.69 287 | 94.96 330 | 88.28 263 | 97.84 89 | 98.24 57 | 91.46 187 | 88.04 344 | 95.80 252 | 79.67 284 | 97.48 361 | 87.02 308 | 84.54 382 | 95.31 332 |
|
| DU-MVS | | | 92.90 208 | 92.04 217 | 95.49 182 | 94.95 331 | 92.83 85 | 97.16 189 | 98.24 57 | 93.02 131 | 90.13 278 | 95.71 259 | 83.47 201 | 97.85 324 | 91.71 199 | 83.93 388 | 95.78 303 |
|
| 9.14 | | | | 96.75 55 | | 98.93 52 | | 97.73 108 | 98.23 60 | 91.28 196 | 97.88 48 | 98.44 57 | 93.00 26 | 99.65 72 | 95.76 98 | 99.47 40 | |
|
| reproduce_model | | | 97.51 16 | 97.51 16 | 97.50 50 | 98.99 48 | 93.01 78 | 97.79 100 | 98.21 61 | 95.73 21 | 97.99 44 | 99.03 13 | 92.63 36 | 99.82 28 | 97.80 28 | 99.42 51 | 99.67 14 |
|
| D2MVS | | | 91.30 282 | 90.95 260 | 92.35 335 | 94.71 346 | 85.52 337 | 96.18 283 | 98.21 61 | 88.89 286 | 86.60 373 | 93.82 357 | 79.92 280 | 97.95 313 | 89.29 257 | 90.95 307 | 93.56 398 |
|
| reproduce-ours | | | 97.53 14 | 97.51 16 | 97.60 47 | 98.97 49 | 93.31 69 | 97.71 113 | 98.20 63 | 95.80 18 | 97.88 48 | 98.98 16 | 92.91 27 | 99.81 30 | 97.68 30 | 99.43 48 | 99.67 14 |
|
| our_new_method | | | 97.53 14 | 97.51 16 | 97.60 47 | 98.97 49 | 93.31 69 | 97.71 113 | 98.20 63 | 95.80 18 | 97.88 48 | 98.98 16 | 92.91 27 | 99.81 30 | 97.68 30 | 99.43 48 | 99.67 14 |
|
| SDMVSNet | | | 94.17 147 | 93.61 154 | 95.86 155 | 98.09 109 | 91.37 145 | 97.35 168 | 98.20 63 | 93.18 125 | 91.79 239 | 97.28 161 | 79.13 292 | 98.93 189 | 94.61 137 | 92.84 273 | 97.28 253 |
|
| XVS | | | 97.18 28 | 96.96 39 | 97.81 28 | 99.38 14 | 94.03 50 | 98.59 13 | 98.20 63 | 94.85 50 | 96.59 92 | 98.29 77 | 91.70 53 | 99.80 35 | 95.66 100 | 99.40 56 | 99.62 22 |
|
| X-MVStestdata | | | 91.71 254 | 89.67 320 | 97.81 28 | 99.38 14 | 94.03 50 | 98.59 13 | 98.20 63 | 94.85 50 | 96.59 92 | 32.69 458 | 91.70 53 | 99.80 35 | 95.66 100 | 99.40 56 | 99.62 22 |
|
| ACMMP_NAP | | | 97.20 27 | 96.86 43 | 98.23 11 | 99.09 36 | 95.16 22 | 97.60 131 | 98.19 68 | 92.82 144 | 97.93 47 | 98.74 39 | 91.60 56 | 99.86 9 | 96.26 72 | 99.52 30 | 99.67 14 |
|
| CP-MVSNet | | | 91.89 250 | 91.24 249 | 93.82 279 | 95.05 327 | 88.57 253 | 97.82 94 | 98.19 68 | 91.70 176 | 88.21 339 | 95.76 257 | 81.96 241 | 97.52 359 | 87.86 283 | 84.65 376 | 95.37 328 |
|
| ZNCC-MVS | | | 96.96 40 | 96.67 58 | 97.85 25 | 99.37 16 | 94.12 46 | 98.49 20 | 98.18 70 | 92.64 150 | 96.39 105 | 98.18 84 | 91.61 55 | 99.88 4 | 95.59 110 | 99.55 26 | 99.57 31 |
|
| SMA-MVS |  | | 97.35 21 | 97.03 34 | 98.30 8 | 99.06 40 | 95.42 10 | 97.94 76 | 98.18 70 | 90.57 234 | 98.85 24 | 98.94 19 | 93.33 23 | 99.83 26 | 96.72 60 | 99.68 4 | 99.63 21 |
| 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 |
| PEN-MVS | | | 91.20 287 | 90.44 283 | 93.48 298 | 94.49 354 | 87.91 278 | 97.76 102 | 98.18 70 | 91.29 193 | 87.78 348 | 95.74 258 | 80.35 271 | 97.33 372 | 85.46 332 | 82.96 398 | 95.19 343 |
|
| DELS-MVS | | | 96.61 65 | 96.38 74 | 97.30 59 | 97.79 133 | 93.19 74 | 95.96 294 | 98.18 70 | 95.23 32 | 95.87 125 | 97.65 133 | 91.45 58 | 99.70 65 | 95.87 92 | 99.44 47 | 99.00 102 |
| 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 |
| tfpnnormal | | | 89.70 339 | 88.40 345 | 93.60 291 | 95.15 322 | 90.10 197 | 97.56 136 | 98.16 74 | 87.28 339 | 86.16 379 | 94.63 312 | 77.57 320 | 98.05 293 | 74.48 418 | 84.59 380 | 92.65 411 |
|
| VNet | | | 95.89 92 | 95.45 95 | 97.21 67 | 98.07 113 | 92.94 81 | 97.50 145 | 98.15 75 | 93.87 94 | 97.52 55 | 97.61 139 | 85.29 169 | 99.53 105 | 95.81 97 | 95.27 226 | 99.16 80 |
|
| DeepPCF-MVS | | 93.97 1 | 96.61 65 | 97.09 27 | 95.15 196 | 98.09 109 | 86.63 310 | 96.00 292 | 98.15 75 | 95.43 25 | 97.95 46 | 98.56 44 | 93.40 21 | 99.36 130 | 96.77 57 | 99.48 39 | 99.45 54 |
|
| SD-MVS | | | 97.41 19 | 97.53 14 | 97.06 78 | 98.57 74 | 94.46 34 | 97.92 79 | 98.14 77 | 94.82 54 | 99.01 14 | 98.55 46 | 94.18 14 | 97.41 368 | 96.94 52 | 99.64 14 | 99.32 69 |
| 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 |
| GST-MVS | | | 96.85 48 | 96.52 64 | 97.82 27 | 99.36 20 | 94.14 45 | 98.29 30 | 98.13 78 | 92.72 147 | 96.70 84 | 98.06 91 | 91.35 62 | 99.86 9 | 94.83 127 | 99.28 68 | 99.47 53 |
|
| UA-Net | | | 95.95 89 | 95.53 91 | 97.20 68 | 97.67 140 | 92.98 80 | 97.65 122 | 98.13 78 | 94.81 56 | 96.61 90 | 98.35 65 | 88.87 99 | 99.51 110 | 90.36 232 | 97.35 166 | 99.11 88 |
|
| QAPM | | | 93.45 184 | 92.27 211 | 96.98 81 | 96.77 209 | 92.62 94 | 98.39 25 | 98.12 80 | 84.50 384 | 88.27 337 | 97.77 122 | 82.39 233 | 99.81 30 | 85.40 333 | 98.81 108 | 98.51 159 |
|
| Vis-MVSNet |  | | 95.23 112 | 94.81 115 | 96.51 105 | 97.18 168 | 91.58 136 | 98.26 35 | 98.12 80 | 94.38 81 | 94.90 153 | 98.15 86 | 82.28 234 | 98.92 191 | 91.45 206 | 98.58 120 | 99.01 99 |
| Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
| OpenMVS |  | 89.19 12 | 92.86 211 | 91.68 232 | 96.40 115 | 95.34 305 | 92.73 90 | 98.27 33 | 98.12 80 | 84.86 379 | 85.78 381 | 97.75 123 | 78.89 302 | 99.74 52 | 87.50 298 | 98.65 115 | 96.73 270 |
|
| TranMVSNet+NR-MVSNet | | | 92.50 220 | 91.63 233 | 95.14 197 | 94.76 342 | 92.07 115 | 97.53 142 | 98.11 83 | 92.90 141 | 89.56 300 | 96.12 236 | 83.16 208 | 97.60 351 | 89.30 256 | 83.20 397 | 95.75 307 |
|
| CPTT-MVS | | | 95.57 102 | 95.19 105 | 96.70 86 | 99.27 28 | 91.48 140 | 98.33 27 | 98.11 83 | 87.79 324 | 95.17 148 | 98.03 94 | 87.09 140 | 99.61 83 | 93.51 158 | 99.42 51 | 99.02 96 |
|
| APD-MVS |  | | 96.95 41 | 96.60 60 | 98.01 20 | 99.03 43 | 94.93 27 | 97.72 111 | 98.10 85 | 91.50 185 | 98.01 43 | 98.32 73 | 92.33 42 | 99.58 91 | 94.85 125 | 99.51 33 | 99.53 43 |
| Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
| mPP-MVS | | | 96.86 46 | 96.60 60 | 97.64 45 | 99.40 11 | 93.44 62 | 98.50 19 | 98.09 86 | 93.27 119 | 95.95 123 | 98.33 71 | 91.04 70 | 99.88 4 | 95.20 114 | 99.57 25 | 99.60 26 |
|
| ZD-MVS | | | | | | 99.05 41 | 94.59 32 | | 98.08 87 | 89.22 272 | 97.03 74 | 98.10 87 | 92.52 39 | 99.65 72 | 94.58 139 | 99.31 66 | |
|
| MTGPA |  | | | | | | | | 98.08 87 | | | | | | | | |
|
| MTAPA | | | 97.08 33 | 96.78 53 | 97.97 23 | 99.37 16 | 94.42 36 | 97.24 178 | 98.08 87 | 95.07 41 | 96.11 115 | 98.59 43 | 90.88 75 | 99.90 2 | 96.18 84 | 99.50 35 | 99.58 30 |
|
| CNVR-MVS | | | 97.68 6 | 97.44 20 | 98.37 7 | 98.90 55 | 95.86 6 | 97.27 176 | 98.08 87 | 95.81 17 | 97.87 51 | 98.31 74 | 94.26 13 | 99.68 68 | 97.02 51 | 99.49 38 | 99.57 31 |
|
| DP-MVS Recon | | | 95.68 97 | 95.12 109 | 97.37 56 | 99.19 33 | 94.19 42 | 97.03 196 | 98.08 87 | 88.35 306 | 95.09 150 | 97.65 133 | 89.97 86 | 99.48 117 | 92.08 191 | 98.59 119 | 98.44 170 |
|
| SR-MVS | | | 97.01 38 | 96.86 43 | 97.47 52 | 99.09 36 | 93.27 71 | 97.98 66 | 98.07 92 | 93.75 97 | 97.45 57 | 98.48 54 | 91.43 60 | 99.59 88 | 96.22 75 | 99.27 69 | 99.54 40 |
|
| MCST-MVS | | | 97.18 28 | 96.84 45 | 98.20 14 | 99.30 26 | 95.35 15 | 97.12 192 | 98.07 92 | 93.54 107 | 96.08 117 | 97.69 128 | 93.86 16 | 99.71 60 | 96.50 67 | 99.39 58 | 99.55 38 |
|
| NR-MVSNet | | | 92.34 229 | 91.27 248 | 95.53 179 | 94.95 331 | 93.05 77 | 97.39 164 | 98.07 92 | 92.65 149 | 84.46 392 | 95.71 259 | 85.00 175 | 97.77 335 | 89.71 244 | 83.52 394 | 95.78 303 |
|
| MP-MVS-pluss | | | 96.70 59 | 96.27 77 | 97.98 22 | 99.23 32 | 94.71 29 | 96.96 207 | 98.06 95 | 90.67 224 | 95.55 139 | 98.78 37 | 91.07 69 | 99.86 9 | 96.58 65 | 99.55 26 | 99.38 65 |
| MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
| APD-MVS_3200maxsize | | | 96.81 52 | 96.71 57 | 97.12 72 | 99.01 47 | 92.31 106 | 97.98 66 | 98.06 95 | 93.11 129 | 97.44 58 | 98.55 46 | 90.93 73 | 99.55 101 | 96.06 85 | 99.25 73 | 99.51 44 |
|
| MP-MVS |  | | 96.77 54 | 96.45 71 | 97.72 39 | 99.39 13 | 93.80 54 | 98.41 24 | 98.06 95 | 93.37 115 | 95.54 141 | 98.34 68 | 90.59 79 | 99.88 4 | 94.83 127 | 99.54 28 | 99.49 49 |
| Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
| HPM-MVS_fast | | | 96.51 68 | 96.27 77 | 97.22 66 | 99.32 24 | 92.74 89 | 98.74 10 | 98.06 95 | 90.57 234 | 96.77 81 | 98.35 65 | 90.21 82 | 99.53 105 | 94.80 130 | 99.63 16 | 99.38 65 |
|
| HPM-MVS |  | | 96.69 61 | 96.45 71 | 97.40 55 | 99.36 20 | 93.11 76 | 98.87 6 | 98.06 95 | 91.17 203 | 96.40 104 | 97.99 98 | 90.99 71 | 99.58 91 | 95.61 107 | 99.61 18 | 99.49 49 |
| Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
| sss | | | 94.51 138 | 93.80 147 | 96.64 88 | 97.07 174 | 91.97 120 | 96.32 271 | 98.06 95 | 88.94 284 | 94.50 165 | 96.78 194 | 84.60 181 | 99.27 140 | 91.90 192 | 96.02 203 | 98.68 145 |
|
| DeepC-MVS | | 93.07 3 | 96.06 83 | 95.66 88 | 97.29 60 | 97.96 121 | 93.17 75 | 97.30 174 | 98.06 95 | 93.92 92 | 93.38 199 | 98.66 40 | 86.83 142 | 99.73 54 | 95.60 109 | 99.22 75 | 98.96 107 |
| Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
| NCCC | | | 97.30 25 | 97.03 34 | 98.11 17 | 98.77 58 | 95.06 25 | 97.34 169 | 98.04 102 | 95.96 12 | 97.09 72 | 97.88 109 | 93.18 25 | 99.71 60 | 95.84 96 | 99.17 84 | 99.56 35 |
|
| DeepC-MVS_fast | | 93.89 2 | 96.93 43 | 96.64 59 | 97.78 32 | 98.64 69 | 94.30 37 | 97.41 159 | 98.04 102 | 94.81 56 | 96.59 92 | 98.37 63 | 91.24 65 | 99.64 80 | 95.16 116 | 99.52 30 | 99.42 60 |
| Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
| SR-MVS-dyc-post | | | 96.88 45 | 96.80 51 | 97.11 74 | 99.02 44 | 92.34 104 | 97.98 66 | 98.03 104 | 93.52 110 | 97.43 60 | 98.51 49 | 91.40 61 | 99.56 99 | 96.05 86 | 99.26 71 | 99.43 58 |
|
| RE-MVS-def | | | | 96.72 56 | | 99.02 44 | 92.34 104 | 97.98 66 | 98.03 104 | 93.52 110 | 97.43 60 | 98.51 49 | 90.71 77 | | 96.05 86 | 99.26 71 | 99.43 58 |
|
| RPMNet | | | 88.98 345 | 87.05 359 | 94.77 223 | 94.45 356 | 87.19 294 | 90.23 431 | 98.03 104 | 77.87 431 | 92.40 217 | 87.55 438 | 80.17 275 | 99.51 110 | 68.84 438 | 93.95 259 | 97.60 238 |
|
| save fliter | | | | | | 98.91 54 | 94.28 38 | 97.02 198 | 98.02 107 | 95.35 28 | | | | | | | |
|
| TEST9 | | | | | | 98.70 61 | 94.19 42 | 96.41 258 | 98.02 107 | 88.17 310 | 96.03 118 | 97.56 145 | 92.74 33 | 99.59 88 | | | |
|
| train_agg | | | 96.30 79 | 95.83 87 | 97.72 39 | 98.70 61 | 94.19 42 | 96.41 258 | 98.02 107 | 88.58 297 | 96.03 118 | 97.56 145 | 92.73 34 | 99.59 88 | 95.04 118 | 99.37 62 | 99.39 63 |
|
| test_8 | | | | | | 98.67 63 | 94.06 49 | 96.37 265 | 98.01 110 | 88.58 297 | 95.98 122 | 97.55 147 | 92.73 34 | 99.58 91 | | | |
|
| agg_prior | | | | | | 98.67 63 | 93.79 55 | | 98.00 111 | | 95.68 135 | | | 99.57 98 | | | |
|
| test_prior | | | | | 97.23 65 | 98.67 63 | 92.99 79 | | 98.00 111 | | | | | 99.41 125 | | | 99.29 70 |
|
| WR-MVS | | | 92.34 229 | 91.53 237 | 94.77 223 | 95.13 324 | 90.83 171 | 96.40 262 | 97.98 113 | 91.88 171 | 89.29 309 | 95.54 270 | 82.50 229 | 97.80 331 | 89.79 243 | 85.27 367 | 95.69 310 |
|
| HPM-MVS++ |  | | 97.34 22 | 96.97 37 | 98.47 5 | 99.08 38 | 96.16 4 | 97.55 141 | 97.97 114 | 95.59 22 | 96.61 90 | 97.89 107 | 92.57 38 | 99.84 23 | 95.95 91 | 99.51 33 | 99.40 61 |
|
| CANet | | | 96.39 74 | 96.02 82 | 97.50 50 | 97.62 147 | 93.38 64 | 97.02 198 | 97.96 115 | 95.42 26 | 94.86 154 | 97.81 119 | 87.38 136 | 99.82 28 | 96.88 54 | 99.20 81 | 99.29 70 |
|
| 114514_t | | | 93.95 162 | 93.06 177 | 96.63 92 | 99.07 39 | 91.61 133 | 97.46 156 | 97.96 115 | 77.99 429 | 93.00 208 | 97.57 143 | 86.14 155 | 99.33 132 | 89.22 260 | 99.15 88 | 98.94 111 |
|
| IU-MVS | | | | | | 99.42 7 | 95.39 11 | | 97.94 117 | 90.40 240 | 98.94 16 | | | | 97.41 46 | 99.66 10 | 99.74 8 |
|
| MSC_two_6792asdad | | | | | 98.86 1 | 98.67 63 | 96.94 1 | | 97.93 118 | | | | | 99.86 9 | 97.68 30 | 99.67 6 | 99.77 2 |
|
| No_MVS | | | | | 98.86 1 | 98.67 63 | 96.94 1 | | 97.93 118 | | | | | 99.86 9 | 97.68 30 | 99.67 6 | 99.77 2 |
|
| fmvsm_s_conf0.1_n_2 | | | 96.33 78 | 96.44 73 | 96.00 147 | 97.30 161 | 90.37 192 | 97.53 142 | 97.92 120 | 96.52 8 | 99.14 12 | 99.08 7 | 83.21 206 | 99.74 52 | 99.22 8 | 98.06 143 | 97.88 218 |
|
| Anonymous20231211 | | | 90.63 311 | 89.42 327 | 94.27 253 | 98.24 94 | 89.19 240 | 98.05 58 | 97.89 121 | 79.95 421 | 88.25 338 | 94.96 293 | 72.56 361 | 98.13 276 | 89.70 245 | 85.14 369 | 95.49 314 |
|
| 原ACMM1 | | | | | 96.38 118 | 98.59 71 | 91.09 161 | | 97.89 121 | 87.41 335 | 95.22 147 | 97.68 129 | 90.25 81 | 99.54 103 | 87.95 282 | 99.12 93 | 98.49 162 |
|
| CDPH-MVS | | | 95.97 88 | 95.38 100 | 97.77 34 | 98.93 52 | 94.44 35 | 96.35 266 | 97.88 123 | 86.98 343 | 96.65 88 | 97.89 107 | 91.99 48 | 99.47 118 | 92.26 180 | 99.46 41 | 99.39 63 |
|
| test11 | | | | | | | | | 97.88 123 | | | | | | | | |
|
| EIA-MVS | | | 95.53 103 | 95.47 94 | 95.71 169 | 97.06 177 | 89.63 213 | 97.82 94 | 97.87 125 | 93.57 103 | 93.92 181 | 95.04 290 | 90.61 78 | 98.95 186 | 94.62 136 | 98.68 113 | 98.54 155 |
|
| CS-MVS | | | 96.86 46 | 97.06 29 | 96.26 128 | 98.16 105 | 91.16 159 | 99.09 3 | 97.87 125 | 95.30 30 | 97.06 73 | 98.03 94 | 91.72 51 | 98.71 222 | 97.10 49 | 99.17 84 | 98.90 120 |
|
| 无先验 | | | | | | | | 95.79 305 | 97.87 125 | 83.87 392 | | | | 99.65 72 | 87.68 292 | | 98.89 124 |
|
| 3Dnovator+ | | 91.43 4 | 95.40 104 | 94.48 131 | 98.16 16 | 96.90 191 | 95.34 16 | 98.48 21 | 97.87 125 | 94.65 67 | 88.53 329 | 98.02 96 | 83.69 197 | 99.71 60 | 93.18 166 | 98.96 103 | 99.44 56 |
|
| VPNet | | | 92.23 237 | 91.31 245 | 94.99 206 | 95.56 289 | 90.96 165 | 97.22 184 | 97.86 129 | 92.96 138 | 90.96 261 | 96.62 211 | 75.06 341 | 98.20 270 | 91.90 192 | 83.65 393 | 95.80 301 |
|
| test_vis1_n_1920 | | | 94.17 147 | 94.58 124 | 92.91 319 | 97.42 159 | 82.02 388 | 97.83 92 | 97.85 130 | 94.68 64 | 98.10 41 | 98.49 51 | 70.15 380 | 99.32 134 | 97.91 27 | 98.82 107 | 97.40 247 |
|
| DVP-MVS |  | | 97.91 3 | 97.81 4 | 98.22 13 | 99.45 3 | 95.36 13 | 98.21 43 | 97.85 130 | 94.92 47 | 98.73 27 | 98.87 28 | 95.08 8 | 99.84 23 | 97.52 39 | 99.67 6 | 99.48 51 |
| 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 |
| TSAR-MVS + MP. | | | 97.42 18 | 97.33 23 | 97.69 42 | 99.25 29 | 94.24 41 | 98.07 56 | 97.85 130 | 93.72 98 | 98.57 31 | 98.35 65 | 93.69 18 | 99.40 126 | 97.06 50 | 99.46 41 | 99.44 56 |
| Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition |
| SPE-MVS-test | | | 96.89 44 | 97.04 33 | 96.45 112 | 98.29 88 | 91.66 132 | 99.03 4 | 97.85 130 | 95.84 15 | 96.90 76 | 97.97 100 | 91.24 65 | 98.75 214 | 96.92 53 | 99.33 64 | 98.94 111 |
|
| test_fmvsmconf0.01_n | | | 96.15 82 | 95.85 86 | 97.03 79 | 92.66 407 | 91.83 124 | 97.97 72 | 97.84 134 | 95.57 23 | 97.53 54 | 99.00 14 | 84.20 190 | 99.76 47 | 98.82 20 | 99.08 95 | 99.48 51 |
|
| GDP-MVS | | | 95.62 99 | 95.13 107 | 97.09 75 | 96.79 203 | 93.26 72 | 97.89 83 | 97.83 135 | 93.58 102 | 96.80 78 | 97.82 117 | 83.06 213 | 99.16 154 | 94.40 141 | 97.95 149 | 98.87 126 |
|
| balanced_conf03 | | | 96.84 50 | 96.89 42 | 96.68 87 | 97.63 146 | 92.22 109 | 98.17 49 | 97.82 136 | 94.44 76 | 98.23 39 | 97.36 156 | 90.97 72 | 99.22 144 | 97.74 29 | 99.66 10 | 98.61 148 |
|
| AdaColmap |  | | 94.34 142 | 93.68 152 | 96.31 122 | 98.59 71 | 91.68 131 | 96.59 249 | 97.81 137 | 89.87 250 | 92.15 227 | 97.06 177 | 83.62 200 | 99.54 103 | 89.34 255 | 98.07 142 | 97.70 231 |
|
| MVSMamba_PlusPlus | | | 96.51 68 | 96.48 66 | 96.59 96 | 98.07 113 | 91.97 120 | 98.14 50 | 97.79 138 | 90.43 238 | 97.34 63 | 97.52 148 | 91.29 64 | 99.19 147 | 98.12 25 | 99.64 14 | 98.60 149 |
|
| KinetiMVS | | | 95.26 109 | 94.75 119 | 96.79 84 | 96.99 186 | 92.05 116 | 97.82 94 | 97.78 139 | 94.77 60 | 96.46 101 | 97.70 126 | 80.62 265 | 99.34 131 | 92.37 179 | 98.28 133 | 98.97 104 |
|
| mamv4 | | | 94.66 135 | 96.10 81 | 90.37 388 | 98.01 116 | 73.41 438 | 96.82 220 | 97.78 139 | 89.95 249 | 94.52 164 | 97.43 152 | 92.91 27 | 99.09 167 | 98.28 24 | 99.16 87 | 98.60 149 |
|
| ETV-MVS | | | 96.02 85 | 95.89 85 | 96.40 115 | 97.16 169 | 92.44 101 | 97.47 154 | 97.77 141 | 94.55 70 | 96.48 99 | 94.51 318 | 91.23 67 | 98.92 191 | 95.65 103 | 98.19 137 | 97.82 226 |
|
| 新几何1 | | | | | 97.32 58 | 98.60 70 | 93.59 59 | | 97.75 142 | 81.58 412 | 95.75 130 | 97.85 113 | 90.04 84 | 99.67 70 | 86.50 314 | 99.13 91 | 98.69 144 |
|
| 旧先验1 | | | | | | 98.38 84 | 93.38 64 | | 97.75 142 | | | 98.09 89 | 92.30 45 | | | 99.01 101 | 99.16 80 |
|
| EC-MVSNet | | | 96.42 72 | 96.47 67 | 96.26 128 | 97.01 184 | 91.52 138 | 98.89 5 | 97.75 142 | 94.42 77 | 96.64 89 | 97.68 129 | 89.32 92 | 98.60 234 | 97.45 43 | 99.11 94 | 98.67 146 |
|
| EI-MVSNet-Vis-set | | | 96.51 68 | 96.47 67 | 96.63 92 | 98.24 94 | 91.20 153 | 96.89 212 | 97.73 145 | 94.74 62 | 96.49 98 | 98.49 51 | 90.88 75 | 99.58 91 | 96.44 69 | 98.32 131 | 99.13 84 |
|
| PAPM_NR | | | 95.01 118 | 94.59 123 | 96.26 128 | 98.89 56 | 90.68 178 | 97.24 178 | 97.73 145 | 91.80 172 | 92.93 213 | 96.62 211 | 89.13 95 | 99.14 159 | 89.21 261 | 97.78 153 | 98.97 104 |
|
| Anonymous20240529 | | | 91.98 246 | 90.73 273 | 95.73 167 | 98.14 106 | 89.40 227 | 97.99 63 | 97.72 147 | 79.63 423 | 93.54 192 | 97.41 154 | 69.94 382 | 99.56 99 | 91.04 214 | 91.11 303 | 98.22 188 |
|
| CHOSEN 280x420 | | | 93.12 196 | 92.72 194 | 94.34 247 | 96.71 213 | 87.27 290 | 90.29 430 | 97.72 147 | 86.61 350 | 91.34 250 | 95.29 278 | 84.29 189 | 98.41 250 | 93.25 164 | 98.94 104 | 97.35 250 |
|
| EI-MVSNet-UG-set | | | 96.34 77 | 96.30 76 | 96.47 109 | 98.20 100 | 90.93 167 | 96.86 215 | 97.72 147 | 94.67 65 | 96.16 114 | 98.46 55 | 90.43 80 | 99.58 91 | 96.23 74 | 97.96 148 | 98.90 120 |
|
| LS3D | | | 93.57 179 | 92.61 199 | 96.47 109 | 97.59 150 | 91.61 133 | 97.67 118 | 97.72 147 | 85.17 374 | 90.29 272 | 98.34 68 | 84.60 181 | 99.73 54 | 83.85 356 | 98.27 134 | 98.06 207 |
|
| PAPR | | | 94.18 146 | 93.42 168 | 96.48 108 | 97.64 144 | 91.42 144 | 95.55 319 | 97.71 151 | 88.99 281 | 92.34 223 | 95.82 251 | 89.19 93 | 99.11 162 | 86.14 320 | 97.38 164 | 98.90 120 |
|
| UGNet | | | 94.04 157 | 93.28 171 | 96.31 122 | 96.85 195 | 91.19 154 | 97.88 84 | 97.68 152 | 94.40 79 | 93.00 208 | 96.18 231 | 73.39 358 | 99.61 83 | 91.72 198 | 98.46 125 | 98.13 196 |
| 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 |
| testdata | | | | | 95.46 186 | 98.18 104 | 88.90 246 | | 97.66 153 | 82.73 403 | 97.03 74 | 98.07 90 | 90.06 83 | 98.85 198 | 89.67 246 | 98.98 102 | 98.64 147 |
|
| test12 | | | | | 97.65 43 | 98.46 75 | 94.26 39 | | 97.66 153 | | 95.52 142 | | 90.89 74 | 99.46 119 | | 99.25 73 | 99.22 77 |
|
| DTE-MVSNet | | | 90.56 312 | 89.75 318 | 93.01 315 | 93.95 369 | 87.25 291 | 97.64 126 | 97.65 155 | 90.74 219 | 87.12 361 | 95.68 262 | 79.97 279 | 97.00 385 | 83.33 357 | 81.66 404 | 94.78 370 |
|
| TAPA-MVS | | 90.10 7 | 92.30 232 | 91.22 251 | 95.56 176 | 98.33 86 | 89.60 215 | 96.79 223 | 97.65 155 | 81.83 409 | 91.52 245 | 97.23 166 | 87.94 118 | 98.91 193 | 71.31 433 | 98.37 129 | 98.17 194 |
| Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
| sd_testset | | | 93.10 197 | 92.45 207 | 95.05 201 | 98.09 109 | 89.21 237 | 96.89 212 | 97.64 157 | 93.18 125 | 91.79 239 | 97.28 161 | 75.35 340 | 98.65 229 | 88.99 266 | 92.84 273 | 97.28 253 |
|
| test_cas_vis1_n_1920 | | | 94.48 140 | 94.55 128 | 94.28 252 | 96.78 207 | 86.45 315 | 97.63 128 | 97.64 157 | 93.32 118 | 97.68 53 | 98.36 64 | 73.75 356 | 99.08 170 | 96.73 59 | 99.05 97 | 97.31 252 |
|
| NormalMVS | | | 96.36 76 | 96.11 80 | 97.12 72 | 99.37 16 | 92.90 83 | 97.99 63 | 97.63 159 | 95.92 13 | 96.57 95 | 97.93 102 | 85.34 167 | 99.50 113 | 94.99 121 | 99.21 76 | 98.97 104 |
|
| Elysia | | | 94.00 159 | 93.12 174 | 96.64 88 | 96.08 268 | 92.72 91 | 97.50 145 | 97.63 159 | 91.15 205 | 94.82 155 | 97.12 172 | 74.98 343 | 99.06 176 | 90.78 219 | 98.02 144 | 98.12 198 |
|
| StellarMVS | | | 94.00 159 | 93.12 174 | 96.64 88 | 96.08 268 | 92.72 91 | 97.50 145 | 97.63 159 | 91.15 205 | 94.82 155 | 97.12 172 | 74.98 343 | 99.06 176 | 90.78 219 | 98.02 144 | 98.12 198 |
|
| cdsmvs_eth3d_5k | | | 23.24 428 | 30.99 430 | 0.00 446 | 0.00 469 | 0.00 471 | 0.00 457 | 97.63 159 | 0.00 464 | 0.00 465 | 96.88 190 | 84.38 186 | 0.00 465 | 0.00 464 | 0.00 463 | 0.00 461 |
|
| DPM-MVS | | | 95.69 96 | 94.92 113 | 98.01 20 | 98.08 112 | 95.71 9 | 95.27 335 | 97.62 163 | 90.43 238 | 95.55 139 | 97.07 176 | 91.72 51 | 99.50 113 | 89.62 248 | 98.94 104 | 98.82 132 |
|
| sasdasda | | | 96.02 85 | 95.45 95 | 97.75 36 | 97.59 150 | 95.15 23 | 98.28 31 | 97.60 164 | 94.52 72 | 96.27 109 | 96.12 236 | 87.65 124 | 99.18 150 | 96.20 80 | 94.82 235 | 98.91 117 |
|
| canonicalmvs | | | 96.02 85 | 95.45 95 | 97.75 36 | 97.59 150 | 95.15 23 | 98.28 31 | 97.60 164 | 94.52 72 | 96.27 109 | 96.12 236 | 87.65 124 | 99.18 150 | 96.20 80 | 94.82 235 | 98.91 117 |
|
| test222 | | | | | | 98.24 94 | 92.21 110 | 95.33 330 | 97.60 164 | 79.22 425 | 95.25 145 | 97.84 115 | 88.80 101 | | | 99.15 88 | 98.72 141 |
|
| cascas | | | 91.20 287 | 90.08 300 | 94.58 233 | 94.97 329 | 89.16 241 | 93.65 395 | 97.59 167 | 79.90 422 | 89.40 304 | 92.92 383 | 75.36 339 | 98.36 258 | 92.14 185 | 94.75 238 | 96.23 280 |
|
| h-mvs33 | | | 94.15 149 | 93.52 160 | 96.04 141 | 97.81 132 | 90.22 196 | 97.62 130 | 97.58 168 | 95.19 33 | 96.74 82 | 97.45 149 | 83.67 198 | 99.61 83 | 95.85 94 | 79.73 411 | 98.29 184 |
|
| MGCFI-Net | | | 95.94 90 | 95.40 99 | 97.56 49 | 97.59 150 | 94.62 31 | 98.21 43 | 97.57 169 | 94.41 78 | 96.17 113 | 96.16 234 | 87.54 129 | 99.17 152 | 96.19 82 | 94.73 240 | 98.91 117 |
|
| MVSFormer | | | 95.37 105 | 95.16 106 | 95.99 148 | 96.34 246 | 91.21 151 | 98.22 41 | 97.57 169 | 91.42 189 | 96.22 111 | 97.32 157 | 86.20 153 | 97.92 318 | 94.07 145 | 99.05 97 | 98.85 128 |
|
| test_djsdf | | | 93.07 199 | 92.76 189 | 94.00 264 | 93.49 386 | 88.70 250 | 98.22 41 | 97.57 169 | 91.42 189 | 90.08 284 | 95.55 269 | 82.85 220 | 97.92 318 | 94.07 145 | 91.58 294 | 95.40 325 |
|
| OMC-MVS | | | 95.09 117 | 94.70 120 | 96.25 131 | 98.46 75 | 91.28 147 | 96.43 256 | 97.57 169 | 92.04 167 | 94.77 159 | 97.96 101 | 87.01 141 | 99.09 167 | 91.31 208 | 96.77 185 | 98.36 177 |
|
| PS-MVSNAJss | | | 93.74 172 | 93.51 161 | 94.44 241 | 93.91 371 | 89.28 235 | 97.75 104 | 97.56 173 | 92.50 151 | 89.94 286 | 96.54 214 | 88.65 104 | 98.18 273 | 93.83 154 | 90.90 308 | 95.86 295 |
|
| casdiffmvs_mvg |  | | 95.81 95 | 95.57 89 | 96.51 105 | 96.87 192 | 91.49 139 | 97.50 145 | 97.56 173 | 93.99 90 | 95.13 149 | 97.92 105 | 87.89 119 | 98.78 207 | 95.97 90 | 97.33 167 | 99.26 74 |
| Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025 |
| jajsoiax | | | 92.42 225 | 91.89 225 | 94.03 263 | 93.33 394 | 88.50 257 | 97.73 108 | 97.53 175 | 92.00 169 | 88.85 321 | 96.50 216 | 75.62 338 | 98.11 280 | 93.88 152 | 91.56 295 | 95.48 315 |
|
| mvs_tets | | | 92.31 231 | 91.76 228 | 93.94 272 | 93.41 391 | 88.29 262 | 97.63 128 | 97.53 175 | 92.04 167 | 88.76 324 | 96.45 218 | 74.62 348 | 98.09 285 | 93.91 150 | 91.48 296 | 95.45 320 |
|
| dcpmvs_2 | | | 96.37 75 | 97.05 32 | 94.31 250 | 98.96 51 | 84.11 362 | 97.56 136 | 97.51 177 | 93.92 92 | 97.43 60 | 98.52 48 | 92.75 32 | 99.32 134 | 97.32 48 | 99.50 35 | 99.51 44 |
|
| HQP_MVS | | | 93.78 171 | 93.43 166 | 94.82 216 | 96.21 250 | 89.99 201 | 97.74 106 | 97.51 177 | 94.85 50 | 91.34 250 | 96.64 204 | 81.32 253 | 98.60 234 | 93.02 172 | 92.23 282 | 95.86 295 |
|
| plane_prior5 | | | | | | | | | 97.51 177 | | | | | 98.60 234 | 93.02 172 | 92.23 282 | 95.86 295 |
|
| viewmanbaseed2359cas | | | 95.24 111 | 95.02 111 | 95.91 151 | 96.87 192 | 89.98 203 | 96.82 220 | 97.49 180 | 92.26 156 | 95.47 143 | 97.82 117 | 86.47 147 | 98.69 223 | 94.80 130 | 97.20 175 | 99.06 94 |
|
| reproduce_monomvs | | | 91.30 282 | 91.10 255 | 91.92 349 | 96.82 200 | 82.48 382 | 97.01 201 | 97.49 180 | 94.64 68 | 88.35 332 | 95.27 281 | 70.53 375 | 98.10 281 | 95.20 114 | 84.60 379 | 95.19 343 |
|
| PS-MVSNAJ | | | 95.37 105 | 95.33 102 | 95.49 182 | 97.35 160 | 90.66 179 | 95.31 332 | 97.48 182 | 93.85 95 | 96.51 97 | 95.70 261 | 88.65 104 | 99.65 72 | 94.80 130 | 98.27 134 | 96.17 284 |
|
| API-MVS | | | 94.84 128 | 94.49 130 | 95.90 152 | 97.90 127 | 92.00 119 | 97.80 98 | 97.48 182 | 89.19 273 | 94.81 157 | 96.71 197 | 88.84 100 | 99.17 152 | 88.91 268 | 98.76 111 | 96.53 273 |
|
| MG-MVS | | | 95.61 100 | 95.38 100 | 96.31 122 | 98.42 79 | 90.53 181 | 96.04 289 | 97.48 182 | 93.47 112 | 95.67 136 | 98.10 87 | 89.17 94 | 99.25 141 | 91.27 209 | 98.77 110 | 99.13 84 |
|
| MAR-MVS | | | 94.22 145 | 93.46 163 | 96.51 105 | 98.00 118 | 92.19 113 | 97.67 118 | 97.47 185 | 88.13 314 | 93.00 208 | 95.84 249 | 84.86 179 | 99.51 110 | 87.99 281 | 98.17 139 | 97.83 225 |
| 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 |
| CLD-MVS | | | 92.98 203 | 92.53 203 | 94.32 248 | 96.12 265 | 89.20 238 | 95.28 333 | 97.47 185 | 92.66 148 | 89.90 287 | 95.62 265 | 80.58 266 | 98.40 251 | 92.73 177 | 92.40 280 | 95.38 327 |
| Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
| UniMVSNet_ETH3D | | | 91.34 280 | 90.22 296 | 94.68 227 | 94.86 338 | 87.86 279 | 97.23 182 | 97.46 187 | 87.99 315 | 89.90 287 | 96.92 188 | 66.35 410 | 98.23 267 | 90.30 233 | 90.99 306 | 97.96 212 |
|
| nrg030 | | | 94.05 156 | 93.31 170 | 96.27 127 | 95.22 316 | 94.59 32 | 98.34 26 | 97.46 187 | 92.93 139 | 91.21 259 | 96.64 204 | 87.23 139 | 98.22 268 | 94.99 121 | 85.80 359 | 95.98 294 |
|
| XVG-OURS | | | 93.72 173 | 93.35 169 | 94.80 221 | 97.07 174 | 88.61 251 | 94.79 350 | 97.46 187 | 91.97 170 | 93.99 178 | 97.86 112 | 81.74 247 | 98.88 195 | 92.64 178 | 92.67 278 | 96.92 265 |
|
| LPG-MVS_test | | | 92.94 206 | 92.56 200 | 94.10 258 | 96.16 260 | 88.26 264 | 97.65 122 | 97.46 187 | 91.29 193 | 90.12 280 | 97.16 169 | 79.05 295 | 98.73 217 | 92.25 182 | 91.89 290 | 95.31 332 |
|
| LGP-MVS_train | | | | | 94.10 258 | 96.16 260 | 88.26 264 | | 97.46 187 | 91.29 193 | 90.12 280 | 97.16 169 | 79.05 295 | 98.73 217 | 92.25 182 | 91.89 290 | 95.31 332 |
|
| MVS | | | 91.71 254 | 90.44 283 | 95.51 180 | 95.20 318 | 91.59 135 | 96.04 289 | 97.45 192 | 73.44 439 | 87.36 357 | 95.60 266 | 85.42 166 | 99.10 164 | 85.97 325 | 97.46 159 | 95.83 299 |
|
| XVG-OURS-SEG-HR | | | 93.86 168 | 93.55 156 | 94.81 218 | 97.06 177 | 88.53 256 | 95.28 333 | 97.45 192 | 91.68 177 | 94.08 177 | 97.68 129 | 82.41 232 | 98.90 194 | 93.84 153 | 92.47 279 | 96.98 261 |
|
| baseline | | | 95.58 101 | 95.42 98 | 96.08 137 | 96.78 207 | 90.41 187 | 97.16 189 | 97.45 192 | 93.69 101 | 95.65 137 | 97.85 113 | 87.29 137 | 98.68 225 | 95.66 100 | 97.25 173 | 99.13 84 |
|
| ab-mvs | | | 93.57 179 | 92.55 201 | 96.64 88 | 97.28 163 | 91.96 122 | 95.40 326 | 97.45 192 | 89.81 255 | 93.22 205 | 96.28 227 | 79.62 286 | 99.46 119 | 90.74 222 | 93.11 270 | 98.50 160 |
|
| xiu_mvs_v2_base | | | 95.32 107 | 95.29 103 | 95.40 187 | 97.22 165 | 90.50 182 | 95.44 325 | 97.44 196 | 93.70 100 | 96.46 101 | 96.18 231 | 88.59 108 | 99.53 105 | 94.79 133 | 97.81 152 | 96.17 284 |
|
| 1314 | | | 92.81 215 | 92.03 218 | 95.14 197 | 95.33 308 | 89.52 222 | 96.04 289 | 97.44 196 | 87.72 328 | 86.25 378 | 95.33 277 | 83.84 195 | 98.79 206 | 89.26 258 | 97.05 180 | 97.11 259 |
|
| casdiffmvs |  | | 95.64 98 | 95.49 92 | 96.08 137 | 96.76 212 | 90.45 184 | 97.29 175 | 97.44 196 | 94.00 89 | 95.46 144 | 97.98 99 | 87.52 132 | 98.73 217 | 95.64 104 | 97.33 167 | 99.08 91 |
| Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025 |
| XXY-MVS | | | 92.16 239 | 91.23 250 | 94.95 212 | 94.75 343 | 90.94 166 | 97.47 154 | 97.43 199 | 89.14 274 | 88.90 317 | 96.43 219 | 79.71 283 | 98.24 266 | 89.56 249 | 87.68 340 | 95.67 311 |
|
| anonymousdsp | | | 92.16 239 | 91.55 236 | 93.97 268 | 92.58 409 | 89.55 219 | 97.51 144 | 97.42 200 | 89.42 267 | 88.40 331 | 94.84 300 | 80.66 264 | 97.88 323 | 91.87 194 | 91.28 300 | 94.48 378 |
|
| Effi-MVS+ | | | 94.93 123 | 94.45 132 | 96.36 120 | 96.61 217 | 91.47 141 | 96.41 258 | 97.41 201 | 91.02 211 | 94.50 165 | 95.92 245 | 87.53 130 | 98.78 207 | 93.89 151 | 96.81 184 | 98.84 131 |
|
| RRT-MVS | | | 94.51 138 | 94.35 135 | 94.98 208 | 96.40 241 | 86.55 313 | 97.56 136 | 97.41 201 | 93.19 123 | 94.93 152 | 97.04 178 | 79.12 293 | 99.30 138 | 96.19 82 | 97.32 169 | 99.09 90 |
|
| HQP3-MVS | | | | | | | | | 97.39 203 | | | | | | | 92.10 287 | |
|
| HQP-MVS | | | 93.19 193 | 92.74 192 | 94.54 236 | 95.86 274 | 89.33 231 | 96.65 240 | 97.39 203 | 93.55 104 | 90.14 274 | 95.87 247 | 80.95 257 | 98.50 244 | 92.13 188 | 92.10 287 | 95.78 303 |
|
| PLC |  | 91.00 6 | 94.11 153 | 93.43 166 | 96.13 136 | 98.58 73 | 91.15 160 | 96.69 236 | 97.39 203 | 87.29 338 | 91.37 249 | 96.71 197 | 88.39 109 | 99.52 109 | 87.33 301 | 97.13 178 | 97.73 229 |
| Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
| v7n | | | 90.76 304 | 89.86 311 | 93.45 300 | 93.54 383 | 87.60 285 | 97.70 116 | 97.37 206 | 88.85 287 | 87.65 350 | 94.08 348 | 81.08 256 | 98.10 281 | 84.68 342 | 83.79 392 | 94.66 375 |
|
| UnsupCasMVSNet_eth | | | 85.99 381 | 84.45 385 | 90.62 384 | 89.97 427 | 82.40 385 | 93.62 396 | 97.37 206 | 89.86 251 | 78.59 429 | 92.37 393 | 65.25 418 | 95.35 419 | 82.27 370 | 70.75 437 | 94.10 389 |
|
| ACMM | | 89.79 8 | 92.96 204 | 92.50 205 | 94.35 245 | 96.30 248 | 88.71 249 | 97.58 132 | 97.36 208 | 91.40 191 | 90.53 267 | 96.65 203 | 79.77 282 | 98.75 214 | 91.24 210 | 91.64 292 | 95.59 313 |
| Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
| xiu_mvs_v1_base_debu | | | 95.01 118 | 94.76 116 | 95.75 164 | 96.58 220 | 91.71 128 | 96.25 276 | 97.35 209 | 92.99 132 | 96.70 84 | 96.63 208 | 82.67 224 | 99.44 122 | 96.22 75 | 97.46 159 | 96.11 290 |
|
| xiu_mvs_v1_base | | | 95.01 118 | 94.76 116 | 95.75 164 | 96.58 220 | 91.71 128 | 96.25 276 | 97.35 209 | 92.99 132 | 96.70 84 | 96.63 208 | 82.67 224 | 99.44 122 | 96.22 75 | 97.46 159 | 96.11 290 |
|
| xiu_mvs_v1_base_debi | | | 95.01 118 | 94.76 116 | 95.75 164 | 96.58 220 | 91.71 128 | 96.25 276 | 97.35 209 | 92.99 132 | 96.70 84 | 96.63 208 | 82.67 224 | 99.44 122 | 96.22 75 | 97.46 159 | 96.11 290 |
|
| diffmvs |  | | 95.25 110 | 95.13 107 | 95.63 172 | 96.43 240 | 89.34 230 | 95.99 293 | 97.35 209 | 92.83 143 | 96.31 107 | 97.37 155 | 86.44 148 | 98.67 226 | 96.26 72 | 97.19 176 | 98.87 126 |
| Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025 |
| WTY-MVS | | | 94.71 134 | 94.02 143 | 96.79 84 | 97.71 138 | 92.05 116 | 96.59 249 | 97.35 209 | 90.61 230 | 94.64 161 | 96.93 185 | 86.41 149 | 99.39 127 | 91.20 211 | 94.71 241 | 98.94 111 |
|
| mamba_test_0407 | | | 94.54 137 | 94.12 142 | 95.80 160 | 96.79 203 | 90.38 189 | 96.79 223 | 97.29 214 | 91.24 197 | 93.68 185 | 97.60 140 | 85.03 173 | 98.67 226 | 92.14 185 | 96.51 193 | 98.35 179 |
|
| mamba_0404 | | | 94.73 133 | 94.31 137 | 95.98 149 | 97.05 179 | 90.90 169 | 97.01 201 | 97.29 214 | 91.24 197 | 94.17 174 | 97.60 140 | 85.03 173 | 98.76 211 | 92.14 185 | 97.30 170 | 98.29 184 |
|
| F-COLMAP | | | 93.58 177 | 92.98 181 | 95.37 188 | 98.40 81 | 88.98 244 | 97.18 187 | 97.29 214 | 87.75 327 | 90.49 268 | 97.10 175 | 85.21 170 | 99.50 113 | 86.70 311 | 96.72 188 | 97.63 233 |
|
| VortexMVS | | | 92.88 210 | 92.64 196 | 93.58 293 | 96.58 220 | 87.53 286 | 96.93 209 | 97.28 217 | 92.78 146 | 89.75 292 | 94.99 291 | 82.73 223 | 97.76 336 | 94.60 138 | 88.16 335 | 95.46 318 |
|
| XVG-ACMP-BASELINE | | | 90.93 300 | 90.21 297 | 93.09 313 | 94.31 362 | 85.89 330 | 95.33 330 | 97.26 218 | 91.06 210 | 89.38 305 | 95.44 275 | 68.61 393 | 98.60 234 | 89.46 251 | 91.05 304 | 94.79 368 |
|
| PCF-MVS | | 89.48 11 | 91.56 264 | 89.95 308 | 96.36 120 | 96.60 218 | 92.52 99 | 92.51 415 | 97.26 218 | 79.41 424 | 88.90 317 | 96.56 213 | 84.04 194 | 99.55 101 | 77.01 409 | 97.30 170 | 97.01 260 |
| Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
| ACMP | | 89.59 10 | 92.62 219 | 92.14 214 | 94.05 261 | 96.40 241 | 88.20 267 | 97.36 167 | 97.25 220 | 91.52 184 | 88.30 335 | 96.64 204 | 78.46 307 | 98.72 221 | 91.86 195 | 91.48 296 | 95.23 339 |
| Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
| icg_test_0407_2 | | | 93.58 177 | 93.46 163 | 93.94 272 | 96.19 254 | 86.16 324 | 93.73 390 | 97.24 221 | 91.54 180 | 93.50 194 | 97.04 178 | 85.64 163 | 96.91 388 | 90.68 224 | 95.59 217 | 98.76 134 |
|
| icg_test_0407 | | | 93.94 163 | 93.75 149 | 94.49 238 | 96.19 254 | 86.16 324 | 96.35 266 | 97.24 221 | 91.54 180 | 93.50 194 | 97.04 178 | 85.64 163 | 98.54 241 | 90.68 224 | 95.59 217 | 98.76 134 |
|
| ICG_test_0404 | | | 92.44 223 | 91.92 223 | 94.00 264 | 96.19 254 | 86.16 324 | 93.84 387 | 97.24 221 | 91.54 180 | 88.17 341 | 97.04 178 | 76.96 325 | 97.09 379 | 90.68 224 | 95.59 217 | 98.76 134 |
|
| icg_test_0403 | | | 93.98 161 | 93.79 148 | 94.55 235 | 96.19 254 | 86.16 324 | 96.35 266 | 97.24 221 | 91.54 180 | 93.59 189 | 97.04 178 | 85.86 158 | 98.73 217 | 90.68 224 | 95.59 217 | 98.76 134 |
|
| OPM-MVS | | | 93.28 189 | 92.76 189 | 94.82 216 | 94.63 349 | 90.77 174 | 96.65 240 | 97.18 225 | 93.72 98 | 91.68 243 | 97.26 164 | 79.33 290 | 98.63 231 | 92.13 188 | 92.28 281 | 95.07 346 |
| Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS). |
| PatchMatch-RL | | | 92.90 208 | 92.02 219 | 95.56 176 | 98.19 102 | 90.80 172 | 95.27 335 | 97.18 225 | 87.96 316 | 91.86 238 | 95.68 262 | 80.44 269 | 98.99 184 | 84.01 351 | 97.54 158 | 96.89 266 |
|
| alignmvs | | | 95.87 94 | 95.23 104 | 97.78 32 | 97.56 156 | 95.19 21 | 97.86 85 | 97.17 227 | 94.39 80 | 96.47 100 | 96.40 221 | 85.89 157 | 99.20 146 | 96.21 79 | 95.11 231 | 98.95 110 |
|
| MVS_Test | | | 94.89 125 | 94.62 122 | 95.68 170 | 96.83 198 | 89.55 219 | 96.70 234 | 97.17 227 | 91.17 203 | 95.60 138 | 96.11 240 | 87.87 121 | 98.76 211 | 93.01 174 | 97.17 177 | 98.72 141 |
|
| Fast-Effi-MVS+ | | | 93.46 183 | 92.75 191 | 95.59 175 | 96.77 209 | 90.03 198 | 96.81 222 | 97.13 229 | 88.19 309 | 91.30 253 | 94.27 336 | 86.21 152 | 98.63 231 | 87.66 293 | 96.46 199 | 98.12 198 |
|
| EI-MVSNet | | | 93.03 201 | 92.88 185 | 93.48 298 | 95.77 280 | 86.98 299 | 96.44 254 | 97.12 230 | 90.66 226 | 91.30 253 | 97.64 136 | 86.56 144 | 98.05 293 | 89.91 239 | 90.55 312 | 95.41 322 |
|
| MVSTER | | | 93.20 192 | 92.81 188 | 94.37 244 | 96.56 224 | 89.59 216 | 97.06 195 | 97.12 230 | 91.24 197 | 91.30 253 | 95.96 243 | 82.02 240 | 98.05 293 | 93.48 159 | 90.55 312 | 95.47 317 |
|
| viewmambaseed2359dif | | | 94.28 143 | 94.14 140 | 94.71 226 | 96.21 250 | 86.97 300 | 95.93 296 | 97.11 232 | 89.00 280 | 95.00 151 | 97.70 126 | 86.02 156 | 98.59 238 | 93.71 156 | 96.59 192 | 98.57 153 |
|
| test_yl | | | 94.78 131 | 94.23 138 | 96.43 113 | 97.74 136 | 91.22 149 | 96.85 216 | 97.10 233 | 91.23 200 | 95.71 132 | 96.93 185 | 84.30 187 | 99.31 136 | 93.10 167 | 95.12 229 | 98.75 138 |
|
| DCV-MVSNet | | | 94.78 131 | 94.23 138 | 96.43 113 | 97.74 136 | 91.22 149 | 96.85 216 | 97.10 233 | 91.23 200 | 95.71 132 | 96.93 185 | 84.30 187 | 99.31 136 | 93.10 167 | 95.12 229 | 98.75 138 |
|
| LTVRE_ROB | | 88.41 13 | 90.99 296 | 89.92 310 | 94.19 254 | 96.18 258 | 89.55 219 | 96.31 272 | 97.09 235 | 87.88 319 | 85.67 382 | 95.91 246 | 78.79 303 | 98.57 239 | 81.50 373 | 89.98 317 | 94.44 381 |
| 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 |
| test_fmvs1_n | | | 92.73 217 | 92.88 185 | 92.29 339 | 96.08 268 | 81.05 396 | 97.98 66 | 97.08 236 | 90.72 221 | 96.79 80 | 98.18 84 | 63.07 422 | 98.45 248 | 97.62 37 | 98.42 128 | 97.36 248 |
|
| v10 | | | 91.04 294 | 90.23 294 | 93.49 297 | 94.12 365 | 88.16 270 | 97.32 172 | 97.08 236 | 88.26 308 | 88.29 336 | 94.22 341 | 82.17 237 | 97.97 305 | 86.45 315 | 84.12 386 | 94.33 384 |
|
| mamba_0408 | | | 93.70 174 | 92.99 178 | 95.83 157 | 96.79 203 | 90.38 189 | 88.69 440 | 97.07 238 | 90.96 213 | 93.68 185 | 97.31 159 | 84.97 176 | 98.76 211 | 90.95 215 | 96.51 193 | 98.35 179 |
|
| mamba_test_0407_2 | | | 93.51 182 | 92.99 178 | 95.05 201 | 96.79 203 | 90.38 189 | 88.69 440 | 97.07 238 | 90.96 213 | 93.68 185 | 97.31 159 | 84.97 176 | 96.42 399 | 90.95 215 | 96.51 193 | 98.35 179 |
|
| v144192 | | | 91.06 293 | 90.28 290 | 93.39 301 | 93.66 380 | 87.23 293 | 96.83 219 | 97.07 238 | 87.43 334 | 89.69 295 | 94.28 335 | 81.48 250 | 98.00 300 | 87.18 305 | 84.92 375 | 94.93 354 |
|
| v1192 | | | 91.07 292 | 90.23 294 | 93.58 293 | 93.70 377 | 87.82 281 | 96.73 230 | 97.07 238 | 87.77 325 | 89.58 298 | 94.32 333 | 80.90 261 | 97.97 305 | 86.52 313 | 85.48 362 | 94.95 350 |
|
| v8 | | | 91.29 284 | 90.53 282 | 93.57 295 | 94.15 364 | 88.12 271 | 97.34 169 | 97.06 242 | 88.99 281 | 88.32 334 | 94.26 338 | 83.08 211 | 98.01 299 | 87.62 295 | 83.92 390 | 94.57 377 |
|
| mvs_anonymous | | | 93.82 169 | 93.74 150 | 94.06 260 | 96.44 239 | 85.41 339 | 95.81 303 | 97.05 243 | 89.85 253 | 90.09 283 | 96.36 223 | 87.44 134 | 97.75 338 | 93.97 147 | 96.69 189 | 99.02 96 |
|
| IterMVS-LS | | | 92.29 233 | 91.94 222 | 93.34 303 | 96.25 249 | 86.97 300 | 96.57 252 | 97.05 243 | 90.67 224 | 89.50 303 | 94.80 303 | 86.59 143 | 97.64 346 | 89.91 239 | 86.11 357 | 95.40 325 |
| Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
| v1921920 | | | 90.85 302 | 90.03 305 | 93.29 305 | 93.55 382 | 86.96 302 | 96.74 229 | 97.04 245 | 87.36 336 | 89.52 302 | 94.34 330 | 80.23 274 | 97.97 305 | 86.27 316 | 85.21 368 | 94.94 352 |
|
| CDS-MVSNet | | | 94.14 152 | 93.54 157 | 95.93 150 | 96.18 258 | 91.46 142 | 96.33 270 | 97.04 245 | 88.97 283 | 93.56 190 | 96.51 215 | 87.55 128 | 97.89 322 | 89.80 242 | 95.95 205 | 98.44 170 |
| Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
| SSC-MVS3.2 | | | 89.74 338 | 89.26 331 | 91.19 373 | 95.16 319 | 80.29 407 | 94.53 357 | 97.03 247 | 91.79 173 | 88.86 320 | 94.10 345 | 69.94 382 | 97.82 328 | 85.29 334 | 86.66 353 | 95.45 320 |
|
| v1144 | | | 91.37 277 | 90.60 278 | 93.68 288 | 93.89 372 | 88.23 266 | 96.84 218 | 97.03 247 | 88.37 305 | 89.69 295 | 94.39 325 | 82.04 239 | 97.98 302 | 87.80 285 | 85.37 364 | 94.84 360 |
|
| v1240 | | | 90.70 308 | 89.85 312 | 93.23 307 | 93.51 385 | 86.80 303 | 96.61 246 | 97.02 249 | 87.16 341 | 89.58 298 | 94.31 334 | 79.55 287 | 97.98 302 | 85.52 331 | 85.44 363 | 94.90 357 |
|
| EPP-MVSNet | | | 95.22 113 | 95.04 110 | 95.76 162 | 97.49 157 | 89.56 218 | 98.67 11 | 97.00 250 | 90.69 222 | 94.24 171 | 97.62 138 | 89.79 89 | 98.81 204 | 93.39 163 | 96.49 197 | 98.92 116 |
|
| V42 | | | 91.58 263 | 90.87 262 | 93.73 283 | 94.05 368 | 88.50 257 | 97.32 172 | 96.97 251 | 88.80 293 | 89.71 293 | 94.33 331 | 82.54 228 | 98.05 293 | 89.01 265 | 85.07 371 | 94.64 376 |
|
| test_fmvs1 | | | 93.21 191 | 93.53 158 | 92.25 342 | 96.55 226 | 81.20 395 | 97.40 163 | 96.96 252 | 90.68 223 | 96.80 78 | 98.04 93 | 69.25 388 | 98.40 251 | 97.58 38 | 98.50 121 | 97.16 258 |
|
| FMVSNet2 | | | 91.31 281 | 90.08 300 | 94.99 206 | 96.51 232 | 92.21 110 | 97.41 159 | 96.95 253 | 88.82 290 | 88.62 326 | 94.75 305 | 73.87 352 | 97.42 367 | 85.20 337 | 88.55 332 | 95.35 329 |
|
| ACMH | | 87.59 16 | 90.53 313 | 89.42 327 | 93.87 277 | 96.21 250 | 87.92 276 | 97.24 178 | 96.94 254 | 88.45 303 | 83.91 402 | 96.27 228 | 71.92 364 | 98.62 233 | 84.43 345 | 89.43 323 | 95.05 348 |
| Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
| GBi-Net | | | 91.35 278 | 90.27 291 | 94.59 229 | 96.51 232 | 91.18 156 | 97.50 145 | 96.93 255 | 88.82 290 | 89.35 306 | 94.51 318 | 73.87 352 | 97.29 374 | 86.12 321 | 88.82 327 | 95.31 332 |
|
| test1 | | | 91.35 278 | 90.27 291 | 94.59 229 | 96.51 232 | 91.18 156 | 97.50 145 | 96.93 255 | 88.82 290 | 89.35 306 | 94.51 318 | 73.87 352 | 97.29 374 | 86.12 321 | 88.82 327 | 95.31 332 |
|
| FMVSNet3 | | | 91.78 252 | 90.69 276 | 95.03 204 | 96.53 229 | 92.27 108 | 97.02 198 | 96.93 255 | 89.79 256 | 89.35 306 | 94.65 311 | 77.01 323 | 97.47 362 | 86.12 321 | 88.82 327 | 95.35 329 |
|
| FMVSNet1 | | | 89.88 333 | 88.31 346 | 94.59 229 | 95.41 298 | 91.18 156 | 97.50 145 | 96.93 255 | 86.62 349 | 87.41 355 | 94.51 318 | 65.94 415 | 97.29 374 | 83.04 360 | 87.43 343 | 95.31 332 |
|
| GeoE | | | 93.89 166 | 93.28 171 | 95.72 168 | 96.96 189 | 89.75 211 | 98.24 39 | 96.92 259 | 89.47 264 | 92.12 229 | 97.21 167 | 84.42 185 | 98.39 256 | 87.71 288 | 96.50 196 | 99.01 99 |
|
| SymmetryMVS | | | 95.94 90 | 95.54 90 | 97.15 70 | 97.85 129 | 92.90 83 | 97.99 63 | 96.91 260 | 95.92 13 | 96.57 95 | 97.93 102 | 85.34 167 | 99.50 113 | 94.99 121 | 96.39 200 | 99.05 95 |
|
| miper_enhance_ethall | | | 91.54 267 | 91.01 258 | 93.15 311 | 95.35 304 | 87.07 298 | 93.97 379 | 96.90 261 | 86.79 347 | 89.17 313 | 93.43 377 | 86.55 145 | 97.64 346 | 89.97 238 | 86.93 348 | 94.74 372 |
|
| eth_miper_zixun_eth | | | 91.02 295 | 90.59 279 | 92.34 337 | 95.33 308 | 84.35 358 | 94.10 376 | 96.90 261 | 88.56 299 | 88.84 322 | 94.33 331 | 84.08 192 | 97.60 351 | 88.77 271 | 84.37 384 | 95.06 347 |
|
| TAMVS | | | 94.01 158 | 93.46 163 | 95.64 171 | 96.16 260 | 90.45 184 | 96.71 233 | 96.89 263 | 89.27 271 | 93.46 197 | 96.92 188 | 87.29 137 | 97.94 315 | 88.70 273 | 95.74 211 | 98.53 156 |
|
| miper_ehance_all_eth | | | 91.59 261 | 91.13 254 | 92.97 317 | 95.55 290 | 86.57 311 | 94.47 360 | 96.88 264 | 87.77 325 | 88.88 319 | 94.01 350 | 86.22 151 | 97.54 355 | 89.49 250 | 86.93 348 | 94.79 368 |
|
| v2v482 | | | 91.59 261 | 90.85 265 | 93.80 280 | 93.87 373 | 88.17 269 | 96.94 208 | 96.88 264 | 89.54 261 | 89.53 301 | 94.90 297 | 81.70 248 | 98.02 298 | 89.25 259 | 85.04 373 | 95.20 340 |
|
| CNLPA | | | 94.28 143 | 93.53 158 | 96.52 101 | 98.38 84 | 92.55 98 | 96.59 249 | 96.88 264 | 90.13 246 | 91.91 235 | 97.24 165 | 85.21 170 | 99.09 167 | 87.64 294 | 97.83 151 | 97.92 215 |
|
| PAPM | | | 91.52 268 | 90.30 289 | 95.20 194 | 95.30 311 | 89.83 209 | 93.38 401 | 96.85 267 | 86.26 357 | 88.59 327 | 95.80 252 | 84.88 178 | 98.15 275 | 75.67 414 | 95.93 206 | 97.63 233 |
|
| c3_l | | | 91.38 275 | 90.89 261 | 92.88 321 | 95.58 288 | 86.30 318 | 94.68 352 | 96.84 268 | 88.17 310 | 88.83 323 | 94.23 339 | 85.65 162 | 97.47 362 | 89.36 254 | 84.63 377 | 94.89 358 |
|
| pm-mvs1 | | | 90.72 307 | 89.65 322 | 93.96 269 | 94.29 363 | 89.63 213 | 97.79 100 | 96.82 269 | 89.07 276 | 86.12 380 | 95.48 274 | 78.61 305 | 97.78 333 | 86.97 309 | 81.67 403 | 94.46 379 |
|
| test_vis1_n | | | 92.37 228 | 92.26 212 | 92.72 327 | 94.75 343 | 82.64 378 | 98.02 60 | 96.80 270 | 91.18 202 | 97.77 52 | 97.93 102 | 58.02 432 | 98.29 264 | 97.63 35 | 98.21 136 | 97.23 256 |
|
| CMPMVS |  | 62.92 21 | 85.62 386 | 84.92 381 | 87.74 410 | 89.14 432 | 73.12 440 | 94.17 374 | 96.80 270 | 73.98 436 | 73.65 438 | 94.93 295 | 66.36 409 | 97.61 350 | 83.95 353 | 91.28 300 | 92.48 416 |
| M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
| MS-PatchMatch | | | 90.27 320 | 89.77 316 | 91.78 358 | 94.33 360 | 84.72 355 | 95.55 319 | 96.73 272 | 86.17 359 | 86.36 377 | 95.28 280 | 71.28 369 | 97.80 331 | 84.09 350 | 98.14 140 | 92.81 408 |
|
| Effi-MVS+-dtu | | | 93.08 198 | 93.21 173 | 92.68 330 | 96.02 271 | 83.25 372 | 97.14 191 | 96.72 273 | 93.85 95 | 91.20 260 | 93.44 374 | 83.08 211 | 98.30 263 | 91.69 201 | 95.73 212 | 96.50 275 |
|
| TSAR-MVS + GP. | | | 96.69 61 | 96.49 65 | 97.27 63 | 98.31 87 | 93.39 63 | 96.79 223 | 96.72 273 | 94.17 84 | 97.44 58 | 97.66 132 | 92.76 31 | 99.33 132 | 96.86 56 | 97.76 155 | 99.08 91 |
|
| 1112_ss | | | 93.37 186 | 92.42 208 | 96.21 132 | 97.05 179 | 90.99 163 | 96.31 272 | 96.72 273 | 86.87 346 | 89.83 290 | 96.69 201 | 86.51 146 | 99.14 159 | 88.12 278 | 93.67 264 | 98.50 160 |
|
| PVSNet | | 86.66 18 | 92.24 236 | 91.74 231 | 93.73 283 | 97.77 134 | 83.69 369 | 92.88 410 | 96.72 273 | 87.91 318 | 93.00 208 | 94.86 299 | 78.51 306 | 99.05 179 | 86.53 312 | 97.45 163 | 98.47 165 |
|
| miper_lstm_enhance | | | 90.50 316 | 90.06 304 | 91.83 354 | 95.33 308 | 83.74 366 | 93.86 385 | 96.70 277 | 87.56 332 | 87.79 347 | 93.81 358 | 83.45 203 | 96.92 387 | 87.39 299 | 84.62 378 | 94.82 363 |
|
| v148 | | | 90.99 296 | 90.38 285 | 92.81 324 | 93.83 374 | 85.80 331 | 96.78 227 | 96.68 278 | 89.45 266 | 88.75 325 | 93.93 354 | 82.96 217 | 97.82 328 | 87.83 284 | 83.25 395 | 94.80 366 |
|
| ACMH+ | | 87.92 14 | 90.20 324 | 89.18 333 | 93.25 306 | 96.48 235 | 86.45 315 | 96.99 204 | 96.68 278 | 88.83 289 | 84.79 391 | 96.22 230 | 70.16 379 | 98.53 242 | 84.42 346 | 88.04 336 | 94.77 371 |
|
| CANet_DTU | | | 94.37 141 | 93.65 153 | 96.55 98 | 96.46 238 | 92.13 114 | 96.21 280 | 96.67 280 | 94.38 81 | 93.53 193 | 97.03 183 | 79.34 289 | 99.71 60 | 90.76 221 | 98.45 126 | 97.82 226 |
|
| cl____ | | | 90.96 299 | 90.32 287 | 92.89 320 | 95.37 302 | 86.21 321 | 94.46 362 | 96.64 281 | 87.82 321 | 88.15 342 | 94.18 342 | 82.98 215 | 97.54 355 | 87.70 289 | 85.59 360 | 94.92 356 |
|
| HY-MVS | | 89.66 9 | 93.87 167 | 92.95 182 | 96.63 92 | 97.10 173 | 92.49 100 | 95.64 316 | 96.64 281 | 89.05 278 | 93.00 208 | 95.79 255 | 85.77 161 | 99.45 121 | 89.16 264 | 94.35 243 | 97.96 212 |
|
| Test_1112_low_res | | | 92.84 213 | 91.84 226 | 95.85 156 | 97.04 181 | 89.97 205 | 95.53 321 | 96.64 281 | 85.38 369 | 89.65 297 | 95.18 285 | 85.86 158 | 99.10 164 | 87.70 289 | 93.58 269 | 98.49 162 |
|
| DIV-MVS_self_test | | | 90.97 298 | 90.33 286 | 92.88 321 | 95.36 303 | 86.19 323 | 94.46 362 | 96.63 284 | 87.82 321 | 88.18 340 | 94.23 339 | 82.99 214 | 97.53 357 | 87.72 286 | 85.57 361 | 94.93 354 |
|
| Fast-Effi-MVS+-dtu | | | 92.29 233 | 91.99 220 | 93.21 309 | 95.27 312 | 85.52 337 | 97.03 196 | 96.63 284 | 92.09 165 | 89.11 315 | 95.14 287 | 80.33 272 | 98.08 286 | 87.54 297 | 94.74 239 | 96.03 293 |
|
| UnsupCasMVSNet_bld | | | 82.13 402 | 79.46 407 | 90.14 391 | 88.00 440 | 82.47 383 | 90.89 428 | 96.62 286 | 78.94 426 | 75.61 433 | 84.40 444 | 56.63 435 | 96.31 401 | 77.30 406 | 66.77 445 | 91.63 426 |
|
| cl22 | | | 91.21 286 | 90.56 281 | 93.14 312 | 96.09 267 | 86.80 303 | 94.41 364 | 96.58 287 | 87.80 323 | 88.58 328 | 93.99 352 | 80.85 262 | 97.62 349 | 89.87 241 | 86.93 348 | 94.99 349 |
|
| jason | | | 94.84 128 | 94.39 134 | 96.18 134 | 95.52 291 | 90.93 167 | 96.09 287 | 96.52 288 | 89.28 270 | 96.01 121 | 97.32 157 | 84.70 180 | 98.77 210 | 95.15 117 | 98.91 106 | 98.85 128 |
| jason: jason. |
| tt0805 | | | 91.09 291 | 90.07 303 | 94.16 256 | 95.61 286 | 88.31 261 | 97.56 136 | 96.51 289 | 89.56 260 | 89.17 313 | 95.64 264 | 67.08 407 | 98.38 257 | 91.07 213 | 88.44 333 | 95.80 301 |
|
| AUN-MVS | | | 91.76 253 | 90.75 271 | 94.81 218 | 97.00 185 | 88.57 253 | 96.65 240 | 96.49 290 | 89.63 258 | 92.15 227 | 96.12 236 | 78.66 304 | 98.50 244 | 90.83 217 | 79.18 414 | 97.36 248 |
|
| hse-mvs2 | | | 93.45 184 | 92.99 178 | 94.81 218 | 97.02 183 | 88.59 252 | 96.69 236 | 96.47 291 | 95.19 33 | 96.74 82 | 96.16 234 | 83.67 198 | 98.48 247 | 95.85 94 | 79.13 415 | 97.35 250 |
|
| SD_0403 | | | 90.01 328 | 90.02 306 | 89.96 394 | 95.65 285 | 76.76 428 | 95.76 307 | 96.46 292 | 90.58 233 | 86.59 374 | 96.29 226 | 82.12 238 | 94.78 423 | 73.00 428 | 93.76 262 | 98.35 179 |
|
| EG-PatchMatch MVS | | | 87.02 368 | 85.44 373 | 91.76 360 | 92.67 406 | 85.00 349 | 96.08 288 | 96.45 293 | 83.41 399 | 79.52 425 | 93.49 371 | 57.10 434 | 97.72 340 | 79.34 397 | 90.87 309 | 92.56 413 |
|
| KD-MVS_self_test | | | 85.95 382 | 84.95 380 | 88.96 404 | 89.55 431 | 79.11 422 | 95.13 342 | 96.42 294 | 85.91 362 | 84.07 400 | 90.48 416 | 70.03 381 | 94.82 422 | 80.04 389 | 72.94 434 | 92.94 406 |
|
| pmmvs6 | | | 87.81 360 | 86.19 368 | 92.69 329 | 91.32 419 | 86.30 318 | 97.34 169 | 96.41 295 | 80.59 420 | 84.05 401 | 94.37 327 | 67.37 402 | 97.67 343 | 84.75 341 | 79.51 413 | 94.09 391 |
|
| PMMVS | | | 92.86 211 | 92.34 209 | 94.42 243 | 94.92 334 | 86.73 306 | 94.53 357 | 96.38 296 | 84.78 381 | 94.27 170 | 95.12 289 | 83.13 210 | 98.40 251 | 91.47 205 | 96.49 197 | 98.12 198 |
|
| RPSCF | | | 90.75 305 | 90.86 263 | 90.42 387 | 96.84 196 | 76.29 431 | 95.61 317 | 96.34 297 | 83.89 390 | 91.38 248 | 97.87 110 | 76.45 329 | 98.78 207 | 87.16 306 | 92.23 282 | 96.20 282 |
|
| BP-MVS1 | | | 95.89 92 | 95.49 92 | 97.08 77 | 96.67 214 | 93.20 73 | 98.08 54 | 96.32 298 | 94.56 69 | 96.32 106 | 97.84 115 | 84.07 193 | 99.15 156 | 96.75 58 | 98.78 109 | 98.90 120 |
|
| MSDG | | | 91.42 273 | 90.24 293 | 94.96 211 | 97.15 171 | 88.91 245 | 93.69 393 | 96.32 298 | 85.72 365 | 86.93 370 | 96.47 217 | 80.24 273 | 98.98 185 | 80.57 386 | 95.05 232 | 96.98 261 |
|
| WBMVS | | | 90.69 310 | 89.99 307 | 92.81 324 | 96.48 235 | 85.00 349 | 95.21 340 | 96.30 300 | 89.46 265 | 89.04 316 | 94.05 349 | 72.45 362 | 97.82 328 | 89.46 251 | 87.41 345 | 95.61 312 |
|
| OurMVSNet-221017-0 | | | 90.51 315 | 90.19 298 | 91.44 366 | 93.41 391 | 81.25 393 | 96.98 205 | 96.28 301 | 91.68 177 | 86.55 375 | 96.30 225 | 74.20 351 | 97.98 302 | 88.96 267 | 87.40 346 | 95.09 345 |
|
| MVP-Stereo | | | 90.74 306 | 90.08 300 | 92.71 328 | 93.19 396 | 88.20 267 | 95.86 300 | 96.27 302 | 86.07 360 | 84.86 390 | 94.76 304 | 77.84 318 | 97.75 338 | 83.88 355 | 98.01 146 | 92.17 423 |
| Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
| lupinMVS | | | 94.99 122 | 94.56 125 | 96.29 126 | 96.34 246 | 91.21 151 | 95.83 302 | 96.27 302 | 88.93 285 | 96.22 111 | 96.88 190 | 86.20 153 | 98.85 198 | 95.27 113 | 99.05 97 | 98.82 132 |
|
| BH-untuned | | | 92.94 206 | 92.62 198 | 93.92 276 | 97.22 165 | 86.16 324 | 96.40 262 | 96.25 304 | 90.06 247 | 89.79 291 | 96.17 233 | 83.19 207 | 98.35 259 | 87.19 304 | 97.27 172 | 97.24 255 |
|
| CL-MVSNet_self_test | | | 86.31 377 | 85.15 377 | 89.80 396 | 88.83 435 | 81.74 391 | 93.93 382 | 96.22 305 | 86.67 348 | 85.03 388 | 90.80 414 | 78.09 314 | 94.50 424 | 74.92 417 | 71.86 436 | 93.15 404 |
|
| IS-MVSNet | | | 94.90 124 | 94.52 129 | 96.05 140 | 97.67 140 | 90.56 180 | 98.44 22 | 96.22 305 | 93.21 120 | 93.99 178 | 97.74 124 | 85.55 165 | 98.45 248 | 89.98 237 | 97.86 150 | 99.14 83 |
|
| FA-MVS(test-final) | | | 93.52 181 | 92.92 183 | 95.31 191 | 96.77 209 | 88.54 255 | 94.82 349 | 96.21 307 | 89.61 259 | 94.20 172 | 95.25 283 | 83.24 205 | 99.14 159 | 90.01 236 | 96.16 202 | 98.25 186 |
|
| GA-MVS | | | 91.38 275 | 90.31 288 | 94.59 229 | 94.65 348 | 87.62 284 | 94.34 367 | 96.19 308 | 90.73 220 | 90.35 271 | 93.83 355 | 71.84 365 | 97.96 309 | 87.22 303 | 93.61 267 | 98.21 189 |
|
| LuminaMVS | | | 94.89 125 | 94.35 135 | 96.53 99 | 95.48 293 | 92.80 87 | 96.88 214 | 96.18 309 | 92.85 142 | 95.92 124 | 96.87 192 | 81.44 251 | 98.83 201 | 96.43 70 | 97.10 179 | 97.94 214 |
|
| IterMVS-SCA-FT | | | 90.31 318 | 89.81 314 | 91.82 355 | 95.52 291 | 84.20 361 | 94.30 370 | 96.15 310 | 90.61 230 | 87.39 356 | 94.27 336 | 75.80 335 | 96.44 398 | 87.34 300 | 86.88 352 | 94.82 363 |
|
| IterMVS | | | 90.15 326 | 89.67 320 | 91.61 362 | 95.48 293 | 83.72 367 | 94.33 368 | 96.12 311 | 89.99 248 | 87.31 359 | 94.15 344 | 75.78 337 | 96.27 402 | 86.97 309 | 86.89 351 | 94.83 361 |
| Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
| DP-MVS | | | 92.76 216 | 91.51 240 | 96.52 101 | 98.77 58 | 90.99 163 | 97.38 166 | 96.08 312 | 82.38 405 | 89.29 309 | 97.87 110 | 83.77 196 | 99.69 66 | 81.37 379 | 96.69 189 | 98.89 124 |
|
| pmmvs4 | | | 90.93 300 | 89.85 312 | 94.17 255 | 93.34 393 | 90.79 173 | 94.60 354 | 96.02 313 | 84.62 382 | 87.45 353 | 95.15 286 | 81.88 245 | 97.45 364 | 87.70 289 | 87.87 338 | 94.27 388 |
|
| ppachtmachnet_test | | | 88.35 355 | 87.29 354 | 91.53 363 | 92.45 412 | 83.57 370 | 93.75 389 | 95.97 314 | 84.28 385 | 85.32 387 | 94.18 342 | 79.00 301 | 96.93 386 | 75.71 413 | 84.99 374 | 94.10 389 |
|
| Anonymous20240521 | | | 86.42 375 | 85.44 373 | 89.34 402 | 90.33 424 | 79.79 413 | 96.73 230 | 95.92 315 | 83.71 395 | 83.25 406 | 91.36 411 | 63.92 420 | 96.01 403 | 78.39 401 | 85.36 365 | 92.22 421 |
|
| ITE_SJBPF | | | | | 92.43 333 | 95.34 305 | 85.37 342 | | 95.92 315 | 91.47 186 | 87.75 349 | 96.39 222 | 71.00 371 | 97.96 309 | 82.36 369 | 89.86 319 | 93.97 394 |
|
| test_fmvs2 | | | 89.77 337 | 89.93 309 | 89.31 403 | 93.68 379 | 76.37 430 | 97.64 126 | 95.90 317 | 89.84 254 | 91.49 246 | 96.26 229 | 58.77 430 | 97.10 378 | 94.65 135 | 91.13 302 | 94.46 379 |
|
| USDC | | | 88.94 346 | 87.83 351 | 92.27 340 | 94.66 347 | 84.96 351 | 93.86 385 | 95.90 317 | 87.34 337 | 83.40 404 | 95.56 268 | 67.43 401 | 98.19 272 | 82.64 368 | 89.67 321 | 93.66 397 |
|
| COLMAP_ROB |  | 87.81 15 | 90.40 317 | 89.28 330 | 93.79 281 | 97.95 122 | 87.13 297 | 96.92 210 | 95.89 319 | 82.83 402 | 86.88 372 | 97.18 168 | 73.77 355 | 99.29 139 | 78.44 400 | 93.62 266 | 94.95 350 |
| Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
| VDD-MVS | | | 93.82 169 | 93.08 176 | 96.02 143 | 97.88 128 | 89.96 206 | 97.72 111 | 95.85 320 | 92.43 152 | 95.86 126 | 98.44 57 | 68.42 397 | 99.39 127 | 96.31 71 | 94.85 233 | 98.71 143 |
|
| VDDNet | | | 93.05 200 | 92.07 215 | 96.02 143 | 96.84 196 | 90.39 188 | 98.08 54 | 95.85 320 | 86.22 358 | 95.79 129 | 98.46 55 | 67.59 400 | 99.19 147 | 94.92 124 | 94.85 233 | 98.47 165 |
|
| mvsmamba | | | 94.57 136 | 94.14 140 | 95.87 153 | 97.03 182 | 89.93 207 | 97.84 89 | 95.85 320 | 91.34 192 | 94.79 158 | 96.80 193 | 80.67 263 | 98.81 204 | 94.85 125 | 98.12 141 | 98.85 128 |
|
| Vis-MVSNet (Re-imp) | | | 94.15 149 | 93.88 146 | 94.95 212 | 97.61 148 | 87.92 276 | 98.10 52 | 95.80 323 | 92.22 158 | 93.02 207 | 97.45 149 | 84.53 183 | 97.91 321 | 88.24 277 | 97.97 147 | 99.02 96 |
|
| MM | | | 97.29 26 | 96.98 36 | 98.23 11 | 98.01 116 | 95.03 26 | 98.07 56 | 95.76 324 | 97.78 1 | 97.52 55 | 98.80 35 | 88.09 114 | 99.86 9 | 99.44 2 | 99.37 62 | 99.80 1 |
|
| KD-MVS_2432*1600 | | | 84.81 392 | 82.64 395 | 91.31 368 | 91.07 421 | 85.34 343 | 91.22 423 | 95.75 325 | 85.56 367 | 83.09 407 | 90.21 419 | 67.21 403 | 95.89 405 | 77.18 407 | 62.48 449 | 92.69 409 |
|
| miper_refine_blended | | | 84.81 392 | 82.64 395 | 91.31 368 | 91.07 421 | 85.34 343 | 91.22 423 | 95.75 325 | 85.56 367 | 83.09 407 | 90.21 419 | 67.21 403 | 95.89 405 | 77.18 407 | 62.48 449 | 92.69 409 |
|
| FE-MVS | | | 92.05 244 | 91.05 256 | 95.08 200 | 96.83 198 | 87.93 275 | 93.91 384 | 95.70 327 | 86.30 355 | 94.15 175 | 94.97 292 | 76.59 327 | 99.21 145 | 84.10 349 | 96.86 182 | 98.09 204 |
|
| tpm cat1 | | | 88.36 354 | 87.21 357 | 91.81 356 | 95.13 324 | 80.55 402 | 92.58 414 | 95.70 327 | 74.97 435 | 87.45 353 | 91.96 404 | 78.01 317 | 98.17 274 | 80.39 388 | 88.74 330 | 96.72 271 |
|
| our_test_3 | | | 88.78 350 | 87.98 350 | 91.20 372 | 92.45 412 | 82.53 380 | 93.61 397 | 95.69 329 | 85.77 364 | 84.88 389 | 93.71 360 | 79.99 278 | 96.78 394 | 79.47 394 | 86.24 354 | 94.28 387 |
|
| BH-w/o | | | 92.14 241 | 91.75 229 | 93.31 304 | 96.99 186 | 85.73 334 | 95.67 311 | 95.69 329 | 88.73 295 | 89.26 311 | 94.82 302 | 82.97 216 | 98.07 290 | 85.26 336 | 96.32 201 | 96.13 289 |
|
| CR-MVSNet | | | 90.82 303 | 89.77 316 | 93.95 270 | 94.45 356 | 87.19 294 | 90.23 431 | 95.68 331 | 86.89 345 | 92.40 217 | 92.36 396 | 80.91 259 | 97.05 381 | 81.09 383 | 93.95 259 | 97.60 238 |
|
| Patchmtry | | | 88.64 352 | 87.25 355 | 92.78 326 | 94.09 366 | 86.64 307 | 89.82 435 | 95.68 331 | 80.81 417 | 87.63 351 | 92.36 396 | 80.91 259 | 97.03 382 | 78.86 398 | 85.12 370 | 94.67 374 |
|
| testing91 | | | 91.90 249 | 91.02 257 | 94.53 237 | 96.54 227 | 86.55 313 | 95.86 300 | 95.64 333 | 91.77 174 | 91.89 236 | 93.47 373 | 69.94 382 | 98.86 196 | 90.23 235 | 93.86 261 | 98.18 191 |
|
| BH-RMVSNet | | | 92.72 218 | 91.97 221 | 94.97 210 | 97.16 169 | 87.99 274 | 96.15 285 | 95.60 334 | 90.62 229 | 91.87 237 | 97.15 171 | 78.41 308 | 98.57 239 | 83.16 358 | 97.60 157 | 98.36 177 |
|
| PVSNet_0 | | 82.17 19 | 85.46 387 | 83.64 390 | 90.92 376 | 95.27 312 | 79.49 418 | 90.55 429 | 95.60 334 | 83.76 394 | 83.00 409 | 89.95 421 | 71.09 370 | 97.97 305 | 82.75 366 | 60.79 451 | 95.31 332 |
|
| guyue | | | 95.17 116 | 94.96 112 | 95.82 158 | 96.97 188 | 89.65 212 | 97.56 136 | 95.58 336 | 94.82 54 | 95.72 131 | 97.42 153 | 82.90 218 | 98.84 200 | 96.71 61 | 96.93 181 | 98.96 107 |
|
| SCA | | | 91.84 251 | 91.18 253 | 93.83 278 | 95.59 287 | 84.95 352 | 94.72 351 | 95.58 336 | 90.82 216 | 92.25 225 | 93.69 362 | 75.80 335 | 98.10 281 | 86.20 318 | 95.98 204 | 98.45 167 |
|
| MonoMVSNet | | | 91.92 247 | 91.77 227 | 92.37 334 | 92.94 400 | 83.11 374 | 97.09 194 | 95.55 338 | 92.91 140 | 90.85 263 | 94.55 315 | 81.27 255 | 96.52 397 | 93.01 174 | 87.76 339 | 97.47 244 |
|
| AllTest | | | 90.23 322 | 88.98 336 | 93.98 266 | 97.94 123 | 86.64 307 | 96.51 253 | 95.54 339 | 85.38 369 | 85.49 384 | 96.77 195 | 70.28 377 | 99.15 156 | 80.02 390 | 92.87 271 | 96.15 287 |
|
| TestCases | | | | | 93.98 266 | 97.94 123 | 86.64 307 | | 95.54 339 | 85.38 369 | 85.49 384 | 96.77 195 | 70.28 377 | 99.15 156 | 80.02 390 | 92.87 271 | 96.15 287 |
|
| mmtdpeth | | | 89.70 339 | 88.96 337 | 91.90 351 | 95.84 279 | 84.42 357 | 97.46 156 | 95.53 341 | 90.27 241 | 94.46 167 | 90.50 415 | 69.74 386 | 98.95 186 | 97.39 47 | 69.48 440 | 92.34 417 |
|
| tpmvs | | | 89.83 336 | 89.15 334 | 91.89 352 | 94.92 334 | 80.30 406 | 93.11 406 | 95.46 342 | 86.28 356 | 88.08 343 | 92.65 386 | 80.44 269 | 98.52 243 | 81.47 375 | 89.92 318 | 96.84 267 |
|
| pmmvs5 | | | 89.86 335 | 88.87 340 | 92.82 323 | 92.86 402 | 86.23 320 | 96.26 275 | 95.39 343 | 84.24 386 | 87.12 361 | 94.51 318 | 74.27 350 | 97.36 371 | 87.61 296 | 87.57 341 | 94.86 359 |
|
| PatchmatchNet |  | | 91.91 248 | 91.35 242 | 93.59 292 | 95.38 300 | 84.11 362 | 93.15 405 | 95.39 343 | 89.54 261 | 92.10 230 | 93.68 364 | 82.82 221 | 98.13 276 | 84.81 340 | 95.32 225 | 98.52 157 |
| Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
| tpmrst | | | 91.44 272 | 91.32 244 | 91.79 357 | 95.15 322 | 79.20 421 | 93.42 400 | 95.37 345 | 88.55 300 | 93.49 196 | 93.67 365 | 82.49 230 | 98.27 265 | 90.41 230 | 89.34 324 | 97.90 216 |
|
| Anonymous20231206 | | | 87.09 367 | 86.14 369 | 89.93 395 | 91.22 420 | 80.35 404 | 96.11 286 | 95.35 346 | 83.57 397 | 84.16 396 | 93.02 381 | 73.54 357 | 95.61 413 | 72.16 430 | 86.14 356 | 93.84 396 |
|
| MIMVSNet1 | | | 84.93 390 | 83.05 392 | 90.56 385 | 89.56 430 | 84.84 354 | 95.40 326 | 95.35 346 | 83.91 389 | 80.38 421 | 92.21 401 | 57.23 433 | 93.34 436 | 70.69 436 | 82.75 401 | 93.50 399 |
|
| TDRefinement | | | 86.53 371 | 84.76 383 | 91.85 353 | 82.23 452 | 84.25 359 | 96.38 264 | 95.35 346 | 84.97 378 | 84.09 399 | 94.94 294 | 65.76 416 | 98.34 262 | 84.60 344 | 74.52 430 | 92.97 405 |
|
| TR-MVS | | | 91.48 271 | 90.59 279 | 94.16 256 | 96.40 241 | 87.33 287 | 95.67 311 | 95.34 349 | 87.68 329 | 91.46 247 | 95.52 271 | 76.77 326 | 98.35 259 | 82.85 363 | 93.61 267 | 96.79 269 |
|
| EPNet_dtu | | | 91.71 254 | 91.28 247 | 92.99 316 | 93.76 376 | 83.71 368 | 96.69 236 | 95.28 350 | 93.15 127 | 87.02 366 | 95.95 244 | 83.37 204 | 97.38 370 | 79.46 395 | 96.84 183 | 97.88 218 |
| Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
| FMVSNet5 | | | 87.29 364 | 85.79 371 | 91.78 358 | 94.80 341 | 87.28 289 | 95.49 323 | 95.28 350 | 84.09 388 | 83.85 403 | 91.82 405 | 62.95 423 | 94.17 428 | 78.48 399 | 85.34 366 | 93.91 395 |
|
| MDTV_nov1_ep13 | | | | 90.76 269 | | 95.22 316 | 80.33 405 | 93.03 408 | 95.28 350 | 88.14 313 | 92.84 214 | 93.83 355 | 81.34 252 | 98.08 286 | 82.86 361 | 94.34 244 | |
|
| LF4IMVS | | | 87.94 358 | 87.25 355 | 89.98 393 | 92.38 414 | 80.05 412 | 94.38 365 | 95.25 353 | 87.59 331 | 84.34 393 | 94.74 306 | 64.31 419 | 97.66 345 | 84.83 339 | 87.45 342 | 92.23 420 |
|
| TransMVSNet (Re) | | | 88.94 346 | 87.56 352 | 93.08 314 | 94.35 359 | 88.45 259 | 97.73 108 | 95.23 354 | 87.47 333 | 84.26 395 | 95.29 278 | 79.86 281 | 97.33 372 | 79.44 396 | 74.44 431 | 93.45 401 |
|
| test20.03 | | | 86.14 380 | 85.40 375 | 88.35 405 | 90.12 425 | 80.06 411 | 95.90 299 | 95.20 355 | 88.59 296 | 81.29 416 | 93.62 367 | 71.43 368 | 92.65 440 | 71.26 434 | 81.17 406 | 92.34 417 |
|
| new-patchmatchnet | | | 83.18 398 | 81.87 401 | 87.11 413 | 86.88 443 | 75.99 432 | 93.70 391 | 95.18 356 | 85.02 377 | 77.30 432 | 88.40 431 | 65.99 414 | 93.88 433 | 74.19 422 | 70.18 438 | 91.47 430 |
|
| MDA-MVSNet_test_wron | | | 85.87 384 | 84.23 387 | 90.80 382 | 92.38 414 | 82.57 379 | 93.17 403 | 95.15 357 | 82.15 406 | 67.65 444 | 92.33 399 | 78.20 310 | 95.51 416 | 77.33 404 | 79.74 410 | 94.31 386 |
|
| YYNet1 | | | 85.87 384 | 84.23 387 | 90.78 383 | 92.38 414 | 82.46 384 | 93.17 403 | 95.14 358 | 82.12 407 | 67.69 442 | 92.36 396 | 78.16 313 | 95.50 417 | 77.31 405 | 79.73 411 | 94.39 382 |
|
| Baseline_NR-MVSNet | | | 91.20 287 | 90.62 277 | 92.95 318 | 93.83 374 | 88.03 273 | 97.01 201 | 95.12 359 | 88.42 304 | 89.70 294 | 95.13 288 | 83.47 201 | 97.44 365 | 89.66 247 | 83.24 396 | 93.37 402 |
|
| thres200 | | | 92.23 237 | 91.39 241 | 94.75 225 | 97.61 148 | 89.03 243 | 96.60 248 | 95.09 360 | 92.08 166 | 93.28 202 | 94.00 351 | 78.39 309 | 99.04 182 | 81.26 382 | 94.18 250 | 96.19 283 |
|
| ADS-MVSNet | | | 89.89 332 | 88.68 342 | 93.53 296 | 95.86 274 | 84.89 353 | 90.93 426 | 95.07 361 | 83.23 400 | 91.28 256 | 91.81 406 | 79.01 299 | 97.85 324 | 79.52 392 | 91.39 298 | 97.84 223 |
|
| pmmvs-eth3d | | | 86.22 378 | 84.45 385 | 91.53 363 | 88.34 439 | 87.25 291 | 94.47 360 | 95.01 362 | 83.47 398 | 79.51 426 | 89.61 424 | 69.75 385 | 95.71 410 | 83.13 359 | 76.73 424 | 91.64 425 |
|
| Anonymous202405211 | | | 92.07 243 | 90.83 267 | 95.76 162 | 98.19 102 | 88.75 248 | 97.58 132 | 95.00 363 | 86.00 361 | 93.64 188 | 97.45 149 | 66.24 412 | 99.53 105 | 90.68 224 | 92.71 276 | 99.01 99 |
|
| MDA-MVSNet-bldmvs | | | 85.00 389 | 82.95 394 | 91.17 374 | 93.13 398 | 83.33 371 | 94.56 356 | 95.00 363 | 84.57 383 | 65.13 448 | 92.65 386 | 70.45 376 | 95.85 407 | 73.57 425 | 77.49 420 | 94.33 384 |
|
| ambc | | | | | 86.56 416 | 83.60 449 | 70.00 443 | 85.69 447 | 94.97 365 | | 80.60 420 | 88.45 430 | 37.42 451 | 96.84 391 | 82.69 367 | 75.44 428 | 92.86 407 |
|
| testgi | | | 87.97 357 | 87.21 357 | 90.24 390 | 92.86 402 | 80.76 397 | 96.67 239 | 94.97 365 | 91.74 175 | 85.52 383 | 95.83 250 | 62.66 425 | 94.47 426 | 76.25 411 | 88.36 334 | 95.48 315 |
|
| myMVS_eth3d28 | | | 91.52 268 | 90.97 259 | 93.17 310 | 96.91 190 | 83.24 373 | 95.61 317 | 94.96 367 | 92.24 157 | 91.98 233 | 93.28 378 | 69.31 387 | 98.40 251 | 88.71 272 | 95.68 214 | 97.88 218 |
|
| dp | | | 88.90 348 | 88.26 348 | 90.81 380 | 94.58 352 | 76.62 429 | 92.85 411 | 94.93 368 | 85.12 375 | 90.07 285 | 93.07 380 | 75.81 334 | 98.12 279 | 80.53 387 | 87.42 344 | 97.71 230 |
|
| test_fmvs3 | | | 83.21 397 | 83.02 393 | 83.78 420 | 86.77 444 | 68.34 446 | 96.76 228 | 94.91 369 | 86.49 351 | 84.14 398 | 89.48 425 | 36.04 452 | 91.73 442 | 91.86 195 | 80.77 408 | 91.26 432 |
|
| test_0402 | | | 86.46 374 | 84.79 382 | 91.45 365 | 95.02 328 | 85.55 336 | 96.29 274 | 94.89 370 | 80.90 414 | 82.21 412 | 93.97 353 | 68.21 398 | 97.29 374 | 62.98 443 | 88.68 331 | 91.51 428 |
|
| tfpn200view9 | | | 92.38 227 | 91.52 238 | 94.95 212 | 97.85 129 | 89.29 233 | 97.41 159 | 94.88 371 | 92.19 162 | 93.27 203 | 94.46 323 | 78.17 311 | 99.08 170 | 81.40 376 | 94.08 254 | 96.48 276 |
|
| CVMVSNet | | | 91.23 285 | 91.75 229 | 89.67 397 | 95.77 280 | 74.69 433 | 96.44 254 | 94.88 371 | 85.81 363 | 92.18 226 | 97.64 136 | 79.07 294 | 95.58 415 | 88.06 280 | 95.86 209 | 98.74 140 |
|
| thres400 | | | 92.42 225 | 91.52 238 | 95.12 199 | 97.85 129 | 89.29 233 | 97.41 159 | 94.88 371 | 92.19 162 | 93.27 203 | 94.46 323 | 78.17 311 | 99.08 170 | 81.40 376 | 94.08 254 | 96.98 261 |
|
| tt0320 | | | 85.39 388 | 83.12 391 | 92.19 344 | 93.44 390 | 85.79 332 | 96.19 282 | 94.87 374 | 71.19 442 | 82.92 410 | 91.76 408 | 58.43 431 | 96.81 392 | 81.03 384 | 78.26 419 | 93.98 393 |
|
| EPNet | | | 95.20 114 | 94.56 125 | 97.14 71 | 92.80 404 | 92.68 93 | 97.85 88 | 94.87 374 | 96.64 6 | 92.46 216 | 97.80 121 | 86.23 150 | 99.65 72 | 93.72 155 | 98.62 117 | 99.10 89 |
| Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
| testing99 | | | 91.62 259 | 90.72 274 | 94.32 248 | 96.48 235 | 86.11 329 | 95.81 303 | 94.76 376 | 91.55 179 | 91.75 241 | 93.44 374 | 68.55 395 | 98.82 202 | 90.43 229 | 93.69 263 | 98.04 208 |
|
| sc_t1 | | | 86.48 373 | 84.10 389 | 93.63 289 | 93.45 389 | 85.76 333 | 96.79 223 | 94.71 377 | 73.06 440 | 86.45 376 | 94.35 328 | 55.13 438 | 97.95 313 | 84.38 347 | 78.55 418 | 97.18 257 |
|
| SixPastTwentyTwo | | | 89.15 344 | 88.54 344 | 90.98 375 | 93.49 386 | 80.28 408 | 96.70 234 | 94.70 378 | 90.78 217 | 84.15 397 | 95.57 267 | 71.78 366 | 97.71 341 | 84.63 343 | 85.07 371 | 94.94 352 |
|
| thres100view900 | | | 92.43 224 | 91.58 235 | 94.98 208 | 97.92 125 | 89.37 229 | 97.71 113 | 94.66 379 | 92.20 160 | 93.31 201 | 94.90 297 | 78.06 315 | 99.08 170 | 81.40 376 | 94.08 254 | 96.48 276 |
|
| thres600view7 | | | 92.49 222 | 91.60 234 | 95.18 195 | 97.91 126 | 89.47 223 | 97.65 122 | 94.66 379 | 92.18 164 | 93.33 200 | 94.91 296 | 78.06 315 | 99.10 164 | 81.61 372 | 94.06 258 | 96.98 261 |
|
| PatchT | | | 88.87 349 | 87.42 353 | 93.22 308 | 94.08 367 | 85.10 347 | 89.51 436 | 94.64 381 | 81.92 408 | 92.36 220 | 88.15 434 | 80.05 277 | 97.01 384 | 72.43 429 | 93.65 265 | 97.54 241 |
|
| baseline1 | | | 92.82 214 | 91.90 224 | 95.55 178 | 97.20 167 | 90.77 174 | 97.19 186 | 94.58 382 | 92.20 160 | 92.36 220 | 96.34 224 | 84.16 191 | 98.21 269 | 89.20 262 | 83.90 391 | 97.68 232 |
|
| AstraMVS | | | 94.82 130 | 94.64 121 | 95.34 190 | 96.36 245 | 88.09 272 | 97.58 132 | 94.56 383 | 94.98 43 | 95.70 134 | 97.92 105 | 81.93 244 | 98.93 189 | 96.87 55 | 95.88 207 | 98.99 103 |
|
| UBG | | | 91.55 265 | 90.76 269 | 93.94 272 | 96.52 231 | 85.06 348 | 95.22 338 | 94.54 384 | 90.47 237 | 91.98 233 | 92.71 385 | 72.02 363 | 98.74 216 | 88.10 279 | 95.26 227 | 98.01 210 |
|
| Gipuma |  | | 67.86 418 | 65.41 420 | 75.18 433 | 92.66 407 | 73.45 437 | 66.50 454 | 94.52 385 | 53.33 453 | 57.80 454 | 66.07 454 | 30.81 454 | 89.20 446 | 48.15 452 | 78.88 417 | 62.90 454 |
| S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
| testing11 | | | 91.68 257 | 90.75 271 | 94.47 239 | 96.53 229 | 86.56 312 | 95.76 307 | 94.51 386 | 91.10 209 | 91.24 258 | 93.59 368 | 68.59 394 | 98.86 196 | 91.10 212 | 94.29 246 | 98.00 211 |
|
| CostFormer | | | 91.18 290 | 90.70 275 | 92.62 331 | 94.84 339 | 81.76 390 | 94.09 377 | 94.43 387 | 84.15 387 | 92.72 215 | 93.77 359 | 79.43 288 | 98.20 270 | 90.70 223 | 92.18 285 | 97.90 216 |
|
| tpm2 | | | 89.96 329 | 89.21 332 | 92.23 343 | 94.91 336 | 81.25 393 | 93.78 388 | 94.42 388 | 80.62 419 | 91.56 244 | 93.44 374 | 76.44 330 | 97.94 315 | 85.60 330 | 92.08 289 | 97.49 242 |
|
| testing3-2 | | | 92.10 242 | 92.05 216 | 92.27 340 | 97.71 138 | 79.56 415 | 97.42 158 | 94.41 389 | 93.53 108 | 93.22 205 | 95.49 272 | 69.16 389 | 99.11 162 | 93.25 164 | 94.22 248 | 98.13 196 |
|
| MVS_0304 | | | 96.74 58 | 96.31 75 | 98.02 19 | 96.87 192 | 94.65 30 | 97.58 132 | 94.39 390 | 96.47 9 | 97.16 67 | 98.39 61 | 87.53 130 | 99.87 7 | 98.97 17 | 99.41 54 | 99.55 38 |
|
| JIA-IIPM | | | 88.26 356 | 87.04 360 | 91.91 350 | 93.52 384 | 81.42 392 | 89.38 437 | 94.38 391 | 80.84 416 | 90.93 262 | 80.74 446 | 79.22 291 | 97.92 318 | 82.76 365 | 91.62 293 | 96.38 279 |
|
| dmvs_re | | | 90.21 323 | 89.50 325 | 92.35 335 | 95.47 297 | 85.15 345 | 95.70 310 | 94.37 392 | 90.94 215 | 88.42 330 | 93.57 369 | 74.63 347 | 95.67 412 | 82.80 364 | 89.57 322 | 96.22 281 |
|
| Patchmatch-test | | | 89.42 342 | 87.99 349 | 93.70 286 | 95.27 312 | 85.11 346 | 88.98 438 | 94.37 392 | 81.11 413 | 87.10 364 | 93.69 362 | 82.28 234 | 97.50 360 | 74.37 420 | 94.76 237 | 98.48 164 |
|
| LCM-MVSNet | | | 72.55 411 | 69.39 415 | 82.03 422 | 70.81 462 | 65.42 451 | 90.12 433 | 94.36 394 | 55.02 452 | 65.88 446 | 81.72 445 | 24.16 460 | 89.96 443 | 74.32 421 | 68.10 443 | 90.71 435 |
|
| ADS-MVSNet2 | | | 89.45 341 | 88.59 343 | 92.03 347 | 95.86 274 | 82.26 386 | 90.93 426 | 94.32 395 | 83.23 400 | 91.28 256 | 91.81 406 | 79.01 299 | 95.99 404 | 79.52 392 | 91.39 298 | 97.84 223 |
|
| mvs5depth | | | 86.53 371 | 85.08 378 | 90.87 377 | 88.74 437 | 82.52 381 | 91.91 419 | 94.23 396 | 86.35 354 | 87.11 363 | 93.70 361 | 66.52 408 | 97.76 336 | 81.37 379 | 75.80 426 | 92.31 419 |
|
| EU-MVSNet | | | 88.72 351 | 88.90 339 | 88.20 407 | 93.15 397 | 74.21 435 | 96.63 245 | 94.22 397 | 85.18 373 | 87.32 358 | 95.97 242 | 76.16 332 | 94.98 421 | 85.27 335 | 86.17 355 | 95.41 322 |
|
| tt0320-xc | | | 84.83 391 | 82.33 399 | 92.31 338 | 93.66 380 | 86.20 322 | 96.17 284 | 94.06 398 | 71.26 441 | 82.04 414 | 92.22 400 | 55.07 439 | 96.72 395 | 81.49 374 | 75.04 429 | 94.02 392 |
|
| MIMVSNet | | | 88.50 353 | 86.76 363 | 93.72 285 | 94.84 339 | 87.77 282 | 91.39 421 | 94.05 399 | 86.41 353 | 87.99 345 | 92.59 389 | 63.27 421 | 95.82 409 | 77.44 403 | 92.84 273 | 97.57 240 |
|
| OpenMVS_ROB |  | 81.14 20 | 84.42 394 | 82.28 400 | 90.83 378 | 90.06 426 | 84.05 364 | 95.73 309 | 94.04 400 | 73.89 438 | 80.17 424 | 91.53 410 | 59.15 429 | 97.64 346 | 66.92 441 | 89.05 326 | 90.80 434 |
|
| TinyColmap | | | 86.82 369 | 85.35 376 | 91.21 370 | 94.91 336 | 82.99 376 | 93.94 381 | 94.02 401 | 83.58 396 | 81.56 415 | 94.68 308 | 62.34 426 | 98.13 276 | 75.78 412 | 87.35 347 | 92.52 415 |
|
| ETVMVS | | | 90.52 314 | 89.14 335 | 94.67 228 | 96.81 202 | 87.85 280 | 95.91 298 | 93.97 402 | 89.71 257 | 92.34 223 | 92.48 391 | 65.41 417 | 97.96 309 | 81.37 379 | 94.27 247 | 98.21 189 |
|
| IB-MVS | | 87.33 17 | 89.91 330 | 88.28 347 | 94.79 222 | 95.26 315 | 87.70 283 | 95.12 343 | 93.95 403 | 89.35 269 | 87.03 365 | 92.49 390 | 70.74 374 | 99.19 147 | 89.18 263 | 81.37 405 | 97.49 242 |
| 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 |
| Syy-MVS | | | 87.13 366 | 87.02 361 | 87.47 411 | 95.16 319 | 73.21 439 | 95.00 345 | 93.93 404 | 88.55 300 | 86.96 367 | 91.99 402 | 75.90 333 | 94.00 430 | 61.59 445 | 94.11 251 | 95.20 340 |
|
| myMVS_eth3d | | | 87.18 365 | 86.38 366 | 89.58 398 | 95.16 319 | 79.53 416 | 95.00 345 | 93.93 404 | 88.55 300 | 86.96 367 | 91.99 402 | 56.23 436 | 94.00 430 | 75.47 416 | 94.11 251 | 95.20 340 |
|
| testing222 | | | 90.31 318 | 88.96 337 | 94.35 245 | 96.54 227 | 87.29 288 | 95.50 322 | 93.84 406 | 90.97 212 | 91.75 241 | 92.96 382 | 62.18 427 | 98.00 300 | 82.86 361 | 94.08 254 | 97.76 228 |
|
| test_f | | | 80.57 404 | 79.62 406 | 83.41 421 | 83.38 450 | 67.80 448 | 93.57 398 | 93.72 407 | 80.80 418 | 77.91 431 | 87.63 437 | 33.40 453 | 92.08 441 | 87.14 307 | 79.04 416 | 90.34 436 |
|
| LCM-MVSNet-Re | | | 92.50 220 | 92.52 204 | 92.44 332 | 96.82 200 | 81.89 389 | 96.92 210 | 93.71 408 | 92.41 153 | 84.30 394 | 94.60 313 | 85.08 172 | 97.03 382 | 91.51 203 | 97.36 165 | 98.40 173 |
|
| tpm | | | 90.25 321 | 89.74 319 | 91.76 360 | 93.92 370 | 79.73 414 | 93.98 378 | 93.54 409 | 88.28 307 | 91.99 232 | 93.25 379 | 77.51 321 | 97.44 365 | 87.30 302 | 87.94 337 | 98.12 198 |
|
| ET-MVSNet_ETH3D | | | 91.49 270 | 90.11 299 | 95.63 172 | 96.40 241 | 91.57 137 | 95.34 329 | 93.48 410 | 90.60 232 | 75.58 434 | 95.49 272 | 80.08 276 | 96.79 393 | 94.25 143 | 89.76 320 | 98.52 157 |
|
| LFMVS | | | 93.60 176 | 92.63 197 | 96.52 101 | 98.13 108 | 91.27 148 | 97.94 76 | 93.39 411 | 90.57 234 | 96.29 108 | 98.31 74 | 69.00 390 | 99.16 154 | 94.18 144 | 95.87 208 | 99.12 87 |
|
| MVStest1 | | | 82.38 401 | 80.04 405 | 89.37 400 | 87.63 442 | 82.83 377 | 95.03 344 | 93.37 412 | 73.90 437 | 73.50 439 | 94.35 328 | 62.89 424 | 93.25 438 | 73.80 423 | 65.92 446 | 92.04 424 |
|
| Patchmatch-RL test | | | 87.38 363 | 86.24 367 | 90.81 380 | 88.74 437 | 78.40 425 | 88.12 445 | 93.17 413 | 87.11 342 | 82.17 413 | 89.29 426 | 81.95 242 | 95.60 414 | 88.64 274 | 77.02 421 | 98.41 172 |
|
| ttmdpeth | | | 85.91 383 | 84.76 383 | 89.36 401 | 89.14 432 | 80.25 409 | 95.66 314 | 93.16 414 | 83.77 393 | 83.39 405 | 95.26 282 | 66.24 412 | 95.26 420 | 80.65 385 | 75.57 427 | 92.57 412 |
|
| test-LLR | | | 91.42 273 | 91.19 252 | 92.12 345 | 94.59 350 | 80.66 399 | 94.29 371 | 92.98 415 | 91.11 207 | 90.76 265 | 92.37 393 | 79.02 297 | 98.07 290 | 88.81 269 | 96.74 186 | 97.63 233 |
|
| test-mter | | | 90.19 325 | 89.54 324 | 92.12 345 | 94.59 350 | 80.66 399 | 94.29 371 | 92.98 415 | 87.68 329 | 90.76 265 | 92.37 393 | 67.67 399 | 98.07 290 | 88.81 269 | 96.74 186 | 97.63 233 |
|
| WB-MVSnew | | | 89.88 333 | 89.56 323 | 90.82 379 | 94.57 353 | 83.06 375 | 95.65 315 | 92.85 417 | 87.86 320 | 90.83 264 | 94.10 345 | 79.66 285 | 96.88 389 | 76.34 410 | 94.19 249 | 92.54 414 |
|
| testing3 | | | 87.67 361 | 86.88 362 | 90.05 392 | 96.14 263 | 80.71 398 | 97.10 193 | 92.85 417 | 90.15 245 | 87.54 352 | 94.55 315 | 55.70 437 | 94.10 429 | 73.77 424 | 94.10 253 | 95.35 329 |
|
| test_method | | | 66.11 419 | 64.89 421 | 69.79 436 | 72.62 460 | 35.23 468 | 65.19 455 | 92.83 419 | 20.35 458 | 65.20 447 | 88.08 435 | 43.14 449 | 82.70 453 | 73.12 427 | 63.46 448 | 91.45 431 |
|
| test0.0.03 1 | | | 89.37 343 | 88.70 341 | 91.41 367 | 92.47 411 | 85.63 335 | 95.22 338 | 92.70 420 | 91.11 207 | 86.91 371 | 93.65 366 | 79.02 297 | 93.19 439 | 78.00 402 | 89.18 325 | 95.41 322 |
|
| new_pmnet | | | 82.89 399 | 81.12 404 | 88.18 408 | 89.63 429 | 80.18 410 | 91.77 420 | 92.57 421 | 76.79 433 | 75.56 435 | 88.23 433 | 61.22 428 | 94.48 425 | 71.43 432 | 82.92 399 | 89.87 437 |
|
| mvsany_test1 | | | 93.93 165 | 93.98 144 | 93.78 282 | 94.94 333 | 86.80 303 | 94.62 353 | 92.55 422 | 88.77 294 | 96.85 77 | 98.49 51 | 88.98 96 | 98.08 286 | 95.03 119 | 95.62 216 | 96.46 278 |
|
| thisisatest0515 | | | 92.29 233 | 91.30 246 | 95.25 193 | 96.60 218 | 88.90 246 | 94.36 366 | 92.32 423 | 87.92 317 | 93.43 198 | 94.57 314 | 77.28 322 | 99.00 183 | 89.42 253 | 95.86 209 | 97.86 222 |
|
| thisisatest0530 | | | 93.03 201 | 92.21 213 | 95.49 182 | 97.07 174 | 89.11 242 | 97.49 153 | 92.19 424 | 90.16 244 | 94.09 176 | 96.41 220 | 76.43 331 | 99.05 179 | 90.38 231 | 95.68 214 | 98.31 183 |
|
| tttt0517 | | | 92.96 204 | 92.33 210 | 94.87 215 | 97.11 172 | 87.16 296 | 97.97 72 | 92.09 425 | 90.63 228 | 93.88 182 | 97.01 184 | 76.50 328 | 99.06 176 | 90.29 234 | 95.45 223 | 98.38 175 |
|
| K. test v3 | | | 87.64 362 | 86.75 364 | 90.32 389 | 93.02 399 | 79.48 419 | 96.61 246 | 92.08 426 | 90.66 226 | 80.25 423 | 94.09 347 | 67.21 403 | 96.65 396 | 85.96 326 | 80.83 407 | 94.83 361 |
|
| TESTMET0.1,1 | | | 90.06 327 | 89.42 327 | 91.97 348 | 94.41 358 | 80.62 401 | 94.29 371 | 91.97 427 | 87.28 339 | 90.44 269 | 92.47 392 | 68.79 391 | 97.67 343 | 88.50 276 | 96.60 191 | 97.61 237 |
|
| PM-MVS | | | 83.48 396 | 81.86 402 | 88.31 406 | 87.83 441 | 77.59 427 | 93.43 399 | 91.75 428 | 86.91 344 | 80.63 419 | 89.91 422 | 44.42 448 | 95.84 408 | 85.17 338 | 76.73 424 | 91.50 429 |
|
| baseline2 | | | 91.63 258 | 90.86 263 | 93.94 272 | 94.33 360 | 86.32 317 | 95.92 297 | 91.64 429 | 89.37 268 | 86.94 369 | 94.69 307 | 81.62 249 | 98.69 223 | 88.64 274 | 94.57 242 | 96.81 268 |
|
| APD_test1 | | | 79.31 406 | 77.70 409 | 84.14 419 | 89.11 434 | 69.07 445 | 92.36 418 | 91.50 430 | 69.07 444 | 73.87 437 | 92.63 388 | 39.93 450 | 94.32 427 | 70.54 437 | 80.25 409 | 89.02 439 |
|
| FPMVS | | | 71.27 412 | 69.85 414 | 75.50 432 | 74.64 457 | 59.03 457 | 91.30 422 | 91.50 430 | 58.80 449 | 57.92 453 | 88.28 432 | 29.98 456 | 85.53 452 | 53.43 450 | 82.84 400 | 81.95 445 |
|
| door | | | | | | | | | 91.13 432 | | | | | | | | |
|
| door-mid | | | | | | | | | 91.06 433 | | | | | | | | |
|
| EGC-MVSNET | | | 68.77 417 | 63.01 423 | 86.07 418 | 92.49 410 | 82.24 387 | 93.96 380 | 90.96 434 | 0.71 463 | 2.62 464 | 90.89 413 | 53.66 440 | 93.46 434 | 57.25 448 | 84.55 381 | 82.51 444 |
|
| mvsany_test3 | | | 83.59 395 | 82.44 398 | 87.03 414 | 83.80 447 | 73.82 436 | 93.70 391 | 90.92 435 | 86.42 352 | 82.51 411 | 90.26 418 | 46.76 447 | 95.71 410 | 90.82 218 | 76.76 423 | 91.57 427 |
|
| pmmvs3 | | | 79.97 405 | 77.50 410 | 87.39 412 | 82.80 451 | 79.38 420 | 92.70 413 | 90.75 436 | 70.69 443 | 78.66 428 | 87.47 439 | 51.34 443 | 93.40 435 | 73.39 426 | 69.65 439 | 89.38 438 |
|
| UWE-MVS | | | 89.91 330 | 89.48 326 | 91.21 370 | 95.88 273 | 78.23 426 | 94.91 348 | 90.26 437 | 89.11 275 | 92.35 222 | 94.52 317 | 68.76 392 | 97.96 309 | 83.95 353 | 95.59 217 | 97.42 246 |
|
| DSMNet-mixed | | | 86.34 376 | 86.12 370 | 87.00 415 | 89.88 428 | 70.43 441 | 94.93 347 | 90.08 438 | 77.97 430 | 85.42 386 | 92.78 384 | 74.44 349 | 93.96 432 | 74.43 419 | 95.14 228 | 96.62 272 |
|
| MVS-HIRNet | | | 82.47 400 | 81.21 403 | 86.26 417 | 95.38 300 | 69.21 444 | 88.96 439 | 89.49 439 | 66.28 446 | 80.79 418 | 74.08 451 | 68.48 396 | 97.39 369 | 71.93 431 | 95.47 222 | 92.18 422 |
|
| WB-MVS | | | 76.77 408 | 76.63 411 | 77.18 427 | 85.32 445 | 56.82 459 | 94.53 357 | 89.39 440 | 82.66 404 | 71.35 440 | 89.18 427 | 75.03 342 | 88.88 447 | 35.42 456 | 66.79 444 | 85.84 441 |
|
| test1111 | | | 93.19 193 | 92.82 187 | 94.30 251 | 97.58 154 | 84.56 356 | 98.21 43 | 89.02 441 | 93.53 108 | 94.58 162 | 98.21 81 | 72.69 359 | 99.05 179 | 93.06 170 | 98.48 124 | 99.28 72 |
|
| SSC-MVS | | | 76.05 409 | 75.83 412 | 76.72 431 | 84.77 446 | 56.22 460 | 94.32 369 | 88.96 442 | 81.82 410 | 70.52 441 | 88.91 428 | 74.79 346 | 88.71 448 | 33.69 457 | 64.71 447 | 85.23 442 |
|
| ECVR-MVS |  | | 93.19 193 | 92.73 193 | 94.57 234 | 97.66 142 | 85.41 339 | 98.21 43 | 88.23 443 | 93.43 113 | 94.70 160 | 98.21 81 | 72.57 360 | 99.07 174 | 93.05 171 | 98.49 122 | 99.25 75 |
|
| EPMVS | | | 90.70 308 | 89.81 314 | 93.37 302 | 94.73 345 | 84.21 360 | 93.67 394 | 88.02 444 | 89.50 263 | 92.38 219 | 93.49 371 | 77.82 319 | 97.78 333 | 86.03 324 | 92.68 277 | 98.11 203 |
|
| ANet_high | | | 63.94 421 | 59.58 424 | 77.02 428 | 61.24 464 | 66.06 449 | 85.66 448 | 87.93 445 | 78.53 428 | 42.94 456 | 71.04 453 | 25.42 459 | 80.71 455 | 52.60 451 | 30.83 457 | 84.28 443 |
|
| PMMVS2 | | | 70.19 413 | 66.92 417 | 80.01 423 | 76.35 456 | 65.67 450 | 86.22 446 | 87.58 446 | 64.83 448 | 62.38 449 | 80.29 448 | 26.78 458 | 88.49 450 | 63.79 442 | 54.07 453 | 85.88 440 |
|
| lessismore_v0 | | | | | 90.45 386 | 91.96 417 | 79.09 423 | | 87.19 447 | | 80.32 422 | 94.39 325 | 66.31 411 | 97.55 354 | 84.00 352 | 76.84 422 | 94.70 373 |
|
| PMVS |  | 53.92 22 | 58.58 422 | 55.40 425 | 68.12 437 | 51.00 465 | 48.64 462 | 78.86 451 | 87.10 448 | 46.77 454 | 35.84 460 | 74.28 450 | 8.76 464 | 86.34 451 | 42.07 454 | 73.91 432 | 69.38 451 |
| Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
| UWE-MVS-28 | | | 86.81 370 | 86.41 365 | 88.02 409 | 92.87 401 | 74.60 434 | 95.38 328 | 86.70 449 | 88.17 310 | 87.28 360 | 94.67 310 | 70.83 373 | 93.30 437 | 67.45 439 | 94.31 245 | 96.17 284 |
|
| test_vis1_rt | | | 86.16 379 | 85.06 379 | 89.46 399 | 93.47 388 | 80.46 403 | 96.41 258 | 86.61 450 | 85.22 372 | 79.15 427 | 88.64 429 | 52.41 442 | 97.06 380 | 93.08 169 | 90.57 311 | 90.87 433 |
|
| testf1 | | | 69.31 415 | 66.76 418 | 76.94 429 | 78.61 454 | 61.93 453 | 88.27 443 | 86.11 451 | 55.62 450 | 59.69 450 | 85.31 442 | 20.19 462 | 89.32 444 | 57.62 446 | 69.44 441 | 79.58 446 |
|
| APD_test2 | | | 69.31 415 | 66.76 418 | 76.94 429 | 78.61 454 | 61.93 453 | 88.27 443 | 86.11 451 | 55.62 450 | 59.69 450 | 85.31 442 | 20.19 462 | 89.32 444 | 57.62 446 | 69.44 441 | 79.58 446 |
|
| gg-mvs-nofinetune | | | 87.82 359 | 85.61 372 | 94.44 241 | 94.46 355 | 89.27 236 | 91.21 425 | 84.61 453 | 80.88 415 | 89.89 289 | 74.98 449 | 71.50 367 | 97.53 357 | 85.75 329 | 97.21 174 | 96.51 274 |
|
| dmvs_testset | | | 81.38 403 | 82.60 397 | 77.73 426 | 91.74 418 | 51.49 461 | 93.03 408 | 84.21 454 | 89.07 276 | 78.28 430 | 91.25 412 | 76.97 324 | 88.53 449 | 56.57 449 | 82.24 402 | 93.16 403 |
|
| GG-mvs-BLEND | | | | | 93.62 290 | 93.69 378 | 89.20 238 | 92.39 417 | 83.33 455 | | 87.98 346 | 89.84 423 | 71.00 371 | 96.87 390 | 82.08 371 | 95.40 224 | 94.80 366 |
|
| MTMP | | | | | | | | 97.86 85 | 82.03 456 | | | | | | | | |
|
| DeepMVS_CX |  | | | | 74.68 434 | 90.84 423 | 64.34 452 | | 81.61 457 | 65.34 447 | 67.47 445 | 88.01 436 | 48.60 446 | 80.13 456 | 62.33 444 | 73.68 433 | 79.58 446 |
|
| E-PMN | | | 53.28 423 | 52.56 427 | 55.43 440 | 74.43 458 | 47.13 463 | 83.63 450 | 76.30 458 | 42.23 455 | 42.59 457 | 62.22 456 | 28.57 457 | 74.40 457 | 31.53 458 | 31.51 456 | 44.78 455 |
|
| test2506 | | | 91.60 260 | 90.78 268 | 94.04 262 | 97.66 142 | 83.81 365 | 98.27 33 | 75.53 459 | 93.43 113 | 95.23 146 | 98.21 81 | 67.21 403 | 99.07 174 | 93.01 174 | 98.49 122 | 99.25 75 |
|
| EMVS | | | 52.08 425 | 51.31 428 | 54.39 441 | 72.62 460 | 45.39 465 | 83.84 449 | 75.51 460 | 41.13 456 | 40.77 458 | 59.65 457 | 30.08 455 | 73.60 458 | 28.31 460 | 29.90 458 | 44.18 456 |
|
| test_vis3_rt | | | 72.73 410 | 70.55 413 | 79.27 424 | 80.02 453 | 68.13 447 | 93.92 383 | 74.30 461 | 76.90 432 | 58.99 452 | 73.58 452 | 20.29 461 | 95.37 418 | 84.16 348 | 72.80 435 | 74.31 449 |
|
| MVE |  | 50.73 23 | 53.25 424 | 48.81 429 | 66.58 439 | 65.34 463 | 57.50 458 | 72.49 453 | 70.94 462 | 40.15 457 | 39.28 459 | 63.51 455 | 6.89 466 | 73.48 459 | 38.29 455 | 42.38 455 | 68.76 453 |
| Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
| tmp_tt | | | 51.94 426 | 53.82 426 | 46.29 442 | 33.73 466 | 45.30 466 | 78.32 452 | 67.24 463 | 18.02 459 | 50.93 455 | 87.05 440 | 52.99 441 | 53.11 461 | 70.76 435 | 25.29 459 | 40.46 457 |
|
| kuosan | | | 65.27 420 | 64.66 422 | 67.11 438 | 83.80 447 | 61.32 456 | 88.53 442 | 60.77 464 | 68.22 445 | 67.67 443 | 80.52 447 | 49.12 445 | 70.76 460 | 29.67 459 | 53.64 454 | 69.26 452 |
|
| dongtai | | | 69.99 414 | 69.33 416 | 71.98 435 | 88.78 436 | 61.64 455 | 89.86 434 | 59.93 465 | 75.67 434 | 74.96 436 | 85.45 441 | 50.19 444 | 81.66 454 | 43.86 453 | 55.27 452 | 72.63 450 |
|
| N_pmnet | | | 78.73 407 | 78.71 408 | 78.79 425 | 92.80 404 | 46.50 464 | 94.14 375 | 43.71 466 | 78.61 427 | 80.83 417 | 91.66 409 | 74.94 345 | 96.36 400 | 67.24 440 | 84.45 383 | 93.50 399 |
|
| wuyk23d | | | 25.11 427 | 24.57 431 | 26.74 443 | 73.98 459 | 39.89 467 | 57.88 456 | 9.80 467 | 12.27 460 | 10.39 461 | 6.97 463 | 7.03 465 | 36.44 462 | 25.43 461 | 17.39 460 | 3.89 460 |
|
| testmvs | | | 13.36 429 | 16.33 432 | 4.48 445 | 5.04 467 | 2.26 470 | 93.18 402 | 3.28 468 | 2.70 461 | 8.24 462 | 21.66 459 | 2.29 468 | 2.19 463 | 7.58 462 | 2.96 461 | 9.00 459 |
|
| test123 | | | 13.04 430 | 15.66 433 | 5.18 444 | 4.51 468 | 3.45 469 | 92.50 416 | 1.81 469 | 2.50 462 | 7.58 463 | 20.15 460 | 3.67 467 | 2.18 464 | 7.13 463 | 1.07 462 | 9.90 458 |
|
| mmdepth | | | 0.00 433 | 0.00 436 | 0.00 446 | 0.00 469 | 0.00 471 | 0.00 457 | 0.00 470 | 0.00 464 | 0.00 465 | 0.00 464 | 0.00 469 | 0.00 465 | 0.00 464 | 0.00 463 | 0.00 461 |
|
| monomultidepth | | | 0.00 433 | 0.00 436 | 0.00 446 | 0.00 469 | 0.00 471 | 0.00 457 | 0.00 470 | 0.00 464 | 0.00 465 | 0.00 464 | 0.00 469 | 0.00 465 | 0.00 464 | 0.00 463 | 0.00 461 |
|
| test_blank | | | 0.00 433 | 0.00 436 | 0.00 446 | 0.00 469 | 0.00 471 | 0.00 457 | 0.00 470 | 0.00 464 | 0.00 465 | 0.00 464 | 0.00 469 | 0.00 465 | 0.00 464 | 0.00 463 | 0.00 461 |
|
| uanet_test | | | 0.00 433 | 0.00 436 | 0.00 446 | 0.00 469 | 0.00 471 | 0.00 457 | 0.00 470 | 0.00 464 | 0.00 465 | 0.00 464 | 0.00 469 | 0.00 465 | 0.00 464 | 0.00 463 | 0.00 461 |
|
| DCPMVS | | | 0.00 433 | 0.00 436 | 0.00 446 | 0.00 469 | 0.00 471 | 0.00 457 | 0.00 470 | 0.00 464 | 0.00 465 | 0.00 464 | 0.00 469 | 0.00 465 | 0.00 464 | 0.00 463 | 0.00 461 |
|
| pcd_1.5k_mvsjas | | | 7.39 432 | 9.85 435 | 0.00 446 | 0.00 469 | 0.00 471 | 0.00 457 | 0.00 470 | 0.00 464 | 0.00 465 | 0.00 464 | 88.65 104 | 0.00 465 | 0.00 464 | 0.00 463 | 0.00 461 |
|
| sosnet-low-res | | | 0.00 433 | 0.00 436 | 0.00 446 | 0.00 469 | 0.00 471 | 0.00 457 | 0.00 470 | 0.00 464 | 0.00 465 | 0.00 464 | 0.00 469 | 0.00 465 | 0.00 464 | 0.00 463 | 0.00 461 |
|
| sosnet | | | 0.00 433 | 0.00 436 | 0.00 446 | 0.00 469 | 0.00 471 | 0.00 457 | 0.00 470 | 0.00 464 | 0.00 465 | 0.00 464 | 0.00 469 | 0.00 465 | 0.00 464 | 0.00 463 | 0.00 461 |
|
| uncertanet | | | 0.00 433 | 0.00 436 | 0.00 446 | 0.00 469 | 0.00 471 | 0.00 457 | 0.00 470 | 0.00 464 | 0.00 465 | 0.00 464 | 0.00 469 | 0.00 465 | 0.00 464 | 0.00 463 | 0.00 461 |
|
| Regformer | | | 0.00 433 | 0.00 436 | 0.00 446 | 0.00 469 | 0.00 471 | 0.00 457 | 0.00 470 | 0.00 464 | 0.00 465 | 0.00 464 | 0.00 469 | 0.00 465 | 0.00 464 | 0.00 463 | 0.00 461 |
|
| n2 | | | | | | | | | 0.00 470 | | | | | | | | |
|
| nn | | | | | | | | | 0.00 470 | | | | | | | | |
|
| ab-mvs-re | | | 8.06 431 | 10.74 434 | 0.00 446 | 0.00 469 | 0.00 471 | 0.00 457 | 0.00 470 | 0.00 464 | 0.00 465 | 96.69 201 | 0.00 469 | 0.00 465 | 0.00 464 | 0.00 463 | 0.00 461 |
|
| uanet | | | 0.00 433 | 0.00 436 | 0.00 446 | 0.00 469 | 0.00 471 | 0.00 457 | 0.00 470 | 0.00 464 | 0.00 465 | 0.00 464 | 0.00 469 | 0.00 465 | 0.00 464 | 0.00 463 | 0.00 461 |
|
| WAC-MVS | | | | | | | 79.53 416 | | | | | | | | 75.56 415 | | |
|
| PC_three_1452 | | | | | | | | | | 90.77 218 | 98.89 23 | 98.28 79 | 96.24 1 | 98.35 259 | 95.76 98 | 99.58 23 | 99.59 27 |
|
| eth-test2 | | | | | | 0.00 469 | | | | | | | | | | | |
|
| eth-test | | | | | | 0.00 469 | | | | | | | | | | | |
|
| OPU-MVS | | | | | 98.55 3 | 98.82 57 | 96.86 3 | 98.25 36 | | | | 98.26 80 | 96.04 2 | 99.24 142 | 95.36 112 | 99.59 19 | 99.56 35 |
|
| test_0728_THIRD | | | | | | | | | | 94.78 58 | 98.73 27 | 98.87 28 | 95.87 4 | 99.84 23 | 97.45 43 | 99.72 2 | 99.77 2 |
|
| GSMVS | | | | | | | | | | | | | | | | | 98.45 167 |
|
| test_part2 | | | | | | 99.28 27 | 95.74 8 | | | | 98.10 41 | | | | | | |
|
| sam_mvs1 | | | | | | | | | | | | | 82.76 222 | | | | 98.45 167 |
|
| sam_mvs | | | | | | | | | | | | | 81.94 243 | | | | |
|
| test_post1 | | | | | | | | 92.81 412 | | | | 16.58 462 | 80.53 267 | 97.68 342 | 86.20 318 | | |
|
| test_post | | | | | | | | | | | | 17.58 461 | 81.76 246 | 98.08 286 | | | |
|
| patchmatchnet-post | | | | | | | | | | | | 90.45 417 | 82.65 227 | 98.10 281 | | | |
|
| gm-plane-assit | | | | | | 93.22 395 | 78.89 424 | | | 84.82 380 | | 93.52 370 | | 98.64 230 | 87.72 286 | | |
|
| test9_res | | | | | | | | | | | | | | | 94.81 129 | 99.38 59 | 99.45 54 |
|
| agg_prior2 | | | | | | | | | | | | | | | 93.94 149 | 99.38 59 | 99.50 47 |
|
| test_prior4 | | | | | | | 93.66 58 | 96.42 257 | | | | | | | | | |
|
| test_prior2 | | | | | | | | 96.35 266 | | 92.80 145 | 96.03 118 | 97.59 142 | 92.01 47 | | 95.01 120 | 99.38 59 | |
|
| 旧先验2 | | | | | | | | 95.94 295 | | 81.66 411 | 97.34 63 | | | 98.82 202 | 92.26 180 | | |
|
| 新几何2 | | | | | | | | 95.79 305 | | | | | | | | | |
|
| 原ACMM2 | | | | | | | | 95.67 311 | | | | | | | | | |
|
| testdata2 | | | | | | | | | | | | | | 99.67 70 | 85.96 326 | | |
|
| segment_acmp | | | | | | | | | | | | | 92.89 30 | | | | |
|
| testdata1 | | | | | | | | 95.26 337 | | 93.10 130 | | | | | | | |
|
| plane_prior7 | | | | | | 96.21 250 | 89.98 203 | | | | | | | | | | |
|
| plane_prior6 | | | | | | 96.10 266 | 90.00 199 | | | | | | 81.32 253 | | | | |
|
| plane_prior4 | | | | | | | | | | | | 96.64 204 | | | | | |
|
| plane_prior3 | | | | | | | 90.00 199 | | | 94.46 75 | 91.34 250 | | | | | | |
|
| plane_prior2 | | | | | | | | 97.74 106 | | 94.85 50 | | | | | | | |
|
| plane_prior1 | | | | | | 96.14 263 | | | | | | | | | | | |
|
| plane_prior | | | | | | | 89.99 201 | 97.24 178 | | 94.06 87 | | | | | | 92.16 286 | |
|
| HQP5-MVS | | | | | | | 89.33 231 | | | | | | | | | | |
|
| HQP-NCC | | | | | | 95.86 274 | | 96.65 240 | | 93.55 104 | 90.14 274 | | | | | | |
|
| ACMP_Plane | | | | | | 95.86 274 | | 96.65 240 | | 93.55 104 | 90.14 274 | | | | | | |
|
| BP-MVS | | | | | | | | | | | | | | | 92.13 188 | | |
|
| HQP4-MVS | | | | | | | | | | | 90.14 274 | | | 98.50 244 | | | 95.78 303 |
|
| HQP2-MVS | | | | | | | | | | | | | 80.95 257 | | | | |
|
| NP-MVS | | | | | | 95.99 272 | 89.81 210 | | | | | 95.87 247 | | | | | |
|
| MDTV_nov1_ep13_2view | | | | | | | 70.35 442 | 93.10 407 | | 83.88 391 | 93.55 191 | | 82.47 231 | | 86.25 317 | | 98.38 175 |
|
| ACMMP++_ref | | | | | | | | | | | | | | | | 90.30 316 | |
|
| ACMMP++ | | | | | | | | | | | | | | | | 91.02 305 | |
|
| Test By Simon | | | | | | | | | | | | | 88.73 103 | | | | |
|