| MCST-MVS | | | 91.08 1 | 91.46 3 | 89.94 4 | 97.66 2 | 73.37 10 | 97.13 2 | 95.58 11 | 89.33 1 | 85.77 71 | 96.26 46 | 72.84 32 | 99.38 1 | 92.64 33 | 95.93 9 | 97.08 11 |
|
| MM | | | 90.87 2 | 91.52 2 | 88.92 15 | 92.12 105 | 71.10 27 | 97.02 3 | 96.04 6 | 88.70 2 | 91.57 19 | 96.19 48 | 70.12 49 | 98.91 21 | 96.83 2 | 95.06 17 | 96.76 15 |
|
| DPM-MVS | | | 90.70 3 | 90.52 9 | 91.24 1 | 89.68 169 | 76.68 2 | 97.29 1 | 95.35 17 | 82.87 37 | 91.58 18 | 97.22 8 | 79.93 5 | 99.10 9 | 83.12 128 | 97.64 2 | 97.94 1 |
|
| DVP-MVS++ | | | 90.53 4 | 91.09 5 | 88.87 16 | 97.31 4 | 69.91 43 | 93.96 88 | 94.37 61 | 72.48 239 | 92.07 11 | 96.85 27 | 83.82 2 | 99.15 2 | 91.53 46 | 97.42 4 | 97.55 4 |
|
| MSP-MVS | | | 90.38 5 | 91.87 1 | 85.88 110 | 92.83 84 | 64.03 236 | 93.06 133 | 94.33 63 | 82.19 45 | 93.65 3 | 96.15 50 | 85.89 1 | 97.19 98 | 91.02 50 | 97.75 1 | 96.43 31 |
| 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 |
| MGCNet | | | 90.32 6 | 90.90 7 | 88.55 23 | 94.05 49 | 70.23 37 | 97.00 5 | 93.73 82 | 87.30 4 | 92.15 8 | 96.15 50 | 66.38 75 | 98.94 20 | 96.71 3 | 94.67 33 | 96.47 28 |
|
| CNVR-MVS | | | 90.32 6 | 90.89 8 | 88.61 22 | 96.76 8 | 70.65 30 | 96.47 14 | 94.83 36 | 84.83 17 | 89.07 43 | 96.80 30 | 70.86 45 | 99.06 15 | 92.64 33 | 95.71 11 | 96.12 40 |
|
| DELS-MVS | | | 90.05 8 | 90.09 11 | 89.94 4 | 93.14 75 | 73.88 9 | 97.01 4 | 94.40 59 | 88.32 3 | 85.71 72 | 94.91 91 | 74.11 23 | 98.91 21 | 87.26 78 | 95.94 8 | 97.03 12 |
| 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 |
| SED-MVS | | | 89.94 9 | 90.36 10 | 88.70 18 | 96.45 12 | 69.38 59 | 96.89 6 | 94.44 55 | 71.65 269 | 92.11 9 | 97.21 9 | 76.79 9 | 99.11 6 | 92.34 35 | 95.36 14 | 97.62 2 |
|
| DeepPCF-MVS | | 81.17 1 | 89.72 10 | 91.38 4 | 84.72 168 | 93.00 80 | 58.16 378 | 96.72 9 | 94.41 57 | 86.50 9 | 90.25 34 | 97.83 1 | 75.46 16 | 98.67 29 | 92.78 32 | 95.49 13 | 97.32 6 |
|
| patch_mono-2 | | | 89.71 11 | 90.99 6 | 85.85 113 | 96.04 25 | 63.70 253 | 95.04 42 | 95.19 22 | 86.74 8 | 91.53 20 | 95.15 84 | 73.86 24 | 97.58 69 | 93.38 27 | 92.00 75 | 96.28 37 |
|
| CANet | | | 89.61 12 | 89.99 12 | 88.46 24 | 94.39 43 | 69.71 52 | 96.53 13 | 93.78 75 | 86.89 7 | 89.68 40 | 95.78 57 | 65.94 80 | 99.10 9 | 92.99 30 | 93.91 46 | 96.58 21 |
|
| DVP-MVS |  | | 89.41 13 | 89.73 14 | 88.45 25 | 96.40 15 | 69.99 39 | 96.64 10 | 94.52 51 | 71.92 255 | 90.55 30 | 96.93 21 | 73.77 25 | 99.08 11 | 91.91 41 | 94.90 22 | 96.29 35 |
| 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 |
| HPM-MVS++ |  | | 89.37 14 | 89.95 13 | 87.64 35 | 95.10 31 | 68.23 101 | 95.24 34 | 94.49 53 | 82.43 42 | 88.90 45 | 96.35 41 | 71.89 42 | 98.63 30 | 88.76 64 | 96.40 6 | 96.06 41 |
|
| balanced_conf03 | | | 89.08 15 | 88.84 23 | 89.81 6 | 93.66 58 | 75.15 5 | 90.61 275 | 93.43 97 | 84.06 24 | 86.20 66 | 90.17 226 | 72.42 37 | 96.98 115 | 93.09 29 | 95.92 10 | 97.29 7 |
|
| NCCC | | | 89.07 16 | 89.46 15 | 87.91 28 | 96.60 10 | 69.05 75 | 96.38 15 | 94.64 46 | 84.42 21 | 86.74 61 | 96.20 47 | 66.56 74 | 98.76 27 | 89.03 63 | 94.56 34 | 95.92 49 |
|
| MED-MVS | | | 88.94 17 | 89.45 16 | 87.42 45 | 94.76 34 | 67.28 126 | 94.47 61 | 94.87 32 | 70.09 301 | 91.27 23 | 96.95 17 | 76.77 11 | 98.98 16 | 91.55 43 | 94.28 37 | 95.99 45 |
|
| DPE-MVS |  | | 88.77 18 | 89.21 19 | 87.45 44 | 96.26 21 | 67.56 119 | 94.17 74 | 94.15 68 | 68.77 322 | 90.74 28 | 97.27 6 | 76.09 14 | 98.49 33 | 90.58 54 | 94.91 21 | 96.30 34 |
| Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025 |
| TestfortrainingZip a | | | 88.66 19 | 88.99 21 | 87.70 33 | 94.76 34 | 68.73 84 | 94.47 61 | 94.87 32 | 73.09 226 | 91.27 23 | 96.95 17 | 76.77 11 | 98.98 16 | 84.41 111 | 94.28 37 | 95.37 69 |
|
| ME-MVS | | | 88.25 20 | 88.55 27 | 87.33 50 | 96.33 18 | 67.28 126 | 93.93 90 | 94.81 37 | 70.09 301 | 88.91 44 | 96.95 17 | 70.12 49 | 98.73 28 | 91.55 43 | 94.28 37 | 95.99 45 |
|
| fmvsm_l_conf0.5_n_9 | | | 88.24 21 | 89.36 17 | 84.85 157 | 88.15 230 | 61.94 303 | 95.65 25 | 89.70 299 | 85.54 12 | 92.07 11 | 97.33 5 | 67.51 66 | 97.27 93 | 96.23 5 | 92.07 74 | 95.35 73 |
|
| fmvsm_s_conf0.5_n_9 | | | 88.14 22 | 89.21 19 | 84.92 152 | 89.29 180 | 61.41 320 | 92.97 138 | 88.36 354 | 86.96 6 | 91.49 21 | 97.49 3 | 69.48 54 | 97.46 76 | 97.00 1 | 89.88 111 | 95.89 50 |
|
| SMA-MVS |  | | 88.14 22 | 88.29 31 | 87.67 34 | 93.21 72 | 68.72 86 | 93.85 96 | 94.03 71 | 74.18 200 | 91.74 15 | 96.67 33 | 65.61 85 | 98.42 37 | 89.24 60 | 96.08 7 | 95.88 51 |
| 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 |
| PS-MVSNAJ | | | 88.14 22 | 87.61 41 | 89.71 7 | 92.06 108 | 76.72 1 | 95.75 20 | 93.26 103 | 83.86 25 | 89.55 41 | 96.06 52 | 53.55 266 | 97.89 51 | 91.10 48 | 93.31 57 | 94.54 131 |
|
| TSAR-MVS + MP. | | | 88.11 25 | 88.64 26 | 86.54 88 | 91.73 123 | 68.04 105 | 90.36 282 | 93.55 89 | 82.89 35 | 91.29 22 | 92.89 146 | 72.27 39 | 96.03 169 | 87.99 68 | 94.77 26 | 95.54 62 |
| Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition |
| fmvsm_s_conf0.5_n_11 | | | 87.99 26 | 89.25 18 | 84.23 194 | 89.07 188 | 61.60 313 | 94.87 49 | 89.06 327 | 85.65 11 | 91.09 26 | 97.41 4 | 68.26 58 | 97.43 80 | 95.07 13 | 92.74 64 | 93.66 183 |
|
| fmvsm_s_conf0.5_n_8 | | | 87.96 27 | 88.93 22 | 85.07 147 | 88.43 217 | 61.78 306 | 94.73 56 | 91.74 178 | 85.87 10 | 91.66 17 | 97.50 2 | 64.03 106 | 98.33 38 | 96.28 4 | 90.08 107 | 95.10 90 |
|
| TSAR-MVS + GP. | | | 87.96 27 | 88.37 30 | 86.70 71 | 93.51 66 | 65.32 190 | 95.15 37 | 93.84 74 | 78.17 130 | 85.93 70 | 94.80 94 | 75.80 15 | 98.21 40 | 89.38 57 | 88.78 123 | 96.59 19 |
|
| DeepC-MVS_fast | | 79.48 2 | 87.95 29 | 88.00 35 | 87.79 31 | 95.86 28 | 68.32 95 | 95.74 21 | 94.11 69 | 83.82 26 | 83.49 97 | 96.19 48 | 64.53 101 | 98.44 35 | 83.42 127 | 94.88 25 | 96.61 18 |
| Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
| fmvsm_s_conf0.5_n_10 | | | 87.93 30 | 88.67 25 | 85.71 120 | 88.69 199 | 63.71 251 | 94.56 59 | 90.22 275 | 85.04 15 | 92.27 6 | 97.05 12 | 63.67 114 | 98.15 42 | 95.09 12 | 91.39 87 | 95.27 81 |
|
| xiu_mvs_v2_base | | | 87.92 31 | 87.38 45 | 89.55 12 | 91.41 135 | 76.43 3 | 95.74 21 | 93.12 111 | 83.53 29 | 89.55 41 | 95.95 55 | 53.45 270 | 97.68 59 | 91.07 49 | 92.62 65 | 94.54 131 |
|
| EPNet | | | 87.84 32 | 88.38 29 | 86.23 100 | 93.30 69 | 66.05 169 | 95.26 33 | 94.84 35 | 87.09 5 | 88.06 48 | 94.53 100 | 66.79 71 | 97.34 86 | 83.89 118 | 91.68 81 | 95.29 78 |
| Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
| lupinMVS | | | 87.74 33 | 87.77 38 | 87.63 39 | 89.24 185 | 71.18 24 | 96.57 12 | 92.90 122 | 82.70 39 | 87.13 56 | 95.27 77 | 64.99 91 | 95.80 185 | 89.34 58 | 91.80 79 | 95.93 48 |
|
| test_fmvsm_n_1920 | | | 87.69 34 | 88.50 28 | 85.27 140 | 87.05 265 | 63.55 260 | 93.69 106 | 91.08 222 | 84.18 23 | 90.17 36 | 97.04 14 | 67.58 65 | 97.99 46 | 95.72 8 | 90.03 108 | 94.26 151 |
|
| fmvsm_l_conf0.5_n_3 | | | 87.54 35 | 88.29 31 | 85.30 137 | 86.92 276 | 62.63 286 | 95.02 44 | 90.28 270 | 84.95 16 | 90.27 33 | 96.86 25 | 65.36 87 | 97.52 74 | 94.93 15 | 90.03 108 | 95.76 54 |
|
| APDe-MVS |  | | 87.54 35 | 87.84 37 | 86.65 74 | 96.07 24 | 66.30 164 | 94.84 51 | 93.78 75 | 69.35 311 | 88.39 47 | 96.34 42 | 67.74 64 | 97.66 64 | 90.62 53 | 93.44 55 | 96.01 44 |
| Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition |
| fmvsm_s_conf0.5_n_6 | | | 87.50 37 | 88.72 24 | 83.84 206 | 86.89 278 | 60.04 354 | 95.05 40 | 92.17 157 | 84.80 18 | 92.27 6 | 96.37 39 | 64.62 98 | 96.54 141 | 94.43 19 | 91.86 77 | 94.94 99 |
|
| fmvsm_l_conf0.5_n | | | 87.49 38 | 88.19 33 | 85.39 131 | 86.95 271 | 64.37 222 | 94.30 71 | 88.45 352 | 80.51 70 | 92.70 4 | 96.86 25 | 69.98 51 | 97.15 103 | 95.83 7 | 88.08 131 | 94.65 124 |
|
| SD-MVS | | | 87.49 38 | 87.49 43 | 87.50 43 | 93.60 60 | 68.82 82 | 93.90 93 | 92.63 137 | 76.86 157 | 87.90 50 | 95.76 58 | 66.17 77 | 97.63 66 | 89.06 62 | 91.48 85 | 96.05 42 |
| 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 |
| fmvsm_l_conf0.5_n_a | | | 87.44 40 | 88.15 34 | 85.30 137 | 87.10 263 | 64.19 231 | 94.41 66 | 88.14 362 | 80.24 81 | 92.54 5 | 96.97 16 | 69.52 53 | 97.17 99 | 95.89 6 | 88.51 126 | 94.56 128 |
|
| dcpmvs_2 | | | 87.37 41 | 87.55 42 | 86.85 62 | 95.04 33 | 68.20 102 | 90.36 282 | 90.66 250 | 79.37 105 | 81.20 121 | 93.67 130 | 74.73 18 | 96.55 140 | 90.88 51 | 92.00 75 | 95.82 52 |
|
| alignmvs | | | 87.28 42 | 86.97 49 | 88.24 27 | 91.30 137 | 71.14 26 | 95.61 26 | 93.56 88 | 79.30 106 | 87.07 58 | 95.25 79 | 68.43 56 | 96.93 123 | 87.87 69 | 84.33 182 | 96.65 17 |
|
| train_agg | | | 87.21 43 | 87.42 44 | 86.60 77 | 94.18 45 | 67.28 126 | 94.16 75 | 93.51 91 | 71.87 260 | 85.52 75 | 95.33 71 | 68.19 59 | 97.27 93 | 89.09 61 | 94.90 22 | 95.25 85 |
|
| MG-MVS | | | 87.11 44 | 86.27 62 | 89.62 8 | 97.79 1 | 76.27 4 | 94.96 46 | 94.49 53 | 78.74 121 | 83.87 93 | 92.94 144 | 64.34 102 | 96.94 121 | 75.19 206 | 94.09 42 | 95.66 57 |
|
| SF-MVS | | | 87.03 45 | 87.09 47 | 86.84 63 | 92.70 90 | 67.45 124 | 93.64 109 | 93.76 78 | 70.78 293 | 86.25 64 | 96.44 38 | 66.98 69 | 97.79 55 | 88.68 65 | 94.56 34 | 95.28 80 |
|
| fmvsm_s_conf0.5_n_3 | | | 86.88 46 | 87.99 36 | 83.58 220 | 87.26 257 | 60.74 334 | 93.21 130 | 87.94 369 | 84.22 22 | 91.70 16 | 97.27 6 | 65.91 82 | 95.02 228 | 93.95 24 | 90.42 103 | 94.99 96 |
|
| CSCG | | | 86.87 47 | 86.26 63 | 88.72 17 | 95.05 32 | 70.79 29 | 93.83 101 | 95.33 18 | 68.48 326 | 77.63 181 | 94.35 109 | 73.04 30 | 98.45 34 | 84.92 103 | 93.71 51 | 96.92 14 |
|
| sasdasda | | | 86.85 48 | 86.25 64 | 88.66 20 | 91.80 121 | 71.92 16 | 93.54 114 | 91.71 181 | 80.26 78 | 87.55 53 | 95.25 79 | 63.59 118 | 96.93 123 | 88.18 66 | 84.34 180 | 97.11 9 |
|
| canonicalmvs | | | 86.85 48 | 86.25 64 | 88.66 20 | 91.80 121 | 71.92 16 | 93.54 114 | 91.71 181 | 80.26 78 | 87.55 53 | 95.25 79 | 63.59 118 | 96.93 123 | 88.18 66 | 84.34 180 | 97.11 9 |
|
| UBG | | | 86.83 50 | 86.70 55 | 87.20 52 | 93.07 78 | 69.81 47 | 93.43 122 | 95.56 13 | 81.52 52 | 81.50 116 | 92.12 167 | 73.58 28 | 96.28 153 | 84.37 112 | 85.20 169 | 95.51 63 |
|
| PHI-MVS | | | 86.83 50 | 86.85 54 | 86.78 67 | 93.47 67 | 65.55 185 | 95.39 31 | 95.10 25 | 71.77 265 | 85.69 73 | 96.52 35 | 62.07 143 | 98.77 26 | 86.06 91 | 95.60 12 | 96.03 43 |
|
| SteuartSystems-ACMMP | | | 86.82 52 | 86.90 52 | 86.58 80 | 90.42 154 | 66.38 161 | 96.09 17 | 93.87 73 | 77.73 139 | 84.01 92 | 95.66 60 | 63.39 121 | 97.94 47 | 87.40 76 | 93.55 54 | 95.42 65 |
| Skip Steuart: Steuart Systems R&D Blog. |
| fmvsm_s_conf0.5_n_4 | | | 86.79 53 | 87.63 39 | 84.27 192 | 86.15 294 | 61.48 317 | 94.69 57 | 91.16 208 | 83.79 28 | 90.51 32 | 96.28 44 | 64.24 103 | 98.22 39 | 95.00 14 | 86.88 143 | 93.11 201 |
|
| PVSNet_Blended | | | 86.73 54 | 86.86 53 | 86.31 99 | 93.76 54 | 67.53 121 | 96.33 16 | 93.61 86 | 82.34 44 | 81.00 127 | 93.08 140 | 63.19 125 | 97.29 89 | 87.08 82 | 91.38 88 | 94.13 160 |
|
| testing11 | | | 86.71 55 | 86.44 60 | 87.55 41 | 93.54 64 | 71.35 21 | 93.65 108 | 95.58 11 | 81.36 59 | 80.69 132 | 92.21 165 | 72.30 38 | 96.46 146 | 85.18 99 | 83.43 197 | 94.82 109 |
|
| test_fmvsmconf_n | | | 86.58 56 | 87.17 46 | 84.82 159 | 85.28 312 | 62.55 287 | 94.26 73 | 89.78 290 | 83.81 27 | 87.78 52 | 96.33 43 | 65.33 88 | 96.98 115 | 94.40 20 | 87.55 137 | 94.95 98 |
|
| BP-MVS1 | | | 86.54 57 | 86.68 57 | 86.13 103 | 87.80 245 | 67.18 133 | 92.97 138 | 95.62 10 | 79.92 86 | 82.84 104 | 94.14 118 | 74.95 17 | 96.46 146 | 82.91 132 | 88.96 122 | 94.74 114 |
|
| jason | | | 86.40 58 | 86.17 66 | 87.11 55 | 86.16 293 | 70.54 32 | 95.71 24 | 92.19 154 | 82.00 47 | 84.58 85 | 94.34 110 | 61.86 146 | 95.53 210 | 87.76 70 | 90.89 96 | 95.27 81 |
| jason: jason. |
| NormalMVS | | | 86.39 59 | 86.66 58 | 85.60 125 | 92.12 105 | 65.95 174 | 94.88 47 | 90.83 238 | 84.69 19 | 83.67 95 | 94.10 119 | 63.16 127 | 96.91 127 | 85.31 95 | 91.15 92 | 93.93 171 |
|
| fmvsm_s_conf0.5_n | | | 86.39 59 | 86.91 51 | 84.82 159 | 87.36 256 | 63.54 261 | 94.74 53 | 90.02 283 | 82.52 40 | 90.14 37 | 96.92 23 | 62.93 132 | 97.84 54 | 95.28 11 | 82.26 208 | 93.07 204 |
|
| fmvsm_s_conf0.5_n_5 | | | 86.38 61 | 86.94 50 | 84.71 170 | 84.67 324 | 63.29 266 | 94.04 84 | 89.99 285 | 82.88 36 | 87.85 51 | 96.03 53 | 62.89 134 | 96.36 150 | 94.15 21 | 89.95 110 | 94.48 141 |
|
| SymmetryMVS | | | 86.32 62 | 86.39 61 | 86.12 104 | 90.52 152 | 65.95 174 | 94.88 47 | 94.58 50 | 84.69 19 | 83.67 95 | 94.10 119 | 63.16 127 | 96.91 127 | 85.31 95 | 86.59 152 | 95.51 63 |
|
| WTY-MVS | | | 86.32 62 | 85.81 74 | 87.85 29 | 92.82 86 | 69.37 61 | 95.20 35 | 95.25 20 | 82.71 38 | 81.91 112 | 94.73 95 | 67.93 63 | 97.63 66 | 79.55 171 | 82.25 210 | 96.54 22 |
|
| myMVS_eth3d28 | | | 86.31 64 | 86.15 67 | 86.78 67 | 93.56 62 | 70.49 33 | 92.94 141 | 95.28 19 | 82.47 41 | 78.70 171 | 92.07 169 | 72.45 36 | 95.41 212 | 82.11 141 | 85.78 162 | 94.44 143 |
|
| MSLP-MVS++ | | | 86.27 65 | 85.91 73 | 87.35 48 | 92.01 112 | 68.97 78 | 95.04 42 | 92.70 128 | 79.04 116 | 81.50 116 | 96.50 37 | 58.98 191 | 96.78 131 | 83.49 126 | 93.93 45 | 96.29 35 |
|
| VNet | | | 86.20 66 | 85.65 78 | 87.84 30 | 93.92 51 | 69.99 39 | 95.73 23 | 95.94 7 | 78.43 126 | 86.00 69 | 93.07 141 | 58.22 201 | 97.00 111 | 85.22 97 | 84.33 182 | 96.52 23 |
|
| MVS_111021_HR | | | 86.19 67 | 85.80 75 | 87.37 47 | 93.17 74 | 69.79 48 | 93.99 87 | 93.76 78 | 79.08 113 | 78.88 167 | 93.99 124 | 62.25 141 | 98.15 42 | 85.93 92 | 91.15 92 | 94.15 159 |
|
| SPE-MVS-test | | | 86.14 68 | 87.01 48 | 83.52 221 | 92.63 92 | 59.36 366 | 95.49 28 | 91.92 167 | 80.09 82 | 85.46 77 | 95.53 66 | 61.82 148 | 95.77 189 | 86.77 86 | 93.37 56 | 95.41 66 |
|
| ACMMP_NAP | | | 86.05 69 | 85.80 75 | 86.80 66 | 91.58 127 | 67.53 121 | 91.79 208 | 93.49 94 | 74.93 190 | 84.61 84 | 95.30 73 | 59.42 181 | 97.92 48 | 86.13 89 | 94.92 20 | 94.94 99 |
|
| testing99 | | | 86.01 70 | 85.47 80 | 87.63 39 | 93.62 59 | 71.25 23 | 93.47 120 | 95.23 21 | 80.42 73 | 80.60 134 | 91.95 176 | 71.73 43 | 96.50 144 | 80.02 168 | 82.22 211 | 95.13 88 |
|
| ETV-MVS | | | 86.01 70 | 86.11 68 | 85.70 121 | 90.21 159 | 67.02 140 | 93.43 122 | 91.92 167 | 81.21 61 | 84.13 91 | 94.07 123 | 60.93 157 | 95.63 199 | 89.28 59 | 89.81 112 | 94.46 142 |
|
| testing91 | | | 85.93 72 | 85.31 84 | 87.78 32 | 93.59 61 | 71.47 19 | 93.50 117 | 95.08 28 | 80.26 78 | 80.53 137 | 91.93 177 | 70.43 47 | 96.51 143 | 80.32 166 | 82.13 213 | 95.37 69 |
|
| APD-MVS |  | | 85.93 72 | 85.99 71 | 85.76 117 | 95.98 27 | 65.21 193 | 93.59 112 | 92.58 139 | 66.54 345 | 86.17 67 | 95.88 56 | 63.83 110 | 97.00 111 | 86.39 88 | 92.94 61 | 95.06 92 |
| Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
| PAPM | | | 85.89 74 | 85.46 81 | 87.18 53 | 88.20 229 | 72.42 15 | 92.41 175 | 92.77 126 | 82.11 46 | 80.34 140 | 93.07 141 | 68.27 57 | 95.02 228 | 78.39 187 | 93.59 53 | 94.09 162 |
|
| CS-MVS | | | 85.80 75 | 86.65 59 | 83.27 233 | 92.00 113 | 58.92 370 | 95.31 32 | 91.86 172 | 79.97 83 | 84.82 83 | 95.40 69 | 62.26 140 | 95.51 211 | 86.11 90 | 92.08 73 | 95.37 69 |
|
| fmvsm_s_conf0.5_n_a | | | 85.75 76 | 86.09 69 | 84.72 168 | 85.73 305 | 63.58 258 | 93.79 102 | 89.32 310 | 81.42 57 | 90.21 35 | 96.91 24 | 62.41 139 | 97.67 61 | 94.48 18 | 80.56 236 | 92.90 210 |
|
| test_fmvsmconf0.1_n | | | 85.71 77 | 86.08 70 | 84.62 178 | 80.83 375 | 62.33 292 | 93.84 99 | 88.81 339 | 83.50 30 | 87.00 59 | 96.01 54 | 63.36 122 | 96.93 123 | 94.04 23 | 87.29 140 | 94.61 126 |
|
| CDPH-MVS | | | 85.71 77 | 85.46 81 | 86.46 90 | 94.75 38 | 67.19 131 | 93.89 94 | 92.83 124 | 70.90 289 | 83.09 102 | 95.28 75 | 63.62 116 | 97.36 84 | 80.63 162 | 94.18 41 | 94.84 105 |
|
| casdiffmvs_mvg |  | | 85.66 79 | 85.18 86 | 87.09 56 | 88.22 228 | 69.35 62 | 93.74 105 | 91.89 170 | 81.47 53 | 80.10 143 | 91.45 189 | 64.80 96 | 96.35 151 | 87.23 79 | 87.69 135 | 95.58 60 |
| Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025 |
| fmvsm_s_conf0.1_n | | | 85.61 80 | 85.93 72 | 84.68 172 | 82.95 356 | 63.48 263 | 94.03 86 | 89.46 304 | 81.69 50 | 89.86 38 | 96.74 31 | 61.85 147 | 97.75 57 | 94.74 17 | 82.01 215 | 92.81 214 |
|
| MGCFI-Net | | | 85.59 81 | 85.73 77 | 85.17 144 | 91.41 135 | 62.44 288 | 92.87 146 | 91.31 198 | 79.65 93 | 86.99 60 | 95.14 85 | 62.90 133 | 96.12 161 | 87.13 81 | 84.13 188 | 96.96 13 |
|
| GDP-MVS | | | 85.54 82 | 85.32 83 | 86.18 101 | 87.64 248 | 67.95 109 | 92.91 144 | 92.36 144 | 77.81 136 | 83.69 94 | 94.31 112 | 72.84 32 | 96.41 148 | 80.39 165 | 85.95 159 | 94.19 155 |
|
| DeepC-MVS | | 77.85 3 | 85.52 83 | 85.24 85 | 86.37 95 | 88.80 197 | 66.64 155 | 92.15 185 | 93.68 84 | 81.07 63 | 76.91 193 | 93.64 131 | 62.59 136 | 98.44 35 | 85.50 93 | 92.84 63 | 94.03 166 |
| Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
| casdiffmvs |  | | 85.37 84 | 84.87 92 | 86.84 63 | 88.25 226 | 69.07 72 | 93.04 135 | 91.76 177 | 81.27 60 | 80.84 130 | 92.07 169 | 64.23 104 | 96.06 167 | 84.98 102 | 87.43 139 | 95.39 67 |
| Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025 |
| ZNCC-MVS | | | 85.33 85 | 85.08 88 | 86.06 105 | 93.09 77 | 65.65 181 | 93.89 94 | 93.41 99 | 73.75 211 | 79.94 145 | 94.68 97 | 60.61 162 | 98.03 45 | 82.63 136 | 93.72 50 | 94.52 133 |
|
| fmvsm_s_conf0.5_n_7 | | | 85.24 86 | 86.69 56 | 80.91 309 | 84.52 329 | 60.10 352 | 93.35 125 | 90.35 263 | 83.41 31 | 86.54 63 | 96.27 45 | 60.50 163 | 90.02 393 | 94.84 16 | 90.38 104 | 92.61 218 |
|
| MP-MVS-pluss | | | 85.24 86 | 85.13 87 | 85.56 126 | 91.42 132 | 65.59 183 | 91.54 227 | 92.51 141 | 74.56 193 | 80.62 133 | 95.64 61 | 59.15 186 | 97.00 111 | 86.94 84 | 93.80 47 | 94.07 164 |
| MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
| testing222 | | | 85.18 88 | 84.69 96 | 86.63 76 | 92.91 82 | 69.91 43 | 92.61 161 | 95.80 9 | 80.31 77 | 80.38 139 | 92.27 161 | 68.73 55 | 95.19 225 | 75.94 200 | 83.27 199 | 94.81 111 |
|
| PAPR | | | 85.15 89 | 84.47 97 | 87.18 53 | 96.02 26 | 68.29 96 | 91.85 206 | 93.00 117 | 76.59 168 | 79.03 163 | 95.00 86 | 61.59 149 | 97.61 68 | 78.16 188 | 89.00 121 | 95.63 58 |
|
| fmvsm_s_conf0.5_n_2 | | | 85.06 90 | 85.60 79 | 83.44 227 | 86.92 276 | 60.53 341 | 94.41 66 | 87.31 377 | 83.30 32 | 88.72 46 | 96.72 32 | 54.28 258 | 97.75 57 | 94.07 22 | 84.68 179 | 92.04 241 |
|
| MP-MVS |  | | 85.02 91 | 84.97 90 | 85.17 144 | 92.60 93 | 64.27 227 | 93.24 127 | 92.27 147 | 73.13 222 | 79.63 155 | 94.43 103 | 61.90 144 | 97.17 99 | 85.00 101 | 92.56 66 | 94.06 165 |
| Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
| baseline | | | 85.01 92 | 84.44 98 | 86.71 70 | 88.33 223 | 68.73 84 | 90.24 287 | 91.82 176 | 81.05 64 | 81.18 122 | 92.50 153 | 63.69 113 | 96.08 166 | 84.45 110 | 86.71 150 | 95.32 76 |
|
| CHOSEN 1792x2688 | | | 84.98 93 | 83.45 118 | 89.57 11 | 89.94 164 | 75.14 6 | 92.07 191 | 92.32 145 | 81.87 48 | 75.68 202 | 88.27 260 | 60.18 167 | 98.60 31 | 80.46 164 | 90.27 106 | 94.96 97 |
|
| MVSMamba_PlusPlus | | | 84.97 94 | 83.65 111 | 88.93 14 | 90.17 160 | 74.04 8 | 87.84 345 | 92.69 131 | 62.18 388 | 81.47 118 | 87.64 274 | 71.47 44 | 96.28 153 | 84.69 105 | 94.74 31 | 96.47 28 |
|
| E3new | | | 84.94 95 | 84.36 100 | 86.69 73 | 89.06 189 | 69.31 63 | 92.68 158 | 91.29 203 | 80.72 67 | 81.03 125 | 92.14 166 | 61.89 145 | 95.91 173 | 84.59 107 | 85.85 161 | 94.86 101 |
|
| viewmanbaseed2359cas | | | 84.89 96 | 84.26 102 | 86.78 67 | 88.50 208 | 69.77 50 | 92.69 157 | 91.13 214 | 81.11 62 | 81.54 115 | 91.98 173 | 60.35 164 | 95.73 191 | 84.47 109 | 86.56 153 | 94.84 105 |
|
| EIA-MVS | | | 84.84 97 | 84.88 91 | 84.69 171 | 91.30 137 | 62.36 291 | 93.85 96 | 92.04 160 | 79.45 101 | 79.33 160 | 94.28 114 | 62.42 138 | 96.35 151 | 80.05 167 | 91.25 91 | 95.38 68 |
|
| lecture | | | 84.77 98 | 84.81 94 | 84.65 174 | 92.12 105 | 62.27 295 | 94.74 53 | 92.64 136 | 68.35 327 | 85.53 74 | 95.30 73 | 59.77 174 | 97.91 49 | 83.73 122 | 91.15 92 | 93.77 180 |
|
| fmvsm_s_conf0.1_n_a | | | 84.76 99 | 84.84 93 | 84.53 180 | 80.23 388 | 63.50 262 | 92.79 148 | 88.73 342 | 80.46 71 | 89.84 39 | 96.65 34 | 60.96 156 | 97.57 71 | 93.80 25 | 80.14 238 | 92.53 223 |
|
| viewcassd2359sk11 | | | 84.74 100 | 84.11 103 | 86.64 75 | 88.57 202 | 69.20 70 | 92.61 161 | 91.23 205 | 80.58 68 | 80.85 129 | 91.96 174 | 61.39 151 | 95.89 175 | 84.28 113 | 85.49 166 | 94.82 109 |
|
| HFP-MVS | | | 84.73 101 | 84.40 99 | 85.72 119 | 93.75 56 | 65.01 199 | 93.50 117 | 93.19 107 | 72.19 249 | 79.22 161 | 94.93 89 | 59.04 189 | 97.67 61 | 81.55 151 | 92.21 69 | 94.49 140 |
|
| MVS | | | 84.66 102 | 82.86 140 | 90.06 2 | 90.93 144 | 74.56 7 | 87.91 343 | 95.54 14 | 68.55 324 | 72.35 259 | 94.71 96 | 59.78 173 | 98.90 23 | 81.29 157 | 94.69 32 | 96.74 16 |
|
| GST-MVS | | | 84.63 103 | 84.29 101 | 85.66 122 | 92.82 86 | 65.27 191 | 93.04 135 | 93.13 110 | 73.20 220 | 78.89 164 | 94.18 117 | 59.41 182 | 97.85 53 | 81.45 153 | 92.48 68 | 93.86 177 |
|
| EC-MVSNet | | | 84.53 104 | 85.04 89 | 83.01 239 | 89.34 176 | 61.37 321 | 94.42 65 | 91.09 218 | 77.91 134 | 83.24 98 | 94.20 116 | 58.37 199 | 95.40 213 | 85.35 94 | 91.41 86 | 92.27 235 |
|
| E2 | | | 84.45 105 | 83.74 107 | 86.56 82 | 87.90 238 | 69.06 73 | 92.53 169 | 91.13 214 | 80.35 75 | 80.58 135 | 91.69 184 | 60.70 158 | 95.84 178 | 83.80 120 | 84.99 171 | 94.79 112 |
|
| E3 | | | 84.45 105 | 83.74 107 | 86.56 82 | 87.90 238 | 69.06 73 | 92.53 169 | 91.13 214 | 80.35 75 | 80.58 135 | 91.69 184 | 60.70 158 | 95.84 178 | 83.80 120 | 84.99 171 | 94.79 112 |
|
| fmvsm_s_conf0.1_n_2 | | | 84.40 107 | 84.78 95 | 83.27 233 | 85.25 313 | 60.41 344 | 94.13 78 | 85.69 402 | 83.05 34 | 87.99 49 | 96.37 39 | 52.75 275 | 97.68 59 | 93.75 26 | 84.05 189 | 91.71 249 |
|
| ACMMPR | | | 84.37 108 | 84.06 104 | 85.28 139 | 93.56 62 | 64.37 222 | 93.50 117 | 93.15 109 | 72.19 249 | 78.85 169 | 94.86 92 | 56.69 225 | 97.45 77 | 81.55 151 | 92.20 70 | 94.02 167 |
|
| region2R | | | 84.36 109 | 84.03 105 | 85.36 135 | 93.54 64 | 64.31 225 | 93.43 122 | 92.95 120 | 72.16 252 | 78.86 168 | 94.84 93 | 56.97 220 | 97.53 73 | 81.38 155 | 92.11 72 | 94.24 153 |
|
| LFMVS | | | 84.34 110 | 82.73 142 | 89.18 13 | 94.76 34 | 73.25 11 | 94.99 45 | 91.89 170 | 71.90 257 | 82.16 111 | 93.49 135 | 47.98 328 | 97.05 106 | 82.55 137 | 84.82 175 | 97.25 8 |
|
| test_yl | | | 84.28 111 | 83.16 131 | 87.64 35 | 94.52 41 | 69.24 68 | 95.78 18 | 95.09 26 | 69.19 314 | 81.09 123 | 92.88 147 | 57.00 218 | 97.44 78 | 81.11 159 | 81.76 219 | 96.23 38 |
|
| DCV-MVSNet | | | 84.28 111 | 83.16 131 | 87.64 35 | 94.52 41 | 69.24 68 | 95.78 18 | 95.09 26 | 69.19 314 | 81.09 123 | 92.88 147 | 57.00 218 | 97.44 78 | 81.11 159 | 81.76 219 | 96.23 38 |
|
| diffmvs |  | | 84.28 111 | 83.83 106 | 85.61 124 | 87.40 254 | 68.02 106 | 90.88 260 | 89.24 313 | 80.54 69 | 81.64 114 | 92.52 152 | 59.83 172 | 94.52 257 | 87.32 77 | 85.11 170 | 94.29 150 |
| Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025 |
| HY-MVS | | 76.49 5 | 84.28 111 | 83.36 124 | 87.02 59 | 92.22 100 | 67.74 114 | 84.65 374 | 94.50 52 | 79.15 110 | 82.23 110 | 87.93 269 | 66.88 70 | 96.94 121 | 80.53 163 | 82.20 212 | 96.39 33 |
|
| ETVMVS | | | 84.22 115 | 83.71 109 | 85.76 117 | 92.58 94 | 68.25 100 | 92.45 173 | 95.53 15 | 79.54 100 | 79.46 157 | 91.64 187 | 70.29 48 | 94.18 271 | 69.16 269 | 82.76 205 | 94.84 105 |
|
| MAR-MVS | | | 84.18 116 | 83.43 119 | 86.44 92 | 96.25 22 | 65.93 176 | 94.28 72 | 94.27 65 | 74.41 195 | 79.16 162 | 95.61 62 | 53.99 261 | 98.88 25 | 69.62 263 | 93.26 58 | 94.50 139 |
| 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 |
| MVS_Test | | | 84.16 117 | 83.20 128 | 87.05 58 | 91.56 128 | 69.82 46 | 89.99 296 | 92.05 159 | 77.77 138 | 82.84 104 | 86.57 291 | 63.93 109 | 96.09 163 | 74.91 211 | 89.18 118 | 95.25 85 |
|
| CANet_DTU | | | 84.09 118 | 83.52 112 | 85.81 114 | 90.30 157 | 66.82 149 | 91.87 204 | 89.01 330 | 85.27 13 | 86.09 68 | 93.74 128 | 47.71 334 | 96.98 115 | 77.90 190 | 89.78 114 | 93.65 184 |
|
| viewdifsd2359ckpt13 | | | 84.08 119 | 83.21 127 | 86.70 71 | 88.49 212 | 69.55 55 | 92.25 179 | 91.14 212 | 79.71 91 | 79.73 152 | 91.72 183 | 58.83 192 | 95.89 175 | 82.06 142 | 84.99 171 | 94.66 123 |
|
| viewmacassd2359aftdt | | | 84.03 120 | 83.18 130 | 86.59 79 | 86.76 279 | 69.44 56 | 92.44 174 | 90.85 237 | 80.38 74 | 80.78 131 | 91.33 195 | 58.54 196 | 95.62 201 | 82.15 140 | 85.41 167 | 94.72 117 |
|
| ET-MVSNet_ETH3D | | | 84.01 121 | 83.15 133 | 86.58 80 | 90.78 149 | 70.89 28 | 94.74 53 | 94.62 47 | 81.44 56 | 58.19 406 | 93.64 131 | 73.64 27 | 92.35 348 | 82.66 135 | 78.66 258 | 96.50 27 |
|
| E4 | | | 84.00 122 | 83.19 129 | 86.46 90 | 86.99 266 | 68.85 80 | 92.39 176 | 90.99 231 | 79.94 84 | 80.17 142 | 91.36 194 | 59.73 175 | 95.79 186 | 82.87 133 | 84.22 186 | 94.74 114 |
|
| diffmvs_AUTHOR | | | 83.97 123 | 83.49 115 | 85.39 131 | 86.09 295 | 67.83 111 | 90.76 265 | 89.05 328 | 79.94 84 | 81.43 119 | 92.23 164 | 59.53 178 | 94.42 260 | 87.18 80 | 85.22 168 | 93.92 173 |
|
| PVSNet_Blended_VisFu | | | 83.97 123 | 83.50 114 | 85.39 131 | 90.02 162 | 66.59 158 | 93.77 103 | 91.73 179 | 77.43 148 | 77.08 192 | 89.81 236 | 63.77 112 | 96.97 118 | 79.67 170 | 88.21 129 | 92.60 219 |
|
| MTAPA | | | 83.91 125 | 83.38 123 | 85.50 127 | 91.89 119 | 65.16 195 | 81.75 406 | 92.23 148 | 75.32 185 | 80.53 137 | 95.21 82 | 56.06 234 | 97.16 102 | 84.86 104 | 92.55 67 | 94.18 156 |
|
| XVS | | | 83.87 126 | 83.47 117 | 85.05 148 | 93.22 70 | 63.78 245 | 92.92 142 | 92.66 133 | 73.99 203 | 78.18 175 | 94.31 112 | 55.25 240 | 97.41 81 | 79.16 177 | 91.58 83 | 93.95 169 |
|
| Effi-MVS+ | | | 83.82 127 | 82.76 141 | 86.99 60 | 89.56 172 | 69.40 57 | 91.35 240 | 86.12 396 | 72.59 236 | 83.22 101 | 92.81 150 | 59.60 177 | 96.01 171 | 81.76 150 | 87.80 134 | 95.56 61 |
|
| test_fmvsmvis_n_1920 | | | 83.80 128 | 83.48 116 | 84.77 163 | 82.51 359 | 63.72 250 | 91.37 236 | 83.99 420 | 81.42 57 | 77.68 180 | 95.74 59 | 58.37 199 | 97.58 69 | 93.38 27 | 86.87 144 | 93.00 207 |
|
| EI-MVSNet-Vis-set | | | 83.77 129 | 83.67 110 | 84.06 197 | 92.79 89 | 63.56 259 | 91.76 213 | 94.81 37 | 79.65 93 | 77.87 178 | 94.09 121 | 63.35 123 | 97.90 50 | 79.35 175 | 79.36 248 | 90.74 270 |
|
| MVSFormer | | | 83.75 130 | 82.88 139 | 86.37 95 | 89.24 185 | 71.18 24 | 89.07 321 | 90.69 247 | 65.80 355 | 87.13 56 | 94.34 110 | 64.99 91 | 92.67 334 | 72.83 228 | 91.80 79 | 95.27 81 |
|
| CP-MVS | | | 83.71 131 | 83.40 122 | 84.65 174 | 93.14 75 | 63.84 243 | 94.59 58 | 92.28 146 | 71.03 287 | 77.41 185 | 94.92 90 | 55.21 243 | 96.19 158 | 81.32 156 | 90.70 98 | 93.91 174 |
|
| test_fmvsmconf0.01_n | | | 83.70 132 | 83.52 112 | 84.25 193 | 75.26 438 | 61.72 310 | 92.17 184 | 87.24 379 | 82.36 43 | 84.91 82 | 95.41 68 | 55.60 238 | 96.83 130 | 92.85 31 | 85.87 160 | 94.21 154 |
|
| baseline2 | | | 83.68 133 | 83.42 121 | 84.48 183 | 87.37 255 | 66.00 171 | 90.06 291 | 95.93 8 | 79.71 91 | 69.08 297 | 90.39 214 | 77.92 6 | 96.28 153 | 78.91 182 | 81.38 223 | 91.16 263 |
|
| E5new | | | 83.62 134 | 82.65 144 | 86.55 84 | 86.98 267 | 69.28 66 | 91.69 217 | 90.96 232 | 79.61 95 | 79.80 147 | 91.25 197 | 58.04 204 | 95.84 178 | 81.83 148 | 83.66 194 | 94.52 133 |
|
| E6new | | | 83.62 134 | 82.65 144 | 86.55 84 | 86.98 267 | 69.29 64 | 91.69 217 | 90.95 234 | 79.60 98 | 79.80 147 | 91.25 197 | 58.04 204 | 95.84 178 | 81.84 146 | 83.67 192 | 94.52 133 |
|
| E6 | | | 83.62 134 | 82.65 144 | 86.55 84 | 86.98 267 | 69.29 64 | 91.69 217 | 90.95 234 | 79.60 98 | 79.80 147 | 91.25 197 | 58.04 204 | 95.84 178 | 81.84 146 | 83.67 192 | 94.52 133 |
|
| E5 | | | 83.62 134 | 82.65 144 | 86.55 84 | 86.98 267 | 69.28 66 | 91.69 217 | 90.96 232 | 79.61 95 | 79.80 147 | 91.25 197 | 58.04 204 | 95.84 178 | 81.83 148 | 83.66 194 | 94.52 133 |
|
| viewdifsd2359ckpt09 | | | 83.52 138 | 82.57 149 | 86.37 95 | 88.02 235 | 68.47 91 | 91.78 210 | 89.63 300 | 79.61 95 | 78.56 173 | 92.00 172 | 59.28 184 | 95.96 172 | 81.94 144 | 82.35 206 | 94.69 118 |
|
| reproduce-ours | | | 83.51 139 | 83.33 125 | 84.06 197 | 92.18 103 | 60.49 342 | 90.74 267 | 92.04 160 | 64.35 365 | 83.24 98 | 95.59 64 | 59.05 187 | 97.27 93 | 83.61 123 | 89.17 119 | 94.41 148 |
|
| our_new_method | | | 83.51 139 | 83.33 125 | 84.06 197 | 92.18 103 | 60.49 342 | 90.74 267 | 92.04 160 | 64.35 365 | 83.24 98 | 95.59 64 | 59.05 187 | 97.27 93 | 83.61 123 | 89.17 119 | 94.41 148 |
|
| thisisatest0515 | | | 83.41 141 | 82.49 151 | 86.16 102 | 89.46 175 | 68.26 98 | 93.54 114 | 94.70 43 | 74.31 198 | 75.75 200 | 90.92 204 | 72.62 34 | 96.52 142 | 69.64 261 | 81.50 222 | 93.71 181 |
|
| PVSNet_BlendedMVS | | | 83.38 142 | 83.43 119 | 83.22 235 | 93.76 54 | 67.53 121 | 94.06 80 | 93.61 86 | 79.13 111 | 81.00 127 | 85.14 309 | 63.19 125 | 97.29 89 | 87.08 82 | 73.91 296 | 84.83 378 |
|
| test2506 | | | 83.29 143 | 82.92 138 | 84.37 187 | 88.39 220 | 63.18 272 | 92.01 194 | 91.35 197 | 77.66 141 | 78.49 174 | 91.42 190 | 64.58 100 | 95.09 227 | 73.19 224 | 89.23 116 | 94.85 102 |
|
| PGM-MVS | | | 83.25 144 | 82.70 143 | 84.92 152 | 92.81 88 | 64.07 235 | 90.44 277 | 92.20 152 | 71.28 281 | 77.23 189 | 94.43 103 | 55.17 244 | 97.31 88 | 79.33 176 | 91.38 88 | 93.37 191 |
|
| HPM-MVS |  | | 83.25 144 | 82.95 137 | 84.17 195 | 92.25 99 | 62.88 281 | 90.91 257 | 91.86 172 | 70.30 298 | 77.12 190 | 93.96 125 | 56.75 223 | 96.28 153 | 82.04 143 | 91.34 90 | 93.34 192 |
| Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
| reproduce_model | | | 83.15 146 | 82.96 135 | 83.73 212 | 92.02 109 | 59.74 358 | 90.37 281 | 92.08 158 | 63.70 372 | 82.86 103 | 95.48 67 | 58.62 194 | 97.17 99 | 83.06 129 | 88.42 127 | 94.26 151 |
|
| EI-MVSNet-UG-set | | | 83.14 147 | 82.96 135 | 83.67 217 | 92.28 98 | 63.19 271 | 91.38 235 | 94.68 44 | 79.22 108 | 76.60 195 | 93.75 127 | 62.64 135 | 97.76 56 | 78.07 189 | 78.01 261 | 90.05 279 |
|
| testing3-2 | | | 83.11 148 | 83.15 133 | 82.98 240 | 91.92 116 | 64.01 238 | 94.39 69 | 95.37 16 | 78.32 127 | 75.53 207 | 90.06 232 | 73.18 29 | 93.18 313 | 74.34 216 | 75.27 285 | 91.77 248 |
|
| VDD-MVS | | | 83.06 149 | 81.81 162 | 86.81 65 | 90.86 147 | 67.70 115 | 95.40 30 | 91.50 192 | 75.46 180 | 81.78 113 | 92.34 160 | 40.09 383 | 97.13 104 | 86.85 85 | 82.04 214 | 95.60 59 |
|
| h-mvs33 | | | 83.01 150 | 82.56 150 | 84.35 188 | 89.34 176 | 62.02 299 | 92.72 151 | 93.76 78 | 81.45 54 | 82.73 107 | 92.25 163 | 60.11 168 | 97.13 104 | 87.69 71 | 62.96 382 | 93.91 174 |
|
| PAPM_NR | | | 82.97 151 | 81.84 161 | 86.37 95 | 94.10 48 | 66.76 152 | 87.66 349 | 92.84 123 | 69.96 304 | 74.07 232 | 93.57 133 | 63.10 130 | 97.50 75 | 70.66 256 | 90.58 100 | 94.85 102 |
|
| mPP-MVS | | | 82.96 152 | 82.44 152 | 84.52 181 | 92.83 84 | 62.92 279 | 92.76 149 | 91.85 174 | 71.52 277 | 75.61 205 | 94.24 115 | 53.48 269 | 96.99 114 | 78.97 180 | 90.73 97 | 93.64 185 |
|
| viewdifsd2359ckpt07 | | | 82.95 153 | 82.04 156 | 85.66 122 | 87.19 260 | 66.73 153 | 91.56 226 | 90.39 262 | 77.58 144 | 77.58 184 | 91.19 201 | 58.57 195 | 95.65 198 | 82.32 138 | 82.01 215 | 94.60 127 |
|
| SR-MVS | | | 82.81 154 | 82.58 148 | 83.50 224 | 93.35 68 | 61.16 324 | 92.23 182 | 91.28 204 | 64.48 364 | 81.27 120 | 95.28 75 | 53.71 265 | 95.86 177 | 82.87 133 | 88.77 124 | 93.49 189 |
|
| DP-MVS Recon | | | 82.73 155 | 81.65 163 | 85.98 107 | 97.31 4 | 67.06 136 | 95.15 37 | 91.99 164 | 69.08 319 | 76.50 197 | 93.89 126 | 54.48 254 | 98.20 41 | 70.76 254 | 85.66 164 | 92.69 215 |
|
| CLD-MVS | | | 82.73 155 | 82.35 154 | 83.86 205 | 87.90 238 | 67.65 117 | 95.45 29 | 92.18 155 | 85.06 14 | 72.58 250 | 92.27 161 | 52.46 278 | 95.78 187 | 84.18 114 | 79.06 253 | 88.16 307 |
| Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
| sss | | | 82.71 157 | 82.38 153 | 83.73 212 | 89.25 182 | 59.58 361 | 92.24 181 | 94.89 31 | 77.96 132 | 79.86 146 | 92.38 158 | 56.70 224 | 97.05 106 | 77.26 193 | 80.86 231 | 94.55 129 |
|
| 3Dnovator | | 73.91 6 | 82.69 158 | 80.82 176 | 88.31 26 | 89.57 171 | 71.26 22 | 92.60 163 | 94.39 60 | 78.84 118 | 67.89 320 | 92.48 156 | 48.42 323 | 98.52 32 | 68.80 274 | 94.40 36 | 95.15 87 |
|
| RRT-MVS | | | 82.61 159 | 81.16 167 | 86.96 61 | 91.10 141 | 68.75 83 | 87.70 348 | 92.20 152 | 76.97 155 | 72.68 246 | 87.10 285 | 51.30 292 | 96.41 148 | 83.56 125 | 87.84 133 | 95.74 55 |
|
| viewmambaseed2359dif | | | 82.60 160 | 81.91 160 | 84.67 173 | 85.83 302 | 66.09 168 | 90.50 276 | 89.01 330 | 75.46 180 | 79.64 154 | 92.01 171 | 59.51 179 | 94.38 262 | 82.99 131 | 82.26 208 | 93.54 187 |
|
| MVSTER | | | 82.47 161 | 82.05 155 | 83.74 210 | 92.68 91 | 69.01 76 | 91.90 203 | 93.21 104 | 79.83 87 | 72.14 260 | 85.71 304 | 74.72 19 | 94.72 242 | 75.72 202 | 72.49 306 | 87.50 314 |
|
| TESTMET0.1,1 | | | 82.41 162 | 81.98 159 | 83.72 214 | 88.08 231 | 63.74 247 | 92.70 153 | 93.77 77 | 79.30 106 | 77.61 182 | 87.57 276 | 58.19 202 | 94.08 276 | 73.91 218 | 86.68 151 | 93.33 194 |
|
| CostFormer | | | 82.33 163 | 81.15 168 | 85.86 112 | 89.01 192 | 68.46 92 | 82.39 403 | 93.01 115 | 75.59 178 | 80.25 141 | 81.57 356 | 72.03 41 | 94.96 232 | 79.06 179 | 77.48 269 | 94.16 158 |
|
| API-MVS | | | 82.28 164 | 80.53 185 | 87.54 42 | 96.13 23 | 70.59 31 | 93.63 110 | 91.04 228 | 65.72 357 | 75.45 208 | 92.83 149 | 56.11 233 | 98.89 24 | 64.10 330 | 89.75 115 | 93.15 199 |
|
| IB-MVS | | 77.80 4 | 82.18 165 | 80.46 187 | 87.35 48 | 89.14 187 | 70.28 36 | 95.59 27 | 95.17 24 | 78.85 117 | 70.19 285 | 85.82 302 | 70.66 46 | 97.67 61 | 72.19 240 | 66.52 351 | 94.09 162 |
| 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 |
| xiu_mvs_v1_base_debu | | | 82.16 166 | 81.12 169 | 85.26 141 | 86.42 286 | 68.72 86 | 92.59 165 | 90.44 259 | 73.12 223 | 84.20 88 | 94.36 105 | 38.04 396 | 95.73 191 | 84.12 115 | 86.81 145 | 91.33 256 |
|
| xiu_mvs_v1_base | | | 82.16 166 | 81.12 169 | 85.26 141 | 86.42 286 | 68.72 86 | 92.59 165 | 90.44 259 | 73.12 223 | 84.20 88 | 94.36 105 | 38.04 396 | 95.73 191 | 84.12 115 | 86.81 145 | 91.33 256 |
|
| xiu_mvs_v1_base_debi | | | 82.16 166 | 81.12 169 | 85.26 141 | 86.42 286 | 68.72 86 | 92.59 165 | 90.44 259 | 73.12 223 | 84.20 88 | 94.36 105 | 38.04 396 | 95.73 191 | 84.12 115 | 86.81 145 | 91.33 256 |
|
| 3Dnovator+ | | 73.60 7 | 82.10 169 | 80.60 183 | 86.60 77 | 90.89 146 | 66.80 151 | 95.20 35 | 93.44 96 | 74.05 202 | 67.42 327 | 92.49 155 | 49.46 313 | 97.65 65 | 70.80 253 | 91.68 81 | 95.33 74 |
|
| MVS_111021_LR | | | 82.02 170 | 81.52 164 | 83.51 223 | 88.42 218 | 62.88 281 | 89.77 299 | 88.93 335 | 76.78 160 | 75.55 206 | 93.10 138 | 50.31 302 | 95.38 215 | 83.82 119 | 87.02 142 | 92.26 236 |
|
| PMMVS | | | 81.98 171 | 82.04 156 | 81.78 277 | 89.76 168 | 56.17 399 | 91.13 253 | 90.69 247 | 77.96 132 | 80.09 144 | 93.57 133 | 46.33 352 | 94.99 231 | 81.41 154 | 87.46 138 | 94.17 157 |
|
| baseline1 | | | 81.84 172 | 81.03 173 | 84.28 191 | 91.60 126 | 66.62 156 | 91.08 254 | 91.66 186 | 81.87 48 | 74.86 218 | 91.67 186 | 69.98 51 | 94.92 235 | 71.76 243 | 64.75 368 | 91.29 261 |
|
| EPP-MVSNet | | | 81.79 173 | 81.52 164 | 82.61 250 | 88.77 198 | 60.21 350 | 93.02 137 | 93.66 85 | 68.52 325 | 72.90 244 | 90.39 214 | 72.19 40 | 94.96 232 | 74.93 210 | 79.29 251 | 92.67 216 |
|
| WBMVS | | | 81.67 174 | 80.98 175 | 83.72 214 | 93.07 78 | 69.40 57 | 94.33 70 | 93.05 113 | 76.84 158 | 72.05 262 | 84.14 322 | 74.49 21 | 93.88 290 | 72.76 231 | 68.09 337 | 87.88 309 |
|
| test_vis1_n_1920 | | | 81.66 175 | 82.01 158 | 80.64 312 | 82.24 361 | 55.09 408 | 94.76 52 | 86.87 383 | 81.67 51 | 84.40 87 | 94.63 98 | 38.17 393 | 94.67 248 | 91.98 40 | 83.34 198 | 92.16 239 |
|
| APD-MVS_3200maxsize | | | 81.64 176 | 81.32 166 | 82.59 252 | 92.36 96 | 58.74 372 | 91.39 233 | 91.01 230 | 63.35 376 | 79.72 153 | 94.62 99 | 51.82 281 | 96.14 160 | 79.71 169 | 87.93 132 | 92.89 211 |
|
| mvsmamba | | | 81.55 177 | 80.72 178 | 84.03 201 | 91.42 132 | 66.93 147 | 83.08 394 | 89.13 321 | 78.55 125 | 67.50 325 | 87.02 286 | 51.79 283 | 90.07 392 | 87.48 74 | 90.49 102 | 95.10 90 |
|
| ACMMP |  | | 81.49 178 | 80.67 180 | 83.93 203 | 91.71 124 | 62.90 280 | 92.13 186 | 92.22 151 | 71.79 264 | 71.68 268 | 93.49 135 | 50.32 301 | 96.96 119 | 78.47 186 | 84.22 186 | 91.93 246 |
| 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 |
| KinetiMVS | | | 81.43 179 | 80.11 189 | 85.38 134 | 86.60 282 | 65.47 189 | 92.90 145 | 93.54 90 | 75.33 184 | 77.31 187 | 90.39 214 | 46.81 343 | 96.75 132 | 71.65 246 | 86.46 156 | 93.93 171 |
|
| CDS-MVSNet | | | 81.43 179 | 80.74 177 | 83.52 221 | 86.26 290 | 64.45 216 | 92.09 189 | 90.65 251 | 75.83 176 | 73.95 234 | 89.81 236 | 63.97 108 | 92.91 324 | 71.27 247 | 82.82 202 | 93.20 198 |
| Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
| mvs_anonymous | | | 81.36 181 | 79.99 193 | 85.46 128 | 90.39 156 | 68.40 93 | 86.88 360 | 90.61 252 | 74.41 195 | 70.31 284 | 84.67 314 | 63.79 111 | 92.32 350 | 73.13 225 | 85.70 163 | 95.67 56 |
|
| ECVR-MVS |  | | 81.29 182 | 80.38 188 | 84.01 202 | 88.39 220 | 61.96 301 | 92.56 168 | 86.79 385 | 77.66 141 | 76.63 194 | 91.42 190 | 46.34 351 | 95.24 224 | 74.36 215 | 89.23 116 | 94.85 102 |
|
| guyue | | | 81.23 183 | 80.57 184 | 83.21 237 | 86.64 280 | 61.85 304 | 92.52 171 | 92.78 125 | 78.69 122 | 74.92 217 | 89.42 240 | 50.07 305 | 95.35 216 | 80.79 161 | 79.31 250 | 92.42 225 |
|
| IMVS_0403 | | | 81.19 184 | 79.88 195 | 85.13 146 | 88.54 203 | 64.75 204 | 88.84 326 | 90.80 241 | 76.73 163 | 75.21 211 | 90.18 220 | 54.22 259 | 96.21 157 | 73.47 220 | 80.95 226 | 94.43 144 |
|
| thisisatest0530 | | | 81.15 185 | 80.07 190 | 84.39 186 | 88.26 225 | 65.63 182 | 91.40 231 | 94.62 47 | 71.27 282 | 70.93 275 | 89.18 245 | 72.47 35 | 96.04 168 | 65.62 315 | 76.89 276 | 91.49 252 |
|
| Fast-Effi-MVS+ | | | 81.14 186 | 80.01 192 | 84.51 182 | 90.24 158 | 65.86 177 | 94.12 79 | 89.15 319 | 73.81 210 | 75.37 210 | 88.26 261 | 57.26 213 | 94.53 256 | 66.97 298 | 84.92 174 | 93.15 199 |
|
| HQP-MVS | | | 81.14 186 | 80.64 181 | 82.64 249 | 87.54 250 | 63.66 256 | 94.06 80 | 91.70 184 | 79.80 88 | 74.18 225 | 90.30 217 | 51.63 286 | 95.61 202 | 77.63 191 | 78.90 254 | 88.63 298 |
|
| hse-mvs2 | | | 81.12 188 | 81.11 172 | 81.16 297 | 86.52 285 | 57.48 387 | 89.40 312 | 91.16 208 | 81.45 54 | 82.73 107 | 90.49 212 | 60.11 168 | 94.58 249 | 87.69 71 | 60.41 409 | 91.41 255 |
|
| SR-MVS-dyc-post | | | 81.06 189 | 80.70 179 | 82.15 268 | 92.02 109 | 58.56 375 | 90.90 258 | 90.45 255 | 62.76 383 | 78.89 164 | 94.46 101 | 51.26 293 | 95.61 202 | 78.77 184 | 86.77 148 | 92.28 232 |
|
| HyFIR lowres test | | | 81.03 190 | 79.56 202 | 85.43 129 | 87.81 244 | 68.11 104 | 90.18 288 | 90.01 284 | 70.65 295 | 72.95 243 | 86.06 298 | 63.61 117 | 94.50 258 | 75.01 209 | 79.75 242 | 93.67 182 |
|
| nrg030 | | | 80.93 191 | 79.86 196 | 84.13 196 | 83.69 345 | 68.83 81 | 93.23 128 | 91.20 206 | 75.55 179 | 75.06 213 | 88.22 264 | 63.04 131 | 94.74 241 | 81.88 145 | 66.88 348 | 88.82 296 |
|
| Vis-MVSNet |  | | 80.92 192 | 79.98 194 | 83.74 210 | 88.48 214 | 61.80 305 | 93.44 121 | 88.26 361 | 73.96 206 | 77.73 179 | 91.76 180 | 49.94 307 | 94.76 239 | 65.84 310 | 90.37 105 | 94.65 124 |
| Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
| test1111 | | | 80.84 193 | 80.02 191 | 83.33 228 | 87.87 241 | 60.76 332 | 92.62 160 | 86.86 384 | 77.86 135 | 75.73 201 | 91.39 192 | 46.35 350 | 94.70 247 | 72.79 230 | 88.68 125 | 94.52 133 |
|
| UWE-MVS | | | 80.81 194 | 81.01 174 | 80.20 322 | 89.33 178 | 57.05 393 | 91.91 202 | 94.71 42 | 75.67 177 | 75.01 214 | 89.37 241 | 63.13 129 | 91.44 375 | 67.19 295 | 82.80 204 | 92.12 240 |
|
| IMVS_0407 | | | 80.80 195 | 79.39 208 | 85.00 151 | 88.54 203 | 64.75 204 | 88.40 334 | 90.80 241 | 76.73 163 | 73.95 234 | 90.18 220 | 51.55 288 | 95.81 184 | 73.47 220 | 80.95 226 | 94.43 144 |
|
| 1314 | | | 80.70 196 | 78.95 216 | 85.94 109 | 87.77 247 | 67.56 119 | 87.91 343 | 92.55 140 | 72.17 251 | 67.44 326 | 93.09 139 | 50.27 303 | 97.04 109 | 71.68 245 | 87.64 136 | 93.23 196 |
|
| AstraMVS | | | 80.66 197 | 79.79 198 | 83.28 232 | 85.07 319 | 61.64 312 | 92.19 183 | 90.58 253 | 79.40 103 | 74.77 220 | 90.18 220 | 45.93 356 | 95.61 202 | 83.04 130 | 76.96 275 | 92.60 219 |
|
| tpmrst | | | 80.57 198 | 79.14 214 | 84.84 158 | 90.10 161 | 68.28 97 | 81.70 407 | 89.72 297 | 77.63 143 | 75.96 199 | 79.54 388 | 64.94 93 | 92.71 331 | 75.43 204 | 77.28 272 | 93.55 186 |
|
| 1112_ss | | | 80.56 199 | 79.83 197 | 82.77 244 | 88.65 200 | 60.78 330 | 92.29 178 | 88.36 354 | 72.58 237 | 72.46 256 | 94.95 87 | 65.09 90 | 93.42 308 | 66.38 304 | 77.71 263 | 94.10 161 |
|
| VDDNet | | | 80.50 200 | 78.26 224 | 87.21 51 | 86.19 291 | 69.79 48 | 94.48 60 | 91.31 198 | 60.42 404 | 79.34 159 | 90.91 205 | 38.48 391 | 96.56 139 | 82.16 139 | 81.05 225 | 95.27 81 |
|
| BH-w/o | | | 80.49 201 | 79.30 210 | 84.05 200 | 90.83 148 | 64.36 224 | 93.60 111 | 89.42 307 | 74.35 197 | 69.09 296 | 90.15 228 | 55.23 242 | 95.61 202 | 64.61 325 | 86.43 157 | 92.17 238 |
|
| test_cas_vis1_n_1920 | | | 80.45 202 | 80.61 182 | 79.97 331 | 78.25 415 | 57.01 395 | 94.04 84 | 88.33 356 | 79.06 115 | 82.81 106 | 93.70 129 | 38.65 388 | 91.63 366 | 90.82 52 | 79.81 240 | 91.27 262 |
|
| icg_test_0407_2 | | | 80.38 203 | 79.22 212 | 83.88 204 | 88.54 203 | 64.75 204 | 86.79 361 | 90.80 241 | 76.73 163 | 73.95 234 | 90.18 220 | 51.55 288 | 92.45 343 | 73.47 220 | 80.95 226 | 94.43 144 |
|
| TAMVS | | | 80.37 204 | 79.45 205 | 83.13 238 | 85.14 316 | 63.37 264 | 91.23 247 | 90.76 246 | 74.81 192 | 72.65 248 | 88.49 254 | 60.63 161 | 92.95 319 | 69.41 265 | 81.95 217 | 93.08 203 |
|
| HQP_MVS | | | 80.34 205 | 79.75 199 | 82.12 270 | 86.94 272 | 62.42 289 | 93.13 131 | 91.31 198 | 78.81 119 | 72.53 251 | 89.14 247 | 50.66 298 | 95.55 208 | 76.74 194 | 78.53 259 | 88.39 304 |
|
| SDMVSNet | | | 80.26 206 | 78.88 217 | 84.40 185 | 89.25 182 | 67.63 118 | 85.35 370 | 93.02 114 | 76.77 161 | 70.84 276 | 87.12 283 | 47.95 331 | 96.09 163 | 85.04 100 | 74.55 287 | 89.48 289 |
|
| HPM-MVS_fast | | | 80.25 207 | 79.55 204 | 82.33 260 | 91.55 129 | 59.95 355 | 91.32 242 | 89.16 318 | 65.23 361 | 74.71 222 | 93.07 141 | 47.81 333 | 95.74 190 | 74.87 213 | 88.23 128 | 91.31 260 |
|
| ab-mvs | | | 80.18 208 | 78.31 223 | 85.80 115 | 88.44 216 | 65.49 188 | 83.00 397 | 92.67 132 | 71.82 263 | 77.36 186 | 85.01 310 | 54.50 251 | 96.59 136 | 76.35 199 | 75.63 283 | 95.32 76 |
|
| IS-MVSNet | | | 80.14 209 | 79.41 206 | 82.33 260 | 87.91 237 | 60.08 353 | 91.97 198 | 88.27 359 | 72.90 232 | 71.44 272 | 91.73 182 | 61.44 150 | 93.66 299 | 62.47 344 | 86.53 154 | 93.24 195 |
|
| test-LLR | | | 80.10 210 | 79.56 202 | 81.72 279 | 86.93 274 | 61.17 322 | 92.70 153 | 91.54 189 | 71.51 278 | 75.62 203 | 86.94 287 | 53.83 262 | 92.38 345 | 72.21 238 | 84.76 177 | 91.60 250 |
|
| PVSNet | | 73.49 8 | 80.05 211 | 78.63 219 | 84.31 189 | 90.92 145 | 64.97 200 | 92.47 172 | 91.05 227 | 79.18 109 | 72.43 257 | 90.51 211 | 37.05 408 | 94.06 278 | 68.06 282 | 86.00 158 | 93.90 176 |
|
| UA-Net | | | 80.02 212 | 79.65 200 | 81.11 300 | 89.33 178 | 57.72 382 | 86.33 366 | 89.00 334 | 77.44 147 | 81.01 126 | 89.15 246 | 59.33 183 | 95.90 174 | 61.01 351 | 84.28 184 | 89.73 285 |
|
| test-mter | | | 79.96 213 | 79.38 209 | 81.72 279 | 86.93 274 | 61.17 322 | 92.70 153 | 91.54 189 | 73.85 208 | 75.62 203 | 86.94 287 | 49.84 309 | 92.38 345 | 72.21 238 | 84.76 177 | 91.60 250 |
|
| QAPM | | | 79.95 214 | 77.39 245 | 87.64 35 | 89.63 170 | 71.41 20 | 93.30 126 | 93.70 83 | 65.34 360 | 67.39 329 | 91.75 181 | 47.83 332 | 98.96 19 | 57.71 367 | 89.81 112 | 92.54 222 |
|
| UGNet | | | 79.87 215 | 78.68 218 | 83.45 226 | 89.96 163 | 61.51 315 | 92.13 186 | 90.79 245 | 76.83 159 | 78.85 169 | 86.33 295 | 38.16 394 | 96.17 159 | 67.93 285 | 87.17 141 | 92.67 216 |
| 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 |
| tpm2 | | | 79.80 216 | 77.95 231 | 85.34 136 | 88.28 224 | 68.26 98 | 81.56 409 | 91.42 195 | 70.11 300 | 77.59 183 | 80.50 374 | 67.40 67 | 94.26 269 | 67.34 292 | 77.35 270 | 93.51 188 |
|
| thres200 | | | 79.66 217 | 78.33 222 | 83.66 218 | 92.54 95 | 65.82 179 | 93.06 133 | 96.31 3 | 74.90 191 | 73.30 240 | 88.66 252 | 59.67 176 | 95.61 202 | 47.84 411 | 78.67 257 | 89.56 288 |
|
| CPTT-MVS | | | 79.59 218 | 79.16 213 | 80.89 310 | 91.54 130 | 59.80 357 | 92.10 188 | 88.54 351 | 60.42 404 | 72.96 242 | 93.28 137 | 48.27 324 | 92.80 328 | 78.89 183 | 86.50 155 | 90.06 278 |
|
| Test_1112_low_res | | | 79.56 219 | 78.60 220 | 82.43 254 | 88.24 227 | 60.39 346 | 92.09 189 | 87.99 366 | 72.10 253 | 71.84 264 | 87.42 278 | 64.62 98 | 93.04 315 | 65.80 311 | 77.30 271 | 93.85 178 |
|
| tttt0517 | | | 79.50 220 | 78.53 221 | 82.41 257 | 87.22 259 | 61.43 319 | 89.75 300 | 94.76 39 | 69.29 312 | 67.91 318 | 88.06 268 | 72.92 31 | 95.63 199 | 62.91 340 | 73.90 297 | 90.16 277 |
|
| reproduce_monomvs | | | 79.49 221 | 79.11 215 | 80.64 312 | 92.91 82 | 61.47 318 | 91.17 252 | 93.28 102 | 83.09 33 | 64.04 359 | 82.38 342 | 66.19 76 | 94.57 251 | 81.19 158 | 57.71 417 | 85.88 361 |
|
| FIs | | | 79.47 222 | 79.41 206 | 79.67 339 | 85.95 298 | 59.40 363 | 91.68 221 | 93.94 72 | 78.06 131 | 68.96 302 | 88.28 259 | 66.61 73 | 91.77 362 | 66.20 307 | 74.99 286 | 87.82 310 |
|
| SSM_0404 | | | 79.46 223 | 77.65 235 | 84.91 154 | 88.37 222 | 67.04 138 | 89.59 301 | 87.03 380 | 67.99 330 | 75.45 208 | 89.32 242 | 47.98 328 | 95.34 218 | 71.23 248 | 81.90 218 | 92.34 228 |
|
| BH-RMVSNet | | | 79.46 223 | 77.65 235 | 84.89 155 | 91.68 125 | 65.66 180 | 93.55 113 | 88.09 364 | 72.93 229 | 73.37 239 | 91.12 203 | 46.20 354 | 96.12 161 | 56.28 373 | 85.61 165 | 92.91 209 |
|
| viewdifsd2359ckpt11 | | | 79.42 225 | 77.95 231 | 83.81 207 | 83.87 342 | 63.85 241 | 89.54 306 | 87.38 373 | 77.39 150 | 74.94 215 | 89.95 233 | 51.11 294 | 94.72 242 | 79.52 172 | 67.90 340 | 92.88 212 |
|
| viewmsd2359difaftdt | | | 79.42 225 | 77.96 230 | 83.81 207 | 83.88 341 | 63.85 241 | 89.54 306 | 87.38 373 | 77.39 150 | 74.94 215 | 89.95 233 | 51.11 294 | 94.72 242 | 79.52 172 | 67.90 340 | 92.88 212 |
|
| PCF-MVS | | 73.15 9 | 79.29 227 | 77.63 237 | 84.29 190 | 86.06 296 | 65.96 173 | 87.03 356 | 91.10 217 | 69.86 306 | 69.79 292 | 90.64 207 | 57.54 212 | 96.59 136 | 64.37 329 | 82.29 207 | 90.32 275 |
| Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
| Vis-MVSNet (Re-imp) | | | 79.24 228 | 79.57 201 | 78.24 360 | 88.46 215 | 52.29 419 | 90.41 279 | 89.12 322 | 74.24 199 | 69.13 295 | 91.91 178 | 65.77 83 | 90.09 391 | 59.00 363 | 88.09 130 | 92.33 229 |
|
| 114514_t | | | 79.17 229 | 77.67 234 | 83.68 216 | 95.32 30 | 65.53 186 | 92.85 147 | 91.60 188 | 63.49 374 | 67.92 317 | 90.63 209 | 46.65 347 | 95.72 196 | 67.01 297 | 83.54 196 | 89.79 283 |
|
| FA-MVS(test-final) | | | 79.12 230 | 77.23 247 | 84.81 162 | 90.54 151 | 63.98 240 | 81.35 412 | 91.71 181 | 71.09 286 | 74.85 219 | 82.94 335 | 52.85 273 | 97.05 106 | 67.97 283 | 81.73 221 | 93.41 190 |
|
| SSM_0407 | | | 79.09 231 | 77.21 248 | 84.75 166 | 88.50 208 | 66.98 143 | 89.21 317 | 87.03 380 | 67.99 330 | 74.12 229 | 89.32 242 | 47.98 328 | 95.29 223 | 71.23 248 | 79.52 243 | 91.98 243 |
|
| VPA-MVSNet | | | 79.03 232 | 78.00 228 | 82.11 273 | 85.95 298 | 64.48 215 | 93.22 129 | 94.66 45 | 75.05 189 | 74.04 233 | 84.95 311 | 52.17 280 | 93.52 301 | 74.90 212 | 67.04 347 | 88.32 306 |
|
| OPM-MVS | | | 79.00 233 | 78.09 226 | 81.73 278 | 83.52 348 | 63.83 244 | 91.64 223 | 90.30 268 | 76.36 172 | 71.97 263 | 89.93 235 | 46.30 353 | 95.17 226 | 75.10 207 | 77.70 264 | 86.19 349 |
| Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS). |
| EI-MVSNet | | | 78.97 234 | 78.22 225 | 81.25 294 | 85.33 309 | 62.73 284 | 89.53 309 | 93.21 104 | 72.39 244 | 72.14 260 | 90.13 229 | 60.99 154 | 94.72 242 | 67.73 287 | 72.49 306 | 86.29 346 |
|
| AdaColmap |  | | 78.94 235 | 77.00 252 | 84.76 165 | 96.34 17 | 65.86 177 | 92.66 159 | 87.97 368 | 62.18 388 | 70.56 278 | 92.37 159 | 43.53 368 | 97.35 85 | 64.50 328 | 82.86 201 | 91.05 265 |
|
| GeoE | | | 78.90 236 | 77.43 241 | 83.29 231 | 88.95 193 | 62.02 299 | 92.31 177 | 86.23 392 | 70.24 299 | 71.34 273 | 89.27 244 | 54.43 255 | 94.04 281 | 63.31 336 | 80.81 233 | 93.81 179 |
|
| miper_enhance_ethall | | | 78.86 237 | 77.97 229 | 81.54 285 | 88.00 236 | 65.17 194 | 91.41 229 | 89.15 319 | 75.19 187 | 68.79 305 | 83.98 325 | 67.17 68 | 92.82 326 | 72.73 232 | 65.30 358 | 86.62 337 |
|
| VPNet | | | 78.82 238 | 77.53 240 | 82.70 247 | 84.52 329 | 66.44 160 | 93.93 90 | 92.23 148 | 80.46 71 | 72.60 249 | 88.38 258 | 49.18 317 | 93.13 314 | 72.47 236 | 63.97 378 | 88.55 301 |
|
| EPNet_dtu | | | 78.80 239 | 79.26 211 | 77.43 368 | 88.06 232 | 49.71 436 | 91.96 199 | 91.95 166 | 77.67 140 | 76.56 196 | 91.28 196 | 58.51 197 | 90.20 389 | 56.37 372 | 80.95 226 | 92.39 226 |
| Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
| tfpn200view9 | | | 78.79 240 | 77.43 241 | 82.88 242 | 92.21 101 | 64.49 213 | 92.05 192 | 96.28 4 | 73.48 217 | 71.75 266 | 88.26 261 | 60.07 170 | 95.32 219 | 45.16 424 | 77.58 266 | 88.83 294 |
|
| TR-MVS | | | 78.77 241 | 77.37 246 | 82.95 241 | 90.49 153 | 60.88 328 | 93.67 107 | 90.07 279 | 70.08 303 | 74.51 223 | 91.37 193 | 45.69 357 | 95.70 197 | 60.12 357 | 80.32 237 | 92.29 231 |
|
| thres400 | | | 78.68 242 | 77.43 241 | 82.43 254 | 92.21 101 | 64.49 213 | 92.05 192 | 96.28 4 | 73.48 217 | 71.75 266 | 88.26 261 | 60.07 170 | 95.32 219 | 45.16 424 | 77.58 266 | 87.48 315 |
|
| BH-untuned | | | 78.68 242 | 77.08 249 | 83.48 225 | 89.84 165 | 63.74 247 | 92.70 153 | 88.59 348 | 71.57 275 | 66.83 336 | 88.65 253 | 51.75 284 | 95.39 214 | 59.03 362 | 84.77 176 | 91.32 259 |
|
| OMC-MVS | | | 78.67 244 | 77.91 233 | 80.95 307 | 85.76 304 | 57.40 389 | 88.49 332 | 88.67 345 | 73.85 208 | 72.43 257 | 92.10 168 | 49.29 316 | 94.55 255 | 72.73 232 | 77.89 262 | 90.91 269 |
|
| tpm | | | 78.58 245 | 77.03 250 | 83.22 235 | 85.94 300 | 64.56 211 | 83.21 393 | 91.14 212 | 78.31 128 | 73.67 237 | 79.68 386 | 64.01 107 | 92.09 356 | 66.07 308 | 71.26 316 | 93.03 205 |
|
| OpenMVS |  | 70.45 11 | 78.54 246 | 75.92 271 | 86.41 94 | 85.93 301 | 71.68 18 | 92.74 150 | 92.51 141 | 66.49 346 | 64.56 353 | 91.96 174 | 43.88 367 | 98.10 44 | 54.61 378 | 90.65 99 | 89.44 291 |
|
| EPMVS | | | 78.49 247 | 75.98 270 | 86.02 106 | 91.21 139 | 69.68 53 | 80.23 421 | 91.20 206 | 75.25 186 | 72.48 255 | 78.11 397 | 54.65 250 | 93.69 298 | 57.66 368 | 83.04 200 | 94.69 118 |
|
| AUN-MVS | | | 78.37 248 | 77.43 241 | 81.17 296 | 86.60 282 | 57.45 388 | 89.46 311 | 91.16 208 | 74.11 201 | 74.40 224 | 90.49 212 | 55.52 239 | 94.57 251 | 74.73 214 | 60.43 408 | 91.48 253 |
|
| thres100view900 | | | 78.37 248 | 77.01 251 | 82.46 253 | 91.89 119 | 63.21 270 | 91.19 251 | 96.33 1 | 72.28 247 | 70.45 281 | 87.89 270 | 60.31 165 | 95.32 219 | 45.16 424 | 77.58 266 | 88.83 294 |
|
| GA-MVS | | | 78.33 250 | 76.23 266 | 84.65 174 | 83.65 346 | 66.30 164 | 91.44 228 | 90.14 277 | 76.01 174 | 70.32 283 | 84.02 324 | 42.50 372 | 94.72 242 | 70.98 251 | 77.00 274 | 92.94 208 |
|
| cascas | | | 78.18 251 | 75.77 273 | 85.41 130 | 87.14 262 | 69.11 71 | 92.96 140 | 91.15 211 | 66.71 344 | 70.47 279 | 86.07 297 | 37.49 402 | 96.48 145 | 70.15 259 | 79.80 241 | 90.65 271 |
|
| UniMVSNet_NR-MVSNet | | | 78.15 252 | 77.55 239 | 79.98 329 | 84.46 332 | 60.26 348 | 92.25 179 | 93.20 106 | 77.50 146 | 68.88 303 | 86.61 290 | 66.10 78 | 92.13 354 | 66.38 304 | 62.55 386 | 87.54 313 |
|
| LuminaMVS | | | 78.14 253 | 76.66 256 | 82.60 251 | 80.82 376 | 64.64 210 | 89.33 313 | 90.45 255 | 68.25 328 | 74.73 221 | 85.51 306 | 41.15 378 | 94.14 272 | 78.96 181 | 80.69 235 | 89.04 292 |
|
| IMVS_0404 | | | 78.11 254 | 76.29 265 | 83.59 219 | 88.54 203 | 64.75 204 | 84.63 375 | 90.80 241 | 76.73 163 | 61.16 382 | 90.18 220 | 40.17 382 | 91.58 368 | 73.47 220 | 80.95 226 | 94.43 144 |
|
| thres600view7 | | | 78.00 255 | 76.66 256 | 82.03 275 | 91.93 115 | 63.69 254 | 91.30 243 | 96.33 1 | 72.43 242 | 70.46 280 | 87.89 270 | 60.31 165 | 94.92 235 | 42.64 436 | 76.64 277 | 87.48 315 |
|
| FC-MVSNet-test | | | 77.99 256 | 78.08 227 | 77.70 363 | 84.89 322 | 55.51 405 | 90.27 285 | 93.75 81 | 76.87 156 | 66.80 337 | 87.59 275 | 65.71 84 | 90.23 388 | 62.89 341 | 73.94 295 | 87.37 318 |
|
| Anonymous202405211 | | | 77.96 257 | 75.33 279 | 85.87 111 | 93.73 57 | 64.52 212 | 94.85 50 | 85.36 405 | 62.52 386 | 76.11 198 | 90.18 220 | 29.43 441 | 97.29 89 | 68.51 276 | 77.24 273 | 95.81 53 |
|
| cl22 | | | 77.94 258 | 76.78 254 | 81.42 287 | 87.57 249 | 64.93 202 | 90.67 270 | 88.86 338 | 72.45 241 | 67.63 324 | 82.68 339 | 64.07 105 | 92.91 324 | 71.79 241 | 65.30 358 | 86.44 339 |
|
| XXY-MVS | | | 77.94 258 | 76.44 259 | 82.43 254 | 82.60 358 | 64.44 217 | 92.01 194 | 91.83 175 | 73.59 216 | 70.00 288 | 85.82 302 | 54.43 255 | 94.76 239 | 69.63 262 | 68.02 339 | 88.10 308 |
|
| MS-PatchMatch | | | 77.90 260 | 76.50 258 | 82.12 270 | 85.99 297 | 69.95 42 | 91.75 215 | 92.70 128 | 73.97 205 | 62.58 376 | 84.44 318 | 41.11 379 | 95.78 187 | 63.76 333 | 92.17 71 | 80.62 427 |
|
| usedtu_dtu_shiyan1 | | | 77.89 261 | 76.39 262 | 82.40 258 | 81.92 366 | 67.01 141 | 91.94 200 | 93.00 117 | 77.01 153 | 68.44 312 | 84.15 320 | 54.78 248 | 93.25 310 | 65.76 312 | 70.53 319 | 86.94 327 |
|
| FE-MVSNET3 | | | 77.89 261 | 76.39 262 | 82.40 258 | 81.92 366 | 67.01 141 | 91.94 200 | 93.00 117 | 77.01 153 | 68.44 312 | 84.15 320 | 54.78 248 | 93.25 310 | 65.76 312 | 70.53 319 | 86.94 327 |
|
| FMVSNet3 | | | 77.73 263 | 76.04 269 | 82.80 243 | 91.20 140 | 68.99 77 | 91.87 204 | 91.99 164 | 73.35 219 | 67.04 332 | 83.19 334 | 56.62 226 | 92.14 353 | 59.80 359 | 69.34 325 | 87.28 321 |
|
| VortexMVS | | | 77.62 264 | 76.44 259 | 81.13 298 | 88.58 201 | 63.73 249 | 91.24 246 | 91.30 202 | 77.81 136 | 65.76 342 | 81.97 348 | 49.69 311 | 93.72 294 | 76.40 198 | 65.26 361 | 85.94 359 |
|
| miper_ehance_all_eth | | | 77.60 265 | 76.44 259 | 81.09 304 | 85.70 306 | 64.41 220 | 90.65 271 | 88.64 347 | 72.31 245 | 67.37 330 | 82.52 340 | 64.77 97 | 92.64 337 | 70.67 255 | 65.30 358 | 86.24 348 |
|
| UniMVSNet (Re) | | | 77.58 266 | 76.78 254 | 79.98 329 | 84.11 338 | 60.80 329 | 91.76 213 | 93.17 108 | 76.56 169 | 69.93 291 | 84.78 313 | 63.32 124 | 92.36 347 | 64.89 322 | 62.51 388 | 86.78 331 |
|
| PatchmatchNet |  | | 77.46 267 | 74.63 286 | 85.96 108 | 89.55 173 | 70.35 35 | 79.97 426 | 89.55 302 | 72.23 248 | 70.94 274 | 76.91 410 | 57.03 216 | 92.79 329 | 54.27 380 | 81.17 224 | 94.74 114 |
| Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
| v2v482 | | | 77.42 268 | 75.65 275 | 82.73 245 | 80.38 384 | 67.13 135 | 91.85 206 | 90.23 273 | 75.09 188 | 69.37 293 | 83.39 331 | 53.79 264 | 94.44 259 | 71.77 242 | 65.00 365 | 86.63 336 |
|
| CHOSEN 280x420 | | | 77.35 269 | 76.95 253 | 78.55 355 | 87.07 264 | 62.68 285 | 69.71 458 | 82.95 428 | 68.80 321 | 71.48 271 | 87.27 282 | 66.03 79 | 84.00 441 | 76.47 197 | 82.81 203 | 88.95 293 |
|
| PS-MVSNAJss | | | 77.26 270 | 76.31 264 | 80.13 324 | 80.64 380 | 59.16 368 | 90.63 274 | 91.06 224 | 72.80 233 | 68.58 309 | 84.57 316 | 53.55 266 | 93.96 286 | 72.97 226 | 71.96 310 | 87.27 322 |
|
| gg-mvs-nofinetune | | | 77.18 271 | 74.31 293 | 85.80 115 | 91.42 132 | 68.36 94 | 71.78 452 | 94.72 41 | 49.61 449 | 77.12 190 | 45.92 479 | 77.41 8 | 93.98 285 | 67.62 288 | 93.16 59 | 95.05 93 |
|
| WB-MVSnew | | | 77.14 272 | 76.18 268 | 80.01 328 | 86.18 292 | 63.24 268 | 91.26 244 | 94.11 69 | 71.72 267 | 73.52 238 | 87.29 281 | 45.14 362 | 93.00 317 | 56.98 370 | 79.42 246 | 83.80 387 |
|
| MVP-Stereo | | | 77.12 273 | 76.23 266 | 79.79 336 | 81.72 368 | 66.34 163 | 89.29 314 | 90.88 236 | 70.56 296 | 62.01 379 | 82.88 336 | 49.34 314 | 94.13 273 | 65.55 317 | 93.80 47 | 78.88 443 |
| Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
| sd_testset | | | 77.08 274 | 75.37 277 | 82.20 266 | 89.25 182 | 62.11 298 | 82.06 404 | 89.09 324 | 76.77 161 | 70.84 276 | 87.12 283 | 41.43 377 | 95.01 230 | 67.23 294 | 74.55 287 | 89.48 289 |
|
| MonoMVSNet | | | 76.99 275 | 75.08 282 | 82.73 245 | 83.32 350 | 63.24 268 | 86.47 365 | 86.37 388 | 79.08 113 | 66.31 340 | 79.30 390 | 49.80 310 | 91.72 363 | 79.37 174 | 65.70 356 | 93.23 196 |
|
| dmvs_re | | | 76.93 276 | 75.36 278 | 81.61 283 | 87.78 246 | 60.71 336 | 80.00 425 | 87.99 366 | 79.42 102 | 69.02 299 | 89.47 239 | 46.77 345 | 94.32 263 | 63.38 335 | 74.45 290 | 89.81 282 |
|
| X-MVStestdata | | | 76.86 277 | 74.13 299 | 85.05 148 | 93.22 70 | 63.78 245 | 92.92 142 | 92.66 133 | 73.99 203 | 78.18 175 | 10.19 494 | 55.25 240 | 97.41 81 | 79.16 177 | 91.58 83 | 93.95 169 |
|
| DU-MVS | | | 76.86 277 | 75.84 272 | 79.91 332 | 82.96 354 | 60.26 348 | 91.26 244 | 91.54 189 | 76.46 171 | 68.88 303 | 86.35 293 | 56.16 231 | 92.13 354 | 66.38 304 | 62.55 386 | 87.35 319 |
|
| Anonymous20240529 | | | 76.84 279 | 74.15 298 | 84.88 156 | 91.02 142 | 64.95 201 | 93.84 99 | 91.09 218 | 53.57 437 | 73.00 241 | 87.42 278 | 35.91 412 | 97.32 87 | 69.14 270 | 72.41 308 | 92.36 227 |
|
| UWE-MVS-28 | | | 76.83 280 | 77.60 238 | 74.51 398 | 84.58 328 | 50.34 432 | 88.22 337 | 94.60 49 | 74.46 194 | 66.66 338 | 88.98 251 | 62.53 137 | 85.50 433 | 57.55 369 | 80.80 234 | 87.69 312 |
|
| c3_l | | | 76.83 280 | 75.47 276 | 80.93 308 | 85.02 320 | 64.18 232 | 90.39 280 | 88.11 363 | 71.66 268 | 66.65 339 | 81.64 354 | 63.58 120 | 92.56 338 | 69.31 267 | 62.86 383 | 86.04 354 |
|
| WR-MVS | | | 76.76 282 | 75.74 274 | 79.82 335 | 84.60 326 | 62.27 295 | 92.60 163 | 92.51 141 | 76.06 173 | 67.87 321 | 85.34 307 | 56.76 222 | 90.24 387 | 62.20 345 | 63.69 380 | 86.94 327 |
|
| v1144 | | | 76.73 283 | 74.88 283 | 82.27 262 | 80.23 388 | 66.60 157 | 91.68 221 | 90.21 276 | 73.69 213 | 69.06 298 | 81.89 349 | 52.73 276 | 94.40 261 | 69.21 268 | 65.23 362 | 85.80 362 |
|
| IterMVS-LS | | | 76.49 284 | 75.18 281 | 80.43 316 | 84.49 331 | 62.74 283 | 90.64 272 | 88.80 340 | 72.40 243 | 65.16 348 | 81.72 352 | 60.98 155 | 92.27 351 | 67.74 286 | 64.65 370 | 86.29 346 |
| Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
| V42 | | | 76.46 285 | 74.55 289 | 82.19 267 | 79.14 402 | 67.82 112 | 90.26 286 | 89.42 307 | 73.75 211 | 68.63 308 | 81.89 349 | 51.31 291 | 94.09 275 | 71.69 244 | 64.84 366 | 84.66 379 |
|
| Elysia | | | 76.45 286 | 74.17 296 | 83.30 229 | 80.43 382 | 64.12 233 | 89.58 302 | 90.83 238 | 61.78 396 | 72.53 251 | 85.92 300 | 34.30 419 | 94.81 237 | 68.10 280 | 84.01 190 | 90.97 266 |
|
| StellarMVS | | | 76.45 286 | 74.17 296 | 83.30 229 | 80.43 382 | 64.12 233 | 89.58 302 | 90.83 238 | 61.78 396 | 72.53 251 | 85.92 300 | 34.30 419 | 94.81 237 | 68.10 280 | 84.01 190 | 90.97 266 |
|
| mamba_0408 | | | 76.22 288 | 73.37 310 | 84.77 163 | 88.50 208 | 66.98 143 | 58.80 479 | 86.18 394 | 69.12 317 | 74.12 229 | 89.01 249 | 47.50 335 | 95.35 216 | 67.57 289 | 79.52 243 | 91.98 243 |
|
| v148 | | | 76.19 289 | 74.47 291 | 81.36 290 | 80.05 390 | 64.44 217 | 91.75 215 | 90.23 273 | 73.68 214 | 67.13 331 | 80.84 369 | 55.92 236 | 93.86 293 | 68.95 272 | 61.73 397 | 85.76 365 |
|
| Effi-MVS+-dtu | | | 76.14 290 | 75.28 280 | 78.72 354 | 83.22 351 | 55.17 407 | 89.87 297 | 87.78 370 | 75.42 182 | 67.98 316 | 81.43 358 | 45.08 363 | 92.52 340 | 75.08 208 | 71.63 311 | 88.48 302 |
|
| cl____ | | | 76.07 291 | 74.67 284 | 80.28 319 | 85.15 315 | 61.76 308 | 90.12 289 | 88.73 342 | 71.16 283 | 65.43 345 | 81.57 356 | 61.15 152 | 92.95 319 | 66.54 301 | 62.17 390 | 86.13 352 |
|
| DIV-MVS_self_test | | | 76.07 291 | 74.67 284 | 80.28 319 | 85.14 316 | 61.75 309 | 90.12 289 | 88.73 342 | 71.16 283 | 65.42 346 | 81.60 355 | 61.15 152 | 92.94 323 | 66.54 301 | 62.16 392 | 86.14 350 |
|
| FMVSNet2 | | | 76.07 291 | 74.01 301 | 82.26 264 | 88.85 194 | 67.66 116 | 91.33 241 | 91.61 187 | 70.84 290 | 65.98 341 | 82.25 344 | 48.03 325 | 92.00 358 | 58.46 364 | 68.73 333 | 87.10 324 |
|
| v144192 | | | 76.05 294 | 74.03 300 | 82.12 270 | 79.50 396 | 66.55 159 | 91.39 233 | 89.71 298 | 72.30 246 | 68.17 314 | 81.33 361 | 51.75 284 | 94.03 283 | 67.94 284 | 64.19 373 | 85.77 363 |
|
| NR-MVSNet | | | 76.05 294 | 74.59 287 | 80.44 315 | 82.96 354 | 62.18 297 | 90.83 262 | 91.73 179 | 77.12 152 | 60.96 384 | 86.35 293 | 59.28 184 | 91.80 361 | 60.74 352 | 61.34 401 | 87.35 319 |
|
| v1192 | | | 75.98 296 | 73.92 302 | 82.15 268 | 79.73 392 | 66.24 166 | 91.22 248 | 89.75 292 | 72.67 235 | 68.49 310 | 81.42 359 | 49.86 308 | 94.27 267 | 67.08 296 | 65.02 364 | 85.95 357 |
|
| FE-MVS | | | 75.97 297 | 73.02 316 | 84.82 159 | 89.78 166 | 65.56 184 | 77.44 437 | 91.07 223 | 64.55 363 | 72.66 247 | 79.85 384 | 46.05 355 | 96.69 134 | 54.97 377 | 80.82 232 | 92.21 237 |
|
| eth_miper_zixun_eth | | | 75.96 298 | 74.40 292 | 80.66 311 | 84.66 325 | 63.02 274 | 89.28 315 | 88.27 359 | 71.88 259 | 65.73 343 | 81.65 353 | 59.45 180 | 92.81 327 | 68.13 279 | 60.53 406 | 86.14 350 |
|
| TranMVSNet+NR-MVSNet | | | 75.86 299 | 74.52 290 | 79.89 333 | 82.44 360 | 60.64 339 | 91.37 236 | 91.37 196 | 76.63 167 | 67.65 323 | 86.21 296 | 52.37 279 | 91.55 369 | 61.84 347 | 60.81 404 | 87.48 315 |
|
| SCA | | | 75.82 300 | 72.76 320 | 85.01 150 | 86.63 281 | 70.08 38 | 81.06 414 | 89.19 316 | 71.60 274 | 70.01 287 | 77.09 408 | 45.53 358 | 90.25 384 | 60.43 354 | 73.27 299 | 94.68 120 |
|
| LPG-MVS_test | | | 75.82 300 | 74.58 288 | 79.56 343 | 84.31 335 | 59.37 364 | 90.44 277 | 89.73 295 | 69.49 309 | 64.86 349 | 88.42 256 | 38.65 388 | 94.30 265 | 72.56 234 | 72.76 303 | 85.01 376 |
|
| GBi-Net | | | 75.65 302 | 73.83 303 | 81.10 301 | 88.85 194 | 65.11 196 | 90.01 293 | 90.32 264 | 70.84 290 | 67.04 332 | 80.25 379 | 48.03 325 | 91.54 370 | 59.80 359 | 69.34 325 | 86.64 333 |
|
| test1 | | | 75.65 302 | 73.83 303 | 81.10 301 | 88.85 194 | 65.11 196 | 90.01 293 | 90.32 264 | 70.84 290 | 67.04 332 | 80.25 379 | 48.03 325 | 91.54 370 | 59.80 359 | 69.34 325 | 86.64 333 |
|
| v1921920 | | | 75.63 304 | 73.49 308 | 82.06 274 | 79.38 397 | 66.35 162 | 91.07 256 | 89.48 303 | 71.98 254 | 67.99 315 | 81.22 364 | 49.16 319 | 93.90 289 | 66.56 300 | 64.56 371 | 85.92 360 |
|
| ACMP | | 71.68 10 | 75.58 305 | 74.23 295 | 79.62 341 | 84.97 321 | 59.64 359 | 90.80 263 | 89.07 326 | 70.39 297 | 62.95 372 | 87.30 280 | 38.28 392 | 93.87 291 | 72.89 227 | 71.45 314 | 85.36 372 |
| Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
| v8 | | | 75.35 306 | 73.26 314 | 81.61 283 | 80.67 379 | 66.82 149 | 89.54 306 | 89.27 312 | 71.65 269 | 63.30 367 | 80.30 378 | 54.99 246 | 94.06 278 | 67.33 293 | 62.33 389 | 83.94 385 |
|
| tpm cat1 | | | 75.30 307 | 72.21 329 | 84.58 179 | 88.52 207 | 67.77 113 | 78.16 435 | 88.02 365 | 61.88 394 | 68.45 311 | 76.37 419 | 60.65 160 | 94.03 283 | 53.77 384 | 74.11 293 | 91.93 246 |
|
| PLC |  | 68.80 14 | 75.23 308 | 73.68 306 | 79.86 334 | 92.93 81 | 58.68 373 | 90.64 272 | 88.30 357 | 60.90 401 | 64.43 357 | 90.53 210 | 42.38 373 | 94.57 251 | 56.52 371 | 76.54 278 | 86.33 345 |
| Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
| v1240 | | | 75.21 309 | 72.98 318 | 81.88 276 | 79.20 399 | 66.00 171 | 90.75 266 | 89.11 323 | 71.63 273 | 67.41 328 | 81.22 364 | 47.36 337 | 93.87 291 | 65.46 318 | 64.72 369 | 85.77 363 |
|
| blend_shiyan4 | | | 75.18 310 | 73.00 317 | 81.69 281 | 75.62 434 | 64.75 204 | 91.78 210 | 91.06 224 | 65.89 354 | 61.35 381 | 77.39 402 | 62.16 142 | 93.71 295 | 68.18 277 | 63.60 381 | 86.61 338 |
|
| Fast-Effi-MVS+-dtu | | | 75.04 311 | 73.37 310 | 80.07 325 | 80.86 374 | 59.52 362 | 91.20 250 | 85.38 404 | 71.90 257 | 65.20 347 | 84.84 312 | 41.46 376 | 92.97 318 | 66.50 303 | 72.96 302 | 87.73 311 |
|
| dp | | | 75.01 312 | 72.09 330 | 83.76 209 | 89.28 181 | 66.22 167 | 79.96 427 | 89.75 292 | 71.16 283 | 67.80 322 | 77.19 407 | 51.81 282 | 92.54 339 | 50.39 394 | 71.44 315 | 92.51 224 |
|
| TAPA-MVS | | 70.22 12 | 74.94 313 | 73.53 307 | 79.17 349 | 90.40 155 | 52.07 420 | 89.19 319 | 89.61 301 | 62.69 385 | 70.07 286 | 92.67 151 | 48.89 322 | 94.32 263 | 38.26 451 | 79.97 239 | 91.12 264 |
| Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
| SSC-MVS3.2 | | | 74.92 314 | 73.32 313 | 79.74 338 | 86.53 284 | 60.31 347 | 89.03 324 | 92.70 128 | 78.61 124 | 68.98 301 | 83.34 332 | 41.93 375 | 92.23 352 | 52.77 388 | 65.97 354 | 86.69 332 |
|
| SSM_04072 | | | 74.86 315 | 73.37 310 | 79.35 346 | 88.50 208 | 66.98 143 | 58.80 479 | 86.18 394 | 69.12 317 | 74.12 229 | 89.01 249 | 47.50 335 | 79.09 463 | 67.57 289 | 79.52 243 | 91.98 243 |
|
| v10 | | | 74.77 316 | 72.54 326 | 81.46 286 | 80.33 386 | 66.71 154 | 89.15 320 | 89.08 325 | 70.94 288 | 63.08 370 | 79.86 383 | 52.52 277 | 94.04 281 | 65.70 314 | 62.17 390 | 83.64 388 |
|
| XVG-OURS-SEG-HR | | | 74.70 317 | 73.08 315 | 79.57 342 | 78.25 415 | 57.33 390 | 80.49 417 | 87.32 375 | 63.22 378 | 68.76 306 | 90.12 231 | 44.89 364 | 91.59 367 | 70.55 257 | 74.09 294 | 89.79 283 |
|
| ACMM | | 69.62 13 | 74.34 318 | 72.73 322 | 79.17 349 | 84.25 337 | 57.87 380 | 90.36 282 | 89.93 286 | 63.17 380 | 65.64 344 | 86.04 299 | 37.79 400 | 94.10 274 | 65.89 309 | 71.52 313 | 85.55 368 |
| Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
| CNLPA | | | 74.31 319 | 72.30 328 | 80.32 317 | 91.49 131 | 61.66 311 | 90.85 261 | 80.72 434 | 56.67 428 | 63.85 362 | 90.64 207 | 46.75 346 | 90.84 378 | 53.79 383 | 75.99 282 | 88.47 303 |
|
| XVG-OURS | | | 74.25 320 | 72.46 327 | 79.63 340 | 78.45 413 | 57.59 386 | 80.33 419 | 87.39 372 | 63.86 370 | 68.76 306 | 89.62 238 | 40.50 381 | 91.72 363 | 69.00 271 | 74.25 292 | 89.58 286 |
|
| test_fmvs1 | | | 74.07 321 | 73.69 305 | 75.22 388 | 78.91 406 | 47.34 449 | 89.06 323 | 74.69 451 | 63.68 373 | 79.41 158 | 91.59 188 | 24.36 452 | 87.77 414 | 85.22 97 | 76.26 280 | 90.55 274 |
|
| CVMVSNet | | | 74.04 322 | 74.27 294 | 73.33 408 | 85.33 309 | 43.94 463 | 89.53 309 | 88.39 353 | 54.33 436 | 70.37 282 | 90.13 229 | 49.17 318 | 84.05 439 | 61.83 348 | 79.36 248 | 91.99 242 |
|
| Baseline_NR-MVSNet | | | 73.99 323 | 72.83 319 | 77.48 367 | 80.78 377 | 59.29 367 | 91.79 208 | 84.55 413 | 68.85 320 | 68.99 300 | 80.70 370 | 56.16 231 | 92.04 357 | 62.67 342 | 60.98 403 | 81.11 421 |
|
| pmmvs4 | | | 73.92 324 | 71.81 334 | 80.25 321 | 79.17 400 | 65.24 192 | 87.43 352 | 87.26 378 | 67.64 337 | 63.46 365 | 83.91 326 | 48.96 321 | 91.53 373 | 62.94 339 | 65.49 357 | 83.96 384 |
|
| D2MVS | | | 73.80 325 | 72.02 331 | 79.15 351 | 79.15 401 | 62.97 275 | 88.58 331 | 90.07 279 | 72.94 228 | 59.22 399 | 78.30 394 | 42.31 374 | 92.70 333 | 65.59 316 | 72.00 309 | 81.79 416 |
|
| SD_0403 | | | 73.79 326 | 73.48 309 | 74.69 395 | 85.33 309 | 45.56 459 | 83.80 382 | 85.57 403 | 76.55 170 | 62.96 371 | 88.45 255 | 50.62 300 | 87.59 418 | 48.80 404 | 79.28 252 | 90.92 268 |
|
| CR-MVSNet | | | 73.79 326 | 70.82 342 | 82.70 247 | 83.15 352 | 67.96 107 | 70.25 455 | 84.00 418 | 73.67 215 | 69.97 289 | 72.41 436 | 57.82 209 | 89.48 397 | 52.99 387 | 73.13 300 | 90.64 272 |
|
| test_djsdf | | | 73.76 328 | 72.56 325 | 77.39 369 | 77.00 427 | 53.93 413 | 89.07 321 | 90.69 247 | 65.80 355 | 63.92 360 | 82.03 347 | 43.14 371 | 92.67 334 | 72.83 228 | 68.53 334 | 85.57 367 |
|
| pmmvs5 | | | 73.35 329 | 71.52 336 | 78.86 353 | 78.64 410 | 60.61 340 | 91.08 254 | 86.90 382 | 67.69 334 | 63.32 366 | 83.64 327 | 44.33 366 | 90.53 381 | 62.04 346 | 66.02 353 | 85.46 370 |
|
| Anonymous20231211 | | | 73.08 330 | 70.39 346 | 81.13 298 | 90.62 150 | 63.33 265 | 91.40 231 | 90.06 281 | 51.84 442 | 64.46 356 | 80.67 372 | 36.49 410 | 94.07 277 | 63.83 332 | 64.17 374 | 85.98 356 |
|
| tt0805 | | | 73.07 331 | 70.73 343 | 80.07 325 | 78.37 414 | 57.05 393 | 87.78 346 | 92.18 155 | 61.23 400 | 67.04 332 | 86.49 292 | 31.35 433 | 94.58 249 | 65.06 321 | 67.12 346 | 88.57 300 |
|
| miper_lstm_enhance | | | 73.05 332 | 71.73 335 | 77.03 374 | 83.80 343 | 58.32 377 | 81.76 405 | 88.88 336 | 69.80 307 | 61.01 383 | 78.23 396 | 57.19 214 | 87.51 420 | 65.34 319 | 59.53 411 | 85.27 375 |
|
| jajsoiax | | | 73.05 332 | 71.51 337 | 77.67 364 | 77.46 424 | 54.83 409 | 88.81 327 | 90.04 282 | 69.13 316 | 62.85 374 | 83.51 329 | 31.16 434 | 92.75 330 | 70.83 252 | 69.80 321 | 85.43 371 |
|
| LCM-MVSNet-Re | | | 72.93 334 | 71.84 333 | 76.18 383 | 88.49 212 | 48.02 444 | 80.07 424 | 70.17 466 | 73.96 206 | 52.25 433 | 80.09 382 | 49.98 306 | 88.24 408 | 67.35 291 | 84.23 185 | 92.28 232 |
|
| pm-mvs1 | | | 72.89 335 | 71.09 339 | 78.26 359 | 79.10 403 | 57.62 384 | 90.80 263 | 89.30 311 | 67.66 335 | 62.91 373 | 81.78 351 | 49.11 320 | 92.95 319 | 60.29 356 | 58.89 414 | 84.22 383 |
|
| tpmvs | | | 72.88 336 | 69.76 352 | 82.22 265 | 90.98 143 | 67.05 137 | 78.22 434 | 88.30 357 | 63.10 381 | 64.35 358 | 74.98 426 | 55.09 245 | 94.27 267 | 43.25 430 | 69.57 324 | 85.34 373 |
|
| test0.0.03 1 | | | 72.76 337 | 72.71 323 | 72.88 412 | 80.25 387 | 47.99 445 | 91.22 248 | 89.45 305 | 71.51 278 | 62.51 377 | 87.66 273 | 53.83 262 | 85.06 435 | 50.16 396 | 67.84 344 | 85.58 366 |
|
| UniMVSNet_ETH3D | | | 72.74 338 | 70.53 345 | 79.36 345 | 78.62 411 | 56.64 397 | 85.01 372 | 89.20 315 | 63.77 371 | 64.84 351 | 84.44 318 | 34.05 421 | 91.86 360 | 63.94 331 | 70.89 318 | 89.57 287 |
|
| mvs_tets | | | 72.71 339 | 71.11 338 | 77.52 365 | 77.41 425 | 54.52 411 | 88.45 333 | 89.76 291 | 68.76 323 | 62.70 375 | 83.26 333 | 29.49 440 | 92.71 331 | 70.51 258 | 69.62 323 | 85.34 373 |
|
| FMVSNet1 | | | 72.71 339 | 69.91 350 | 81.10 301 | 83.60 347 | 65.11 196 | 90.01 293 | 90.32 264 | 63.92 369 | 63.56 364 | 80.25 379 | 36.35 411 | 91.54 370 | 54.46 379 | 66.75 349 | 86.64 333 |
|
| test_fmvs1_n | | | 72.69 341 | 71.92 332 | 74.99 393 | 71.15 453 | 47.08 451 | 87.34 354 | 75.67 446 | 63.48 375 | 78.08 177 | 91.17 202 | 20.16 466 | 87.87 411 | 84.65 106 | 75.57 284 | 90.01 280 |
|
| IterMVS | | | 72.65 342 | 70.83 340 | 78.09 361 | 82.17 362 | 62.96 276 | 87.64 350 | 86.28 390 | 71.56 276 | 60.44 390 | 78.85 392 | 45.42 360 | 86.66 424 | 63.30 337 | 61.83 394 | 84.65 380 |
| Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
| myMVS_eth3d | | | 72.58 343 | 72.74 321 | 72.10 420 | 87.87 241 | 49.45 438 | 88.07 339 | 89.01 330 | 72.91 230 | 63.11 368 | 88.10 265 | 63.63 115 | 85.54 430 | 32.73 467 | 69.23 328 | 81.32 419 |
|
| wanda-best-256-512 | | | 72.42 344 | 69.43 354 | 81.37 288 | 75.39 435 | 64.24 229 | 91.58 224 | 91.09 218 | 66.36 347 | 60.64 386 | 76.86 411 | 47.20 339 | 93.47 303 | 64.80 323 | 50.98 438 | 86.40 340 |
|
| FE-blended-shiyan7 | | | 72.42 344 | 69.43 354 | 81.37 288 | 75.39 435 | 64.24 229 | 91.58 224 | 91.09 218 | 66.36 347 | 60.64 386 | 76.86 411 | 47.20 339 | 93.47 303 | 64.80 323 | 50.98 438 | 86.40 340 |
|
| blended_shiyan8 | | | 72.26 346 | 69.25 358 | 81.29 292 | 75.23 440 | 64.03 236 | 91.36 239 | 91.04 228 | 66.11 352 | 60.42 391 | 76.73 415 | 46.79 344 | 93.45 306 | 64.58 327 | 51.00 437 | 86.37 343 |
|
| blended_shiyan6 | | | 72.26 346 | 69.26 357 | 81.27 293 | 75.24 439 | 64.00 239 | 91.37 236 | 91.06 224 | 66.12 351 | 60.34 392 | 76.75 414 | 46.82 342 | 93.45 306 | 64.61 325 | 50.98 438 | 86.37 343 |
|
| PatchMatch-RL | | | 72.06 348 | 69.98 347 | 78.28 358 | 89.51 174 | 55.70 404 | 83.49 386 | 83.39 426 | 61.24 399 | 63.72 363 | 82.76 337 | 34.77 416 | 93.03 316 | 53.37 386 | 77.59 265 | 86.12 353 |
|
| PVSNet_0 | | 68.08 15 | 71.81 349 | 68.32 365 | 82.27 262 | 84.68 323 | 62.31 294 | 88.68 329 | 90.31 267 | 75.84 175 | 57.93 411 | 80.65 373 | 37.85 399 | 94.19 270 | 69.94 260 | 29.05 482 | 90.31 276 |
|
| MIMVSNet | | | 71.64 350 | 68.44 363 | 81.23 295 | 81.97 365 | 64.44 217 | 73.05 449 | 88.80 340 | 69.67 308 | 64.59 352 | 74.79 428 | 32.79 425 | 87.82 412 | 53.99 381 | 76.35 279 | 91.42 254 |
|
| test_vis1_n | | | 71.63 351 | 70.73 343 | 74.31 402 | 69.63 460 | 47.29 450 | 86.91 358 | 72.11 459 | 63.21 379 | 75.18 212 | 90.17 226 | 20.40 464 | 85.76 429 | 84.59 107 | 74.42 291 | 89.87 281 |
|
| IterMVS-SCA-FT | | | 71.55 352 | 69.97 348 | 76.32 381 | 81.48 370 | 60.67 338 | 87.64 350 | 85.99 397 | 66.17 350 | 59.50 397 | 78.88 391 | 45.53 358 | 83.65 443 | 62.58 343 | 61.93 393 | 84.63 382 |
|
| v7n | | | 71.31 353 | 68.65 360 | 79.28 347 | 76.40 429 | 60.77 331 | 86.71 362 | 89.45 305 | 64.17 368 | 58.77 404 | 78.24 395 | 44.59 365 | 93.54 300 | 57.76 366 | 61.75 396 | 83.52 391 |
|
| anonymousdsp | | | 71.14 354 | 69.37 356 | 76.45 380 | 72.95 448 | 54.71 410 | 84.19 379 | 88.88 336 | 61.92 393 | 62.15 378 | 79.77 385 | 38.14 395 | 91.44 375 | 68.90 273 | 67.45 345 | 83.21 397 |
|
| usedtu_blend_shiyan5 | | | 71.06 355 | 67.54 368 | 81.62 282 | 75.39 435 | 64.75 204 | 85.67 368 | 86.47 387 | 56.48 429 | 60.64 386 | 76.85 413 | 47.20 339 | 93.71 295 | 68.18 277 | 50.98 438 | 86.40 340 |
|
| F-COLMAP | | | 70.66 356 | 68.44 363 | 77.32 370 | 86.37 289 | 55.91 402 | 88.00 341 | 86.32 389 | 56.94 426 | 57.28 415 | 88.07 267 | 33.58 423 | 92.49 341 | 51.02 391 | 68.37 335 | 83.55 389 |
|
| WR-MVS_H | | | 70.59 357 | 69.94 349 | 72.53 414 | 81.03 373 | 51.43 424 | 87.35 353 | 92.03 163 | 67.38 338 | 60.23 394 | 80.70 370 | 55.84 237 | 83.45 446 | 46.33 419 | 58.58 416 | 82.72 404 |
|
| CP-MVSNet | | | 70.50 358 | 69.91 350 | 72.26 417 | 80.71 378 | 51.00 428 | 87.23 355 | 90.30 268 | 67.84 333 | 59.64 396 | 82.69 338 | 50.23 304 | 82.30 454 | 51.28 390 | 59.28 412 | 83.46 393 |
|
| RPMNet | | | 70.42 359 | 65.68 380 | 84.63 177 | 83.15 352 | 67.96 107 | 70.25 455 | 90.45 255 | 46.83 458 | 69.97 289 | 65.10 461 | 56.48 230 | 95.30 222 | 35.79 456 | 73.13 300 | 90.64 272 |
|
| testing3 | | | 70.38 360 | 70.83 340 | 69.03 434 | 85.82 303 | 43.93 464 | 90.72 269 | 90.56 254 | 68.06 329 | 60.24 393 | 86.82 289 | 64.83 95 | 84.12 437 | 26.33 475 | 64.10 375 | 79.04 441 |
|
| tfpnnormal | | | 70.10 361 | 67.36 370 | 78.32 357 | 83.45 349 | 60.97 327 | 88.85 325 | 92.77 126 | 64.85 362 | 60.83 385 | 78.53 393 | 43.52 369 | 93.48 302 | 31.73 470 | 61.70 398 | 80.52 428 |
|
| TransMVSNet (Re) | | | 70.07 362 | 67.66 367 | 77.31 371 | 80.62 381 | 59.13 369 | 91.78 210 | 84.94 409 | 65.97 353 | 60.08 395 | 80.44 375 | 50.78 297 | 91.87 359 | 48.84 403 | 45.46 455 | 80.94 423 |
|
| CL-MVSNet_self_test | | | 69.92 363 | 68.09 366 | 75.41 386 | 73.25 447 | 55.90 403 | 90.05 292 | 89.90 287 | 69.96 304 | 61.96 380 | 76.54 416 | 51.05 296 | 87.64 415 | 49.51 400 | 50.59 443 | 82.70 406 |
|
| DP-MVS | | | 69.90 364 | 66.48 372 | 80.14 323 | 95.36 29 | 62.93 277 | 89.56 304 | 76.11 444 | 50.27 448 | 57.69 413 | 85.23 308 | 39.68 384 | 95.73 191 | 33.35 461 | 71.05 317 | 81.78 417 |
|
| PS-CasMVS | | | 69.86 365 | 69.13 359 | 72.07 421 | 80.35 385 | 50.57 431 | 87.02 357 | 89.75 292 | 67.27 339 | 59.19 400 | 82.28 343 | 46.58 348 | 82.24 455 | 50.69 393 | 59.02 413 | 83.39 395 |
|
| Syy-MVS | | | 69.65 366 | 69.52 353 | 70.03 429 | 87.87 241 | 43.21 465 | 88.07 339 | 89.01 330 | 72.91 230 | 63.11 368 | 88.10 265 | 45.28 361 | 85.54 430 | 22.07 480 | 69.23 328 | 81.32 419 |
|
| MSDG | | | 69.54 367 | 65.73 379 | 80.96 306 | 85.11 318 | 63.71 251 | 84.19 379 | 83.28 427 | 56.95 425 | 54.50 422 | 84.03 323 | 31.50 431 | 96.03 169 | 42.87 434 | 69.13 330 | 83.14 399 |
|
| PEN-MVS | | | 69.46 368 | 68.56 361 | 72.17 419 | 79.27 398 | 49.71 436 | 86.90 359 | 89.24 313 | 67.24 342 | 59.08 401 | 82.51 341 | 47.23 338 | 83.54 445 | 48.42 406 | 57.12 418 | 83.25 396 |
|
| LS3D | | | 69.17 369 | 66.40 374 | 77.50 366 | 91.92 116 | 56.12 400 | 85.12 371 | 80.37 436 | 46.96 456 | 56.50 417 | 87.51 277 | 37.25 403 | 93.71 295 | 32.52 469 | 79.40 247 | 82.68 407 |
|
| PatchT | | | 69.11 370 | 65.37 384 | 80.32 317 | 82.07 364 | 63.68 255 | 67.96 465 | 87.62 371 | 50.86 446 | 69.37 293 | 65.18 460 | 57.09 215 | 88.53 404 | 41.59 440 | 66.60 350 | 88.74 297 |
|
| KD-MVS_2432*1600 | | | 69.03 371 | 66.37 375 | 77.01 375 | 85.56 307 | 61.06 325 | 81.44 410 | 90.25 271 | 67.27 339 | 58.00 409 | 76.53 417 | 54.49 252 | 87.63 416 | 48.04 408 | 35.77 473 | 82.34 410 |
|
| miper_refine_blended | | | 69.03 371 | 66.37 375 | 77.01 375 | 85.56 307 | 61.06 325 | 81.44 410 | 90.25 271 | 67.27 339 | 58.00 409 | 76.53 417 | 54.49 252 | 87.63 416 | 48.04 408 | 35.77 473 | 82.34 410 |
|
| mvsany_test1 | | | 68.77 373 | 68.56 361 | 69.39 432 | 73.57 446 | 45.88 458 | 80.93 415 | 60.88 480 | 59.65 410 | 71.56 269 | 90.26 219 | 43.22 370 | 75.05 467 | 74.26 217 | 62.70 385 | 87.25 323 |
|
| ACMH | | 63.93 17 | 68.62 374 | 64.81 386 | 80.03 327 | 85.22 314 | 63.25 267 | 87.72 347 | 84.66 411 | 60.83 402 | 51.57 437 | 79.43 389 | 27.29 447 | 94.96 232 | 41.76 438 | 64.84 366 | 81.88 415 |
| Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
| EG-PatchMatch MVS | | | 68.55 375 | 65.41 383 | 77.96 362 | 78.69 409 | 62.93 277 | 89.86 298 | 89.17 317 | 60.55 403 | 50.27 443 | 77.73 401 | 22.60 460 | 94.06 278 | 47.18 415 | 72.65 305 | 76.88 455 |
|
| ADS-MVSNet | | | 68.54 376 | 64.38 393 | 81.03 305 | 88.06 232 | 66.90 148 | 68.01 463 | 84.02 417 | 57.57 419 | 64.48 354 | 69.87 448 | 38.68 386 | 89.21 399 | 40.87 442 | 67.89 342 | 86.97 325 |
|
| DTE-MVSNet | | | 68.46 377 | 67.33 371 | 71.87 423 | 77.94 419 | 49.00 442 | 86.16 367 | 88.58 349 | 66.36 347 | 58.19 406 | 82.21 345 | 46.36 349 | 83.87 442 | 44.97 427 | 55.17 425 | 82.73 403 |
|
| mmtdpeth | | | 68.33 378 | 66.37 375 | 74.21 403 | 82.81 357 | 51.73 421 | 84.34 377 | 80.42 435 | 67.01 343 | 71.56 269 | 68.58 452 | 30.52 438 | 92.35 348 | 75.89 201 | 36.21 471 | 78.56 448 |
|
| our_test_3 | | | 68.29 379 | 64.69 388 | 79.11 352 | 78.92 404 | 64.85 203 | 88.40 334 | 85.06 407 | 60.32 406 | 52.68 431 | 76.12 421 | 40.81 380 | 89.80 396 | 44.25 429 | 55.65 423 | 82.67 408 |
|
| Patchmatch-RL test | | | 68.17 380 | 64.49 391 | 79.19 348 | 71.22 452 | 53.93 413 | 70.07 457 | 71.54 463 | 69.22 313 | 56.79 416 | 62.89 465 | 56.58 227 | 88.61 401 | 69.53 264 | 52.61 433 | 95.03 95 |
|
| XVG-ACMP-BASELINE | | | 68.04 381 | 65.53 382 | 75.56 385 | 74.06 445 | 52.37 418 | 78.43 431 | 85.88 398 | 62.03 391 | 58.91 403 | 81.21 366 | 20.38 465 | 91.15 377 | 60.69 353 | 68.18 336 | 83.16 398 |
|
| FMVSNet5 | | | 68.04 381 | 65.66 381 | 75.18 390 | 84.43 333 | 57.89 379 | 83.54 384 | 86.26 391 | 61.83 395 | 53.64 428 | 73.30 431 | 37.15 406 | 85.08 434 | 48.99 402 | 61.77 395 | 82.56 409 |
|
| ppachtmachnet_test | | | 67.72 383 | 63.70 396 | 79.77 337 | 78.92 404 | 66.04 170 | 88.68 329 | 82.90 429 | 60.11 408 | 55.45 419 | 75.96 422 | 39.19 385 | 90.55 380 | 39.53 446 | 52.55 434 | 82.71 405 |
|
| ACMH+ | | 65.35 16 | 67.65 384 | 64.55 389 | 76.96 377 | 84.59 327 | 57.10 392 | 88.08 338 | 80.79 433 | 58.59 417 | 53.00 430 | 81.09 368 | 26.63 449 | 92.95 319 | 46.51 417 | 61.69 399 | 80.82 424 |
|
| pmmvs6 | | | 67.57 385 | 64.76 387 | 76.00 384 | 72.82 450 | 53.37 415 | 88.71 328 | 86.78 386 | 53.19 438 | 57.58 414 | 78.03 398 | 35.33 415 | 92.41 344 | 55.56 375 | 54.88 427 | 82.21 412 |
|
| Anonymous20231206 | | | 67.53 386 | 65.78 378 | 72.79 413 | 74.95 441 | 47.59 447 | 88.23 336 | 87.32 375 | 61.75 398 | 58.07 408 | 77.29 405 | 37.79 400 | 87.29 422 | 42.91 432 | 63.71 379 | 83.48 392 |
|
| Patchmtry | | | 67.53 386 | 63.93 395 | 78.34 356 | 82.12 363 | 64.38 221 | 68.72 460 | 84.00 418 | 48.23 455 | 59.24 398 | 72.41 436 | 57.82 209 | 89.27 398 | 46.10 420 | 56.68 422 | 81.36 418 |
|
| USDC | | | 67.43 388 | 64.51 390 | 76.19 382 | 77.94 419 | 55.29 406 | 78.38 432 | 85.00 408 | 73.17 221 | 48.36 451 | 80.37 376 | 21.23 462 | 92.48 342 | 52.15 389 | 64.02 377 | 80.81 425 |
|
| ADS-MVSNet2 | | | 66.90 389 | 63.44 398 | 77.26 372 | 88.06 232 | 60.70 337 | 68.01 463 | 75.56 448 | 57.57 419 | 64.48 354 | 69.87 448 | 38.68 386 | 84.10 438 | 40.87 442 | 67.89 342 | 86.97 325 |
|
| FE-MVSNET2 | | | 66.80 390 | 64.06 394 | 75.03 391 | 69.84 458 | 57.11 391 | 86.57 363 | 88.57 350 | 67.94 332 | 50.97 441 | 72.16 440 | 33.79 422 | 87.55 419 | 53.94 382 | 52.74 431 | 80.45 429 |
|
| CMPMVS |  | 48.56 21 | 66.77 391 | 64.41 392 | 73.84 405 | 70.65 456 | 50.31 433 | 77.79 436 | 85.73 401 | 45.54 461 | 44.76 462 | 82.14 346 | 35.40 414 | 90.14 390 | 63.18 338 | 74.54 289 | 81.07 422 |
| M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
| OpenMVS_ROB |  | 61.12 18 | 66.39 392 | 62.92 401 | 76.80 379 | 76.51 428 | 57.77 381 | 89.22 316 | 83.41 425 | 55.48 433 | 53.86 426 | 77.84 399 | 26.28 450 | 93.95 287 | 34.90 458 | 68.76 332 | 78.68 446 |
|
| LTVRE_ROB | | 59.60 19 | 66.27 393 | 63.54 397 | 74.45 399 | 84.00 340 | 51.55 423 | 67.08 467 | 83.53 423 | 58.78 415 | 54.94 421 | 80.31 377 | 34.54 417 | 93.23 312 | 40.64 444 | 68.03 338 | 78.58 447 |
| 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 |
| JIA-IIPM | | | 66.06 394 | 62.45 404 | 76.88 378 | 81.42 372 | 54.45 412 | 57.49 481 | 88.67 345 | 49.36 450 | 63.86 361 | 46.86 478 | 56.06 234 | 90.25 384 | 49.53 399 | 68.83 331 | 85.95 357 |
|
| Patchmatch-test | | | 65.86 395 | 60.94 410 | 80.62 314 | 83.75 344 | 58.83 371 | 58.91 478 | 75.26 450 | 44.50 465 | 50.95 442 | 77.09 408 | 58.81 193 | 87.90 410 | 35.13 457 | 64.03 376 | 95.12 89 |
|
| UnsupCasMVSNet_eth | | | 65.79 396 | 63.10 399 | 73.88 404 | 70.71 455 | 50.29 434 | 81.09 413 | 89.88 288 | 72.58 237 | 49.25 448 | 74.77 429 | 32.57 427 | 87.43 421 | 55.96 374 | 41.04 463 | 83.90 386 |
|
| test_fmvs2 | | | 65.78 397 | 64.84 385 | 68.60 436 | 66.54 467 | 41.71 468 | 83.27 390 | 69.81 467 | 54.38 435 | 67.91 318 | 84.54 317 | 15.35 472 | 81.22 459 | 75.65 203 | 66.16 352 | 82.88 400 |
|
| dmvs_testset | | | 65.55 398 | 66.45 373 | 62.86 448 | 79.87 391 | 22.35 494 | 76.55 439 | 71.74 461 | 77.42 149 | 55.85 418 | 87.77 272 | 51.39 290 | 80.69 460 | 31.51 473 | 65.92 355 | 85.55 368 |
|
| pmmvs-eth3d | | | 65.53 399 | 62.32 405 | 75.19 389 | 69.39 461 | 59.59 360 | 82.80 398 | 83.43 424 | 62.52 386 | 51.30 439 | 72.49 434 | 32.86 424 | 87.16 423 | 55.32 376 | 50.73 442 | 78.83 444 |
|
| mamv4 | | | 65.18 400 | 67.43 369 | 58.44 452 | 77.88 421 | 49.36 441 | 69.40 459 | 70.99 465 | 48.31 454 | 57.78 412 | 85.53 305 | 59.01 190 | 51.88 490 | 73.67 219 | 64.32 372 | 74.07 460 |
|
| SixPastTwentyTwo | | | 64.92 401 | 61.78 408 | 74.34 401 | 78.74 408 | 49.76 435 | 83.42 389 | 79.51 439 | 62.86 382 | 50.27 443 | 77.35 403 | 30.92 436 | 90.49 382 | 45.89 421 | 47.06 449 | 82.78 401 |
|
| OurMVSNet-221017-0 | | | 64.68 402 | 62.17 406 | 72.21 418 | 76.08 432 | 47.35 448 | 80.67 416 | 81.02 432 | 56.19 430 | 51.60 436 | 79.66 387 | 27.05 448 | 88.56 403 | 53.60 385 | 53.63 430 | 80.71 426 |
|
| test_0402 | | | 64.54 403 | 61.09 409 | 74.92 394 | 84.10 339 | 60.75 333 | 87.95 342 | 79.71 438 | 52.03 440 | 52.41 432 | 77.20 406 | 32.21 429 | 91.64 365 | 23.14 478 | 61.03 402 | 72.36 466 |
|
| testgi | | | 64.48 404 | 62.87 402 | 69.31 433 | 71.24 451 | 40.62 471 | 85.49 369 | 79.92 437 | 65.36 359 | 54.18 424 | 83.49 330 | 23.74 455 | 84.55 436 | 41.60 439 | 60.79 405 | 82.77 402 |
|
| RPSCF | | | 64.24 405 | 61.98 407 | 71.01 426 | 76.10 431 | 45.00 460 | 75.83 444 | 75.94 445 | 46.94 457 | 58.96 402 | 84.59 315 | 31.40 432 | 82.00 456 | 47.76 413 | 60.33 410 | 86.04 354 |
|
| EU-MVSNet | | | 64.01 406 | 63.01 400 | 67.02 442 | 74.40 444 | 38.86 477 | 83.27 390 | 86.19 393 | 45.11 463 | 54.27 423 | 81.15 367 | 36.91 409 | 80.01 462 | 48.79 405 | 57.02 419 | 82.19 413 |
|
| test20.03 | | | 63.83 407 | 62.65 403 | 67.38 441 | 70.58 457 | 39.94 473 | 86.57 363 | 84.17 415 | 63.29 377 | 51.86 435 | 77.30 404 | 37.09 407 | 82.47 452 | 38.87 450 | 54.13 429 | 79.73 435 |
|
| sc_t1 | | | 63.81 408 | 59.39 416 | 77.10 373 | 77.62 422 | 56.03 401 | 84.32 378 | 73.56 455 | 46.66 459 | 58.22 405 | 73.06 432 | 23.28 458 | 90.62 379 | 50.93 392 | 46.84 450 | 84.64 381 |
|
| MDA-MVSNet_test_wron | | | 63.78 409 | 60.16 412 | 74.64 396 | 78.15 417 | 60.41 344 | 83.49 386 | 84.03 416 | 56.17 432 | 39.17 473 | 71.59 443 | 37.22 404 | 83.24 449 | 42.87 434 | 48.73 445 | 80.26 432 |
|
| YYNet1 | | | 63.76 410 | 60.14 413 | 74.62 397 | 78.06 418 | 60.19 351 | 83.46 388 | 83.99 420 | 56.18 431 | 39.25 472 | 71.56 444 | 37.18 405 | 83.34 447 | 42.90 433 | 48.70 446 | 80.32 431 |
|
| K. test v3 | | | 63.09 411 | 59.61 415 | 73.53 407 | 76.26 430 | 49.38 440 | 83.27 390 | 77.15 442 | 64.35 365 | 47.77 453 | 72.32 438 | 28.73 442 | 87.79 413 | 49.93 398 | 36.69 470 | 83.41 394 |
|
| COLMAP_ROB |  | 57.96 20 | 62.98 412 | 59.65 414 | 72.98 411 | 81.44 371 | 53.00 417 | 83.75 383 | 75.53 449 | 48.34 453 | 48.81 450 | 81.40 360 | 24.14 453 | 90.30 383 | 32.95 464 | 60.52 407 | 75.65 458 |
| Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
| Anonymous20240521 | | | 62.09 413 | 59.08 417 | 71.10 425 | 67.19 465 | 48.72 443 | 83.91 381 | 85.23 406 | 50.38 447 | 47.84 452 | 71.22 446 | 20.74 463 | 85.51 432 | 46.47 418 | 58.75 415 | 79.06 440 |
|
| tt0320 | | | 61.85 414 | 57.45 423 | 75.03 391 | 77.49 423 | 57.60 385 | 82.74 399 | 73.65 454 | 43.65 469 | 53.65 427 | 68.18 454 | 25.47 451 | 88.66 400 | 45.56 423 | 46.68 451 | 78.81 445 |
|
| AllTest | | | 61.66 415 | 58.06 419 | 72.46 415 | 79.57 393 | 51.42 425 | 80.17 422 | 68.61 469 | 51.25 444 | 45.88 456 | 81.23 362 | 19.86 467 | 86.58 425 | 38.98 448 | 57.01 420 | 79.39 437 |
|
| UnsupCasMVSNet_bld | | | 61.60 416 | 57.71 420 | 73.29 409 | 68.73 462 | 51.64 422 | 78.61 430 | 89.05 328 | 57.20 424 | 46.11 455 | 61.96 469 | 28.70 443 | 88.60 402 | 50.08 397 | 38.90 468 | 79.63 436 |
|
| MDA-MVSNet-bldmvs | | | 61.54 417 | 57.70 421 | 73.05 410 | 79.53 395 | 57.00 396 | 83.08 394 | 81.23 431 | 57.57 419 | 34.91 477 | 72.45 435 | 32.79 425 | 86.26 427 | 35.81 455 | 41.95 461 | 75.89 457 |
|
| tt0320-xc | | | 61.51 418 | 56.89 427 | 75.37 387 | 78.50 412 | 58.61 374 | 82.61 401 | 71.27 464 | 44.31 466 | 53.17 429 | 68.03 456 | 23.38 456 | 88.46 405 | 47.77 412 | 43.00 460 | 79.03 442 |
|
| mvs5depth | | | 61.03 419 | 57.65 422 | 71.18 424 | 67.16 466 | 47.04 453 | 72.74 450 | 77.49 440 | 57.47 422 | 60.52 389 | 72.53 433 | 22.84 459 | 88.38 406 | 49.15 401 | 38.94 467 | 78.11 451 |
|
| KD-MVS_self_test | | | 60.87 420 | 58.60 418 | 67.68 439 | 66.13 468 | 39.93 474 | 75.63 446 | 84.70 410 | 57.32 423 | 49.57 446 | 68.45 453 | 29.55 439 | 82.87 450 | 48.09 407 | 47.94 447 | 80.25 433 |
|
| kuosan | | | 60.86 421 | 60.24 411 | 62.71 449 | 81.57 369 | 46.43 455 | 75.70 445 | 85.88 398 | 57.98 418 | 48.95 449 | 69.53 450 | 58.42 198 | 76.53 465 | 28.25 474 | 35.87 472 | 65.15 473 |
|
| FE-MVSNET | | | 60.52 422 | 57.18 426 | 70.53 427 | 67.53 464 | 50.68 430 | 82.62 400 | 76.28 443 | 59.33 413 | 46.71 454 | 71.10 447 | 30.54 437 | 83.61 444 | 33.15 463 | 47.37 448 | 77.29 454 |
|
| TinyColmap | | | 60.32 423 | 56.42 430 | 72.00 422 | 78.78 407 | 53.18 416 | 78.36 433 | 75.64 447 | 52.30 439 | 41.59 471 | 75.82 424 | 14.76 475 | 88.35 407 | 35.84 454 | 54.71 428 | 74.46 459 |
|
| MVS-HIRNet | | | 60.25 424 | 55.55 431 | 74.35 400 | 84.37 334 | 56.57 398 | 71.64 453 | 74.11 452 | 34.44 476 | 45.54 460 | 42.24 484 | 31.11 435 | 89.81 394 | 40.36 445 | 76.10 281 | 76.67 456 |
|
| MIMVSNet1 | | | 60.16 425 | 57.33 424 | 68.67 435 | 69.71 459 | 44.13 462 | 78.92 429 | 84.21 414 | 55.05 434 | 44.63 463 | 71.85 441 | 23.91 454 | 81.54 458 | 32.63 468 | 55.03 426 | 80.35 430 |
|
| PM-MVS | | | 59.40 426 | 56.59 428 | 67.84 437 | 63.63 471 | 41.86 466 | 76.76 438 | 63.22 477 | 59.01 414 | 51.07 440 | 72.27 439 | 11.72 479 | 83.25 448 | 61.34 349 | 50.28 444 | 78.39 449 |
|
| new-patchmatchnet | | | 59.30 427 | 56.48 429 | 67.79 438 | 65.86 469 | 44.19 461 | 82.47 402 | 81.77 430 | 59.94 409 | 43.65 467 | 66.20 459 | 27.67 446 | 81.68 457 | 39.34 447 | 41.40 462 | 77.50 453 |
|
| test_vis1_rt | | | 59.09 428 | 57.31 425 | 64.43 445 | 68.44 463 | 46.02 457 | 83.05 396 | 48.63 489 | 51.96 441 | 49.57 446 | 63.86 464 | 16.30 470 | 80.20 461 | 71.21 250 | 62.79 384 | 67.07 472 |
|
| usedtu_dtu_shiyan2 | | | 57.76 429 | 53.69 435 | 69.95 430 | 57.60 481 | 41.80 467 | 83.50 385 | 83.67 422 | 45.26 462 | 43.79 466 | 62.82 466 | 17.63 469 | 85.93 428 | 42.56 437 | 46.40 453 | 82.12 414 |
|
| test_fmvs3 | | | 56.82 430 | 54.86 433 | 62.69 450 | 53.59 483 | 35.47 480 | 75.87 443 | 65.64 474 | 43.91 467 | 55.10 420 | 71.43 445 | 6.91 487 | 74.40 470 | 68.64 275 | 52.63 432 | 78.20 450 |
|
| DSMNet-mixed | | | 56.78 431 | 54.44 434 | 63.79 446 | 63.21 472 | 29.44 489 | 64.43 470 | 64.10 476 | 42.12 473 | 51.32 438 | 71.60 442 | 31.76 430 | 75.04 468 | 36.23 453 | 65.20 363 | 86.87 330 |
|
| pmmvs3 | | | 55.51 432 | 51.50 438 | 67.53 440 | 57.90 480 | 50.93 429 | 80.37 418 | 73.66 453 | 40.63 474 | 44.15 465 | 64.75 462 | 16.30 470 | 78.97 464 | 44.77 428 | 40.98 465 | 72.69 464 |
|
| TDRefinement | | | 55.28 433 | 51.58 437 | 66.39 443 | 59.53 479 | 46.15 456 | 76.23 441 | 72.80 456 | 44.60 464 | 42.49 469 | 76.28 420 | 15.29 473 | 82.39 453 | 33.20 462 | 43.75 457 | 70.62 468 |
|
| dongtai | | | 55.18 434 | 55.46 432 | 54.34 460 | 76.03 433 | 36.88 478 | 76.07 442 | 84.61 412 | 51.28 443 | 43.41 468 | 64.61 463 | 56.56 228 | 67.81 478 | 18.09 483 | 28.50 483 | 58.32 476 |
|
| LF4IMVS | | | 54.01 435 | 52.12 436 | 59.69 451 | 62.41 474 | 39.91 475 | 68.59 461 | 68.28 471 | 42.96 471 | 44.55 464 | 75.18 425 | 14.09 477 | 68.39 477 | 41.36 441 | 51.68 435 | 70.78 467 |
|
| ttmdpeth | | | 53.34 436 | 49.96 439 | 63.45 447 | 62.07 476 | 40.04 472 | 72.06 451 | 65.64 474 | 42.54 472 | 51.88 434 | 77.79 400 | 13.94 478 | 76.48 466 | 32.93 465 | 30.82 481 | 73.84 461 |
|
| MVStest1 | | | 51.35 437 | 46.89 441 | 64.74 444 | 65.06 470 | 51.10 427 | 67.33 466 | 72.58 457 | 30.20 480 | 35.30 475 | 74.82 427 | 27.70 445 | 69.89 475 | 24.44 477 | 24.57 484 | 73.22 462 |
|
| N_pmnet | | | 50.55 438 | 49.11 440 | 54.88 458 | 77.17 426 | 4.02 502 | 84.36 376 | 2.00 500 | 48.59 451 | 45.86 458 | 68.82 451 | 32.22 428 | 82.80 451 | 31.58 471 | 51.38 436 | 77.81 452 |
|
| new_pmnet | | | 49.31 439 | 46.44 442 | 57.93 453 | 62.84 473 | 40.74 470 | 68.47 462 | 62.96 478 | 36.48 475 | 35.09 476 | 57.81 473 | 14.97 474 | 72.18 472 | 32.86 466 | 46.44 452 | 60.88 475 |
|
| mvsany_test3 | | | 48.86 440 | 46.35 443 | 56.41 454 | 46.00 489 | 31.67 485 | 62.26 472 | 47.25 490 | 43.71 468 | 45.54 460 | 68.15 455 | 10.84 480 | 64.44 486 | 57.95 365 | 35.44 475 | 73.13 463 |
|
| test_f | | | 46.58 441 | 43.45 445 | 55.96 455 | 45.18 490 | 32.05 484 | 61.18 473 | 49.49 488 | 33.39 477 | 42.05 470 | 62.48 468 | 7.00 486 | 65.56 482 | 47.08 416 | 43.21 459 | 70.27 469 |
|
| WB-MVS | | | 46.23 442 | 44.94 444 | 50.11 463 | 62.13 475 | 21.23 496 | 76.48 440 | 55.49 482 | 45.89 460 | 35.78 474 | 61.44 471 | 35.54 413 | 72.83 471 | 9.96 490 | 21.75 485 | 56.27 478 |
|
| FPMVS | | | 45.64 443 | 43.10 447 | 53.23 461 | 51.42 486 | 36.46 479 | 64.97 469 | 71.91 460 | 29.13 481 | 27.53 481 | 61.55 470 | 9.83 482 | 65.01 484 | 16.00 487 | 55.58 424 | 58.22 477 |
|
| SSC-MVS | | | 44.51 444 | 43.35 446 | 47.99 467 | 61.01 478 | 18.90 498 | 74.12 448 | 54.36 483 | 43.42 470 | 34.10 478 | 60.02 472 | 34.42 418 | 70.39 474 | 9.14 492 | 19.57 486 | 54.68 479 |
|
| EGC-MVSNET | | | 42.35 445 | 38.09 448 | 55.11 457 | 74.57 442 | 46.62 454 | 71.63 454 | 55.77 481 | 0.04 495 | 0.24 496 | 62.70 467 | 14.24 476 | 74.91 469 | 17.59 484 | 46.06 454 | 43.80 481 |
|
| LCM-MVSNet | | | 40.54 446 | 35.79 451 | 54.76 459 | 36.92 496 | 30.81 486 | 51.41 484 | 69.02 468 | 22.07 483 | 24.63 483 | 45.37 480 | 4.56 491 | 65.81 481 | 33.67 460 | 34.50 476 | 67.67 470 |
|
| APD_test1 | | | 40.50 447 | 37.31 450 | 50.09 464 | 51.88 484 | 35.27 481 | 59.45 477 | 52.59 485 | 21.64 484 | 26.12 482 | 57.80 474 | 4.56 491 | 66.56 480 | 22.64 479 | 39.09 466 | 48.43 480 |
|
| test_vis3_rt | | | 40.46 448 | 37.79 449 | 48.47 466 | 44.49 491 | 33.35 483 | 66.56 468 | 32.84 497 | 32.39 478 | 29.65 479 | 39.13 487 | 3.91 494 | 68.65 476 | 50.17 395 | 40.99 464 | 43.40 482 |
|
| ANet_high | | | 40.27 449 | 35.20 452 | 55.47 456 | 34.74 497 | 34.47 482 | 63.84 471 | 71.56 462 | 48.42 452 | 18.80 486 | 41.08 485 | 9.52 483 | 64.45 485 | 20.18 481 | 8.66 493 | 67.49 471 |
|
| test_method | | | 38.59 450 | 35.16 453 | 48.89 465 | 54.33 482 | 21.35 495 | 45.32 487 | 53.71 484 | 7.41 492 | 28.74 480 | 51.62 476 | 8.70 484 | 52.87 489 | 33.73 459 | 32.89 477 | 72.47 465 |
|
| PMMVS2 | | | 37.93 451 | 33.61 454 | 50.92 462 | 46.31 488 | 24.76 492 | 60.55 476 | 50.05 486 | 28.94 482 | 20.93 484 | 47.59 477 | 4.41 493 | 65.13 483 | 25.14 476 | 18.55 488 | 62.87 474 |
|
| Gipuma |  | | 34.91 452 | 31.44 455 | 45.30 468 | 70.99 454 | 39.64 476 | 19.85 491 | 72.56 458 | 20.10 486 | 16.16 490 | 21.47 491 | 5.08 490 | 71.16 473 | 13.07 488 | 43.70 458 | 25.08 488 |
| S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
| testf1 | | | 32.77 453 | 29.47 456 | 42.67 470 | 41.89 493 | 30.81 486 | 52.07 482 | 43.45 491 | 15.45 487 | 18.52 487 | 44.82 481 | 2.12 495 | 58.38 487 | 16.05 485 | 30.87 479 | 38.83 483 |
|
| APD_test2 | | | 32.77 453 | 29.47 456 | 42.67 470 | 41.89 493 | 30.81 486 | 52.07 482 | 43.45 491 | 15.45 487 | 18.52 487 | 44.82 481 | 2.12 495 | 58.38 487 | 16.05 485 | 30.87 479 | 38.83 483 |
|
| PMVS |  | 26.43 22 | 31.84 455 | 28.16 458 | 42.89 469 | 25.87 499 | 27.58 490 | 50.92 485 | 49.78 487 | 21.37 485 | 14.17 491 | 40.81 486 | 2.01 497 | 66.62 479 | 9.61 491 | 38.88 469 | 34.49 487 |
| Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
| E-PMN | | | 24.61 456 | 24.00 460 | 26.45 474 | 43.74 492 | 18.44 499 | 60.86 474 | 39.66 493 | 15.11 489 | 9.53 493 | 22.10 490 | 6.52 488 | 46.94 492 | 8.31 493 | 10.14 490 | 13.98 490 |
|
| MVE |  | 24.84 23 | 24.35 457 | 19.77 463 | 38.09 472 | 34.56 498 | 26.92 491 | 26.57 489 | 38.87 495 | 11.73 491 | 11.37 492 | 27.44 488 | 1.37 498 | 50.42 491 | 11.41 489 | 14.60 489 | 36.93 485 |
| Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
| EMVS | | | 23.76 458 | 23.20 462 | 25.46 475 | 41.52 495 | 16.90 500 | 60.56 475 | 38.79 496 | 14.62 490 | 8.99 494 | 20.24 493 | 7.35 485 | 45.82 493 | 7.25 494 | 9.46 491 | 13.64 491 |
|
| tmp_tt | | | 22.26 459 | 23.75 461 | 17.80 476 | 5.23 500 | 12.06 501 | 35.26 488 | 39.48 494 | 2.82 494 | 18.94 485 | 44.20 483 | 22.23 461 | 24.64 495 | 36.30 452 | 9.31 492 | 16.69 489 |
|
| cdsmvs_eth3d_5k | | | 19.86 460 | 26.47 459 | 0.00 480 | 0.00 503 | 0.00 505 | 0.00 492 | 93.45 95 | 0.00 498 | 0.00 499 | 95.27 77 | 49.56 312 | 0.00 499 | 0.00 498 | 0.00 496 | 0.00 495 |
|
| wuyk23d | | | 11.30 461 | 10.95 464 | 12.33 477 | 48.05 487 | 19.89 497 | 25.89 490 | 1.92 501 | 3.58 493 | 3.12 495 | 1.37 495 | 0.64 499 | 15.77 496 | 6.23 495 | 7.77 494 | 1.35 492 |
|
| ab-mvs-re | | | 7.91 462 | 10.55 465 | 0.00 480 | 0.00 503 | 0.00 505 | 0.00 492 | 0.00 504 | 0.00 498 | 0.00 499 | 94.95 87 | 0.00 502 | 0.00 499 | 0.00 498 | 0.00 496 | 0.00 495 |
|
| testmvs | | | 7.23 463 | 9.62 466 | 0.06 479 | 0.04 501 | 0.02 504 | 84.98 373 | 0.02 502 | 0.03 496 | 0.18 497 | 1.21 496 | 0.01 501 | 0.02 497 | 0.14 496 | 0.01 495 | 0.13 494 |
|
| test123 | | | 6.92 464 | 9.21 467 | 0.08 478 | 0.03 502 | 0.05 503 | 81.65 408 | 0.01 503 | 0.02 497 | 0.14 498 | 0.85 497 | 0.03 500 | 0.02 497 | 0.12 497 | 0.00 496 | 0.16 493 |
|
| pcd_1.5k_mvsjas | | | 4.46 465 | 5.95 468 | 0.00 480 | 0.00 503 | 0.00 505 | 0.00 492 | 0.00 504 | 0.00 498 | 0.00 499 | 0.00 498 | 53.55 266 | 0.00 499 | 0.00 498 | 0.00 496 | 0.00 495 |
|
| mmdepth | | | 0.00 466 | 0.00 469 | 0.00 480 | 0.00 503 | 0.00 505 | 0.00 492 | 0.00 504 | 0.00 498 | 0.00 499 | 0.00 498 | 0.00 502 | 0.00 499 | 0.00 498 | 0.00 496 | 0.00 495 |
|
| monomultidepth | | | 0.00 466 | 0.00 469 | 0.00 480 | 0.00 503 | 0.00 505 | 0.00 492 | 0.00 504 | 0.00 498 | 0.00 499 | 0.00 498 | 0.00 502 | 0.00 499 | 0.00 498 | 0.00 496 | 0.00 495 |
|
| test_blank | | | 0.00 466 | 0.00 469 | 0.00 480 | 0.00 503 | 0.00 505 | 0.00 492 | 0.00 504 | 0.00 498 | 0.00 499 | 0.00 498 | 0.00 502 | 0.00 499 | 0.00 498 | 0.00 496 | 0.00 495 |
|
| uanet_test | | | 0.00 466 | 0.00 469 | 0.00 480 | 0.00 503 | 0.00 505 | 0.00 492 | 0.00 504 | 0.00 498 | 0.00 499 | 0.00 498 | 0.00 502 | 0.00 499 | 0.00 498 | 0.00 496 | 0.00 495 |
|
| DCPMVS | | | 0.00 466 | 0.00 469 | 0.00 480 | 0.00 503 | 0.00 505 | 0.00 492 | 0.00 504 | 0.00 498 | 0.00 499 | 0.00 498 | 0.00 502 | 0.00 499 | 0.00 498 | 0.00 496 | 0.00 495 |
|
| sosnet-low-res | | | 0.00 466 | 0.00 469 | 0.00 480 | 0.00 503 | 0.00 505 | 0.00 492 | 0.00 504 | 0.00 498 | 0.00 499 | 0.00 498 | 0.00 502 | 0.00 499 | 0.00 498 | 0.00 496 | 0.00 495 |
|
| sosnet | | | 0.00 466 | 0.00 469 | 0.00 480 | 0.00 503 | 0.00 505 | 0.00 492 | 0.00 504 | 0.00 498 | 0.00 499 | 0.00 498 | 0.00 502 | 0.00 499 | 0.00 498 | 0.00 496 | 0.00 495 |
|
| uncertanet | | | 0.00 466 | 0.00 469 | 0.00 480 | 0.00 503 | 0.00 505 | 0.00 492 | 0.00 504 | 0.00 498 | 0.00 499 | 0.00 498 | 0.00 502 | 0.00 499 | 0.00 498 | 0.00 496 | 0.00 495 |
|
| Regformer | | | 0.00 466 | 0.00 469 | 0.00 480 | 0.00 503 | 0.00 505 | 0.00 492 | 0.00 504 | 0.00 498 | 0.00 499 | 0.00 498 | 0.00 502 | 0.00 499 | 0.00 498 | 0.00 496 | 0.00 495 |
|
| uanet | | | 0.00 466 | 0.00 469 | 0.00 480 | 0.00 503 | 0.00 505 | 0.00 492 | 0.00 504 | 0.00 498 | 0.00 499 | 0.00 498 | 0.00 502 | 0.00 499 | 0.00 498 | 0.00 496 | 0.00 495 |
|
| MED-MVS test | | | | | 87.42 45 | 94.76 34 | 67.28 126 | 94.47 61 | 94.87 32 | 73.09 226 | 91.27 23 | 96.95 17 | | 98.98 16 | 91.55 43 | 94.28 37 | 95.99 45 |
|
| TestfortrainingZip | | | | | | | | 94.47 61 | | | | | | | | | |
|
| WAC-MVS | | | | | | | 49.45 438 | | | | | | | | 31.56 472 | | |
|
| FOURS1 | | | | | | 93.95 50 | 61.77 307 | 93.96 88 | 91.92 167 | 62.14 390 | 86.57 62 | | | | | | |
|
| MSC_two_6792asdad | | | | | 89.60 9 | 97.31 4 | 73.22 12 | | 95.05 29 | | | | | 99.07 13 | 92.01 38 | 94.77 26 | 96.51 24 |
|
| PC_three_1452 | | | | | | | | | | 80.91 65 | 94.07 2 | 96.83 29 | 83.57 4 | 99.12 5 | 95.70 10 | 97.42 4 | 97.55 4 |
|
| No_MVS | | | | | 89.60 9 | 97.31 4 | 73.22 12 | | 95.05 29 | | | | | 99.07 13 | 92.01 38 | 94.77 26 | 96.51 24 |
|
| test_one_0601 | | | | | | 96.32 19 | 69.74 51 | | 94.18 66 | 71.42 280 | 90.67 29 | 96.85 27 | 74.45 22 | | | | |
|
| eth-test2 | | | | | | 0.00 503 | | | | | | | | | | | |
|
| eth-test | | | | | | 0.00 503 | | | | | | | | | | | |
|
| ZD-MVS | | | | | | 96.63 9 | 65.50 187 | | 93.50 93 | 70.74 294 | 85.26 80 | 95.19 83 | 64.92 94 | 97.29 89 | 87.51 73 | 93.01 60 | |
|
| RE-MVS-def | | | | 80.48 186 | | 92.02 109 | 58.56 375 | 90.90 258 | 90.45 255 | 62.76 383 | 78.89 164 | 94.46 101 | 49.30 315 | | 78.77 184 | 86.77 148 | 92.28 232 |
|
| IU-MVS | | | | | | 96.46 11 | 69.91 43 | | 95.18 23 | 80.75 66 | 95.28 1 | | | | 92.34 35 | 95.36 14 | 96.47 28 |
|
| OPU-MVS | | | | | 89.97 3 | 97.52 3 | 73.15 14 | 96.89 6 | | | | 97.00 15 | 83.82 2 | 99.15 2 | 95.72 8 | 97.63 3 | 97.62 2 |
|
| test_241102_TWO | | | | | | | | | 94.41 57 | 71.65 269 | 92.07 11 | 97.21 9 | 74.58 20 | 99.11 6 | 92.34 35 | 95.36 14 | 96.59 19 |
|
| test_241102_ONE | | | | | | 96.45 12 | 69.38 59 | | 94.44 55 | 71.65 269 | 92.11 9 | 97.05 12 | 76.79 9 | 99.11 6 | | | |
|
| 9.14 | | | | 87.63 39 | | 93.86 52 | | 94.41 66 | 94.18 66 | 72.76 234 | 86.21 65 | 96.51 36 | 66.64 72 | 97.88 52 | 90.08 55 | 94.04 43 | |
|
| save fliter | | | | | | 93.84 53 | 67.89 110 | 95.05 40 | 92.66 133 | 78.19 129 | | | | | | | |
|
| test_0728_THIRD | | | | | | | | | | 72.48 239 | 90.55 30 | 96.93 21 | 76.24 13 | 99.08 11 | 91.53 46 | 94.99 18 | 96.43 31 |
|
| test_0728_SECOND | | | | | 88.70 18 | 96.45 12 | 70.43 34 | 96.64 10 | 94.37 61 | | | | | 99.15 2 | 91.91 41 | 94.90 22 | 96.51 24 |
|
| test0726 | | | | | | 96.40 15 | 69.99 39 | 96.76 8 | 94.33 63 | 71.92 255 | 91.89 14 | 97.11 11 | 73.77 25 | | | | |
|
| GSMVS | | | | | | | | | | | | | | | | | 94.68 120 |
|
| test_part2 | | | | | | 96.29 20 | 68.16 103 | | | | 90.78 27 | | | | | | |
|
| sam_mvs1 | | | | | | | | | | | | | 57.85 208 | | | | 94.68 120 |
|
| sam_mvs | | | | | | | | | | | | | 54.91 247 | | | | |
|
| ambc | | | | | 69.61 431 | 61.38 477 | 41.35 469 | 49.07 486 | 85.86 400 | | 50.18 445 | 66.40 458 | 10.16 481 | 88.14 409 | 45.73 422 | 44.20 456 | 79.32 439 |
|
| MTGPA |  | | | | | | | | 92.23 148 | | | | | | | | |
|
| test_post1 | | | | | | | | 78.95 428 | | | | 20.70 492 | 53.05 271 | 91.50 374 | 60.43 354 | | |
|
| test_post | | | | | | | | | | | | 23.01 489 | 56.49 229 | 92.67 334 | | | |
|
| patchmatchnet-post | | | | | | | | | | | | 67.62 457 | 57.62 211 | 90.25 384 | | | |
|
| GG-mvs-BLEND | | | | | 86.53 89 | 91.91 118 | 69.67 54 | 75.02 447 | 94.75 40 | | 78.67 172 | 90.85 206 | 77.91 7 | 94.56 254 | 72.25 237 | 93.74 49 | 95.36 72 |
|
| MTMP | | | | | | | | 93.77 103 | 32.52 498 | | | | | | | | |
|
| gm-plane-assit | | | | | | 88.42 218 | 67.04 138 | | | 78.62 123 | | 91.83 179 | | 97.37 83 | 76.57 196 | | |
|
| test9_res | | | | | | | | | | | | | | | 89.41 56 | 94.96 19 | 95.29 78 |
|
| TEST9 | | | | | | 94.18 45 | 67.28 126 | 94.16 75 | 93.51 91 | 71.75 266 | 85.52 75 | 95.33 71 | 68.01 61 | 97.27 93 | | | |
|
| test_8 | | | | | | 94.19 44 | 67.19 131 | 94.15 77 | 93.42 98 | 71.87 260 | 85.38 78 | 95.35 70 | 68.19 59 | 96.95 120 | | | |
|
| agg_prior2 | | | | | | | | | | | | | | | 86.41 87 | 94.75 30 | 95.33 74 |
|
| agg_prior | | | | | | 94.16 47 | 66.97 146 | | 93.31 101 | | 84.49 86 | | | 96.75 132 | | | |
|
| TestCases | | | | | 72.46 415 | 79.57 393 | 51.42 425 | | 68.61 469 | 51.25 444 | 45.88 456 | 81.23 362 | 19.86 467 | 86.58 425 | 38.98 448 | 57.01 420 | 79.39 437 |
|
| test_prior4 | | | | | | | 67.18 133 | 93.92 92 | | | | | | | | | |
|
| test_prior2 | | | | | | | | 95.10 39 | | 75.40 183 | 85.25 81 | 95.61 62 | 67.94 62 | | 87.47 75 | 94.77 26 | |
|
| test_prior | | | | | 86.42 93 | 94.71 39 | 67.35 125 | | 93.10 112 | | | | | 96.84 129 | | | 95.05 93 |
|
| 旧先验2 | | | | | | | | 92.00 197 | | 59.37 412 | 87.54 55 | | | 93.47 303 | 75.39 205 | | |
|
| æ–°å‡ ä½•2 | | | | | | | | 91.41 229 | | | | | | | | | |
|
| æ–°å‡ ä½•1 | | | | | 84.73 167 | 92.32 97 | 64.28 226 | | 91.46 194 | 59.56 411 | 79.77 151 | 92.90 145 | 56.95 221 | 96.57 138 | 63.40 334 | 92.91 62 | 93.34 192 |
|
| 旧先验1 | | | | | | 91.94 114 | 60.74 334 | | 91.50 192 | | | 94.36 105 | 65.23 89 | | | 91.84 78 | 94.55 129 |
|
| æ— å…ˆéªŒ | | | | | | | | 92.71 152 | 92.61 138 | 62.03 391 | | | | 97.01 110 | 66.63 299 | | 93.97 168 |
|
| 原ACMM2 | | | | | | | | 92.01 194 | | | | | | | | | |
|
| 原ACMM1 | | | | | 84.42 184 | 93.21 72 | 64.27 227 | | 93.40 100 | 65.39 358 | 79.51 156 | 92.50 153 | 58.11 203 | 96.69 134 | 65.27 320 | 93.96 44 | 92.32 230 |
|
| test222 | | | | | | 89.77 167 | 61.60 313 | 89.55 305 | 89.42 307 | 56.83 427 | 77.28 188 | 92.43 157 | 52.76 274 | | | 91.14 95 | 93.09 202 |
|
| testdata2 | | | | | | | | | | | | | | 96.09 163 | 61.26 350 | | |
|
| segment_acmp | | | | | | | | | | | | | 65.94 80 | | | | |
|
| testdata | | | | | 81.34 291 | 89.02 191 | 57.72 382 | | 89.84 289 | 58.65 416 | 85.32 79 | 94.09 121 | 57.03 216 | 93.28 309 | 69.34 266 | 90.56 101 | 93.03 205 |
|
| testdata1 | | | | | | | | 89.21 317 | | 77.55 145 | | | | | | | |
|
| test12 | | | | | 87.09 56 | 94.60 40 | 68.86 79 | | 92.91 121 | | 82.67 109 | | 65.44 86 | 97.55 72 | | 93.69 52 | 94.84 105 |
|
| plane_prior7 | | | | | | 86.94 272 | 61.51 315 | | | | | | | | | | |
|
| plane_prior6 | | | | | | 87.23 258 | 62.32 293 | | | | | | 50.66 298 | | | | |
|
| plane_prior5 | | | | | | | | | 91.31 198 | | | | | 95.55 208 | 76.74 194 | 78.53 259 | 88.39 304 |
|
| plane_prior4 | | | | | | | | | | | | 89.14 247 | | | | | |
|
| plane_prior3 | | | | | | | 61.95 302 | | | 79.09 112 | 72.53 251 | | | | | | |
|
| plane_prior2 | | | | | | | | 93.13 131 | | 78.81 119 | | | | | | | |
|
| plane_prior1 | | | | | | 87.15 261 | | | | | | | | | | | |
|
| plane_prior | | | | | | | 62.42 289 | 93.85 96 | | 79.38 104 | | | | | | 78.80 256 | |
|
| n2 | | | | | | | | | 0.00 504 | | | | | | | | |
|
| nn | | | | | | | | | 0.00 504 | | | | | | | | |
|
| door-mid | | | | | | | | | 66.01 473 | | | | | | | | |
|
| lessismore_v0 | | | | | 73.72 406 | 72.93 449 | 47.83 446 | | 61.72 479 | | 45.86 458 | 73.76 430 | 28.63 444 | 89.81 394 | 47.75 414 | 31.37 478 | 83.53 390 |
|
| LGP-MVS_train | | | | | 79.56 343 | 84.31 335 | 59.37 364 | | 89.73 295 | 69.49 309 | 64.86 349 | 88.42 256 | 38.65 388 | 94.30 265 | 72.56 234 | 72.76 303 | 85.01 376 |
|
| test11 | | | | | | | | | 93.01 115 | | | | | | | | |
|
| door | | | | | | | | | 66.57 472 | | | | | | | | |
|
| HQP5-MVS | | | | | | | 63.66 256 | | | | | | | | | | |
|
| HQP-NCC | | | | | | 87.54 250 | | 94.06 80 | | 79.80 88 | 74.18 225 | | | | | | |
|
| ACMP_Plane | | | | | | 87.54 250 | | 94.06 80 | | 79.80 88 | 74.18 225 | | | | | | |
|
| BP-MVS | | | | | | | | | | | | | | | 77.63 191 | | |
|
| HQP4-MVS | | | | | | | | | | | 74.18 225 | | | 95.61 202 | | | 88.63 298 |
|
| HQP3-MVS | | | | | | | | | 91.70 184 | | | | | | | 78.90 254 | |
|
| HQP2-MVS | | | | | | | | | | | | | 51.63 286 | | | | |
|
| NP-MVS | | | | | | 87.41 253 | 63.04 273 | | | | | 90.30 217 | | | | | |
|
| MDTV_nov1_ep13_2view | | | | | | | 59.90 356 | 80.13 423 | | 67.65 336 | 72.79 245 | | 54.33 257 | | 59.83 358 | | 92.58 221 |
|
| MDTV_nov1_ep13 | | | | 72.61 324 | | 89.06 189 | 68.48 90 | 80.33 419 | 90.11 278 | 71.84 262 | 71.81 265 | 75.92 423 | 53.01 272 | 93.92 288 | 48.04 408 | 73.38 298 | |
|
| ACMMP++_ref | | | | | | | | | | | | | | | | 71.63 311 | |
|
| ACMMP++ | | | | | | | | | | | | | | | | 69.72 322 | |
|
| Test By Simon | | | | | | | | | | | | | 54.21 260 | | | | |
|
| ITE_SJBPF | | | | | 70.43 428 | 74.44 443 | 47.06 452 | | 77.32 441 | 60.16 407 | 54.04 425 | 83.53 328 | 23.30 457 | 84.01 440 | 43.07 431 | 61.58 400 | 80.21 434 |
|
| DeepMVS_CX |  | | | | 34.71 473 | 51.45 485 | 24.73 493 | | 28.48 499 | 31.46 479 | 17.49 489 | 52.75 475 | 5.80 489 | 42.60 494 | 18.18 482 | 19.42 487 | 36.81 486 |
|