| LCM-MVSNet | | | 95.70 1 | 96.40 1 | 93.61 3 | 98.67 1 | 85.39 37 | 95.54 5 | 97.36 1 | 96.97 1 | 99.04 1 | 99.05 1 | 96.61 1 | 95.92 16 | 85.07 69 | 99.27 1 | 99.54 1 |
|
| mamv4 | | | 95.37 2 | 94.51 2 | 97.96 1 | 96.31 10 | 98.41 1 | 91.05 47 | 97.23 2 | 95.32 2 | 99.01 2 | 97.26 9 | 80.16 143 | 98.99 1 | 95.15 1 | 99.14 2 | 96.47 35 |
|
| TDRefinement | | | 93.52 3 | 93.39 5 | 93.88 2 | 95.94 15 | 90.26 4 | 95.70 4 | 96.46 3 | 90.58 9 | 92.86 51 | 96.29 22 | 88.16 36 | 94.17 102 | 86.07 54 | 98.48 18 | 97.22 18 |
|
| LTVRE_ROB | | 86.10 1 | 93.04 4 | 93.44 4 | 91.82 22 | 93.73 66 | 85.72 34 | 96.79 1 | 95.51 10 | 88.86 16 | 95.63 10 | 96.99 13 | 84.81 76 | 93.16 145 | 91.10 2 | 97.53 77 | 96.58 33 |
| 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 |
| reproduce_model | | | 92.89 5 | 93.18 8 | 92.01 13 | 94.20 51 | 88.23 9 | 92.87 13 | 94.32 22 | 90.25 11 | 95.65 9 | 95.74 33 | 87.75 42 | 95.72 36 | 89.60 5 | 98.27 28 | 92.08 225 |
|
| reproduce-ours | | | 92.86 6 | 93.22 6 | 91.76 23 | 94.39 46 | 87.71 11 | 92.40 28 | 94.38 20 | 89.82 13 | 95.51 12 | 95.49 42 | 89.64 22 | 95.82 26 | 89.13 7 | 98.26 30 | 91.76 236 |
|
| our_new_method | | | 92.86 6 | 93.22 6 | 91.76 23 | 94.39 46 | 87.71 11 | 92.40 28 | 94.38 20 | 89.82 13 | 95.51 12 | 95.49 42 | 89.64 22 | 95.82 26 | 89.13 7 | 98.26 30 | 91.76 236 |
|
| HPM-MVS_fast | | | 92.50 8 | 92.54 10 | 92.37 6 | 95.93 16 | 85.81 33 | 92.99 12 | 94.23 28 | 85.21 44 | 92.51 59 | 95.13 52 | 90.65 10 | 95.34 55 | 88.06 16 | 98.15 39 | 95.95 46 |
|
| lecture | | | 92.43 9 | 93.50 3 | 89.21 66 | 94.43 44 | 79.31 84 | 92.69 19 | 95.72 8 | 88.48 22 | 94.43 20 | 95.73 34 | 91.34 4 | 94.68 78 | 90.26 4 | 98.44 20 | 93.63 140 |
|
| SR-MVS-dyc-post | | | 92.41 10 | 92.41 11 | 92.39 5 | 94.13 57 | 88.95 6 | 92.87 13 | 94.16 33 | 88.75 18 | 93.79 33 | 94.43 76 | 88.83 27 | 95.51 47 | 87.16 37 | 97.60 71 | 92.73 182 |
|
| SR-MVS | | | 92.23 11 | 92.34 12 | 91.91 17 | 94.89 38 | 87.85 10 | 92.51 25 | 93.87 52 | 88.20 24 | 93.24 43 | 94.02 100 | 90.15 17 | 95.67 38 | 86.82 41 | 97.34 81 | 92.19 220 |
|
| HPM-MVS |  | | 92.13 12 | 92.20 14 | 91.91 17 | 95.58 26 | 84.67 46 | 93.51 8 | 94.85 16 | 82.88 70 | 91.77 72 | 93.94 108 | 90.55 13 | 95.73 35 | 88.50 12 | 98.23 33 | 95.33 61 |
| Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
| APD-MVS_3200maxsize | | | 92.05 13 | 92.24 13 | 91.48 25 | 93.02 85 | 85.17 39 | 92.47 27 | 95.05 15 | 87.65 28 | 93.21 44 | 94.39 81 | 90.09 18 | 95.08 66 | 86.67 43 | 97.60 71 | 94.18 110 |
|
| COLMAP_ROB |  | 83.01 3 | 91.97 14 | 91.95 15 | 92.04 11 | 93.68 67 | 86.15 24 | 93.37 10 | 95.10 14 | 90.28 10 | 92.11 64 | 95.03 54 | 89.75 21 | 94.93 70 | 79.95 128 | 98.27 28 | 95.04 74 |
| Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
| ACMMP |  | | 91.91 15 | 91.87 20 | 92.03 12 | 95.53 27 | 85.91 28 | 93.35 11 | 94.16 33 | 82.52 73 | 92.39 62 | 94.14 93 | 89.15 26 | 95.62 39 | 87.35 32 | 98.24 32 | 94.56 90 |
| 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 |
| mPP-MVS | | | 91.69 16 | 91.47 27 | 92.37 6 | 96.04 13 | 88.48 8 | 92.72 18 | 92.60 108 | 83.09 67 | 91.54 74 | 94.25 87 | 87.67 45 | 95.51 47 | 87.21 36 | 98.11 40 | 93.12 165 |
|
| CP-MVS | | | 91.67 17 | 91.58 24 | 91.96 14 | 95.29 31 | 87.62 13 | 93.38 9 | 93.36 69 | 83.16 66 | 91.06 84 | 94.00 101 | 88.26 33 | 95.71 37 | 87.28 35 | 98.39 23 | 92.55 195 |
|
| XVS | | | 91.54 18 | 91.36 29 | 92.08 9 | 95.64 24 | 86.25 22 | 92.64 20 | 93.33 71 | 85.07 45 | 89.99 106 | 94.03 99 | 86.57 56 | 95.80 28 | 87.35 32 | 97.62 69 | 94.20 107 |
|
| MTAPA | | | 91.52 19 | 91.60 23 | 91.29 30 | 96.59 4 | 86.29 21 | 92.02 34 | 91.81 135 | 84.07 55 | 92.00 67 | 94.40 80 | 86.63 55 | 95.28 58 | 88.59 11 | 98.31 26 | 92.30 212 |
|
| UA-Net | | | 91.49 20 | 91.53 25 | 91.39 27 | 94.98 35 | 82.95 58 | 93.52 7 | 92.79 100 | 88.22 23 | 88.53 142 | 97.64 6 | 83.45 90 | 94.55 86 | 86.02 58 | 98.60 13 | 96.67 30 |
|
| ACMMPR | | | 91.49 20 | 91.35 31 | 91.92 16 | 95.74 20 | 85.88 30 | 92.58 23 | 93.25 77 | 81.99 76 | 91.40 76 | 94.17 92 | 87.51 46 | 95.87 20 | 87.74 21 | 97.76 60 | 93.99 117 |
|
| LPG-MVS_test | | | 91.47 22 | 91.68 21 | 90.82 37 | 94.75 41 | 81.69 63 | 90.00 63 | 94.27 25 | 82.35 74 | 93.67 38 | 94.82 60 | 91.18 5 | 95.52 45 | 85.36 65 | 98.73 7 | 95.23 66 |
|
| region2R | | | 91.44 23 | 91.30 35 | 91.87 19 | 95.75 19 | 85.90 29 | 92.63 22 | 93.30 75 | 81.91 78 | 90.88 91 | 94.21 88 | 87.75 42 | 95.87 20 | 87.60 26 | 97.71 63 | 93.83 126 |
|
| HFP-MVS | | | 91.30 24 | 91.39 28 | 91.02 33 | 95.43 29 | 84.66 47 | 92.58 23 | 93.29 76 | 81.99 76 | 91.47 75 | 93.96 105 | 88.35 32 | 95.56 42 | 87.74 21 | 97.74 62 | 92.85 179 |
|
| ZNCC-MVS | | | 91.26 25 | 91.34 32 | 91.01 34 | 95.73 21 | 83.05 56 | 92.18 32 | 94.22 30 | 80.14 99 | 91.29 80 | 93.97 102 | 87.93 41 | 95.87 20 | 88.65 10 | 97.96 51 | 94.12 114 |
|
| APDe-MVS |  | | 91.22 26 | 91.92 16 | 89.14 68 | 92.97 87 | 78.04 96 | 92.84 16 | 94.14 37 | 83.33 64 | 93.90 29 | 95.73 34 | 88.77 28 | 96.41 3 | 87.60 26 | 97.98 48 | 92.98 175 |
| Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition |
| PGM-MVS | | | 91.20 27 | 90.95 44 | 91.93 15 | 95.67 23 | 85.85 31 | 90.00 63 | 93.90 49 | 80.32 96 | 91.74 73 | 94.41 79 | 88.17 35 | 95.98 13 | 86.37 47 | 97.99 46 | 93.96 119 |
|
| SteuartSystems-ACMMP | | | 91.16 28 | 91.36 29 | 90.55 41 | 93.91 62 | 80.97 70 | 91.49 41 | 93.48 67 | 82.82 71 | 92.60 58 | 93.97 102 | 88.19 34 | 96.29 6 | 87.61 25 | 98.20 36 | 94.39 102 |
| Skip Steuart: Steuart Systems R&D Blog. |
| MP-MVS |  | | 91.14 29 | 90.91 45 | 91.83 20 | 96.18 11 | 86.88 17 | 92.20 31 | 93.03 90 | 82.59 72 | 88.52 143 | 94.37 82 | 86.74 54 | 95.41 53 | 86.32 48 | 98.21 34 | 93.19 161 |
| Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
| GST-MVS | | | 90.96 30 | 91.01 41 | 90.82 37 | 95.45 28 | 82.73 59 | 91.75 39 | 93.74 55 | 80.98 89 | 91.38 77 | 93.80 112 | 87.20 50 | 95.80 28 | 87.10 39 | 97.69 65 | 93.93 120 |
|
| MP-MVS-pluss | | | 90.81 31 | 91.08 38 | 89.99 50 | 95.97 14 | 79.88 77 | 88.13 105 | 94.51 19 | 75.79 155 | 92.94 48 | 94.96 55 | 88.36 31 | 95.01 68 | 90.70 3 | 98.40 22 | 95.09 73 |
| MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
| ACMH+ | | 77.89 11 | 90.73 32 | 91.50 26 | 88.44 81 | 93.00 86 | 76.26 122 | 89.65 76 | 95.55 9 | 87.72 27 | 93.89 31 | 94.94 56 | 91.62 3 | 93.44 136 | 78.35 150 | 98.76 4 | 95.61 55 |
|
| ACMMP_NAP | | | 90.65 33 | 91.07 40 | 89.42 62 | 95.93 16 | 79.54 82 | 89.95 67 | 93.68 59 | 77.65 134 | 91.97 68 | 94.89 57 | 88.38 30 | 95.45 51 | 89.27 6 | 97.87 56 | 93.27 156 |
|
| ACMM | | 79.39 9 | 90.65 33 | 90.99 42 | 89.63 58 | 95.03 34 | 83.53 51 | 89.62 77 | 93.35 70 | 79.20 112 | 93.83 32 | 93.60 122 | 90.81 8 | 92.96 152 | 85.02 72 | 98.45 19 | 92.41 202 |
| Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
| LS3D | | | 90.60 35 | 90.34 52 | 91.38 28 | 89.03 199 | 84.23 49 | 93.58 6 | 94.68 18 | 90.65 8 | 90.33 100 | 93.95 107 | 84.50 78 | 95.37 54 | 80.87 118 | 95.50 153 | 94.53 93 |
|
| ACMP | | 79.16 10 | 90.54 36 | 90.60 50 | 90.35 45 | 94.36 48 | 80.98 69 | 89.16 87 | 94.05 42 | 79.03 115 | 92.87 50 | 93.74 117 | 90.60 12 | 95.21 61 | 82.87 96 | 98.76 4 | 94.87 78 |
| Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
| DPE-MVS |  | | 90.53 37 | 91.08 38 | 88.88 71 | 93.38 76 | 78.65 90 | 89.15 88 | 94.05 42 | 84.68 49 | 93.90 29 | 94.11 95 | 88.13 37 | 96.30 5 | 84.51 79 | 97.81 58 | 91.70 240 |
| Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025 |
| SED-MVS | | | 90.46 38 | 91.64 22 | 86.93 105 | 94.18 52 | 72.65 152 | 90.47 56 | 93.69 57 | 83.77 58 | 94.11 27 | 94.27 83 | 90.28 15 | 95.84 24 | 86.03 55 | 97.92 52 | 92.29 214 |
|
| SMA-MVS |  | | 90.31 39 | 90.48 51 | 89.83 55 | 95.31 30 | 79.52 83 | 90.98 48 | 93.24 78 | 75.37 164 | 92.84 52 | 95.28 48 | 85.58 69 | 96.09 8 | 87.92 18 | 97.76 60 | 93.88 123 |
| 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 |
| SF-MVS | | | 90.27 40 | 90.80 47 | 88.68 78 | 92.86 91 | 77.09 111 | 91.19 45 | 95.74 6 | 81.38 84 | 92.28 63 | 93.80 112 | 86.89 53 | 94.64 81 | 85.52 64 | 97.51 78 | 94.30 106 |
|
| v7n | | | 90.13 41 | 90.96 43 | 87.65 96 | 91.95 118 | 71.06 184 | 89.99 65 | 93.05 87 | 86.53 35 | 94.29 23 | 96.27 23 | 82.69 97 | 94.08 105 | 86.25 51 | 97.63 67 | 97.82 8 |
|
| PMVS |  | 80.48 6 | 90.08 42 | 90.66 49 | 88.34 84 | 96.71 3 | 92.97 2 | 90.31 60 | 89.57 210 | 88.51 21 | 90.11 102 | 95.12 53 | 90.98 7 | 88.92 271 | 77.55 164 | 97.07 88 | 83.13 397 |
| Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
| DVP-MVS++ | | | 90.07 43 | 91.09 37 | 87.00 103 | 91.55 135 | 72.64 154 | 96.19 2 | 94.10 40 | 85.33 42 | 93.49 40 | 94.64 68 | 81.12 131 | 95.88 18 | 87.41 30 | 95.94 133 | 92.48 198 |
|
| DVP-MVS |  | | 90.06 44 | 91.32 33 | 86.29 117 | 94.16 55 | 72.56 158 | 90.54 53 | 91.01 161 | 83.61 61 | 93.75 35 | 94.65 65 | 89.76 19 | 95.78 32 | 86.42 45 | 97.97 49 | 90.55 277 |
| 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 |
| PS-CasMVS | | | 90.06 44 | 91.92 16 | 84.47 167 | 96.56 6 | 58.83 350 | 89.04 89 | 92.74 102 | 91.40 6 | 96.12 5 | 96.06 29 | 87.23 49 | 95.57 41 | 79.42 138 | 98.74 6 | 99.00 2 |
|
| PEN-MVS | | | 90.03 46 | 91.88 19 | 84.48 166 | 96.57 5 | 58.88 347 | 88.95 90 | 93.19 79 | 91.62 5 | 96.01 7 | 96.16 27 | 87.02 51 | 95.60 40 | 78.69 146 | 98.72 9 | 98.97 3 |
|
| OurMVSNet-221017-0 | | | 90.01 47 | 89.74 57 | 90.83 36 | 93.16 83 | 80.37 74 | 91.91 37 | 93.11 83 | 81.10 87 | 95.32 14 | 97.24 10 | 72.94 244 | 94.85 72 | 85.07 69 | 97.78 59 | 97.26 16 |
|
| DTE-MVSNet | | | 89.98 48 | 91.91 18 | 84.21 176 | 96.51 7 | 57.84 358 | 88.93 91 | 92.84 98 | 91.92 4 | 96.16 4 | 96.23 24 | 86.95 52 | 95.99 12 | 79.05 142 | 98.57 15 | 98.80 6 |
|
| XVG-ACMP-BASELINE | | | 89.98 48 | 89.84 55 | 90.41 43 | 94.91 37 | 84.50 48 | 89.49 82 | 93.98 44 | 79.68 104 | 92.09 65 | 93.89 110 | 83.80 85 | 93.10 148 | 82.67 100 | 98.04 41 | 93.64 139 |
|
| 3Dnovator+ | | 83.92 2 | 89.97 50 | 89.66 58 | 90.92 35 | 91.27 144 | 81.66 66 | 91.25 43 | 94.13 38 | 88.89 15 | 88.83 134 | 94.26 86 | 77.55 172 | 95.86 23 | 84.88 73 | 95.87 139 | 95.24 65 |
|
| WR-MVS_H | | | 89.91 51 | 91.31 34 | 85.71 134 | 96.32 9 | 62.39 294 | 89.54 80 | 93.31 74 | 90.21 12 | 95.57 11 | 95.66 37 | 81.42 128 | 95.90 17 | 80.94 117 | 98.80 3 | 98.84 5 |
|
| OPM-MVS | | | 89.80 52 | 89.97 53 | 89.27 64 | 94.76 40 | 79.86 78 | 86.76 132 | 92.78 101 | 78.78 118 | 92.51 59 | 93.64 121 | 88.13 37 | 93.84 116 | 84.83 75 | 97.55 74 | 94.10 115 |
| Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS). |
| mvs_tets | | | 89.78 53 | 89.27 64 | 91.30 29 | 93.51 70 | 84.79 44 | 89.89 69 | 90.63 171 | 70.00 248 | 94.55 19 | 96.67 17 | 87.94 40 | 93.59 128 | 84.27 81 | 95.97 129 | 95.52 56 |
|
| anonymousdsp | | | 89.73 54 | 88.88 74 | 92.27 8 | 89.82 180 | 86.67 18 | 90.51 55 | 90.20 192 | 69.87 249 | 95.06 15 | 96.14 28 | 84.28 81 | 93.07 149 | 87.68 23 | 96.34 111 | 97.09 20 |
|
| test_djsdf | | | 89.62 55 | 89.01 68 | 91.45 26 | 92.36 103 | 82.98 57 | 91.98 35 | 90.08 195 | 71.54 228 | 94.28 25 | 96.54 19 | 81.57 126 | 94.27 92 | 86.26 49 | 96.49 105 | 97.09 20 |
|
| XVG-OURS-SEG-HR | | | 89.59 56 | 89.37 62 | 90.28 46 | 94.47 43 | 85.95 27 | 86.84 128 | 93.91 48 | 80.07 100 | 86.75 191 | 93.26 128 | 93.64 2 | 90.93 210 | 84.60 78 | 90.75 306 | 93.97 118 |
|
| APD-MVS |  | | 89.54 57 | 89.63 59 | 89.26 65 | 92.57 96 | 81.34 68 | 90.19 62 | 93.08 86 | 80.87 91 | 91.13 82 | 93.19 130 | 86.22 63 | 95.97 14 | 82.23 106 | 97.18 86 | 90.45 279 |
| Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
| jajsoiax | | | 89.41 58 | 88.81 77 | 91.19 32 | 93.38 76 | 84.72 45 | 89.70 72 | 90.29 189 | 69.27 256 | 94.39 21 | 96.38 21 | 86.02 66 | 93.52 132 | 83.96 83 | 95.92 135 | 95.34 60 |
|
| CPTT-MVS | | | 89.39 59 | 88.98 70 | 90.63 40 | 95.09 33 | 86.95 16 | 92.09 33 | 92.30 117 | 79.74 103 | 87.50 175 | 92.38 162 | 81.42 128 | 93.28 141 | 83.07 92 | 97.24 84 | 91.67 241 |
|
| ACMH | | 76.49 14 | 89.34 60 | 91.14 36 | 83.96 183 | 92.50 99 | 70.36 193 | 89.55 78 | 93.84 53 | 81.89 79 | 94.70 17 | 95.44 44 | 90.69 9 | 88.31 287 | 83.33 88 | 98.30 27 | 93.20 160 |
| Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
| testf1 | | | 89.30 61 | 89.12 65 | 89.84 53 | 88.67 210 | 85.64 35 | 90.61 51 | 93.17 80 | 86.02 38 | 93.12 45 | 95.30 46 | 84.94 73 | 89.44 263 | 74.12 212 | 96.10 124 | 94.45 96 |
|
| APD_test2 | | | 89.30 61 | 89.12 65 | 89.84 53 | 88.67 210 | 85.64 35 | 90.61 51 | 93.17 80 | 86.02 38 | 93.12 45 | 95.30 46 | 84.94 73 | 89.44 263 | 74.12 212 | 96.10 124 | 94.45 96 |
|
| CP-MVSNet | | | 89.27 63 | 90.91 45 | 84.37 168 | 96.34 8 | 58.61 353 | 88.66 98 | 92.06 124 | 90.78 7 | 95.67 8 | 95.17 51 | 81.80 123 | 95.54 44 | 79.00 143 | 98.69 10 | 98.95 4 |
|
| XVG-OURS | | | 89.18 64 | 88.83 76 | 90.23 47 | 94.28 49 | 86.11 26 | 85.91 147 | 93.60 62 | 80.16 98 | 89.13 131 | 93.44 124 | 83.82 84 | 90.98 207 | 83.86 85 | 95.30 161 | 93.60 143 |
|
| DeepC-MVS | | 82.31 4 | 89.15 65 | 89.08 67 | 89.37 63 | 93.64 68 | 79.07 86 | 88.54 101 | 94.20 31 | 73.53 189 | 89.71 114 | 94.82 60 | 85.09 72 | 95.77 34 | 84.17 82 | 98.03 43 | 93.26 158 |
| Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
| UniMVSNet_ETH3D | | | 89.12 66 | 90.72 48 | 84.31 174 | 97.00 2 | 64.33 265 | 89.67 75 | 88.38 230 | 88.84 17 | 94.29 23 | 97.57 7 | 90.48 14 | 91.26 198 | 72.57 242 | 97.65 66 | 97.34 15 |
|
| MSP-MVS | | | 89.08 67 | 88.16 84 | 91.83 20 | 95.76 18 | 86.14 25 | 92.75 17 | 93.90 49 | 78.43 123 | 89.16 129 | 92.25 171 | 72.03 258 | 96.36 4 | 88.21 13 | 90.93 298 | 92.98 175 |
| 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 |
| SD-MVS | | | 88.96 68 | 89.88 54 | 86.22 121 | 91.63 129 | 77.07 112 | 89.82 70 | 93.77 54 | 78.90 116 | 92.88 49 | 92.29 169 | 86.11 64 | 90.22 237 | 86.24 52 | 97.24 84 | 91.36 249 |
| 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 |
| HPM-MVS++ |  | | 88.93 69 | 88.45 80 | 90.38 44 | 94.92 36 | 85.85 31 | 89.70 72 | 91.27 153 | 78.20 126 | 86.69 195 | 92.28 170 | 80.36 141 | 95.06 67 | 86.17 53 | 96.49 105 | 90.22 283 |
|
| Elysia | | | 88.71 70 | 88.89 72 | 88.19 87 | 91.26 145 | 72.96 148 | 88.10 106 | 93.59 63 | 84.31 51 | 90.42 96 | 94.10 96 | 74.07 222 | 94.82 73 | 88.19 14 | 95.92 135 | 96.80 27 |
|
| StellarMVS | | | 88.71 70 | 88.89 72 | 88.19 87 | 91.26 145 | 72.96 148 | 88.10 106 | 93.59 63 | 84.31 51 | 90.42 96 | 94.10 96 | 74.07 222 | 94.82 73 | 88.19 14 | 95.92 135 | 96.80 27 |
|
| test_0402 | | | 88.65 72 | 89.58 61 | 85.88 130 | 92.55 97 | 72.22 166 | 84.01 193 | 89.44 213 | 88.63 20 | 94.38 22 | 95.77 32 | 86.38 62 | 93.59 128 | 79.84 129 | 95.21 162 | 91.82 234 |
|
| DP-MVS | | | 88.60 73 | 89.01 68 | 87.36 98 | 91.30 142 | 77.50 104 | 87.55 114 | 92.97 94 | 87.95 26 | 89.62 118 | 92.87 146 | 84.56 77 | 93.89 113 | 77.65 162 | 96.62 100 | 90.70 269 |
|
| APD_test1 | | | 88.40 74 | 87.91 86 | 89.88 52 | 89.50 186 | 86.65 20 | 89.98 66 | 91.91 130 | 84.26 53 | 90.87 92 | 93.92 109 | 82.18 112 | 89.29 267 | 73.75 220 | 94.81 181 | 93.70 134 |
|
| Anonymous20231211 | | | 88.40 74 | 89.62 60 | 84.73 158 | 90.46 165 | 65.27 254 | 88.86 92 | 93.02 91 | 87.15 30 | 93.05 47 | 97.10 11 | 82.28 110 | 92.02 178 | 76.70 174 | 97.99 46 | 96.88 26 |
|
| PS-MVSNAJss | | | 88.31 76 | 87.90 87 | 89.56 60 | 93.31 78 | 77.96 99 | 87.94 110 | 91.97 127 | 70.73 239 | 94.19 26 | 96.67 17 | 76.94 184 | 94.57 84 | 83.07 92 | 96.28 113 | 96.15 38 |
|
| OMC-MVS | | | 88.19 77 | 87.52 91 | 90.19 48 | 91.94 120 | 81.68 65 | 87.49 117 | 93.17 80 | 76.02 149 | 88.64 139 | 91.22 208 | 84.24 82 | 93.37 139 | 77.97 160 | 97.03 89 | 95.52 56 |
|
| CS-MVS | | | 88.14 78 | 87.67 90 | 89.54 61 | 89.56 184 | 79.18 85 | 90.47 56 | 94.77 17 | 79.37 110 | 84.32 258 | 89.33 272 | 83.87 83 | 94.53 87 | 82.45 102 | 94.89 177 | 94.90 76 |
|
| TSAR-MVS + MP. | | | 88.14 78 | 87.82 88 | 89.09 69 | 95.72 22 | 76.74 115 | 92.49 26 | 91.19 156 | 67.85 282 | 86.63 196 | 94.84 59 | 79.58 149 | 95.96 15 | 87.62 24 | 94.50 190 | 94.56 90 |
| Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition |
| tt0805 | | | 88.09 80 | 89.79 56 | 82.98 214 | 93.26 80 | 63.94 269 | 91.10 46 | 89.64 207 | 85.07 45 | 90.91 88 | 91.09 213 | 89.16 25 | 91.87 183 | 82.03 107 | 95.87 139 | 93.13 163 |
|
| EC-MVSNet | | | 88.01 81 | 88.32 83 | 87.09 100 | 89.28 191 | 72.03 169 | 90.31 60 | 96.31 4 | 80.88 90 | 85.12 234 | 89.67 266 | 84.47 79 | 95.46 50 | 82.56 101 | 96.26 116 | 93.77 132 |
|
| RPSCF | | | 88.00 82 | 86.93 105 | 91.22 31 | 90.08 173 | 89.30 5 | 89.68 74 | 91.11 157 | 79.26 111 | 89.68 115 | 94.81 63 | 82.44 101 | 87.74 298 | 76.54 179 | 88.74 341 | 96.61 32 |
|
| AllTest | | | 87.97 83 | 87.40 95 | 89.68 56 | 91.59 130 | 83.40 52 | 89.50 81 | 95.44 11 | 79.47 106 | 88.00 158 | 93.03 137 | 82.66 98 | 91.47 191 | 70.81 255 | 96.14 121 | 94.16 111 |
|
| TranMVSNet+NR-MVSNet | | | 87.86 84 | 88.76 78 | 85.18 146 | 94.02 60 | 64.13 266 | 84.38 185 | 91.29 150 | 84.88 48 | 92.06 66 | 93.84 111 | 86.45 59 | 93.73 118 | 73.22 233 | 98.66 11 | 97.69 9 |
|
| nrg030 | | | 87.85 85 | 88.49 79 | 85.91 128 | 90.07 175 | 69.73 201 | 87.86 111 | 94.20 31 | 74.04 181 | 92.70 57 | 94.66 64 | 85.88 67 | 91.50 190 | 79.72 131 | 97.32 82 | 96.50 34 |
|
| CNVR-MVS | | | 87.81 86 | 87.68 89 | 88.21 86 | 92.87 89 | 77.30 110 | 85.25 163 | 91.23 154 | 77.31 139 | 87.07 185 | 91.47 198 | 82.94 95 | 94.71 77 | 84.67 77 | 96.27 115 | 92.62 190 |
|
| HQP_MVS | | | 87.75 87 | 87.43 94 | 88.70 77 | 93.45 72 | 76.42 119 | 89.45 83 | 93.61 60 | 79.44 108 | 86.55 197 | 92.95 143 | 74.84 209 | 95.22 59 | 80.78 120 | 95.83 141 | 94.46 94 |
|
| sc_t1 | | | 87.70 88 | 88.94 71 | 83.99 181 | 93.47 71 | 67.15 232 | 85.05 168 | 88.21 237 | 86.81 32 | 91.87 70 | 97.65 5 | 85.51 71 | 87.91 293 | 74.22 207 | 97.63 67 | 96.92 25 |
|
| MM | | | 87.64 89 | 87.15 97 | 89.09 69 | 89.51 185 | 76.39 121 | 88.68 97 | 86.76 268 | 84.54 50 | 83.58 277 | 93.78 114 | 73.36 239 | 96.48 2 | 87.98 17 | 96.21 117 | 94.41 101 |
|
| MVSMamba_PlusPlus | | | 87.53 90 | 88.86 75 | 83.54 200 | 92.03 116 | 62.26 298 | 91.49 41 | 92.62 106 | 88.07 25 | 88.07 155 | 96.17 26 | 72.24 253 | 95.79 31 | 84.85 74 | 94.16 203 | 92.58 193 |
|
| NCCC | | | 87.36 91 | 86.87 106 | 88.83 72 | 92.32 106 | 78.84 89 | 86.58 136 | 91.09 159 | 78.77 119 | 84.85 245 | 90.89 223 | 80.85 134 | 95.29 56 | 81.14 115 | 95.32 158 | 92.34 210 |
|
| DeepPCF-MVS | | 81.24 5 | 87.28 92 | 86.21 116 | 90.49 42 | 91.48 139 | 84.90 42 | 83.41 216 | 92.38 113 | 70.25 245 | 89.35 126 | 90.68 233 | 82.85 96 | 94.57 84 | 79.55 135 | 95.95 132 | 92.00 229 |
|
| SixPastTwentyTwo | | | 87.20 93 | 87.45 93 | 86.45 114 | 92.52 98 | 69.19 211 | 87.84 112 | 88.05 238 | 81.66 81 | 94.64 18 | 96.53 20 | 65.94 295 | 94.75 76 | 83.02 94 | 96.83 94 | 95.41 58 |
|
| fmvsm_s_conf0.5_n_9 | | | 87.04 94 | 87.02 102 | 87.08 101 | 89.67 182 | 75.87 126 | 84.60 178 | 89.74 202 | 74.40 178 | 89.92 110 | 93.41 125 | 80.45 139 | 90.63 225 | 86.66 44 | 94.37 196 | 94.73 87 |
|
| SPE-MVS-test | | | 87.00 95 | 86.43 112 | 88.71 76 | 89.46 187 | 77.46 105 | 89.42 85 | 95.73 7 | 77.87 132 | 81.64 318 | 87.25 317 | 82.43 102 | 94.53 87 | 77.65 162 | 96.46 107 | 94.14 113 |
|
| UniMVSNet (Re) | | | 86.87 96 | 86.98 104 | 86.55 112 | 93.11 84 | 68.48 221 | 83.80 203 | 92.87 96 | 80.37 94 | 89.61 120 | 91.81 184 | 77.72 168 | 94.18 100 | 75.00 201 | 98.53 16 | 96.99 24 |
|
| Vis-MVSNet |  | | 86.86 97 | 86.58 109 | 87.72 94 | 92.09 113 | 77.43 107 | 87.35 118 | 92.09 123 | 78.87 117 | 84.27 263 | 94.05 98 | 78.35 160 | 93.65 121 | 80.54 124 | 91.58 285 | 92.08 225 |
| Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
| UniMVSNet_NR-MVSNet | | | 86.84 98 | 87.06 100 | 86.17 124 | 92.86 91 | 67.02 236 | 82.55 243 | 91.56 140 | 83.08 68 | 90.92 86 | 91.82 183 | 78.25 161 | 93.99 107 | 74.16 210 | 98.35 24 | 97.49 13 |
|
| DU-MVS | | | 86.80 99 | 86.99 103 | 86.21 122 | 93.24 81 | 67.02 236 | 83.16 226 | 92.21 118 | 81.73 80 | 90.92 86 | 91.97 176 | 77.20 178 | 93.99 107 | 74.16 210 | 98.35 24 | 97.61 10 |
|
| casdiffmvs_mvg |  | | 86.72 100 | 87.51 92 | 84.36 170 | 87.09 259 | 65.22 255 | 84.16 189 | 94.23 28 | 77.89 130 | 91.28 81 | 93.66 120 | 84.35 80 | 92.71 158 | 80.07 125 | 94.87 180 | 95.16 71 |
| Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025 |
| test_fmvsmconf0.01_n | | | 86.68 101 | 86.52 110 | 87.18 99 | 85.94 295 | 78.30 92 | 86.93 125 | 92.20 119 | 65.94 302 | 89.16 129 | 93.16 132 | 83.10 93 | 89.89 252 | 87.81 20 | 94.43 194 | 93.35 151 |
|
| tt0320-xc | | | 86.67 102 | 88.41 81 | 81.44 255 | 93.45 72 | 60.44 325 | 83.96 195 | 88.50 226 | 87.26 29 | 90.90 90 | 97.90 3 | 85.61 68 | 86.40 324 | 70.14 267 | 98.01 45 | 97.47 14 |
|
| IS-MVSNet | | | 86.66 103 | 86.82 108 | 86.17 124 | 92.05 115 | 66.87 239 | 91.21 44 | 88.64 223 | 86.30 37 | 89.60 121 | 92.59 155 | 69.22 276 | 94.91 71 | 73.89 217 | 97.89 55 | 96.72 29 |
|
| tt0320 | | | 86.63 104 | 88.36 82 | 81.41 256 | 93.57 69 | 60.73 322 | 84.37 186 | 88.61 225 | 87.00 31 | 90.75 93 | 97.98 2 | 85.54 70 | 86.45 322 | 69.75 272 | 97.70 64 | 97.06 22 |
|
| v10 | | | 86.54 105 | 87.10 99 | 84.84 152 | 88.16 225 | 63.28 276 | 86.64 135 | 92.20 119 | 75.42 163 | 92.81 54 | 94.50 72 | 74.05 225 | 94.06 106 | 83.88 84 | 96.28 113 | 97.17 19 |
|
| pmmvs6 | | | 86.52 106 | 88.06 85 | 81.90 241 | 92.22 109 | 62.28 297 | 84.66 177 | 89.15 217 | 83.54 63 | 89.85 111 | 97.32 8 | 88.08 39 | 86.80 315 | 70.43 264 | 97.30 83 | 96.62 31 |
|
| NormalMVS | | | 86.47 107 | 85.32 139 | 89.94 51 | 94.43 44 | 80.42 72 | 88.63 99 | 93.59 63 | 74.56 173 | 85.12 234 | 90.34 246 | 66.19 292 | 94.20 97 | 76.57 177 | 98.44 20 | 95.19 68 |
|
| PHI-MVS | | | 86.38 108 | 85.81 126 | 88.08 89 | 88.44 219 | 77.34 108 | 89.35 86 | 93.05 87 | 73.15 202 | 84.76 247 | 87.70 306 | 78.87 154 | 94.18 100 | 80.67 122 | 96.29 112 | 92.73 182 |
|
| CSCG | | | 86.26 109 | 86.47 111 | 85.60 136 | 90.87 157 | 74.26 136 | 87.98 109 | 91.85 131 | 80.35 95 | 89.54 124 | 88.01 293 | 79.09 152 | 92.13 174 | 75.51 194 | 95.06 169 | 90.41 280 |
|
| DeepC-MVS_fast | | 80.27 8 | 86.23 110 | 85.65 132 | 87.96 92 | 91.30 142 | 76.92 113 | 87.19 120 | 91.99 126 | 70.56 240 | 84.96 240 | 90.69 232 | 80.01 145 | 95.14 64 | 78.37 149 | 95.78 145 | 91.82 234 |
| Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
| v8 | | | 86.22 111 | 86.83 107 | 84.36 170 | 87.82 233 | 62.35 296 | 86.42 139 | 91.33 149 | 76.78 143 | 92.73 56 | 94.48 74 | 73.41 236 | 93.72 119 | 83.10 91 | 95.41 154 | 97.01 23 |
|
| Anonymous20240529 | | | 86.20 112 | 87.13 98 | 83.42 202 | 90.19 170 | 64.55 262 | 84.55 180 | 90.71 168 | 85.85 40 | 89.94 109 | 95.24 50 | 82.13 113 | 90.40 232 | 69.19 279 | 96.40 110 | 95.31 62 |
|
| fmvsm_s_conf0.5_n_3 | | | 86.19 113 | 87.27 96 | 82.95 216 | 86.91 266 | 70.38 192 | 85.31 162 | 92.61 107 | 75.59 159 | 88.32 150 | 92.87 146 | 82.22 111 | 88.63 279 | 88.80 9 | 92.82 248 | 89.83 293 |
|
| test_fmvsmconf0.1_n | | | 86.18 114 | 85.88 124 | 87.08 101 | 85.26 310 | 78.25 93 | 85.82 151 | 91.82 133 | 65.33 316 | 88.55 141 | 92.35 168 | 82.62 100 | 89.80 254 | 86.87 40 | 94.32 198 | 93.18 162 |
|
| CDPH-MVS | | | 86.17 115 | 85.54 133 | 88.05 91 | 92.25 107 | 75.45 129 | 83.85 200 | 92.01 125 | 65.91 304 | 86.19 208 | 91.75 188 | 83.77 86 | 94.98 69 | 77.43 167 | 96.71 98 | 93.73 133 |
|
| NR-MVSNet | | | 86.00 116 | 86.22 115 | 85.34 143 | 93.24 81 | 64.56 261 | 82.21 257 | 90.46 177 | 80.99 88 | 88.42 146 | 91.97 176 | 77.56 171 | 93.85 114 | 72.46 243 | 98.65 12 | 97.61 10 |
|
| train_agg | | | 85.98 117 | 85.28 140 | 88.07 90 | 92.34 104 | 79.70 80 | 83.94 196 | 90.32 184 | 65.79 306 | 84.49 252 | 90.97 217 | 81.93 119 | 93.63 123 | 81.21 114 | 96.54 103 | 90.88 263 |
|
| KinetiMVS | | | 85.95 118 | 86.10 119 | 85.50 140 | 87.56 243 | 69.78 199 | 83.70 206 | 89.83 201 | 80.42 93 | 87.76 169 | 93.24 129 | 73.76 230 | 91.54 189 | 85.03 71 | 93.62 223 | 95.19 68 |
|
| FC-MVSNet-test | | | 85.93 119 | 87.05 101 | 82.58 227 | 92.25 107 | 56.44 369 | 85.75 152 | 93.09 85 | 77.33 138 | 91.94 69 | 94.65 65 | 74.78 211 | 93.41 138 | 75.11 200 | 98.58 14 | 97.88 7 |
|
| test_fmvsmconf_n | | | 85.88 120 | 85.51 134 | 86.99 104 | 84.77 319 | 78.21 94 | 85.40 161 | 91.39 147 | 65.32 317 | 87.72 171 | 91.81 184 | 82.33 105 | 89.78 255 | 86.68 42 | 94.20 201 | 92.99 173 |
|
| Effi-MVS+-dtu | | | 85.82 121 | 83.38 186 | 93.14 4 | 87.13 254 | 91.15 3 | 87.70 113 | 88.42 229 | 74.57 172 | 83.56 278 | 85.65 341 | 78.49 159 | 94.21 96 | 72.04 245 | 92.88 244 | 94.05 116 |
|
| TAPA-MVS | | 77.73 12 | 85.71 122 | 84.83 149 | 88.37 83 | 88.78 209 | 79.72 79 | 87.15 122 | 93.50 66 | 69.17 257 | 85.80 218 | 89.56 267 | 80.76 135 | 92.13 174 | 73.21 238 | 95.51 152 | 93.25 159 |
| Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
| sasdasda | | | 85.50 123 | 86.14 117 | 83.58 196 | 87.97 227 | 67.13 233 | 87.55 114 | 94.32 22 | 73.44 192 | 88.47 144 | 87.54 309 | 86.45 59 | 91.06 205 | 75.76 192 | 93.76 214 | 92.54 196 |
|
| canonicalmvs | | | 85.50 123 | 86.14 117 | 83.58 196 | 87.97 227 | 67.13 233 | 87.55 114 | 94.32 22 | 73.44 192 | 88.47 144 | 87.54 309 | 86.45 59 | 91.06 205 | 75.76 192 | 93.76 214 | 92.54 196 |
|
| fmvsm_s_conf0.5_n_8 | | | 85.48 125 | 85.75 129 | 84.68 161 | 87.10 257 | 69.98 197 | 84.28 187 | 92.68 103 | 74.77 169 | 87.90 162 | 92.36 167 | 73.94 226 | 90.41 231 | 85.95 60 | 92.74 250 | 93.66 135 |
|
| EPP-MVSNet | | | 85.47 126 | 85.04 144 | 86.77 109 | 91.52 138 | 69.37 206 | 91.63 40 | 87.98 241 | 81.51 83 | 87.05 186 | 91.83 182 | 66.18 294 | 95.29 56 | 70.75 258 | 96.89 91 | 95.64 53 |
|
| GeoE | | | 85.45 127 | 85.81 126 | 84.37 168 | 90.08 173 | 67.07 235 | 85.86 150 | 91.39 147 | 72.33 219 | 87.59 173 | 90.25 251 | 84.85 75 | 92.37 168 | 78.00 158 | 91.94 275 | 93.66 135 |
|
| MVS_0304 | | | 85.37 128 | 84.58 158 | 87.75 93 | 85.28 309 | 73.36 141 | 86.54 138 | 85.71 285 | 77.56 137 | 81.78 316 | 92.47 160 | 70.29 270 | 96.02 11 | 85.59 63 | 95.96 130 | 93.87 124 |
|
| FIs | | | 85.35 129 | 86.27 114 | 82.60 226 | 91.86 122 | 57.31 362 | 85.10 167 | 93.05 87 | 75.83 154 | 91.02 85 | 93.97 102 | 73.57 232 | 92.91 156 | 73.97 216 | 98.02 44 | 97.58 12 |
|
| test_fmvsmvis_n_1920 | | | 85.22 130 | 85.36 138 | 84.81 154 | 85.80 297 | 76.13 125 | 85.15 166 | 92.32 116 | 61.40 352 | 91.33 78 | 90.85 226 | 83.76 87 | 86.16 330 | 84.31 80 | 93.28 232 | 92.15 223 |
|
| casdiffmvs |  | | 85.21 131 | 85.85 125 | 83.31 205 | 86.17 288 | 62.77 283 | 83.03 228 | 93.93 47 | 74.69 171 | 88.21 152 | 92.68 154 | 82.29 109 | 91.89 182 | 77.87 161 | 93.75 217 | 95.27 64 |
| Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025 |
| baseline | | | 85.20 132 | 85.93 122 | 83.02 212 | 86.30 283 | 62.37 295 | 84.55 180 | 93.96 45 | 74.48 175 | 87.12 180 | 92.03 175 | 82.30 107 | 91.94 179 | 78.39 148 | 94.21 200 | 94.74 86 |
|
| SSM_0404 | | | 85.16 133 | 85.09 142 | 85.36 142 | 90.14 172 | 69.52 204 | 86.17 144 | 91.58 138 | 74.41 176 | 86.55 197 | 91.49 195 | 78.54 155 | 93.97 109 | 73.71 221 | 93.21 236 | 92.59 192 |
|
| K. test v3 | | | 85.14 134 | 84.73 151 | 86.37 115 | 91.13 151 | 69.63 203 | 85.45 159 | 76.68 370 | 84.06 56 | 92.44 61 | 96.99 13 | 62.03 322 | 94.65 80 | 80.58 123 | 93.24 233 | 94.83 83 |
|
| mmtdpeth | | | 85.13 135 | 85.78 128 | 83.17 210 | 84.65 321 | 74.71 132 | 85.87 149 | 90.35 183 | 77.94 129 | 83.82 270 | 96.96 15 | 77.75 166 | 80.03 389 | 78.44 147 | 96.21 117 | 94.79 85 |
|
| EI-MVSNet-Vis-set | | | 85.12 136 | 84.53 161 | 86.88 106 | 84.01 334 | 72.76 151 | 83.91 199 | 85.18 295 | 80.44 92 | 88.75 136 | 85.49 345 | 80.08 144 | 91.92 180 | 82.02 108 | 90.85 303 | 95.97 44 |
|
| fmvsm_l_conf0.5_n_3 | | | 85.11 137 | 84.96 146 | 85.56 137 | 87.49 246 | 75.69 128 | 84.71 175 | 90.61 173 | 67.64 286 | 84.88 243 | 92.05 174 | 82.30 107 | 88.36 285 | 83.84 86 | 91.10 291 | 92.62 190 |
|
| MGCFI-Net | | | 85.04 138 | 85.95 121 | 82.31 235 | 87.52 244 | 63.59 272 | 86.23 143 | 93.96 45 | 73.46 190 | 88.07 155 | 87.83 304 | 86.46 58 | 90.87 215 | 76.17 186 | 93.89 211 | 92.47 200 |
|
| EI-MVSNet-UG-set | | | 85.04 138 | 84.44 164 | 86.85 107 | 83.87 338 | 72.52 160 | 83.82 201 | 85.15 296 | 80.27 97 | 88.75 136 | 85.45 347 | 79.95 146 | 91.90 181 | 81.92 111 | 90.80 305 | 96.13 39 |
|
| X-MVStestdata | | | 85.04 138 | 82.70 204 | 92.08 9 | 95.64 24 | 86.25 22 | 92.64 20 | 93.33 71 | 85.07 45 | 89.99 106 | 16.05 466 | 86.57 56 | 95.80 28 | 87.35 32 | 97.62 69 | 94.20 107 |
|
| MSLP-MVS++ | | | 85.00 141 | 86.03 120 | 81.90 241 | 91.84 125 | 71.56 179 | 86.75 133 | 93.02 91 | 75.95 152 | 87.12 180 | 89.39 270 | 77.98 163 | 89.40 266 | 77.46 165 | 94.78 182 | 84.75 369 |
|
| F-COLMAP | | | 84.97 142 | 83.42 185 | 89.63 58 | 92.39 102 | 83.40 52 | 88.83 93 | 91.92 129 | 73.19 201 | 80.18 340 | 89.15 276 | 77.04 182 | 93.28 141 | 65.82 312 | 92.28 264 | 92.21 219 |
|
| SSM_0407 | | | 84.89 143 | 84.85 148 | 85.01 150 | 89.13 195 | 68.97 214 | 85.60 156 | 91.58 138 | 74.41 176 | 85.68 219 | 91.49 195 | 78.54 155 | 93.69 120 | 73.71 221 | 93.47 225 | 92.38 207 |
|
| balanced_conf03 | | | 84.80 144 | 85.40 136 | 83.00 213 | 88.95 202 | 61.44 306 | 90.42 59 | 92.37 115 | 71.48 230 | 88.72 138 | 93.13 133 | 70.16 272 | 95.15 63 | 79.26 140 | 94.11 204 | 92.41 202 |
|
| 3Dnovator | | 80.37 7 | 84.80 144 | 84.71 154 | 85.06 149 | 86.36 281 | 74.71 132 | 88.77 95 | 90.00 197 | 75.65 157 | 84.96 240 | 93.17 131 | 74.06 224 | 91.19 200 | 78.28 152 | 91.09 292 | 89.29 303 |
|
| SymmetryMVS | | | 84.79 146 | 83.54 180 | 88.55 79 | 92.44 101 | 80.42 72 | 88.63 99 | 82.37 330 | 74.56 173 | 85.12 234 | 90.34 246 | 66.19 292 | 94.20 97 | 76.57 177 | 95.68 149 | 91.03 257 |
|
| IterMVS-LS | | | 84.73 147 | 84.98 145 | 83.96 183 | 87.35 248 | 63.66 270 | 83.25 221 | 89.88 200 | 76.06 147 | 89.62 118 | 92.37 165 | 73.40 238 | 92.52 163 | 78.16 155 | 94.77 184 | 95.69 51 |
| Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
| MVS_111021_HR | | | 84.63 148 | 84.34 168 | 85.49 141 | 90.18 171 | 75.86 127 | 79.23 308 | 87.13 258 | 73.35 194 | 85.56 226 | 89.34 271 | 83.60 89 | 90.50 228 | 76.64 176 | 94.05 208 | 90.09 289 |
|
| HQP-MVS | | | 84.61 149 | 84.06 173 | 86.27 118 | 91.19 147 | 70.66 187 | 84.77 170 | 92.68 103 | 73.30 197 | 80.55 332 | 90.17 256 | 72.10 254 | 94.61 82 | 77.30 169 | 94.47 192 | 93.56 147 |
|
| v1192 | | | 84.57 150 | 84.69 156 | 84.21 176 | 87.75 235 | 62.88 280 | 83.02 229 | 91.43 144 | 69.08 259 | 89.98 108 | 90.89 223 | 72.70 248 | 93.62 126 | 82.41 103 | 94.97 174 | 96.13 39 |
|
| fmvsm_s_conf0.5_n_5 | | | 84.56 151 | 84.71 154 | 84.11 179 | 87.92 230 | 72.09 168 | 84.80 169 | 88.64 223 | 64.43 326 | 88.77 135 | 91.78 186 | 78.07 162 | 87.95 292 | 85.85 61 | 92.18 268 | 92.30 212 |
|
| FMVSNet1 | | | 84.55 152 | 85.45 135 | 81.85 243 | 90.27 169 | 61.05 313 | 86.83 129 | 88.27 234 | 78.57 122 | 89.66 117 | 95.64 38 | 75.43 201 | 90.68 222 | 69.09 280 | 95.33 157 | 93.82 127 |
|
| v1144 | | | 84.54 153 | 84.72 153 | 84.00 180 | 87.67 239 | 62.55 287 | 82.97 231 | 90.93 164 | 70.32 244 | 89.80 112 | 90.99 216 | 73.50 233 | 93.48 134 | 81.69 113 | 94.65 188 | 95.97 44 |
|
| Gipuma |  | | 84.44 154 | 86.33 113 | 78.78 300 | 84.20 331 | 73.57 140 | 89.55 78 | 90.44 178 | 84.24 54 | 84.38 255 | 94.89 57 | 76.35 197 | 80.40 386 | 76.14 187 | 96.80 96 | 82.36 407 |
| S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
| fmvsm_s_conf0.5_n_4 | | | 84.38 155 | 84.27 169 | 84.74 157 | 87.25 250 | 70.84 186 | 83.55 211 | 88.45 228 | 68.64 268 | 86.29 207 | 91.31 204 | 74.97 207 | 88.42 283 | 87.87 19 | 90.07 320 | 94.95 75 |
|
| MCST-MVS | | | 84.36 156 | 83.93 176 | 85.63 135 | 91.59 130 | 71.58 177 | 83.52 212 | 92.13 121 | 61.82 345 | 83.96 268 | 89.75 264 | 79.93 147 | 93.46 135 | 78.33 151 | 94.34 197 | 91.87 233 |
|
| VDDNet | | | 84.35 157 | 85.39 137 | 81.25 258 | 95.13 32 | 59.32 339 | 85.42 160 | 81.11 341 | 86.41 36 | 87.41 176 | 96.21 25 | 73.61 231 | 90.61 226 | 66.33 305 | 96.85 92 | 93.81 130 |
|
| ETV-MVS | | | 84.31 158 | 83.91 177 | 85.52 138 | 88.58 215 | 70.40 191 | 84.50 184 | 93.37 68 | 78.76 120 | 84.07 266 | 78.72 420 | 80.39 140 | 95.13 65 | 73.82 219 | 92.98 242 | 91.04 256 |
|
| v1240 | | | 84.30 159 | 84.51 162 | 83.65 193 | 87.65 240 | 61.26 310 | 82.85 235 | 91.54 141 | 67.94 279 | 90.68 95 | 90.65 237 | 71.71 262 | 93.64 122 | 82.84 97 | 94.78 182 | 96.07 41 |
|
| MVS_111021_LR | | | 84.28 160 | 83.76 178 | 85.83 132 | 89.23 193 | 83.07 55 | 80.99 278 | 83.56 318 | 72.71 211 | 86.07 211 | 89.07 278 | 81.75 125 | 86.19 329 | 77.11 171 | 93.36 228 | 88.24 322 |
|
| h-mvs33 | | | 84.25 161 | 82.76 203 | 88.72 75 | 91.82 127 | 82.60 60 | 84.00 194 | 84.98 302 | 71.27 231 | 86.70 193 | 90.55 242 | 63.04 319 | 93.92 112 | 78.26 153 | 94.20 201 | 89.63 295 |
|
| v144192 | | | 84.24 162 | 84.41 165 | 83.71 192 | 87.59 242 | 61.57 305 | 82.95 232 | 91.03 160 | 67.82 283 | 89.80 112 | 90.49 243 | 73.28 240 | 93.51 133 | 81.88 112 | 94.89 177 | 96.04 43 |
|
| dcpmvs_2 | | | 84.23 163 | 85.14 141 | 81.50 253 | 88.61 214 | 61.98 302 | 82.90 234 | 93.11 83 | 68.66 267 | 92.77 55 | 92.39 161 | 78.50 158 | 87.63 301 | 76.99 173 | 92.30 261 | 94.90 76 |
|
| v1921920 | | | 84.23 163 | 84.37 167 | 83.79 188 | 87.64 241 | 61.71 304 | 82.91 233 | 91.20 155 | 67.94 279 | 90.06 103 | 90.34 246 | 72.04 257 | 93.59 128 | 82.32 104 | 94.91 175 | 96.07 41 |
|
| VDD-MVS | | | 84.23 163 | 84.58 158 | 83.20 208 | 91.17 150 | 65.16 257 | 83.25 221 | 84.97 303 | 79.79 102 | 87.18 179 | 94.27 83 | 74.77 212 | 90.89 213 | 69.24 276 | 96.54 103 | 93.55 149 |
|
| v2v482 | | | 84.09 166 | 84.24 170 | 83.62 194 | 87.13 254 | 61.40 307 | 82.71 238 | 89.71 205 | 72.19 222 | 89.55 122 | 91.41 199 | 70.70 268 | 93.20 143 | 81.02 116 | 93.76 214 | 96.25 37 |
|
| EG-PatchMatch MVS | | | 84.08 167 | 84.11 172 | 83.98 182 | 92.22 109 | 72.61 157 | 82.20 259 | 87.02 264 | 72.63 212 | 88.86 132 | 91.02 215 | 78.52 157 | 91.11 203 | 73.41 228 | 91.09 292 | 88.21 323 |
|
| fmvsm_s_conf0.5_n_6 | | | 84.05 168 | 84.14 171 | 83.81 186 | 87.75 235 | 71.17 182 | 83.42 215 | 91.10 158 | 67.90 281 | 84.53 250 | 90.70 231 | 73.01 243 | 88.73 277 | 85.09 68 | 93.72 219 | 91.53 246 |
|
| DP-MVS Recon | | | 84.05 168 | 83.22 189 | 86.52 113 | 91.73 128 | 75.27 130 | 83.23 223 | 92.40 111 | 72.04 224 | 82.04 307 | 88.33 289 | 77.91 165 | 93.95 111 | 66.17 306 | 95.12 167 | 90.34 282 |
|
| viewmacassd2359aftdt | | | 84.04 170 | 84.78 150 | 81.81 246 | 86.43 275 | 60.32 327 | 81.95 261 | 92.82 99 | 71.56 227 | 86.06 212 | 92.98 139 | 81.79 124 | 90.28 233 | 76.18 185 | 93.24 233 | 94.82 84 |
|
| TransMVSNet (Re) | | | 84.02 171 | 85.74 130 | 78.85 299 | 91.00 154 | 55.20 381 | 82.29 253 | 87.26 253 | 79.65 105 | 88.38 148 | 95.52 41 | 83.00 94 | 86.88 313 | 67.97 294 | 96.60 101 | 94.45 96 |
|
| Baseline_NR-MVSNet | | | 84.00 172 | 85.90 123 | 78.29 311 | 91.47 140 | 53.44 393 | 82.29 253 | 87.00 267 | 79.06 114 | 89.55 122 | 95.72 36 | 77.20 178 | 86.14 331 | 72.30 244 | 98.51 17 | 95.28 63 |
|
| fmvsm_l_conf0.5_n_9 | | | 83.98 173 | 84.46 163 | 82.53 230 | 86.11 291 | 70.65 189 | 82.45 248 | 89.17 216 | 67.72 285 | 86.74 192 | 91.49 195 | 79.20 150 | 85.86 340 | 84.71 76 | 92.60 254 | 91.07 255 |
|
| TSAR-MVS + GP. | | | 83.95 174 | 82.69 205 | 87.72 94 | 89.27 192 | 81.45 67 | 83.72 205 | 81.58 339 | 74.73 170 | 85.66 222 | 86.06 336 | 72.56 250 | 92.69 160 | 75.44 196 | 95.21 162 | 89.01 316 |
|
| LuminaMVS | | | 83.94 175 | 83.51 181 | 85.23 144 | 89.78 181 | 71.74 172 | 84.76 173 | 87.27 252 | 72.60 213 | 89.31 127 | 90.60 241 | 64.04 308 | 90.95 208 | 79.08 141 | 94.11 204 | 92.99 173 |
|
| alignmvs | | | 83.94 175 | 83.98 175 | 83.80 187 | 87.80 234 | 67.88 228 | 84.54 182 | 91.42 146 | 73.27 200 | 88.41 147 | 87.96 294 | 72.33 251 | 90.83 216 | 76.02 189 | 94.11 204 | 92.69 186 |
|
| Effi-MVS+ | | | 83.90 177 | 84.01 174 | 83.57 198 | 87.22 252 | 65.61 253 | 86.55 137 | 92.40 111 | 78.64 121 | 81.34 323 | 84.18 366 | 83.65 88 | 92.93 154 | 74.22 207 | 87.87 355 | 92.17 222 |
|
| fmvsm_s_conf0.1_n_2 | | | 83.82 178 | 83.49 182 | 84.84 152 | 85.99 294 | 70.19 195 | 80.93 279 | 87.58 248 | 67.26 292 | 87.94 161 | 92.37 165 | 71.40 264 | 88.01 289 | 86.03 55 | 91.87 276 | 96.31 36 |
|
| mvs5depth | | | 83.82 178 | 84.54 160 | 81.68 249 | 82.23 363 | 68.65 219 | 86.89 126 | 89.90 199 | 80.02 101 | 87.74 170 | 97.86 4 | 64.19 307 | 82.02 374 | 76.37 181 | 95.63 151 | 94.35 103 |
|
| CANet | | | 83.79 180 | 82.85 202 | 86.63 110 | 86.17 288 | 72.21 167 | 83.76 204 | 91.43 144 | 77.24 140 | 74.39 395 | 87.45 313 | 75.36 202 | 95.42 52 | 77.03 172 | 92.83 247 | 92.25 218 |
|
| pm-mvs1 | | | 83.69 181 | 84.95 147 | 79.91 285 | 90.04 177 | 59.66 336 | 82.43 249 | 87.44 249 | 75.52 161 | 87.85 165 | 95.26 49 | 81.25 130 | 85.65 344 | 68.74 286 | 96.04 126 | 94.42 100 |
|
| AdaColmap |  | | 83.66 182 | 83.69 179 | 83.57 198 | 90.05 176 | 72.26 165 | 86.29 141 | 90.00 197 | 78.19 127 | 81.65 317 | 87.16 319 | 83.40 91 | 94.24 95 | 61.69 348 | 94.76 185 | 84.21 379 |
|
| MIMVSNet1 | | | 83.63 183 | 84.59 157 | 80.74 269 | 94.06 59 | 62.77 283 | 82.72 237 | 84.53 310 | 77.57 136 | 90.34 99 | 95.92 31 | 76.88 190 | 85.83 341 | 61.88 346 | 97.42 79 | 93.62 141 |
|
| fmvsm_s_conf0.5_n_2 | | | 83.62 184 | 83.29 188 | 84.62 162 | 85.43 307 | 70.18 196 | 80.61 284 | 87.24 254 | 67.14 293 | 87.79 167 | 91.87 178 | 71.79 261 | 87.98 291 | 86.00 59 | 91.77 279 | 95.71 50 |
|
| test_fmvsm_n_1920 | | | 83.60 185 | 82.89 199 | 85.74 133 | 85.22 311 | 77.74 102 | 84.12 191 | 90.48 175 | 59.87 372 | 86.45 206 | 91.12 212 | 75.65 199 | 85.89 338 | 82.28 105 | 90.87 301 | 93.58 145 |
|
| WR-MVS | | | 83.56 186 | 84.40 166 | 81.06 263 | 93.43 75 | 54.88 382 | 78.67 317 | 85.02 300 | 81.24 85 | 90.74 94 | 91.56 193 | 72.85 245 | 91.08 204 | 68.00 293 | 98.04 41 | 97.23 17 |
|
| CNLPA | | | 83.55 187 | 83.10 194 | 84.90 151 | 89.34 190 | 83.87 50 | 84.54 182 | 88.77 220 | 79.09 113 | 83.54 279 | 88.66 286 | 74.87 208 | 81.73 376 | 66.84 300 | 92.29 263 | 89.11 309 |
|
| LCM-MVSNet-Re | | | 83.48 188 | 85.06 143 | 78.75 301 | 85.94 295 | 55.75 375 | 80.05 290 | 94.27 25 | 76.47 144 | 96.09 6 | 94.54 71 | 83.31 92 | 89.75 258 | 59.95 359 | 94.89 177 | 90.75 266 |
|
| hse-mvs2 | | | 83.47 189 | 81.81 220 | 88.47 80 | 91.03 153 | 82.27 61 | 82.61 239 | 83.69 316 | 71.27 231 | 86.70 193 | 86.05 337 | 63.04 319 | 92.41 166 | 78.26 153 | 93.62 223 | 90.71 268 |
|
| V42 | | | 83.47 189 | 83.37 187 | 83.75 190 | 83.16 357 | 63.33 275 | 81.31 272 | 90.23 191 | 69.51 253 | 90.91 88 | 90.81 228 | 74.16 221 | 92.29 172 | 80.06 126 | 90.22 318 | 95.62 54 |
|
| VPA-MVSNet | | | 83.47 189 | 84.73 151 | 79.69 290 | 90.29 168 | 57.52 361 | 81.30 274 | 88.69 222 | 76.29 145 | 87.58 174 | 94.44 75 | 80.60 138 | 87.20 307 | 66.60 303 | 96.82 95 | 94.34 104 |
|
| mamba_0408 | | | 83.44 192 | 82.88 200 | 85.11 147 | 89.13 195 | 68.97 214 | 72.73 392 | 91.28 151 | 72.90 205 | 85.68 219 | 90.61 239 | 76.78 191 | 93.97 109 | 73.37 230 | 93.47 225 | 92.38 207 |
|
| PAPM_NR | | | 83.23 193 | 83.19 191 | 83.33 204 | 90.90 156 | 65.98 249 | 88.19 104 | 90.78 167 | 78.13 128 | 80.87 328 | 87.92 298 | 73.49 235 | 92.42 165 | 70.07 268 | 88.40 344 | 91.60 243 |
|
| CLD-MVS | | | 83.18 194 | 82.64 206 | 84.79 155 | 89.05 198 | 67.82 229 | 77.93 327 | 92.52 109 | 68.33 271 | 85.07 237 | 81.54 395 | 82.06 116 | 92.96 152 | 69.35 275 | 97.91 54 | 93.57 146 |
| Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
| ANet_high | | | 83.17 195 | 85.68 131 | 75.65 349 | 81.24 375 | 45.26 437 | 79.94 292 | 92.91 95 | 83.83 57 | 91.33 78 | 96.88 16 | 80.25 142 | 85.92 334 | 68.89 283 | 95.89 138 | 95.76 48 |
|
| FA-MVS(test-final) | | | 83.13 196 | 83.02 195 | 83.43 201 | 86.16 290 | 66.08 248 | 88.00 108 | 88.36 231 | 75.55 160 | 85.02 238 | 92.75 152 | 65.12 301 | 92.50 164 | 74.94 202 | 91.30 289 | 91.72 238 |
|
| 114514_t | | | 83.10 197 | 82.54 209 | 84.77 156 | 92.90 88 | 69.10 213 | 86.65 134 | 90.62 172 | 54.66 404 | 81.46 320 | 90.81 228 | 76.98 183 | 94.38 90 | 72.62 241 | 96.18 119 | 90.82 265 |
|
| RRT-MVS | | | 82.97 198 | 83.44 183 | 81.57 251 | 85.06 314 | 58.04 356 | 87.20 119 | 90.37 181 | 77.88 131 | 88.59 140 | 93.70 119 | 63.17 316 | 93.05 150 | 76.49 180 | 88.47 343 | 93.62 141 |
|
| viewmanbaseed2359cas | | | 82.95 199 | 83.43 184 | 81.52 252 | 85.18 312 | 60.03 332 | 81.36 271 | 92.38 113 | 69.55 252 | 84.84 246 | 91.38 200 | 79.85 148 | 90.09 246 | 74.22 207 | 92.09 270 | 94.43 99 |
|
| BP-MVS1 | | | 82.81 200 | 81.67 222 | 86.23 119 | 87.88 232 | 68.53 220 | 86.06 146 | 84.36 311 | 75.65 157 | 85.14 233 | 90.19 253 | 45.84 408 | 94.42 89 | 85.18 67 | 94.72 186 | 95.75 49 |
|
| UGNet | | | 82.78 201 | 81.64 223 | 86.21 122 | 86.20 287 | 76.24 123 | 86.86 127 | 85.68 286 | 77.07 141 | 73.76 399 | 92.82 148 | 69.64 273 | 91.82 185 | 69.04 282 | 93.69 220 | 90.56 276 |
| 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 |
| LF4IMVS | | | 82.75 202 | 81.93 218 | 85.19 145 | 82.08 364 | 80.15 76 | 85.53 157 | 88.76 221 | 68.01 276 | 85.58 225 | 87.75 305 | 71.80 260 | 86.85 314 | 74.02 215 | 93.87 212 | 88.58 319 |
|
| EI-MVSNet | | | 82.61 203 | 82.42 211 | 83.20 208 | 83.25 354 | 63.66 270 | 83.50 213 | 85.07 297 | 76.06 147 | 86.55 197 | 85.10 353 | 73.41 236 | 90.25 234 | 78.15 157 | 90.67 312 | 95.68 52 |
|
| QAPM | | | 82.59 204 | 82.59 208 | 82.58 227 | 86.44 274 | 66.69 240 | 89.94 68 | 90.36 182 | 67.97 278 | 84.94 242 | 92.58 157 | 72.71 247 | 92.18 173 | 70.63 261 | 87.73 358 | 88.85 317 |
|
| fmvsm_s_conf0.1_n_a | | | 82.58 205 | 81.93 218 | 84.50 165 | 87.68 238 | 73.35 142 | 86.14 145 | 77.70 359 | 61.64 350 | 85.02 238 | 91.62 190 | 77.75 166 | 86.24 326 | 82.79 98 | 87.07 366 | 93.91 122 |
|
| Fast-Effi-MVS+-dtu | | | 82.54 206 | 81.41 232 | 85.90 129 | 85.60 302 | 76.53 118 | 83.07 227 | 89.62 209 | 73.02 204 | 79.11 350 | 83.51 371 | 80.74 136 | 90.24 236 | 68.76 285 | 89.29 331 | 90.94 260 |
|
| MVS_Test | | | 82.47 207 | 83.22 189 | 80.22 281 | 82.62 362 | 57.75 360 | 82.54 244 | 91.96 128 | 71.16 235 | 82.89 290 | 92.52 159 | 77.41 173 | 90.50 228 | 80.04 127 | 87.84 357 | 92.40 204 |
|
| viewdifsd2359ckpt11 | | | 82.46 208 | 82.98 197 | 80.88 266 | 83.53 341 | 61.00 316 | 79.46 301 | 85.97 281 | 69.48 254 | 87.89 163 | 91.31 204 | 82.10 114 | 88.61 280 | 74.28 205 | 92.86 245 | 93.02 169 |
|
| viewmsd2359difaftdt | | | 82.46 208 | 82.99 196 | 80.88 266 | 83.52 342 | 61.00 316 | 79.46 301 | 85.97 281 | 69.48 254 | 87.89 163 | 91.31 204 | 82.10 114 | 88.61 280 | 74.28 205 | 92.86 245 | 93.02 169 |
|
| v148 | | | 82.31 210 | 82.48 210 | 81.81 246 | 85.59 303 | 59.66 336 | 81.47 269 | 86.02 279 | 72.85 207 | 88.05 157 | 90.65 237 | 70.73 267 | 90.91 212 | 75.15 199 | 91.79 277 | 94.87 78 |
|
| API-MVS | | | 82.28 211 | 82.61 207 | 81.30 257 | 86.29 284 | 69.79 198 | 88.71 96 | 87.67 247 | 78.42 124 | 82.15 303 | 84.15 367 | 77.98 163 | 91.59 188 | 65.39 315 | 92.75 249 | 82.51 406 |
|
| MVSFormer | | | 82.23 212 | 81.57 228 | 84.19 178 | 85.54 304 | 69.26 208 | 91.98 35 | 90.08 195 | 71.54 228 | 76.23 375 | 85.07 356 | 58.69 344 | 94.27 92 | 86.26 49 | 88.77 339 | 89.03 314 |
|
| viewdifsd2359ckpt13 | | | 82.22 213 | 81.98 217 | 82.95 216 | 85.48 306 | 64.44 263 | 83.17 225 | 92.11 122 | 65.97 301 | 83.72 273 | 89.73 265 | 77.60 170 | 90.80 218 | 70.61 262 | 89.42 329 | 93.59 144 |
|
| fmvsm_s_conf0.5_n_a | | | 82.21 214 | 81.51 231 | 84.32 173 | 86.56 271 | 73.35 142 | 85.46 158 | 77.30 363 | 61.81 346 | 84.51 251 | 90.88 225 | 77.36 174 | 86.21 328 | 82.72 99 | 86.97 371 | 93.38 150 |
|
| EIA-MVS | | | 82.19 215 | 81.23 239 | 85.10 148 | 87.95 229 | 69.17 212 | 83.22 224 | 93.33 71 | 70.42 241 | 78.58 355 | 79.77 411 | 77.29 175 | 94.20 97 | 71.51 251 | 88.96 337 | 91.93 232 |
|
| GDP-MVS | | | 82.17 216 | 80.85 247 | 86.15 126 | 88.65 212 | 68.95 217 | 85.65 155 | 93.02 91 | 68.42 269 | 83.73 272 | 89.54 268 | 45.07 419 | 94.31 91 | 79.66 133 | 93.87 212 | 95.19 68 |
|
| fmvsm_s_conf0.1_n | | | 82.17 216 | 81.59 226 | 83.94 185 | 86.87 269 | 71.57 178 | 85.19 165 | 77.42 362 | 62.27 344 | 84.47 254 | 91.33 202 | 76.43 194 | 85.91 336 | 83.14 89 | 87.14 364 | 94.33 105 |
|
| PCF-MVS | | 74.62 15 | 82.15 218 | 80.92 245 | 85.84 131 | 89.43 188 | 72.30 164 | 80.53 285 | 91.82 133 | 57.36 388 | 87.81 166 | 89.92 261 | 77.67 169 | 93.63 123 | 58.69 364 | 95.08 168 | 91.58 244 |
| Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
| PLC |  | 73.85 16 | 82.09 219 | 80.31 254 | 87.45 97 | 90.86 158 | 80.29 75 | 85.88 148 | 90.65 170 | 68.17 274 | 76.32 374 | 86.33 331 | 73.12 242 | 92.61 162 | 61.40 351 | 90.02 322 | 89.44 298 |
| Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
| fmvsm_l_conf0.5_n | | | 82.06 220 | 81.54 230 | 83.60 195 | 83.94 335 | 73.90 138 | 83.35 218 | 86.10 275 | 58.97 374 | 83.80 271 | 90.36 245 | 74.23 219 | 86.94 312 | 82.90 95 | 90.22 318 | 89.94 291 |
|
| fmvsm_s_conf0.5_n_7 | | | 82.04 221 | 82.05 215 | 82.01 239 | 86.98 265 | 71.07 183 | 78.70 315 | 89.45 212 | 68.07 275 | 78.14 357 | 91.61 191 | 74.19 220 | 85.92 334 | 79.61 134 | 91.73 280 | 89.05 313 |
|
| GBi-Net | | | 82.02 222 | 82.07 213 | 81.85 243 | 86.38 278 | 61.05 313 | 86.83 129 | 88.27 234 | 72.43 214 | 86.00 213 | 95.64 38 | 63.78 312 | 90.68 222 | 65.95 308 | 93.34 229 | 93.82 127 |
|
| test1 | | | 82.02 222 | 82.07 213 | 81.85 243 | 86.38 278 | 61.05 313 | 86.83 129 | 88.27 234 | 72.43 214 | 86.00 213 | 95.64 38 | 63.78 312 | 90.68 222 | 65.95 308 | 93.34 229 | 93.82 127 |
|
| OpenMVS |  | 76.72 13 | 81.98 224 | 82.00 216 | 81.93 240 | 84.42 326 | 68.22 223 | 88.50 102 | 89.48 211 | 66.92 296 | 81.80 314 | 91.86 179 | 72.59 249 | 90.16 240 | 71.19 254 | 91.25 290 | 87.40 339 |
|
| KD-MVS_self_test | | | 81.93 225 | 83.14 193 | 78.30 310 | 84.75 320 | 52.75 397 | 80.37 287 | 89.42 214 | 70.24 246 | 90.26 101 | 93.39 126 | 74.55 218 | 86.77 316 | 68.61 288 | 96.64 99 | 95.38 59 |
|
| fmvsm_s_conf0.5_n | | | 81.91 226 | 81.30 236 | 83.75 190 | 86.02 293 | 71.56 179 | 84.73 174 | 77.11 366 | 62.44 341 | 84.00 267 | 90.68 233 | 76.42 195 | 85.89 338 | 83.14 89 | 87.11 365 | 93.81 130 |
|
| SDMVSNet | | | 81.90 227 | 83.17 192 | 78.10 314 | 88.81 207 | 62.45 293 | 76.08 361 | 86.05 278 | 73.67 186 | 83.41 280 | 93.04 135 | 82.35 104 | 80.65 383 | 70.06 269 | 95.03 170 | 91.21 251 |
|
| tfpnnormal | | | 81.79 228 | 82.95 198 | 78.31 309 | 88.93 203 | 55.40 377 | 80.83 282 | 82.85 325 | 76.81 142 | 85.90 217 | 94.14 93 | 74.58 216 | 86.51 320 | 66.82 301 | 95.68 149 | 93.01 172 |
|
| AstraMVS | | | 81.67 229 | 81.40 233 | 82.48 232 | 87.06 262 | 66.47 243 | 81.41 270 | 81.68 336 | 68.78 264 | 88.00 158 | 90.95 221 | 65.70 297 | 87.86 297 | 76.66 175 | 92.38 258 | 93.12 165 |
|
| c3_l | | | 81.64 230 | 81.59 226 | 81.79 248 | 80.86 381 | 59.15 344 | 78.61 318 | 90.18 193 | 68.36 270 | 87.20 178 | 87.11 321 | 69.39 274 | 91.62 187 | 78.16 155 | 94.43 194 | 94.60 89 |
|
| guyue | | | 81.57 231 | 81.37 235 | 82.15 236 | 86.39 276 | 66.13 247 | 81.54 268 | 83.21 320 | 69.79 250 | 87.77 168 | 89.95 259 | 65.36 300 | 87.64 300 | 75.88 190 | 92.49 256 | 92.67 187 |
|
| PVSNet_Blended_VisFu | | | 81.55 232 | 80.49 252 | 84.70 160 | 91.58 133 | 73.24 146 | 84.21 188 | 91.67 137 | 62.86 335 | 80.94 326 | 87.16 319 | 67.27 286 | 92.87 157 | 69.82 271 | 88.94 338 | 87.99 329 |
|
| fmvsm_l_conf0.5_n_a | | | 81.46 233 | 80.87 246 | 83.25 206 | 83.73 340 | 73.21 147 | 83.00 230 | 85.59 288 | 58.22 380 | 82.96 289 | 90.09 258 | 72.30 252 | 86.65 318 | 81.97 110 | 89.95 323 | 89.88 292 |
|
| SSM_04072 | | | 81.44 234 | 82.88 200 | 77.10 329 | 89.13 195 | 68.97 214 | 72.73 392 | 91.28 151 | 72.90 205 | 85.68 219 | 90.61 239 | 76.78 191 | 69.94 426 | 73.37 230 | 93.47 225 | 92.38 207 |
|
| DELS-MVS | | | 81.44 234 | 81.25 237 | 82.03 238 | 84.27 330 | 62.87 281 | 76.47 355 | 92.49 110 | 70.97 237 | 81.64 318 | 83.83 368 | 75.03 205 | 92.70 159 | 74.29 204 | 92.22 267 | 90.51 278 |
| 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 |
| FMVSNet2 | | | 81.31 236 | 81.61 225 | 80.41 277 | 86.38 278 | 58.75 351 | 83.93 198 | 86.58 270 | 72.43 214 | 87.65 172 | 92.98 139 | 63.78 312 | 90.22 237 | 66.86 298 | 93.92 210 | 92.27 216 |
|
| TinyColmap | | | 81.25 237 | 82.34 212 | 77.99 317 | 85.33 308 | 60.68 323 | 82.32 252 | 88.33 232 | 71.26 233 | 86.97 187 | 92.22 173 | 77.10 181 | 86.98 311 | 62.37 340 | 95.17 164 | 86.31 352 |
|
| diffmvs_AUTHOR | | | 81.24 238 | 81.55 229 | 80.30 279 | 80.61 386 | 60.22 328 | 77.98 326 | 90.48 175 | 67.77 284 | 83.34 282 | 89.50 269 | 74.69 214 | 87.42 303 | 78.78 145 | 90.81 304 | 93.27 156 |
|
| AUN-MVS | | | 81.18 239 | 78.78 277 | 88.39 82 | 90.93 155 | 82.14 62 | 82.51 245 | 83.67 317 | 64.69 325 | 80.29 336 | 85.91 340 | 51.07 383 | 92.38 167 | 76.29 184 | 93.63 222 | 90.65 273 |
|
| IMVS_0407 | | | 81.08 240 | 81.23 239 | 80.62 274 | 85.76 298 | 62.46 289 | 82.46 246 | 87.91 242 | 65.23 318 | 82.12 304 | 87.92 298 | 77.27 176 | 90.18 239 | 71.67 247 | 90.74 307 | 89.20 304 |
|
| tttt0517 | | | 81.07 241 | 79.58 267 | 85.52 138 | 88.99 201 | 66.45 244 | 87.03 124 | 75.51 378 | 73.76 185 | 88.32 150 | 90.20 252 | 37.96 440 | 94.16 104 | 79.36 139 | 95.13 165 | 95.93 47 |
|
| Fast-Effi-MVS+ | | | 81.04 242 | 80.57 249 | 82.46 233 | 87.50 245 | 63.22 277 | 78.37 321 | 89.63 208 | 68.01 276 | 81.87 310 | 82.08 389 | 82.31 106 | 92.65 161 | 67.10 297 | 88.30 350 | 91.51 247 |
|
| BH-untuned | | | 80.96 243 | 80.99 243 | 80.84 268 | 88.55 216 | 68.23 222 | 80.33 288 | 88.46 227 | 72.79 210 | 86.55 197 | 86.76 325 | 74.72 213 | 91.77 186 | 61.79 347 | 88.99 336 | 82.52 405 |
|
| IMVS_0403 | | | 80.93 244 | 81.00 242 | 80.72 271 | 85.76 298 | 62.46 289 | 81.82 262 | 87.91 242 | 65.23 318 | 82.07 306 | 87.92 298 | 75.91 198 | 90.50 228 | 71.67 247 | 90.74 307 | 89.20 304 |
|
| eth_miper_zixun_eth | | | 80.84 245 | 80.22 258 | 82.71 224 | 81.41 373 | 60.98 318 | 77.81 329 | 90.14 194 | 67.31 291 | 86.95 188 | 87.24 318 | 64.26 305 | 92.31 170 | 75.23 198 | 91.61 283 | 94.85 82 |
|
| xiu_mvs_v1_base_debu | | | 80.84 245 | 80.14 260 | 82.93 219 | 88.31 220 | 71.73 173 | 79.53 297 | 87.17 255 | 65.43 312 | 79.59 342 | 82.73 383 | 76.94 184 | 90.14 243 | 73.22 233 | 88.33 346 | 86.90 346 |
|
| xiu_mvs_v1_base | | | 80.84 245 | 80.14 260 | 82.93 219 | 88.31 220 | 71.73 173 | 79.53 297 | 87.17 255 | 65.43 312 | 79.59 342 | 82.73 383 | 76.94 184 | 90.14 243 | 73.22 233 | 88.33 346 | 86.90 346 |
|
| xiu_mvs_v1_base_debi | | | 80.84 245 | 80.14 260 | 82.93 219 | 88.31 220 | 71.73 173 | 79.53 297 | 87.17 255 | 65.43 312 | 79.59 342 | 82.73 383 | 76.94 184 | 90.14 243 | 73.22 233 | 88.33 346 | 86.90 346 |
|
| IterMVS-SCA-FT | | | 80.64 249 | 79.41 268 | 84.34 172 | 83.93 336 | 69.66 202 | 76.28 357 | 81.09 342 | 72.43 214 | 86.47 204 | 90.19 253 | 60.46 329 | 93.15 146 | 77.45 166 | 86.39 377 | 90.22 283 |
|
| BH-RMVSNet | | | 80.53 250 | 80.22 258 | 81.49 254 | 87.19 253 | 66.21 246 | 77.79 330 | 86.23 273 | 74.21 180 | 83.69 274 | 88.50 287 | 73.25 241 | 90.75 219 | 63.18 336 | 87.90 354 | 87.52 337 |
|
| VortexMVS | | | 80.51 251 | 80.63 248 | 80.15 283 | 83.36 350 | 61.82 303 | 80.63 283 | 88.00 240 | 67.11 294 | 87.23 177 | 89.10 277 | 63.98 309 | 88.00 290 | 73.63 225 | 92.63 253 | 90.64 274 |
|
| Anonymous202405211 | | | 80.51 251 | 81.19 241 | 78.49 306 | 88.48 217 | 57.26 363 | 76.63 350 | 82.49 328 | 81.21 86 | 84.30 261 | 92.24 172 | 67.99 282 | 86.24 326 | 62.22 341 | 95.13 165 | 91.98 231 |
|
| DIV-MVS_self_test | | | 80.43 253 | 80.23 256 | 81.02 264 | 79.99 391 | 59.25 341 | 77.07 343 | 87.02 264 | 67.38 288 | 86.19 208 | 89.22 273 | 63.09 317 | 90.16 240 | 76.32 182 | 95.80 143 | 93.66 135 |
|
| cl____ | | | 80.42 254 | 80.23 256 | 81.02 264 | 79.99 391 | 59.25 341 | 77.07 343 | 87.02 264 | 67.37 289 | 86.18 210 | 89.21 274 | 63.08 318 | 90.16 240 | 76.31 183 | 95.80 143 | 93.65 138 |
|
| diffmvs |  | | 80.40 255 | 80.48 253 | 80.17 282 | 79.02 404 | 60.04 330 | 77.54 334 | 90.28 190 | 66.65 299 | 82.40 297 | 87.33 316 | 73.50 233 | 87.35 305 | 77.98 159 | 89.62 327 | 93.13 163 |
| Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025 |
| EPNet | | | 80.37 256 | 78.41 285 | 86.23 119 | 76.75 418 | 73.28 144 | 87.18 121 | 77.45 361 | 76.24 146 | 68.14 429 | 88.93 280 | 65.41 299 | 93.85 114 | 69.47 274 | 96.12 123 | 91.55 245 |
| Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
| miper_ehance_all_eth | | | 80.34 257 | 80.04 263 | 81.24 260 | 79.82 394 | 58.95 346 | 77.66 331 | 89.66 206 | 65.75 309 | 85.99 216 | 85.11 352 | 68.29 281 | 91.42 195 | 76.03 188 | 92.03 271 | 93.33 152 |
|
| MG-MVS | | | 80.32 258 | 80.94 244 | 78.47 307 | 88.18 223 | 52.62 400 | 82.29 253 | 85.01 301 | 72.01 225 | 79.24 349 | 92.54 158 | 69.36 275 | 93.36 140 | 70.65 260 | 89.19 334 | 89.45 297 |
|
| mvsmamba | | | 80.30 259 | 78.87 274 | 84.58 164 | 88.12 226 | 67.55 230 | 92.35 30 | 84.88 304 | 63.15 333 | 85.33 230 | 90.91 222 | 50.71 385 | 95.20 62 | 66.36 304 | 87.98 353 | 90.99 258 |
|
| VPNet | | | 80.25 260 | 81.68 221 | 75.94 345 | 92.46 100 | 47.98 424 | 76.70 348 | 81.67 337 | 73.45 191 | 84.87 244 | 92.82 148 | 74.66 215 | 86.51 320 | 61.66 349 | 96.85 92 | 93.33 152 |
|
| MAR-MVS | | | 80.24 261 | 78.74 279 | 84.73 158 | 86.87 269 | 78.18 95 | 85.75 152 | 87.81 246 | 65.67 311 | 77.84 361 | 78.50 421 | 73.79 229 | 90.53 227 | 61.59 350 | 90.87 301 | 85.49 362 |
| 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 |
| PM-MVS | | | 80.20 262 | 79.00 273 | 83.78 189 | 88.17 224 | 86.66 19 | 81.31 272 | 66.81 434 | 69.64 251 | 88.33 149 | 90.19 253 | 64.58 302 | 83.63 365 | 71.99 246 | 90.03 321 | 81.06 425 |
|
| Anonymous20240521 | | | 80.18 263 | 81.25 237 | 76.95 331 | 83.15 358 | 60.84 320 | 82.46 246 | 85.99 280 | 68.76 265 | 86.78 189 | 93.73 118 | 59.13 341 | 77.44 400 | 73.71 221 | 97.55 74 | 92.56 194 |
|
| LFMVS | | | 80.15 264 | 80.56 250 | 78.89 298 | 89.19 194 | 55.93 371 | 85.22 164 | 73.78 390 | 82.96 69 | 84.28 262 | 92.72 153 | 57.38 353 | 90.07 248 | 63.80 330 | 95.75 146 | 90.68 270 |
|
| DPM-MVS | | | 80.10 265 | 79.18 272 | 82.88 222 | 90.71 161 | 69.74 200 | 78.87 313 | 90.84 165 | 60.29 368 | 75.64 384 | 85.92 339 | 67.28 285 | 93.11 147 | 71.24 253 | 91.79 277 | 85.77 358 |
|
| MSDG | | | 80.06 266 | 79.99 265 | 80.25 280 | 83.91 337 | 68.04 227 | 77.51 335 | 89.19 215 | 77.65 134 | 81.94 308 | 83.45 373 | 76.37 196 | 86.31 325 | 63.31 335 | 86.59 374 | 86.41 350 |
|
| FE-MVS | | | 79.98 267 | 78.86 275 | 83.36 203 | 86.47 273 | 66.45 244 | 89.73 71 | 84.74 308 | 72.80 209 | 84.22 265 | 91.38 200 | 44.95 420 | 93.60 127 | 63.93 328 | 91.50 286 | 90.04 290 |
|
| sd_testset | | | 79.95 268 | 81.39 234 | 75.64 350 | 88.81 207 | 58.07 355 | 76.16 360 | 82.81 326 | 73.67 186 | 83.41 280 | 93.04 135 | 80.96 133 | 77.65 399 | 58.62 365 | 95.03 170 | 91.21 251 |
|
| ab-mvs | | | 79.67 269 | 80.56 250 | 76.99 330 | 88.48 217 | 56.93 365 | 84.70 176 | 86.06 277 | 68.95 262 | 80.78 329 | 93.08 134 | 75.30 203 | 84.62 352 | 56.78 374 | 90.90 299 | 89.43 299 |
|
| VNet | | | 79.31 270 | 80.27 255 | 76.44 339 | 87.92 230 | 53.95 389 | 75.58 367 | 84.35 312 | 74.39 179 | 82.23 301 | 90.72 230 | 72.84 246 | 84.39 357 | 60.38 357 | 93.98 209 | 90.97 259 |
|
| thisisatest0530 | | | 79.07 271 | 77.33 295 | 84.26 175 | 87.13 254 | 64.58 260 | 83.66 208 | 75.95 373 | 68.86 263 | 85.22 232 | 87.36 315 | 38.10 437 | 93.57 131 | 75.47 195 | 94.28 199 | 94.62 88 |
|
| cl22 | | | 78.97 272 | 78.21 287 | 81.24 260 | 77.74 408 | 59.01 345 | 77.46 338 | 87.13 258 | 65.79 306 | 84.32 258 | 85.10 353 | 58.96 343 | 90.88 214 | 75.36 197 | 92.03 271 | 93.84 125 |
|
| patch_mono-2 | | | 78.89 273 | 79.39 269 | 77.41 326 | 84.78 318 | 68.11 225 | 75.60 365 | 83.11 322 | 60.96 360 | 79.36 346 | 89.89 262 | 75.18 204 | 72.97 415 | 73.32 232 | 92.30 261 | 91.15 253 |
|
| RPMNet | | | 78.88 274 | 78.28 286 | 80.68 273 | 79.58 395 | 62.64 285 | 82.58 241 | 94.16 33 | 74.80 168 | 75.72 382 | 92.59 155 | 48.69 392 | 95.56 42 | 73.48 227 | 82.91 413 | 83.85 384 |
|
| PAPR | | | 78.84 275 | 78.10 288 | 81.07 262 | 85.17 313 | 60.22 328 | 82.21 257 | 90.57 174 | 62.51 337 | 75.32 388 | 84.61 361 | 74.99 206 | 92.30 171 | 59.48 362 | 88.04 352 | 90.68 270 |
|
| viewmambaseed2359dif | | | 78.80 276 | 78.47 284 | 79.78 286 | 80.26 390 | 59.28 340 | 77.31 340 | 87.13 258 | 60.42 366 | 82.37 298 | 88.67 285 | 74.58 216 | 87.87 296 | 67.78 296 | 87.73 358 | 92.19 220 |
|
| PVSNet_BlendedMVS | | | 78.80 276 | 77.84 289 | 81.65 250 | 84.43 324 | 63.41 273 | 79.49 300 | 90.44 178 | 61.70 349 | 75.43 385 | 87.07 322 | 69.11 277 | 91.44 193 | 60.68 355 | 92.24 265 | 90.11 288 |
|
| FMVSNet3 | | | 78.80 276 | 78.55 281 | 79.57 292 | 82.89 361 | 56.89 367 | 81.76 263 | 85.77 284 | 69.04 260 | 86.00 213 | 90.44 244 | 51.75 381 | 90.09 246 | 65.95 308 | 93.34 229 | 91.72 238 |
|
| test_yl | | | 78.71 279 | 78.51 282 | 79.32 295 | 84.32 328 | 58.84 348 | 78.38 319 | 85.33 292 | 75.99 150 | 82.49 295 | 86.57 327 | 58.01 347 | 90.02 250 | 62.74 337 | 92.73 251 | 89.10 310 |
|
| DCV-MVSNet | | | 78.71 279 | 78.51 282 | 79.32 295 | 84.32 328 | 58.84 348 | 78.38 319 | 85.33 292 | 75.99 150 | 82.49 295 | 86.57 327 | 58.01 347 | 90.02 250 | 62.74 337 | 92.73 251 | 89.10 310 |
|
| test1111 | | | 78.53 281 | 78.85 276 | 77.56 323 | 92.22 109 | 47.49 426 | 82.61 239 | 69.24 422 | 72.43 214 | 85.28 231 | 94.20 89 | 51.91 379 | 90.07 248 | 65.36 316 | 96.45 108 | 95.11 72 |
|
| FE-MVSNET | | | 78.46 282 | 79.36 270 | 75.75 347 | 86.53 272 | 54.53 384 | 78.03 323 | 85.35 291 | 69.01 261 | 85.41 229 | 90.68 233 | 64.27 304 | 85.73 342 | 62.59 339 | 92.35 260 | 87.00 345 |
|
| icg_test_0407_2 | | | 78.46 282 | 79.68 266 | 74.78 357 | 85.76 298 | 62.46 289 | 68.51 421 | 87.91 242 | 65.23 318 | 82.12 304 | 87.92 298 | 77.27 176 | 72.67 416 | 71.67 247 | 90.74 307 | 89.20 304 |
|
| ECVR-MVS |  | | 78.44 284 | 78.63 280 | 77.88 319 | 91.85 123 | 48.95 420 | 83.68 207 | 69.91 418 | 72.30 220 | 84.26 264 | 94.20 89 | 51.89 380 | 89.82 253 | 63.58 331 | 96.02 127 | 94.87 78 |
|
| pmmvs-eth3d | | | 78.42 285 | 77.04 298 | 82.57 229 | 87.44 247 | 74.41 135 | 80.86 281 | 79.67 350 | 55.68 397 | 84.69 248 | 90.31 250 | 60.91 327 | 85.42 345 | 62.20 342 | 91.59 284 | 87.88 333 |
|
| mvs_anonymous | | | 78.13 286 | 78.76 278 | 76.23 344 | 79.24 401 | 50.31 416 | 78.69 316 | 84.82 306 | 61.60 351 | 83.09 288 | 92.82 148 | 73.89 228 | 87.01 308 | 68.33 292 | 86.41 376 | 91.37 248 |
|
| TAMVS | | | 78.08 287 | 76.36 305 | 83.23 207 | 90.62 162 | 72.87 150 | 79.08 309 | 80.01 349 | 61.72 348 | 81.35 322 | 86.92 324 | 63.96 311 | 88.78 275 | 50.61 413 | 93.01 241 | 88.04 328 |
|
| miper_enhance_ethall | | | 77.83 288 | 76.93 299 | 80.51 275 | 76.15 425 | 58.01 357 | 75.47 369 | 88.82 219 | 58.05 382 | 83.59 276 | 80.69 399 | 64.41 303 | 91.20 199 | 73.16 239 | 92.03 271 | 92.33 211 |
|
| Vis-MVSNet (Re-imp) | | | 77.82 289 | 77.79 290 | 77.92 318 | 88.82 206 | 51.29 410 | 83.28 219 | 71.97 406 | 74.04 181 | 82.23 301 | 89.78 263 | 57.38 353 | 89.41 265 | 57.22 373 | 95.41 154 | 93.05 168 |
|
| CANet_DTU | | | 77.81 290 | 77.05 297 | 80.09 284 | 81.37 374 | 59.90 334 | 83.26 220 | 88.29 233 | 69.16 258 | 67.83 432 | 83.72 369 | 60.93 326 | 89.47 260 | 69.22 278 | 89.70 326 | 90.88 263 |
|
| OpenMVS_ROB |  | 70.19 17 | 77.77 291 | 77.46 292 | 78.71 302 | 84.39 327 | 61.15 311 | 81.18 276 | 82.52 327 | 62.45 340 | 83.34 282 | 87.37 314 | 66.20 291 | 88.66 278 | 64.69 323 | 85.02 393 | 86.32 351 |
|
| SSC-MVS | | | 77.55 292 | 81.64 223 | 65.29 423 | 90.46 165 | 20.33 470 | 73.56 385 | 68.28 424 | 85.44 41 | 88.18 154 | 94.64 68 | 70.93 266 | 81.33 378 | 71.25 252 | 92.03 271 | 94.20 107 |
|
| MDA-MVSNet-bldmvs | | | 77.47 293 | 76.90 300 | 79.16 297 | 79.03 403 | 64.59 259 | 66.58 433 | 75.67 376 | 73.15 202 | 88.86 132 | 88.99 279 | 66.94 287 | 81.23 379 | 64.71 322 | 88.22 351 | 91.64 242 |
|
| jason | | | 77.42 294 | 75.75 311 | 82.43 234 | 87.10 257 | 69.27 207 | 77.99 325 | 81.94 334 | 51.47 424 | 77.84 361 | 85.07 356 | 60.32 331 | 89.00 269 | 70.74 259 | 89.27 333 | 89.03 314 |
| jason: jason. |
| CDS-MVSNet | | | 77.32 295 | 75.40 315 | 83.06 211 | 89.00 200 | 72.48 161 | 77.90 328 | 82.17 332 | 60.81 361 | 78.94 352 | 83.49 372 | 59.30 339 | 88.76 276 | 54.64 393 | 92.37 259 | 87.93 332 |
| Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
| IMVS_0404 | | | 77.24 296 | 77.75 291 | 75.73 348 | 85.76 298 | 62.46 289 | 70.84 407 | 87.91 242 | 65.23 318 | 72.21 407 | 87.92 298 | 67.48 284 | 75.53 408 | 71.67 247 | 90.74 307 | 89.20 304 |
|
| xiu_mvs_v2_base | | | 77.19 297 | 76.75 302 | 78.52 305 | 87.01 263 | 61.30 309 | 75.55 368 | 87.12 262 | 61.24 357 | 74.45 394 | 78.79 419 | 77.20 178 | 90.93 210 | 64.62 325 | 84.80 400 | 83.32 393 |
|
| MVSTER | | | 77.09 298 | 75.70 312 | 81.25 258 | 75.27 433 | 61.08 312 | 77.49 337 | 85.07 297 | 60.78 362 | 86.55 197 | 88.68 283 | 43.14 429 | 90.25 234 | 73.69 224 | 90.67 312 | 92.42 201 |
|
| PS-MVSNAJ | | | 77.04 299 | 76.53 304 | 78.56 304 | 87.09 259 | 61.40 307 | 75.26 370 | 87.13 258 | 61.25 356 | 74.38 396 | 77.22 433 | 76.94 184 | 90.94 209 | 64.63 324 | 84.83 399 | 83.35 392 |
|
| IterMVS | | | 76.91 300 | 76.34 306 | 78.64 303 | 80.91 379 | 64.03 267 | 76.30 356 | 79.03 353 | 64.88 324 | 83.11 286 | 89.16 275 | 59.90 335 | 84.46 355 | 68.61 288 | 85.15 391 | 87.42 338 |
| Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
| D2MVS | | | 76.84 301 | 75.67 313 | 80.34 278 | 80.48 388 | 62.16 301 | 73.50 386 | 84.80 307 | 57.61 386 | 82.24 300 | 87.54 309 | 51.31 382 | 87.65 299 | 70.40 265 | 93.19 237 | 91.23 250 |
|
| CL-MVSNet_self_test | | | 76.81 302 | 77.38 294 | 75.12 353 | 86.90 267 | 51.34 408 | 73.20 389 | 80.63 346 | 68.30 272 | 81.80 314 | 88.40 288 | 66.92 288 | 80.90 380 | 55.35 387 | 94.90 176 | 93.12 165 |
|
| TR-MVS | | | 76.77 303 | 75.79 310 | 79.72 289 | 86.10 292 | 65.79 251 | 77.14 341 | 83.02 323 | 65.20 322 | 81.40 321 | 82.10 387 | 66.30 290 | 90.73 221 | 55.57 384 | 85.27 387 | 82.65 400 |
|
| MonoMVSNet | | | 76.66 304 | 77.26 296 | 74.86 355 | 79.86 393 | 54.34 386 | 86.26 142 | 86.08 276 | 71.08 236 | 85.59 224 | 88.68 283 | 53.95 371 | 85.93 333 | 63.86 329 | 80.02 429 | 84.32 375 |
|
| USDC | | | 76.63 305 | 76.73 303 | 76.34 341 | 83.46 345 | 57.20 364 | 80.02 291 | 88.04 239 | 52.14 420 | 83.65 275 | 91.25 207 | 63.24 315 | 86.65 318 | 54.66 392 | 94.11 204 | 85.17 364 |
|
| BH-w/o | | | 76.57 306 | 76.07 309 | 78.10 314 | 86.88 268 | 65.92 250 | 77.63 332 | 86.33 271 | 65.69 310 | 80.89 327 | 79.95 408 | 68.97 279 | 90.74 220 | 53.01 403 | 85.25 388 | 77.62 436 |
|
| Patchmtry | | | 76.56 307 | 77.46 292 | 73.83 363 | 79.37 400 | 46.60 430 | 82.41 250 | 76.90 367 | 73.81 184 | 85.56 226 | 92.38 162 | 48.07 395 | 83.98 362 | 63.36 334 | 95.31 160 | 90.92 261 |
|
| PVSNet_Blended | | | 76.49 308 | 75.40 315 | 79.76 288 | 84.43 324 | 63.41 273 | 75.14 371 | 90.44 178 | 57.36 388 | 75.43 385 | 78.30 422 | 69.11 277 | 91.44 193 | 60.68 355 | 87.70 360 | 84.42 374 |
|
| miper_lstm_enhance | | | 76.45 309 | 76.10 308 | 77.51 324 | 76.72 419 | 60.97 319 | 64.69 437 | 85.04 299 | 63.98 329 | 83.20 285 | 88.22 290 | 56.67 357 | 78.79 396 | 73.22 233 | 93.12 238 | 92.78 181 |
|
| lupinMVS | | | 76.37 310 | 74.46 324 | 82.09 237 | 85.54 304 | 69.26 208 | 76.79 346 | 80.77 345 | 50.68 431 | 76.23 375 | 82.82 381 | 58.69 344 | 88.94 270 | 69.85 270 | 88.77 339 | 88.07 325 |
|
| cascas | | | 76.29 311 | 74.81 320 | 80.72 271 | 84.47 323 | 62.94 279 | 73.89 383 | 87.34 250 | 55.94 395 | 75.16 390 | 76.53 438 | 63.97 310 | 91.16 201 | 65.00 319 | 90.97 297 | 88.06 327 |
|
| SD_0403 | | | 76.08 312 | 76.77 301 | 73.98 361 | 87.08 261 | 49.45 419 | 83.62 209 | 84.68 309 | 63.31 330 | 75.13 391 | 87.47 312 | 71.85 259 | 84.56 353 | 49.97 415 | 87.86 356 | 87.94 331 |
|
| WB-MVS | | | 76.06 313 | 80.01 264 | 64.19 426 | 89.96 179 | 20.58 469 | 72.18 396 | 68.19 425 | 83.21 65 | 86.46 205 | 93.49 123 | 70.19 271 | 78.97 394 | 65.96 307 | 90.46 317 | 93.02 169 |
|
| thres600view7 | | | 75.97 314 | 75.35 317 | 77.85 321 | 87.01 263 | 51.84 406 | 80.45 286 | 73.26 395 | 75.20 165 | 83.10 287 | 86.31 333 | 45.54 410 | 89.05 268 | 55.03 390 | 92.24 265 | 92.66 188 |
|
| GA-MVS | | | 75.83 315 | 74.61 321 | 79.48 294 | 81.87 366 | 59.25 341 | 73.42 387 | 82.88 324 | 68.68 266 | 79.75 341 | 81.80 392 | 50.62 386 | 89.46 261 | 66.85 299 | 85.64 384 | 89.72 294 |
|
| MVP-Stereo | | | 75.81 316 | 73.51 333 | 82.71 224 | 89.35 189 | 73.62 139 | 80.06 289 | 85.20 294 | 60.30 367 | 73.96 397 | 87.94 295 | 57.89 351 | 89.45 262 | 52.02 407 | 74.87 447 | 85.06 366 |
| Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
| test_fmvs3 | | | 75.72 317 | 75.20 318 | 77.27 327 | 75.01 436 | 69.47 205 | 78.93 310 | 84.88 304 | 46.67 438 | 87.08 184 | 87.84 303 | 50.44 388 | 71.62 421 | 77.42 168 | 88.53 342 | 90.72 267 |
|
| thres100view900 | | | 75.45 318 | 75.05 319 | 76.66 337 | 87.27 249 | 51.88 405 | 81.07 277 | 73.26 395 | 75.68 156 | 83.25 284 | 86.37 330 | 45.54 410 | 88.80 272 | 51.98 408 | 90.99 294 | 89.31 301 |
|
| ET-MVSNet_ETH3D | | | 75.28 319 | 72.77 342 | 82.81 223 | 83.03 360 | 68.11 225 | 77.09 342 | 76.51 371 | 60.67 364 | 77.60 366 | 80.52 403 | 38.04 438 | 91.15 202 | 70.78 257 | 90.68 311 | 89.17 308 |
|
| thres400 | | | 75.14 320 | 74.23 326 | 77.86 320 | 86.24 285 | 52.12 402 | 79.24 306 | 73.87 388 | 73.34 195 | 81.82 312 | 84.60 362 | 46.02 403 | 88.80 272 | 51.98 408 | 90.99 294 | 92.66 188 |
|
| wuyk23d | | | 75.13 321 | 79.30 271 | 62.63 429 | 75.56 429 | 75.18 131 | 80.89 280 | 73.10 397 | 75.06 167 | 94.76 16 | 95.32 45 | 87.73 44 | 52.85 461 | 34.16 459 | 97.11 87 | 59.85 457 |
|
| EU-MVSNet | | | 75.12 322 | 74.43 325 | 77.18 328 | 83.11 359 | 59.48 338 | 85.71 154 | 82.43 329 | 39.76 458 | 85.64 223 | 88.76 281 | 44.71 422 | 87.88 295 | 73.86 218 | 85.88 383 | 84.16 380 |
|
| HyFIR lowres test | | | 75.12 322 | 72.66 344 | 82.50 231 | 91.44 141 | 65.19 256 | 72.47 394 | 87.31 251 | 46.79 437 | 80.29 336 | 84.30 364 | 52.70 376 | 92.10 177 | 51.88 412 | 86.73 372 | 90.22 283 |
|
| CMPMVS |  | 59.41 20 | 75.12 322 | 73.57 331 | 79.77 287 | 75.84 428 | 67.22 231 | 81.21 275 | 82.18 331 | 50.78 429 | 76.50 371 | 87.66 307 | 55.20 367 | 82.99 368 | 62.17 344 | 90.64 316 | 89.09 312 |
| M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
| pmmvs4 | | | 74.92 325 | 72.98 340 | 80.73 270 | 84.95 315 | 71.71 176 | 76.23 358 | 77.59 360 | 52.83 414 | 77.73 365 | 86.38 329 | 56.35 360 | 84.97 349 | 57.72 372 | 87.05 367 | 85.51 361 |
|
| tfpn200view9 | | | 74.86 326 | 74.23 326 | 76.74 336 | 86.24 285 | 52.12 402 | 79.24 306 | 73.87 388 | 73.34 195 | 81.82 312 | 84.60 362 | 46.02 403 | 88.80 272 | 51.98 408 | 90.99 294 | 89.31 301 |
|
| 1112_ss | | | 74.82 327 | 73.74 329 | 78.04 316 | 89.57 183 | 60.04 330 | 76.49 354 | 87.09 263 | 54.31 405 | 73.66 400 | 79.80 409 | 60.25 332 | 86.76 317 | 58.37 366 | 84.15 404 | 87.32 340 |
|
| EGC-MVSNET | | | 74.79 328 | 69.99 372 | 89.19 67 | 94.89 38 | 87.00 15 | 91.89 38 | 86.28 272 | 1.09 467 | 2.23 469 | 95.98 30 | 81.87 122 | 89.48 259 | 79.76 130 | 95.96 130 | 91.10 254 |
|
| ppachtmachnet_test | | | 74.73 329 | 74.00 328 | 76.90 333 | 80.71 384 | 56.89 367 | 71.53 402 | 78.42 355 | 58.24 379 | 79.32 348 | 82.92 380 | 57.91 350 | 84.26 359 | 65.60 314 | 91.36 288 | 89.56 296 |
|
| Patchmatch-RL test | | | 74.48 330 | 73.68 330 | 76.89 334 | 84.83 317 | 66.54 241 | 72.29 395 | 69.16 423 | 57.70 384 | 86.76 190 | 86.33 331 | 45.79 409 | 82.59 369 | 69.63 273 | 90.65 315 | 81.54 416 |
|
| PatchMatch-RL | | | 74.48 330 | 73.22 337 | 78.27 312 | 87.70 237 | 85.26 38 | 75.92 363 | 70.09 416 | 64.34 327 | 76.09 378 | 81.25 397 | 65.87 296 | 78.07 398 | 53.86 395 | 83.82 406 | 71.48 445 |
|
| XXY-MVS | | | 74.44 332 | 76.19 307 | 69.21 398 | 84.61 322 | 52.43 401 | 71.70 399 | 77.18 365 | 60.73 363 | 80.60 330 | 90.96 219 | 75.44 200 | 69.35 429 | 56.13 379 | 88.33 346 | 85.86 357 |
|
| test2506 | | | 74.12 333 | 73.39 334 | 76.28 342 | 91.85 123 | 44.20 440 | 84.06 192 | 48.20 465 | 72.30 220 | 81.90 309 | 94.20 89 | 27.22 465 | 89.77 256 | 64.81 321 | 96.02 127 | 94.87 78 |
|
| reproduce_monomvs | | | 74.09 334 | 73.23 336 | 76.65 338 | 76.52 420 | 54.54 383 | 77.50 336 | 81.40 340 | 65.85 305 | 82.86 292 | 86.67 326 | 27.38 463 | 84.53 354 | 70.24 266 | 90.66 314 | 90.89 262 |
|
| CR-MVSNet | | | 74.00 335 | 73.04 339 | 76.85 335 | 79.58 395 | 62.64 285 | 82.58 241 | 76.90 367 | 50.50 432 | 75.72 382 | 92.38 162 | 48.07 395 | 84.07 361 | 68.72 287 | 82.91 413 | 83.85 384 |
|
| SSC-MVS3.2 | | | 73.90 336 | 75.67 313 | 68.61 406 | 84.11 333 | 41.28 448 | 64.17 439 | 72.83 398 | 72.09 223 | 79.08 351 | 87.94 295 | 70.31 269 | 73.89 414 | 55.99 380 | 94.49 191 | 90.67 272 |
|
| Test_1112_low_res | | | 73.90 336 | 73.08 338 | 76.35 340 | 90.35 167 | 55.95 370 | 73.40 388 | 86.17 274 | 50.70 430 | 73.14 401 | 85.94 338 | 58.31 346 | 85.90 337 | 56.51 376 | 83.22 410 | 87.20 342 |
|
| test20.03 | | | 73.75 338 | 74.59 323 | 71.22 384 | 81.11 377 | 51.12 412 | 70.15 413 | 72.10 405 | 70.42 241 | 80.28 338 | 91.50 194 | 64.21 306 | 74.72 412 | 46.96 433 | 94.58 189 | 87.82 335 |
|
| test_fmvs2 | | | 73.57 339 | 72.80 341 | 75.90 346 | 72.74 450 | 68.84 218 | 77.07 343 | 84.32 313 | 45.14 444 | 82.89 290 | 84.22 365 | 48.37 393 | 70.36 425 | 73.40 229 | 87.03 368 | 88.52 320 |
|
| SCA | | | 73.32 340 | 72.57 346 | 75.58 351 | 81.62 370 | 55.86 373 | 78.89 312 | 71.37 411 | 61.73 347 | 74.93 392 | 83.42 374 | 60.46 329 | 87.01 308 | 58.11 370 | 82.63 418 | 83.88 381 |
|
| baseline1 | | | 73.26 341 | 73.54 332 | 72.43 377 | 84.92 316 | 47.79 425 | 79.89 293 | 74.00 386 | 65.93 303 | 78.81 353 | 86.28 334 | 56.36 359 | 81.63 377 | 56.63 375 | 79.04 436 | 87.87 334 |
|
| 1314 | | | 73.22 342 | 72.56 347 | 75.20 352 | 80.41 389 | 57.84 358 | 81.64 266 | 85.36 290 | 51.68 423 | 73.10 402 | 76.65 437 | 61.45 324 | 85.19 347 | 63.54 332 | 79.21 434 | 82.59 401 |
|
| MVS | | | 73.21 343 | 72.59 345 | 75.06 354 | 80.97 378 | 60.81 321 | 81.64 266 | 85.92 283 | 46.03 442 | 71.68 410 | 77.54 428 | 68.47 280 | 89.77 256 | 55.70 383 | 85.39 385 | 74.60 442 |
|
| HY-MVS | | 64.64 18 | 73.03 344 | 72.47 348 | 74.71 358 | 83.36 350 | 54.19 387 | 82.14 260 | 81.96 333 | 56.76 394 | 69.57 424 | 86.21 335 | 60.03 333 | 84.83 351 | 49.58 420 | 82.65 416 | 85.11 365 |
|
| thisisatest0515 | | | 73.00 345 | 70.52 364 | 80.46 276 | 81.45 372 | 59.90 334 | 73.16 390 | 74.31 385 | 57.86 383 | 76.08 379 | 77.78 425 | 37.60 441 | 92.12 176 | 65.00 319 | 91.45 287 | 89.35 300 |
|
| EPNet_dtu | | | 72.87 346 | 71.33 358 | 77.49 325 | 77.72 409 | 60.55 324 | 82.35 251 | 75.79 374 | 66.49 300 | 58.39 460 | 81.06 398 | 53.68 372 | 85.98 332 | 53.55 398 | 92.97 243 | 85.95 355 |
| Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
| CVMVSNet | | | 72.62 347 | 71.41 357 | 76.28 342 | 83.25 354 | 60.34 326 | 83.50 213 | 79.02 354 | 37.77 462 | 76.33 373 | 85.10 353 | 49.60 391 | 87.41 304 | 70.54 263 | 77.54 442 | 81.08 423 |
|
| CHOSEN 1792x2688 | | | 72.45 348 | 70.56 363 | 78.13 313 | 90.02 178 | 63.08 278 | 68.72 420 | 83.16 321 | 42.99 452 | 75.92 380 | 85.46 346 | 57.22 355 | 85.18 348 | 49.87 418 | 81.67 420 | 86.14 353 |
|
| testgi | | | 72.36 349 | 74.61 321 | 65.59 420 | 80.56 387 | 42.82 445 | 68.29 422 | 73.35 394 | 66.87 297 | 81.84 311 | 89.93 260 | 72.08 256 | 66.92 443 | 46.05 437 | 92.54 255 | 87.01 344 |
|
| thres200 | | | 72.34 350 | 71.55 356 | 74.70 359 | 83.48 344 | 51.60 407 | 75.02 372 | 73.71 391 | 70.14 247 | 78.56 356 | 80.57 402 | 46.20 401 | 88.20 288 | 46.99 432 | 89.29 331 | 84.32 375 |
|
| FPMVS | | | 72.29 351 | 72.00 350 | 73.14 368 | 88.63 213 | 85.00 40 | 74.65 376 | 67.39 428 | 71.94 226 | 77.80 363 | 87.66 307 | 50.48 387 | 75.83 406 | 49.95 416 | 79.51 430 | 58.58 459 |
|
| FMVSNet5 | | | 72.10 352 | 71.69 352 | 73.32 366 | 81.57 371 | 53.02 396 | 76.77 347 | 78.37 356 | 63.31 330 | 76.37 372 | 91.85 180 | 36.68 442 | 78.98 393 | 47.87 429 | 92.45 257 | 87.95 330 |
|
| our_test_3 | | | 71.85 353 | 71.59 353 | 72.62 374 | 80.71 384 | 53.78 390 | 69.72 416 | 71.71 410 | 58.80 376 | 78.03 358 | 80.51 404 | 56.61 358 | 78.84 395 | 62.20 342 | 86.04 382 | 85.23 363 |
|
| PAPM | | | 71.77 354 | 70.06 370 | 76.92 332 | 86.39 276 | 53.97 388 | 76.62 351 | 86.62 269 | 53.44 409 | 63.97 449 | 84.73 360 | 57.79 352 | 92.34 169 | 39.65 449 | 81.33 424 | 84.45 373 |
|
| ttmdpeth | | | 71.72 355 | 70.67 361 | 74.86 355 | 73.08 447 | 55.88 372 | 77.41 339 | 69.27 421 | 55.86 396 | 78.66 354 | 93.77 116 | 38.01 439 | 75.39 409 | 60.12 358 | 89.87 324 | 93.31 154 |
|
| IB-MVS | | 62.13 19 | 71.64 356 | 68.97 382 | 79.66 291 | 80.80 383 | 62.26 298 | 73.94 382 | 76.90 367 | 63.27 332 | 68.63 428 | 76.79 435 | 33.83 446 | 91.84 184 | 59.28 363 | 87.26 362 | 84.88 367 |
| 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 |
| UnsupCasMVSNet_eth | | | 71.63 357 | 72.30 349 | 69.62 395 | 76.47 422 | 52.70 399 | 70.03 414 | 80.97 343 | 59.18 373 | 79.36 346 | 88.21 291 | 60.50 328 | 69.12 430 | 58.33 368 | 77.62 441 | 87.04 343 |
|
| testing3 | | | 71.53 358 | 70.79 360 | 73.77 364 | 88.89 205 | 41.86 447 | 76.60 353 | 59.12 454 | 72.83 208 | 80.97 324 | 82.08 389 | 19.80 471 | 87.33 306 | 65.12 318 | 91.68 282 | 92.13 224 |
|
| test_vis3_rt | | | 71.42 359 | 70.67 361 | 73.64 365 | 69.66 457 | 70.46 190 | 66.97 432 | 89.73 203 | 42.68 454 | 88.20 153 | 83.04 376 | 43.77 424 | 60.07 455 | 65.35 317 | 86.66 373 | 90.39 281 |
|
| Anonymous20231206 | | | 71.38 360 | 71.88 351 | 69.88 392 | 86.31 282 | 54.37 385 | 70.39 411 | 74.62 381 | 52.57 416 | 76.73 370 | 88.76 281 | 59.94 334 | 72.06 418 | 44.35 441 | 93.23 235 | 83.23 395 |
|
| test_vis1_n_1920 | | | 71.30 361 | 71.58 355 | 70.47 387 | 77.58 411 | 59.99 333 | 74.25 377 | 84.22 314 | 51.06 426 | 74.85 393 | 79.10 415 | 55.10 368 | 68.83 432 | 68.86 284 | 79.20 435 | 82.58 402 |
|
| MIMVSNet | | | 71.09 362 | 71.59 353 | 69.57 396 | 87.23 251 | 50.07 417 | 78.91 311 | 71.83 407 | 60.20 370 | 71.26 411 | 91.76 187 | 55.08 369 | 76.09 404 | 41.06 446 | 87.02 369 | 82.54 404 |
|
| test_fmvs1_n | | | 70.94 363 | 70.41 367 | 72.53 376 | 73.92 438 | 66.93 238 | 75.99 362 | 84.21 315 | 43.31 451 | 79.40 345 | 79.39 413 | 43.47 425 | 68.55 434 | 69.05 281 | 84.91 396 | 82.10 410 |
|
| MS-PatchMatch | | | 70.93 364 | 70.22 368 | 73.06 369 | 81.85 367 | 62.50 288 | 73.82 384 | 77.90 357 | 52.44 417 | 75.92 380 | 81.27 396 | 55.67 364 | 81.75 375 | 55.37 386 | 77.70 440 | 74.94 441 |
|
| pmmvs5 | | | 70.73 365 | 70.07 369 | 72.72 372 | 77.03 416 | 52.73 398 | 74.14 378 | 75.65 377 | 50.36 433 | 72.17 408 | 85.37 350 | 55.42 366 | 80.67 382 | 52.86 404 | 87.59 361 | 84.77 368 |
|
| testing3-2 | | | 70.72 366 | 70.97 359 | 69.95 391 | 88.93 203 | 34.80 461 | 69.85 415 | 66.59 435 | 78.42 124 | 77.58 367 | 85.55 342 | 31.83 452 | 82.08 373 | 46.28 434 | 93.73 218 | 92.98 175 |
|
| PatchT | | | 70.52 367 | 72.76 343 | 63.79 428 | 79.38 399 | 33.53 462 | 77.63 332 | 65.37 439 | 73.61 188 | 71.77 409 | 92.79 151 | 44.38 423 | 75.65 407 | 64.53 326 | 85.37 386 | 82.18 409 |
|
| test_vis1_n | | | 70.29 368 | 69.99 372 | 71.20 385 | 75.97 427 | 66.50 242 | 76.69 349 | 80.81 344 | 44.22 447 | 75.43 385 | 77.23 432 | 50.00 389 | 68.59 433 | 66.71 302 | 82.85 415 | 78.52 435 |
|
| N_pmnet | | | 70.20 369 | 68.80 384 | 74.38 360 | 80.91 379 | 84.81 43 | 59.12 450 | 76.45 372 | 55.06 400 | 75.31 389 | 82.36 386 | 55.74 363 | 54.82 460 | 47.02 431 | 87.24 363 | 83.52 388 |
|
| tpmvs | | | 70.16 370 | 69.56 375 | 71.96 380 | 74.71 437 | 48.13 422 | 79.63 295 | 75.45 379 | 65.02 323 | 70.26 419 | 81.88 391 | 45.34 415 | 85.68 343 | 58.34 367 | 75.39 446 | 82.08 411 |
|
| new-patchmatchnet | | | 70.10 371 | 73.37 335 | 60.29 437 | 81.23 376 | 16.95 472 | 59.54 448 | 74.62 381 | 62.93 334 | 80.97 324 | 87.93 297 | 62.83 321 | 71.90 419 | 55.24 388 | 95.01 173 | 92.00 229 |
|
| YYNet1 | | | 70.06 372 | 70.44 365 | 68.90 400 | 73.76 440 | 53.42 394 | 58.99 451 | 67.20 430 | 58.42 378 | 87.10 182 | 85.39 349 | 59.82 336 | 67.32 440 | 59.79 360 | 83.50 409 | 85.96 354 |
|
| MVStest1 | | | 70.05 373 | 69.26 376 | 72.41 378 | 58.62 469 | 55.59 376 | 76.61 352 | 65.58 437 | 53.44 409 | 89.28 128 | 93.32 127 | 22.91 469 | 71.44 423 | 74.08 214 | 89.52 328 | 90.21 287 |
|
| MDA-MVSNet_test_wron | | | 70.05 373 | 70.44 365 | 68.88 401 | 73.84 439 | 53.47 392 | 58.93 452 | 67.28 429 | 58.43 377 | 87.09 183 | 85.40 348 | 59.80 337 | 67.25 441 | 59.66 361 | 83.54 408 | 85.92 356 |
|
| CostFormer | | | 69.98 375 | 68.68 385 | 73.87 362 | 77.14 414 | 50.72 414 | 79.26 305 | 74.51 383 | 51.94 422 | 70.97 414 | 84.75 359 | 45.16 418 | 87.49 302 | 55.16 389 | 79.23 433 | 83.40 391 |
|
| testing91 | | | 69.94 376 | 68.99 381 | 72.80 371 | 83.81 339 | 45.89 433 | 71.57 401 | 73.64 393 | 68.24 273 | 70.77 417 | 77.82 424 | 34.37 445 | 84.44 356 | 53.64 397 | 87.00 370 | 88.07 325 |
|
| baseline2 | | | 69.77 377 | 66.89 394 | 78.41 308 | 79.51 397 | 58.09 354 | 76.23 358 | 69.57 419 | 57.50 387 | 64.82 447 | 77.45 430 | 46.02 403 | 88.44 282 | 53.08 400 | 77.83 438 | 88.70 318 |
|
| PatchmatchNet |  | | 69.71 378 | 68.83 383 | 72.33 379 | 77.66 410 | 53.60 391 | 79.29 304 | 69.99 417 | 57.66 385 | 72.53 405 | 82.93 379 | 46.45 400 | 80.08 388 | 60.91 354 | 72.09 450 | 83.31 394 |
| Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
| test_fmvs1 | | | 69.57 379 | 69.05 379 | 71.14 386 | 69.15 458 | 65.77 252 | 73.98 381 | 83.32 319 | 42.83 453 | 77.77 364 | 78.27 423 | 43.39 428 | 68.50 435 | 68.39 291 | 84.38 403 | 79.15 433 |
|
| JIA-IIPM | | | 69.41 380 | 66.64 398 | 77.70 322 | 73.19 444 | 71.24 181 | 75.67 364 | 65.56 438 | 70.42 241 | 65.18 443 | 92.97 142 | 33.64 448 | 83.06 366 | 53.52 399 | 69.61 456 | 78.79 434 |
|
| Syy-MVS | | | 69.40 381 | 70.03 371 | 67.49 411 | 81.72 368 | 38.94 453 | 71.00 404 | 61.99 445 | 61.38 353 | 70.81 415 | 72.36 449 | 61.37 325 | 79.30 391 | 64.50 327 | 85.18 389 | 84.22 377 |
|
| testing99 | | | 69.27 382 | 68.15 389 | 72.63 373 | 83.29 352 | 45.45 435 | 71.15 403 | 71.08 412 | 67.34 290 | 70.43 418 | 77.77 426 | 32.24 451 | 84.35 358 | 53.72 396 | 86.33 378 | 88.10 324 |
|
| UnsupCasMVSNet_bld | | | 69.21 383 | 69.68 374 | 67.82 409 | 79.42 398 | 51.15 411 | 67.82 426 | 75.79 374 | 54.15 406 | 77.47 368 | 85.36 351 | 59.26 340 | 70.64 424 | 48.46 426 | 79.35 432 | 81.66 414 |
|
| test_cas_vis1_n_1920 | | | 69.20 384 | 69.12 377 | 69.43 397 | 73.68 441 | 62.82 282 | 70.38 412 | 77.21 364 | 46.18 441 | 80.46 335 | 78.95 417 | 52.03 378 | 65.53 448 | 65.77 313 | 77.45 443 | 79.95 431 |
|
| gg-mvs-nofinetune | | | 68.96 385 | 69.11 378 | 68.52 407 | 76.12 426 | 45.32 436 | 83.59 210 | 55.88 459 | 86.68 33 | 64.62 448 | 97.01 12 | 30.36 456 | 83.97 363 | 44.78 440 | 82.94 412 | 76.26 438 |
|
| WBMVS | | | 68.76 386 | 68.43 386 | 69.75 394 | 83.29 352 | 40.30 451 | 67.36 428 | 72.21 404 | 57.09 391 | 77.05 369 | 85.53 344 | 33.68 447 | 80.51 384 | 48.79 424 | 90.90 299 | 88.45 321 |
|
| WB-MVSnew | | | 68.72 387 | 69.01 380 | 67.85 408 | 83.22 356 | 43.98 441 | 74.93 373 | 65.98 436 | 55.09 399 | 73.83 398 | 79.11 414 | 65.63 298 | 71.89 420 | 38.21 454 | 85.04 392 | 87.69 336 |
|
| tpm2 | | | 68.45 388 | 66.83 395 | 73.30 367 | 78.93 405 | 48.50 421 | 79.76 294 | 71.76 408 | 47.50 436 | 69.92 421 | 83.60 370 | 42.07 431 | 88.40 284 | 48.44 427 | 79.51 430 | 83.01 398 |
|
| tpm | | | 67.95 389 | 68.08 390 | 67.55 410 | 78.74 406 | 43.53 443 | 75.60 365 | 67.10 433 | 54.92 401 | 72.23 406 | 88.10 292 | 42.87 430 | 75.97 405 | 52.21 406 | 80.95 428 | 83.15 396 |
|
| WTY-MVS | | | 67.91 390 | 68.35 387 | 66.58 416 | 80.82 382 | 48.12 423 | 65.96 434 | 72.60 399 | 53.67 408 | 71.20 412 | 81.68 394 | 58.97 342 | 69.06 431 | 48.57 425 | 81.67 420 | 82.55 403 |
|
| testing11 | | | 67.38 391 | 65.93 399 | 71.73 382 | 83.37 349 | 46.60 430 | 70.95 406 | 69.40 420 | 62.47 339 | 66.14 436 | 76.66 436 | 31.22 453 | 84.10 360 | 49.10 422 | 84.10 405 | 84.49 371 |
|
| test-LLR | | | 67.21 392 | 66.74 396 | 68.63 404 | 76.45 423 | 55.21 379 | 67.89 423 | 67.14 431 | 62.43 342 | 65.08 444 | 72.39 447 | 43.41 426 | 69.37 427 | 61.00 352 | 84.89 397 | 81.31 418 |
|
| testing222 | | | 66.93 393 | 65.30 406 | 71.81 381 | 83.38 348 | 45.83 434 | 72.06 397 | 67.50 427 | 64.12 328 | 69.68 423 | 76.37 439 | 27.34 464 | 83.00 367 | 38.88 450 | 88.38 345 | 86.62 349 |
|
| sss | | | 66.92 394 | 67.26 392 | 65.90 418 | 77.23 413 | 51.10 413 | 64.79 436 | 71.72 409 | 52.12 421 | 70.13 420 | 80.18 406 | 57.96 349 | 65.36 449 | 50.21 414 | 81.01 426 | 81.25 420 |
|
| KD-MVS_2432*1600 | | | 66.87 395 | 65.81 402 | 70.04 389 | 67.50 459 | 47.49 426 | 62.56 442 | 79.16 351 | 61.21 358 | 77.98 359 | 80.61 400 | 25.29 467 | 82.48 370 | 53.02 401 | 84.92 394 | 80.16 429 |
|
| miper_refine_blended | | | 66.87 395 | 65.81 402 | 70.04 389 | 67.50 459 | 47.49 426 | 62.56 442 | 79.16 351 | 61.21 358 | 77.98 359 | 80.61 400 | 25.29 467 | 82.48 370 | 53.02 401 | 84.92 394 | 80.16 429 |
|
| dmvs_re | | | 66.81 397 | 66.98 393 | 66.28 417 | 76.87 417 | 58.68 352 | 71.66 400 | 72.24 402 | 60.29 368 | 69.52 425 | 73.53 446 | 52.38 377 | 64.40 451 | 44.90 439 | 81.44 423 | 75.76 439 |
|
| tpm cat1 | | | 66.76 398 | 65.21 407 | 71.42 383 | 77.09 415 | 50.62 415 | 78.01 324 | 73.68 392 | 44.89 445 | 68.64 427 | 79.00 416 | 45.51 412 | 82.42 372 | 49.91 417 | 70.15 453 | 81.23 422 |
|
| UWE-MVS | | | 66.43 399 | 65.56 405 | 69.05 399 | 84.15 332 | 40.98 449 | 73.06 391 | 64.71 441 | 54.84 402 | 76.18 377 | 79.62 412 | 29.21 458 | 80.50 385 | 38.54 453 | 89.75 325 | 85.66 359 |
|
| PVSNet | | 58.17 21 | 66.41 400 | 65.63 404 | 68.75 402 | 81.96 365 | 49.88 418 | 62.19 444 | 72.51 401 | 51.03 427 | 68.04 430 | 75.34 443 | 50.84 384 | 74.77 410 | 45.82 438 | 82.96 411 | 81.60 415 |
|
| tpmrst | | | 66.28 401 | 66.69 397 | 65.05 424 | 72.82 449 | 39.33 452 | 78.20 322 | 70.69 415 | 53.16 412 | 67.88 431 | 80.36 405 | 48.18 394 | 74.75 411 | 58.13 369 | 70.79 452 | 81.08 423 |
|
| Patchmatch-test | | | 65.91 402 | 67.38 391 | 61.48 434 | 75.51 430 | 43.21 444 | 68.84 419 | 63.79 443 | 62.48 338 | 72.80 404 | 83.42 374 | 44.89 421 | 59.52 457 | 48.27 428 | 86.45 375 | 81.70 413 |
|
| ADS-MVSNet2 | | | 65.87 403 | 63.64 412 | 72.55 375 | 73.16 445 | 56.92 366 | 67.10 430 | 74.81 380 | 49.74 434 | 66.04 438 | 82.97 377 | 46.71 398 | 77.26 401 | 42.29 443 | 69.96 454 | 83.46 389 |
|
| myMVS_eth3d28 | | | 65.83 404 | 65.85 400 | 65.78 419 | 83.42 347 | 35.71 459 | 67.29 429 | 68.01 426 | 67.58 287 | 69.80 422 | 77.72 427 | 32.29 450 | 74.30 413 | 37.49 455 | 89.06 335 | 87.32 340 |
|
| test_vis1_rt | | | 65.64 405 | 64.09 409 | 70.31 388 | 66.09 463 | 70.20 194 | 61.16 445 | 81.60 338 | 38.65 459 | 72.87 403 | 69.66 452 | 52.84 374 | 60.04 456 | 56.16 378 | 77.77 439 | 80.68 427 |
|
| mvsany_test3 | | | 65.48 406 | 62.97 415 | 73.03 370 | 69.99 456 | 76.17 124 | 64.83 435 | 43.71 467 | 43.68 449 | 80.25 339 | 87.05 323 | 52.83 375 | 63.09 454 | 51.92 411 | 72.44 449 | 79.84 432 |
|
| test-mter | | | 65.00 407 | 63.79 411 | 68.63 404 | 76.45 423 | 55.21 379 | 67.89 423 | 67.14 431 | 50.98 428 | 65.08 444 | 72.39 447 | 28.27 461 | 69.37 427 | 61.00 352 | 84.89 397 | 81.31 418 |
|
| ETVMVS | | | 64.67 408 | 63.34 414 | 68.64 403 | 83.44 346 | 41.89 446 | 69.56 418 | 61.70 450 | 61.33 355 | 68.74 426 | 75.76 441 | 28.76 459 | 79.35 390 | 34.65 458 | 86.16 381 | 84.67 370 |
|
| myMVS_eth3d | | | 64.66 409 | 63.89 410 | 66.97 414 | 81.72 368 | 37.39 456 | 71.00 404 | 61.99 445 | 61.38 353 | 70.81 415 | 72.36 449 | 20.96 470 | 79.30 391 | 49.59 419 | 85.18 389 | 84.22 377 |
|
| test0.0.03 1 | | | 64.66 409 | 64.36 408 | 65.57 421 | 75.03 435 | 46.89 429 | 64.69 437 | 61.58 451 | 62.43 342 | 71.18 413 | 77.54 428 | 43.41 426 | 68.47 436 | 40.75 448 | 82.65 416 | 81.35 417 |
|
| UBG | | | 64.34 411 | 63.35 413 | 67.30 412 | 83.50 343 | 40.53 450 | 67.46 427 | 65.02 440 | 54.77 403 | 67.54 434 | 74.47 445 | 32.99 449 | 78.50 397 | 40.82 447 | 83.58 407 | 82.88 399 |
|
| test_f | | | 64.31 412 | 65.85 400 | 59.67 438 | 66.54 462 | 62.24 300 | 57.76 454 | 70.96 413 | 40.13 456 | 84.36 256 | 82.09 388 | 46.93 397 | 51.67 462 | 61.99 345 | 81.89 419 | 65.12 453 |
|
| pmmvs3 | | | 62.47 413 | 60.02 426 | 69.80 393 | 71.58 453 | 64.00 268 | 70.52 410 | 58.44 457 | 39.77 457 | 66.05 437 | 75.84 440 | 27.10 466 | 72.28 417 | 46.15 436 | 84.77 401 | 73.11 443 |
|
| EPMVS | | | 62.47 413 | 62.63 417 | 62.01 430 | 70.63 455 | 38.74 454 | 74.76 374 | 52.86 461 | 53.91 407 | 67.71 433 | 80.01 407 | 39.40 435 | 66.60 444 | 55.54 385 | 68.81 458 | 80.68 427 |
|
| ADS-MVSNet | | | 61.90 415 | 62.19 419 | 61.03 435 | 73.16 445 | 36.42 458 | 67.10 430 | 61.75 448 | 49.74 434 | 66.04 438 | 82.97 377 | 46.71 398 | 63.21 452 | 42.29 443 | 69.96 454 | 83.46 389 |
|
| PMMVS | | | 61.65 416 | 60.38 423 | 65.47 422 | 65.40 466 | 69.26 208 | 63.97 440 | 61.73 449 | 36.80 463 | 60.11 455 | 68.43 454 | 59.42 338 | 66.35 445 | 48.97 423 | 78.57 437 | 60.81 456 |
|
| E-PMN | | | 61.59 417 | 61.62 420 | 61.49 433 | 66.81 461 | 55.40 377 | 53.77 457 | 60.34 453 | 66.80 298 | 58.90 458 | 65.50 457 | 40.48 434 | 66.12 446 | 55.72 382 | 86.25 379 | 62.95 455 |
|
| TESTMET0.1,1 | | | 61.29 418 | 60.32 424 | 64.19 426 | 72.06 451 | 51.30 409 | 67.89 423 | 62.09 444 | 45.27 443 | 60.65 454 | 69.01 453 | 27.93 462 | 64.74 450 | 56.31 377 | 81.65 422 | 76.53 437 |
|
| MVS-HIRNet | | | 61.16 419 | 62.92 416 | 55.87 441 | 79.09 402 | 35.34 460 | 71.83 398 | 57.98 458 | 46.56 439 | 59.05 457 | 91.14 211 | 49.95 390 | 76.43 403 | 38.74 451 | 71.92 451 | 55.84 460 |
|
| EMVS | | | 61.10 420 | 60.81 422 | 61.99 431 | 65.96 464 | 55.86 373 | 53.10 458 | 58.97 456 | 67.06 295 | 56.89 462 | 63.33 458 | 40.98 432 | 67.03 442 | 54.79 391 | 86.18 380 | 63.08 454 |
|
| DSMNet-mixed | | | 60.98 421 | 61.61 421 | 59.09 440 | 72.88 448 | 45.05 438 | 74.70 375 | 46.61 466 | 26.20 464 | 65.34 442 | 90.32 249 | 55.46 365 | 63.12 453 | 41.72 445 | 81.30 425 | 69.09 449 |
|
| dp | | | 60.70 422 | 60.29 425 | 61.92 432 | 72.04 452 | 38.67 455 | 70.83 408 | 64.08 442 | 51.28 425 | 60.75 453 | 77.28 431 | 36.59 443 | 71.58 422 | 47.41 430 | 62.34 460 | 75.52 440 |
|
| dmvs_testset | | | 60.59 423 | 62.54 418 | 54.72 443 | 77.26 412 | 27.74 466 | 74.05 380 | 61.00 452 | 60.48 365 | 65.62 441 | 67.03 456 | 55.93 362 | 68.23 438 | 32.07 462 | 69.46 457 | 68.17 450 |
|
| CHOSEN 280x420 | | | 59.08 424 | 56.52 430 | 66.76 415 | 76.51 421 | 64.39 264 | 49.62 459 | 59.00 455 | 43.86 448 | 55.66 463 | 68.41 455 | 35.55 444 | 68.21 439 | 43.25 442 | 76.78 445 | 67.69 451 |
|
| mvsany_test1 | | | 58.48 425 | 56.47 431 | 64.50 425 | 65.90 465 | 68.21 224 | 56.95 455 | 42.11 468 | 38.30 460 | 65.69 440 | 77.19 434 | 56.96 356 | 59.35 458 | 46.16 435 | 58.96 461 | 65.93 452 |
|
| UWE-MVS-28 | | | 58.44 426 | 57.71 428 | 60.65 436 | 73.58 442 | 31.23 463 | 69.68 417 | 48.80 464 | 53.12 413 | 61.79 451 | 78.83 418 | 30.98 454 | 68.40 437 | 21.58 465 | 80.99 427 | 82.33 408 |
|
| PVSNet_0 | | 51.08 22 | 56.10 427 | 54.97 432 | 59.48 439 | 75.12 434 | 53.28 395 | 55.16 456 | 61.89 447 | 44.30 446 | 59.16 456 | 62.48 459 | 54.22 370 | 65.91 447 | 35.40 457 | 47.01 462 | 59.25 458 |
|
| new_pmnet | | | 55.69 428 | 57.66 429 | 49.76 444 | 75.47 431 | 30.59 464 | 59.56 447 | 51.45 462 | 43.62 450 | 62.49 450 | 75.48 442 | 40.96 433 | 49.15 464 | 37.39 456 | 72.52 448 | 69.55 448 |
|
| PMMVS2 | | | 55.64 429 | 59.27 427 | 44.74 445 | 64.30 467 | 12.32 473 | 40.60 460 | 49.79 463 | 53.19 411 | 65.06 446 | 84.81 358 | 53.60 373 | 49.76 463 | 32.68 461 | 89.41 330 | 72.15 444 |
|
| MVE |  | 40.22 23 | 51.82 430 | 50.47 433 | 55.87 441 | 62.66 468 | 51.91 404 | 31.61 462 | 39.28 469 | 40.65 455 | 50.76 464 | 74.98 444 | 56.24 361 | 44.67 465 | 33.94 460 | 64.11 459 | 71.04 447 |
| Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
| dongtai | | | 41.90 431 | 42.65 434 | 39.67 446 | 70.86 454 | 21.11 468 | 61.01 446 | 21.42 473 | 57.36 388 | 57.97 461 | 50.06 462 | 16.40 472 | 58.73 459 | 21.03 466 | 27.69 466 | 39.17 462 |
|
| kuosan | | | 30.83 432 | 32.17 435 | 26.83 448 | 53.36 470 | 19.02 471 | 57.90 453 | 20.44 474 | 38.29 461 | 38.01 465 | 37.82 464 | 15.18 473 | 33.45 467 | 7.74 468 | 20.76 467 | 28.03 463 |
|
| test_method | | | 30.46 433 | 29.60 436 | 33.06 447 | 17.99 472 | 3.84 475 | 13.62 463 | 73.92 387 | 2.79 466 | 18.29 468 | 53.41 461 | 28.53 460 | 43.25 466 | 22.56 463 | 35.27 464 | 52.11 461 |
|
| cdsmvs_eth3d_5k | | | 20.81 434 | 27.75 437 | 0.00 453 | 0.00 476 | 0.00 478 | 0.00 464 | 85.44 289 | 0.00 471 | 0.00 472 | 82.82 381 | 81.46 127 | 0.00 472 | 0.00 471 | 0.00 470 | 0.00 468 |
|
| tmp_tt | | | 20.25 435 | 24.50 438 | 7.49 450 | 4.47 473 | 8.70 474 | 34.17 461 | 25.16 471 | 1.00 468 | 32.43 467 | 18.49 465 | 39.37 436 | 9.21 469 | 21.64 464 | 43.75 463 | 4.57 465 |
|
| ab-mvs-re | | | 6.65 436 | 8.87 439 | 0.00 453 | 0.00 476 | 0.00 478 | 0.00 464 | 0.00 477 | 0.00 471 | 0.00 472 | 79.80 409 | 0.00 476 | 0.00 472 | 0.00 471 | 0.00 470 | 0.00 468 |
|
| pcd_1.5k_mvsjas | | | 6.41 437 | 8.55 440 | 0.00 453 | 0.00 476 | 0.00 478 | 0.00 464 | 0.00 477 | 0.00 471 | 0.00 472 | 0.00 471 | 76.94 184 | 0.00 472 | 0.00 471 | 0.00 470 | 0.00 468 |
|
| test123 | | | 6.27 438 | 8.08 441 | 0.84 451 | 1.11 475 | 0.57 476 | 62.90 441 | 0.82 475 | 0.54 469 | 1.07 471 | 2.75 470 | 1.26 474 | 0.30 470 | 1.04 469 | 1.26 469 | 1.66 466 |
|
| testmvs | | | 5.91 439 | 7.65 442 | 0.72 452 | 1.20 474 | 0.37 477 | 59.14 449 | 0.67 476 | 0.49 470 | 1.11 470 | 2.76 469 | 0.94 475 | 0.24 471 | 1.02 470 | 1.47 468 | 1.55 467 |
|
| mmdepth | | | 0.00 440 | 0.00 443 | 0.00 453 | 0.00 476 | 0.00 478 | 0.00 464 | 0.00 477 | 0.00 471 | 0.00 472 | 0.00 471 | 0.00 476 | 0.00 472 | 0.00 471 | 0.00 470 | 0.00 468 |
|
| monomultidepth | | | 0.00 440 | 0.00 443 | 0.00 453 | 0.00 476 | 0.00 478 | 0.00 464 | 0.00 477 | 0.00 471 | 0.00 472 | 0.00 471 | 0.00 476 | 0.00 472 | 0.00 471 | 0.00 470 | 0.00 468 |
|
| test_blank | | | 0.00 440 | 0.00 443 | 0.00 453 | 0.00 476 | 0.00 478 | 0.00 464 | 0.00 477 | 0.00 471 | 0.00 472 | 0.00 471 | 0.00 476 | 0.00 472 | 0.00 471 | 0.00 470 | 0.00 468 |
|
| uanet_test | | | 0.00 440 | 0.00 443 | 0.00 453 | 0.00 476 | 0.00 478 | 0.00 464 | 0.00 477 | 0.00 471 | 0.00 472 | 0.00 471 | 0.00 476 | 0.00 472 | 0.00 471 | 0.00 470 | 0.00 468 |
|
| DCPMVS | | | 0.00 440 | 0.00 443 | 0.00 453 | 0.00 476 | 0.00 478 | 0.00 464 | 0.00 477 | 0.00 471 | 0.00 472 | 0.00 471 | 0.00 476 | 0.00 472 | 0.00 471 | 0.00 470 | 0.00 468 |
|
| sosnet-low-res | | | 0.00 440 | 0.00 443 | 0.00 453 | 0.00 476 | 0.00 478 | 0.00 464 | 0.00 477 | 0.00 471 | 0.00 472 | 0.00 471 | 0.00 476 | 0.00 472 | 0.00 471 | 0.00 470 | 0.00 468 |
|
| sosnet | | | 0.00 440 | 0.00 443 | 0.00 453 | 0.00 476 | 0.00 478 | 0.00 464 | 0.00 477 | 0.00 471 | 0.00 472 | 0.00 471 | 0.00 476 | 0.00 472 | 0.00 471 | 0.00 470 | 0.00 468 |
|
| uncertanet | | | 0.00 440 | 0.00 443 | 0.00 453 | 0.00 476 | 0.00 478 | 0.00 464 | 0.00 477 | 0.00 471 | 0.00 472 | 0.00 471 | 0.00 476 | 0.00 472 | 0.00 471 | 0.00 470 | 0.00 468 |
|
| Regformer | | | 0.00 440 | 0.00 443 | 0.00 453 | 0.00 476 | 0.00 478 | 0.00 464 | 0.00 477 | 0.00 471 | 0.00 472 | 0.00 471 | 0.00 476 | 0.00 472 | 0.00 471 | 0.00 470 | 0.00 468 |
|
| uanet | | | 0.00 440 | 0.00 443 | 0.00 453 | 0.00 476 | 0.00 478 | 0.00 464 | 0.00 477 | 0.00 471 | 0.00 472 | 0.00 471 | 0.00 476 | 0.00 472 | 0.00 471 | 0.00 470 | 0.00 468 |
|
| WAC-MVS | | | | | | | 37.39 456 | | | | | | | | 52.61 405 | | |
|
| FOURS1 | | | | | | 96.08 12 | 87.41 14 | 96.19 2 | 95.83 5 | 92.95 3 | 96.57 3 | | | | | | |
|
| MSC_two_6792asdad | | | | | 88.81 73 | 91.55 135 | 77.99 97 | | 91.01 161 | | | | | 96.05 9 | 87.45 28 | 98.17 37 | 92.40 204 |
|
| PC_three_1452 | | | | | | | | | | 58.96 375 | 90.06 103 | 91.33 202 | 80.66 137 | 93.03 151 | 75.78 191 | 95.94 133 | 92.48 198 |
|
| No_MVS | | | | | 88.81 73 | 91.55 135 | 77.99 97 | | 91.01 161 | | | | | 96.05 9 | 87.45 28 | 98.17 37 | 92.40 204 |
|
| test_one_0601 | | | | | | 93.85 64 | 73.27 145 | | 94.11 39 | 86.57 34 | 93.47 42 | 94.64 68 | 88.42 29 | | | | |
|
| eth-test2 | | | | | | 0.00 476 | | | | | | | | | | | |
|
| eth-test | | | | | | 0.00 476 | | | | | | | | | | | |
|
| ZD-MVS | | | | | | 92.22 109 | 80.48 71 | | 91.85 131 | 71.22 234 | 90.38 98 | 92.98 139 | 86.06 65 | 96.11 7 | 81.99 109 | 96.75 97 | |
|
| RE-MVS-def | | | | 92.61 9 | | 94.13 57 | 88.95 6 | 92.87 13 | 94.16 33 | 88.75 18 | 93.79 33 | 94.43 76 | 90.64 11 | | 87.16 37 | 97.60 71 | 92.73 182 |
|
| IU-MVS | | | | | | 94.18 52 | 72.64 154 | | 90.82 166 | 56.98 392 | 89.67 116 | | | | 85.78 62 | 97.92 52 | 93.28 155 |
|
| OPU-MVS | | | | | 88.27 85 | 91.89 121 | 77.83 100 | 90.47 56 | | | | 91.22 208 | 81.12 131 | 94.68 78 | 74.48 203 | 95.35 156 | 92.29 214 |
|
| test_241102_TWO | | | | | | | | | 93.71 56 | 83.77 58 | 93.49 40 | 94.27 83 | 89.27 24 | 95.84 24 | 86.03 55 | 97.82 57 | 92.04 227 |
|
| test_241102_ONE | | | | | | 94.18 52 | 72.65 152 | | 93.69 57 | 83.62 60 | 94.11 27 | 93.78 114 | 90.28 15 | 95.50 49 | | | |
|
| 9.14 | | | | 89.29 63 | | 91.84 125 | | 88.80 94 | 95.32 13 | 75.14 166 | 91.07 83 | 92.89 145 | 87.27 48 | 93.78 117 | 83.69 87 | 97.55 74 | |
|
| save fliter | | | | | | 93.75 65 | 77.44 106 | 86.31 140 | 89.72 204 | 70.80 238 | | | | | | | |
|
| test_0728_THIRD | | | | | | | | | | 85.33 42 | 93.75 35 | 94.65 65 | 87.44 47 | 95.78 32 | 87.41 30 | 98.21 34 | 92.98 175 |
|
| test_0728_SECOND | | | | | 86.79 108 | 94.25 50 | 72.45 162 | 90.54 53 | 94.10 40 | | | | | 95.88 18 | 86.42 45 | 97.97 49 | 92.02 228 |
|
| test0726 | | | | | | 94.16 55 | 72.56 158 | 90.63 50 | 93.90 49 | 83.61 61 | 93.75 35 | 94.49 73 | 89.76 19 | | | | |
|
| GSMVS | | | | | | | | | | | | | | | | | 83.88 381 |
|
| test_part2 | | | | | | 93.86 63 | 77.77 101 | | | | 92.84 52 | | | | | | |
|
| sam_mvs1 | | | | | | | | | | | | | 46.11 402 | | | | 83.88 381 |
|
| sam_mvs | | | | | | | | | | | | | 45.92 407 | | | | |
|
| ambc | | | | | 82.98 214 | 90.55 164 | 64.86 258 | 88.20 103 | 89.15 217 | | 89.40 125 | 93.96 105 | 71.67 263 | 91.38 197 | 78.83 144 | 96.55 102 | 92.71 185 |
|
| MTGPA |  | | | | | | | | 91.81 135 | | | | | | | | |
|
| test_post1 | | | | | | | | 78.85 314 | | | | 3.13 467 | 45.19 417 | 80.13 387 | 58.11 370 | | |
|
| test_post | | | | | | | | | | | | 3.10 468 | 45.43 413 | 77.22 402 | | | |
|
| patchmatchnet-post | | | | | | | | | | | | 81.71 393 | 45.93 406 | 87.01 308 | | | |
|
| GG-mvs-BLEND | | | | | 67.16 413 | 73.36 443 | 46.54 432 | 84.15 190 | 55.04 460 | | 58.64 459 | 61.95 460 | 29.93 457 | 83.87 364 | 38.71 452 | 76.92 444 | 71.07 446 |
|
| MTMP | | | | | | | | 90.66 49 | 33.14 470 | | | | | | | | |
|
| gm-plane-assit | | | | | | 75.42 432 | 44.97 439 | | | 52.17 418 | | 72.36 449 | | 87.90 294 | 54.10 394 | | |
|
| test9_res | | | | | | | | | | | | | | | 80.83 119 | 96.45 108 | 90.57 275 |
|
| TEST9 | | | | | | 92.34 104 | 79.70 80 | 83.94 196 | 90.32 184 | 65.41 315 | 84.49 252 | 90.97 217 | 82.03 117 | 93.63 123 | | | |
|
| test_8 | | | | | | 92.09 113 | 78.87 88 | 83.82 201 | 90.31 186 | 65.79 306 | 84.36 256 | 90.96 219 | 81.93 119 | 93.44 136 | | | |
|
| agg_prior2 | | | | | | | | | | | | | | | 79.68 132 | 96.16 120 | 90.22 283 |
|
| agg_prior | | | | | | 91.58 133 | 77.69 103 | | 90.30 187 | | 84.32 258 | | | 93.18 144 | | | |
|
| TestCases | | | | | 89.68 56 | 91.59 130 | 83.40 52 | | 95.44 11 | 79.47 106 | 88.00 158 | 93.03 137 | 82.66 98 | 91.47 191 | 70.81 255 | 96.14 121 | 94.16 111 |
|
| test_prior4 | | | | | | | 78.97 87 | 84.59 179 | | | | | | | | | |
|
| test_prior2 | | | | | | | | 83.37 217 | | 75.43 162 | 84.58 249 | 91.57 192 | 81.92 121 | | 79.54 136 | 96.97 90 | |
|
| test_prior | | | | | 86.32 116 | 90.59 163 | 71.99 170 | | 92.85 97 | | | | | 94.17 102 | | | 92.80 180 |
|
| 旧先验2 | | | | | | | | 81.73 264 | | 56.88 393 | 86.54 203 | | | 84.90 350 | 72.81 240 | | |
|
| 新几何2 | | | | | | | | 81.72 265 | | | | | | | | | |
|
| 新几何1 | | | | | 82.95 216 | 93.96 61 | 78.56 91 | | 80.24 347 | 55.45 398 | 83.93 269 | 91.08 214 | 71.19 265 | 88.33 286 | 65.84 311 | 93.07 239 | 81.95 412 |
|
| 旧先验1 | | | | | | 91.97 117 | 71.77 171 | | 81.78 335 | | | 91.84 181 | 73.92 227 | | | 93.65 221 | 83.61 387 |
|
| 无先验 | | | | | | | | 82.81 236 | 85.62 287 | 58.09 381 | | | | 91.41 196 | 67.95 295 | | 84.48 372 |
|
| 原ACMM2 | | | | | | | | 82.26 256 | | | | | | | | | |
|
| 原ACMM1 | | | | | 84.60 163 | 92.81 94 | 74.01 137 | | 91.50 142 | 62.59 336 | 82.73 294 | 90.67 236 | 76.53 193 | 94.25 94 | 69.24 276 | 95.69 148 | 85.55 360 |
|
| test222 | | | | | | 93.31 78 | 76.54 116 | 79.38 303 | 77.79 358 | 52.59 415 | 82.36 299 | 90.84 227 | 66.83 289 | | | 91.69 281 | 81.25 420 |
|
| testdata2 | | | | | | | | | | | | | | 86.43 323 | 63.52 333 | | |
|
| segment_acmp | | | | | | | | | | | | | 81.94 118 | | | | |
|
| testdata | | | | | 79.54 293 | 92.87 89 | 72.34 163 | | 80.14 348 | 59.91 371 | 85.47 228 | 91.75 188 | 67.96 283 | 85.24 346 | 68.57 290 | 92.18 268 | 81.06 425 |
|
| testdata1 | | | | | | | | 79.62 296 | | 73.95 183 | | | | | | | |
|
| test12 | | | | | 86.57 111 | 90.74 159 | 72.63 156 | | 90.69 169 | | 82.76 293 | | 79.20 150 | 94.80 75 | | 95.32 158 | 92.27 216 |
|
| plane_prior7 | | | | | | 93.45 72 | 77.31 109 | | | | | | | | | | |
|
| plane_prior6 | | | | | | 92.61 95 | 76.54 116 | | | | | | 74.84 209 | | | | |
|
| plane_prior5 | | | | | | | | | 93.61 60 | | | | | 95.22 59 | 80.78 120 | 95.83 141 | 94.46 94 |
|
| plane_prior4 | | | | | | | | | | | | 92.95 143 | | | | | |
|
| plane_prior3 | | | | | | | 76.85 114 | | | 77.79 133 | 86.55 197 | | | | | | |
|
| plane_prior2 | | | | | | | | 89.45 83 | | 79.44 108 | | | | | | | |
|
| plane_prior1 | | | | | | 92.83 93 | | | | | | | | | | | |
|
| plane_prior | | | | | | | 76.42 119 | 87.15 122 | | 75.94 153 | | | | | | 95.03 170 | |
|
| n2 | | | | | | | | | 0.00 477 | | | | | | | | |
|
| nn | | | | | | | | | 0.00 477 | | | | | | | | |
|
| door-mid | | | | | | | | | 74.45 384 | | | | | | | | |
|
| lessismore_v0 | | | | | 85.95 127 | 91.10 152 | 70.99 185 | | 70.91 414 | | 91.79 71 | 94.42 78 | 61.76 323 | 92.93 154 | 79.52 137 | 93.03 240 | 93.93 120 |
|
| LGP-MVS_train | | | | | 90.82 37 | 94.75 41 | 81.69 63 | | 94.27 25 | 82.35 74 | 93.67 38 | 94.82 60 | 91.18 5 | 95.52 45 | 85.36 65 | 98.73 7 | 95.23 66 |
|
| test11 | | | | | | | | | 91.46 143 | | | | | | | | |
|
| door | | | | | | | | | 72.57 400 | | | | | | | | |
|
| HQP5-MVS | | | | | | | 70.66 187 | | | | | | | | | | |
|
| HQP-NCC | | | | | | 91.19 147 | | 84.77 170 | | 73.30 197 | 80.55 332 | | | | | | |
|
| ACMP_Plane | | | | | | 91.19 147 | | 84.77 170 | | 73.30 197 | 80.55 332 | | | | | | |
|
| BP-MVS | | | | | | | | | | | | | | | 77.30 169 | | |
|
| HQP4-MVS | | | | | | | | | | | 80.56 331 | | | 94.61 82 | | | 93.56 147 |
|
| HQP3-MVS | | | | | | | | | 92.68 103 | | | | | | | 94.47 192 | |
|
| HQP2-MVS | | | | | | | | | | | | | 72.10 254 | | | | |
|
| NP-MVS | | | | | | 91.95 118 | 74.55 134 | | | | | 90.17 256 | | | | | |
|
| MDTV_nov1_ep13_2view | | | | | | | 27.60 467 | 70.76 409 | | 46.47 440 | 61.27 452 | | 45.20 416 | | 49.18 421 | | 83.75 386 |
|
| MDTV_nov1_ep13 | | | | 68.29 388 | | 78.03 407 | 43.87 442 | 74.12 379 | 72.22 403 | 52.17 418 | 67.02 435 | 85.54 343 | 45.36 414 | 80.85 381 | 55.73 381 | 84.42 402 | |
|
| ACMMP++_ref | | | | | | | | | | | | | | | | 95.74 147 | |
|
| ACMMP++ | | | | | | | | | | | | | | | | 97.35 80 | |
|
| Test By Simon | | | | | | | | | | | | | 79.09 152 | | | | |
|
| ITE_SJBPF | | | | | 90.11 49 | 90.72 160 | 84.97 41 | | 90.30 187 | 81.56 82 | 90.02 105 | 91.20 210 | 82.40 103 | 90.81 217 | 73.58 226 | 94.66 187 | 94.56 90 |
|
| DeepMVS_CX |  | | | | 24.13 449 | 32.95 471 | 29.49 465 | | 21.63 472 | 12.07 465 | 37.95 466 | 45.07 463 | 30.84 455 | 19.21 468 | 17.94 467 | 33.06 465 | 23.69 464 |
|