PGM-MVS | | | 98.86 28 | 99.35 23 | 98.29 32 | 99.77 1 | 99.63 30 | 99.67 6 | 95.63 41 | 98.66 110 | 95.27 47 | 99.11 24 | 99.82 38 | 99.67 4 | 99.33 22 | 99.19 20 | 99.73 60 | 99.74 74 |
|
SMA-MVS | | | 99.38 3 | 99.60 2 | 99.12 7 | 99.76 2 | 99.62 34 | 99.39 28 | 98.23 15 | 99.52 14 | 98.03 13 | 99.45 9 | 99.98 1 | 99.64 5 | 99.58 6 | 99.30 11 | 99.68 96 | 99.76 59 |
|
CSCG | | | 98.90 27 | 98.93 46 | 98.85 22 | 99.75 3 | 99.72 4 | 99.49 19 | 96.58 38 | 99.38 21 | 98.05 12 | 98.97 30 | 97.87 65 | 99.49 18 | 97.78 125 | 98.92 32 | 99.78 39 | 99.90 4 |
|
APDe-MVS | | | 99.49 1 | 99.64 1 | 99.32 1 | 99.74 4 | 99.74 3 | 99.75 1 | 98.34 2 | 99.56 9 | 98.72 3 | 99.57 5 | 99.97 5 | 99.53 15 | 99.65 2 | 99.25 14 | 99.84 6 | 99.77 54 |
|
ACMMP_Plus | | | 99.05 22 | 99.45 10 | 98.58 28 | 99.73 5 | 99.60 43 | 99.64 9 | 98.28 11 | 99.23 45 | 94.57 61 | 99.35 13 | 99.97 5 | 99.55 13 | 99.63 3 | 98.66 45 | 99.70 85 | 99.74 74 |
|
zzz-MVS | | | 99.31 5 | 99.44 13 | 99.16 5 | 99.73 5 | 99.65 21 | 99.63 11 | 98.26 12 | 99.27 38 | 98.01 14 | 99.27 15 | 99.97 5 | 99.60 7 | 99.59 5 | 98.58 51 | 99.71 76 | 99.73 78 |
|
v1.0 | | | 91.56 211 | 85.17 231 | 99.01 16 | 99.70 7 | 99.69 12 | 99.40 27 | 98.31 6 | 98.94 82 | 97.70 19 | 99.40 11 | 99.97 5 | 99.17 43 | 99.54 9 | 98.67 44 | 99.78 39 | 0.00 246 |
|
HFP-MVS | | | 99.32 4 | 99.53 6 | 99.07 11 | 99.69 8 | 99.59 46 | 99.63 11 | 98.31 6 | 99.56 9 | 97.37 23 | 99.27 15 | 99.97 5 | 99.70 3 | 99.35 20 | 99.24 16 | 99.71 76 | 99.76 59 |
|
HPM-MVS++ | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 99.10 18 | 99.30 24 | 98.86 21 | 99.69 8 | 99.48 62 | 99.59 14 | 98.34 2 | 99.26 41 | 96.55 34 | 99.10 25 | 99.96 11 | 99.36 26 | 99.25 25 | 98.37 66 | 99.64 126 | 99.66 123 |
|
APD-MVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 99.25 10 | 99.38 18 | 99.09 9 | 99.69 8 | 99.58 49 | 99.56 15 | 98.32 5 | 98.85 89 | 97.87 16 | 98.91 37 | 99.92 26 | 99.30 32 | 99.45 14 | 99.38 8 | 99.79 36 | 99.58 139 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
HSP-MVS | | | 99.31 5 | 99.43 15 | 99.17 3 | 99.68 11 | 99.75 2 | 99.72 2 | 98.31 6 | 99.45 18 | 98.16 10 | 99.28 14 | 99.98 1 | 99.30 32 | 99.34 21 | 98.41 61 | 99.81 27 | 99.81 33 |
|
X-MVS | | | 98.93 26 | 99.37 19 | 98.42 29 | 99.67 12 | 99.62 34 | 99.60 13 | 98.15 20 | 99.08 65 | 93.81 84 | 98.46 60 | 99.95 16 | 99.59 9 | 99.49 12 | 99.21 19 | 99.68 96 | 99.75 70 |
|
MCST-MVS | | | 99.11 17 | 99.27 26 | 98.93 19 | 99.67 12 | 99.33 84 | 99.51 18 | 98.31 6 | 99.28 36 | 96.57 33 | 99.10 25 | 99.90 29 | 99.71 2 | 99.19 26 | 98.35 68 | 99.82 14 | 99.71 94 |
|
ACMMPR | | | 99.30 7 | 99.54 5 | 99.03 14 | 99.66 14 | 99.64 26 | 99.68 5 | 98.25 13 | 99.56 9 | 97.12 27 | 99.19 18 | 99.95 16 | 99.72 1 | 99.43 15 | 99.25 14 | 99.72 66 | 99.77 54 |
|
SteuartSystems-ACMMP | | | 99.20 13 | 99.51 7 | 98.83 24 | 99.66 14 | 99.66 20 | 99.71 4 | 98.12 24 | 99.14 57 | 96.62 31 | 99.16 20 | 99.98 1 | 99.12 51 | 99.63 3 | 99.19 20 | 99.78 39 | 99.83 27 |
Skip Steuart: Steuart Systems R&D Blog. |
CNVR-MVS | | | 99.23 12 | 99.28 25 | 99.17 3 | 99.65 16 | 99.34 82 | 99.46 22 | 98.21 16 | 99.28 36 | 98.47 5 | 98.89 39 | 99.94 24 | 99.50 16 | 99.42 16 | 98.61 48 | 99.73 60 | 99.52 150 |
|
ESAPD | | | 99.39 2 | 99.55 4 | 99.20 2 | 99.63 17 | 99.71 8 | 99.66 7 | 98.33 4 | 99.29 35 | 98.40 8 | 99.64 4 | 99.98 1 | 99.31 30 | 99.56 7 | 98.96 29 | 99.85 4 | 99.70 96 |
|
MP-MVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 99.07 20 | 99.36 20 | 98.74 25 | 99.63 17 | 99.57 51 | 99.66 7 | 98.25 13 | 99.00 77 | 95.62 40 | 98.97 30 | 99.94 24 | 99.54 14 | 99.51 11 | 98.79 42 | 99.71 76 | 99.73 78 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
NCCC | | | 99.05 22 | 99.08 34 | 99.02 15 | 99.62 19 | 99.38 74 | 99.43 26 | 98.21 16 | 99.36 25 | 97.66 20 | 97.79 78 | 99.90 29 | 99.45 21 | 99.17 27 | 98.43 59 | 99.77 44 | 99.51 154 |
|
CP-MVS | | | 99.27 8 | 99.44 13 | 99.08 10 | 99.62 19 | 99.58 49 | 99.53 16 | 98.16 18 | 99.21 48 | 97.79 17 | 99.15 21 | 99.96 11 | 99.59 9 | 99.54 9 | 98.86 37 | 99.78 39 | 99.74 74 |
|
AdaColmap | ![Method available as binary. binary](img/icon_binary.png) | | 99.06 21 | 98.98 44 | 99.15 6 | 99.60 21 | 99.30 89 | 99.38 29 | 98.16 18 | 99.02 76 | 98.55 4 | 98.71 47 | 99.57 50 | 99.58 12 | 99.09 31 | 97.84 98 | 99.64 126 | 99.36 167 |
|
CPTT-MVS | | | 99.14 16 | 99.20 29 | 99.06 12 | 99.58 22 | 99.53 56 | 99.45 23 | 97.80 32 | 99.19 51 | 98.32 9 | 98.58 54 | 99.95 16 | 99.60 7 | 99.28 24 | 98.20 79 | 99.64 126 | 99.69 103 |
|
QAPM | | | 98.62 37 | 99.04 40 | 98.13 36 | 99.57 23 | 99.48 62 | 99.17 37 | 94.78 51 | 99.57 8 | 96.16 36 | 96.73 104 | 99.80 39 | 99.33 28 | 98.79 50 | 99.29 13 | 99.75 48 | 99.64 130 |
|
3Dnovator | | 96.92 7 | 98.67 34 | 99.05 37 | 98.23 35 | 99.57 23 | 99.45 66 | 99.11 40 | 94.66 54 | 99.69 3 | 96.80 30 | 96.55 112 | 99.61 47 | 99.40 24 | 98.87 46 | 99.49 3 | 99.85 4 | 99.66 123 |
|
DeepC-MVS_fast | | 98.34 1 | 99.17 14 | 99.45 10 | 98.85 22 | 99.55 25 | 99.37 76 | 99.64 9 | 98.05 27 | 99.53 12 | 96.58 32 | 98.93 32 | 99.92 26 | 99.49 18 | 99.46 13 | 99.32 10 | 99.80 34 | 99.64 130 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
mPP-MVS | | | | | | 99.53 26 | | | | | | | 99.89 31 | | | | | |
|
3Dnovator+ | | 96.92 7 | 98.71 33 | 99.05 37 | 98.32 31 | 99.53 26 | 99.34 82 | 99.06 44 | 94.61 55 | 99.65 4 | 97.49 21 | 96.75 102 | 99.86 34 | 99.44 22 | 98.78 51 | 99.30 11 | 99.81 27 | 99.67 115 |
|
MSLP-MVS++ | | | 99.15 15 | 99.24 27 | 99.04 13 | 99.52 28 | 99.49 61 | 99.09 42 | 98.07 26 | 99.37 23 | 98.47 5 | 97.79 78 | 99.89 31 | 99.50 16 | 98.93 40 | 99.45 4 | 99.61 141 | 99.76 59 |
|
OpenMVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 96.23 11 | 97.95 50 | 98.45 58 | 97.35 48 | 99.52 28 | 99.42 70 | 98.91 50 | 94.61 55 | 98.87 86 | 92.24 105 | 94.61 145 | 99.05 55 | 99.10 54 | 98.64 63 | 99.05 24 | 99.74 54 | 99.51 154 |
|
PLC | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 97.93 2 | 99.02 25 | 98.94 45 | 99.11 8 | 99.46 30 | 99.24 97 | 99.06 44 | 97.96 29 | 99.31 32 | 99.16 1 | 97.90 76 | 99.79 41 | 99.36 26 | 98.71 57 | 98.12 83 | 99.65 115 | 99.52 150 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
MVS_111021_HR | | | 98.59 38 | 99.36 20 | 97.68 44 | 99.42 31 | 99.61 39 | 98.14 85 | 94.81 50 | 99.31 32 | 95.00 53 | 99.51 7 | 99.79 41 | 99.00 61 | 98.94 39 | 98.83 39 | 99.69 87 | 99.57 144 |
|
OMC-MVS | | | 98.84 29 | 99.01 43 | 98.65 27 | 99.39 32 | 99.23 98 | 99.22 34 | 96.70 37 | 99.40 20 | 97.77 18 | 97.89 77 | 99.80 39 | 99.21 36 | 99.02 35 | 98.65 46 | 99.57 163 | 99.07 183 |
|
TSAR-MVS + ACMM | | | 98.77 30 | 99.45 10 | 97.98 40 | 99.37 33 | 99.46 64 | 99.44 25 | 98.13 23 | 99.65 4 | 92.30 104 | 98.91 37 | 99.95 16 | 99.05 57 | 99.42 16 | 98.95 30 | 99.58 159 | 99.82 28 |
|
MVS_111021_LR | | | 98.67 34 | 99.41 17 | 97.81 43 | 99.37 33 | 99.53 56 | 98.51 61 | 95.52 43 | 99.27 38 | 94.85 56 | 99.56 6 | 99.69 45 | 99.04 58 | 99.36 19 | 98.88 35 | 99.60 149 | 99.58 139 |
|
train_agg | | | 98.73 32 | 99.11 32 | 98.28 33 | 99.36 35 | 99.35 80 | 99.48 21 | 97.96 29 | 98.83 93 | 93.86 83 | 98.70 49 | 99.86 34 | 99.44 22 | 99.08 33 | 98.38 64 | 99.61 141 | 99.58 139 |
|
ACMMP | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 98.74 31 | 99.03 41 | 98.40 30 | 99.36 35 | 99.64 26 | 99.20 35 | 97.75 33 | 98.82 95 | 95.24 48 | 98.85 40 | 99.87 33 | 99.17 43 | 98.74 56 | 97.50 116 | 99.71 76 | 99.76 59 |
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 |
MAR-MVS | | | 97.71 56 | 98.04 81 | 97.32 49 | 99.35 37 | 98.91 113 | 97.65 105 | 91.68 107 | 98.00 139 | 97.01 28 | 97.72 82 | 94.83 101 | 98.85 65 | 98.44 77 | 98.86 37 | 99.41 187 | 99.52 150 |
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 |
abl_6 | | | | | 98.09 37 | 99.33 38 | 99.22 99 | 98.79 54 | 94.96 49 | 98.52 119 | 97.00 29 | 97.30 88 | 99.86 34 | 98.76 67 | | | 99.69 87 | 99.41 164 |
|
CDPH-MVS | | | 98.41 40 | 99.10 33 | 97.61 46 | 99.32 39 | 99.36 78 | 99.49 19 | 96.15 40 | 98.82 95 | 91.82 107 | 98.41 61 | 99.66 46 | 99.10 54 | 98.93 40 | 98.97 28 | 99.75 48 | 99.58 139 |
|
CNLPA | | | 99.03 24 | 99.05 37 | 99.01 16 | 99.27 40 | 99.22 99 | 99.03 46 | 97.98 28 | 99.34 30 | 99.00 2 | 98.25 67 | 99.71 44 | 99.31 30 | 98.80 49 | 98.82 40 | 99.48 177 | 99.17 176 |
|
MSDG | | | 98.27 44 | 98.29 68 | 98.24 34 | 99.20 41 | 99.22 99 | 99.20 35 | 97.82 31 | 99.37 23 | 94.43 70 | 95.90 126 | 97.31 71 | 99.12 51 | 98.76 53 | 98.35 68 | 99.67 104 | 99.14 180 |
|
PHI-MVS | | | 99.08 19 | 99.43 15 | 98.67 26 | 99.15 42 | 99.59 46 | 99.11 40 | 97.35 35 | 99.14 57 | 97.30 24 | 99.44 10 | 99.96 11 | 99.32 29 | 98.89 44 | 99.39 7 | 99.79 36 | 99.58 139 |
|
PatchMatch-RL | | | 97.77 54 | 98.25 69 | 97.21 54 | 99.11 43 | 99.25 95 | 97.06 128 | 94.09 66 | 98.72 108 | 95.14 50 | 98.47 59 | 96.29 81 | 98.43 80 | 98.65 60 | 97.44 121 | 99.45 181 | 98.94 186 |
|
TAPA-MVS | | 97.53 5 | 98.41 40 | 98.84 50 | 97.91 41 | 99.08 44 | 99.33 84 | 99.15 38 | 97.13 36 | 99.34 30 | 93.20 92 | 97.75 80 | 99.19 53 | 99.20 37 | 98.66 59 | 98.13 82 | 99.66 109 | 99.48 159 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
EPNet | | | 98.05 48 | 98.86 48 | 97.10 57 | 99.02 45 | 99.43 69 | 98.47 62 | 94.73 52 | 99.05 73 | 95.62 40 | 98.93 32 | 97.62 69 | 95.48 166 | 98.59 70 | 98.55 53 | 99.29 195 | 99.84 23 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
EPNet_dtu | | | 96.30 109 | 98.53 56 | 93.70 131 | 98.97 46 | 98.24 157 | 97.36 113 | 94.23 63 | 98.85 89 | 79.18 200 | 99.19 18 | 98.47 60 | 94.09 203 | 97.89 120 | 98.21 78 | 98.39 211 | 98.85 192 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
COLMAP_ROB | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 96.15 12 | 97.78 53 | 98.17 76 | 97.32 49 | 98.84 47 | 99.45 66 | 99.28 32 | 95.43 44 | 99.48 17 | 91.80 108 | 94.83 143 | 98.36 62 | 98.90 62 | 98.09 104 | 97.85 97 | 99.68 96 | 99.15 177 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
DeepPCF-MVS | | 97.74 3 | 98.34 42 | 99.46 9 | 97.04 61 | 98.82 48 | 99.33 84 | 96.28 144 | 97.47 34 | 99.58 7 | 94.70 60 | 98.99 29 | 99.85 37 | 97.24 116 | 99.55 8 | 99.34 9 | 97.73 221 | 99.56 145 |
|
SD-MVS | | | 99.25 10 | 99.50 8 | 98.96 18 | 98.79 49 | 99.55 54 | 99.33 31 | 98.29 10 | 99.75 1 | 97.96 15 | 99.15 21 | 99.95 16 | 99.61 6 | 99.17 27 | 99.06 23 | 99.81 27 | 99.84 23 |
|
TSAR-MVS + MP. | | | 99.27 8 | 99.57 3 | 98.92 20 | 98.78 50 | 99.53 56 | 99.72 2 | 98.11 25 | 99.73 2 | 97.43 22 | 99.15 21 | 99.96 11 | 99.59 9 | 99.73 1 | 99.07 22 | 99.88 1 | 99.82 28 |
|
PCF-MVS | | 97.50 6 | 98.18 46 | 98.35 64 | 97.99 39 | 98.65 51 | 99.36 78 | 98.94 49 | 98.14 22 | 98.59 112 | 93.62 87 | 96.61 108 | 99.76 43 | 99.03 59 | 97.77 126 | 97.45 120 | 99.57 163 | 98.89 191 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
DeepC-MVS | | 97.63 4 | 98.33 43 | 98.57 54 | 98.04 38 | 98.62 52 | 99.65 21 | 99.45 23 | 98.15 20 | 99.51 16 | 92.80 98 | 95.74 131 | 96.44 78 | 99.46 20 | 99.37 18 | 99.50 2 | 99.78 39 | 99.81 33 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
CANet | | | 98.46 39 | 99.16 30 | 97.64 45 | 98.48 53 | 99.64 26 | 99.35 30 | 94.71 53 | 99.53 12 | 95.17 49 | 97.63 84 | 99.59 48 | 98.38 82 | 98.88 45 | 98.99 27 | 99.74 54 | 99.86 19 |
|
LS3D | | | 97.79 52 | 98.25 69 | 97.26 53 | 98.40 54 | 99.63 30 | 99.53 16 | 98.63 1 | 99.25 43 | 88.13 129 | 96.93 100 | 94.14 115 | 99.19 39 | 99.14 29 | 99.23 17 | 99.69 87 | 99.42 163 |
|
CHOSEN 280x420 | | | 97.99 49 | 99.24 27 | 96.53 82 | 98.34 55 | 99.61 39 | 98.36 75 | 89.80 149 | 99.27 38 | 95.08 51 | 99.81 1 | 98.58 58 | 98.64 72 | 99.02 35 | 98.92 32 | 98.93 203 | 99.48 159 |
|
DELS-MVS | | | 98.19 45 | 98.77 51 | 97.52 47 | 98.29 56 | 99.71 8 | 99.12 39 | 94.58 58 | 98.80 98 | 95.38 46 | 96.24 118 | 98.24 63 | 97.92 100 | 99.06 34 | 99.52 1 | 99.82 14 | 99.79 43 |
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 |
RPSCF | | | 97.61 60 | 98.16 77 | 96.96 73 | 98.10 57 | 99.00 106 | 98.84 52 | 93.76 83 | 99.45 18 | 94.78 59 | 99.39 12 | 99.31 52 | 98.53 78 | 96.61 160 | 95.43 169 | 97.74 219 | 97.93 208 |
|
PVSNet_BlendedMVS | | | 97.51 64 | 97.71 91 | 97.28 51 | 98.06 58 | 99.61 39 | 97.31 115 | 95.02 47 | 99.08 65 | 95.51 43 | 98.05 71 | 90.11 136 | 98.07 96 | 98.91 42 | 98.40 62 | 99.72 66 | 99.78 46 |
|
PVSNet_Blended | | | 97.51 64 | 97.71 91 | 97.28 51 | 98.06 58 | 99.61 39 | 97.31 115 | 95.02 47 | 99.08 65 | 95.51 43 | 98.05 71 | 90.11 136 | 98.07 96 | 98.91 42 | 98.40 62 | 99.72 66 | 99.78 46 |
|
MVS_0304 | | | 98.14 47 | 99.03 41 | 97.10 57 | 98.05 60 | 99.63 30 | 99.27 33 | 94.33 60 | 99.63 6 | 93.06 95 | 97.32 87 | 99.05 55 | 98.09 95 | 98.82 48 | 98.87 36 | 99.81 27 | 99.89 8 |
|
CHOSEN 1792x2688 | | | 96.41 104 | 96.99 114 | 95.74 106 | 98.01 61 | 99.72 4 | 97.70 104 | 90.78 128 | 99.13 61 | 90.03 122 | 87.35 213 | 95.36 93 | 98.33 85 | 98.59 70 | 98.91 34 | 99.59 155 | 99.87 14 |
|
HyFIR lowres test | | | 95.99 116 | 96.56 122 | 95.32 111 | 97.99 62 | 99.65 21 | 96.54 138 | 88.86 157 | 98.44 121 | 89.77 125 | 84.14 225 | 97.05 74 | 99.03 59 | 98.55 72 | 98.19 80 | 99.73 60 | 99.86 19 |
|
OPM-MVS | | | 96.22 111 | 95.85 146 | 96.65 79 | 97.75 63 | 98.54 138 | 99.00 48 | 95.53 42 | 96.88 188 | 89.88 123 | 95.95 125 | 86.46 158 | 98.07 96 | 97.65 134 | 96.63 137 | 99.67 104 | 98.83 193 |
|
tmp_tt | | | | | 82.25 229 | 97.73 64 | 88.71 241 | 80.18 236 | 68.65 244 | 99.15 54 | 86.98 137 | 99.47 8 | 85.31 169 | 68.35 241 | 87.51 236 | 83.81 237 | 91.64 240 | |
|
TSAR-MVS + COLMAP | | | 96.79 88 | 96.55 123 | 97.06 60 | 97.70 65 | 98.46 142 | 99.07 43 | 96.23 39 | 99.38 21 | 91.32 112 | 98.80 41 | 85.61 165 | 98.69 70 | 97.64 135 | 96.92 131 | 99.37 190 | 99.06 184 |
|
PVSNet_Blended_VisFu | | | 97.41 66 | 98.49 57 | 96.15 91 | 97.49 66 | 99.76 1 | 96.02 147 | 93.75 85 | 99.26 41 | 93.38 90 | 93.73 152 | 99.35 51 | 96.47 139 | 98.96 37 | 98.46 57 | 99.77 44 | 99.90 4 |
|
MS-PatchMatch | | | 95.99 116 | 97.26 108 | 94.51 118 | 97.46 67 | 98.76 123 | 97.27 117 | 86.97 179 | 99.09 63 | 89.83 124 | 93.51 155 | 97.78 66 | 96.18 144 | 97.53 139 | 95.71 166 | 99.35 191 | 98.41 199 |
|
XVS | | | | | | 97.42 68 | 99.62 34 | 98.59 59 | | | 93.81 84 | | 99.95 16 | | | | 99.69 87 | |
|
X-MVStestdata | | | | | | 97.42 68 | 99.62 34 | 98.59 59 | | | 93.81 84 | | 99.95 16 | | | | 99.69 87 | |
|
LGP-MVS_train | | | 96.23 110 | 96.89 116 | 95.46 110 | 97.32 70 | 98.77 121 | 98.81 53 | 93.60 86 | 98.58 113 | 85.52 145 | 99.08 27 | 86.67 155 | 97.83 106 | 97.87 121 | 97.51 115 | 99.69 87 | 99.73 78 |
|
CMPMVS | ![Method available as binary. binary](img/icon_binary.png) | 70.31 18 | 90.74 214 | 91.06 222 | 90.36 204 | 97.32 70 | 97.43 200 | 92.97 209 | 87.82 173 | 93.50 230 | 75.34 218 | 83.27 228 | 84.90 173 | 92.19 218 | 92.64 225 | 91.21 233 | 96.50 235 | 94.46 229 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
HQP-MVS | | | 96.37 105 | 96.58 121 | 96.13 93 | 97.31 72 | 98.44 145 | 98.45 63 | 95.22 45 | 98.86 87 | 88.58 127 | 98.33 65 | 87.00 147 | 97.67 107 | 97.23 148 | 96.56 140 | 99.56 166 | 99.62 133 |
|
ACMM | | 96.26 9 | 96.67 99 | 96.69 120 | 96.66 78 | 97.29 73 | 98.46 142 | 96.48 141 | 95.09 46 | 99.21 48 | 93.19 93 | 98.78 43 | 86.73 154 | 98.17 90 | 97.84 123 | 96.32 146 | 99.74 54 | 99.49 158 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
UA-Net | | | 97.13 79 | 99.14 31 | 94.78 115 | 97.21 74 | 99.38 74 | 97.56 107 | 92.04 100 | 98.48 120 | 88.03 130 | 98.39 63 | 99.91 28 | 94.03 204 | 99.33 22 | 99.23 17 | 99.81 27 | 99.25 172 |
|
UGNet | | | 97.66 59 | 99.07 36 | 96.01 100 | 97.19 75 | 99.65 21 | 97.09 126 | 93.39 89 | 99.35 26 | 94.40 72 | 98.79 42 | 99.59 48 | 94.24 201 | 98.04 113 | 98.29 75 | 99.73 60 | 99.80 36 |
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 |
TSAR-MVS + GP. | | | 98.66 36 | 99.36 20 | 97.85 42 | 97.16 76 | 99.46 64 | 99.03 46 | 94.59 57 | 99.09 63 | 97.19 26 | 99.73 3 | 99.95 16 | 99.39 25 | 98.95 38 | 98.69 43 | 99.75 48 | 99.65 126 |
|
CANet_DTU | | | 96.64 100 | 99.08 34 | 93.81 127 | 97.10 77 | 99.42 70 | 98.85 51 | 90.01 143 | 99.31 32 | 79.98 186 | 99.78 2 | 99.10 54 | 97.42 113 | 98.35 80 | 98.05 88 | 99.47 179 | 99.53 148 |
|
IB-MVS | | 93.96 15 | 95.02 134 | 96.44 135 | 93.36 141 | 97.05 78 | 99.28 92 | 90.43 221 | 93.39 89 | 98.02 138 | 96.02 37 | 94.92 142 | 92.07 130 | 83.52 232 | 95.38 187 | 95.82 162 | 99.72 66 | 99.59 137 |
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 |
ACMP | | 96.25 10 | 96.62 102 | 96.72 119 | 96.50 85 | 96.96 79 | 98.75 124 | 97.80 101 | 94.30 61 | 98.85 89 | 93.12 94 | 98.78 43 | 86.61 156 | 97.23 117 | 97.73 129 | 96.61 138 | 99.62 138 | 99.71 94 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
ACMH | | 95.42 14 | 95.27 131 | 95.96 142 | 94.45 119 | 96.83 80 | 98.78 120 | 94.72 187 | 91.67 108 | 98.95 79 | 86.82 139 | 96.42 115 | 83.67 182 | 97.00 121 | 97.48 141 | 96.68 136 | 99.69 87 | 99.76 59 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
CLD-MVS | | | 96.74 92 | 96.51 126 | 97.01 68 | 96.71 81 | 98.62 133 | 98.73 55 | 94.38 59 | 98.94 82 | 94.46 68 | 97.33 86 | 87.03 146 | 98.07 96 | 97.20 150 | 96.87 132 | 99.72 66 | 99.54 147 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
TDRefinement | | | 93.04 171 | 93.57 197 | 92.41 151 | 96.58 82 | 98.77 121 | 97.78 103 | 91.96 103 | 98.12 134 | 80.84 172 | 89.13 190 | 79.87 221 | 87.78 224 | 96.44 166 | 94.50 209 | 99.54 171 | 98.15 203 |
|
Anonymous202405211 | | | | 97.40 101 | | 96.45 83 | 99.54 55 | 98.08 89 | 93.79 82 | 98.24 128 | | 93.55 153 | 94.41 108 | 98.88 64 | 98.04 113 | 98.24 77 | 99.75 48 | 99.76 59 |
|
Anonymous20240521 | | | 97.56 62 | 98.36 63 | 96.62 81 | 96.44 84 | 98.36 152 | 98.37 73 | 91.73 106 | 99.11 62 | 94.80 58 | 98.36 64 | 96.28 82 | 98.60 74 | 98.12 100 | 98.44 58 | 99.76 46 | 99.87 14 |
|
ACMH+ | | 95.51 13 | 95.40 126 | 96.00 140 | 94.70 116 | 96.33 85 | 98.79 118 | 96.79 133 | 91.32 117 | 98.77 104 | 87.18 136 | 95.60 136 | 85.46 167 | 96.97 122 | 97.15 151 | 96.59 139 | 99.59 155 | 99.65 126 |
|
tfpn1000 | | | 97.60 61 | 98.21 74 | 96.89 75 | 96.32 86 | 99.60 43 | 97.99 95 | 93.85 79 | 99.21 48 | 95.03 52 | 98.49 57 | 93.69 119 | 98.31 86 | 98.50 75 | 98.31 74 | 99.86 2 | 99.70 96 |
|
Anonymous20231211 | | | 97.10 80 | 97.06 112 | 97.14 55 | 96.32 86 | 99.52 59 | 98.16 84 | 93.76 83 | 98.84 92 | 95.98 38 | 90.92 169 | 94.58 107 | 98.90 62 | 97.72 130 | 98.10 85 | 99.71 76 | 99.75 70 |
|
tfpn111 | | | 96.96 85 | 96.91 115 | 97.03 62 | 96.31 88 | 99.67 14 | 98.41 65 | 93.99 69 | 97.35 164 | 94.50 65 | 98.65 51 | 86.93 148 | 99.14 46 | 98.26 86 | 97.80 101 | 99.82 14 | 99.70 96 |
|
tfpn_ndepth | | | 97.71 56 | 98.30 67 | 97.02 66 | 96.31 88 | 99.56 52 | 98.05 91 | 93.94 77 | 98.95 79 | 95.59 42 | 98.40 62 | 94.79 103 | 98.39 81 | 98.40 79 | 98.42 60 | 99.86 2 | 99.56 145 |
|
conf200view11 | | | 96.75 90 | 96.51 126 | 97.03 62 | 96.31 88 | 99.67 14 | 98.41 65 | 93.99 69 | 97.35 164 | 94.50 65 | 95.90 126 | 86.93 148 | 99.14 46 | 98.26 86 | 97.80 101 | 99.82 14 | 99.70 96 |
|
thres100view900 | | | 96.72 93 | 96.47 130 | 97.00 70 | 96.31 88 | 99.52 59 | 98.28 79 | 94.01 67 | 97.35 164 | 94.52 63 | 95.90 126 | 86.93 148 | 99.09 56 | 98.07 107 | 97.87 96 | 99.81 27 | 99.63 132 |
|
tfpn200view9 | | | 96.75 90 | 96.51 126 | 97.03 62 | 96.31 88 | 99.67 14 | 98.41 65 | 93.99 69 | 97.35 164 | 94.52 63 | 95.90 126 | 86.93 148 | 99.14 46 | 98.26 86 | 97.80 101 | 99.82 14 | 99.70 96 |
|
thres200 | | | 96.76 89 | 96.53 124 | 97.03 62 | 96.31 88 | 99.67 14 | 98.37 73 | 93.99 69 | 97.68 159 | 94.49 67 | 95.83 130 | 86.77 153 | 99.18 41 | 98.26 86 | 97.82 100 | 99.82 14 | 99.66 123 |
|
conf0.01 | | | 96.35 106 | 95.71 147 | 97.10 57 | 96.30 94 | 99.65 21 | 98.41 65 | 94.10 65 | 97.35 164 | 94.82 57 | 95.44 139 | 81.88 209 | 99.14 46 | 98.16 98 | 97.80 101 | 99.82 14 | 99.69 103 |
|
conf0.002 | | | 96.31 108 | 95.63 149 | 97.11 56 | 96.29 95 | 99.64 26 | 98.41 65 | 94.11 64 | 97.35 164 | 94.86 55 | 95.49 138 | 81.06 214 | 99.14 46 | 98.14 99 | 98.02 90 | 99.82 14 | 99.69 103 |
|
view800 | | | 96.70 95 | 96.45 133 | 96.99 72 | 96.29 95 | 99.69 12 | 98.39 72 | 93.95 76 | 97.92 146 | 94.25 76 | 96.23 119 | 85.57 166 | 99.22 34 | 98.28 83 | 97.71 107 | 99.82 14 | 99.76 59 |
|
tfpn | | | 96.22 111 | 95.62 150 | 96.93 74 | 96.29 95 | 99.72 4 | 98.34 77 | 93.94 77 | 97.96 143 | 93.94 79 | 96.45 114 | 79.09 224 | 99.22 34 | 98.28 83 | 98.06 87 | 99.83 10 | 99.78 46 |
|
view600 | | | 96.70 95 | 96.44 135 | 97.01 68 | 96.28 98 | 99.67 14 | 98.42 64 | 93.99 69 | 97.87 149 | 94.34 74 | 95.99 123 | 85.94 162 | 99.20 37 | 98.26 86 | 97.64 109 | 99.82 14 | 99.73 78 |
|
thres600view7 | | | 96.69 97 | 96.43 137 | 97.00 70 | 96.28 98 | 99.67 14 | 98.41 65 | 93.99 69 | 97.85 152 | 94.29 75 | 95.96 124 | 85.91 163 | 99.19 39 | 98.26 86 | 97.63 110 | 99.82 14 | 99.73 78 |
|
thres400 | | | 96.71 94 | 96.45 133 | 97.02 66 | 96.28 98 | 99.63 30 | 98.41 65 | 94.00 68 | 97.82 154 | 94.42 71 | 95.74 131 | 86.26 159 | 99.18 41 | 98.20 95 | 97.79 105 | 99.81 27 | 99.70 96 |
|
canonicalmvs | | | 97.31 72 | 97.81 89 | 96.72 76 | 96.20 101 | 99.45 66 | 98.21 81 | 91.60 109 | 99.22 46 | 95.39 45 | 98.48 58 | 90.95 134 | 99.16 45 | 97.66 132 | 99.05 24 | 99.76 46 | 99.90 4 |
|
conf0.05thres1000 | | | 96.34 107 | 96.47 130 | 96.17 90 | 96.16 102 | 99.71 8 | 97.82 99 | 93.46 87 | 98.10 135 | 90.69 114 | 96.75 102 | 85.26 170 | 99.11 53 | 98.05 111 | 97.65 108 | 99.82 14 | 99.80 36 |
|
thresconf0.02 | | | 97.18 77 | 97.81 89 | 96.45 87 | 96.11 103 | 99.20 102 | 98.21 81 | 94.26 62 | 99.14 57 | 91.72 109 | 98.65 51 | 91.51 133 | 98.57 75 | 98.22 92 | 98.47 56 | 99.82 14 | 99.50 156 |
|
tfpn_n400 | | | 97.32 69 | 98.38 61 | 96.09 94 | 96.07 104 | 99.30 89 | 98.00 93 | 93.84 80 | 99.35 26 | 90.50 117 | 98.93 32 | 94.24 112 | 98.30 87 | 98.65 60 | 98.60 49 | 99.83 10 | 99.60 135 |
|
tfpnconf | | | 97.32 69 | 98.38 61 | 96.09 94 | 96.07 104 | 99.30 89 | 98.00 93 | 93.84 80 | 99.35 26 | 90.50 117 | 98.93 32 | 94.24 112 | 98.30 87 | 98.65 60 | 98.60 49 | 99.83 10 | 99.60 135 |
|
tfpnview11 | | | 97.32 69 | 98.33 65 | 96.14 92 | 96.07 104 | 99.31 87 | 98.08 89 | 93.96 75 | 99.25 43 | 90.50 117 | 98.93 32 | 94.24 112 | 98.38 82 | 98.61 65 | 98.36 67 | 99.84 6 | 99.59 137 |
|
IS_MVSNet | | | 97.86 51 | 98.86 48 | 96.68 77 | 96.02 107 | 99.72 4 | 98.35 76 | 93.37 91 | 98.75 107 | 94.01 77 | 96.88 101 | 98.40 61 | 98.48 79 | 99.09 31 | 99.42 5 | 99.83 10 | 99.80 36 |
|
USDC | | | 94.26 148 | 94.83 160 | 93.59 133 | 96.02 107 | 98.44 145 | 97.84 98 | 88.65 161 | 98.86 87 | 82.73 164 | 94.02 149 | 80.56 215 | 96.76 129 | 97.28 147 | 96.15 153 | 99.55 167 | 98.50 197 |
|
FC-MVSNet-train | | | 97.04 81 | 97.91 87 | 96.03 98 | 96.00 109 | 98.41 148 | 96.53 140 | 93.42 88 | 99.04 75 | 93.02 96 | 98.03 73 | 94.32 110 | 97.47 112 | 97.93 118 | 97.77 106 | 99.75 48 | 99.88 12 |
|
casdiffmvs1 | | | 97.69 58 | 98.72 52 | 96.49 86 | 96.00 109 | 99.40 72 | 98.26 80 | 91.54 112 | 99.52 14 | 94.56 62 | 98.61 53 | 96.41 79 | 98.79 66 | 98.60 68 | 98.58 51 | 99.80 34 | 99.91 3 |
|
Vis-MVSNet (Re-imp) | | | 97.40 67 | 98.89 47 | 95.66 108 | 95.99 111 | 99.62 34 | 97.82 99 | 93.22 94 | 98.82 95 | 91.40 111 | 96.94 99 | 98.56 59 | 95.70 155 | 99.14 29 | 99.41 6 | 99.79 36 | 99.75 70 |
|
MVSTER | | | 97.16 78 | 97.71 91 | 96.52 83 | 95.97 112 | 98.48 140 | 98.63 58 | 92.10 99 | 98.68 109 | 95.96 39 | 99.23 17 | 91.79 131 | 96.87 126 | 98.76 53 | 97.37 124 | 99.57 163 | 99.68 110 |
|
TinyColmap | | | 94.00 152 | 94.35 171 | 93.60 132 | 95.89 113 | 98.26 155 | 97.49 110 | 88.82 158 | 98.56 115 | 83.21 158 | 91.28 168 | 80.48 217 | 96.68 131 | 97.34 145 | 96.26 149 | 99.53 173 | 98.24 202 |
|
diffmvs1 | | | 97.31 72 | 98.41 59 | 96.03 98 | 95.86 114 | 99.31 87 | 98.04 92 | 90.88 123 | 99.35 26 | 93.31 91 | 98.71 47 | 95.25 94 | 98.56 76 | 98.22 92 | 98.14 81 | 99.54 171 | 99.87 14 |
|
DWT-MVSNet_training | | | 95.38 127 | 95.05 156 | 95.78 103 | 95.86 114 | 98.88 114 | 97.55 108 | 90.09 142 | 98.23 129 | 96.49 35 | 97.62 85 | 86.92 152 | 97.16 118 | 92.03 229 | 94.12 211 | 97.52 224 | 97.50 211 |
|
EPMVS | | | 95.05 133 | 96.86 118 | 92.94 148 | 95.84 116 | 98.96 111 | 96.68 134 | 79.87 218 | 99.05 73 | 90.15 120 | 97.12 94 | 95.99 88 | 97.49 111 | 95.17 196 | 94.75 204 | 97.59 223 | 96.96 220 |
|
PMMVS | | | 97.52 63 | 98.39 60 | 96.51 84 | 95.82 117 | 98.73 127 | 97.80 101 | 93.05 96 | 98.76 105 | 94.39 73 | 99.07 28 | 97.03 75 | 98.55 77 | 98.31 82 | 97.61 111 | 99.43 185 | 99.21 175 |
|
casdiffmvs | | | 97.36 68 | 98.33 65 | 96.23 88 | 95.78 118 | 99.37 76 | 97.62 106 | 91.41 115 | 99.07 71 | 94.45 69 | 98.68 50 | 94.90 99 | 98.37 84 | 98.27 85 | 98.12 83 | 99.75 48 | 99.87 14 |
|
MVS_Test | | | 97.30 74 | 98.54 55 | 95.87 101 | 95.74 119 | 99.28 92 | 98.19 83 | 91.40 116 | 99.18 52 | 91.59 110 | 98.17 68 | 96.18 84 | 98.63 73 | 98.61 65 | 98.55 53 | 99.66 109 | 99.78 46 |
|
diffmvs | | | 96.92 86 | 97.86 88 | 95.82 102 | 95.70 120 | 99.28 92 | 97.98 96 | 91.13 122 | 99.08 65 | 92.48 103 | 98.09 70 | 92.81 125 | 98.18 89 | 98.11 101 | 97.83 99 | 99.44 183 | 99.81 33 |
|
tpmrst | | | 93.86 157 | 95.88 144 | 91.50 176 | 95.69 121 | 98.62 133 | 95.64 153 | 79.41 223 | 98.80 98 | 83.76 154 | 95.63 135 | 96.13 85 | 97.25 115 | 92.92 221 | 92.31 226 | 97.27 229 | 96.74 223 |
|
ADS-MVSNet | | | 94.65 140 | 97.04 113 | 91.88 169 | 95.68 122 | 98.99 108 | 95.89 148 | 79.03 227 | 99.15 54 | 85.81 144 | 96.96 98 | 98.21 64 | 97.10 119 | 94.48 215 | 94.24 210 | 97.74 219 | 97.21 216 |
|
EPP-MVSNet | | | 97.75 55 | 98.71 53 | 96.63 80 | 95.68 122 | 99.56 52 | 97.51 109 | 93.10 95 | 99.22 46 | 94.99 54 | 97.18 93 | 97.30 72 | 98.65 71 | 98.83 47 | 98.93 31 | 99.84 6 | 99.92 1 |
|
DI_MVS_plusplus_trai | | | 96.90 87 | 97.49 97 | 96.21 89 | 95.61 124 | 99.40 72 | 98.72 56 | 92.11 98 | 99.14 57 | 92.98 97 | 93.08 163 | 95.14 96 | 98.13 94 | 98.05 111 | 97.91 94 | 99.74 54 | 99.73 78 |
|
thisisatest0530 | | | 97.23 75 | 98.25 69 | 96.05 96 | 95.60 125 | 99.59 46 | 96.96 130 | 93.23 92 | 99.17 53 | 92.60 100 | 98.75 45 | 96.19 83 | 98.17 90 | 98.19 96 | 96.10 154 | 99.72 66 | 99.77 54 |
|
tttt0517 | | | 97.23 75 | 98.24 72 | 96.04 97 | 95.60 125 | 99.60 43 | 96.94 131 | 93.23 92 | 99.15 54 | 92.56 101 | 98.74 46 | 96.12 86 | 98.17 90 | 98.21 94 | 96.10 154 | 99.73 60 | 99.78 46 |
|
dps | | | 94.63 141 | 95.31 155 | 93.84 126 | 95.53 127 | 98.71 128 | 96.54 138 | 80.12 217 | 97.81 156 | 97.21 25 | 96.98 97 | 92.37 127 | 96.34 141 | 92.46 226 | 91.77 230 | 97.26 230 | 97.08 218 |
|
PatchmatchNet | ![Method available under a permissive open source license. permissive](img/icon_permissive.png) | | 94.70 138 | 97.08 111 | 91.92 166 | 95.53 127 | 98.85 116 | 95.77 150 | 79.54 222 | 98.95 79 | 85.98 142 | 98.52 55 | 96.45 76 | 97.39 114 | 95.32 188 | 94.09 212 | 97.32 228 | 97.38 215 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
test-LLR | | | 95.50 124 | 97.32 104 | 93.37 140 | 95.49 129 | 98.74 125 | 96.44 142 | 90.82 126 | 98.18 130 | 82.75 162 | 96.60 109 | 94.67 105 | 95.54 162 | 98.09 104 | 96.00 156 | 99.20 198 | 98.93 187 |
|
test0.0.03 1 | | | 96.69 97 | 98.12 79 | 95.01 113 | 95.49 129 | 98.99 108 | 95.86 149 | 90.82 126 | 98.38 123 | 92.54 102 | 96.66 106 | 97.33 70 | 95.75 153 | 97.75 128 | 98.34 70 | 99.60 149 | 99.40 165 |
|
CostFormer | | | 94.25 149 | 94.88 159 | 93.51 137 | 95.43 131 | 98.34 153 | 96.21 145 | 80.64 214 | 97.94 145 | 94.01 77 | 98.30 66 | 86.20 161 | 97.52 109 | 92.71 222 | 92.69 222 | 97.23 232 | 98.02 207 |
|
MDTV_nov1_ep13 | | | 95.57 122 | 97.48 98 | 93.35 142 | 95.43 131 | 98.97 110 | 97.19 121 | 83.72 209 | 98.92 85 | 87.91 132 | 97.75 80 | 96.12 86 | 97.88 104 | 96.84 159 | 95.64 167 | 97.96 217 | 98.10 204 |
|
tpm cat1 | | | 94.06 150 | 94.90 158 | 93.06 145 | 95.42 133 | 98.52 139 | 96.64 136 | 80.67 213 | 97.82 154 | 92.63 99 | 93.39 157 | 95.00 98 | 96.06 148 | 91.36 233 | 91.58 232 | 96.98 233 | 96.66 225 |
|
tpmp4_e23 | | | 93.84 159 | 94.58 166 | 92.98 147 | 95.41 134 | 98.29 154 | 96.81 132 | 80.57 215 | 98.15 133 | 90.53 116 | 97.00 96 | 84.39 178 | 96.91 124 | 93.69 218 | 92.45 224 | 97.67 222 | 98.06 205 |
|
Vis-MVSNet | ![Method available under a permissive open source license. permissive](img/icon_permissive.png) | | 96.16 113 | 98.22 73 | 93.75 128 | 95.33 135 | 99.70 11 | 97.27 117 | 90.85 125 | 98.30 125 | 85.51 146 | 95.72 133 | 96.45 76 | 93.69 210 | 98.70 58 | 99.00 26 | 99.84 6 | 99.69 103 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
CVMVSNet | | | 95.33 130 | 97.09 110 | 93.27 143 | 95.23 136 | 98.39 150 | 95.49 156 | 92.58 97 | 97.71 158 | 83.00 161 | 94.44 148 | 93.28 122 | 93.92 207 | 97.79 124 | 98.54 55 | 99.41 187 | 99.45 161 |
|
IterMVS-LS | | | 96.12 114 | 97.48 98 | 94.53 117 | 95.19 137 | 97.56 191 | 97.15 122 | 89.19 155 | 99.08 65 | 88.23 128 | 94.97 141 | 94.73 104 | 97.84 105 | 97.86 122 | 98.26 76 | 99.60 149 | 99.88 12 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
Effi-MVS+ | | | 95.81 118 | 97.31 107 | 94.06 123 | 95.09 138 | 99.35 80 | 97.24 119 | 88.22 166 | 98.54 116 | 85.38 147 | 98.52 55 | 88.68 140 | 98.70 69 | 98.32 81 | 97.93 92 | 99.74 54 | 99.84 23 |
|
testgi | | | 95.67 121 | 97.48 98 | 93.56 134 | 95.07 139 | 99.00 106 | 95.33 160 | 88.47 163 | 98.80 98 | 86.90 138 | 97.30 88 | 92.33 128 | 95.97 150 | 97.66 132 | 97.91 94 | 99.60 149 | 99.38 166 |
|
RPMNet | | | 94.66 139 | 97.16 109 | 91.75 172 | 94.98 140 | 98.59 135 | 97.00 129 | 78.37 231 | 97.98 140 | 83.78 152 | 96.27 117 | 94.09 117 | 96.91 124 | 97.36 144 | 96.73 134 | 99.48 177 | 99.09 182 |
|
LP | | | 92.12 204 | 94.60 164 | 89.22 212 | 94.96 141 | 98.45 144 | 93.01 208 | 77.58 232 | 97.85 152 | 77.26 209 | 89.80 184 | 93.00 124 | 94.54 194 | 93.69 218 | 92.58 223 | 98.00 216 | 96.83 222 |
|
CR-MVSNet | | | 94.57 145 | 97.34 103 | 91.33 180 | 94.90 142 | 98.59 135 | 97.15 122 | 79.14 225 | 97.98 140 | 80.42 179 | 96.59 111 | 93.50 121 | 96.85 127 | 98.10 102 | 97.49 117 | 99.50 176 | 99.15 177 |
|
gg-mvs-nofinetune | | | 90.85 213 | 94.14 173 | 87.02 219 | 94.89 143 | 99.25 95 | 98.64 57 | 76.29 236 | 88.24 238 | 57.50 240 | 79.93 233 | 95.45 92 | 95.18 188 | 98.77 52 | 98.07 86 | 99.62 138 | 99.24 173 |
|
IterMVS | | | 94.81 137 | 97.71 91 | 91.42 178 | 94.83 144 | 97.63 184 | 97.38 112 | 85.08 194 | 98.93 84 | 75.67 215 | 94.02 149 | 97.64 67 | 96.66 133 | 98.45 76 | 97.60 112 | 98.90 204 | 99.72 90 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
PatchT | | | 93.96 154 | 97.36 102 | 90.00 207 | 94.76 145 | 98.65 131 | 90.11 224 | 78.57 230 | 97.96 143 | 80.42 179 | 96.07 121 | 94.10 116 | 96.85 127 | 98.10 102 | 97.49 117 | 99.26 196 | 99.15 177 |
|
CDS-MVSNet | | | 96.59 103 | 98.02 83 | 94.92 114 | 94.45 146 | 98.96 111 | 97.46 111 | 91.75 105 | 97.86 151 | 90.07 121 | 96.02 122 | 97.25 73 | 96.21 142 | 98.04 113 | 98.38 64 | 99.60 149 | 99.65 126 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
tpm | | | 92.38 194 | 94.79 161 | 89.56 210 | 94.30 147 | 97.50 196 | 94.24 201 | 78.97 228 | 97.72 157 | 74.93 219 | 97.97 75 | 82.91 193 | 96.60 135 | 93.65 220 | 94.81 202 | 98.33 212 | 98.98 185 |
|
Fast-Effi-MVS+ | | | 95.38 127 | 96.52 125 | 94.05 124 | 94.15 148 | 99.14 104 | 97.24 119 | 86.79 180 | 98.53 117 | 87.62 134 | 94.51 146 | 87.06 145 | 98.76 67 | 98.60 68 | 98.04 89 | 99.72 66 | 99.77 54 |
|
Effi-MVS+-dtu | | | 95.74 120 | 98.04 81 | 93.06 145 | 93.92 149 | 99.16 103 | 97.90 97 | 88.16 169 | 99.07 71 | 82.02 167 | 98.02 74 | 94.32 110 | 96.74 130 | 98.53 73 | 97.56 113 | 99.61 141 | 99.62 133 |
|
Fast-Effi-MVS+-dtu | | | 95.38 127 | 98.20 75 | 92.09 158 | 93.91 150 | 98.87 115 | 97.35 114 | 85.01 196 | 99.08 65 | 81.09 171 | 98.10 69 | 96.36 80 | 95.62 159 | 98.43 78 | 97.03 128 | 99.55 167 | 99.50 156 |
|
testpf | | | 91.80 209 | 94.43 170 | 88.74 213 | 93.89 151 | 95.30 229 | 92.05 214 | 71.77 240 | 97.52 161 | 87.24 135 | 94.77 144 | 92.68 126 | 91.48 220 | 91.75 232 | 92.11 229 | 96.02 237 | 96.89 221 |
|
TAMVS | | | 95.53 123 | 96.50 129 | 94.39 120 | 93.86 152 | 99.03 105 | 96.67 135 | 89.55 152 | 97.33 170 | 90.64 115 | 93.02 164 | 91.58 132 | 96.21 142 | 97.72 130 | 97.43 122 | 99.43 185 | 99.36 167 |
|
GBi-Net | | | 96.98 83 | 98.00 84 | 95.78 103 | 93.81 153 | 97.98 162 | 98.09 86 | 91.32 117 | 98.80 98 | 93.92 80 | 97.21 90 | 95.94 89 | 97.89 101 | 98.07 107 | 98.34 70 | 99.68 96 | 99.67 115 |
|
test1 | | | 96.98 83 | 98.00 84 | 95.78 103 | 93.81 153 | 97.98 162 | 98.09 86 | 91.32 117 | 98.80 98 | 93.92 80 | 97.21 90 | 95.94 89 | 97.89 101 | 98.07 107 | 98.34 70 | 99.68 96 | 99.67 115 |
|
FMVSNet2 | | | 96.64 100 | 97.50 96 | 95.63 109 | 93.81 153 | 97.98 162 | 98.09 86 | 90.87 124 | 98.99 78 | 93.48 88 | 93.17 160 | 95.25 94 | 97.89 101 | 98.63 64 | 98.80 41 | 99.68 96 | 99.67 115 |
|
MVS-HIRNet | | | 92.51 188 | 95.97 141 | 88.48 216 | 93.73 156 | 98.37 151 | 90.33 222 | 75.36 239 | 98.32 124 | 77.78 206 | 89.15 189 | 94.87 100 | 95.14 189 | 97.62 136 | 96.39 144 | 98.51 207 | 97.11 217 |
|
GA-MVS | | | 93.93 155 | 96.31 139 | 91.16 186 | 93.61 157 | 98.79 118 | 95.39 159 | 90.69 131 | 98.25 127 | 73.28 223 | 96.15 120 | 88.42 141 | 94.39 199 | 97.76 127 | 95.35 173 | 99.58 159 | 99.45 161 |
|
FC-MVSNet-test | | | 96.07 115 | 97.94 86 | 93.89 125 | 93.60 158 | 98.67 130 | 96.62 137 | 90.30 138 | 98.76 105 | 88.62 126 | 95.57 137 | 97.63 68 | 94.48 197 | 97.97 116 | 97.48 119 | 99.71 76 | 99.52 150 |
|
FMVSNet3 | | | 97.02 82 | 98.12 79 | 95.73 107 | 93.59 159 | 97.98 162 | 98.34 77 | 91.32 117 | 98.80 98 | 93.92 80 | 97.21 90 | 95.94 89 | 97.63 108 | 98.61 65 | 98.62 47 | 99.61 141 | 99.65 126 |
|
FMVSNet1 | | | 95.77 119 | 96.41 138 | 95.03 112 | 93.42 160 | 97.86 169 | 97.11 125 | 89.89 146 | 98.53 117 | 92.00 106 | 89.17 188 | 93.23 123 | 98.15 93 | 98.07 107 | 98.34 70 | 99.61 141 | 99.69 103 |
|
tfpnnormal | | | 93.85 158 | 94.12 175 | 93.54 136 | 93.22 161 | 98.24 157 | 95.45 157 | 91.96 103 | 94.61 226 | 83.91 150 | 90.74 171 | 81.75 211 | 97.04 120 | 97.49 140 | 96.16 152 | 99.68 96 | 99.84 23 |
|
TransMVSNet (Re) | | | 93.45 162 | 94.08 177 | 92.72 150 | 92.83 162 | 97.62 187 | 94.94 166 | 91.54 112 | 95.65 222 | 83.06 160 | 88.93 191 | 83.53 183 | 94.25 200 | 97.41 142 | 97.03 128 | 99.67 104 | 98.40 201 |
|
LTVRE_ROB | | 93.20 16 | 92.84 174 | 94.92 157 | 90.43 203 | 92.83 162 | 98.63 132 | 97.08 127 | 87.87 172 | 97.91 147 | 68.42 229 | 93.54 154 | 79.46 223 | 96.62 134 | 97.55 138 | 97.40 123 | 99.74 54 | 99.92 1 |
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 |
TESTMET0.1,1 | | | 94.95 135 | 97.32 104 | 92.20 155 | 92.62 164 | 98.74 125 | 96.44 142 | 86.67 182 | 98.18 130 | 82.75 162 | 96.60 109 | 94.67 105 | 95.54 162 | 98.09 104 | 96.00 156 | 99.20 198 | 98.93 187 |
|
pm-mvs1 | | | 94.27 147 | 95.57 151 | 92.75 149 | 92.58 165 | 98.13 160 | 94.87 172 | 90.71 130 | 96.70 194 | 83.78 152 | 89.94 183 | 89.85 139 | 94.96 192 | 97.58 137 | 97.07 127 | 99.61 141 | 99.72 90 |
|
NR-MVSNet | | | 94.01 151 | 94.51 167 | 93.44 138 | 92.56 166 | 97.77 170 | 95.67 151 | 91.57 110 | 97.17 175 | 85.84 143 | 93.13 161 | 80.53 216 | 95.29 185 | 97.01 155 | 96.17 151 | 99.69 87 | 99.75 70 |
|
EG-PatchMatch MVS | | | 92.45 189 | 93.92 186 | 90.72 198 | 92.56 166 | 98.43 147 | 94.88 171 | 84.54 200 | 97.18 174 | 79.55 194 | 86.12 223 | 83.23 187 | 93.15 214 | 97.22 149 | 96.00 156 | 99.67 104 | 99.27 171 |
|
test-mter | | | 94.86 136 | 97.32 104 | 92.00 162 | 92.41 168 | 98.82 117 | 96.18 146 | 86.35 186 | 98.05 137 | 82.28 165 | 96.48 113 | 94.39 109 | 95.46 172 | 98.17 97 | 96.20 150 | 99.32 193 | 99.13 181 |
|
our_test_3 | | | | | | 92.30 169 | 97.58 189 | 90.09 225 | | | | | | | | | | |
|
pmmvs4 | | | 95.09 132 | 95.90 143 | 94.14 122 | 92.29 170 | 97.70 176 | 95.45 157 | 90.31 136 | 98.60 111 | 90.70 113 | 93.25 158 | 89.90 138 | 96.67 132 | 97.13 152 | 95.42 170 | 99.44 183 | 99.28 170 |
|
FMVSNet5 | | | 95.42 125 | 96.47 130 | 94.20 121 | 92.26 171 | 95.99 216 | 95.66 152 | 87.15 176 | 97.87 149 | 93.46 89 | 96.68 105 | 93.79 118 | 97.52 109 | 97.10 154 | 97.21 126 | 99.11 201 | 96.62 226 |
|
UniMVSNet (Re) | | | 94.58 144 | 95.34 153 | 93.71 130 | 92.25 172 | 98.08 161 | 94.97 165 | 91.29 121 | 97.03 181 | 87.94 131 | 93.97 151 | 86.25 160 | 96.07 147 | 96.27 175 | 95.97 159 | 99.72 66 | 99.79 43 |
|
v18 | | | 92.63 186 | 93.67 192 | 91.43 177 | 92.13 173 | 95.65 217 | 95.09 162 | 85.44 191 | 97.06 179 | 80.78 173 | 90.06 176 | 83.06 188 | 95.47 171 | 95.16 200 | 95.01 188 | 99.64 126 | 99.67 115 |
|
v16 | | | 92.66 185 | 93.80 189 | 91.32 181 | 92.13 173 | 95.62 219 | 94.89 168 | 85.12 193 | 97.20 173 | 80.66 174 | 89.96 182 | 83.93 180 | 95.49 165 | 95.17 196 | 95.04 183 | 99.63 132 | 99.68 110 |
|
v17 | | | 92.55 187 | 93.65 193 | 91.27 183 | 92.11 175 | 95.63 218 | 94.89 168 | 85.15 192 | 97.12 178 | 80.39 182 | 90.02 177 | 83.02 189 | 95.45 173 | 95.17 196 | 94.92 198 | 99.66 109 | 99.68 110 |
|
SixPastTwentyTwo | | | 93.44 163 | 95.32 154 | 91.24 184 | 92.11 175 | 98.40 149 | 92.77 210 | 88.64 162 | 98.09 136 | 77.83 205 | 93.51 155 | 85.74 164 | 96.52 138 | 96.91 157 | 94.89 201 | 99.59 155 | 99.73 78 |
|
v8 | | | 92.87 173 | 93.87 188 | 91.72 174 | 92.05 177 | 97.50 196 | 94.79 179 | 88.20 167 | 96.85 190 | 80.11 185 | 90.01 178 | 82.86 195 | 95.48 166 | 95.15 204 | 94.90 199 | 99.66 109 | 99.80 36 |
|
v6 | | | 93.11 167 | 93.98 181 | 92.10 157 | 92.01 178 | 97.71 173 | 94.86 175 | 90.15 139 | 96.96 184 | 80.47 178 | 90.01 178 | 83.26 186 | 95.48 166 | 95.17 196 | 95.01 188 | 99.64 126 | 99.76 59 |
|
thisisatest0515 | | | 94.61 142 | 96.89 116 | 91.95 164 | 92.00 179 | 98.47 141 | 92.01 215 | 90.73 129 | 98.18 130 | 83.96 149 | 94.51 146 | 95.13 97 | 93.38 211 | 97.38 143 | 94.74 205 | 99.61 141 | 99.79 43 |
|
v1neww | | | 93.06 168 | 93.94 183 | 92.03 160 | 91.99 180 | 97.70 176 | 94.79 179 | 90.14 140 | 96.93 186 | 80.13 183 | 89.97 180 | 83.01 190 | 95.48 166 | 95.16 200 | 95.01 188 | 99.63 132 | 99.76 59 |
|
v7new | | | 93.06 168 | 93.94 183 | 92.03 160 | 91.99 180 | 97.70 176 | 94.79 179 | 90.14 140 | 96.93 186 | 80.13 183 | 89.97 180 | 83.01 190 | 95.48 166 | 95.16 200 | 95.01 188 | 99.63 132 | 99.76 59 |
|
WR-MVS_H | | | 93.54 161 | 94.67 163 | 92.22 153 | 91.95 182 | 97.91 167 | 94.58 195 | 88.75 159 | 96.64 198 | 83.88 151 | 90.66 173 | 85.13 171 | 94.40 198 | 96.54 165 | 95.91 161 | 99.73 60 | 99.89 8 |
|
V42 | | | 93.05 170 | 93.90 187 | 92.04 159 | 91.91 183 | 97.66 182 | 94.91 167 | 89.91 145 | 96.85 190 | 80.58 176 | 89.66 185 | 83.43 185 | 95.37 178 | 95.03 210 | 94.90 199 | 99.59 155 | 99.78 46 |
|
EU-MVSNet | | | 92.80 177 | 94.76 162 | 90.51 201 | 91.88 184 | 96.74 213 | 92.48 212 | 88.69 160 | 96.21 209 | 79.00 201 | 91.51 165 | 87.82 142 | 91.83 219 | 95.87 183 | 96.27 147 | 99.21 197 | 98.92 190 |
|
N_pmnet | | | 92.21 201 | 94.60 164 | 89.42 211 | 91.88 184 | 97.38 203 | 89.15 227 | 89.74 150 | 97.89 148 | 73.75 221 | 87.94 210 | 92.23 129 | 93.85 208 | 96.10 179 | 93.20 218 | 98.15 215 | 97.43 214 |
|
UniMVSNet_NR-MVSNet | | | 94.59 143 | 95.47 152 | 93.55 135 | 91.85 186 | 97.89 168 | 95.03 163 | 92.00 101 | 97.33 170 | 86.12 140 | 93.19 159 | 87.29 144 | 96.60 135 | 96.12 178 | 96.70 135 | 99.72 66 | 99.80 36 |
|
v15 | | | 92.27 199 | 93.33 204 | 91.04 188 | 91.83 187 | 95.60 220 | 94.79 179 | 84.88 197 | 96.66 196 | 79.66 192 | 88.72 196 | 82.45 202 | 95.40 176 | 95.19 195 | 95.00 192 | 99.65 115 | 99.67 115 |
|
v7 | | | 92.97 172 | 94.11 176 | 91.65 175 | 91.83 187 | 97.55 193 | 94.86 175 | 88.19 168 | 96.96 184 | 79.72 191 | 88.16 205 | 84.68 175 | 95.63 157 | 96.33 172 | 95.30 175 | 99.65 115 | 99.77 54 |
|
pmmvs6 | | | 91.90 208 | 92.53 219 | 91.17 185 | 91.81 189 | 97.63 184 | 93.23 206 | 88.37 165 | 93.43 231 | 80.61 175 | 77.32 235 | 87.47 143 | 94.12 202 | 96.58 162 | 95.72 165 | 98.88 205 | 99.53 148 |
|
V14 | | | 92.31 198 | 93.41 202 | 91.03 189 | 91.80 190 | 95.59 222 | 94.79 179 | 84.70 198 | 96.58 201 | 79.83 187 | 88.79 194 | 82.98 192 | 95.41 175 | 95.22 190 | 95.02 187 | 99.65 115 | 99.67 115 |
|
v1 | | | 92.81 175 | 93.57 197 | 91.94 165 | 91.79 191 | 97.70 176 | 94.80 178 | 90.32 134 | 96.52 204 | 79.75 189 | 88.47 201 | 82.46 201 | 95.32 182 | 95.14 206 | 94.96 195 | 99.63 132 | 99.73 78 |
|
v10 | | | 92.79 179 | 94.06 178 | 91.31 182 | 91.78 192 | 97.29 207 | 94.87 172 | 86.10 187 | 96.97 183 | 79.82 188 | 88.16 205 | 84.56 176 | 95.63 157 | 96.33 172 | 95.31 174 | 99.65 115 | 99.80 36 |
|
V9 | | | 92.24 200 | 93.32 206 | 90.98 191 | 91.76 193 | 95.58 224 | 94.83 177 | 84.50 202 | 96.68 195 | 79.73 190 | 88.66 197 | 82.39 203 | 95.39 177 | 95.22 190 | 95.03 185 | 99.65 115 | 99.67 115 |
|
v1141 | | | 92.79 179 | 93.61 194 | 91.84 171 | 91.75 194 | 97.71 173 | 94.74 185 | 90.33 133 | 96.58 201 | 79.21 199 | 88.59 198 | 82.53 200 | 95.36 179 | 95.16 200 | 94.96 195 | 99.63 132 | 99.72 90 |
|
divwei89l23v2f112 | | | 92.80 177 | 93.60 196 | 91.86 170 | 91.75 194 | 97.71 173 | 94.75 184 | 90.32 134 | 96.54 203 | 79.35 196 | 88.59 198 | 82.55 199 | 95.35 180 | 95.15 204 | 94.96 195 | 99.63 132 | 99.72 90 |
|
v13 | | | 92.16 203 | 93.28 208 | 90.85 196 | 91.75 194 | 95.58 224 | 94.65 192 | 84.23 206 | 96.49 207 | 79.51 195 | 88.40 203 | 82.58 198 | 95.31 184 | 95.21 193 | 95.03 185 | 99.66 109 | 99.68 110 |
|
MIMVSNet | | | 94.49 146 | 97.59 95 | 90.87 195 | 91.74 197 | 98.70 129 | 94.68 189 | 78.73 229 | 97.98 140 | 83.71 155 | 97.71 83 | 94.81 102 | 96.96 123 | 97.97 116 | 97.92 93 | 99.40 189 | 98.04 206 |
|
v11 | | | 92.43 191 | 93.77 190 | 90.85 196 | 91.72 198 | 95.58 224 | 94.87 172 | 84.07 208 | 96.98 182 | 79.28 197 | 88.03 208 | 84.22 179 | 95.53 164 | 96.55 164 | 95.36 172 | 99.65 115 | 99.70 96 |
|
v12 | | | 92.18 202 | 93.29 207 | 90.88 194 | 91.70 199 | 95.59 222 | 94.61 193 | 84.36 204 | 96.65 197 | 79.59 193 | 88.85 192 | 82.03 207 | 95.35 180 | 95.22 190 | 95.04 183 | 99.65 115 | 99.68 110 |
|
v1144 | | | 92.81 175 | 94.03 179 | 91.40 179 | 91.68 200 | 97.60 188 | 94.73 186 | 88.40 164 | 96.71 193 | 78.48 203 | 88.14 207 | 84.46 177 | 95.45 173 | 96.31 174 | 95.22 177 | 99.65 115 | 99.76 59 |
|
DU-MVS | | | 93.98 153 | 94.44 169 | 93.44 138 | 91.66 201 | 97.77 170 | 95.03 163 | 91.57 110 | 97.17 175 | 86.12 140 | 93.13 161 | 81.13 213 | 96.60 135 | 95.10 207 | 97.01 130 | 99.67 104 | 99.80 36 |
|
Baseline_NR-MVSNet | | | 93.87 156 | 93.98 181 | 93.75 128 | 91.66 201 | 97.02 208 | 95.53 155 | 91.52 114 | 97.16 177 | 87.77 133 | 87.93 211 | 83.69 181 | 96.35 140 | 95.10 207 | 97.23 125 | 99.68 96 | 99.73 78 |
|
CP-MVSNet | | | 93.25 165 | 94.00 180 | 92.38 152 | 91.65 203 | 97.56 191 | 94.38 198 | 89.20 154 | 96.05 214 | 83.16 159 | 89.51 186 | 81.97 208 | 96.16 146 | 96.43 167 | 96.56 140 | 99.71 76 | 99.89 8 |
|
v148 | | | 92.36 196 | 92.88 213 | 91.75 172 | 91.63 204 | 97.66 182 | 92.64 211 | 90.55 132 | 96.09 212 | 83.34 157 | 88.19 204 | 80.00 219 | 92.74 215 | 93.98 217 | 94.58 208 | 99.58 159 | 99.69 103 |
|
PS-CasMVS | | | 92.72 182 | 93.36 203 | 91.98 163 | 91.62 205 | 97.52 194 | 94.13 202 | 88.98 156 | 95.94 217 | 81.51 170 | 87.35 213 | 79.95 220 | 95.91 151 | 96.37 169 | 96.49 142 | 99.70 85 | 99.89 8 |
|
v2v482 | | | 92.77 181 | 93.52 201 | 91.90 168 | 91.59 206 | 97.63 184 | 94.57 196 | 90.31 136 | 96.80 192 | 79.22 198 | 88.74 195 | 81.55 212 | 96.04 149 | 95.26 189 | 94.97 194 | 99.66 109 | 99.69 103 |
|
v1192 | | | 92.43 191 | 93.61 194 | 91.05 187 | 91.53 207 | 97.43 200 | 94.61 193 | 87.99 170 | 96.60 199 | 76.72 211 | 87.11 215 | 82.74 196 | 95.85 152 | 96.35 171 | 95.30 175 | 99.60 149 | 99.74 74 |
|
WR-MVS | | | 93.43 164 | 94.48 168 | 92.21 154 | 91.52 208 | 97.69 180 | 94.66 191 | 89.98 144 | 96.86 189 | 83.43 156 | 90.12 175 | 85.03 172 | 93.94 206 | 96.02 181 | 95.82 162 | 99.71 76 | 99.82 28 |
|
v144192 | | | 92.38 194 | 93.55 200 | 91.00 190 | 91.44 209 | 97.47 199 | 94.27 199 | 87.41 175 | 96.52 204 | 78.03 204 | 87.50 212 | 82.65 197 | 95.32 182 | 95.82 184 | 95.15 179 | 99.55 167 | 99.78 46 |
|
pmmvs5 | | | 92.71 184 | 94.27 172 | 90.90 193 | 91.42 210 | 97.74 172 | 93.23 206 | 86.66 183 | 95.99 216 | 78.96 202 | 91.45 166 | 83.44 184 | 95.55 161 | 97.30 146 | 95.05 182 | 99.58 159 | 98.93 187 |
|
v1921920 | | | 92.36 196 | 93.57 197 | 90.94 192 | 91.39 211 | 97.39 202 | 94.70 188 | 87.63 174 | 96.60 199 | 76.63 212 | 86.98 216 | 82.89 194 | 95.75 153 | 96.26 176 | 95.14 180 | 99.55 167 | 99.73 78 |
|
gm-plane-assit | | | 89.44 220 | 92.82 217 | 85.49 223 | 91.37 212 | 95.34 228 | 79.55 238 | 82.12 211 | 91.68 234 | 64.79 235 | 87.98 209 | 80.26 218 | 95.66 156 | 98.51 74 | 97.56 113 | 99.45 181 | 98.41 199 |
|
v1240 | | | 91.99 205 | 93.33 204 | 90.44 202 | 91.29 213 | 97.30 206 | 94.25 200 | 86.79 180 | 96.43 208 | 75.49 217 | 86.34 221 | 81.85 210 | 95.29 185 | 96.42 168 | 95.22 177 | 99.52 174 | 99.73 78 |
|
PEN-MVS | | | 92.72 182 | 93.20 209 | 92.15 156 | 91.29 213 | 97.31 205 | 94.67 190 | 89.81 147 | 96.19 210 | 81.83 168 | 88.58 200 | 79.06 225 | 95.61 160 | 95.21 193 | 96.27 147 | 99.72 66 | 99.82 28 |
|
TranMVSNet+NR-MVSNet | | | 93.67 160 | 94.14 173 | 93.13 144 | 91.28 215 | 97.58 189 | 95.60 154 | 91.97 102 | 97.06 179 | 84.05 148 | 90.64 174 | 82.22 204 | 96.17 145 | 94.94 211 | 96.78 133 | 99.69 87 | 99.78 46 |
|
anonymousdsp | | | 93.12 166 | 95.86 145 | 89.93 209 | 91.09 216 | 98.25 156 | 95.12 161 | 85.08 194 | 97.44 162 | 73.30 222 | 90.89 170 | 90.78 135 | 95.25 187 | 97.91 119 | 95.96 160 | 99.71 76 | 99.82 28 |
|
MDTV_nov1_ep13_2view | | | 92.44 190 | 95.66 148 | 88.68 214 | 91.05 217 | 97.92 166 | 92.17 213 | 79.64 220 | 98.83 93 | 76.20 213 | 91.45 166 | 93.51 120 | 95.04 190 | 95.68 185 | 93.70 215 | 97.96 217 | 98.53 196 |
|
DTE-MVSNet | | | 92.42 193 | 92.85 215 | 91.91 167 | 90.87 218 | 96.97 209 | 94.53 197 | 89.81 147 | 95.86 219 | 81.59 169 | 88.83 193 | 77.88 228 | 95.01 191 | 94.34 216 | 96.35 145 | 99.64 126 | 99.73 78 |
|
V4 | | | 91.92 207 | 93.10 210 | 90.55 200 | 90.64 219 | 97.51 195 | 93.93 204 | 87.02 177 | 95.81 221 | 77.61 208 | 86.93 217 | 82.19 205 | 94.50 196 | 94.72 212 | 94.68 207 | 99.62 138 | 99.85 21 |
|
v52 | | | 91.94 206 | 93.10 210 | 90.57 199 | 90.62 220 | 97.50 196 | 93.98 203 | 87.02 177 | 95.86 219 | 77.67 207 | 86.93 217 | 82.16 206 | 94.53 195 | 94.71 213 | 94.70 206 | 99.61 141 | 99.85 21 |
|
v748 | | | 91.12 212 | 91.95 220 | 90.16 205 | 90.60 221 | 97.35 204 | 91.11 216 | 87.92 171 | 94.75 225 | 80.54 177 | 86.26 222 | 75.97 230 | 91.13 221 | 94.63 214 | 94.81 202 | 99.65 115 | 99.90 4 |
|
v7n | | | 91.61 210 | 92.95 212 | 90.04 206 | 90.56 222 | 97.69 180 | 93.74 205 | 85.59 189 | 95.89 218 | 76.95 210 | 86.60 220 | 78.60 227 | 93.76 209 | 97.01 155 | 94.99 193 | 99.65 115 | 99.87 14 |
|
test20.03 | | | 90.65 216 | 93.71 191 | 87.09 218 | 90.44 223 | 96.24 214 | 89.74 226 | 85.46 190 | 95.59 223 | 72.99 224 | 90.68 172 | 85.33 168 | 84.41 231 | 95.94 182 | 95.10 181 | 99.52 174 | 97.06 219 |
|
FPMVS | | | 83.82 227 | 84.61 233 | 82.90 228 | 90.39 224 | 90.71 236 | 90.85 220 | 84.10 207 | 95.47 224 | 65.15 233 | 83.44 226 | 74.46 232 | 75.48 234 | 81.63 238 | 79.42 240 | 91.42 241 | 87.14 238 |
|
Anonymous20231206 | | | 90.70 215 | 93.93 185 | 86.92 220 | 90.21 225 | 96.79 211 | 90.30 223 | 86.61 184 | 96.05 214 | 69.25 228 | 88.46 202 | 84.86 174 | 85.86 228 | 97.11 153 | 96.47 143 | 99.30 194 | 97.80 210 |
|
new_pmnet | | | 90.45 217 | 92.84 216 | 87.66 217 | 88.96 226 | 96.16 215 | 88.71 228 | 84.66 199 | 97.56 160 | 71.91 227 | 85.60 224 | 86.58 157 | 93.28 212 | 96.07 180 | 93.54 216 | 98.46 209 | 94.39 230 |
|
testus | | | 88.77 222 | 92.77 218 | 84.10 226 | 88.24 227 | 93.95 232 | 87.16 231 | 84.24 205 | 97.37 163 | 61.54 239 | 95.70 134 | 73.10 233 | 84.90 230 | 95.56 186 | 95.82 162 | 98.51 207 | 97.88 209 |
|
test2356 | | | 88.81 221 | 92.86 214 | 84.09 227 | 87.85 228 | 93.46 234 | 87.07 232 | 83.60 210 | 96.50 206 | 62.08 238 | 97.06 95 | 75.04 231 | 85.17 229 | 95.08 209 | 95.42 170 | 98.75 206 | 97.46 212 |
|
PM-MVS | | | 89.55 219 | 90.30 224 | 88.67 215 | 87.06 229 | 95.60 220 | 90.88 219 | 84.51 201 | 96.14 211 | 75.75 214 | 86.89 219 | 63.47 240 | 94.64 193 | 96.85 158 | 93.89 213 | 99.17 200 | 99.29 169 |
|
pmmvs-eth3d | | | 89.81 218 | 89.65 225 | 90.00 207 | 86.94 230 | 95.38 227 | 91.08 217 | 86.39 185 | 94.57 227 | 82.27 166 | 83.03 229 | 64.94 237 | 93.96 205 | 96.57 163 | 93.82 214 | 99.35 191 | 99.24 173 |
|
new-patchmatchnet | | | 86.12 226 | 87.30 227 | 84.74 224 | 86.92 231 | 95.19 231 | 83.57 235 | 84.42 203 | 92.67 232 | 65.66 232 | 80.32 232 | 64.72 238 | 89.41 223 | 92.33 228 | 89.21 234 | 98.43 210 | 96.69 224 |
|
pmmvs3 | | | 88.19 224 | 91.27 221 | 84.60 225 | 85.60 232 | 93.66 233 | 85.68 233 | 81.13 212 | 92.36 233 | 63.66 237 | 89.51 186 | 77.10 229 | 93.22 213 | 96.37 169 | 92.40 225 | 98.30 213 | 97.46 212 |
|
testmv | | | 81.83 229 | 86.26 228 | 76.66 232 | 84.10 233 | 89.42 239 | 74.29 242 | 79.65 219 | 90.61 235 | 51.85 244 | 82.11 230 | 63.06 242 | 72.61 237 | 91.94 230 | 92.75 220 | 97.49 225 | 93.94 232 |
|
test1235678 | | | 81.83 229 | 86.26 228 | 76.66 232 | 84.10 233 | 89.41 240 | 74.29 242 | 79.64 220 | 90.60 236 | 51.84 245 | 82.11 230 | 63.07 241 | 72.61 237 | 91.94 230 | 92.75 220 | 97.49 225 | 93.94 232 |
|
Gipuma | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 81.40 231 | 81.78 234 | 80.96 230 | 83.21 235 | 85.61 244 | 79.73 237 | 76.25 237 | 97.33 170 | 64.21 236 | 55.32 241 | 55.55 244 | 86.04 227 | 92.43 227 | 92.20 228 | 96.32 236 | 93.99 231 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
test12356 | | | 80.53 232 | 84.80 232 | 75.54 234 | 82.31 236 | 88.05 243 | 75.99 239 | 79.31 224 | 88.53 237 | 53.24 243 | 83.30 227 | 56.38 243 | 65.16 243 | 90.87 234 | 93.10 219 | 97.25 231 | 93.34 235 |
|
1111 | | | 82.87 228 | 85.67 230 | 79.62 231 | 81.86 237 | 89.62 237 | 74.44 240 | 68.81 242 | 87.44 239 | 66.59 230 | 76.83 236 | 70.33 235 | 87.71 225 | 92.65 223 | 93.37 217 | 98.28 214 | 89.42 236 |
|
.test1245 | | | 69.67 235 | 72.22 238 | 66.70 239 | 81.86 237 | 89.62 237 | 74.44 240 | 68.81 242 | 87.44 239 | 66.59 230 | 76.83 236 | 70.33 235 | 87.71 225 | 92.65 223 | 37.65 243 | 20.79 247 | 51.04 243 |
|
MDA-MVSNet-bldmvs | | | 87.84 225 | 89.22 226 | 86.23 221 | 81.74 239 | 96.77 212 | 83.74 234 | 89.57 151 | 94.50 228 | 72.83 225 | 96.64 107 | 64.47 239 | 92.71 216 | 81.43 239 | 92.28 227 | 96.81 234 | 98.47 198 |
|
MIMVSNet1 | | | 88.61 223 | 90.68 223 | 86.19 222 | 81.56 240 | 95.30 229 | 87.78 229 | 85.98 188 | 94.19 229 | 72.30 226 | 78.84 234 | 78.90 226 | 90.06 222 | 96.59 161 | 95.47 168 | 99.46 180 | 95.49 228 |
|
PMVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 72.60 17 | 76.39 234 | 77.66 237 | 74.92 235 | 81.04 241 | 69.37 249 | 68.47 245 | 80.54 216 | 85.39 241 | 65.07 234 | 73.52 238 | 72.91 234 | 65.67 242 | 80.35 240 | 76.81 241 | 88.71 243 | 85.25 242 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
ambc | | | | 80.99 235 | | 80.04 242 | 90.84 235 | 90.91 218 | | 96.09 212 | 74.18 220 | 62.81 240 | 30.59 250 | 82.44 233 | 96.25 177 | 91.77 230 | 95.91 238 | 98.56 195 |
|
PMMVS2 | | | 77.26 233 | 79.47 236 | 74.70 236 | 76.00 243 | 88.37 242 | 74.22 244 | 76.34 235 | 78.31 242 | 54.13 241 | 69.96 239 | 52.50 245 | 70.14 240 | 84.83 237 | 88.71 235 | 97.35 227 | 93.58 234 |
|
EMVS | | | 68.12 238 | 68.11 240 | 68.14 238 | 75.51 244 | 71.76 247 | 55.38 248 | 77.20 234 | 77.78 243 | 37.79 248 | 53.59 242 | 43.61 246 | 74.72 235 | 67.05 244 | 76.70 242 | 88.27 245 | 86.24 240 |
|
E-PMN | | | 68.30 237 | 68.43 239 | 68.15 237 | 74.70 245 | 71.56 248 | 55.64 247 | 77.24 233 | 77.48 244 | 39.46 247 | 51.95 244 | 41.68 248 | 73.28 236 | 70.65 242 | 79.51 239 | 88.61 244 | 86.20 241 |
|
no-one | | | 66.79 239 | 67.62 241 | 65.81 240 | 73.06 246 | 81.79 245 | 51.90 250 | 76.20 238 | 61.07 246 | 54.05 242 | 51.62 245 | 41.72 247 | 49.18 244 | 67.26 243 | 82.83 238 | 90.47 242 | 87.07 239 |
|
MVE | ![Method available under a permissive open source license. permissive](img/icon_permissive.png) | 67.97 19 | 65.53 240 | 67.43 242 | 63.31 241 | 59.33 247 | 74.20 246 | 53.09 249 | 70.43 241 | 66.27 245 | 43.13 246 | 45.98 246 | 30.62 249 | 70.65 239 | 79.34 241 | 86.30 236 | 83.25 246 | 89.33 237 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
testmvs | | | 31.24 241 | 40.15 243 | 20.86 243 | 12.61 248 | 17.99 250 | 25.16 251 | 13.30 245 | 48.42 247 | 24.82 249 | 53.07 243 | 30.13 251 | 28.47 245 | 42.73 245 | 37.65 243 | 20.79 247 | 51.04 243 |
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test123 | | | 26.75 242 | 34.25 244 | 18.01 244 | 7.93 249 | 17.18 251 | 24.85 252 | 12.36 246 | 44.83 248 | 16.52 250 | 41.80 247 | 18.10 252 | 28.29 246 | 33.08 246 | 34.79 245 | 18.10 249 | 49.95 245 |
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GG-mvs-BLEND | | | 69.11 236 | 98.13 78 | 35.26 242 | 3.49 250 | 98.20 159 | 94.89 168 | 2.38 247 | 98.42 122 | 5.82 251 | 96.37 116 | 98.60 57 | 5.97 247 | 98.75 55 | 97.98 91 | 99.01 202 | 98.61 194 |
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sosnet-low-res | | | 0.00 243 | 0.00 245 | 0.00 245 | 0.00 251 | 0.00 252 | 0.00 253 | 0.00 248 | 0.00 249 | 0.00 252 | 0.00 248 | 0.00 253 | 0.00 248 | 0.00 247 | 0.00 246 | 0.00 250 | 0.00 246 |
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sosnet | | | 0.00 243 | 0.00 245 | 0.00 245 | 0.00 251 | 0.00 252 | 0.00 253 | 0.00 248 | 0.00 249 | 0.00 252 | 0.00 248 | 0.00 253 | 0.00 248 | 0.00 247 | 0.00 246 | 0.00 250 | 0.00 246 |
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MTAPA | | | | | | | | | | | 98.09 11 | | 99.97 5 | | | | | |
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MTMP | | | | | | | | | | | 98.46 7 | | 99.96 11 | | | | | |
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Patchmatch-RL test | | | | | | | | 66.86 246 | | | | | | | | | | |
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NP-MVS | | | | | | | | | | 98.57 114 | | | | | | | | |
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Patchmtry | | | | | | | 98.59 135 | 97.15 122 | 79.14 225 | | 80.42 179 | | | | | | | |
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DeepMVS_CX | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | | | | | 96.85 210 | 87.43 230 | 89.27 153 | 98.30 125 | 75.55 216 | 95.05 140 | 79.47 222 | 92.62 217 | 89.48 235 | | 95.18 239 | 95.96 227 |
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