ESAPD | | | 95.53 1 | 96.13 1 | 94.82 1 | 96.81 21 | 98.05 1 | 97.42 1 | 93.09 2 | 94.31 5 | 91.49 4 | 97.12 1 | 95.03 2 | 93.27 2 | 95.55 4 | 94.58 8 | 96.86 2 | 98.25 1 |
|
v1.0 | | | 87.80 59 | 81.94 97 | 94.63 3 | 97.35 6 | 97.95 2 | 97.09 3 | 93.48 1 | 93.91 10 | 90.13 13 | 96.41 3 | 95.14 1 | 92.88 5 | 95.64 3 | 94.53 9 | 96.86 2 | 0.00 246 |
|
HSP-MVS | | | 94.83 3 | 95.37 3 | 94.21 6 | 96.82 20 | 97.94 3 | 96.69 4 | 92.37 8 | 93.97 9 | 90.29 11 | 96.16 4 | 93.71 4 | 92.70 6 | 94.80 14 | 93.13 33 | 96.37 9 | 97.90 6 |
|
APDe-MVS | | | 95.23 2 | 95.69 2 | 94.70 2 | 97.12 11 | 97.81 4 | 97.19 2 | 92.83 3 | 95.06 2 | 90.98 6 | 96.47 2 | 92.77 9 | 93.38 1 | 95.34 7 | 94.21 13 | 96.68 5 | 98.17 2 |
|
CSCG | | | 92.76 22 | 93.16 24 | 92.29 26 | 96.30 24 | 97.74 5 | 94.67 29 | 88.98 31 | 92.46 20 | 89.73 17 | 86.67 34 | 92.15 13 | 88.69 37 | 92.26 47 | 92.92 37 | 95.40 47 | 97.89 7 |
|
SMA-MVS | | | 94.70 4 | 95.35 4 | 93.93 9 | 97.57 2 | 97.57 6 | 95.98 10 | 91.91 10 | 94.50 3 | 90.35 9 | 93.46 14 | 92.72 10 | 91.89 15 | 95.89 1 | 95.22 1 | 95.88 21 | 98.10 3 |
|
SteuartSystems-ACMMP | | | 94.06 10 | 94.65 8 | 93.38 16 | 96.97 16 | 97.36 7 | 96.12 8 | 91.78 11 | 92.05 25 | 87.34 27 | 94.42 9 | 90.87 20 | 91.87 16 | 95.47 6 | 94.59 7 | 96.21 14 | 97.77 8 |
Skip Steuart: Steuart Systems R&D Blog. |
ACMMP_Plus | | | 93.94 12 | 94.49 11 | 93.30 17 | 97.03 14 | 97.31 8 | 95.96 11 | 91.30 15 | 93.41 15 | 88.55 21 | 93.00 16 | 90.33 23 | 91.43 23 | 95.53 5 | 94.41 11 | 95.53 43 | 97.47 12 |
|
CNVR-MVS | | | 94.37 8 | 94.65 8 | 94.04 8 | 97.29 7 | 97.11 9 | 96.00 9 | 92.43 7 | 93.45 13 | 89.85 16 | 90.92 22 | 93.04 7 | 92.59 8 | 95.77 2 | 94.82 4 | 96.11 16 | 97.42 13 |
|
PHI-MVS | | | 92.05 28 | 93.74 18 | 90.08 40 | 94.96 36 | 97.06 10 | 93.11 41 | 87.71 39 | 90.71 33 | 80.78 61 | 92.40 19 | 91.03 18 | 87.68 48 | 94.32 23 | 94.48 10 | 96.21 14 | 96.16 36 |
|
SD-MVS | | | 94.53 6 | 95.22 5 | 93.73 12 | 95.69 32 | 97.03 11 | 95.77 18 | 91.95 9 | 94.41 4 | 91.35 5 | 94.97 5 | 93.34 6 | 91.80 17 | 94.72 17 | 93.99 17 | 95.82 28 | 98.07 4 |
|
TSAR-MVS + MP. | | | 94.48 7 | 94.97 6 | 93.90 10 | 95.53 33 | 97.01 12 | 96.69 4 | 90.71 19 | 94.24 6 | 90.92 7 | 94.97 5 | 92.19 12 | 93.03 3 | 94.83 13 | 93.60 23 | 96.51 8 | 97.97 5 |
|
APD-MVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 94.37 8 | 94.47 12 | 94.26 4 | 97.18 9 | 96.99 13 | 96.53 6 | 92.68 4 | 92.45 21 | 89.96 14 | 94.53 8 | 91.63 16 | 92.89 4 | 94.58 19 | 93.82 20 | 96.31 12 | 97.26 14 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
ACMMPR | | | 93.72 15 | 93.94 16 | 93.48 15 | 97.07 12 | 96.93 14 | 95.78 17 | 90.66 21 | 93.88 11 | 89.24 18 | 93.53 13 | 89.08 33 | 92.24 10 | 93.89 29 | 93.50 26 | 95.88 21 | 96.73 27 |
|
DeepPCF-MVS | | 88.51 2 | 92.64 25 | 94.42 13 | 90.56 37 | 94.84 39 | 96.92 15 | 91.31 59 | 89.61 27 | 95.16 1 | 84.55 43 | 89.91 26 | 91.45 17 | 90.15 30 | 95.12 9 | 94.81 5 | 92.90 171 | 97.58 10 |
|
DeepC-MVS | | 87.86 3 | 92.26 27 | 91.86 30 | 92.73 22 | 96.18 25 | 96.87 16 | 95.19 24 | 91.76 12 | 92.17 24 | 86.58 32 | 81.79 46 | 85.85 45 | 90.88 26 | 94.57 20 | 94.61 6 | 95.80 29 | 97.18 15 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
HFP-MVS | | | 94.02 11 | 94.22 14 | 93.78 11 | 97.25 8 | 96.85 17 | 95.81 16 | 90.94 18 | 94.12 7 | 90.29 11 | 94.09 11 | 89.98 26 | 92.52 9 | 93.94 27 | 93.49 28 | 95.87 23 | 97.10 19 |
|
MCST-MVS | | | 93.81 13 | 94.06 15 | 93.53 14 | 96.79 22 | 96.85 17 | 95.95 12 | 91.69 13 | 92.20 23 | 87.17 29 | 90.83 24 | 93.41 5 | 91.96 13 | 94.49 21 | 93.50 26 | 97.61 1 | 97.12 18 |
|
MP-MVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 93.35 17 | 93.59 20 | 93.08 20 | 97.39 4 | 96.82 19 | 95.38 21 | 90.71 19 | 90.82 32 | 88.07 24 | 92.83 18 | 90.29 24 | 91.32 24 | 94.03 24 | 93.19 32 | 95.61 39 | 97.16 16 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
MVS_0304 | | | 90.88 35 | 91.35 33 | 90.34 38 | 93.91 47 | 96.79 20 | 94.49 30 | 86.54 45 | 86.57 53 | 82.85 52 | 81.68 49 | 89.70 28 | 87.57 50 | 94.64 18 | 93.93 18 | 96.67 6 | 96.15 37 |
|
CANet | | | 91.33 33 | 91.46 31 | 91.18 32 | 95.01 35 | 96.71 21 | 93.77 34 | 87.39 41 | 87.72 49 | 87.26 28 | 81.77 47 | 89.73 27 | 87.32 53 | 94.43 22 | 93.86 19 | 96.31 12 | 96.02 39 |
|
XVS | | | | | | 93.11 55 | 96.70 22 | 91.91 51 | | | 83.95 45 | | 88.82 35 | | | | 95.79 30 | |
|
X-MVStestdata | | | | | | 93.11 55 | 96.70 22 | 91.91 51 | | | 83.95 45 | | 88.82 35 | | | | 95.79 30 | |
|
X-MVS | | | 92.36 26 | 92.75 27 | 91.90 29 | 96.89 17 | 96.70 22 | 95.25 23 | 90.48 24 | 91.50 30 | 83.95 45 | 88.20 28 | 88.82 35 | 89.11 33 | 93.75 30 | 93.43 29 | 95.75 33 | 96.83 25 |
|
PGM-MVS | | | 92.76 22 | 93.03 25 | 92.45 25 | 97.03 14 | 96.67 25 | 95.73 19 | 87.92 37 | 90.15 39 | 86.53 33 | 92.97 17 | 88.33 39 | 91.69 18 | 93.62 32 | 93.03 34 | 95.83 27 | 96.41 33 |
|
NCCC | | | 93.69 16 | 93.66 19 | 93.72 13 | 97.37 5 | 96.66 26 | 95.93 14 | 92.50 6 | 93.40 16 | 88.35 22 | 87.36 32 | 92.33 11 | 92.18 11 | 94.89 12 | 94.09 15 | 96.00 17 | 96.91 22 |
|
TSAR-MVS + ACMM | | | 92.97 20 | 94.51 10 | 91.16 33 | 95.88 30 | 96.59 27 | 95.09 25 | 90.45 25 | 93.42 14 | 83.01 51 | 94.68 7 | 90.74 22 | 88.74 36 | 94.75 15 | 93.78 21 | 93.82 145 | 97.63 9 |
|
ACMMP | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 92.03 29 | 92.16 28 | 91.87 30 | 95.88 30 | 96.55 28 | 94.47 31 | 89.49 28 | 91.71 28 | 85.26 39 | 91.52 21 | 84.48 50 | 90.21 29 | 92.82 42 | 91.63 46 | 95.92 19 | 96.42 32 |
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 |
HPM-MVS++ | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 94.60 5 | 94.91 7 | 94.24 5 | 97.86 1 | 96.53 29 | 96.14 7 | 92.51 5 | 93.87 12 | 90.76 8 | 93.45 15 | 93.84 3 | 92.62 7 | 95.11 10 | 94.08 16 | 95.58 41 | 97.48 11 |
|
CP-MVS | | | 93.25 18 | 93.26 23 | 93.24 18 | 96.84 19 | 96.51 30 | 95.52 20 | 90.61 22 | 92.37 22 | 88.88 19 | 90.91 23 | 89.52 29 | 91.91 14 | 93.64 31 | 92.78 39 | 95.69 34 | 97.09 20 |
|
DeepC-MVS_fast | | 88.76 1 | 93.10 19 | 93.02 26 | 93.19 19 | 97.13 10 | 96.51 30 | 95.35 22 | 91.19 16 | 93.14 18 | 88.14 23 | 85.26 38 | 89.49 30 | 91.45 20 | 95.17 8 | 95.07 2 | 95.85 26 | 96.48 30 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
3Dnovator+ | | 86.06 4 | 91.60 31 | 90.86 37 | 92.47 24 | 96.00 29 | 96.50 32 | 94.70 28 | 87.83 38 | 90.49 35 | 89.92 15 | 74.68 81 | 89.35 31 | 90.66 27 | 94.02 25 | 94.14 14 | 95.67 36 | 96.85 23 |
|
zzz-MVS | | | 93.80 14 | 93.45 22 | 94.20 7 | 97.53 3 | 96.43 33 | 95.88 15 | 91.12 17 | 94.09 8 | 92.74 3 | 87.68 30 | 90.77 21 | 92.04 12 | 94.74 16 | 93.56 25 | 95.91 20 | 96.85 23 |
|
abl_6 | | | | | 90.66 36 | 94.65 42 | 96.27 34 | 92.21 47 | 86.94 43 | 90.23 37 | 86.38 34 | 85.50 37 | 92.96 8 | 88.37 40 | | | 95.40 47 | 95.46 49 |
|
DELS-MVS | | | 89.71 41 | 89.68 42 | 89.74 43 | 93.75 49 | 96.22 35 | 93.76 35 | 85.84 48 | 82.53 68 | 85.05 41 | 78.96 61 | 84.24 51 | 84.25 69 | 94.91 11 | 94.91 3 | 95.78 32 | 96.02 39 |
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 |
CDPH-MVS | | | 91.14 34 | 92.01 29 | 90.11 39 | 96.18 25 | 96.18 36 | 94.89 27 | 88.80 33 | 88.76 45 | 77.88 78 | 89.18 27 | 87.71 42 | 87.29 54 | 93.13 36 | 93.31 31 | 95.62 38 | 95.84 41 |
|
PCF-MVS | | 84.60 6 | 88.66 48 | 87.75 61 | 89.73 44 | 93.06 57 | 96.02 37 | 93.22 40 | 90.00 26 | 82.44 70 | 80.02 65 | 77.96 68 | 85.16 48 | 87.36 52 | 88.54 115 | 88.54 112 | 94.72 90 | 95.61 46 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
TSAR-MVS + GP. | | | 92.71 24 | 93.91 17 | 91.30 31 | 91.96 67 | 96.00 38 | 93.43 37 | 87.94 36 | 92.53 19 | 86.27 37 | 93.57 12 | 91.94 14 | 91.44 22 | 93.29 34 | 92.89 38 | 96.78 4 | 97.15 17 |
|
QAPM | | | 89.49 43 | 89.58 43 | 89.38 48 | 94.73 40 | 95.94 39 | 92.35 46 | 85.00 55 | 85.69 57 | 80.03 64 | 76.97 72 | 87.81 41 | 87.87 44 | 92.18 51 | 92.10 42 | 96.33 10 | 96.40 34 |
|
canonicalmvs | | | 89.36 44 | 89.92 39 | 88.70 55 | 91.38 69 | 95.92 40 | 91.81 54 | 82.61 89 | 90.37 36 | 82.73 54 | 82.09 44 | 79.28 77 | 88.30 41 | 91.17 61 | 93.59 24 | 95.36 51 | 97.04 21 |
|
MSLP-MVS++ | | | 92.02 30 | 91.40 32 | 92.75 21 | 96.01 28 | 95.88 41 | 93.73 36 | 89.00 29 | 89.89 40 | 90.31 10 | 81.28 51 | 88.85 34 | 91.45 20 | 92.88 41 | 94.24 12 | 96.00 17 | 96.76 26 |
|
3Dnovator | | 85.17 5 | 90.48 37 | 89.90 41 | 91.16 33 | 94.88 38 | 95.74 42 | 93.82 33 | 85.36 52 | 89.28 41 | 87.81 25 | 74.34 83 | 87.40 43 | 88.56 38 | 93.07 37 | 93.74 22 | 96.53 7 | 95.71 43 |
|
MVS_111021_HR | | | 90.56 36 | 91.29 34 | 89.70 45 | 94.71 41 | 95.63 43 | 91.81 54 | 86.38 46 | 87.53 50 | 81.29 58 | 87.96 29 | 85.43 47 | 87.69 47 | 93.90 28 | 92.93 36 | 96.33 10 | 95.69 44 |
|
casdiffmvs1 | | | 89.19 45 | 89.09 45 | 89.31 49 | 91.86 68 | 95.44 44 | 92.81 45 | 82.23 96 | 88.97 44 | 85.78 38 | 82.59 43 | 81.31 63 | 87.87 44 | 89.06 106 | 90.78 56 | 95.34 53 | 95.46 49 |
|
PVSNet_Blended_VisFu | | | 87.40 63 | 87.80 57 | 86.92 67 | 92.86 59 | 95.40 45 | 88.56 94 | 83.45 74 | 79.55 96 | 82.26 55 | 74.49 82 | 84.03 52 | 79.24 138 | 92.97 40 | 91.53 47 | 95.15 63 | 96.65 29 |
|
train_agg | | | 92.87 21 | 93.53 21 | 92.09 27 | 96.88 18 | 95.38 46 | 95.94 13 | 90.59 23 | 90.65 34 | 83.65 48 | 94.31 10 | 91.87 15 | 90.30 28 | 93.38 33 | 92.42 40 | 95.17 61 | 96.73 27 |
|
OMC-MVS | | | 90.23 39 | 90.40 38 | 90.03 41 | 93.45 52 | 95.29 47 | 91.89 53 | 86.34 47 | 93.25 17 | 84.94 42 | 81.72 48 | 86.65 44 | 88.90 34 | 91.69 54 | 90.27 67 | 94.65 95 | 93.95 72 |
|
CPTT-MVS | | | 91.39 32 | 90.95 35 | 91.91 28 | 95.06 34 | 95.24 48 | 95.02 26 | 88.98 31 | 91.02 31 | 86.71 31 | 84.89 40 | 88.58 38 | 91.60 19 | 90.82 79 | 89.67 85 | 94.08 122 | 96.45 31 |
|
AdaColmap | ![Method available as binary. binary](img/icon_binary.png) | | 90.29 38 | 88.38 51 | 92.53 23 | 96.10 27 | 95.19 49 | 92.98 42 | 91.40 14 | 89.08 43 | 88.65 20 | 78.35 65 | 81.44 62 | 91.30 25 | 90.81 80 | 90.21 68 | 94.72 90 | 93.59 79 |
|
casdiffmvs | | | 87.83 58 | 87.45 63 | 88.28 61 | 91.01 74 | 95.16 50 | 91.42 58 | 82.08 98 | 84.68 62 | 83.26 50 | 80.75 54 | 77.48 86 | 86.53 60 | 89.82 94 | 89.84 79 | 95.38 50 | 94.43 64 |
|
UA-Net | | | 86.07 69 | 87.78 58 | 84.06 95 | 92.85 60 | 95.11 51 | 87.73 101 | 84.38 58 | 73.22 148 | 73.18 96 | 79.99 55 | 89.22 32 | 71.47 186 | 93.22 35 | 93.03 34 | 94.76 87 | 90.69 152 |
|
MAR-MVS | | | 88.39 53 | 88.44 50 | 88.33 60 | 94.90 37 | 95.06 52 | 90.51 62 | 83.59 68 | 85.27 58 | 79.07 68 | 77.13 70 | 82.89 57 | 87.70 46 | 92.19 50 | 92.32 41 | 94.23 117 | 94.20 70 |
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 |
OpenMVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 82.53 11 | 87.71 60 | 86.84 66 | 88.73 54 | 94.42 43 | 95.06 52 | 91.02 61 | 83.49 71 | 82.50 69 | 82.24 56 | 67.62 127 | 85.48 46 | 85.56 63 | 91.19 60 | 91.30 48 | 95.67 36 | 94.75 59 |
|
Vis-MVSNet | ![Method available under a permissive open source license. permissive](img/icon_permissive.png) | | 84.38 84 | 86.68 69 | 81.70 127 | 87.65 124 | 94.89 54 | 88.14 96 | 80.90 110 | 74.48 132 | 68.23 123 | 77.53 69 | 80.72 65 | 69.98 192 | 92.68 43 | 91.90 43 | 95.33 55 | 94.58 63 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
TAPA-MVS | | 84.37 7 | 88.91 47 | 88.93 47 | 88.89 52 | 93.00 58 | 94.85 55 | 92.00 50 | 84.84 56 | 91.68 29 | 80.05 63 | 79.77 56 | 84.56 49 | 88.17 42 | 90.11 90 | 89.00 106 | 95.30 56 | 92.57 104 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
PVSNet_BlendedMVS | | | 88.19 56 | 88.00 55 | 88.42 58 | 92.71 63 | 94.82 56 | 89.08 79 | 83.81 64 | 84.91 59 | 86.38 34 | 79.14 59 | 78.11 82 | 82.66 75 | 93.05 38 | 91.10 49 | 95.86 24 | 94.86 57 |
|
PVSNet_Blended | | | 88.19 56 | 88.00 55 | 88.42 58 | 92.71 63 | 94.82 56 | 89.08 79 | 83.81 64 | 84.91 59 | 86.38 34 | 79.14 59 | 78.11 82 | 82.66 75 | 93.05 38 | 91.10 49 | 95.86 24 | 94.86 57 |
|
IS_MVSNet | | | 86.18 68 | 88.18 53 | 83.85 99 | 91.02 73 | 94.72 58 | 87.48 105 | 82.46 91 | 81.05 82 | 70.28 104 | 76.98 71 | 82.20 60 | 76.65 153 | 93.97 26 | 93.38 30 | 95.18 60 | 94.97 54 |
|
EPP-MVSNet | | | 86.55 66 | 87.76 59 | 85.15 76 | 90.52 83 | 94.41 59 | 87.24 113 | 82.32 94 | 81.79 75 | 73.60 94 | 78.57 63 | 82.41 58 | 82.07 80 | 91.23 57 | 90.39 65 | 95.14 64 | 95.48 48 |
|
CNLPA | | | 88.40 51 | 87.00 65 | 90.03 41 | 93.73 50 | 94.28 60 | 89.56 73 | 85.81 49 | 91.87 26 | 87.55 26 | 69.53 115 | 81.49 61 | 89.23 32 | 89.45 101 | 88.59 111 | 94.31 116 | 93.82 75 |
|
CLD-MVS | | | 88.66 48 | 88.52 49 | 88.82 53 | 91.37 70 | 94.22 61 | 92.82 44 | 82.08 98 | 88.27 47 | 85.14 40 | 81.86 45 | 78.53 81 | 85.93 62 | 91.17 61 | 90.61 61 | 95.55 42 | 95.00 53 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
UGNet | | | 85.90 72 | 88.23 52 | 83.18 107 | 88.96 111 | 94.10 62 | 87.52 104 | 83.60 67 | 81.66 76 | 77.90 77 | 80.76 53 | 83.19 55 | 66.70 206 | 91.13 72 | 90.71 60 | 94.39 113 | 96.06 38 |
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 |
OPM-MVS | | | 87.56 62 | 85.80 76 | 89.62 46 | 93.90 48 | 94.09 63 | 94.12 32 | 88.18 34 | 75.40 122 | 77.30 82 | 76.41 73 | 77.93 84 | 88.79 35 | 92.20 49 | 90.82 55 | 95.40 47 | 93.72 78 |
|
MVS_111021_LR | | | 90.14 40 | 90.89 36 | 89.26 50 | 93.23 54 | 94.05 64 | 90.43 63 | 84.65 57 | 90.16 38 | 84.52 44 | 90.14 25 | 83.80 53 | 87.99 43 | 92.50 45 | 90.92 54 | 94.74 88 | 94.70 61 |
|
ACMP | | 83.90 8 | 88.32 54 | 88.06 54 | 88.62 56 | 92.18 65 | 93.98 65 | 91.28 60 | 85.24 53 | 86.69 52 | 81.23 59 | 85.62 36 | 75.13 94 | 87.01 56 | 89.83 93 | 89.77 82 | 94.79 84 | 95.43 51 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
TSAR-MVS + COLMAP | | | 88.40 51 | 89.09 45 | 87.60 64 | 92.72 62 | 93.92 66 | 92.21 47 | 85.57 51 | 91.73 27 | 73.72 93 | 91.75 20 | 73.22 107 | 87.64 49 | 91.49 55 | 89.71 84 | 93.73 151 | 91.82 126 |
|
diffmvs1 | | | 87.00 64 | 87.76 59 | 86.12 69 | 88.93 112 | 93.78 67 | 90.40 64 | 80.42 113 | 88.25 48 | 78.77 70 | 79.52 58 | 79.73 73 | 85.07 65 | 89.30 103 | 89.49 94 | 93.11 167 | 94.28 67 |
|
HQP-MVS | | | 89.13 46 | 89.58 43 | 88.60 57 | 93.53 51 | 93.67 68 | 93.29 39 | 87.58 40 | 88.53 46 | 75.50 83 | 87.60 31 | 80.32 67 | 87.07 55 | 90.66 85 | 89.95 75 | 94.62 98 | 96.35 35 |
|
LGP-MVS_train | | | 88.25 55 | 88.55 48 | 87.89 62 | 92.84 61 | 93.66 69 | 93.35 38 | 85.22 54 | 85.77 55 | 74.03 92 | 86.60 35 | 76.29 90 | 86.62 58 | 91.20 59 | 90.58 63 | 95.29 57 | 95.75 42 |
|
EPNet | | | 89.60 42 | 89.91 40 | 89.24 51 | 96.45 23 | 93.61 70 | 92.95 43 | 88.03 35 | 85.74 56 | 83.36 49 | 87.29 33 | 83.05 56 | 80.98 86 | 92.22 48 | 91.85 44 | 93.69 152 | 95.58 47 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
IB-MVS | | 79.09 12 | 82.60 98 | 82.19 95 | 83.07 108 | 91.08 72 | 93.55 71 | 80.90 194 | 81.35 106 | 76.56 113 | 80.87 60 | 64.81 149 | 69.97 116 | 68.87 196 | 85.64 156 | 90.06 71 | 95.36 51 | 94.74 60 |
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 |
MVS_Test | | | 86.93 65 | 87.24 64 | 86.56 68 | 90.10 98 | 93.47 72 | 90.31 65 | 80.12 121 | 83.55 65 | 78.12 73 | 79.58 57 | 79.80 71 | 85.45 64 | 90.17 89 | 90.59 62 | 95.29 57 | 93.53 80 |
|
Effi-MVS+ | | | 85.33 76 | 85.08 79 | 85.63 73 | 89.69 105 | 93.42 73 | 89.90 68 | 80.31 118 | 79.32 97 | 72.48 100 | 73.52 93 | 74.03 98 | 86.55 59 | 90.99 75 | 89.98 73 | 94.83 82 | 94.27 69 |
|
HyFIR lowres test | | | 81.62 116 | 79.45 139 | 84.14 94 | 91.00 75 | 93.38 74 | 88.27 95 | 78.19 149 | 76.28 115 | 70.18 105 | 48.78 221 | 73.69 102 | 83.52 70 | 87.05 131 | 87.83 124 | 93.68 153 | 89.15 162 |
|
diffmvs | | | 85.72 74 | 86.10 72 | 85.27 75 | 88.43 117 | 93.34 75 | 88.98 88 | 80.17 120 | 84.21 63 | 77.41 81 | 78.53 64 | 76.01 91 | 83.28 72 | 88.09 120 | 88.61 110 | 93.34 161 | 93.92 73 |
|
LS3D | | | 85.96 71 | 84.37 83 | 87.81 63 | 94.13 45 | 93.27 76 | 90.26 66 | 89.00 29 | 84.91 59 | 72.84 98 | 71.74 101 | 72.47 109 | 87.45 51 | 89.53 100 | 89.09 103 | 93.20 165 | 89.60 159 |
|
conf0.002 | | | 82.54 100 | 80.83 119 | 84.54 79 | 90.28 95 | 93.24 77 | 89.05 81 | 82.75 79 | 75.14 123 | 69.75 107 | 67.99 123 | 57.12 203 | 80.38 99 | 91.16 64 | 89.79 80 | 95.02 69 | 91.36 140 |
|
conf0.01 | | | 82.64 97 | 81.02 111 | 84.53 81 | 90.30 90 | 93.22 78 | 89.05 81 | 82.75 79 | 75.14 123 | 69.69 108 | 67.15 129 | 59.19 192 | 80.38 99 | 91.16 64 | 89.51 89 | 95.00 71 | 91.76 129 |
|
DI_MVS_plusplus_trai | | | 86.41 67 | 85.54 77 | 87.42 65 | 89.24 107 | 93.13 79 | 92.16 49 | 82.65 87 | 82.30 71 | 80.75 62 | 68.30 122 | 80.41 66 | 85.01 66 | 90.56 86 | 90.07 70 | 94.70 92 | 94.01 71 |
|
tfpn111 | | | 83.51 89 | 82.68 92 | 84.47 83 | 90.30 90 | 93.09 80 | 89.05 81 | 82.72 81 | 75.14 123 | 69.49 112 | 74.24 84 | 63.13 143 | 80.38 99 | 91.15 66 | 89.51 89 | 94.91 75 | 92.50 110 |
|
conf200view11 | | | 82.85 94 | 81.46 102 | 84.47 83 | 90.30 90 | 93.09 80 | 89.05 81 | 82.72 81 | 75.14 123 | 69.49 112 | 65.72 135 | 63.13 143 | 80.38 99 | 91.15 66 | 89.51 89 | 94.91 75 | 92.50 110 |
|
tfpn200view9 | | | 82.86 93 | 81.46 102 | 84.48 82 | 90.30 90 | 93.09 80 | 89.05 81 | 82.71 83 | 75.14 123 | 69.56 109 | 65.72 135 | 63.13 143 | 80.38 99 | 91.15 66 | 89.51 89 | 94.91 75 | 92.50 110 |
|
thres600view7 | | | 82.53 101 | 81.02 111 | 84.28 89 | 90.61 79 | 93.05 83 | 88.57 92 | 82.67 85 | 74.12 138 | 68.56 121 | 65.09 144 | 62.13 163 | 80.40 98 | 91.15 66 | 89.02 105 | 94.88 79 | 92.59 101 |
|
thres200 | | | 82.77 95 | 81.25 107 | 84.54 79 | 90.38 87 | 93.05 83 | 89.13 78 | 82.67 85 | 74.40 133 | 69.53 111 | 65.69 138 | 63.03 148 | 80.63 93 | 91.15 66 | 89.42 95 | 94.88 79 | 92.04 120 |
|
CANet_DTU | | | 85.43 75 | 87.72 62 | 82.76 111 | 90.95 77 | 93.01 85 | 89.99 67 | 75.46 182 | 82.67 67 | 64.91 156 | 83.14 41 | 80.09 68 | 80.68 91 | 92.03 53 | 91.03 51 | 94.57 101 | 92.08 118 |
|
Anonymous202405211 | | | | 82.75 91 | | 89.58 106 | 92.97 86 | 89.04 86 | 84.13 62 | 78.72 102 | | 57.18 201 | 76.64 89 | 83.13 73 | 89.55 99 | 89.92 76 | 93.38 160 | 94.28 67 |
|
view800 | | | 82.38 103 | 80.93 116 | 84.06 95 | 90.59 81 | 92.96 87 | 88.11 97 | 82.44 92 | 73.92 139 | 68.10 124 | 65.07 145 | 61.64 165 | 80.10 110 | 91.17 61 | 89.24 99 | 95.01 70 | 92.56 105 |
|
view600 | | | 82.51 102 | 81.00 115 | 84.27 90 | 90.56 82 | 92.95 88 | 88.57 92 | 82.57 90 | 74.16 137 | 68.70 120 | 65.13 143 | 62.15 162 | 80.36 104 | 91.15 66 | 88.98 107 | 94.87 81 | 92.48 113 |
|
tfpn | | | 81.79 110 | 80.06 128 | 83.82 100 | 90.61 79 | 92.91 89 | 87.62 103 | 82.34 93 | 73.66 145 | 67.46 127 | 64.99 146 | 55.50 211 | 79.77 122 | 91.12 73 | 89.62 86 | 95.14 64 | 92.59 101 |
|
thres400 | | | 82.68 96 | 81.15 108 | 84.47 83 | 90.52 83 | 92.89 90 | 88.95 89 | 82.71 83 | 74.33 134 | 69.22 116 | 65.31 140 | 62.61 152 | 80.63 93 | 90.96 77 | 89.50 93 | 94.79 84 | 92.45 115 |
|
Vis-MVSNet (Re-imp) | | | 83.65 88 | 86.81 68 | 79.96 162 | 90.46 86 | 92.71 91 | 84.84 162 | 82.00 100 | 80.93 84 | 62.44 173 | 76.29 74 | 82.32 59 | 65.54 209 | 92.29 46 | 91.66 45 | 94.49 107 | 91.47 138 |
|
Anonymous20231211 | | | 84.42 83 | 83.02 88 | 86.05 70 | 88.85 113 | 92.70 92 | 88.92 90 | 83.40 75 | 79.99 90 | 78.31 72 | 55.83 208 | 78.92 79 | 83.33 71 | 89.06 106 | 89.76 83 | 93.50 157 | 94.90 55 |
|
tttt0517 | | | 85.11 79 | 85.81 75 | 84.30 88 | 89.24 107 | 92.68 93 | 87.12 126 | 80.11 122 | 81.98 73 | 74.31 91 | 78.08 67 | 73.57 103 | 79.90 114 | 91.01 74 | 89.58 87 | 95.11 68 | 93.77 76 |
|
PLC | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 83.76 9 | 88.61 50 | 86.83 67 | 90.70 35 | 94.22 44 | 92.63 94 | 91.50 56 | 87.19 42 | 89.16 42 | 86.87 30 | 75.51 78 | 80.87 64 | 89.98 31 | 90.01 91 | 89.20 100 | 94.41 112 | 90.45 157 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
thisisatest0530 | | | 85.15 78 | 85.86 74 | 84.33 87 | 89.19 109 | 92.57 95 | 87.22 114 | 80.11 122 | 82.15 72 | 74.41 89 | 78.15 66 | 73.80 101 | 79.90 114 | 90.99 75 | 89.58 87 | 95.13 66 | 93.75 77 |
|
thres100view900 | | | 82.55 99 | 81.01 114 | 84.34 86 | 90.30 90 | 92.27 96 | 89.04 86 | 82.77 78 | 75.14 123 | 69.56 109 | 65.72 135 | 63.13 143 | 79.62 126 | 89.97 92 | 89.26 98 | 94.73 89 | 91.61 136 |
|
gg-mvs-nofinetune | | | 75.64 201 | 77.26 177 | 73.76 206 | 87.92 120 | 92.20 97 | 87.32 109 | 64.67 225 | 51.92 232 | 35.35 237 | 46.44 225 | 77.05 88 | 71.97 180 | 92.64 44 | 91.02 52 | 95.34 53 | 89.53 160 |
|
ACMM | | 83.27 10 | 87.68 61 | 86.09 73 | 89.54 47 | 93.26 53 | 92.19 98 | 91.43 57 | 86.74 44 | 86.02 54 | 82.85 52 | 75.63 77 | 75.14 93 | 88.41 39 | 90.68 84 | 89.99 72 | 94.59 99 | 92.97 88 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
conf0.05thres1000 | | | 81.00 121 | 79.12 140 | 83.20 106 | 90.14 97 | 92.15 99 | 87.05 127 | 82.09 97 | 68.11 191 | 66.19 138 | 59.67 189 | 61.10 176 | 79.05 139 | 90.47 88 | 89.11 102 | 94.68 93 | 93.22 82 |
|
CHOSEN 1792x2688 | | | 82.16 104 | 80.91 118 | 83.61 102 | 91.14 71 | 92.01 100 | 89.55 74 | 79.15 141 | 79.87 92 | 70.29 103 | 52.51 217 | 72.56 108 | 81.39 82 | 88.87 110 | 88.17 117 | 90.15 198 | 92.37 116 |
|
Fast-Effi-MVS+ | | | 83.77 87 | 82.98 89 | 84.69 78 | 87.98 119 | 91.87 101 | 88.10 98 | 77.70 155 | 78.10 106 | 73.04 97 | 69.13 117 | 68.51 122 | 86.66 57 | 90.49 87 | 89.85 78 | 94.67 94 | 92.88 90 |
|
UniMVSNet (Re) | | | 81.22 117 | 81.08 110 | 81.39 134 | 85.35 145 | 91.76 102 | 84.93 161 | 82.88 77 | 76.13 116 | 65.02 155 | 64.94 147 | 63.09 147 | 75.17 160 | 87.71 124 | 89.04 104 | 94.97 72 | 94.88 56 |
|
tfpn_ndepth | | | 81.77 112 | 82.29 94 | 81.15 142 | 89.79 104 | 91.71 103 | 85.49 154 | 81.63 105 | 79.17 99 | 64.76 157 | 73.04 95 | 68.14 126 | 70.62 190 | 88.72 111 | 87.88 121 | 94.63 97 | 87.38 177 |
|
FC-MVSNet-train | | | 85.18 77 | 85.31 78 | 85.03 77 | 90.67 78 | 91.62 104 | 87.66 102 | 83.61 66 | 79.75 93 | 74.37 90 | 78.69 62 | 71.21 113 | 78.91 140 | 91.23 57 | 89.96 74 | 94.96 73 | 94.69 62 |
|
tfpn1000 | | | 81.03 120 | 81.70 99 | 80.25 160 | 90.18 96 | 91.35 105 | 83.96 169 | 81.15 109 | 78.00 107 | 62.11 176 | 73.37 94 | 65.75 130 | 69.17 195 | 88.68 113 | 87.44 126 | 94.93 74 | 87.29 179 |
|
thresconf0.02 | | | 81.14 119 | 80.93 116 | 81.39 134 | 90.01 102 | 91.31 106 | 86.79 135 | 82.28 95 | 76.97 112 | 61.46 185 | 74.24 84 | 62.08 164 | 72.98 178 | 88.70 112 | 87.90 119 | 94.81 83 | 85.28 196 |
|
UniMVSNet_NR-MVSNet | | | 81.87 107 | 81.33 106 | 82.50 112 | 85.31 146 | 91.30 107 | 85.70 148 | 84.25 59 | 75.89 117 | 64.21 159 | 66.95 130 | 64.65 136 | 80.22 106 | 87.07 130 | 89.18 101 | 95.27 59 | 94.29 65 |
|
ACMH | | 78.52 14 | 81.86 108 | 80.45 125 | 83.51 104 | 90.51 85 | 91.22 108 | 85.62 151 | 84.23 60 | 70.29 172 | 62.21 174 | 69.04 119 | 64.05 141 | 84.48 68 | 87.57 125 | 88.45 114 | 94.01 127 | 92.54 108 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
tfpn_n400 | | | 80.63 125 | 80.79 120 | 80.43 157 | 90.02 100 | 91.08 109 | 85.34 157 | 81.79 103 | 72.93 151 | 59.27 198 | 73.54 90 | 64.40 137 | 71.61 184 | 89.05 108 | 88.21 115 | 94.56 102 | 86.32 188 |
|
tfpnconf | | | 80.63 125 | 80.79 120 | 80.43 157 | 90.02 100 | 91.08 109 | 85.34 157 | 81.79 103 | 72.93 151 | 59.27 198 | 73.54 90 | 64.40 137 | 71.61 184 | 89.05 108 | 88.21 115 | 94.56 102 | 86.32 188 |
|
tfpnview11 | | | 80.84 123 | 81.10 109 | 80.54 154 | 90.10 98 | 90.96 111 | 85.44 155 | 81.84 101 | 75.77 118 | 59.27 198 | 73.54 90 | 64.40 137 | 71.69 183 | 89.16 104 | 87.97 118 | 94.91 75 | 85.92 193 |
|
DU-MVS | | | 81.20 118 | 80.30 126 | 82.25 114 | 84.98 153 | 90.94 112 | 85.70 148 | 83.58 69 | 75.74 119 | 64.21 159 | 65.30 141 | 59.60 189 | 80.22 106 | 86.89 134 | 89.31 96 | 94.77 86 | 94.29 65 |
|
NR-MVSNet | | | 80.25 131 | 79.98 131 | 80.56 153 | 85.20 148 | 90.94 112 | 85.65 150 | 83.58 69 | 75.74 119 | 61.36 187 | 65.30 141 | 56.75 205 | 72.38 179 | 88.46 117 | 88.80 108 | 95.16 62 | 93.87 74 |
|
ACMH+ | | 79.08 13 | 81.84 109 | 80.06 128 | 83.91 98 | 89.92 103 | 90.62 114 | 86.21 143 | 83.48 73 | 73.88 141 | 65.75 147 | 66.38 132 | 65.30 133 | 84.63 67 | 85.90 151 | 87.25 130 | 93.45 158 | 91.13 142 |
|
Effi-MVS+-dtu | | | 82.05 105 | 81.76 98 | 82.38 113 | 87.72 122 | 90.56 115 | 86.90 132 | 78.05 151 | 73.85 142 | 66.85 133 | 71.29 103 | 71.90 111 | 82.00 81 | 86.64 141 | 85.48 176 | 92.76 173 | 92.58 103 |
|
v1neww | | | 80.09 133 | 78.45 148 | 82.00 118 | 83.97 167 | 90.49 116 | 87.18 120 | 79.67 131 | 71.49 160 | 67.44 128 | 61.24 163 | 62.41 158 | 79.83 118 | 85.49 164 | 86.19 156 | 93.88 139 | 91.86 123 |
|
v7new | | | 80.09 133 | 78.45 148 | 82.00 118 | 83.97 167 | 90.49 116 | 87.18 120 | 79.67 131 | 71.49 160 | 67.44 128 | 61.24 163 | 62.41 158 | 79.83 118 | 85.49 164 | 86.19 156 | 93.88 139 | 91.86 123 |
|
v6 | | | 80.11 132 | 78.47 146 | 82.01 117 | 83.97 167 | 90.49 116 | 87.19 119 | 79.67 131 | 71.59 159 | 67.51 126 | 61.26 161 | 62.46 157 | 79.81 121 | 85.49 164 | 86.18 159 | 93.89 137 | 91.86 123 |
|
TranMVSNet+NR-MVSNet | | | 80.52 127 | 79.84 133 | 81.33 137 | 84.92 155 | 90.39 119 | 85.53 153 | 84.22 61 | 74.27 135 | 60.68 192 | 64.93 148 | 59.96 184 | 77.48 149 | 86.75 139 | 89.28 97 | 95.12 67 | 93.29 81 |
|
v1 | | | 79.76 142 | 78.06 158 | 81.74 125 | 83.89 174 | 90.38 120 | 87.20 116 | 79.88 129 | 70.23 173 | 66.17 143 | 60.92 171 | 61.56 166 | 79.50 134 | 85.37 169 | 86.17 160 | 93.81 146 | 91.77 127 |
|
v1141 | | | 79.75 144 | 78.04 160 | 81.75 123 | 83.89 174 | 90.37 121 | 87.20 116 | 79.89 128 | 70.23 173 | 66.18 140 | 60.92 171 | 61.48 170 | 79.54 130 | 85.36 170 | 86.17 160 | 93.81 146 | 91.76 129 |
|
divwei89l23v2f112 | | | 79.75 144 | 78.04 160 | 81.75 123 | 83.90 171 | 90.37 121 | 87.21 115 | 79.90 127 | 70.20 175 | 66.18 140 | 60.92 171 | 61.48 170 | 79.52 133 | 85.36 170 | 86.17 160 | 93.81 146 | 91.77 127 |
|
GA-MVS | | | 79.52 151 | 79.71 136 | 79.30 166 | 85.68 141 | 90.36 123 | 84.55 164 | 78.44 146 | 70.47 171 | 57.87 205 | 68.52 121 | 61.38 174 | 76.21 155 | 89.40 102 | 87.89 120 | 93.04 170 | 89.96 158 |
|
v7 | | | 79.79 141 | 78.28 153 | 81.54 133 | 83.73 181 | 90.34 124 | 87.27 111 | 78.27 148 | 70.50 169 | 65.59 148 | 60.59 178 | 60.47 179 | 80.46 96 | 86.90 133 | 86.63 139 | 93.92 133 | 92.56 105 |
|
CDS-MVSNet | | | 81.63 115 | 82.09 96 | 81.09 144 | 87.21 129 | 90.28 125 | 87.46 107 | 80.33 117 | 69.06 185 | 70.66 101 | 71.30 102 | 73.87 99 | 67.99 199 | 89.58 98 | 89.87 77 | 92.87 172 | 90.69 152 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
v1144 | | | 79.38 153 | 77.83 167 | 81.18 140 | 83.62 183 | 90.23 126 | 87.15 124 | 78.35 147 | 69.13 184 | 64.02 163 | 60.20 186 | 59.41 190 | 80.14 109 | 86.78 137 | 86.57 141 | 93.81 146 | 92.53 109 |
|
EG-PatchMatch MVS | | | 76.40 191 | 75.47 200 | 77.48 179 | 85.86 139 | 90.22 127 | 82.45 178 | 73.96 188 | 59.64 222 | 59.60 196 | 52.75 216 | 62.20 161 | 68.44 198 | 88.23 119 | 87.50 125 | 94.55 104 | 87.78 174 |
|
v2v482 | | | 79.84 138 | 78.07 156 | 81.90 121 | 83.75 180 | 90.21 128 | 87.17 122 | 79.85 130 | 70.65 167 | 65.93 146 | 61.93 158 | 60.07 183 | 80.82 87 | 85.25 173 | 86.71 136 | 93.88 139 | 91.70 134 |
|
FMVSNet3 | | | 84.44 82 | 84.64 82 | 84.21 91 | 84.32 159 | 90.13 129 | 89.85 69 | 80.37 114 | 81.17 78 | 75.50 83 | 69.63 110 | 79.69 74 | 79.62 126 | 89.72 96 | 90.52 64 | 95.59 40 | 91.58 137 |
|
GG-mvs-BLEND | | | 57.56 228 | 82.61 93 | 28.34 241 | 0.22 248 | 90.10 130 | 79.37 202 | 0.14 246 | 79.56 95 | 0.40 249 | 71.25 104 | 83.40 54 | 0.30 247 | 86.27 147 | 83.87 188 | 89.59 202 | 83.83 202 |
|
GBi-Net | | | 84.51 80 | 84.80 80 | 84.17 92 | 84.20 160 | 89.95 131 | 89.70 70 | 80.37 114 | 81.17 78 | 75.50 83 | 69.63 110 | 79.69 74 | 79.75 123 | 90.73 81 | 90.72 57 | 95.52 44 | 91.71 131 |
|
test1 | | | 84.51 80 | 84.80 80 | 84.17 92 | 84.20 160 | 89.95 131 | 89.70 70 | 80.37 114 | 81.17 78 | 75.50 83 | 69.63 110 | 79.69 74 | 79.75 123 | 90.73 81 | 90.72 57 | 95.52 44 | 91.71 131 |
|
FMVSNet2 | | | 83.87 85 | 83.73 86 | 84.05 97 | 84.20 160 | 89.95 131 | 89.70 70 | 80.21 119 | 79.17 99 | 74.89 87 | 65.91 133 | 77.49 85 | 79.75 123 | 90.87 78 | 91.00 53 | 95.52 44 | 91.71 131 |
|
MVSTER | | | 86.03 70 | 86.12 71 | 85.93 71 | 88.62 115 | 89.93 134 | 89.33 76 | 79.91 126 | 81.87 74 | 81.35 57 | 81.07 52 | 74.91 95 | 80.66 92 | 92.13 52 | 90.10 69 | 95.68 35 | 92.80 93 |
|
v1192 | | | 78.94 162 | 77.33 174 | 80.82 149 | 83.25 187 | 89.90 135 | 86.91 131 | 77.72 154 | 68.63 188 | 62.61 172 | 59.17 192 | 57.53 201 | 80.62 95 | 86.89 134 | 86.47 144 | 93.79 150 | 92.75 96 |
|
Anonymous20240521 | | | 85.88 73 | 86.17 70 | 85.54 74 | 89.10 110 | 89.85 136 | 89.34 75 | 80.70 111 | 83.04 66 | 78.08 75 | 76.19 75 | 79.00 78 | 82.42 78 | 89.67 97 | 90.30 66 | 93.63 155 | 95.12 52 |
|
v8 | | | 79.90 137 | 78.39 151 | 81.66 129 | 83.97 167 | 89.81 137 | 87.16 123 | 77.40 157 | 71.49 160 | 67.71 125 | 61.24 163 | 62.49 155 | 79.83 118 | 85.48 168 | 86.17 160 | 93.89 137 | 92.02 122 |
|
MSDG | | | 83.87 85 | 81.02 111 | 87.19 66 | 92.17 66 | 89.80 138 | 89.15 77 | 85.72 50 | 80.61 87 | 79.24 67 | 66.66 131 | 68.75 121 | 82.69 74 | 87.95 123 | 87.44 126 | 94.19 118 | 85.92 193 |
|
v144192 | | | 78.81 163 | 77.22 179 | 80.67 151 | 82.95 192 | 89.79 139 | 86.40 141 | 77.42 156 | 68.26 190 | 63.13 168 | 59.50 190 | 58.13 199 | 80.08 111 | 85.93 150 | 86.08 165 | 94.06 124 | 92.83 92 |
|
WR-MVS | | | 76.63 183 | 78.02 162 | 75.02 200 | 84.14 163 | 89.76 140 | 78.34 208 | 80.64 112 | 69.56 182 | 52.32 213 | 61.26 161 | 61.24 175 | 60.66 214 | 84.45 184 | 87.07 132 | 93.99 128 | 92.77 94 |
|
V42 | | | 79.59 149 | 78.43 150 | 80.94 148 | 82.79 197 | 89.71 141 | 86.66 136 | 76.73 164 | 71.38 163 | 67.42 130 | 61.01 169 | 62.30 160 | 78.39 143 | 85.56 160 | 86.48 143 | 93.65 154 | 92.60 100 |
|
v10 | | | 79.62 148 | 78.19 154 | 81.28 138 | 83.73 181 | 89.69 142 | 87.27 111 | 76.86 162 | 70.50 169 | 65.46 149 | 60.58 180 | 60.47 179 | 80.44 97 | 86.91 132 | 86.63 139 | 93.93 131 | 92.55 107 |
|
COLMAP_ROB | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 76.78 15 | 80.50 129 | 78.49 145 | 82.85 109 | 90.96 76 | 89.65 143 | 86.20 144 | 83.40 75 | 77.15 110 | 66.54 134 | 62.27 157 | 65.62 132 | 77.89 147 | 85.23 174 | 84.70 184 | 92.11 178 | 84.83 199 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
v1921920 | | | 78.57 168 | 76.99 182 | 80.41 159 | 82.93 193 | 89.63 144 | 86.38 142 | 77.14 159 | 68.31 189 | 61.80 180 | 58.89 196 | 56.79 204 | 80.19 108 | 86.50 145 | 86.05 167 | 94.02 126 | 92.76 95 |
|
PatchMatch-RL | | | 83.34 91 | 81.36 105 | 85.65 72 | 90.33 89 | 89.52 145 | 84.36 166 | 81.82 102 | 80.87 86 | 79.29 66 | 74.04 86 | 62.85 150 | 86.05 61 | 88.40 118 | 87.04 133 | 92.04 179 | 86.77 184 |
|
WR-MVS_H | | | 75.84 199 | 76.93 183 | 74.57 205 | 82.86 195 | 89.50 146 | 78.34 208 | 79.36 139 | 66.90 195 | 52.51 212 | 60.20 186 | 59.71 186 | 59.73 215 | 83.61 189 | 85.77 170 | 94.65 95 | 92.84 91 |
|
v1240 | | | 78.15 170 | 76.53 185 | 80.04 161 | 82.85 196 | 89.48 147 | 85.61 152 | 76.77 163 | 67.05 193 | 61.18 190 | 58.37 198 | 56.16 209 | 79.89 117 | 86.11 149 | 86.08 165 | 93.92 133 | 92.47 114 |
|
pm-mvs1 | | | 78.51 169 | 77.75 169 | 79.40 165 | 84.83 156 | 89.30 148 | 83.55 173 | 79.38 138 | 62.64 213 | 63.68 165 | 58.73 197 | 64.68 135 | 70.78 189 | 89.79 95 | 87.84 122 | 94.17 119 | 91.28 141 |
|
MS-PatchMatch | | | 81.79 110 | 81.44 104 | 82.19 116 | 90.35 88 | 89.29 149 | 88.08 99 | 75.36 183 | 77.60 108 | 69.00 117 | 64.37 152 | 78.87 80 | 77.14 152 | 88.03 122 | 85.70 172 | 93.19 166 | 86.24 190 |
|
LTVRE_ROB | | 74.41 16 | 75.78 200 | 74.72 206 | 77.02 186 | 85.88 138 | 89.22 150 | 82.44 179 | 77.17 158 | 50.57 233 | 45.45 225 | 65.44 139 | 52.29 221 | 81.25 83 | 85.50 163 | 87.42 128 | 89.94 200 | 92.62 99 |
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 |
FMVSNet1 | | | 81.64 114 | 80.61 123 | 82.84 110 | 82.36 201 | 89.20 151 | 88.67 91 | 79.58 135 | 70.79 166 | 72.63 99 | 58.95 195 | 72.26 110 | 79.34 137 | 90.73 81 | 90.72 57 | 94.47 108 | 91.62 135 |
|
v148 | | | 78.59 167 | 76.84 184 | 80.62 152 | 83.61 184 | 89.16 152 | 83.65 172 | 79.24 140 | 69.38 183 | 69.34 115 | 59.88 188 | 60.41 181 | 75.19 159 | 83.81 188 | 84.63 185 | 92.70 174 | 90.63 154 |
|
Fast-Effi-MVS+-dtu | | | 79.95 136 | 80.69 122 | 79.08 167 | 86.36 135 | 89.14 153 | 85.85 146 | 72.28 193 | 72.85 153 | 59.32 197 | 70.43 108 | 68.42 123 | 77.57 148 | 86.14 148 | 86.44 145 | 93.11 167 | 91.39 139 |
|
USDC | | | 80.69 124 | 79.89 132 | 81.62 130 | 86.48 134 | 89.11 154 | 86.53 139 | 78.86 142 | 81.15 81 | 63.48 166 | 72.98 97 | 59.12 195 | 81.16 84 | 87.10 129 | 85.01 180 | 93.23 164 | 84.77 200 |
|
v7n | | | 77.22 178 | 76.23 188 | 78.38 175 | 81.89 204 | 89.10 155 | 82.24 183 | 76.36 165 | 65.96 201 | 61.21 189 | 56.56 203 | 55.79 210 | 75.07 162 | 86.55 142 | 86.68 137 | 93.52 156 | 92.95 89 |
|
TDRefinement | | | 79.05 158 | 77.05 181 | 81.39 134 | 88.45 116 | 89.00 156 | 86.92 130 | 82.65 87 | 74.21 136 | 64.41 158 | 59.17 192 | 59.16 193 | 74.52 166 | 85.23 174 | 85.09 179 | 91.37 186 | 87.51 176 |
|
tfpnnormal | | | 77.46 177 | 74.86 205 | 80.49 155 | 86.34 136 | 88.92 157 | 84.33 167 | 81.26 107 | 61.39 217 | 61.70 182 | 51.99 218 | 53.66 218 | 74.84 163 | 88.63 114 | 87.38 129 | 94.50 106 | 92.08 118 |
|
thisisatest0515 | | | 79.76 142 | 80.59 124 | 78.80 170 | 84.40 158 | 88.91 158 | 79.48 200 | 76.94 161 | 72.29 156 | 67.33 131 | 67.82 126 | 65.99 129 | 70.80 188 | 88.50 116 | 87.84 122 | 93.86 142 | 92.75 96 |
|
CP-MVSNet | | | 76.36 192 | 76.41 186 | 76.32 192 | 82.73 198 | 88.64 159 | 79.39 201 | 79.62 134 | 67.21 192 | 53.70 209 | 60.72 175 | 55.22 213 | 67.91 201 | 83.52 190 | 86.34 147 | 94.55 104 | 93.19 83 |
|
IterMVS-LS | | | 83.28 92 | 82.95 90 | 83.65 101 | 88.39 118 | 88.63 160 | 86.80 134 | 78.64 145 | 76.56 113 | 73.43 95 | 72.52 100 | 75.35 92 | 80.81 89 | 86.43 146 | 88.51 113 | 93.84 144 | 92.66 98 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
CostFormer | | | 80.94 122 | 80.21 127 | 81.79 122 | 87.69 123 | 88.58 161 | 87.47 106 | 70.66 199 | 80.02 89 | 77.88 78 | 73.03 96 | 71.40 112 | 78.24 144 | 79.96 210 | 79.63 206 | 88.82 205 | 88.84 163 |
|
V4 | | | 76.55 185 | 75.89 194 | 77.32 181 | 79.95 214 | 88.50 162 | 81.07 192 | 73.62 189 | 65.47 206 | 61.71 181 | 56.31 205 | 58.87 198 | 74.28 170 | 83.48 191 | 85.62 174 | 93.28 162 | 92.98 87 |
|
v52 | | | 76.55 185 | 75.89 194 | 77.31 182 | 79.94 215 | 88.49 163 | 81.07 192 | 73.62 189 | 65.49 205 | 61.66 183 | 56.29 206 | 58.90 196 | 74.30 169 | 83.47 192 | 85.62 174 | 93.28 162 | 92.99 86 |
|
PS-CasMVS | | | 75.90 198 | 75.86 196 | 75.96 194 | 82.59 199 | 88.46 164 | 79.23 204 | 79.56 136 | 66.00 200 | 52.77 211 | 59.48 191 | 54.35 216 | 67.14 204 | 83.37 193 | 86.23 148 | 94.47 108 | 93.10 85 |
|
pmmvs5 | | | 76.93 180 | 76.33 187 | 77.62 178 | 81.97 203 | 88.40 165 | 81.32 188 | 74.35 186 | 65.42 207 | 61.42 186 | 63.07 155 | 57.95 200 | 73.23 176 | 85.60 158 | 85.35 178 | 93.41 159 | 88.55 165 |
|
DWT-MVSNet_training | | | 80.51 128 | 78.05 159 | 83.39 105 | 88.64 114 | 88.33 166 | 86.11 145 | 76.33 167 | 79.65 94 | 78.64 71 | 69.62 113 | 58.89 197 | 80.82 87 | 80.50 207 | 82.03 202 | 89.77 201 | 87.36 178 |
|
v748 | | | 76.17 194 | 75.10 204 | 77.43 180 | 81.60 206 | 88.01 167 | 79.02 205 | 76.28 170 | 64.47 209 | 64.14 161 | 56.55 204 | 56.26 208 | 70.40 191 | 82.50 199 | 85.77 170 | 93.11 167 | 92.15 117 |
|
PEN-MVS | | | 76.02 196 | 76.07 189 | 75.95 195 | 83.17 189 | 87.97 168 | 79.65 198 | 80.07 125 | 66.57 197 | 51.45 215 | 60.94 170 | 55.47 212 | 66.81 205 | 82.72 196 | 86.80 135 | 94.59 99 | 92.03 121 |
|
anonymousdsp | | | 77.94 172 | 79.00 141 | 76.71 188 | 79.03 216 | 87.83 169 | 79.58 199 | 72.87 192 | 65.80 202 | 58.86 204 | 65.82 134 | 62.48 156 | 75.99 156 | 86.77 138 | 88.66 109 | 93.92 133 | 95.68 45 |
|
SixPastTwentyTwo | | | 76.02 196 | 75.72 197 | 76.36 191 | 83.38 185 | 87.54 170 | 75.50 215 | 76.22 171 | 65.50 204 | 57.05 206 | 70.64 105 | 53.97 217 | 74.54 165 | 80.96 205 | 82.12 200 | 91.44 184 | 89.35 161 |
|
pmmvs6 | | | 74.83 204 | 72.89 211 | 77.09 184 | 82.11 202 | 87.50 171 | 80.88 195 | 76.97 160 | 52.79 231 | 61.91 179 | 46.66 224 | 60.49 178 | 69.28 194 | 86.74 140 | 85.46 177 | 91.39 185 | 90.56 155 |
|
RPSCF | | | 83.46 90 | 83.36 87 | 83.59 103 | 87.75 121 | 87.35 172 | 84.82 163 | 79.46 137 | 83.84 64 | 78.12 73 | 82.69 42 | 79.87 69 | 82.60 77 | 82.47 200 | 81.13 204 | 88.78 206 | 86.13 191 |
|
TransMVSNet (Re) | | | 76.57 184 | 75.16 203 | 78.22 176 | 85.60 142 | 87.24 173 | 82.46 177 | 81.23 108 | 59.80 221 | 59.05 203 | 57.07 202 | 59.14 194 | 66.60 207 | 88.09 120 | 86.82 134 | 94.37 114 | 87.95 173 |
|
DTE-MVSNet | | | 75.14 203 | 75.44 201 | 74.80 202 | 83.18 188 | 87.19 174 | 78.25 210 | 80.11 122 | 66.05 199 | 48.31 221 | 60.88 174 | 54.67 214 | 64.54 211 | 82.57 198 | 86.17 160 | 94.43 111 | 90.53 156 |
|
pmmvs4 | | | 79.99 135 | 78.08 155 | 82.22 115 | 83.04 191 | 87.16 175 | 84.95 160 | 78.80 144 | 78.64 103 | 74.53 88 | 64.61 150 | 59.41 190 | 79.45 136 | 84.13 186 | 84.54 186 | 92.53 175 | 88.08 170 |
|
tpmp4_e23 | | | 79.82 140 | 77.96 164 | 82.00 118 | 87.59 125 | 86.93 176 | 87.81 100 | 72.21 194 | 79.99 90 | 78.02 76 | 67.83 125 | 64.77 134 | 78.74 141 | 79.99 209 | 78.90 209 | 87.65 211 | 87.29 179 |
|
MDTV_nov1_ep13 | | | 79.14 154 | 79.49 138 | 78.74 171 | 85.40 144 | 86.89 177 | 84.32 168 | 70.29 201 | 78.85 101 | 69.42 114 | 75.37 79 | 73.29 106 | 75.64 158 | 80.61 206 | 79.48 208 | 87.36 212 | 81.91 208 |
|
v18 | | | 79.71 146 | 77.98 163 | 81.73 126 | 84.02 166 | 86.67 178 | 87.37 108 | 76.35 166 | 72.61 154 | 68.86 118 | 61.35 160 | 62.65 151 | 79.94 112 | 85.49 164 | 86.21 151 | 93.85 143 | 90.92 145 |
|
v17 | | | 79.59 149 | 77.88 166 | 81.60 131 | 84.03 165 | 86.66 179 | 87.13 125 | 76.31 169 | 72.09 157 | 68.29 122 | 61.15 167 | 62.57 153 | 79.90 114 | 85.55 161 | 86.20 154 | 93.93 131 | 90.93 144 |
|
Baseline_NR-MVSNet | | | 79.84 138 | 78.37 152 | 81.55 132 | 84.98 153 | 86.66 179 | 85.06 159 | 83.49 71 | 75.57 121 | 63.31 167 | 58.22 199 | 60.97 177 | 78.00 146 | 86.89 134 | 87.13 131 | 94.47 108 | 93.15 84 |
|
v16 | | | 79.65 147 | 77.91 165 | 81.69 128 | 84.04 164 | 86.65 181 | 87.20 116 | 76.32 168 | 72.41 155 | 68.71 119 | 61.13 168 | 62.52 154 | 79.93 113 | 85.55 161 | 86.22 149 | 93.92 133 | 90.91 146 |
|
dps | | | 78.02 171 | 75.94 193 | 80.44 156 | 86.06 137 | 86.62 182 | 82.58 176 | 69.98 203 | 75.14 123 | 77.76 80 | 69.08 118 | 59.93 185 | 78.47 142 | 79.47 212 | 77.96 213 | 87.78 209 | 83.40 204 |
|
v15 | | | 79.13 155 | 77.37 171 | 81.19 139 | 83.90 171 | 86.56 183 | 87.01 128 | 76.15 173 | 70.20 175 | 66.48 135 | 60.71 176 | 61.55 167 | 79.60 128 | 85.59 159 | 86.19 156 | 93.98 129 | 90.80 151 |
|
V14 | | | 79.11 156 | 77.35 173 | 81.16 141 | 83.90 171 | 86.54 184 | 86.94 129 | 76.10 175 | 70.14 177 | 66.41 137 | 60.59 178 | 61.54 168 | 79.59 129 | 85.64 156 | 86.20 154 | 94.04 125 | 90.82 149 |
|
V9 | | | 79.08 157 | 77.32 175 | 81.14 143 | 83.89 174 | 86.52 185 | 86.85 133 | 76.06 176 | 70.02 178 | 66.42 136 | 60.44 181 | 61.52 169 | 79.54 130 | 85.68 155 | 86.21 151 | 94.08 122 | 90.83 148 |
|
v12 | | | 79.03 159 | 77.28 176 | 81.06 145 | 83.88 178 | 86.49 186 | 86.62 137 | 76.02 177 | 69.99 179 | 66.18 140 | 60.34 184 | 61.44 172 | 79.54 130 | 85.70 154 | 86.21 151 | 94.11 121 | 90.82 149 |
|
v11 | | | 79.02 160 | 77.36 172 | 80.95 147 | 83.89 174 | 86.48 187 | 86.53 139 | 75.77 181 | 69.69 181 | 65.21 154 | 60.36 183 | 60.24 182 | 80.32 105 | 87.20 128 | 86.54 142 | 93.96 130 | 91.02 143 |
|
v13 | | | 78.99 161 | 77.25 178 | 81.02 146 | 83.87 179 | 86.47 188 | 86.60 138 | 75.96 179 | 69.87 180 | 66.07 144 | 60.25 185 | 61.41 173 | 79.49 135 | 85.72 153 | 86.22 149 | 94.14 120 | 90.84 147 |
|
PatchmatchNet | ![Method available under a permissive open source license. permissive](img/icon_permissive.png) | | 78.67 166 | 78.85 143 | 78.46 174 | 86.85 133 | 86.03 189 | 83.77 171 | 68.11 210 | 80.88 85 | 66.19 138 | 72.90 98 | 73.40 105 | 78.06 145 | 79.25 214 | 77.71 215 | 87.75 210 | 81.75 209 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
EPNet_dtu | | | 81.98 106 | 83.82 85 | 79.83 164 | 94.10 46 | 85.97 190 | 87.29 110 | 84.08 63 | 80.61 87 | 59.96 194 | 81.62 50 | 77.19 87 | 62.91 213 | 87.21 127 | 86.38 146 | 90.66 194 | 87.77 175 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
IterMVS | | | 78.79 164 | 79.71 136 | 77.71 177 | 85.26 147 | 85.91 191 | 84.54 165 | 69.84 205 | 73.38 147 | 61.25 188 | 70.53 107 | 70.35 114 | 74.43 167 | 85.21 176 | 83.80 190 | 90.95 192 | 88.77 164 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
tpm cat1 | | | 77.78 174 | 75.28 202 | 80.70 150 | 87.14 130 | 85.84 192 | 85.81 147 | 70.40 200 | 77.44 109 | 78.80 69 | 63.72 153 | 64.01 142 | 76.55 154 | 75.60 224 | 75.21 223 | 85.51 222 | 85.12 197 |
|
FC-MVSNet-test | | | 76.53 188 | 81.62 101 | 70.58 213 | 84.99 152 | 85.73 193 | 74.81 216 | 78.85 143 | 77.00 111 | 39.13 236 | 75.90 76 | 73.50 104 | 54.08 221 | 86.54 143 | 85.99 168 | 91.65 182 | 86.68 185 |
|
CMPMVS | ![Method available as binary. binary](img/icon_binary.png) | 56.49 17 | 73.84 209 | 71.73 214 | 76.31 193 | 85.20 148 | 85.67 194 | 75.80 214 | 73.23 191 | 62.26 214 | 65.40 150 | 53.40 215 | 59.70 187 | 71.77 182 | 80.25 208 | 79.56 207 | 86.45 217 | 81.28 211 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
TinyColmap | | | 76.73 181 | 73.95 208 | 79.96 162 | 85.16 150 | 85.64 195 | 82.34 180 | 78.19 149 | 70.63 168 | 62.06 177 | 60.69 177 | 49.61 225 | 80.81 89 | 85.12 177 | 83.69 191 | 91.22 190 | 82.27 207 |
|
TAMVS | | | 76.42 189 | 77.16 180 | 75.56 196 | 83.05 190 | 85.55 196 | 80.58 196 | 71.43 196 | 65.40 208 | 61.04 191 | 67.27 128 | 69.22 120 | 67.99 199 | 84.88 180 | 84.78 183 | 89.28 204 | 83.01 206 |
|
Patchmtry | | | | | | | 85.54 197 | 82.30 181 | 68.23 208 | | 65.37 151 | | | | | | | |
|
MIMVSNet | | | 74.69 205 | 75.60 199 | 73.62 207 | 76.02 226 | 85.31 198 | 81.21 191 | 67.43 211 | 71.02 165 | 59.07 202 | 54.48 209 | 64.07 140 | 66.14 208 | 86.52 144 | 86.64 138 | 91.83 181 | 81.17 212 |
|
CR-MVSNet | | | 78.71 165 | 78.86 142 | 78.55 173 | 85.85 140 | 85.15 199 | 82.30 181 | 68.23 208 | 74.71 130 | 65.37 151 | 64.39 151 | 69.59 118 | 77.18 150 | 85.10 178 | 84.87 181 | 92.34 177 | 88.21 168 |
|
RPMNet | | | 77.07 179 | 77.63 170 | 76.42 190 | 85.56 143 | 85.15 199 | 81.37 186 | 65.27 222 | 74.71 130 | 60.29 193 | 63.71 154 | 66.59 128 | 73.64 172 | 82.71 197 | 82.12 200 | 92.38 176 | 88.39 166 |
|
test0.0.03 1 | | | 76.03 195 | 78.51 144 | 73.12 210 | 87.47 126 | 85.13 201 | 76.32 213 | 78.05 151 | 73.19 150 | 50.98 218 | 70.64 105 | 69.28 119 | 55.53 217 | 85.33 172 | 84.38 187 | 90.39 196 | 81.63 210 |
|
EPMVS | | | 77.53 176 | 78.07 156 | 76.90 187 | 86.89 132 | 84.91 202 | 82.18 184 | 66.64 216 | 81.00 83 | 64.11 162 | 72.75 99 | 69.68 117 | 74.42 168 | 79.36 213 | 78.13 212 | 87.14 214 | 80.68 214 |
|
CVMVSNet | | | 76.70 182 | 78.46 147 | 74.64 204 | 83.34 186 | 84.48 203 | 81.83 185 | 74.58 184 | 68.88 186 | 51.23 217 | 69.77 109 | 70.05 115 | 67.49 202 | 84.27 185 | 83.81 189 | 89.38 203 | 87.96 172 |
|
PatchT | | | 76.42 189 | 77.81 168 | 74.80 202 | 78.46 219 | 84.30 204 | 71.82 222 | 65.03 224 | 73.89 140 | 65.37 151 | 61.58 159 | 66.70 127 | 77.18 150 | 85.10 178 | 84.87 181 | 90.94 193 | 88.21 168 |
|
MDTV_nov1_ep13_2view | | | 73.21 210 | 72.91 210 | 73.56 208 | 80.01 212 | 84.28 205 | 78.62 206 | 66.43 217 | 68.64 187 | 59.12 201 | 60.39 182 | 59.69 188 | 69.81 193 | 78.82 216 | 77.43 217 | 87.36 212 | 81.11 213 |
|
our_test_3 | | | | | | 81.81 205 | 83.96 206 | 76.61 212 | | | | | | | | | | |
|
testgi | | | 71.92 211 | 74.20 207 | 69.27 216 | 84.58 157 | 83.06 207 | 73.40 218 | 74.39 185 | 64.04 211 | 46.17 224 | 68.90 120 | 57.15 202 | 48.89 227 | 84.07 187 | 83.08 194 | 88.18 208 | 79.09 219 |
|
tpmrst | | | 76.55 185 | 75.99 192 | 77.20 183 | 87.32 128 | 83.05 208 | 82.86 175 | 65.62 220 | 78.61 104 | 67.22 132 | 69.19 116 | 65.71 131 | 75.87 157 | 76.75 221 | 75.33 222 | 84.31 226 | 83.28 205 |
|
tpm | | | 76.30 193 | 76.05 191 | 76.59 189 | 86.97 131 | 83.01 209 | 83.83 170 | 67.06 214 | 71.83 158 | 63.87 164 | 69.56 114 | 62.88 149 | 73.41 175 | 79.79 211 | 78.59 210 | 84.41 225 | 86.68 185 |
|
test-mter | | | 77.79 173 | 80.02 130 | 75.18 199 | 81.18 210 | 82.85 210 | 80.52 197 | 62.03 230 | 73.62 146 | 62.16 175 | 73.55 89 | 73.83 100 | 73.81 171 | 84.67 181 | 83.34 192 | 91.37 186 | 88.31 167 |
|
pmmvs-eth3d | | | 74.32 207 | 71.96 213 | 77.08 185 | 77.33 222 | 82.71 211 | 78.41 207 | 76.02 177 | 66.65 196 | 65.98 145 | 54.23 212 | 49.02 227 | 73.14 177 | 82.37 201 | 82.69 197 | 91.61 183 | 86.05 192 |
|
PMMVS | | | 81.65 113 | 84.05 84 | 78.86 169 | 78.56 218 | 82.63 212 | 83.10 174 | 67.22 213 | 81.39 77 | 70.11 106 | 84.91 39 | 79.74 72 | 82.12 79 | 87.31 126 | 85.70 172 | 92.03 180 | 86.67 187 |
|
PM-MVS | | | 74.17 208 | 73.10 209 | 75.41 197 | 76.07 225 | 82.53 213 | 77.56 211 | 71.69 195 | 71.04 164 | 61.92 178 | 61.23 166 | 47.30 228 | 74.82 164 | 81.78 203 | 79.80 205 | 90.42 195 | 88.05 171 |
|
Anonymous20231206 | | | 70.80 212 | 70.59 216 | 71.04 212 | 81.60 206 | 82.49 214 | 74.64 217 | 75.87 180 | 64.17 210 | 49.27 219 | 44.85 228 | 53.59 219 | 54.68 220 | 83.07 194 | 82.34 199 | 90.17 197 | 83.65 203 |
|
test-LLR | | | 79.47 152 | 79.84 133 | 79.03 168 | 87.47 126 | 82.40 215 | 81.24 189 | 78.05 151 | 73.72 143 | 62.69 170 | 73.76 87 | 74.42 96 | 73.49 173 | 84.61 182 | 82.99 195 | 91.25 188 | 87.01 182 |
|
TESTMET0.1,1 | | | 77.78 174 | 79.84 133 | 75.38 198 | 80.86 211 | 82.40 215 | 81.24 189 | 62.72 229 | 73.72 143 | 62.69 170 | 73.76 87 | 74.42 96 | 73.49 173 | 84.61 182 | 82.99 195 | 91.25 188 | 87.01 182 |
|
MDA-MVSNet-bldmvs | | | 66.22 219 | 64.49 223 | 68.24 217 | 61.67 238 | 82.11 217 | 70.07 224 | 76.16 172 | 59.14 223 | 47.94 222 | 54.35 211 | 35.82 239 | 67.33 203 | 64.94 237 | 75.68 221 | 86.30 218 | 79.36 216 |
|
FMVSNet5 | | | 75.50 202 | 76.07 189 | 74.83 201 | 76.16 224 | 81.19 218 | 81.34 187 | 70.21 202 | 73.20 149 | 61.59 184 | 58.97 194 | 68.33 124 | 68.50 197 | 85.87 152 | 85.85 169 | 91.18 191 | 79.11 218 |
|
test20.03 | | | 68.31 217 | 70.05 217 | 66.28 221 | 82.41 200 | 80.84 219 | 67.35 226 | 76.11 174 | 58.44 224 | 40.80 231 | 53.77 213 | 54.54 215 | 42.28 233 | 83.07 194 | 81.96 203 | 88.73 207 | 77.76 221 |
|
ADS-MVSNet | | | 74.53 206 | 75.69 198 | 73.17 209 | 81.57 208 | 80.71 220 | 79.27 203 | 63.03 228 | 79.27 98 | 59.94 195 | 67.86 124 | 68.32 125 | 71.08 187 | 77.33 218 | 76.83 218 | 84.12 228 | 79.53 215 |
|
MIMVSNet1 | | | 65.00 220 | 66.24 221 | 63.55 224 | 58.41 242 | 80.01 221 | 69.00 225 | 74.03 187 | 55.81 229 | 41.88 229 | 36.81 237 | 49.48 226 | 47.89 228 | 81.32 204 | 82.40 198 | 90.08 199 | 77.88 220 |
|
EU-MVSNet | | | 69.98 214 | 72.30 212 | 67.28 219 | 75.67 227 | 79.39 222 | 73.12 219 | 69.94 204 | 63.59 212 | 42.80 228 | 62.93 156 | 56.71 206 | 55.07 219 | 79.13 215 | 78.55 211 | 87.06 215 | 85.82 195 |
|
LP | | | 68.35 216 | 67.23 218 | 69.67 215 | 77.49 221 | 79.38 223 | 72.84 221 | 61.37 231 | 66.94 194 | 55.08 207 | 47.00 223 | 50.35 223 | 65.16 210 | 75.61 223 | 76.03 219 | 86.08 220 | 75.28 224 |
|
CHOSEN 280x420 | | | 80.28 130 | 81.66 100 | 78.67 172 | 82.92 194 | 79.24 224 | 85.36 156 | 66.79 215 | 78.11 105 | 70.32 102 | 75.03 80 | 79.87 69 | 81.09 85 | 89.07 105 | 83.16 193 | 85.54 221 | 87.17 181 |
|
gm-plane-assit | | | 70.29 213 | 70.65 215 | 69.88 214 | 85.03 151 | 78.50 225 | 58.41 235 | 65.47 221 | 50.39 234 | 40.88 230 | 49.60 220 | 50.11 224 | 75.14 161 | 91.43 56 | 89.78 81 | 94.32 115 | 84.73 201 |
|
new-patchmatchnet | | | 63.80 223 | 63.31 226 | 64.37 222 | 76.49 223 | 75.99 226 | 63.73 230 | 70.99 198 | 57.27 225 | 43.08 227 | 45.86 226 | 43.80 229 | 45.13 232 | 73.20 228 | 70.68 233 | 86.80 216 | 76.34 223 |
|
MVS-HIRNet | | | 68.83 215 | 66.39 220 | 71.68 211 | 77.58 220 | 75.52 227 | 66.45 227 | 65.05 223 | 62.16 215 | 62.84 169 | 44.76 229 | 56.60 207 | 71.96 181 | 78.04 217 | 75.06 224 | 86.18 219 | 72.56 227 |
|
N_pmnet | | | 66.85 218 | 66.63 219 | 67.11 220 | 78.73 217 | 74.66 228 | 70.53 223 | 71.07 197 | 66.46 198 | 46.54 223 | 51.68 219 | 51.91 222 | 55.48 218 | 74.68 225 | 72.38 229 | 80.29 233 | 74.65 225 |
|
ambc | | | | 61.92 227 | | 70.98 235 | 73.54 229 | 63.64 231 | | 60.06 220 | 52.23 214 | 38.44 232 | 19.17 246 | 57.12 216 | 82.33 202 | 75.03 225 | 83.21 229 | 84.89 198 |
|
testus | | | 63.31 225 | 64.48 224 | 61.94 227 | 73.99 229 | 71.99 230 | 63.56 232 | 63.25 227 | 57.01 227 | 39.41 235 | 54.38 210 | 38.73 238 | 46.24 231 | 77.01 219 | 77.93 214 | 85.20 223 | 74.29 226 |
|
test2356 | | | 63.96 221 | 64.10 225 | 63.78 223 | 74.71 228 | 71.55 231 | 65.83 228 | 67.38 212 | 57.11 226 | 40.41 232 | 53.58 214 | 41.13 234 | 49.35 226 | 77.00 220 | 77.57 216 | 85.01 224 | 70.79 228 |
|
FPMVS | | | 63.63 224 | 60.08 230 | 67.78 218 | 80.01 212 | 71.50 232 | 72.88 220 | 69.41 207 | 61.82 216 | 53.11 210 | 45.12 227 | 42.11 232 | 50.86 224 | 66.69 234 | 63.84 236 | 80.41 232 | 69.46 232 |
|
pmmvs3 | | | 61.89 226 | 61.74 228 | 62.06 226 | 64.30 236 | 70.83 233 | 64.22 229 | 52.14 240 | 48.78 235 | 44.47 226 | 41.67 231 | 41.70 233 | 63.03 212 | 76.06 222 | 76.02 220 | 84.18 227 | 77.14 222 |
|
testpf | | | 63.91 222 | 65.23 222 | 62.38 225 | 81.32 209 | 69.95 234 | 62.71 233 | 54.16 238 | 61.29 218 | 48.73 220 | 57.31 200 | 52.50 220 | 50.97 223 | 67.50 233 | 68.86 234 | 76.36 236 | 79.21 217 |
|
new_pmnet | | | 59.28 227 | 61.47 229 | 56.73 232 | 61.66 239 | 68.29 235 | 59.57 234 | 54.91 236 | 60.83 219 | 34.38 238 | 44.66 230 | 43.65 230 | 49.90 225 | 71.66 231 | 71.56 232 | 79.94 234 | 69.67 231 |
|
testmv | | | 56.62 230 | 56.41 232 | 56.86 230 | 71.92 231 | 67.58 236 | 52.17 238 | 65.69 218 | 40.60 239 | 28.53 240 | 37.90 233 | 31.52 240 | 40.10 235 | 72.64 229 | 74.73 226 | 82.78 230 | 69.91 229 |
|
test1235678 | | | 56.61 231 | 56.40 233 | 56.86 230 | 71.92 231 | 67.58 236 | 52.17 238 | 65.69 218 | 40.58 240 | 28.52 241 | 37.89 234 | 31.49 241 | 40.10 235 | 72.64 229 | 74.72 227 | 82.78 230 | 69.90 230 |
|
1111 | | | 57.32 229 | 57.20 231 | 57.46 229 | 71.89 233 | 67.50 238 | 52.34 236 | 58.78 233 | 46.57 236 | 39.69 233 | 37.38 235 | 38.78 236 | 46.37 229 | 74.15 226 | 74.36 228 | 75.70 237 | 61.66 235 |
|
.test1245 | | | 41.43 237 | 38.48 239 | 44.88 235 | 71.89 233 | 67.50 238 | 52.34 236 | 58.78 233 | 46.57 236 | 39.69 233 | 37.38 235 | 38.78 236 | 46.37 229 | 74.15 226 | 1.18 243 | 0.20 247 | 3.76 244 |
|
no-one | | | 44.14 235 | 43.91 238 | 44.40 236 | 59.91 240 | 61.10 240 | 34.07 245 | 60.09 232 | 27.71 243 | 14.44 245 | 19.11 241 | 19.28 245 | 23.90 242 | 47.36 241 | 66.69 235 | 73.98 239 | 66.11 234 |
|
test12356 | | | 50.02 233 | 51.22 235 | 48.61 234 | 63.00 237 | 60.15 241 | 47.60 242 | 56.49 235 | 38.02 241 | 24.74 243 | 36.14 239 | 25.93 243 | 24.79 240 | 66.19 235 | 71.68 231 | 75.07 238 | 60.44 237 |
|
PMMVS2 | | | 41.68 236 | 44.74 237 | 38.10 237 | 46.97 245 | 52.32 242 | 40.63 244 | 48.08 241 | 35.51 242 | 7.36 248 | 26.86 240 | 24.64 244 | 16.72 243 | 55.24 239 | 59.03 238 | 68.85 241 | 59.59 238 |
|
PMVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 50.48 18 | 55.81 232 | 51.93 234 | 60.33 228 | 72.90 230 | 49.34 243 | 48.78 240 | 69.51 206 | 43.49 238 | 54.25 208 | 36.26 238 | 41.04 235 | 39.71 237 | 65.07 236 | 60.70 237 | 76.85 235 | 67.58 233 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
Gipuma | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 49.17 234 | 47.05 236 | 51.65 233 | 59.67 241 | 48.39 244 | 41.98 243 | 63.47 226 | 55.64 230 | 33.33 239 | 14.90 242 | 13.78 247 | 41.34 234 | 69.31 232 | 72.30 230 | 70.11 240 | 55.00 239 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
DeepMVS_CX | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | | | | | 48.31 245 | 48.03 241 | 26.08 243 | 56.42 228 | 25.77 242 | 47.51 222 | 31.31 242 | 51.30 222 | 48.49 240 | | 53.61 242 | 61.52 236 |
|
MVE | ![Method available under a permissive open source license. permissive](img/icon_permissive.png) | 30.17 19 | 30.88 239 | 33.52 240 | 27.80 242 | 23.78 247 | 39.16 246 | 18.69 249 | 46.90 242 | 21.88 246 | 15.39 244 | 14.37 244 | 7.31 250 | 24.41 241 | 41.63 242 | 56.22 239 | 37.64 246 | 54.07 240 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
E-PMN | | | 31.40 238 | 26.80 241 | 36.78 238 | 51.39 244 | 29.96 247 | 20.20 247 | 54.17 237 | 25.93 245 | 12.75 246 | 14.73 243 | 8.58 249 | 34.10 239 | 27.36 243 | 37.83 241 | 48.07 244 | 43.18 241 |
|
EMVS | | | 30.49 240 | 25.44 242 | 36.39 239 | 51.47 243 | 29.89 248 | 20.17 248 | 54.00 239 | 26.49 244 | 12.02 247 | 13.94 245 | 8.84 248 | 34.37 238 | 25.04 244 | 34.37 242 | 46.29 245 | 39.53 242 |
|
tmp_tt | | | | | 32.73 240 | 43.96 246 | 21.15 249 | 26.71 246 | 8.99 244 | 65.67 203 | 51.39 216 | 56.01 207 | 42.64 231 | 11.76 244 | 56.60 238 | 50.81 240 | 53.55 243 | |
|
testmvs | | | 1.03 241 | 1.63 243 | 0.34 243 | 0.09 249 | 0.35 250 | 0.61 251 | 0.16 245 | 1.49 247 | 0.10 250 | 3.15 246 | 0.15 251 | 0.86 246 | 1.32 245 | 1.18 243 | 0.20 247 | 3.76 244 |
|
test123 | | | 0.87 242 | 1.40 244 | 0.25 244 | 0.03 250 | 0.25 251 | 0.35 252 | 0.08 247 | 1.21 248 | 0.05 251 | 2.84 247 | 0.03 252 | 0.89 245 | 0.43 246 | 1.16 245 | 0.13 249 | 3.87 243 |
|
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 |
|
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 |
|
MTAPA | | | | | | | | | | | 92.97 2 | | 91.03 18 | | | | | |
|
MTMP | | | | | | | | | | | 93.14 1 | | 90.21 25 | | | | | |
|
Patchmatch-RL test | | | | | | | | 8.55 250 | | | | | | | | | | |
|
mPP-MVS | | | | | | 97.06 13 | | | | | | | 88.08 40 | | | | | |
|
NP-MVS | | | | | | | | | | 87.47 51 | | | | | | | | |
|