APDe-MVS | | | 98.87 1 | 98.96 1 | 98.77 1 | 99.58 1 | 99.53 2 | 99.44 1 | 97.81 1 | 98.22 7 | 97.33 2 | 98.70 2 | 99.33 6 | 98.86 7 | 98.96 3 | 98.40 10 | 99.63 3 | 99.57 5 |
|
PGM-MVS | | | 97.81 21 | 98.11 24 | 97.46 25 | 99.55 2 | 99.34 12 | 99.32 6 | 94.51 39 | 96.21 54 | 93.07 33 | 98.05 10 | 97.95 35 | 98.82 10 | 98.22 26 | 97.89 28 | 99.48 21 | 99.09 43 |
|
ACMMP_Plus | | | 98.20 14 | 98.49 9 | 97.85 21 | 99.50 3 | 99.40 6 | 99.26 9 | 97.64 7 | 97.47 26 | 92.62 42 | 97.59 16 | 99.09 15 | 98.71 14 | 98.82 8 | 97.86 29 | 99.40 48 | 99.19 34 |
|
MPTG | | | 98.43 8 | 98.31 19 | 98.57 2 | 99.48 4 | 99.40 6 | 99.32 6 | 97.62 8 | 97.70 16 | 96.67 6 | 96.59 27 | 99.09 15 | 98.86 7 | 98.65 9 | 97.56 36 | 99.45 30 | 99.17 38 |
|
APD-MVS | | | 98.36 11 | 98.32 18 | 98.41 5 | 99.47 5 | 99.26 18 | 99.12 12 | 97.77 4 | 96.73 42 | 96.12 12 | 97.27 23 | 98.88 18 | 98.46 21 | 98.47 14 | 98.39 11 | 99.52 13 | 99.22 30 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
CSCG | | | 97.44 28 | 97.18 35 | 97.75 23 | 99.47 5 | 99.52 3 | 98.55 28 | 95.41 34 | 97.69 18 | 95.72 15 | 94.29 46 | 95.53 53 | 98.10 26 | 96.20 98 | 97.38 43 | 99.24 75 | 99.62 2 |
|
ESAPD | | | 98.59 2 | 98.77 3 | 98.39 6 | 99.46 7 | 99.50 4 | 99.11 13 | 97.80 2 | 97.20 32 | 96.06 13 | 98.56 3 | 99.83 1 | 98.43 22 | 98.84 6 | 98.03 22 | 99.45 30 | 99.45 10 |
|
HPM-MVS++ | | | 98.34 12 | 98.47 11 | 98.18 12 | 99.46 7 | 99.15 26 | 99.10 14 | 97.69 5 | 97.67 19 | 94.93 22 | 97.62 15 | 99.70 3 | 98.60 17 | 98.45 15 | 97.46 39 | 99.31 64 | 99.26 25 |
|
HSP-MVS | | | 98.59 2 | 98.65 6 | 98.52 3 | 99.44 9 | 99.57 1 | 99.34 3 | 97.65 6 | 97.36 28 | 96.62 8 | 98.49 5 | 99.65 4 | 98.67 16 | 98.60 10 | 97.44 40 | 99.40 48 | 99.46 9 |
|
ACMMPR | | | 98.40 9 | 98.49 9 | 98.28 9 | 99.41 10 | 99.40 6 | 99.36 2 | 97.35 16 | 98.30 4 | 95.02 21 | 97.79 13 | 98.39 30 | 99.04 2 | 98.26 23 | 98.10 17 | 99.50 19 | 99.22 30 |
|
X-MVS | | | 97.84 20 | 98.19 23 | 97.42 26 | 99.40 11 | 99.35 9 | 99.06 15 | 97.25 20 | 97.38 27 | 90.85 50 | 96.06 31 | 98.72 23 | 98.53 20 | 98.41 18 | 98.15 16 | 99.46 26 | 99.28 20 |
|
MCST-MVS | | | 98.20 14 | 98.36 14 | 98.01 18 | 99.40 11 | 99.05 29 | 99.00 18 | 97.62 8 | 97.59 23 | 93.70 29 | 97.42 22 | 99.30 7 | 98.77 12 | 98.39 19 | 97.48 38 | 99.59 4 | 99.31 19 |
|
CNVR-MVS | | | 98.47 7 | 98.46 12 | 98.48 4 | 99.40 11 | 99.05 29 | 99.02 17 | 97.54 11 | 97.73 14 | 96.65 7 | 97.20 24 | 99.13 13 | 98.85 9 | 98.91 5 | 98.10 17 | 99.41 46 | 99.08 44 |
|
HFP-MVS | | | 98.48 6 | 98.62 7 | 98.32 7 | 99.39 14 | 99.33 13 | 99.27 8 | 97.42 13 | 98.27 5 | 95.25 19 | 98.34 8 | 98.83 20 | 99.08 1 | 98.26 23 | 98.08 19 | 99.48 21 | 99.26 25 |
|
NCCC | | | 98.10 17 | 98.05 26 | 98.17 14 | 99.38 15 | 99.05 29 | 99.00 18 | 97.53 12 | 98.04 10 | 95.12 20 | 94.80 43 | 99.18 11 | 98.58 18 | 98.49 13 | 97.78 31 | 99.39 50 | 98.98 60 |
|
MP-MVS | | | 98.09 18 | 98.30 20 | 97.84 22 | 99.34 16 | 99.19 24 | 99.23 10 | 97.40 14 | 97.09 36 | 93.03 36 | 97.58 17 | 98.85 19 | 98.57 19 | 98.44 17 | 97.69 32 | 99.48 21 | 99.23 28 |
|
CP-MVS | | | 98.32 13 | 98.34 17 | 98.29 8 | 99.34 16 | 99.30 14 | 99.15 11 | 97.35 16 | 97.49 25 | 95.58 17 | 97.72 14 | 98.62 27 | 98.82 10 | 98.29 21 | 97.67 33 | 99.51 17 | 99.28 20 |
|
SteuartSystems-ACMMP | | | 98.38 10 | 98.71 5 | 97.99 19 | 99.34 16 | 99.46 5 | 99.34 3 | 97.33 19 | 97.31 29 | 94.25 25 | 98.06 9 | 99.17 12 | 98.13 25 | 98.98 2 | 98.46 8 | 99.55 9 | 99.54 6 |
Skip Steuart: Steuart Systems R&D Blog. |
mPP-MVS | | | | | | 99.21 19 | | | | | | | 98.29 31 | | | | | |
|
AdaColmap | | | 97.53 26 | 96.93 39 | 98.24 10 | 99.21 19 | 98.77 60 | 98.47 30 | 97.34 18 | 96.68 44 | 96.52 10 | 95.11 40 | 96.12 49 | 98.72 13 | 97.19 54 | 96.24 70 | 99.17 87 | 98.39 102 |
|
DeepC-MVS_fast | | 96.13 1 | 98.13 16 | 98.27 21 | 97.97 20 | 99.16 21 | 99.03 34 | 99.05 16 | 97.24 21 | 98.22 7 | 94.17 27 | 95.82 32 | 98.07 32 | 98.69 15 | 98.83 7 | 98.80 2 | 99.52 13 | 99.10 41 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
MSLP-MVS++ | | | 98.04 19 | 97.93 28 | 98.18 12 | 99.10 22 | 99.09 28 | 98.34 32 | 96.99 26 | 97.54 24 | 96.60 9 | 94.82 42 | 98.45 29 | 98.89 5 | 97.46 47 | 98.77 4 | 99.17 87 | 99.37 13 |
|
3Dnovator | | 93.79 8 | 97.08 33 | 97.20 33 | 96.95 33 | 99.09 23 | 99.03 34 | 98.20 34 | 93.33 47 | 97.99 11 | 93.82 28 | 90.61 80 | 96.80 42 | 97.82 30 | 97.90 37 | 98.78 3 | 99.47 24 | 99.26 25 |
|
QAPM | | | 96.78 40 | 97.14 36 | 96.36 38 | 99.05 24 | 99.14 27 | 98.02 37 | 93.26 49 | 97.27 31 | 90.84 53 | 91.16 72 | 97.31 37 | 97.64 35 | 97.70 41 | 98.20 14 | 99.33 59 | 99.18 37 |
|
OpenMVS | | 92.33 11 | 95.50 47 | 95.22 61 | 95.82 46 | 98.98 25 | 98.97 40 | 97.67 45 | 93.04 57 | 94.64 88 | 89.18 75 | 84.44 129 | 94.79 55 | 96.79 56 | 97.23 51 | 97.61 34 | 99.24 75 | 98.88 69 |
|
PLC | | 94.95 3 | 97.37 29 | 96.77 42 | 98.07 16 | 98.97 26 | 98.21 84 | 97.94 40 | 96.85 29 | 97.66 20 | 97.58 1 | 93.33 51 | 96.84 41 | 98.01 29 | 97.13 56 | 96.20 73 | 99.09 99 | 98.01 120 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
train_agg | | | 97.65 25 | 98.06 25 | 97.18 29 | 98.94 27 | 98.91 53 | 98.98 21 | 97.07 25 | 96.71 43 | 90.66 55 | 97.43 21 | 99.08 17 | 98.20 23 | 97.96 35 | 97.14 48 | 99.22 81 | 99.19 34 |
|
CDPH-MVS | | | 96.84 38 | 97.49 30 | 96.09 42 | 98.92 28 | 98.85 57 | 98.61 25 | 95.09 35 | 96.00 61 | 87.29 95 | 95.45 37 | 97.42 36 | 97.16 42 | 97.83 39 | 97.94 25 | 99.44 39 | 98.92 65 |
|
CPTT-MVS | | | 97.78 22 | 97.54 29 | 98.05 17 | 98.91 29 | 99.05 29 | 99.00 18 | 96.96 27 | 97.14 34 | 95.92 14 | 95.50 35 | 98.78 22 | 98.99 4 | 97.20 52 | 96.07 74 | 98.54 162 | 99.04 52 |
|
3Dnovator+ | | 93.91 7 | 97.23 31 | 97.22 32 | 97.24 28 | 98.89 30 | 98.85 57 | 98.26 33 | 93.25 51 | 97.99 11 | 95.56 18 | 90.01 86 | 98.03 34 | 98.05 27 | 97.91 36 | 98.43 9 | 99.44 39 | 99.35 15 |
|
ACMMP | | | 97.37 29 | 97.48 31 | 97.25 27 | 98.88 31 | 99.28 16 | 98.47 30 | 96.86 28 | 97.04 38 | 92.15 43 | 97.57 18 | 96.05 51 | 97.67 33 | 97.27 50 | 95.99 78 | 99.46 26 | 99.14 40 |
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 |
PHI-MVS | | | 97.78 22 | 98.44 13 | 97.02 32 | 98.73 32 | 99.25 20 | 98.11 35 | 95.54 33 | 96.66 45 | 92.79 39 | 98.52 4 | 99.38 5 | 97.50 37 | 97.84 38 | 98.39 11 | 99.45 30 | 99.03 53 |
|
OMC-MVS | | | 97.00 35 | 96.92 40 | 97.09 30 | 98.69 33 | 98.66 66 | 97.85 41 | 95.02 36 | 98.09 9 | 94.47 23 | 93.15 52 | 96.90 39 | 97.38 38 | 97.16 55 | 96.82 56 | 99.13 94 | 97.65 144 |
|
MAR-MVS | | | 95.50 47 | 95.60 55 | 95.39 53 | 98.67 34 | 98.18 85 | 95.89 84 | 89.81 99 | 94.55 90 | 91.97 45 | 92.99 53 | 90.21 76 | 97.30 39 | 96.79 65 | 97.49 37 | 98.72 149 | 98.99 58 |
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 |
TSAR-MVS + ACMM | | | 97.71 24 | 98.60 8 | 96.66 35 | 98.64 35 | 99.05 29 | 98.85 22 | 97.23 22 | 98.45 2 | 89.40 73 | 97.51 19 | 99.27 9 | 96.88 55 | 98.53 11 | 97.81 30 | 98.96 111 | 99.59 4 |
|
abl_6 | | | | | 96.82 34 | 98.60 36 | 98.74 61 | 97.74 43 | 93.73 43 | 96.25 52 | 94.37 24 | 94.55 45 | 98.60 28 | 97.25 40 | | | 99.27 70 | 98.61 84 |
|
CNLPA | | | 96.90 37 | 96.28 48 | 97.64 24 | 98.56 37 | 98.63 70 | 96.85 57 | 96.60 30 | 97.73 14 | 97.08 4 | 89.78 88 | 96.28 48 | 97.80 32 | 96.73 70 | 96.63 58 | 98.94 112 | 98.14 116 |
|
EPNet | | | 96.27 44 | 96.97 38 | 95.46 51 | 98.47 38 | 98.28 79 | 97.41 48 | 93.67 44 | 95.86 66 | 92.86 38 | 97.51 19 | 93.79 59 | 91.76 128 | 97.03 57 | 97.03 49 | 98.61 158 | 99.28 20 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
MVS_111021_LR | | | 97.16 32 | 98.01 27 | 96.16 41 | 98.47 38 | 98.98 39 | 96.94 54 | 93.89 42 | 97.64 21 | 91.44 47 | 98.89 1 | 96.41 44 | 97.20 41 | 98.02 34 | 97.29 47 | 99.04 107 | 98.85 72 |
|
MVS_111021_HR | | | 97.04 34 | 98.20 22 | 95.69 47 | 98.44 40 | 99.29 15 | 96.59 70 | 93.20 52 | 97.70 16 | 89.94 65 | 98.46 6 | 96.89 40 | 96.71 58 | 98.11 31 | 97.95 24 | 99.27 70 | 99.01 56 |
|
MSDG | | | 94.82 59 | 93.73 89 | 96.09 42 | 98.34 41 | 97.43 97 | 97.06 51 | 96.05 31 | 95.84 67 | 90.56 56 | 86.30 120 | 89.10 83 | 95.55 73 | 96.13 101 | 95.61 96 | 99.00 108 | 95.73 182 |
|
TAPA-MVS | | 94.18 5 | 96.38 42 | 96.49 46 | 96.25 39 | 98.26 42 | 98.66 66 | 98.00 38 | 94.96 37 | 97.17 33 | 89.48 70 | 92.91 55 | 96.35 45 | 97.53 36 | 96.59 77 | 95.90 82 | 99.28 68 | 97.82 133 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
DeepC-MVS | | 94.87 4 | 96.76 41 | 96.50 45 | 97.05 31 | 98.21 43 | 99.28 16 | 98.67 24 | 97.38 15 | 97.31 29 | 90.36 61 | 89.19 90 | 93.58 61 | 98.19 24 | 98.31 20 | 98.50 6 | 99.51 17 | 99.36 14 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
SD-MVS | | | 98.52 4 | 98.77 3 | 98.23 11 | 98.15 44 | 99.26 18 | 98.79 23 | 97.59 10 | 98.52 1 | 96.25 11 | 97.99 11 | 99.75 2 | 99.01 3 | 98.27 22 | 97.97 23 | 99.59 4 | 99.63 1 |
|
TSAR-MVS + MP. | | | 98.49 5 | 98.78 2 | 98.15 15 | 98.14 45 | 99.17 25 | 99.34 3 | 97.18 23 | 98.44 3 | 95.72 15 | 97.84 12 | 99.28 8 | 98.87 6 | 99.05 1 | 98.05 20 | 99.66 1 | 99.60 3 |
|
EPNet_dtu | | | 92.45 105 | 95.02 65 | 89.46 134 | 98.02 46 | 95.47 152 | 94.79 110 | 92.62 58 | 94.97 85 | 70.11 198 | 94.76 44 | 92.61 66 | 84.07 204 | 95.94 104 | 95.56 97 | 97.15 190 | 95.82 181 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
CANet | | | 96.84 38 | 97.20 33 | 96.42 36 | 97.92 47 | 99.24 22 | 98.60 26 | 93.51 46 | 97.11 35 | 93.07 33 | 91.16 72 | 97.24 38 | 96.21 65 | 98.24 25 | 98.05 20 | 99.22 81 | 99.35 15 |
|
LS3D | | | 95.46 49 | 95.14 62 | 95.84 45 | 97.91 48 | 98.90 55 | 98.58 27 | 97.79 3 | 97.07 37 | 83.65 108 | 88.71 93 | 88.64 86 | 97.82 30 | 97.49 46 | 97.42 41 | 99.26 74 | 97.72 143 |
|
DELS-MVS | | | 96.06 45 | 96.04 51 | 96.07 44 | 97.77 49 | 99.25 20 | 98.10 36 | 93.26 49 | 94.42 91 | 92.79 39 | 88.52 97 | 93.48 62 | 95.06 80 | 98.51 12 | 98.83 1 | 99.45 30 | 99.28 20 |
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 |
COLMAP_ROB | | 90.49 14 | 93.27 96 | 92.71 100 | 93.93 79 | 97.75 50 | 97.44 96 | 96.07 81 | 93.17 53 | 95.40 75 | 83.86 106 | 83.76 134 | 88.72 85 | 93.87 94 | 94.25 137 | 94.11 130 | 98.87 117 | 95.28 188 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
PCF-MVS | | 93.95 6 | 95.65 46 | 95.14 62 | 96.25 39 | 97.73 51 | 98.73 63 | 97.59 46 | 97.13 24 | 92.50 124 | 89.09 77 | 89.85 87 | 96.65 43 | 96.90 54 | 94.97 125 | 94.89 111 | 99.08 100 | 98.38 103 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
PatchMatch-RL | | | 94.69 64 | 94.41 74 | 95.02 57 | 97.63 52 | 98.15 87 | 94.50 115 | 91.99 75 | 95.32 77 | 91.31 48 | 95.47 36 | 83.44 113 | 96.02 68 | 96.56 80 | 95.23 104 | 98.69 153 | 96.67 172 |
|
PVSNet_BlendedMVS | | | 95.41 51 | 95.28 59 | 95.57 49 | 97.42 53 | 99.02 36 | 95.89 84 | 93.10 54 | 96.16 55 | 93.12 31 | 91.99 64 | 85.27 102 | 94.66 82 | 98.09 32 | 97.34 44 | 99.24 75 | 99.08 44 |
|
PVSNet_Blended | | | 95.41 51 | 95.28 59 | 95.57 49 | 97.42 53 | 99.02 36 | 95.89 84 | 93.10 54 | 96.16 55 | 93.12 31 | 91.99 64 | 85.27 102 | 94.66 82 | 98.09 32 | 97.34 44 | 99.24 75 | 99.08 44 |
|
DeepPCF-MVS | | 95.28 2 | 97.00 35 | 98.35 16 | 95.42 52 | 97.30 55 | 98.94 42 | 94.82 109 | 96.03 32 | 98.24 6 | 92.11 44 | 95.80 33 | 98.64 26 | 95.51 74 | 98.95 4 | 98.66 5 | 96.78 193 | 99.20 33 |
|
CHOSEN 280x420 | | | 95.46 49 | 97.01 37 | 93.66 86 | 97.28 56 | 97.98 90 | 96.40 77 | 85.39 150 | 96.10 59 | 91.07 49 | 96.53 28 | 96.34 47 | 95.61 71 | 97.65 42 | 96.95 52 | 96.21 197 | 97.49 146 |
|
MVS_0304 | | | 96.31 43 | 96.91 41 | 95.62 48 | 97.21 57 | 99.20 23 | 98.55 28 | 93.10 54 | 97.04 38 | 89.73 67 | 90.30 82 | 96.35 45 | 95.71 69 | 98.14 28 | 97.93 27 | 99.38 52 | 99.40 12 |
|
CHOSEN 1792x2688 | | | 92.66 102 | 92.49 106 | 92.85 96 | 97.13 58 | 98.89 56 | 95.90 82 | 88.50 113 | 95.32 77 | 83.31 109 | 71.99 200 | 88.96 84 | 94.10 93 | 96.69 71 | 96.49 60 | 98.15 175 | 99.10 41 |
|
HyFIR lowres test | | | 92.03 106 | 91.55 124 | 92.58 100 | 97.13 58 | 98.72 64 | 94.65 112 | 86.54 135 | 93.58 105 | 82.56 112 | 67.75 215 | 90.47 75 | 95.67 70 | 95.87 106 | 95.54 98 | 98.91 115 | 98.93 64 |
|
OPM-MVS | | | 93.61 85 | 92.43 109 | 95.00 59 | 96.94 60 | 97.34 98 | 97.78 42 | 94.23 40 | 89.64 159 | 85.53 99 | 88.70 94 | 82.81 119 | 96.28 64 | 96.28 95 | 95.00 110 | 99.24 75 | 97.22 155 |
|
XVS | | | | | | 96.60 61 | 99.35 9 | 96.82 58 | | | 90.85 50 | | 98.72 23 | | | | 99.46 26 | |
|
X-MVStestdata | | | | | | 96.60 61 | 99.35 9 | 96.82 58 | | | 90.85 50 | | 98.72 23 | | | | 99.46 26 | |
|
TSAR-MVS + COLMAP | | | 94.79 61 | 94.51 72 | 95.11 55 | 96.50 63 | 97.54 93 | 97.99 39 | 94.54 38 | 97.81 13 | 85.88 98 | 96.73 26 | 81.28 126 | 96.99 53 | 96.29 94 | 95.21 105 | 98.76 146 | 96.73 171 |
|
PVSNet_Blended_VisFu | | | 94.77 63 | 95.54 57 | 93.87 81 | 96.48 64 | 98.97 40 | 94.33 117 | 91.84 78 | 94.93 86 | 90.37 60 | 85.04 125 | 94.99 54 | 90.87 144 | 98.12 30 | 97.30 46 | 99.30 66 | 99.45 10 |
|
LGP-MVS_train | | | 94.12 72 | 94.62 69 | 93.53 87 | 96.44 65 | 97.54 93 | 97.40 49 | 91.84 78 | 94.66 87 | 81.09 123 | 95.70 34 | 83.36 117 | 95.10 79 | 96.36 92 | 95.71 91 | 99.32 61 | 99.03 53 |
|
HQP-MVS | | | 94.43 67 | 94.57 70 | 94.27 74 | 96.41 66 | 97.23 100 | 96.89 55 | 93.98 41 | 95.94 63 | 83.68 107 | 95.01 41 | 84.46 107 | 95.58 72 | 95.47 116 | 94.85 113 | 99.07 102 | 99.00 57 |
|
ACMM | | 92.75 10 | 94.41 69 | 93.84 86 | 95.09 56 | 96.41 66 | 96.80 110 | 94.88 108 | 93.54 45 | 96.41 48 | 90.16 62 | 92.31 62 | 83.11 118 | 96.32 62 | 96.22 97 | 94.65 115 | 99.22 81 | 97.35 151 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
RPSCF | | | 94.05 73 | 94.00 82 | 94.12 76 | 96.20 68 | 96.41 124 | 96.61 68 | 91.54 82 | 95.83 68 | 89.73 67 | 96.94 25 | 92.80 65 | 95.35 77 | 91.63 189 | 90.44 197 | 95.27 209 | 93.94 198 |
|
UA-Net | | | 93.96 75 | 95.95 52 | 91.64 107 | 96.06 69 | 98.59 72 | 95.29 99 | 90.00 95 | 91.06 142 | 82.87 110 | 90.64 79 | 98.06 33 | 86.06 192 | 98.14 28 | 98.20 14 | 99.58 6 | 96.96 165 |
|
UGNet | | | 94.92 56 | 96.63 43 | 92.93 95 | 96.03 70 | 98.63 70 | 94.53 114 | 91.52 83 | 96.23 53 | 90.03 63 | 92.87 56 | 96.10 50 | 86.28 191 | 96.68 72 | 96.60 59 | 99.16 90 | 99.32 18 |
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 |
ACMP | | 92.88 9 | 94.43 67 | 94.38 75 | 94.50 72 | 96.01 71 | 97.69 92 | 95.85 87 | 92.09 72 | 95.74 70 | 89.12 76 | 95.14 39 | 82.62 121 | 94.77 81 | 95.73 111 | 94.67 114 | 99.14 93 | 99.06 48 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
IB-MVS | | 89.56 15 | 91.71 111 | 92.50 105 | 90.79 117 | 95.94 72 | 98.44 76 | 87.05 203 | 91.38 84 | 93.15 109 | 92.98 37 | 84.78 126 | 85.14 105 | 78.27 211 | 92.47 164 | 94.44 126 | 99.10 98 | 99.08 44 |
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 |
MS-PatchMatch | | | 91.82 109 | 92.51 104 | 91.02 111 | 95.83 73 | 96.88 105 | 95.05 102 | 84.55 165 | 93.85 100 | 82.01 113 | 82.51 142 | 91.71 67 | 90.52 157 | 95.07 123 | 93.03 151 | 98.13 176 | 94.52 191 |
|
CANet_DTU | | | 93.92 76 | 96.57 44 | 90.83 115 | 95.63 74 | 98.39 77 | 96.99 53 | 87.38 127 | 96.26 51 | 71.97 185 | 96.31 29 | 93.02 63 | 94.53 85 | 97.38 48 | 96.83 55 | 98.49 165 | 97.79 135 |
|
ACMH | | 90.77 13 | 91.51 116 | 91.63 122 | 91.38 109 | 95.62 75 | 96.87 107 | 91.76 174 | 89.66 101 | 91.58 137 | 78.67 132 | 86.73 109 | 78.12 135 | 93.77 97 | 94.59 128 | 94.54 122 | 98.78 140 | 98.98 60 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
TSAR-MVS + GP. | | | 97.45 27 | 98.36 14 | 96.39 37 | 95.56 76 | 98.93 47 | 97.74 43 | 93.31 48 | 97.61 22 | 94.24 26 | 98.44 7 | 99.19 10 | 98.03 28 | 97.60 43 | 97.41 42 | 99.44 39 | 99.33 17 |
|
view800 | | | 93.45 93 | 92.37 113 | 94.71 69 | 95.42 77 | 98.92 51 | 96.51 73 | 92.19 70 | 93.14 110 | 87.62 90 | 86.72 110 | 76.54 147 | 97.08 51 | 96.86 59 | 95.74 90 | 99.45 30 | 98.70 79 |
|
tfpn | | | 92.91 99 | 91.44 126 | 94.63 71 | 95.42 77 | 98.92 51 | 96.41 76 | 92.10 71 | 93.19 108 | 87.34 94 | 86.85 107 | 69.20 204 | 97.01 52 | 96.88 58 | 96.28 66 | 99.47 24 | 98.75 78 |
|
thres600view7 | | | 93.49 92 | 92.37 113 | 94.79 68 | 95.42 77 | 98.93 47 | 96.58 71 | 92.31 63 | 93.04 112 | 87.88 88 | 86.62 113 | 76.94 144 | 97.09 50 | 96.82 61 | 95.63 94 | 99.45 30 | 98.63 82 |
|
view600 | | | 93.50 91 | 92.39 112 | 94.80 67 | 95.41 80 | 98.93 47 | 96.60 69 | 92.30 67 | 93.09 111 | 87.96 87 | 86.67 112 | 76.97 143 | 97.12 45 | 96.83 60 | 95.64 93 | 99.43 44 | 98.62 83 |
|
thres400 | | | 93.56 87 | 92.43 109 | 94.87 65 | 95.40 81 | 98.91 53 | 96.70 66 | 92.38 62 | 92.93 114 | 88.19 86 | 86.69 111 | 77.35 141 | 97.13 43 | 96.75 69 | 95.85 86 | 99.42 45 | 98.56 86 |
|
thres200 | | | 93.62 84 | 92.54 103 | 94.88 63 | 95.36 82 | 98.93 47 | 96.75 65 | 92.31 63 | 92.84 115 | 88.28 83 | 86.99 106 | 77.81 140 | 97.13 43 | 96.82 61 | 95.92 80 | 99.45 30 | 98.49 95 |
|
conf200view11 | | | 93.64 81 | 92.57 101 | 94.88 63 | 95.33 83 | 98.94 42 | 96.82 58 | 92.31 63 | 92.63 117 | 88.26 84 | 87.21 103 | 78.01 137 | 97.12 45 | 96.82 61 | 95.85 86 | 99.45 30 | 98.56 86 |
|
thres100view900 | | | 93.55 90 | 92.47 108 | 94.81 66 | 95.33 83 | 98.74 61 | 96.78 64 | 92.30 67 | 92.63 117 | 88.29 81 | 87.21 103 | 78.01 137 | 96.78 57 | 96.38 90 | 95.92 80 | 99.38 52 | 98.40 101 |
|
tfpn200view9 | | | 93.64 81 | 92.57 101 | 94.89 62 | 95.33 83 | 98.94 42 | 96.82 58 | 92.31 63 | 92.63 117 | 88.29 81 | 87.21 103 | 78.01 137 | 97.12 45 | 96.82 61 | 95.85 86 | 99.45 30 | 98.56 86 |
|
conf0.01 | | | 93.33 94 | 91.89 119 | 95.00 59 | 95.32 86 | 98.94 42 | 96.82 58 | 92.41 61 | 92.63 117 | 88.91 79 | 88.02 101 | 72.75 177 | 97.12 45 | 96.78 66 | 95.85 86 | 99.44 39 | 98.27 109 |
|
conf0.002 | | | 93.20 97 | 91.63 122 | 95.02 57 | 95.31 87 | 98.94 42 | 96.82 58 | 92.43 60 | 92.63 117 | 88.99 78 | 88.16 100 | 70.49 196 | 97.12 45 | 96.77 67 | 96.30 62 | 99.44 39 | 98.16 115 |
|
IS_MVSNet | | | 95.28 53 | 96.43 47 | 93.94 78 | 95.30 88 | 99.01 38 | 95.90 82 | 91.12 86 | 94.13 97 | 87.50 92 | 91.23 71 | 94.45 57 | 94.17 91 | 98.45 15 | 98.50 6 | 99.65 2 | 99.23 28 |
|
CMPMVS | | 65.18 17 | 84.76 201 | 83.10 210 | 86.69 188 | 95.29 89 | 95.05 172 | 88.37 198 | 85.51 149 | 80.27 218 | 71.31 189 | 68.37 213 | 73.85 161 | 85.25 195 | 87.72 209 | 87.75 208 | 94.38 218 | 88.70 218 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
tfpn1000 | | | 94.14 71 | 94.54 71 | 93.67 85 | 95.27 90 | 98.50 75 | 95.36 98 | 91.84 78 | 96.31 50 | 87.38 93 | 92.98 54 | 84.04 109 | 92.60 115 | 96.49 87 | 95.62 95 | 99.55 9 | 97.82 133 |
|
canonicalmvs | | | 95.25 55 | 95.45 58 | 95.00 59 | 95.27 90 | 98.72 64 | 96.89 55 | 89.82 98 | 96.51 46 | 90.84 53 | 93.72 47 | 86.01 97 | 97.66 34 | 95.78 110 | 97.94 25 | 99.54 11 | 99.50 7 |
|
Vis-MVSNet (Re-imp) | | | 94.46 66 | 96.24 49 | 92.40 101 | 95.23 92 | 98.64 68 | 95.56 94 | 90.99 87 | 94.42 91 | 85.02 101 | 90.88 78 | 94.65 56 | 88.01 181 | 98.17 27 | 98.37 13 | 99.57 8 | 98.53 90 |
|
conf0.05thres1000 | | | 92.47 104 | 91.39 127 | 93.73 84 | 95.21 93 | 98.52 73 | 95.66 90 | 91.56 81 | 90.87 145 | 84.27 103 | 82.79 140 | 76.12 148 | 96.29 63 | 96.59 77 | 95.68 92 | 99.39 50 | 99.19 34 |
|
CLD-MVS | | | 94.79 61 | 94.36 76 | 95.30 54 | 95.21 93 | 97.46 95 | 97.23 50 | 92.24 69 | 96.43 47 | 91.77 46 | 92.69 57 | 84.31 108 | 96.06 66 | 95.52 115 | 95.03 107 | 99.31 64 | 99.06 48 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
tfpn_ndepth | | | 94.36 70 | 94.64 68 | 94.04 77 | 95.16 95 | 98.51 74 | 95.58 92 | 92.09 72 | 95.78 69 | 88.52 80 | 92.38 61 | 85.74 99 | 93.34 105 | 96.39 88 | 95.90 82 | 99.54 11 | 97.79 135 |
|
TDRefinement | | | 89.07 147 | 88.15 155 | 90.14 127 | 95.16 95 | 96.88 105 | 95.55 95 | 90.20 93 | 89.68 157 | 76.42 143 | 76.67 160 | 74.30 159 | 84.85 198 | 93.11 154 | 91.91 188 | 98.64 157 | 94.47 192 |
|
ACMH+ | | 90.88 12 | 91.41 117 | 91.13 129 | 91.74 106 | 95.11 97 | 96.95 104 | 93.13 132 | 89.48 104 | 92.42 126 | 79.93 127 | 85.13 124 | 78.02 136 | 93.82 96 | 93.49 149 | 93.88 136 | 98.94 112 | 97.99 121 |
|
tfpnview11 | | | 93.63 83 | 94.42 73 | 92.71 97 | 95.08 98 | 98.26 82 | 95.58 92 | 92.06 74 | 96.32 49 | 81.88 114 | 93.44 48 | 83.43 114 | 92.14 120 | 96.58 79 | 95.88 84 | 99.52 13 | 97.07 162 |
|
tfpn_n400 | | | 93.56 87 | 94.36 76 | 92.63 98 | 95.07 99 | 98.28 79 | 95.50 96 | 91.98 76 | 95.48 73 | 81.88 114 | 93.44 48 | 83.43 114 | 92.01 123 | 96.60 75 | 96.27 67 | 99.34 57 | 97.04 163 |
|
tfpnconf | | | 93.56 87 | 94.36 76 | 92.63 98 | 95.07 99 | 98.28 79 | 95.50 96 | 91.98 76 | 95.48 73 | 81.88 114 | 93.44 48 | 83.43 114 | 92.01 123 | 96.60 75 | 96.27 67 | 99.34 57 | 97.04 163 |
|
thresconf0.02 | | | 93.57 86 | 93.84 86 | 93.25 92 | 95.03 101 | 98.16 86 | 95.80 89 | 92.46 59 | 96.12 57 | 83.88 105 | 92.61 58 | 80.39 127 | 92.83 113 | 96.11 102 | 96.21 72 | 99.49 20 | 97.28 154 |
|
FC-MVSNet-train | | | 93.85 77 | 93.91 83 | 93.78 83 | 94.94 102 | 96.79 113 | 94.29 118 | 91.13 85 | 93.84 101 | 88.26 84 | 90.40 81 | 85.23 104 | 94.65 84 | 96.54 82 | 95.31 102 | 99.38 52 | 99.28 20 |
|
EPP-MVSNet | | | 95.27 54 | 96.18 50 | 94.20 75 | 94.88 103 | 98.64 68 | 94.97 104 | 90.70 88 | 95.34 76 | 89.67 69 | 91.66 68 | 93.84 58 | 95.42 76 | 97.32 49 | 97.00 50 | 99.58 6 | 99.47 8 |
|
MVS_Test | | | 94.82 59 | 95.66 53 | 93.84 82 | 94.79 104 | 98.35 78 | 96.49 74 | 89.10 109 | 96.12 57 | 87.09 96 | 92.58 59 | 90.61 74 | 96.48 61 | 96.51 86 | 96.89 53 | 99.11 97 | 98.54 89 |
|
diffmvs | | | 94.83 58 | 95.64 54 | 93.89 80 | 94.73 105 | 97.96 91 | 96.49 74 | 89.13 108 | 96.82 41 | 89.47 71 | 91.66 68 | 93.63 60 | 95.15 78 | 94.76 126 | 95.93 79 | 98.36 172 | 98.69 80 |
|
MVSTER | | | 94.89 57 | 95.07 64 | 94.68 70 | 94.71 106 | 96.68 116 | 97.00 52 | 90.57 90 | 95.18 83 | 93.05 35 | 95.21 38 | 86.41 94 | 93.72 98 | 97.59 44 | 95.88 84 | 99.00 108 | 98.50 94 |
|
EPMVS | | | 90.88 122 | 92.12 115 | 89.44 135 | 94.71 106 | 97.24 99 | 93.55 124 | 76.81 205 | 95.89 64 | 81.77 118 | 91.49 70 | 86.47 93 | 93.87 94 | 90.21 198 | 90.07 199 | 95.92 199 | 93.49 204 |
|
DWT-MVSNet_training | | | 91.30 118 | 89.73 140 | 93.13 94 | 94.64 108 | 96.87 107 | 94.93 105 | 86.17 140 | 94.22 95 | 93.18 30 | 89.11 91 | 73.28 171 | 93.59 101 | 88.00 208 | 90.73 195 | 96.26 196 | 95.87 179 |
|
DI_MVS_plusplus_trai | | | 94.01 74 | 93.63 91 | 94.44 73 | 94.54 109 | 98.26 82 | 97.51 47 | 90.63 89 | 95.88 65 | 89.34 74 | 80.54 150 | 89.36 80 | 95.48 75 | 96.33 93 | 96.27 67 | 99.17 87 | 98.78 76 |
|
ADS-MVSNet | | | 89.80 136 | 91.33 128 | 88.00 165 | 94.43 110 | 96.71 115 | 92.29 153 | 74.95 217 | 96.07 60 | 77.39 136 | 88.67 95 | 86.09 96 | 93.26 107 | 88.44 206 | 89.57 201 | 95.68 202 | 93.81 201 |
|
tpmrst | | | 88.86 151 | 89.62 141 | 87.97 166 | 94.33 111 | 95.98 132 | 92.62 139 | 76.36 210 | 94.62 89 | 76.94 139 | 85.98 121 | 82.80 120 | 92.80 114 | 86.90 212 | 87.15 213 | 94.77 214 | 93.93 199 |
|
PMMVS | | | 94.61 65 | 95.56 56 | 93.50 88 | 94.30 112 | 96.74 114 | 94.91 107 | 89.56 103 | 95.58 72 | 87.72 89 | 96.15 30 | 92.86 64 | 96.06 66 | 95.47 116 | 95.02 108 | 98.43 170 | 97.09 158 |
|
CostFormer | | | 90.69 123 | 90.48 137 | 90.93 113 | 94.18 113 | 96.08 130 | 94.03 120 | 78.20 201 | 93.47 106 | 89.96 64 | 90.97 77 | 80.30 128 | 93.72 98 | 87.66 211 | 88.75 203 | 95.51 205 | 96.12 176 |
|
USDC | | | 90.69 123 | 90.52 136 | 90.88 114 | 94.17 114 | 96.43 123 | 95.82 88 | 86.76 133 | 93.92 98 | 76.27 145 | 86.49 115 | 74.30 159 | 93.67 100 | 95.04 124 | 93.36 145 | 98.61 158 | 94.13 196 |
|
Vis-MVSNet | | | 92.77 100 | 95.00 66 | 90.16 125 | 94.10 115 | 98.79 59 | 94.76 111 | 88.26 114 | 92.37 129 | 79.95 126 | 88.19 99 | 91.58 68 | 84.38 201 | 97.59 44 | 97.58 35 | 99.52 13 | 98.91 67 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
Effi-MVS+ | | | 92.93 98 | 93.86 85 | 91.86 103 | 94.07 116 | 98.09 89 | 95.59 91 | 85.98 143 | 94.27 94 | 79.54 130 | 91.12 75 | 81.81 123 | 96.71 58 | 96.67 73 | 96.06 75 | 99.27 70 | 98.98 60 |
|
tpmp4_e23 | | | 89.82 135 | 89.31 146 | 90.42 121 | 94.01 117 | 95.45 153 | 94.63 113 | 78.37 198 | 93.59 104 | 87.09 96 | 86.62 113 | 76.59 146 | 93.06 111 | 88.50 205 | 88.52 204 | 95.36 206 | 95.88 178 |
|
IterMVS-LS | | | 92.56 103 | 93.18 98 | 91.84 104 | 93.90 118 | 94.97 174 | 94.99 103 | 86.20 139 | 94.18 96 | 82.68 111 | 85.81 122 | 87.36 91 | 94.43 86 | 95.31 118 | 96.02 77 | 98.87 117 | 98.60 85 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
dps | | | 90.11 133 | 89.37 145 | 90.98 112 | 93.89 119 | 96.21 128 | 93.49 126 | 77.61 203 | 91.95 135 | 92.74 41 | 88.85 92 | 78.77 134 | 92.37 118 | 87.71 210 | 87.71 209 | 95.80 200 | 94.38 194 |
|
tpm cat1 | | | 88.90 149 | 87.78 168 | 90.22 124 | 93.88 120 | 95.39 162 | 93.79 123 | 78.11 202 | 92.55 123 | 89.43 72 | 81.31 146 | 79.84 130 | 91.40 131 | 84.95 217 | 86.34 219 | 94.68 217 | 94.09 197 |
|
PatchmatchNet | | | 90.56 125 | 92.49 106 | 88.31 153 | 93.83 121 | 96.86 109 | 92.42 143 | 76.50 209 | 95.96 62 | 78.31 133 | 91.96 66 | 89.66 79 | 93.48 103 | 90.04 200 | 89.20 202 | 95.32 207 | 93.73 202 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
TinyColmap | | | 89.42 139 | 88.58 150 | 90.40 122 | 93.80 122 | 95.45 153 | 93.96 122 | 86.54 135 | 92.24 132 | 76.49 142 | 80.83 148 | 70.44 197 | 93.37 104 | 94.45 132 | 93.30 148 | 98.26 174 | 93.37 206 |
|
RPMNet | | | 90.19 131 | 92.03 117 | 88.05 162 | 93.46 123 | 95.95 135 | 93.41 127 | 74.59 218 | 92.40 127 | 75.91 147 | 84.22 130 | 86.41 94 | 92.49 116 | 94.42 133 | 93.85 138 | 98.44 168 | 96.96 165 |
|
gg-mvs-nofinetune | | | 86.17 195 | 88.57 151 | 83.36 204 | 93.44 124 | 98.15 87 | 96.58 71 | 72.05 223 | 74.12 223 | 49.23 230 | 64.81 218 | 90.85 72 | 89.90 172 | 97.83 39 | 96.84 54 | 98.97 110 | 97.41 149 |
|
MDTV_nov1_ep13 | | | 91.57 114 | 93.18 98 | 89.70 131 | 93.39 125 | 96.97 103 | 93.53 125 | 80.91 193 | 95.70 71 | 81.86 117 | 92.40 60 | 89.93 77 | 93.25 108 | 91.97 187 | 90.80 194 | 95.25 210 | 94.46 193 |
|
CR-MVSNet | | | 90.16 132 | 91.96 118 | 88.06 161 | 93.32 126 | 95.95 135 | 93.36 128 | 75.99 212 | 92.40 127 | 75.19 154 | 83.18 137 | 85.37 101 | 92.05 121 | 95.21 120 | 94.56 120 | 98.47 167 | 97.08 160 |
|
test-LLR | | | 91.62 113 | 93.56 94 | 89.35 137 | 93.31 127 | 96.57 119 | 92.02 169 | 87.06 131 | 92.34 130 | 75.05 157 | 90.20 83 | 88.64 86 | 90.93 140 | 96.19 99 | 94.07 131 | 97.75 185 | 96.90 168 |
|
test0.0.03 1 | | | 91.97 107 | 93.91 83 | 89.72 130 | 93.31 127 | 96.40 125 | 91.34 179 | 87.06 131 | 93.86 99 | 81.67 119 | 91.15 74 | 89.16 82 | 86.02 193 | 95.08 122 | 95.09 106 | 98.91 115 | 96.64 174 |
|
CVMVSNet | | | 89.77 137 | 91.66 121 | 87.56 179 | 93.21 129 | 95.45 153 | 91.94 173 | 89.22 106 | 89.62 160 | 69.34 203 | 83.99 132 | 85.90 98 | 84.81 199 | 94.30 136 | 95.28 103 | 96.85 192 | 97.09 158 |
|
PatchT | | | 89.13 146 | 91.71 120 | 86.11 196 | 92.92 130 | 95.59 148 | 83.64 210 | 75.09 216 | 91.87 136 | 75.19 154 | 82.63 141 | 85.06 106 | 92.05 121 | 95.21 120 | 94.56 120 | 97.76 184 | 97.08 160 |
|
Fast-Effi-MVS+ | | | 91.87 108 | 92.08 116 | 91.62 108 | 92.91 131 | 97.21 101 | 94.93 105 | 84.60 162 | 93.61 103 | 81.49 121 | 83.50 135 | 78.95 132 | 96.62 60 | 96.55 81 | 96.22 71 | 99.16 90 | 98.51 93 |
|
IterMVS | | | 90.20 130 | 92.43 109 | 87.61 177 | 92.82 132 | 94.31 192 | 94.11 119 | 81.54 190 | 92.97 113 | 69.90 199 | 84.71 127 | 88.16 90 | 89.96 171 | 95.25 119 | 94.17 129 | 97.31 188 | 97.46 147 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
CDS-MVSNet | | | 92.77 100 | 93.60 92 | 91.80 105 | 92.63 133 | 96.80 110 | 95.24 100 | 89.14 107 | 90.30 153 | 84.58 102 | 86.76 108 | 90.65 73 | 90.42 160 | 95.89 105 | 96.49 60 | 98.79 133 | 98.32 107 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
tpm | | | 87.95 165 | 89.44 144 | 86.21 194 | 92.53 134 | 94.62 187 | 91.40 177 | 76.36 210 | 91.46 138 | 69.80 201 | 87.43 102 | 75.14 154 | 91.55 130 | 89.85 203 | 90.60 196 | 95.61 203 | 96.96 165 |
|
Effi-MVS+-dtu | | | 91.78 110 | 93.59 93 | 89.68 133 | 92.44 135 | 97.11 102 | 94.40 116 | 84.94 158 | 92.43 125 | 75.48 149 | 91.09 76 | 83.75 112 | 93.55 102 | 96.61 74 | 95.47 99 | 97.24 189 | 98.67 81 |
|
testgi | | | 89.42 139 | 91.50 125 | 87.00 186 | 92.40 136 | 95.59 148 | 89.15 197 | 85.27 155 | 92.78 116 | 72.42 183 | 91.75 67 | 76.00 151 | 84.09 203 | 94.38 134 | 93.82 140 | 98.65 156 | 96.15 175 |
|
LP | | | 84.43 203 | 85.10 204 | 83.66 202 | 92.31 137 | 93.89 194 | 87.13 201 | 72.88 220 | 90.81 146 | 67.08 207 | 70.65 208 | 75.76 152 | 86.87 187 | 86.43 215 | 87.15 213 | 95.70 201 | 90.98 212 |
|
Fast-Effi-MVS+-dtu | | | 91.19 119 | 93.64 90 | 88.33 152 | 92.19 138 | 96.46 122 | 93.99 121 | 81.52 191 | 92.59 122 | 71.82 186 | 92.17 63 | 85.54 100 | 91.68 129 | 95.73 111 | 94.64 116 | 98.80 127 | 98.34 104 |
|
FC-MVSNet-test | | | 91.63 112 | 93.82 88 | 89.08 138 | 92.02 139 | 96.40 125 | 93.26 130 | 87.26 128 | 93.72 102 | 77.26 137 | 88.61 96 | 89.86 78 | 85.50 194 | 95.72 113 | 95.02 108 | 99.16 90 | 97.44 148 |
|
GA-MVS | | | 89.28 142 | 90.75 135 | 87.57 178 | 91.77 140 | 96.48 121 | 92.29 153 | 87.58 126 | 90.61 150 | 65.77 209 | 84.48 128 | 76.84 145 | 89.46 173 | 95.84 107 | 93.68 141 | 98.52 163 | 97.34 152 |
|
TAMVS | | | 90.54 127 | 90.87 134 | 90.16 125 | 91.48 141 | 96.61 118 | 93.26 130 | 86.08 141 | 87.71 188 | 81.66 120 | 83.11 139 | 84.04 109 | 90.42 160 | 94.54 129 | 94.60 117 | 98.04 180 | 95.48 186 |
|
tfpnnormal | | | 88.50 152 | 87.01 188 | 90.23 123 | 91.36 142 | 95.78 143 | 92.74 136 | 90.09 94 | 83.65 211 | 76.33 144 | 71.46 205 | 69.58 202 | 91.84 126 | 95.54 114 | 94.02 133 | 99.06 105 | 99.03 53 |
|
GBi-Net | | | 93.81 78 | 94.18 79 | 93.38 89 | 91.34 143 | 95.86 138 | 96.22 78 | 88.68 110 | 95.23 80 | 90.40 57 | 86.39 116 | 91.16 69 | 94.40 88 | 96.52 83 | 96.30 62 | 99.21 84 | 97.79 135 |
|
test1 | | | 93.81 78 | 94.18 79 | 93.38 89 | 91.34 143 | 95.86 138 | 96.22 78 | 88.68 110 | 95.23 80 | 90.40 57 | 86.39 116 | 91.16 69 | 94.40 88 | 96.52 83 | 96.30 62 | 99.21 84 | 97.79 135 |
|
FMVSNet2 | | | 93.30 95 | 93.36 97 | 93.22 93 | 91.34 143 | 95.86 138 | 96.22 78 | 88.24 115 | 95.15 84 | 89.92 66 | 81.64 144 | 89.36 80 | 94.40 88 | 96.77 67 | 96.98 51 | 99.21 84 | 97.79 135 |
|
FMVSNet3 | | | 93.79 80 | 94.17 81 | 93.35 91 | 91.21 146 | 95.99 131 | 96.62 67 | 88.68 110 | 95.23 80 | 90.40 57 | 86.39 116 | 91.16 69 | 94.11 92 | 95.96 103 | 96.67 57 | 99.07 102 | 97.79 135 |
|
testpf | | | 83.57 206 | 85.70 199 | 81.08 207 | 90.99 147 | 88.96 217 | 82.71 213 | 65.32 231 | 90.22 155 | 73.86 169 | 81.58 145 | 76.10 149 | 81.19 208 | 84.14 221 | 85.41 221 | 92.43 224 | 93.45 205 |
|
TransMVSNet (Re) | | | 87.73 174 | 86.79 190 | 88.83 143 | 90.76 148 | 94.40 190 | 91.33 180 | 89.62 102 | 84.73 207 | 75.41 151 | 72.73 196 | 71.41 192 | 86.80 188 | 94.53 130 | 93.93 135 | 99.06 105 | 95.83 180 |
|
LTVRE_ROB | | 87.32 16 | 87.55 176 | 88.25 154 | 86.73 187 | 90.66 149 | 95.80 142 | 93.05 133 | 84.77 159 | 83.35 212 | 60.32 218 | 83.12 138 | 67.39 209 | 93.32 106 | 94.36 135 | 94.86 112 | 98.28 173 | 98.87 71 |
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 |
EG-PatchMatch MVS | | | 86.68 189 | 87.24 180 | 86.02 197 | 90.58 150 | 96.26 127 | 91.08 183 | 81.59 189 | 84.96 206 | 69.80 201 | 71.35 206 | 75.08 156 | 84.23 202 | 94.24 138 | 93.35 146 | 98.82 120 | 95.46 187 |
|
TESTMET0.1,1 | | | 91.07 120 | 93.56 94 | 88.17 156 | 90.43 151 | 96.57 119 | 92.02 169 | 82.83 176 | 92.34 130 | 75.05 157 | 90.20 83 | 88.64 86 | 90.93 140 | 96.19 99 | 94.07 131 | 97.75 185 | 96.90 168 |
|
pm-mvs1 | | | 89.19 145 | 89.02 147 | 89.38 136 | 90.40 152 | 95.74 144 | 92.05 166 | 88.10 117 | 86.13 202 | 77.70 134 | 73.72 191 | 79.44 131 | 88.97 176 | 95.81 109 | 94.51 124 | 99.08 100 | 97.78 141 |
|
NR-MVSNet | | | 89.34 141 | 88.66 149 | 90.13 128 | 90.40 152 | 95.61 146 | 93.04 134 | 89.91 96 | 91.22 140 | 78.96 131 | 77.72 158 | 68.90 206 | 89.16 175 | 94.24 138 | 93.95 134 | 99.32 61 | 98.99 58 |
|
FMVSNet1 | | | 91.54 115 | 90.93 132 | 92.26 102 | 90.35 154 | 95.27 166 | 95.22 101 | 87.16 130 | 91.37 139 | 87.62 90 | 75.45 163 | 83.84 111 | 94.43 86 | 96.52 83 | 96.30 62 | 98.82 120 | 97.74 142 |
|
test-mter | | | 90.95 121 | 93.54 96 | 87.93 167 | 90.28 155 | 96.80 110 | 91.44 176 | 82.68 178 | 92.15 134 | 74.37 165 | 89.57 89 | 88.23 89 | 90.88 143 | 96.37 91 | 94.31 127 | 97.93 182 | 97.37 150 |
|
pmmvs4 | | | 90.55 126 | 89.91 139 | 91.30 110 | 90.26 156 | 94.95 175 | 92.73 137 | 87.94 121 | 93.44 107 | 85.35 100 | 82.28 143 | 76.09 150 | 93.02 112 | 93.56 147 | 92.26 186 | 98.51 164 | 96.77 170 |
|
MVS-HIRNet | | | 85.36 199 | 86.89 189 | 83.57 203 | 90.13 157 | 94.51 188 | 83.57 211 | 72.61 221 | 88.27 184 | 71.22 190 | 68.97 211 | 81.81 123 | 88.91 177 | 93.08 155 | 91.94 187 | 94.97 213 | 89.64 217 |
|
SixPastTwentyTwo | | | 88.37 156 | 89.47 143 | 87.08 184 | 90.01 158 | 95.93 137 | 87.41 200 | 85.32 152 | 90.26 154 | 70.26 196 | 86.34 119 | 71.95 187 | 90.93 140 | 92.89 159 | 91.72 190 | 98.55 161 | 97.22 155 |
|
UniMVSNet (Re) | | | 90.03 134 | 89.61 142 | 90.51 120 | 89.97 159 | 96.12 129 | 92.32 149 | 89.26 105 | 90.99 143 | 80.95 124 | 78.25 157 | 75.08 156 | 91.14 135 | 93.78 142 | 93.87 137 | 99.41 46 | 99.21 32 |
|
v18 | | | 87.93 166 | 87.61 174 | 88.31 153 | 89.74 160 | 92.04 201 | 92.59 140 | 82.71 177 | 89.70 156 | 75.32 152 | 75.23 165 | 73.55 165 | 90.74 147 | 92.11 178 | 92.77 166 | 98.78 140 | 97.87 129 |
|
v16 | | | 87.87 171 | 87.60 175 | 88.19 155 | 89.70 161 | 92.01 203 | 92.37 144 | 82.54 180 | 89.67 158 | 75.00 159 | 75.02 169 | 73.65 163 | 90.73 149 | 92.14 174 | 92.80 160 | 98.77 144 | 97.90 126 |
|
UniMVSNet_NR-MVSNet | | | 90.35 129 | 89.96 138 | 90.80 116 | 89.66 162 | 95.83 141 | 92.48 141 | 90.53 91 | 90.96 144 | 79.57 128 | 79.33 154 | 77.14 142 | 93.21 109 | 92.91 158 | 94.50 125 | 99.37 55 | 99.05 50 |
|
v17 | | | 87.83 172 | 87.56 176 | 88.13 157 | 89.65 163 | 92.02 202 | 92.34 148 | 82.55 179 | 89.38 161 | 74.76 160 | 75.14 166 | 73.59 164 | 90.70 150 | 92.15 173 | 92.78 164 | 98.78 140 | 97.89 127 |
|
v8 | | | 88.21 159 | 87.94 165 | 88.51 149 | 89.62 164 | 95.01 173 | 92.31 150 | 84.99 157 | 88.94 168 | 74.70 161 | 75.03 168 | 73.51 166 | 90.67 153 | 92.11 178 | 92.74 172 | 98.80 127 | 98.24 110 |
|
WR-MVS_H | | | 87.93 166 | 87.85 166 | 88.03 164 | 89.62 164 | 95.58 150 | 90.47 188 | 85.55 148 | 87.20 194 | 76.83 140 | 74.42 178 | 72.67 183 | 86.37 190 | 93.22 153 | 93.04 150 | 99.33 59 | 98.83 73 |
|
v1neww | | | 88.41 154 | 88.00 161 | 88.89 140 | 89.61 166 | 95.44 156 | 92.31 150 | 87.65 124 | 89.09 164 | 74.30 166 | 75.02 169 | 73.42 169 | 90.68 151 | 92.12 175 | 92.77 166 | 98.79 133 | 98.18 112 |
|
v7new | | | 88.41 154 | 88.00 161 | 88.89 140 | 89.61 166 | 95.44 156 | 92.31 150 | 87.65 124 | 89.09 164 | 74.30 166 | 75.02 169 | 73.42 169 | 90.68 151 | 92.12 175 | 92.77 166 | 98.79 133 | 98.18 112 |
|
v6 | | | 88.43 153 | 88.01 158 | 88.92 139 | 89.60 168 | 95.43 158 | 92.36 145 | 87.66 123 | 89.07 166 | 74.50 163 | 75.06 167 | 73.47 167 | 90.59 156 | 92.11 178 | 92.76 170 | 98.79 133 | 98.18 112 |
|
pmmvs5 | | | 87.83 172 | 88.09 156 | 87.51 181 | 89.59 169 | 95.48 151 | 89.75 195 | 84.73 160 | 86.07 204 | 71.44 188 | 80.57 149 | 70.09 200 | 90.74 147 | 94.47 131 | 92.87 156 | 98.82 120 | 97.10 157 |
|
gm-plane-assit | | | 83.26 207 | 85.29 202 | 80.89 208 | 89.52 170 | 89.89 215 | 70.26 224 | 78.24 200 | 77.11 221 | 58.01 223 | 74.16 183 | 66.90 211 | 90.63 155 | 97.20 52 | 96.05 76 | 98.66 155 | 95.68 183 |
|
v7 | | | 88.18 160 | 88.01 158 | 88.39 150 | 89.45 171 | 95.14 170 | 92.36 145 | 85.37 151 | 89.29 163 | 72.94 182 | 73.98 187 | 72.77 175 | 91.38 132 | 93.59 143 | 92.87 156 | 98.82 120 | 98.42 98 |
|
v1141 | | | 88.17 161 | 87.69 170 | 88.74 145 | 89.44 172 | 95.41 159 | 92.25 158 | 87.98 118 | 88.38 178 | 73.54 177 | 74.43 177 | 72.71 181 | 90.45 158 | 92.08 182 | 92.72 174 | 98.79 133 | 98.09 117 |
|
divwei89l23v2f112 | | | 88.17 161 | 87.69 170 | 88.74 145 | 89.44 172 | 95.41 159 | 92.26 156 | 87.97 120 | 88.29 182 | 73.57 176 | 74.45 176 | 72.75 177 | 90.42 160 | 92.08 182 | 92.72 174 | 98.81 124 | 98.09 117 |
|
v10 | | | 88.00 164 | 87.96 163 | 88.05 162 | 89.44 172 | 94.68 183 | 92.36 145 | 83.35 172 | 89.37 162 | 72.96 180 | 73.98 187 | 72.79 174 | 91.35 133 | 93.59 143 | 92.88 155 | 98.81 124 | 98.42 98 |
|
v1 | | | 88.17 161 | 87.66 172 | 88.77 144 | 89.44 172 | 95.40 161 | 92.29 153 | 87.98 118 | 88.21 185 | 73.75 171 | 74.41 179 | 72.75 177 | 90.36 166 | 92.07 185 | 92.71 177 | 98.80 127 | 98.09 117 |
|
V42 | | | 88.31 157 | 87.95 164 | 88.73 147 | 89.44 172 | 95.34 163 | 92.23 160 | 87.21 129 | 88.83 170 | 74.49 164 | 74.89 173 | 73.43 168 | 90.41 163 | 92.08 182 | 92.77 166 | 98.60 160 | 98.33 105 |
|
v15 | | | 87.46 180 | 87.16 183 | 87.81 168 | 89.41 177 | 91.96 204 | 92.26 156 | 82.28 183 | 88.42 176 | 73.72 172 | 74.29 182 | 72.73 180 | 90.41 163 | 92.17 172 | 92.76 170 | 98.79 133 | 97.83 132 |
|
v148 | | | 87.51 177 | 86.79 190 | 88.36 151 | 89.39 178 | 95.21 168 | 89.84 194 | 88.20 116 | 87.61 190 | 77.56 135 | 73.38 194 | 70.32 199 | 86.80 188 | 90.70 195 | 92.31 183 | 98.37 171 | 97.98 123 |
|
V14 | | | 87.47 179 | 87.19 182 | 87.80 169 | 89.37 179 | 91.95 205 | 92.25 158 | 82.12 184 | 88.39 177 | 73.83 170 | 74.31 180 | 72.84 173 | 90.44 159 | 92.20 170 | 92.78 164 | 98.80 127 | 97.84 131 |
|
v11 | | | 87.58 175 | 87.50 177 | 87.67 174 | 89.34 180 | 91.91 208 | 92.22 162 | 81.63 188 | 89.01 167 | 72.95 181 | 74.11 185 | 72.51 185 | 91.08 137 | 94.01 141 | 93.00 152 | 98.77 144 | 97.93 124 |
|
V9 | | | 87.41 181 | 87.15 184 | 87.72 172 | 89.33 181 | 91.93 206 | 92.23 160 | 82.02 185 | 88.35 179 | 73.59 175 | 74.13 184 | 72.77 175 | 90.37 165 | 92.21 169 | 92.80 160 | 98.79 133 | 97.86 130 |
|
v13 | | | 87.34 184 | 87.11 187 | 87.62 176 | 89.30 182 | 91.91 208 | 92.04 167 | 81.86 187 | 88.35 179 | 73.36 178 | 73.88 189 | 72.69 182 | 90.34 167 | 92.23 167 | 92.82 158 | 98.80 127 | 97.88 128 |
|
v12 | | | 87.38 183 | 87.13 185 | 87.68 173 | 89.30 182 | 91.92 207 | 92.01 171 | 81.94 186 | 88.35 179 | 73.69 173 | 74.10 186 | 72.57 184 | 90.33 168 | 92.23 167 | 92.82 158 | 98.80 127 | 97.91 125 |
|
CP-MVSNet | | | 87.89 170 | 87.27 179 | 88.62 148 | 89.30 182 | 95.06 171 | 90.60 187 | 85.78 145 | 87.43 192 | 75.98 146 | 74.60 174 | 68.14 208 | 90.76 145 | 93.07 156 | 93.60 142 | 99.30 66 | 98.98 60 |
|
v1144 | | | 87.92 169 | 87.79 167 | 88.07 159 | 89.27 185 | 95.15 169 | 92.17 163 | 85.62 147 | 88.52 174 | 71.52 187 | 73.80 190 | 72.40 186 | 91.06 138 | 93.54 148 | 92.80 160 | 98.81 124 | 98.33 105 |
|
DU-MVS | | | 89.67 138 | 88.84 148 | 90.63 119 | 89.26 186 | 95.61 146 | 92.48 141 | 89.91 96 | 91.22 140 | 79.57 128 | 77.72 158 | 71.18 193 | 93.21 109 | 92.53 162 | 94.57 119 | 99.35 56 | 99.05 50 |
|
WR-MVS | | | 87.93 166 | 88.09 156 | 87.75 170 | 89.26 186 | 95.28 164 | 90.81 185 | 86.69 134 | 88.90 169 | 75.29 153 | 74.31 180 | 73.72 162 | 85.19 197 | 92.26 165 | 93.32 147 | 99.27 70 | 98.81 74 |
|
Baseline_NR-MVSNet | | | 89.27 143 | 88.01 158 | 90.73 118 | 89.26 186 | 93.71 195 | 92.71 138 | 89.78 100 | 90.73 147 | 81.28 122 | 73.53 192 | 72.85 172 | 92.30 119 | 92.53 162 | 93.84 139 | 99.07 102 | 98.88 69 |
|
N_pmnet | | | 84.80 200 | 85.10 204 | 84.45 200 | 89.25 189 | 92.86 198 | 84.04 209 | 86.21 137 | 88.78 171 | 66.73 208 | 72.41 199 | 74.87 158 | 85.21 196 | 88.32 207 | 86.45 217 | 95.30 208 | 92.04 208 |
|
v2v482 | | | 88.25 158 | 87.71 169 | 88.88 142 | 89.23 190 | 95.28 164 | 92.10 164 | 87.89 122 | 88.69 173 | 73.31 179 | 75.32 164 | 71.64 189 | 91.89 125 | 92.10 181 | 92.92 154 | 98.86 119 | 97.99 121 |
|
PS-CasMVS | | | 87.33 185 | 86.68 193 | 88.10 158 | 89.22 191 | 94.93 176 | 90.35 190 | 85.70 146 | 86.44 198 | 74.01 168 | 73.43 193 | 66.59 214 | 90.04 170 | 92.92 157 | 93.52 143 | 99.28 68 | 98.91 67 |
|
TranMVSNet+NR-MVSNet | | | 89.23 144 | 88.48 152 | 90.11 129 | 89.07 192 | 95.25 167 | 92.91 135 | 90.43 92 | 90.31 152 | 77.10 138 | 76.62 161 | 71.57 191 | 91.83 127 | 92.12 175 | 94.59 118 | 99.32 61 | 98.92 65 |
|
v1192 | | | 87.51 177 | 87.31 178 | 87.74 171 | 89.04 193 | 94.87 181 | 92.07 165 | 85.03 156 | 88.49 175 | 70.32 195 | 72.65 197 | 70.35 198 | 91.21 134 | 93.59 143 | 92.80 160 | 98.78 140 | 98.42 98 |
|
v144192 | | | 87.40 182 | 87.20 181 | 87.64 175 | 88.89 194 | 94.88 180 | 91.65 175 | 84.70 161 | 87.80 187 | 71.17 192 | 73.20 195 | 70.91 194 | 90.75 146 | 92.69 160 | 92.49 179 | 98.71 150 | 98.43 97 |
|
PEN-MVS | | | 87.22 187 | 86.50 197 | 88.07 159 | 88.88 195 | 94.44 189 | 90.99 184 | 86.21 137 | 86.53 197 | 73.66 174 | 74.97 172 | 66.56 215 | 89.42 174 | 91.20 191 | 93.48 144 | 99.24 75 | 98.31 108 |
|
v1921920 | | | 87.31 186 | 87.13 185 | 87.52 180 | 88.87 196 | 94.72 182 | 91.96 172 | 84.59 163 | 88.28 183 | 69.86 200 | 72.50 198 | 70.03 201 | 91.10 136 | 93.33 151 | 92.61 178 | 98.71 150 | 98.44 96 |
|
pmmvs6 | | | 85.98 196 | 84.89 206 | 87.25 183 | 88.83 197 | 94.35 191 | 89.36 196 | 85.30 154 | 78.51 220 | 75.44 150 | 62.71 221 | 75.41 153 | 87.65 183 | 93.58 146 | 92.40 181 | 96.89 191 | 97.29 153 |
|
v1240 | | | 86.89 188 | 86.75 192 | 87.06 185 | 88.75 198 | 94.65 185 | 91.30 181 | 84.05 166 | 87.49 191 | 68.94 204 | 71.96 201 | 68.86 207 | 90.65 154 | 93.33 151 | 92.72 174 | 98.67 154 | 98.24 110 |
|
anonymousdsp | | | 88.90 149 | 91.00 131 | 86.44 192 | 88.74 199 | 95.97 133 | 90.40 189 | 82.86 175 | 88.77 172 | 67.33 206 | 81.18 147 | 81.44 125 | 90.22 169 | 96.23 96 | 94.27 128 | 99.12 96 | 99.16 39 |
|
EU-MVSNet | | | 85.62 198 | 87.65 173 | 83.24 205 | 88.54 200 | 92.77 199 | 87.12 202 | 85.32 152 | 86.71 195 | 64.54 211 | 78.52 156 | 75.11 155 | 78.35 210 | 92.25 166 | 92.28 185 | 95.58 204 | 95.93 177 |
|
DTE-MVSNet | | | 86.67 190 | 86.09 198 | 87.35 182 | 88.45 201 | 94.08 193 | 90.65 186 | 86.05 142 | 86.13 202 | 72.19 184 | 74.58 175 | 66.77 213 | 87.61 184 | 90.31 197 | 93.12 149 | 99.13 94 | 97.62 145 |
|
v748 | | | 85.88 197 | 85.66 200 | 86.14 195 | 88.03 202 | 94.63 186 | 87.02 204 | 84.59 163 | 84.30 208 | 74.56 162 | 70.94 207 | 67.27 210 | 83.94 205 | 90.96 194 | 92.74 172 | 98.71 150 | 98.81 74 |
|
FMVSNet5 | | | 90.36 128 | 90.93 132 | 89.70 131 | 87.99 203 | 92.25 200 | 92.03 168 | 83.51 169 | 92.20 133 | 84.13 104 | 85.59 123 | 86.48 92 | 92.43 117 | 94.61 127 | 94.52 123 | 98.13 176 | 90.85 213 |
|
v7n | | | 86.43 193 | 86.52 196 | 86.33 193 | 87.91 204 | 94.93 176 | 90.15 191 | 83.05 173 | 86.57 196 | 70.21 197 | 71.48 204 | 66.78 212 | 87.72 182 | 94.19 140 | 92.96 153 | 98.92 114 | 98.76 77 |
|
test20.03 | | | 82.92 208 | 85.52 201 | 79.90 211 | 87.75 205 | 91.84 210 | 82.80 212 | 82.99 174 | 82.65 216 | 60.32 218 | 78.90 155 | 70.50 195 | 67.10 222 | 92.05 186 | 90.89 193 | 98.44 168 | 91.80 209 |
|
MDTV_nov1_ep13_2view | | | 86.30 194 | 88.27 153 | 84.01 201 | 87.71 206 | 94.67 184 | 88.08 199 | 76.78 206 | 90.59 151 | 68.66 205 | 80.46 151 | 80.12 129 | 87.58 185 | 89.95 202 | 88.20 206 | 95.25 210 | 93.90 200 |
|
V4 | | | 86.56 192 | 86.61 195 | 86.50 190 | 87.49 207 | 94.90 178 | 89.87 193 | 83.39 170 | 86.25 200 | 71.20 191 | 71.57 202 | 71.58 190 | 88.30 180 | 91.14 192 | 92.31 183 | 98.75 147 | 98.52 91 |
|
v52 | | | 86.57 191 | 86.63 194 | 86.50 190 | 87.47 208 | 94.89 179 | 89.90 192 | 83.39 170 | 86.36 199 | 71.17 192 | 71.53 203 | 71.65 188 | 88.34 179 | 91.14 192 | 92.32 182 | 98.74 148 | 98.52 91 |
|
Anonymous20231206 | | | 83.84 205 | 85.19 203 | 82.26 206 | 87.38 209 | 92.87 197 | 85.49 207 | 83.65 168 | 86.07 204 | 63.44 214 | 68.42 212 | 69.01 205 | 75.45 214 | 93.34 150 | 92.44 180 | 98.12 178 | 94.20 195 |
|
FPMVS | | | 75.84 217 | 74.59 217 | 77.29 218 | 86.92 210 | 83.89 224 | 85.01 208 | 80.05 196 | 82.91 214 | 60.61 217 | 65.25 217 | 60.41 219 | 63.86 223 | 75.60 226 | 73.60 228 | 87.29 229 | 80.47 225 |
|
MIMVSNet | | | 88.99 148 | 91.07 130 | 86.57 189 | 86.78 211 | 95.62 145 | 91.20 182 | 75.40 215 | 90.65 149 | 76.57 141 | 84.05 131 | 82.44 122 | 91.01 139 | 95.84 107 | 95.38 101 | 98.48 166 | 93.50 203 |
|
tmp_tt | | | | | 66.88 225 | 86.07 212 | 73.86 231 | 68.22 225 | 33.38 233 | 96.88 40 | 80.67 125 | 88.23 98 | 78.82 133 | 49.78 229 | 82.68 223 | 77.47 225 | 83.19 232 | |
|
PM-MVS | | | 84.72 202 | 84.47 207 | 85.03 199 | 84.67 213 | 91.57 211 | 86.27 206 | 82.31 182 | 87.65 189 | 70.62 194 | 76.54 162 | 56.41 226 | 88.75 178 | 92.59 161 | 89.85 200 | 97.54 187 | 96.66 173 |
|
testus | | | 81.33 210 | 84.13 208 | 78.06 214 | 84.54 214 | 87.72 218 | 79.66 217 | 80.42 194 | 87.36 193 | 54.13 229 | 83.83 133 | 56.63 224 | 73.21 219 | 90.51 196 | 91.74 189 | 96.40 194 | 91.11 211 |
|
test2356 | | | 81.26 211 | 84.10 209 | 77.95 216 | 84.35 215 | 87.38 220 | 79.56 218 | 79.53 197 | 86.17 201 | 54.14 228 | 83.24 136 | 60.71 218 | 73.77 215 | 90.01 201 | 91.18 192 | 96.33 195 | 90.01 215 |
|
pmmvs-eth3d | | | 84.33 204 | 82.94 211 | 85.96 198 | 84.16 216 | 90.94 212 | 86.55 205 | 83.79 167 | 84.25 209 | 75.85 148 | 70.64 209 | 56.43 225 | 87.44 186 | 92.20 170 | 90.41 198 | 97.97 181 | 95.68 183 |
|
new-patchmatchnet | | | 78.49 215 | 78.19 216 | 78.84 213 | 84.13 217 | 90.06 214 | 77.11 223 | 80.39 195 | 79.57 219 | 59.64 222 | 66.01 216 | 55.65 227 | 75.62 213 | 84.55 220 | 80.70 223 | 96.14 198 | 90.77 214 |
|
new_pmnet | | | 81.53 209 | 82.68 212 | 80.20 209 | 83.47 218 | 89.47 216 | 82.21 215 | 78.36 199 | 87.86 186 | 60.14 220 | 67.90 214 | 69.43 203 | 82.03 207 | 89.22 204 | 87.47 210 | 94.99 212 | 87.39 219 |
|
Anonymous20231211 | | | 75.89 216 | 74.18 221 | 77.88 217 | 81.42 219 | 87.72 218 | 79.33 220 | 81.05 192 | 66.49 230 | 60.00 221 | 45.74 229 | 51.46 229 | 71.22 220 | 85.70 216 | 86.91 216 | 94.25 219 | 95.25 189 |
|
testmv | | | 72.66 219 | 74.40 218 | 70.62 220 | 80.64 220 | 81.51 227 | 64.99 229 | 76.60 207 | 68.76 226 | 44.81 231 | 63.78 219 | 48.00 230 | 62.52 224 | 84.74 218 | 87.17 211 | 94.19 220 | 86.86 220 |
|
test1235678 | | | 72.65 220 | 74.40 218 | 70.62 220 | 80.64 220 | 81.50 228 | 64.99 229 | 76.59 208 | 68.74 227 | 44.81 231 | 63.78 219 | 47.99 231 | 62.51 225 | 84.73 219 | 87.17 211 | 94.19 220 | 86.85 221 |
|
pmmvs3 | | | 79.16 214 | 80.12 215 | 78.05 215 | 79.36 222 | 86.59 222 | 78.13 222 | 73.87 219 | 76.42 222 | 57.51 224 | 70.59 210 | 57.02 223 | 84.66 200 | 90.10 199 | 88.32 205 | 94.75 215 | 91.77 210 |
|
1111 | | | 73.35 218 | 74.40 218 | 72.12 219 | 78.22 223 | 82.24 225 | 65.06 227 | 65.61 229 | 70.28 224 | 55.42 225 | 56.30 224 | 57.35 221 | 73.66 216 | 86.73 213 | 88.16 207 | 94.75 215 | 79.76 227 |
|
.test1245 | | | 56.65 226 | 56.09 227 | 57.30 227 | 78.22 223 | 82.24 225 | 65.06 227 | 65.61 229 | 70.28 224 | 55.42 225 | 56.30 224 | 57.35 221 | 73.66 216 | 86.73 213 | 15.01 232 | 5.84 236 | 24.75 233 |
|
PMVS | | 63.12 18 | 67.27 223 | 66.39 225 | 68.30 223 | 77.98 225 | 60.24 234 | 59.53 233 | 76.82 204 | 66.65 229 | 60.74 216 | 54.39 226 | 59.82 220 | 51.24 228 | 73.92 229 | 70.52 229 | 83.48 231 | 79.17 228 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
test12356 | | | 69.55 221 | 71.53 223 | 67.24 224 | 77.70 226 | 78.48 229 | 65.92 226 | 75.55 214 | 68.39 228 | 44.26 233 | 61.80 222 | 40.70 233 | 47.92 232 | 81.45 224 | 87.01 215 | 92.09 225 | 82.89 223 |
|
MDA-MVSNet-bldmvs | | | 80.11 212 | 80.24 214 | 79.94 210 | 77.01 227 | 93.21 196 | 78.86 221 | 85.94 144 | 82.71 215 | 60.86 215 | 79.71 153 | 51.77 228 | 83.71 206 | 75.60 226 | 86.37 218 | 93.28 222 | 92.35 207 |
|
ambc | | | | 73.83 222 | | 76.23 228 | 85.13 223 | 82.27 214 | | 84.16 210 | 65.58 210 | 52.82 227 | 23.31 238 | 73.55 218 | 91.41 190 | 85.26 222 | 92.97 223 | 94.70 190 |
|
Gipuma | | | 68.35 222 | 66.71 224 | 70.27 222 | 74.16 229 | 68.78 233 | 63.93 232 | 71.77 224 | 83.34 213 | 54.57 227 | 34.37 230 | 31.88 234 | 68.69 221 | 83.30 222 | 85.53 220 | 88.48 228 | 79.78 226 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
MIMVSNet1 | | | 80.03 213 | 80.93 213 | 78.97 212 | 72.46 230 | 90.73 213 | 80.81 216 | 82.44 181 | 80.39 217 | 63.64 213 | 57.57 223 | 64.93 216 | 76.37 212 | 91.66 188 | 91.55 191 | 98.07 179 | 89.70 216 |
|
no-one | | | 55.96 227 | 55.63 228 | 56.35 228 | 68.48 231 | 73.29 232 | 43.03 234 | 72.52 222 | 44.01 234 | 34.80 234 | 32.83 231 | 29.11 235 | 35.21 233 | 56.63 231 | 75.72 226 | 84.04 230 | 77.79 229 |
|
PMMVS2 | | | 64.36 225 | 65.94 226 | 62.52 226 | 67.37 232 | 77.44 230 | 64.39 231 | 69.32 228 | 61.47 231 | 34.59 235 | 46.09 228 | 41.03 232 | 48.02 231 | 74.56 228 | 78.23 224 | 91.43 226 | 82.76 224 |
|
EMVS | | | 49.98 229 | 46.76 231 | 53.74 230 | 64.96 233 | 51.29 236 | 37.81 236 | 69.35 227 | 51.83 232 | 22.69 238 | 29.57 233 | 25.06 236 | 57.28 226 | 44.81 233 | 56.11 231 | 70.32 234 | 68.64 232 |
|
E-PMN | | | 50.67 228 | 47.85 230 | 53.96 229 | 64.13 234 | 50.98 237 | 38.06 235 | 69.51 226 | 51.40 233 | 24.60 237 | 29.46 234 | 24.39 237 | 56.07 227 | 48.17 232 | 59.70 230 | 71.40 233 | 70.84 231 |
|
MVE | | 50.86 19 | 49.54 230 | 51.43 229 | 47.33 231 | 44.14 235 | 59.20 235 | 36.45 237 | 60.59 232 | 41.47 235 | 31.14 236 | 29.58 232 | 17.06 239 | 48.52 230 | 62.22 230 | 74.63 227 | 63.12 235 | 75.87 230 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
testmvs | | | 12.09 231 | 16.94 232 | 6.42 233 | 3.15 236 | 6.08 238 | 9.51 239 | 3.84 234 | 21.46 236 | 5.31 239 | 27.49 235 | 6.76 240 | 10.89 234 | 17.06 234 | 15.01 232 | 5.84 236 | 24.75 233 |
|
GG-mvs-BLEND | | | 66.17 224 | 94.91 67 | 32.63 232 | 1.32 237 | 96.64 117 | 91.40 177 | 0.85 236 | 94.39 93 | 2.20 240 | 90.15 85 | 95.70 52 | 2.27 236 | 96.39 88 | 95.44 100 | 97.78 183 | 95.68 183 |
|
test123 | | | 9.58 232 | 13.53 233 | 4.97 234 | 1.31 238 | 5.47 239 | 8.32 240 | 2.95 235 | 18.14 237 | 2.03 241 | 20.82 236 | 2.34 241 | 10.60 235 | 10.00 235 | 14.16 234 | 4.60 238 | 23.77 235 |
|
sosnet-low-res | | | 0.00 233 | 0.00 234 | 0.00 235 | 0.00 239 | 0.00 240 | 0.00 241 | 0.00 237 | 0.00 238 | 0.00 242 | 0.00 237 | 0.00 242 | 0.00 237 | 0.00 236 | 0.00 235 | 0.00 239 | 0.00 236 |
|
sosnet | | | 0.00 233 | 0.00 234 | 0.00 235 | 0.00 239 | 0.00 240 | 0.00 241 | 0.00 237 | 0.00 238 | 0.00 242 | 0.00 237 | 0.00 242 | 0.00 237 | 0.00 236 | 0.00 235 | 0.00 239 | 0.00 236 |
|
MTAPA | | | | | | | | | | | 96.83 5 | | 99.12 14 | | | | | |
|
MTMP | | | | | | | | | | | 97.18 3 | | 98.83 20 | | | | | |
|
Patchmatch-RL test | | | | | | | | 34.61 238 | | | | | | | | | | |
|
NP-MVS | | | | | | | | | | 95.32 77 | | | | | | | | |
|
Patchmtry | | | | | | | 95.96 134 | 93.36 128 | 75.99 212 | | 75.19 154 | | | | | | | |
|
DeepMVS_CX | | | | | | | 86.86 221 | 79.50 219 | 70.43 225 | 90.73 147 | 63.66 212 | 80.36 152 | 60.83 217 | 79.68 209 | 76.23 225 | | 89.46 227 | 86.53 222 |
|