CNVR-MVS | | | 99.39 2 | 99.75 11 | 98.98 1 | 99.69 1 | 99.95 12 | 99.76 5 | 96.91 6 | 99.98 3 | 97.59 5 | 99.64 19 | 100.00 1 | 99.93 1 | 99.94 2 | 98.75 46 | 99.97 10 | 99.97 80 |
|
SMA-MVS | | | 99.14 13 | 99.79 4 | 98.39 16 | 99.68 2 | 99.94 17 | 99.74 13 | 96.86 11 | 99.97 6 | 94.36 28 | 99.22 40 | 100.00 1 | 99.89 5 | 99.84 12 | 99.58 13 | 99.83 34 | 99.95 108 |
|
PGM-MVS | | | 98.47 29 | 99.73 14 | 97.00 33 | 99.68 2 | 99.94 17 | 99.76 5 | 91.74 41 | 99.84 44 | 91.17 52 | 100.00 1 | 99.69 46 | 99.81 11 | 99.38 25 | 99.30 24 | 99.82 39 | 99.95 108 |
|
AdaColmap | | | 99.21 9 | 99.45 35 | 98.92 3 | 99.67 4 | 99.95 12 | 99.65 23 | 96.77 17 | 99.97 6 | 97.67 3 | 100.00 1 | 99.69 46 | 99.93 1 | 99.26 27 | 97.25 85 | 99.85 27 | 100.00 1 |
|
NCCC | | | 99.24 7 | 99.75 11 | 98.65 10 | 99.63 5 | 99.96 7 | 99.76 5 | 96.91 6 | 99.97 6 | 95.86 18 | 99.67 11 | 100.00 1 | 99.75 13 | 99.85 10 | 98.80 42 | 99.98 9 | 99.97 80 |
|
ESAPD | | | 99.25 6 | 99.69 18 | 98.74 8 | 99.62 6 | 99.94 17 | 99.79 2 | 96.87 9 | 99.93 24 | 96.33 14 | 99.59 23 | 100.00 1 | 99.84 8 | 99.88 8 | 98.50 53 | 100.00 1 | 100.00 1 |
|
ACMMP_Plus | | | 98.68 25 | 99.58 29 | 97.62 27 | 99.62 6 | 99.92 35 | 99.72 18 | 96.78 16 | 99.71 61 | 90.13 68 | 99.66 15 | 99.99 26 | 99.64 23 | 99.78 13 | 98.14 60 | 99.82 39 | 99.89 137 |
|
HPM-MVS++ | | | 98.98 21 | 99.62 25 | 98.22 20 | 99.62 6 | 99.94 17 | 99.74 13 | 96.95 3 | 99.87 37 | 93.76 30 | 99.49 31 | 100.00 1 | 99.39 35 | 99.73 16 | 98.35 55 | 99.89 22 | 99.96 99 |
|
APDe-MVS | | | 99.40 1 | 99.81 2 | 98.92 3 | 99.62 6 | 99.96 7 | 99.76 5 | 96.87 9 | 99.95 20 | 97.66 4 | 99.57 26 | 100.00 1 | 99.63 24 | 99.88 8 | 99.28 25 | 100.00 1 | 100.00 1 |
|
HSP-MVS | | | 99.36 4 | 99.79 4 | 98.85 6 | 99.61 10 | 99.96 7 | 99.71 19 | 96.94 4 | 99.97 6 | 97.11 8 | 99.60 22 | 100.00 1 | 99.70 16 | 99.96 1 | 99.12 30 | 100.00 1 | 99.96 99 |
|
APD-MVS | | | 99.33 5 | 99.85 1 | 98.73 9 | 99.61 10 | 99.92 35 | 99.77 4 | 96.91 6 | 99.93 24 | 96.31 15 | 99.59 23 | 99.95 33 | 99.84 8 | 99.73 16 | 99.84 8 | 99.95 13 | 100.00 1 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
MSLP-MVS++ | | | 99.39 2 | 99.76 8 | 98.95 2 | 99.60 12 | 99.99 1 | 99.83 1 | 96.82 13 | 99.92 29 | 97.58 6 | 99.58 25 | 100.00 1 | 99.93 1 | 98.98 31 | 99.86 7 | 99.96 11 | 100.00 1 |
|
CSCG | | | 98.22 31 | 98.37 60 | 98.04 22 | 99.60 12 | 99.82 56 | 99.45 29 | 93.59 37 | 99.16 98 | 96.46 13 | 98.22 73 | 95.86 91 | 99.41 34 | 96.33 123 | 99.22 27 | 99.75 86 | 99.94 113 |
|
MCST-MVS | | | 99.08 17 | 99.72 16 | 98.33 18 | 99.59 14 | 99.97 3 | 99.78 3 | 96.96 2 | 99.95 20 | 93.72 31 | 99.67 11 | 100.00 1 | 99.90 4 | 99.91 5 | 98.55 51 | 100.00 1 | 100.00 1 |
|
zzz-MVS | | | 99.12 15 | 99.52 34 | 98.65 10 | 99.58 15 | 99.93 29 | 99.74 13 | 96.72 20 | 99.44 83 | 96.47 12 | 99.62 21 | 100.00 1 | 99.63 24 | 99.74 15 | 97.97 63 | 99.77 66 | 99.94 113 |
|
HFP-MVS | | | 99.19 10 | 99.77 7 | 98.51 14 | 99.55 16 | 99.94 17 | 99.76 5 | 96.84 12 | 99.88 34 | 95.27 22 | 99.67 11 | 100.00 1 | 99.85 7 | 99.56 21 | 99.36 20 | 99.79 54 | 99.97 80 |
|
X-MVS | | | 98.62 26 | 99.75 11 | 97.29 28 | 99.50 17 | 99.94 17 | 99.71 19 | 96.55 25 | 99.85 41 | 88.58 83 | 99.65 16 | 99.98 28 | 99.67 20 | 99.60 20 | 99.26 26 | 99.77 66 | 99.97 80 |
|
DeepC-MVS_fast | | 98.03 2 | 99.05 19 | 99.78 6 | 98.21 21 | 99.47 18 | 99.97 3 | 99.75 11 | 96.80 14 | 99.97 6 | 93.58 34 | 98.68 62 | 99.94 34 | 99.69 17 | 99.93 4 | 99.95 2 | 99.96 11 | 99.98 67 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
PLC | | 98.06 1 | 99.17 11 | 99.38 37 | 98.92 3 | 99.47 18 | 99.90 43 | 99.48 28 | 96.47 27 | 99.96 16 | 98.73 1 | 99.52 29 | 100.00 1 | 99.55 29 | 98.54 51 | 97.73 76 | 99.84 29 | 99.99 47 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
ACMMPR | | | 99.12 15 | 99.76 8 | 98.36 17 | 99.45 20 | 99.94 17 | 99.75 11 | 96.70 21 | 99.93 24 | 94.65 26 | 99.65 16 | 99.96 31 | 99.84 8 | 99.51 23 | 99.35 21 | 99.79 54 | 99.96 99 |
|
CP-MVS | | | 99.14 13 | 99.67 20 | 98.53 13 | 99.45 20 | 99.94 17 | 99.63 25 | 96.62 24 | 99.82 46 | 95.92 17 | 99.65 16 | 100.00 1 | 99.71 15 | 99.76 14 | 98.56 50 | 99.83 34 | 100.00 1 |
|
CPTT-MVS | | | 99.08 17 | 99.53 33 | 98.57 12 | 99.44 22 | 99.93 29 | 99.60 26 | 95.92 32 | 99.77 53 | 97.01 9 | 99.67 11 | 100.00 1 | 99.72 14 | 99.56 21 | 97.76 73 | 99.70 110 | 99.98 67 |
|
SteuartSystems-ACMMP | | | 98.95 22 | 99.80 3 | 97.95 24 | 99.43 23 | 99.96 7 | 99.76 5 | 96.45 28 | 99.82 46 | 93.63 32 | 99.64 19 | 100.00 1 | 98.56 72 | 99.90 7 | 99.31 23 | 99.84 29 | 100.00 1 |
Skip Steuart: Steuart Systems R&D Blog. |
MP-MVS | | | 98.82 24 | 99.63 23 | 97.88 26 | 99.41 24 | 99.91 42 | 99.74 13 | 96.76 18 | 99.88 34 | 91.89 41 | 99.50 30 | 99.94 34 | 99.65 22 | 99.71 19 | 98.49 54 | 99.82 39 | 99.97 80 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
OMC-MVS | | | 98.59 28 | 99.07 39 | 98.03 23 | 99.41 24 | 99.90 43 | 99.26 34 | 94.33 36 | 99.94 22 | 96.03 16 | 96.68 87 | 99.72 45 | 99.42 32 | 98.86 34 | 98.84 39 | 99.72 106 | 99.58 175 |
|
3Dnovator | | 95.01 8 | 97.98 37 | 98.89 43 | 96.92 35 | 99.36 26 | 99.76 72 | 98.72 48 | 89.98 54 | 99.98 3 | 93.99 29 | 94.60 112 | 99.43 56 | 99.50 30 | 98.55 48 | 99.91 4 | 99.99 6 | 99.98 67 |
|
QAPM | | | 97.90 39 | 98.89 43 | 96.74 36 | 99.35 27 | 99.80 68 | 98.84 44 | 90.20 53 | 99.94 22 | 92.85 35 | 94.17 115 | 99.78 42 | 99.42 32 | 98.71 37 | 99.87 6 | 99.79 54 | 99.98 67 |
|
CNLPA | | | 99.24 7 | 99.58 29 | 98.85 6 | 99.34 28 | 99.95 12 | 99.32 31 | 96.65 22 | 99.96 16 | 98.44 2 | 98.97 51 | 100.00 1 | 99.57 27 | 98.66 39 | 99.56 15 | 99.76 73 | 99.97 80 |
|
MAR-MVS | | | 97.03 50 | 98.00 72 | 95.89 46 | 99.32 29 | 99.74 73 | 96.76 90 | 84.89 102 | 99.97 6 | 94.86 24 | 98.29 68 | 90.58 114 | 99.67 20 | 98.02 79 | 99.50 16 | 99.82 39 | 99.92 121 |
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 | | 94.03 11 | 96.87 52 | 98.10 69 | 95.44 52 | 99.29 30 | 99.78 70 | 98.46 56 | 89.92 57 | 99.47 81 | 85.78 97 | 91.05 136 | 98.50 65 | 99.30 39 | 98.49 56 | 99.41 17 | 99.89 22 | 99.98 67 |
|
3Dnovator+ | | 95.21 7 | 98.17 32 | 99.08 38 | 97.12 31 | 99.28 31 | 99.78 70 | 98.61 50 | 89.93 56 | 99.93 24 | 95.36 21 | 95.50 98 | 100.00 1 | 99.56 28 | 98.58 46 | 99.80 9 | 99.95 13 | 99.97 80 |
|
MVS_111021_HR | | | 97.94 38 | 99.59 27 | 96.02 45 | 99.27 32 | 99.97 3 | 97.03 84 | 90.44 49 | 99.89 31 | 90.75 56 | 100.00 1 | 99.73 43 | 98.68 71 | 98.67 38 | 98.89 37 | 99.95 13 | 99.97 80 |
|
mPP-MVS | | | | | | 99.23 33 | | | | | | | 99.87 37 | | | | | |
|
MVS_111021_LR | | | 98.15 34 | 99.69 18 | 96.36 41 | 99.23 33 | 99.93 29 | 97.79 61 | 91.84 40 | 99.87 37 | 90.53 63 | 100.00 1 | 99.57 51 | 98.93 59 | 99.44 24 | 99.08 32 | 99.85 27 | 99.95 108 |
|
abl_6 | | | | | 97.06 32 | 99.17 35 | 99.82 56 | 98.68 49 | 90.86 47 | 100.00 1 | 94.53 27 | 97.40 82 | 100.00 1 | 99.17 50 | | | 99.93 16 | 99.99 47 |
|
TSAR-MVS + ACMM | | | 98.30 30 | 99.64 22 | 96.74 36 | 99.08 36 | 99.94 17 | 99.67 22 | 96.73 19 | 99.97 6 | 86.30 96 | 98.30 67 | 99.99 26 | 98.78 66 | 99.73 16 | 99.57 14 | 99.88 25 | 99.98 67 |
|
train_agg | | | 98.62 26 | 99.76 8 | 97.28 29 | 99.03 37 | 99.93 29 | 99.65 23 | 96.37 29 | 99.98 3 | 89.24 78 | 99.53 27 | 99.83 39 | 99.59 26 | 99.85 10 | 99.19 28 | 99.80 50 | 100.00 1 |
|
CDPH-MVS | | | 97.88 40 | 99.59 27 | 95.89 46 | 98.90 38 | 99.95 12 | 99.40 30 | 92.86 39 | 99.86 40 | 85.33 99 | 98.62 63 | 99.45 55 | 99.06 57 | 99.29 26 | 99.94 3 | 99.81 47 | 100.00 1 |
|
ACMMP | | | 98.16 33 | 99.01 40 | 97.18 30 | 98.86 39 | 99.92 35 | 98.77 47 | 95.73 33 | 99.31 94 | 91.15 53 | 100.00 1 | 99.81 41 | 98.82 65 | 98.11 72 | 95.91 122 | 99.77 66 | 99.97 80 |
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 |
PatchMatch-RL | | | 96.84 53 | 98.03 71 | 95.47 49 | 98.84 40 | 99.81 64 | 95.61 109 | 89.20 64 | 99.65 64 | 91.28 50 | 99.39 33 | 93.46 105 | 98.18 79 | 98.05 75 | 96.28 109 | 99.69 116 | 99.55 180 |
|
tmp_tt | | | | | 78.81 211 | 98.80 41 | 85.73 227 | 70.08 228 | 77.87 166 | 98.68 114 | 83.71 105 | 99.53 27 | 74.55 167 | 54.97 233 | 78.28 228 | 72.43 231 | 87.45 232 | |
|
TAPA-MVS | | 96.62 5 | 97.60 42 | 98.46 58 | 96.60 39 | 98.73 42 | 99.90 43 | 99.30 32 | 94.96 35 | 99.46 82 | 87.57 88 | 96.05 96 | 98.53 64 | 99.26 46 | 98.04 77 | 97.33 84 | 99.77 66 | 99.88 140 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
MSDG | | | 97.29 45 | 97.55 81 | 97.00 33 | 98.66 43 | 99.71 74 | 99.03 40 | 96.15 30 | 99.59 69 | 89.67 75 | 92.77 129 | 94.86 95 | 98.75 67 | 98.22 67 | 97.94 64 | 99.72 106 | 99.76 160 |
|
PHI-MVS | | | 98.85 23 | 99.67 20 | 97.89 25 | 98.63 44 | 99.93 29 | 98.95 42 | 95.20 34 | 99.84 44 | 94.94 23 | 99.74 10 | 100.00 1 | 99.69 17 | 98.40 58 | 99.75 10 | 99.93 16 | 99.99 47 |
|
DeepPCF-MVS | | 97.16 4 | 97.58 43 | 99.72 16 | 95.07 57 | 98.45 45 | 99.96 7 | 93.83 134 | 95.93 31 | 100.00 1 | 90.79 55 | 98.38 66 | 99.85 38 | 95.28 124 | 99.94 2 | 99.97 1 | 96.15 221 | 99.97 80 |
|
EPNet | | | 98.11 35 | 99.63 23 | 96.34 42 | 98.44 46 | 99.88 49 | 98.55 51 | 90.25 52 | 99.93 24 | 92.60 38 | 100.00 1 | 99.73 43 | 98.41 73 | 98.87 33 | 99.02 33 | 99.82 39 | 99.97 80 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
EPNet_dtu | | | 95.10 93 | 98.81 48 | 90.78 107 | 98.38 47 | 98.47 125 | 96.54 92 | 89.36 61 | 99.78 52 | 65.65 190 | 99.31 37 | 98.24 73 | 94.79 129 | 98.28 64 | 99.35 21 | 99.93 16 | 98.27 202 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
SD-MVS | | | 99.16 12 | 99.73 14 | 98.49 15 | 97.93 48 | 99.95 12 | 99.74 13 | 96.94 4 | 99.96 16 | 96.60 11 | 99.47 32 | 100.00 1 | 99.88 6 | 99.15 29 | 99.59 12 | 99.84 29 | 100.00 1 |
|
TSAR-MVS + MP. | | | 98.99 20 | 99.61 26 | 98.27 19 | 97.88 49 | 99.92 35 | 99.71 19 | 96.80 14 | 99.96 16 | 95.58 20 | 98.71 61 | 100.00 1 | 99.68 19 | 99.91 5 | 98.78 44 | 99.99 6 | 100.00 1 |
|
COLMAP_ROB | | 93.56 12 | 96.03 66 | 96.83 100 | 95.11 55 | 97.87 50 | 99.52 85 | 98.81 46 | 91.40 44 | 99.42 85 | 84.97 101 | 90.46 138 | 96.82 83 | 98.05 81 | 96.46 119 | 96.19 112 | 99.54 135 | 98.92 197 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
PCF-MVS | | 97.20 3 | 97.49 44 | 98.20 66 | 96.66 38 | 97.62 51 | 99.92 35 | 98.93 43 | 96.64 23 | 98.53 125 | 88.31 86 | 94.04 117 | 99.58 50 | 98.94 58 | 97.53 91 | 97.79 71 | 99.54 135 | 99.97 80 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
DeepC-MVS | | 96.33 6 | 97.05 47 | 97.59 80 | 96.42 40 | 97.37 52 | 99.92 35 | 99.10 38 | 96.54 26 | 99.34 93 | 86.64 95 | 91.93 133 | 93.15 107 | 99.11 55 | 99.11 30 | 99.68 11 | 99.73 102 | 99.97 80 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
CANet | | | 97.62 41 | 98.94 42 | 96.08 44 | 97.19 53 | 99.93 29 | 99.29 33 | 90.38 50 | 99.87 37 | 91.00 54 | 95.79 97 | 99.51 52 | 98.72 70 | 98.53 52 | 99.00 34 | 99.90 21 | 99.99 47 |
|
DELS-MVS | | | 97.05 47 | 98.05 70 | 95.88 48 | 97.09 54 | 99.99 1 | 98.82 45 | 90.30 51 | 98.44 130 | 91.40 47 | 92.91 126 | 96.57 84 | 97.68 94 | 98.56 47 | 99.88 5 | 100.00 1 | 100.00 1 |
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 |
LS3D | | | 96.44 60 | 97.31 86 | 95.41 53 | 97.06 55 | 99.87 50 | 99.51 27 | 97.48 1 | 99.57 70 | 79.00 122 | 95.39 100 | 89.19 120 | 99.81 11 | 98.55 48 | 98.84 39 | 99.62 124 | 99.78 158 |
|
CHOSEN 280x420 | | | 97.16 46 | 99.58 29 | 94.35 78 | 96.95 56 | 99.97 3 | 97.19 80 | 81.55 140 | 99.92 29 | 91.75 42 | 100.00 1 | 100.00 1 | 98.84 64 | 98.55 48 | 98.65 47 | 99.79 54 | 99.97 80 |
|
RPSCF | | | 95.86 70 | 96.94 99 | 94.61 72 | 96.52 57 | 98.67 123 | 98.54 52 | 88.43 83 | 99.56 72 | 90.51 66 | 99.39 33 | 98.70 62 | 97.72 91 | 93.77 168 | 92.00 176 | 95.93 222 | 96.50 217 |
|
PVSNet_BlendedMVS | | | 96.01 67 | 96.48 110 | 95.46 50 | 96.47 58 | 99.89 47 | 95.64 106 | 91.23 45 | 99.75 57 | 91.59 43 | 96.80 84 | 82.44 145 | 98.05 81 | 98.53 52 | 97.92 68 | 99.80 50 | 100.00 1 |
|
PVSNet_Blended | | | 96.01 67 | 96.48 110 | 95.46 50 | 96.47 58 | 99.89 47 | 95.64 106 | 91.23 45 | 99.75 57 | 91.59 43 | 96.80 84 | 82.44 145 | 98.05 81 | 98.53 52 | 97.92 68 | 99.80 50 | 100.00 1 |
|
MVS_0304 | | | 97.04 49 | 98.72 51 | 95.08 56 | 96.32 60 | 99.90 43 | 99.15 36 | 89.61 60 | 99.89 31 | 87.22 93 | 95.47 99 | 98.22 74 | 98.22 78 | 98.63 43 | 98.90 36 | 99.93 16 | 100.00 1 |
|
TSAR-MVS + COLMAP | | | 95.20 89 | 95.03 131 | 95.41 53 | 96.17 61 | 98.69 122 | 99.11 37 | 93.40 38 | 99.97 6 | 84.89 103 | 98.23 72 | 75.01 165 | 99.34 37 | 97.27 107 | 96.37 108 | 99.58 129 | 99.64 170 |
|
CHOSEN 1792x2688 | | | 93.69 108 | 94.89 133 | 92.28 100 | 96.17 61 | 99.84 51 | 95.69 105 | 83.17 121 | 98.54 124 | 82.04 115 | 77.58 205 | 91.15 112 | 96.90 104 | 98.36 61 | 98.82 41 | 99.73 102 | 99.98 67 |
|
HyFIR lowres test | | | 93.13 117 | 94.48 137 | 91.56 104 | 96.12 63 | 99.68 76 | 93.52 136 | 79.98 148 | 97.24 149 | 81.73 118 | 72.66 216 | 95.74 93 | 98.29 77 | 98.27 65 | 97.79 71 | 99.70 110 | 100.00 1 |
|
OPM-MVS | | | 93.50 111 | 93.00 148 | 94.07 79 | 95.82 64 | 98.26 132 | 98.49 55 | 91.62 42 | 94.69 174 | 81.93 116 | 92.82 128 | 76.18 163 | 96.82 106 | 96.12 128 | 94.57 139 | 99.74 90 | 98.39 200 |
|
MS-PatchMatch | | | 93.46 115 | 95.91 124 | 90.61 110 | 95.48 65 | 99.31 97 | 95.62 108 | 77.23 170 | 99.42 85 | 81.88 117 | 88.92 143 | 96.06 90 | 93.80 141 | 96.45 120 | 93.11 163 | 99.65 120 | 98.10 206 |
|
CMPMVS | | 65.66 17 | 84.62 206 | 85.02 214 | 84.15 188 | 95.40 66 | 97.79 140 | 88.35 197 | 79.22 155 | 89.66 221 | 60.71 209 | 72.20 217 | 73.94 171 | 87.32 208 | 86.73 221 | 84.55 226 | 93.90 228 | 90.31 226 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
PVSNet_Blended_VisFu | | | 95.37 87 | 97.44 84 | 92.95 94 | 95.20 67 | 99.80 68 | 92.68 141 | 88.41 84 | 99.12 100 | 87.64 87 | 88.31 146 | 99.10 60 | 94.07 139 | 98.27 65 | 97.51 81 | 99.73 102 | 100.00 1 |
|
HQP-MVS | | | 94.48 99 | 95.39 129 | 93.42 86 | 95.10 68 | 98.35 128 | 98.19 57 | 91.41 43 | 99.77 53 | 79.79 119 | 99.30 38 | 77.08 155 | 96.25 114 | 96.93 109 | 96.28 109 | 99.76 73 | 99.99 47 |
|
XVS | | | | | | 95.09 69 | 99.94 17 | 97.49 71 | | | 88.58 83 | | 99.98 28 | | | | 99.78 61 | |
|
X-MVStestdata | | | | | | 95.09 69 | 99.94 17 | 97.49 71 | | | 88.58 83 | | 99.98 28 | | | | 99.78 61 | |
|
LGP-MVS_train | | | 93.60 109 | 95.05 130 | 91.90 103 | 94.90 71 | 98.29 131 | 97.93 59 | 88.06 85 | 99.14 99 | 74.83 137 | 99.26 39 | 76.50 159 | 96.07 116 | 96.31 124 | 95.90 124 | 99.59 127 | 99.97 80 |
|
ACMM | | 94.44 10 | 94.26 104 | 94.62 135 | 93.84 81 | 94.86 72 | 97.73 141 | 93.48 137 | 90.76 48 | 99.27 95 | 87.46 89 | 99.04 46 | 76.60 158 | 96.76 109 | 96.37 122 | 93.76 154 | 99.74 90 | 99.55 180 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
CANet_DTU | | | 94.90 95 | 98.98 41 | 90.13 115 | 94.74 73 | 99.81 64 | 98.53 53 | 82.23 131 | 99.97 6 | 66.76 178 | 100.00 1 | 98.50 65 | 98.74 68 | 97.52 92 | 97.19 93 | 99.76 73 | 99.88 140 |
|
TSAR-MVS + GP. | | | 98.06 36 | 99.55 32 | 96.32 43 | 94.72 74 | 99.92 35 | 99.22 35 | 89.98 54 | 99.97 6 | 94.77 25 | 99.94 9 | 100.00 1 | 99.43 31 | 98.52 55 | 98.53 52 | 99.79 54 | 100.00 1 |
|
UGNet | | | 96.05 65 | 98.55 55 | 93.13 89 | 94.64 75 | 99.65 78 | 94.70 123 | 87.78 87 | 99.40 88 | 89.69 74 | 98.25 70 | 99.25 59 | 92.12 156 | 96.50 115 | 97.08 95 | 99.84 29 | 99.72 164 |
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 |
IB-MVS | | 90.59 15 | 92.70 126 | 95.70 125 | 89.21 123 | 94.62 76 | 99.45 91 | 83.77 210 | 88.92 69 | 99.53 73 | 92.82 36 | 98.86 55 | 86.08 129 | 75.24 222 | 92.81 186 | 93.17 161 | 99.89 22 | 100.00 1 |
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 |
UA-Net | | | 94.95 94 | 98.66 52 | 90.63 109 | 94.60 77 | 98.94 115 | 96.03 100 | 85.28 97 | 98.01 143 | 78.92 123 | 97.42 81 | 99.96 31 | 89.09 198 | 98.95 32 | 98.80 42 | 99.82 39 | 98.57 199 |
|
ACMP | | 94.49 9 | 94.19 105 | 94.74 134 | 93.56 84 | 94.25 78 | 98.32 130 | 96.02 101 | 89.35 63 | 98.90 112 | 87.28 92 | 99.14 43 | 76.41 161 | 94.94 127 | 96.07 131 | 94.35 148 | 99.49 147 | 99.99 47 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
ACMH | | 92.34 14 | 91.59 138 | 93.02 147 | 89.92 117 | 93.97 79 | 97.98 137 | 90.10 180 | 84.70 103 | 98.46 128 | 76.80 131 | 93.38 124 | 71.94 180 | 94.39 134 | 95.34 142 | 94.04 150 | 99.54 135 | 100.00 1 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
CLD-MVS | | | 94.53 97 | 94.45 138 | 94.61 72 | 93.85 80 | 98.36 127 | 98.12 58 | 89.68 58 | 99.35 92 | 89.62 77 | 95.19 102 | 77.08 155 | 96.66 111 | 95.51 138 | 95.67 127 | 99.74 90 | 100.00 1 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
TDRefinement | | | 87.79 169 | 88.76 195 | 86.66 149 | 93.54 81 | 98.02 135 | 95.76 103 | 85.18 100 | 96.57 155 | 67.90 164 | 80.51 181 | 66.51 217 | 78.37 219 | 93.20 180 | 89.73 212 | 99.22 204 | 96.75 214 |
|
tfpn_ndepth | | | 96.84 53 | 98.58 54 | 94.81 67 | 93.18 82 | 99.62 81 | 96.83 88 | 88.75 76 | 99.73 59 | 92.38 39 | 98.45 65 | 96.34 88 | 97.90 87 | 98.34 63 | 97.59 79 | 99.84 29 | 99.99 47 |
|
conf0.01 | | | 96.20 64 | 97.19 90 | 95.05 59 | 93.11 83 | 99.83 52 | 97.67 63 | 89.06 67 | 98.62 117 | 91.38 48 | 99.19 41 | 89.09 121 | 99.28 41 | 97.48 94 | 96.10 113 | 99.76 73 | 100.00 1 |
|
conf0.002 | | | 96.51 57 | 97.75 77 | 95.07 57 | 93.11 83 | 99.83 52 | 97.67 63 | 89.10 66 | 98.62 117 | 91.47 46 | 99.39 33 | 91.68 110 | 99.28 41 | 97.49 93 | 97.24 86 | 99.76 73 | 100.00 1 |
|
tfpn111 | | | 95.79 73 | 96.55 104 | 94.89 61 | 93.10 85 | 99.82 56 | 97.67 63 | 88.85 70 | 98.62 117 | 90.69 57 | 99.07 44 | 84.86 132 | 99.28 41 | 97.41 98 | 96.10 113 | 99.76 73 | 99.99 47 |
|
conf200view11 | | | 95.78 74 | 96.54 106 | 94.89 61 | 93.10 85 | 99.82 56 | 97.67 63 | 88.85 70 | 98.62 117 | 90.69 57 | 99.00 47 | 84.86 132 | 99.28 41 | 97.41 98 | 96.10 113 | 99.76 73 | 99.99 47 |
|
thres100view900 | | | 95.86 70 | 96.62 102 | 94.97 60 | 93.10 85 | 99.83 52 | 97.76 62 | 89.15 65 | 98.62 117 | 90.69 57 | 99.00 47 | 84.86 132 | 99.30 39 | 97.57 90 | 96.48 103 | 99.81 47 | 100.00 1 |
|
tfpn200view9 | | | 95.78 74 | 96.54 106 | 94.89 61 | 93.10 85 | 99.82 56 | 97.67 63 | 88.85 70 | 98.62 117 | 90.69 57 | 99.00 47 | 84.86 132 | 99.28 41 | 97.41 98 | 96.10 113 | 99.76 73 | 99.99 47 |
|
thres200 | | | 95.77 77 | 96.55 104 | 94.86 64 | 93.09 89 | 99.82 56 | 97.63 69 | 88.85 70 | 98.49 126 | 90.66 61 | 98.99 50 | 84.86 132 | 99.20 47 | 97.41 98 | 96.28 109 | 99.76 73 | 100.00 1 |
|
tfpn1000 | | | 96.58 56 | 98.37 60 | 94.50 74 | 93.04 90 | 99.59 82 | 96.53 93 | 88.54 80 | 99.73 59 | 91.59 43 | 98.28 69 | 95.76 92 | 97.46 96 | 98.19 68 | 97.10 94 | 99.82 39 | 99.96 99 |
|
ACMH+ | | 92.61 13 | 91.80 136 | 93.03 146 | 90.37 112 | 93.03 91 | 98.17 133 | 94.00 132 | 84.13 113 | 98.12 140 | 77.39 129 | 91.95 132 | 74.62 166 | 94.36 136 | 94.62 153 | 93.82 153 | 99.32 194 | 99.87 144 |
|
thres400 | | | 95.72 81 | 96.48 110 | 94.84 65 | 93.00 92 | 99.83 52 | 97.55 70 | 88.93 68 | 98.49 126 | 90.61 62 | 98.86 55 | 84.63 137 | 99.20 47 | 97.45 95 | 96.10 113 | 99.77 66 | 99.99 47 |
|
view600 | | | 95.64 82 | 96.38 113 | 94.79 68 | 92.96 93 | 99.82 56 | 97.48 74 | 88.85 70 | 98.38 131 | 90.52 64 | 98.84 57 | 84.61 138 | 99.15 51 | 97.41 98 | 95.60 130 | 99.76 73 | 99.99 47 |
|
view800 | | | 95.62 84 | 96.38 113 | 94.73 70 | 92.96 93 | 99.81 64 | 97.38 76 | 88.75 76 | 98.35 136 | 90.43 67 | 98.81 59 | 84.54 140 | 99.13 54 | 97.35 104 | 95.82 125 | 99.76 73 | 99.98 67 |
|
canonicalmvs | | | 95.80 72 | 97.02 94 | 94.37 76 | 92.96 93 | 99.47 89 | 97.49 71 | 84.58 104 | 99.44 83 | 92.05 40 | 98.54 64 | 86.65 127 | 99.37 36 | 96.18 126 | 98.93 35 | 99.77 66 | 99.92 121 |
|
thres600view7 | | | 95.64 82 | 96.38 113 | 94.79 68 | 92.96 93 | 99.82 56 | 97.48 74 | 88.85 70 | 98.38 131 | 90.52 64 | 98.84 57 | 84.61 138 | 99.15 51 | 97.41 98 | 95.60 130 | 99.76 73 | 99.99 47 |
|
tfpn | | | 95.93 69 | 97.06 93 | 94.62 71 | 92.94 97 | 99.81 64 | 97.25 78 | 88.71 79 | 98.32 137 | 89.98 70 | 98.79 60 | 88.55 123 | 99.11 55 | 97.26 108 | 96.71 101 | 99.75 86 | 99.98 67 |
|
thresconf0.02 | | | 96.46 58 | 98.87 45 | 93.64 83 | 92.77 98 | 99.11 102 | 97.05 83 | 89.36 61 | 99.64 66 | 85.14 100 | 99.07 44 | 96.84 82 | 97.72 91 | 98.72 36 | 98.76 45 | 99.78 61 | 99.95 108 |
|
MVSTER | | | 97.00 51 | 98.85 46 | 94.83 66 | 92.71 99 | 97.43 146 | 99.03 40 | 85.52 95 | 99.82 46 | 92.74 37 | 99.15 42 | 99.94 34 | 99.19 49 | 98.66 39 | 96.99 99 | 99.79 54 | 99.98 67 |
|
conf0.05thres1000 | | | 94.50 98 | 95.70 125 | 93.11 90 | 92.68 100 | 99.67 77 | 96.04 99 | 87.81 86 | 97.52 148 | 83.71 105 | 96.20 94 | 84.52 141 | 98.73 69 | 96.39 121 | 95.66 128 | 99.71 108 | 99.92 121 |
|
tfpnview11 | | | 95.78 74 | 98.17 68 | 93.01 93 | 92.58 101 | 99.04 108 | 96.64 91 | 88.72 78 | 99.63 68 | 83.08 109 | 98.90 52 | 94.24 100 | 97.25 99 | 98.35 62 | 97.21 87 | 99.77 66 | 99.80 157 |
|
DWT-MVSNet_training | | | 96.26 63 | 98.44 59 | 93.72 82 | 92.58 101 | 99.34 96 | 96.15 98 | 83.00 123 | 99.76 55 | 93.63 32 | 97.89 77 | 99.46 53 | 97.23 100 | 94.43 155 | 98.19 58 | 99.70 110 | 100.00 1 |
|
USDC | | | 90.36 146 | 91.68 155 | 88.82 128 | 92.58 101 | 98.02 135 | 96.27 97 | 79.83 149 | 98.37 134 | 70.61 157 | 89.05 142 | 67.50 212 | 94.17 137 | 95.77 134 | 94.43 143 | 99.46 157 | 98.62 198 |
|
PMMVS | | | 96.45 59 | 98.24 63 | 94.36 77 | 92.58 101 | 99.01 109 | 97.08 82 | 87.42 90 | 99.88 34 | 90.06 69 | 99.39 33 | 94.63 96 | 99.33 38 | 97.85 84 | 96.99 99 | 99.70 110 | 99.96 99 |
|
tfpn_n400 | | | 95.76 78 | 98.21 64 | 92.90 95 | 92.57 105 | 99.05 106 | 96.42 94 | 88.50 81 | 99.49 76 | 83.08 109 | 98.90 52 | 94.24 100 | 97.07 101 | 98.10 73 | 97.93 66 | 99.74 90 | 99.76 160 |
|
tfpnconf | | | 95.76 78 | 98.21 64 | 92.90 95 | 92.57 105 | 99.05 106 | 96.42 94 | 88.50 81 | 99.49 76 | 83.08 109 | 98.90 52 | 94.24 100 | 97.07 101 | 98.10 73 | 97.93 66 | 99.74 90 | 99.76 160 |
|
EPMVS | | | 94.08 106 | 98.54 56 | 88.87 126 | 92.51 107 | 99.47 89 | 94.18 130 | 66.53 211 | 99.68 63 | 82.40 113 | 95.24 101 | 99.40 57 | 97.86 88 | 98.12 71 | 97.99 62 | 99.75 86 | 99.88 140 |
|
TinyColmap | | | 89.94 147 | 90.88 161 | 88.84 127 | 92.43 108 | 97.91 139 | 95.59 110 | 80.10 147 | 98.12 140 | 71.33 154 | 84.56 157 | 67.46 213 | 94.15 138 | 95.57 137 | 94.27 149 | 99.43 166 | 98.26 203 |
|
diffmvs | | | 96.35 61 | 98.76 50 | 93.54 85 | 92.41 109 | 99.55 84 | 97.22 79 | 83.75 115 | 99.57 70 | 89.64 76 | 96.86 83 | 98.33 70 | 98.37 74 | 98.42 57 | 98.61 48 | 99.88 25 | 99.99 47 |
|
IS_MVSNet | | | 96.66 55 | 98.62 53 | 94.38 75 | 92.41 109 | 99.70 75 | 97.19 80 | 87.67 89 | 99.05 105 | 91.27 51 | 95.09 104 | 98.46 69 | 97.95 86 | 98.64 41 | 99.37 18 | 99.79 54 | 100.00 1 |
|
Vis-MVSNet (Re-imp) | | | 95.60 85 | 98.52 57 | 92.19 101 | 92.37 111 | 99.56 83 | 96.37 96 | 87.41 91 | 98.95 108 | 84.77 104 | 94.88 110 | 98.48 68 | 92.44 153 | 98.63 43 | 99.37 18 | 99.76 73 | 99.77 159 |
|
FC-MVSNet-train | | | 94.61 96 | 96.27 117 | 92.68 99 | 92.35 112 | 97.14 149 | 93.45 138 | 87.73 88 | 98.93 109 | 87.31 90 | 96.42 90 | 89.35 118 | 95.67 119 | 96.06 132 | 96.01 120 | 99.56 132 | 99.98 67 |
|
MVS_Test | | | 95.74 80 | 98.18 67 | 92.90 95 | 92.16 113 | 99.49 88 | 97.36 77 | 84.30 109 | 99.79 50 | 84.94 102 | 96.65 88 | 93.63 104 | 98.85 63 | 98.61 45 | 99.10 31 | 99.81 47 | 100.00 1 |
|
tpmrst | | | 92.52 130 | 97.45 83 | 86.77 148 | 92.15 114 | 99.36 95 | 92.53 144 | 65.95 216 | 99.53 73 | 72.50 142 | 92.22 131 | 99.83 39 | 97.81 90 | 95.18 145 | 96.05 119 | 99.69 116 | 100.00 1 |
|
ADS-MVSNet | | | 92.91 122 | 97.97 73 | 87.01 145 | 92.07 115 | 99.27 98 | 92.70 140 | 65.39 220 | 99.85 41 | 75.40 135 | 94.93 109 | 98.26 71 | 96.86 105 | 96.09 129 | 97.52 80 | 99.65 120 | 99.84 149 |
|
dps | | | 94.29 103 | 97.33 85 | 90.75 108 | 92.02 116 | 99.21 99 | 94.31 128 | 66.97 210 | 99.50 75 | 95.61 19 | 96.22 93 | 98.64 63 | 96.08 115 | 93.71 170 | 94.03 151 | 99.52 139 | 99.98 67 |
|
PatchmatchNet | | | 93.48 114 | 98.84 47 | 87.22 143 | 91.93 117 | 99.39 93 | 92.55 143 | 66.06 215 | 99.71 61 | 75.61 134 | 98.24 71 | 99.59 49 | 97.35 97 | 97.87 83 | 97.64 78 | 99.83 34 | 99.43 185 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
tpm cat1 | | | 93.29 116 | 96.53 108 | 89.50 121 | 91.84 118 | 99.18 101 | 94.70 123 | 67.70 206 | 98.38 131 | 86.67 94 | 89.16 141 | 99.38 58 | 96.66 111 | 94.33 156 | 95.30 133 | 99.43 166 | 100.00 1 |
|
tpmp4_e23 | | | 92.95 121 | 96.28 116 | 89.06 125 | 91.80 119 | 98.81 120 | 94.95 119 | 67.56 209 | 99.21 96 | 82.97 112 | 96.54 89 | 88.52 124 | 97.47 95 | 94.47 154 | 96.42 106 | 99.61 125 | 100.00 1 |
|
EPP-MVSNet | | | 96.29 62 | 98.34 62 | 93.90 80 | 91.77 120 | 99.38 94 | 95.45 114 | 87.25 92 | 99.38 89 | 91.36 49 | 94.86 111 | 98.49 67 | 97.83 89 | 98.01 80 | 98.23 57 | 99.75 86 | 99.99 47 |
|
DI_MVS_plusplus_trai | | | 95.29 88 | 97.02 94 | 93.28 88 | 91.76 121 | 99.52 85 | 97.84 60 | 85.67 94 | 99.08 104 | 87.29 91 | 87.76 149 | 97.46 80 | 97.31 98 | 97.83 85 | 97.48 82 | 99.83 34 | 100.00 1 |
|
MDTV_nov1_ep13 | | | 94.32 101 | 98.77 49 | 89.14 124 | 91.70 122 | 99.52 85 | 95.21 116 | 72.09 202 | 99.80 49 | 78.91 124 | 96.32 91 | 99.62 48 | 97.71 93 | 98.39 59 | 97.71 77 | 99.22 204 | 100.00 1 |
|
test-LLR | | | 93.71 107 | 97.23 88 | 89.60 119 | 91.69 123 | 99.10 103 | 94.68 125 | 83.60 116 | 99.36 90 | 71.94 148 | 93.82 119 | 96.51 85 | 95.96 117 | 97.42 96 | 94.37 145 | 99.74 90 | 99.99 47 |
|
test0.0.03 1 | | | 95.15 92 | 97.87 76 | 91.99 102 | 91.69 123 | 98.82 119 | 93.04 139 | 83.60 116 | 99.65 64 | 88.80 81 | 94.15 116 | 97.67 78 | 94.97 126 | 96.62 114 | 98.16 59 | 99.83 34 | 100.00 1 |
|
CostFormer | | | 93.50 111 | 96.50 109 | 90.00 116 | 91.69 123 | 98.65 124 | 93.88 133 | 67.64 207 | 98.97 106 | 89.16 79 | 97.79 78 | 88.92 122 | 97.97 85 | 95.14 146 | 96.06 118 | 99.63 122 | 100.00 1 |
|
CVMVSNet | | | 92.13 134 | 95.40 128 | 88.32 137 | 91.29 126 | 97.29 148 | 91.85 148 | 86.42 93 | 96.71 154 | 71.84 150 | 89.56 140 | 91.18 111 | 88.98 200 | 96.17 127 | 97.76 73 | 99.51 143 | 99.14 193 |
|
Vis-MVSNet | | | 93.08 119 | 96.76 101 | 88.78 130 | 91.14 127 | 99.63 80 | 94.85 120 | 83.34 119 | 97.19 150 | 74.78 138 | 91.92 134 | 93.15 107 | 88.81 201 | 97.59 89 | 98.35 55 | 99.78 61 | 99.49 184 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
IterMVS-LS | | | 93.50 111 | 96.22 118 | 90.33 114 | 90.93 128 | 95.50 190 | 94.83 121 | 80.54 144 | 98.92 110 | 79.11 121 | 90.64 137 | 93.70 103 | 96.79 107 | 96.93 109 | 97.85 70 | 99.78 61 | 99.99 47 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
Effi-MVS+ | | | 93.06 120 | 95.94 123 | 89.70 118 | 90.82 129 | 99.45 91 | 95.71 104 | 78.94 160 | 98.72 113 | 74.71 139 | 97.92 76 | 80.73 149 | 98.35 75 | 97.72 86 | 97.05 98 | 99.70 110 | 100.00 1 |
|
RPMNet | | | 92.64 128 | 97.88 75 | 86.53 150 | 90.79 130 | 98.95 113 | 95.13 117 | 64.44 224 | 99.09 102 | 72.36 144 | 93.58 122 | 99.01 61 | 96.74 110 | 98.05 75 | 96.45 105 | 99.71 108 | 100.00 1 |
|
testgi | | | 92.47 131 | 95.68 127 | 88.73 131 | 90.68 131 | 98.35 128 | 91.67 151 | 79.50 153 | 98.96 107 | 77.12 130 | 95.17 103 | 85.84 130 | 93.95 140 | 95.75 135 | 96.47 104 | 99.45 160 | 99.21 191 |
|
LP | | | 88.31 159 | 93.18 144 | 82.63 202 | 90.66 132 | 97.98 137 | 87.32 202 | 63.49 227 | 97.17 151 | 63.02 201 | 82.08 167 | 90.47 115 | 91.92 158 | 92.75 187 | 93.42 159 | 99.38 188 | 98.37 201 |
|
CR-MVSNet | | | 92.32 133 | 97.97 73 | 85.74 163 | 90.63 133 | 98.95 113 | 95.46 112 | 65.50 218 | 99.09 102 | 67.51 169 | 94.20 114 | 98.18 75 | 95.59 122 | 98.16 69 | 97.20 91 | 99.74 90 | 100.00 1 |
|
gg-mvs-nofinetune | | | 86.69 193 | 91.30 159 | 81.30 207 | 90.42 134 | 99.64 79 | 98.50 54 | 61.68 229 | 79.23 230 | 40.35 233 | 66.58 224 | 97.14 81 | 96.92 103 | 98.64 41 | 97.94 64 | 99.91 20 | 99.97 80 |
|
PatchT | | | 91.06 141 | 97.66 78 | 83.36 199 | 90.32 135 | 98.96 112 | 82.30 215 | 64.72 223 | 98.45 129 | 67.51 169 | 93.28 125 | 97.60 79 | 95.59 122 | 98.16 69 | 97.20 91 | 99.70 110 | 100.00 1 |
|
IterMVS | | | 91.65 137 | 96.62 102 | 85.85 160 | 90.27 136 | 95.80 180 | 95.32 115 | 74.15 185 | 98.91 111 | 60.95 207 | 88.79 145 | 97.76 77 | 94.69 132 | 98.04 77 | 97.07 96 | 99.73 102 | 100.00 1 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
tpm | | | 89.60 149 | 94.93 132 | 83.39 197 | 89.94 137 | 97.11 150 | 90.09 181 | 65.28 221 | 98.67 115 | 60.03 211 | 96.79 86 | 84.38 142 | 95.66 121 | 91.90 191 | 95.65 129 | 99.32 194 | 99.98 67 |
|
CDS-MVSNet | | | 94.32 101 | 97.00 96 | 91.19 106 | 89.82 138 | 98.71 121 | 95.51 111 | 85.14 101 | 96.85 152 | 82.33 114 | 92.48 130 | 96.40 87 | 94.71 130 | 96.86 111 | 97.76 73 | 99.63 122 | 99.92 121 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
testpf | | | 91.26 140 | 97.28 87 | 84.23 186 | 89.52 139 | 97.45 145 | 88.08 199 | 56.08 233 | 99.76 55 | 78.71 125 | 95.06 108 | 98.26 71 | 93.44 145 | 94.72 151 | 95.69 126 | 99.57 130 | 99.99 47 |
|
FMVSNet3 | | | 95.59 86 | 97.51 82 | 93.34 87 | 89.48 140 | 96.57 158 | 97.67 63 | 84.17 110 | 99.48 78 | 89.76 71 | 95.09 104 | 94.35 97 | 99.14 53 | 98.37 60 | 98.86 38 | 99.82 39 | 99.89 137 |
|
GBi-Net | | | 95.19 90 | 96.99 97 | 93.09 91 | 89.11 141 | 96.47 160 | 96.90 85 | 84.17 110 | 99.48 78 | 89.76 71 | 95.09 104 | 94.35 97 | 98.87 60 | 96.50 115 | 97.21 87 | 99.74 90 | 99.81 153 |
|
test1 | | | 95.19 90 | 96.99 97 | 93.09 91 | 89.11 141 | 96.47 160 | 96.90 85 | 84.17 110 | 99.48 78 | 89.76 71 | 95.09 104 | 94.35 97 | 98.87 60 | 96.50 115 | 97.21 87 | 99.74 90 | 99.81 153 |
|
FMVSNet2 | | | 94.48 99 | 95.95 122 | 92.77 98 | 89.11 141 | 96.47 160 | 96.90 85 | 83.38 118 | 99.11 101 | 88.64 82 | 87.50 154 | 92.26 109 | 98.87 60 | 97.91 82 | 98.60 49 | 99.74 90 | 99.81 153 |
|
Fast-Effi-MVS+ | | | 92.11 135 | 94.33 139 | 89.52 120 | 89.06 144 | 99.00 110 | 95.13 117 | 76.72 175 | 98.59 123 | 78.21 128 | 89.99 139 | 77.35 154 | 98.34 76 | 97.97 81 | 97.44 83 | 99.67 118 | 99.96 99 |
|
Fast-Effi-MVS+-dtu | | | 92.73 125 | 97.62 79 | 87.02 144 | 88.91 145 | 98.83 118 | 95.79 102 | 73.98 189 | 99.89 31 | 68.62 163 | 97.73 79 | 93.30 106 | 95.21 125 | 97.67 87 | 95.96 121 | 99.59 127 | 100.00 1 |
|
MVS-HIRNet | | | 88.27 160 | 94.05 141 | 81.51 206 | 88.90 146 | 98.93 116 | 83.38 213 | 60.52 232 | 98.06 142 | 63.78 197 | 80.67 179 | 90.36 116 | 92.94 148 | 97.29 106 | 96.41 107 | 99.56 132 | 96.66 215 |
|
Effi-MVS+-dtu | | | 93.13 117 | 97.13 91 | 88.47 134 | 88.86 147 | 99.19 100 | 96.79 89 | 79.08 157 | 99.64 66 | 70.01 158 | 97.51 80 | 89.38 117 | 96.53 113 | 97.60 88 | 96.55 102 | 99.57 130 | 100.00 1 |
|
TAMVS | | | 92.43 132 | 94.21 140 | 90.35 113 | 88.68 148 | 98.85 117 | 94.15 131 | 81.53 141 | 95.58 161 | 83.61 107 | 87.05 155 | 86.45 128 | 94.71 130 | 96.27 125 | 95.91 122 | 99.42 172 | 99.38 187 |
|
GA-MVS | | | 90.38 145 | 94.59 136 | 85.46 168 | 88.30 149 | 98.44 126 | 92.18 145 | 83.30 120 | 97.89 144 | 58.05 214 | 92.86 127 | 84.25 143 | 91.27 181 | 96.65 113 | 92.61 171 | 99.66 119 | 99.43 185 |
|
FC-MVSNet-test | | | 92.78 124 | 96.19 120 | 88.80 129 | 88.00 150 | 97.54 143 | 93.60 135 | 82.36 130 | 98.16 138 | 79.71 120 | 91.55 135 | 95.41 94 | 89.65 193 | 96.09 129 | 95.23 134 | 99.49 147 | 99.31 188 |
|
FMVSNet1 | | | 92.55 129 | 93.66 142 | 91.26 105 | 87.91 151 | 96.12 167 | 94.75 122 | 81.69 139 | 97.67 145 | 85.63 98 | 80.56 180 | 87.88 126 | 98.15 80 | 96.50 115 | 97.21 87 | 99.41 184 | 99.71 165 |
|
tfpnnormal | | | 89.09 153 | 89.71 170 | 88.38 135 | 87.37 152 | 96.78 154 | 91.46 152 | 85.20 99 | 90.33 217 | 72.35 145 | 83.45 161 | 69.30 208 | 94.45 133 | 95.29 143 | 92.86 166 | 99.44 165 | 99.93 116 |
|
TESTMET0.1,1 | | | 92.87 123 | 97.23 88 | 87.79 140 | 86.96 153 | 99.10 103 | 94.68 125 | 77.46 169 | 99.36 90 | 71.94 148 | 93.82 119 | 96.51 85 | 95.96 117 | 97.42 96 | 94.37 145 | 99.74 90 | 99.99 47 |
|
FMVSNet5 | | | 93.53 110 | 96.09 121 | 90.56 111 | 86.74 154 | 92.84 208 | 92.64 142 | 77.50 168 | 99.41 87 | 88.97 80 | 98.02 75 | 97.81 76 | 98.00 84 | 94.85 149 | 95.43 132 | 99.50 146 | 94.25 222 |
|
test-mter | | | 92.67 127 | 97.13 91 | 87.47 142 | 86.72 155 | 99.07 105 | 94.28 129 | 76.90 173 | 99.21 96 | 71.53 152 | 93.63 121 | 96.32 89 | 95.67 119 | 97.32 105 | 94.36 147 | 99.74 90 | 99.99 47 |
|
TransMVSNet (Re) | | | 88.33 158 | 89.55 176 | 86.91 147 | 86.65 156 | 95.56 187 | 90.48 163 | 84.44 108 | 92.02 216 | 71.07 156 | 80.13 182 | 72.48 178 | 89.41 195 | 95.05 148 | 94.44 142 | 99.39 186 | 97.14 212 |
|
LTVRE_ROB | | 88.65 16 | 87.87 167 | 91.11 160 | 84.10 189 | 86.64 157 | 97.47 144 | 94.40 127 | 78.41 164 | 96.13 158 | 52.02 222 | 87.95 147 | 65.92 218 | 93.59 144 | 95.29 143 | 95.09 136 | 99.52 139 | 99.95 108 |
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 | | | 86.96 187 | 89.56 175 | 83.93 193 | 86.29 158 | 97.61 142 | 90.75 159 | 73.31 194 | 95.43 165 | 66.08 186 | 75.88 213 | 71.31 186 | 87.55 207 | 94.79 150 | 92.74 168 | 99.61 125 | 99.13 194 |
|
pm-mvs1 | | | 89.68 148 | 92.00 151 | 86.96 146 | 86.23 159 | 96.62 157 | 90.36 169 | 83.05 122 | 93.97 186 | 72.15 147 | 81.77 175 | 82.10 147 | 90.69 187 | 95.38 141 | 94.50 141 | 99.29 198 | 99.65 167 |
|
NR-MVSNet | | | 89.52 150 | 90.71 162 | 88.14 139 | 86.19 160 | 96.20 164 | 92.07 146 | 84.58 104 | 95.54 162 | 75.27 136 | 87.52 152 | 67.96 211 | 91.24 183 | 94.33 156 | 93.45 158 | 99.49 147 | 99.97 80 |
|
our_test_3 | | | | | | 85.89 161 | 96.09 170 | 82.15 216 | | | | | | | | | | |
|
pmmvs4 | | | 91.41 139 | 93.05 145 | 89.49 122 | 85.85 162 | 96.52 159 | 91.70 150 | 82.49 125 | 98.14 139 | 83.17 108 | 87.57 151 | 81.76 148 | 94.39 134 | 95.47 139 | 92.62 170 | 99.33 193 | 99.29 189 |
|
UniMVSNet (Re) | | | 90.41 144 | 91.96 152 | 88.59 133 | 85.71 163 | 96.73 155 | 90.82 156 | 84.11 114 | 95.23 167 | 78.54 126 | 88.91 144 | 76.41 161 | 92.84 150 | 93.40 177 | 93.05 164 | 99.55 134 | 100.00 1 |
|
v18 | | | 87.14 185 | 88.96 188 | 85.01 172 | 85.57 164 | 92.03 210 | 90.89 155 | 74.62 183 | 94.80 173 | 67.90 164 | 82.02 168 | 71.28 187 | 91.63 167 | 91.53 197 | 91.44 188 | 99.47 154 | 99.60 172 |
|
v16 | | | 87.15 184 | 89.13 183 | 84.83 175 | 85.55 165 | 91.94 212 | 90.50 161 | 74.13 187 | 95.06 169 | 67.72 166 | 81.84 172 | 72.55 177 | 91.65 164 | 91.50 200 | 91.42 192 | 99.42 172 | 99.60 172 |
|
v17 | | | 86.99 186 | 88.90 191 | 84.76 177 | 85.52 166 | 91.96 211 | 90.50 161 | 74.17 184 | 94.88 171 | 67.33 174 | 81.94 169 | 71.21 188 | 91.57 170 | 91.49 201 | 91.20 201 | 99.48 151 | 99.60 172 |
|
v8 | | | 87.54 172 | 89.33 182 | 85.45 169 | 85.41 167 | 95.50 190 | 90.32 174 | 78.94 160 | 94.35 184 | 66.93 177 | 81.90 170 | 70.99 193 | 91.62 168 | 91.49 201 | 91.22 200 | 99.48 151 | 99.87 144 |
|
SixPastTwentyTwo | | | 88.35 157 | 91.51 157 | 84.66 178 | 85.39 168 | 96.96 152 | 86.57 203 | 79.62 152 | 96.57 155 | 63.73 198 | 87.86 148 | 75.18 164 | 93.43 146 | 94.03 160 | 90.37 208 | 99.24 203 | 99.58 175 |
|
v6 | | | 87.96 164 | 89.58 173 | 86.08 152 | 85.34 169 | 96.14 166 | 90.44 164 | 82.19 132 | 94.56 175 | 67.43 173 | 81.90 170 | 71.57 185 | 91.62 168 | 91.54 196 | 91.43 189 | 99.43 166 | 99.92 121 |
|
v1neww | | | 87.88 165 | 89.51 179 | 85.97 157 | 85.32 170 | 96.12 167 | 90.33 171 | 82.17 133 | 94.51 176 | 66.96 175 | 81.84 172 | 71.21 188 | 91.64 165 | 91.52 198 | 91.43 189 | 99.42 172 | 99.92 121 |
|
v7new | | | 87.88 165 | 89.51 179 | 85.97 157 | 85.32 170 | 96.12 167 | 90.33 171 | 82.17 133 | 94.51 176 | 66.96 175 | 81.84 172 | 71.21 188 | 91.64 165 | 91.52 198 | 91.43 189 | 99.42 172 | 99.92 121 |
|
MIMVSNet | | | 91.01 142 | 96.22 118 | 84.93 174 | 85.24 172 | 98.09 134 | 90.40 167 | 64.96 222 | 97.55 147 | 72.65 140 | 96.23 92 | 90.81 113 | 96.79 107 | 96.69 112 | 97.06 97 | 99.52 139 | 97.09 213 |
|
V42 | | | 87.84 168 | 89.42 181 | 85.99 156 | 85.16 173 | 96.01 176 | 90.52 160 | 81.78 138 | 94.43 182 | 67.59 167 | 81.32 176 | 71.87 181 | 91.48 172 | 91.25 208 | 91.16 203 | 99.43 166 | 99.92 121 |
|
WR-MVS_H | | | 88.47 156 | 90.55 164 | 86.04 153 | 85.13 174 | 96.07 173 | 89.86 189 | 79.80 150 | 94.37 183 | 72.32 146 | 83.12 163 | 74.44 169 | 89.60 194 | 93.52 174 | 92.40 172 | 99.51 143 | 99.96 99 |
|
N_pmnet | | | 87.31 180 | 91.51 157 | 82.41 205 | 85.13 174 | 95.57 186 | 80.59 218 | 81.79 137 | 96.20 157 | 58.52 213 | 78.62 200 | 85.66 131 | 89.36 196 | 94.64 152 | 92.14 175 | 99.08 209 | 97.72 211 |
|
EU-MVSNet | | | 87.20 182 | 90.47 165 | 83.38 198 | 85.11 176 | 93.85 206 | 86.10 205 | 79.76 151 | 93.30 206 | 65.39 193 | 84.41 158 | 78.43 151 | 85.04 212 | 92.20 190 | 93.03 165 | 98.86 211 | 98.05 207 |
|
UniMVSNet_NR-MVSNet | | | 90.50 143 | 92.31 150 | 88.38 135 | 85.04 177 | 96.34 163 | 90.94 153 | 85.32 96 | 95.87 160 | 75.69 132 | 87.68 150 | 78.49 150 | 93.78 142 | 93.21 179 | 94.60 138 | 99.53 138 | 99.97 80 |
|
v7 | | | 87.72 171 | 89.75 169 | 85.35 170 | 85.01 178 | 95.79 181 | 90.43 166 | 78.98 158 | 94.50 179 | 66.39 181 | 78.87 196 | 73.65 173 | 91.85 161 | 93.69 171 | 91.86 180 | 99.45 160 | 99.92 121 |
|
v15 | | | 86.50 196 | 88.32 200 | 84.37 180 | 85.00 179 | 91.86 213 | 90.30 175 | 73.76 190 | 93.90 189 | 66.28 184 | 79.78 187 | 70.37 200 | 91.45 174 | 91.48 203 | 91.27 197 | 99.43 166 | 99.58 175 |
|
V14 | | | 86.54 195 | 88.41 199 | 84.35 181 | 84.94 180 | 91.83 214 | 90.28 176 | 73.48 192 | 93.73 194 | 66.50 180 | 79.89 185 | 71.12 192 | 91.46 173 | 91.48 203 | 91.25 198 | 99.42 172 | 99.58 175 |
|
v10 | | | 87.40 178 | 89.62 172 | 84.80 176 | 84.93 181 | 95.07 198 | 90.44 164 | 75.63 179 | 94.51 176 | 66.52 179 | 78.87 196 | 73.47 174 | 91.86 160 | 93.69 171 | 91.87 179 | 99.45 160 | 99.86 147 |
|
v1 | | | 87.48 174 | 88.91 190 | 85.81 161 | 84.93 181 | 96.07 173 | 90.33 171 | 82.45 128 | 93.65 196 | 66.39 181 | 79.38 193 | 70.40 199 | 91.33 178 | 91.58 195 | 91.38 195 | 99.42 172 | 99.93 116 |
|
pmmvs6 | | | 85.75 203 | 86.97 211 | 84.34 183 | 84.88 183 | 95.59 185 | 87.41 201 | 79.19 156 | 87.81 223 | 67.56 168 | 63.05 227 | 77.76 152 | 89.15 197 | 93.45 176 | 91.90 178 | 97.83 218 | 99.21 191 |
|
V9 | | | 86.42 197 | 88.26 201 | 84.27 185 | 84.88 183 | 91.80 215 | 90.34 170 | 73.18 196 | 93.92 188 | 66.37 183 | 79.68 189 | 70.25 201 | 91.42 175 | 91.43 205 | 91.23 199 | 99.42 172 | 99.55 180 |
|
v1141 | | | 87.45 177 | 88.98 186 | 85.67 166 | 84.86 185 | 96.08 171 | 90.23 177 | 82.46 126 | 93.75 191 | 65.64 192 | 79.57 190 | 70.52 197 | 91.41 176 | 91.63 193 | 91.39 193 | 99.42 172 | 99.92 121 |
|
divwei89l23v2f112 | | | 87.46 175 | 88.97 187 | 85.70 165 | 84.85 186 | 96.08 171 | 90.23 177 | 82.46 126 | 93.69 195 | 65.83 188 | 79.57 190 | 70.54 196 | 91.39 177 | 91.60 194 | 91.39 193 | 99.43 166 | 99.92 121 |
|
v13 | | | 86.27 199 | 88.16 204 | 84.06 192 | 84.85 186 | 91.77 218 | 90.00 183 | 72.77 200 | 93.56 198 | 66.06 187 | 79.25 194 | 70.50 198 | 91.25 182 | 91.35 207 | 91.15 204 | 99.42 172 | 99.55 180 |
|
v11 | | | 86.74 191 | 89.01 184 | 84.09 191 | 84.79 188 | 91.79 217 | 90.39 168 | 72.53 201 | 94.47 180 | 65.75 189 | 78.64 199 | 72.96 176 | 91.66 163 | 93.92 163 | 91.69 184 | 99.42 172 | 99.61 171 |
|
v12 | | | 86.32 198 | 88.22 202 | 84.10 189 | 84.76 189 | 91.80 215 | 89.94 186 | 72.97 198 | 93.85 190 | 66.18 185 | 79.98 184 | 69.72 207 | 91.33 178 | 91.40 206 | 91.20 201 | 99.42 172 | 99.56 179 |
|
v1144 | | | 87.49 173 | 89.64 171 | 84.97 173 | 84.73 190 | 95.84 179 | 90.17 179 | 79.30 154 | 93.96 187 | 64.65 195 | 78.83 198 | 73.38 175 | 91.51 171 | 93.77 168 | 91.77 181 | 99.45 160 | 99.93 116 |
|
DU-MVS | | | 89.49 151 | 90.60 163 | 88.19 138 | 84.71 191 | 96.20 164 | 90.94 153 | 84.58 104 | 95.54 162 | 75.69 132 | 87.52 152 | 68.74 210 | 93.78 142 | 91.10 209 | 95.13 135 | 99.47 154 | 99.97 80 |
|
Baseline_NR-MVSNet | | | 89.13 152 | 89.53 177 | 88.66 132 | 84.71 191 | 94.43 201 | 91.79 149 | 84.49 107 | 95.54 162 | 78.28 127 | 78.52 202 | 72.46 179 | 93.29 147 | 91.10 209 | 94.82 137 | 99.42 172 | 99.86 147 |
|
v148 | | | 86.63 194 | 87.79 206 | 85.28 171 | 84.65 193 | 95.97 177 | 86.46 204 | 82.84 124 | 92.91 209 | 71.52 153 | 78.99 195 | 66.74 216 | 86.83 209 | 89.28 217 | 90.69 206 | 99.41 184 | 99.94 113 |
|
CP-MVSNet | | | 88.09 162 | 89.57 174 | 86.36 151 | 84.63 194 | 95.46 192 | 89.48 191 | 80.53 145 | 93.42 201 | 71.26 155 | 81.25 177 | 69.90 203 | 92.78 151 | 93.30 178 | 93.69 155 | 99.47 154 | 99.96 99 |
|
v2v482 | | | 87.46 175 | 88.90 191 | 85.78 162 | 84.58 195 | 95.95 178 | 89.90 188 | 82.43 129 | 94.19 185 | 65.65 190 | 79.80 186 | 69.12 209 | 92.67 152 | 91.88 192 | 91.46 187 | 99.45 160 | 99.93 116 |
|
PS-CasMVS | | | 87.24 181 | 88.52 198 | 85.73 164 | 84.58 195 | 95.35 194 | 89.03 194 | 80.17 146 | 93.11 208 | 68.86 162 | 77.71 204 | 66.89 214 | 92.30 154 | 93.13 182 | 93.50 157 | 99.46 157 | 99.96 99 |
|
v1192 | | | 86.93 188 | 89.01 184 | 84.50 179 | 84.46 197 | 95.51 189 | 89.93 187 | 78.65 162 | 93.75 191 | 62.29 203 | 77.19 206 | 70.88 194 | 92.28 155 | 93.84 165 | 91.96 177 | 99.38 188 | 99.90 133 |
|
WR-MVS | | | 88.23 161 | 90.15 166 | 86.00 155 | 84.39 198 | 95.64 183 | 89.96 185 | 81.80 136 | 94.46 181 | 71.60 151 | 82.10 166 | 74.36 170 | 88.76 202 | 92.48 188 | 92.20 174 | 99.46 157 | 99.83 151 |
|
v144192 | | | 86.80 190 | 88.90 191 | 84.35 181 | 84.33 199 | 95.56 187 | 89.34 192 | 77.74 167 | 93.60 197 | 64.03 196 | 77.82 203 | 70.76 195 | 91.28 180 | 92.91 185 | 91.74 183 | 99.37 190 | 99.90 133 |
|
pmmvs5 | | | 87.33 179 | 90.01 167 | 84.20 187 | 84.31 200 | 96.04 175 | 87.63 200 | 76.59 176 | 93.17 207 | 65.35 194 | 84.30 160 | 71.68 182 | 91.91 159 | 95.41 140 | 91.37 196 | 99.39 186 | 98.13 204 |
|
v1921920 | | | 86.81 189 | 88.93 189 | 84.33 184 | 84.23 201 | 95.41 193 | 90.09 181 | 78.10 165 | 93.74 193 | 62.17 204 | 76.98 208 | 71.14 191 | 92.05 157 | 93.69 171 | 91.69 184 | 99.32 194 | 99.88 140 |
|
gm-plane-assit | | | 84.93 205 | 91.61 156 | 77.14 215 | 84.14 202 | 91.29 220 | 66.18 231 | 69.70 204 | 85.22 226 | 47.95 228 | 78.58 201 | 89.24 119 | 94.90 128 | 98.82 35 | 98.12 61 | 99.99 6 | 100.00 1 |
|
TranMVSNet+NR-MVSNet | | | 88.88 155 | 89.90 168 | 87.69 141 | 84.06 203 | 95.68 182 | 91.88 147 | 85.23 98 | 95.16 168 | 72.54 141 | 83.06 164 | 70.14 202 | 92.93 149 | 90.81 212 | 94.53 140 | 99.48 151 | 99.89 137 |
|
v1240 | | | 86.24 200 | 88.56 197 | 83.54 194 | 84.05 204 | 95.21 197 | 89.27 193 | 76.76 174 | 93.42 201 | 60.68 210 | 75.99 212 | 69.80 205 | 91.21 184 | 93.83 167 | 91.76 182 | 99.29 198 | 99.91 132 |
|
PEN-MVS | | | 87.20 182 | 88.22 202 | 86.01 154 | 84.01 205 | 94.93 200 | 90.00 183 | 81.52 143 | 93.46 200 | 69.29 160 | 79.69 188 | 65.51 219 | 91.72 162 | 91.01 211 | 93.12 162 | 99.49 147 | 99.84 149 |
|
MDTV_nov1_ep13_2view | | | 87.75 170 | 93.32 143 | 81.26 208 | 83.74 206 | 96.64 156 | 85.66 206 | 66.20 214 | 98.36 135 | 61.61 205 | 84.34 159 | 87.95 125 | 91.12 186 | 94.01 161 | 92.66 169 | 99.22 204 | 99.27 190 |
|
anonymousdsp | | | 87.98 163 | 92.38 149 | 82.85 200 | 83.68 207 | 96.79 153 | 90.78 158 | 74.06 188 | 95.29 166 | 57.91 215 | 83.33 162 | 83.12 144 | 91.15 185 | 95.96 133 | 92.37 173 | 99.52 139 | 99.76 160 |
|
Anonymous20240521 | | | 89.08 154 | 91.78 153 | 85.93 159 | 83.53 208 | 97.10 151 | 90.80 157 | 78.98 158 | 93.39 203 | 72.49 143 | 86.21 156 | 77.40 153 | 90.27 190 | 95.66 136 | 92.80 167 | 99.51 143 | 99.93 116 |
|
DTE-MVSNet | | | 86.70 192 | 87.66 210 | 85.58 167 | 83.30 209 | 94.29 202 | 89.74 190 | 81.53 141 | 92.77 210 | 68.93 161 | 80.13 182 | 64.00 222 | 90.62 188 | 89.45 216 | 93.34 160 | 99.32 194 | 99.67 166 |
|
FPMVS | | | 73.80 220 | 74.62 225 | 72.84 222 | 83.09 210 | 84.44 228 | 83.89 208 | 73.64 191 | 92.20 214 | 48.50 226 | 72.19 218 | 59.51 225 | 63.16 228 | 69.13 231 | 66.26 236 | 84.74 233 | 78.59 236 |
|
V4 | | | 85.78 202 | 87.74 207 | 83.50 196 | 82.90 211 | 95.33 195 | 88.62 196 | 77.05 171 | 92.14 215 | 63.45 200 | 76.91 209 | 69.85 204 | 89.72 192 | 90.07 213 | 90.05 211 | 99.27 201 | 99.81 153 |
|
v52 | | | 85.80 201 | 87.74 207 | 83.53 195 | 82.87 212 | 95.31 196 | 88.71 195 | 77.04 172 | 92.23 213 | 63.53 199 | 76.91 209 | 69.80 205 | 89.78 191 | 90.05 214 | 90.07 210 | 99.26 202 | 99.82 152 |
|
v748 | | | 84.47 207 | 86.06 212 | 82.62 203 | 82.85 213 | 95.02 199 | 83.73 211 | 78.48 163 | 90.20 219 | 67.45 172 | 75.86 214 | 61.27 224 | 83.84 213 | 89.87 215 | 90.28 209 | 99.34 191 | 99.90 133 |
|
v7n | | | 85.39 204 | 87.70 209 | 82.70 201 | 82.77 214 | 95.64 183 | 88.27 198 | 74.83 181 | 92.30 212 | 62.58 202 | 76.37 211 | 64.80 221 | 88.38 204 | 94.29 158 | 90.61 207 | 99.34 191 | 99.87 144 |
|
test20.03 | | | 83.86 210 | 88.73 196 | 78.16 213 | 82.60 215 | 93.00 207 | 81.61 217 | 74.68 182 | 92.36 211 | 57.50 216 | 83.01 165 | 74.48 168 | 73.30 225 | 92.40 189 | 91.14 205 | 99.29 198 | 94.75 221 |
|
Anonymous20231206 | | | 84.28 208 | 89.53 177 | 78.17 212 | 82.31 216 | 94.16 204 | 82.57 214 | 76.51 177 | 93.38 205 | 52.98 220 | 79.47 192 | 73.74 172 | 75.45 221 | 95.07 147 | 94.41 144 | 99.18 207 | 96.46 218 |
|
new_pmnet | | | 84.12 209 | 87.89 205 | 79.72 210 | 80.43 217 | 94.14 205 | 80.26 219 | 74.14 186 | 96.01 159 | 56.30 219 | 74.94 215 | 76.45 160 | 88.59 203 | 93.11 183 | 89.31 213 | 98.59 214 | 91.27 225 |
|
testus | | | 82.22 214 | 88.82 194 | 74.52 220 | 79.14 218 | 89.37 223 | 78.38 221 | 72.99 197 | 97.57 146 | 44.54 232 | 93.44 123 | 58.13 226 | 74.20 224 | 92.96 184 | 93.67 156 | 97.89 217 | 96.58 216 |
|
test2356 | | | 83.84 211 | 91.77 154 | 74.59 219 | 78.71 219 | 89.10 224 | 78.24 223 | 72.07 203 | 96.78 153 | 45.18 231 | 96.19 95 | 76.77 157 | 74.87 223 | 93.17 181 | 94.01 152 | 98.44 215 | 96.38 219 |
|
PM-MVS | | | 82.79 213 | 84.51 215 | 80.77 209 | 77.22 220 | 92.13 209 | 83.61 212 | 73.31 194 | 93.50 199 | 61.06 206 | 77.15 207 | 46.52 231 | 90.55 189 | 94.14 159 | 89.05 216 | 98.85 212 | 99.12 195 |
|
pmmvs-eth3d | | | 82.92 212 | 83.31 217 | 82.47 204 | 76.97 221 | 91.76 219 | 83.79 209 | 76.10 178 | 90.33 217 | 69.95 159 | 71.04 220 | 48.09 228 | 89.02 199 | 93.85 164 | 89.14 214 | 99.02 210 | 98.96 196 |
|
new-patchmatchnet | | | 78.17 218 | 80.82 219 | 75.07 218 | 76.93 222 | 91.20 221 | 71.90 227 | 73.32 193 | 86.59 225 | 48.91 225 | 67.11 223 | 47.85 230 | 81.19 215 | 88.18 218 | 87.02 221 | 98.19 216 | 97.79 210 |
|
pmmvs3 | | | 80.91 215 | 85.62 213 | 75.42 217 | 75.01 223 | 89.09 225 | 75.31 225 | 68.70 205 | 86.99 224 | 46.74 230 | 81.18 178 | 62.91 223 | 87.95 205 | 93.84 165 | 89.06 215 | 98.80 213 | 96.23 220 |
|
Anonymous20231211 | | | 74.10 219 | 74.22 227 | 73.97 221 | 74.36 224 | 87.76 226 | 75.92 224 | 72.78 199 | 74.83 235 | 52.25 221 | 44.18 234 | 42.42 234 | 73.07 226 | 86.16 222 | 86.24 224 | 95.44 226 | 97.94 208 |
|
testmv | | | 71.50 222 | 77.62 222 | 64.36 225 | 72.64 225 | 81.28 232 | 59.32 237 | 66.24 212 | 83.91 227 | 35.02 237 | 69.74 221 | 46.18 232 | 57.12 231 | 85.60 224 | 87.48 219 | 95.84 223 | 89.16 228 |
|
test1235678 | | | 71.50 222 | 77.61 223 | 64.36 225 | 72.64 225 | 81.26 233 | 59.31 238 | 66.22 213 | 83.90 228 | 35.02 237 | 69.74 221 | 46.18 232 | 57.12 231 | 85.60 224 | 87.47 220 | 95.84 223 | 89.15 229 |
|
Gipuma | | | 71.02 224 | 72.60 229 | 69.19 223 | 71.31 227 | 75.11 236 | 66.36 230 | 61.65 230 | 94.93 170 | 47.29 229 | 38.74 235 | 38.52 236 | 75.52 220 | 86.09 223 | 85.92 225 | 93.01 229 | 88.87 230 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
test12356 | | | 69.94 226 | 75.85 224 | 63.04 227 | 70.04 228 | 79.32 235 | 61.62 233 | 65.84 217 | 80.56 229 | 36.30 236 | 71.45 219 | 39.38 235 | 48.79 237 | 83.64 226 | 88.02 218 | 95.64 225 | 88.56 231 |
|
1111 | | | 73.79 221 | 78.62 221 | 68.16 224 | 69.34 229 | 81.48 230 | 59.42 235 | 52.46 235 | 78.55 231 | 50.42 223 | 62.43 228 | 71.67 183 | 80.43 217 | 86.79 219 | 88.22 217 | 96.87 219 | 81.17 235 |
|
.test1245 | | | 70.78 225 | 79.90 220 | 60.13 230 | 69.34 229 | 81.48 230 | 59.42 235 | 52.46 235 | 78.55 231 | 50.42 223 | 62.43 228 | 71.67 183 | 80.43 217 | 86.79 219 | 78.71 228 | 48.74 239 | 99.65 167 |
|
MDA-MVSNet-bldmvs | | | 80.30 217 | 82.83 218 | 77.34 214 | 69.16 231 | 94.29 202 | 72.16 226 | 81.97 135 | 90.14 220 | 57.32 217 | 94.01 118 | 47.97 229 | 86.81 210 | 68.74 232 | 86.82 222 | 96.63 220 | 97.86 209 |
|
MIMVSNet1 | | | 80.64 216 | 83.97 216 | 76.76 216 | 68.91 232 | 91.15 222 | 78.32 222 | 75.47 180 | 89.58 222 | 56.64 218 | 65.10 225 | 65.17 220 | 82.14 214 | 93.51 175 | 91.64 186 | 99.10 208 | 91.66 224 |
|
PMVS | | 60.14 18 | 62.67 229 | 64.05 231 | 61.06 229 | 68.32 233 | 53.27 243 | 52.23 240 | 67.63 208 | 75.07 234 | 48.30 227 | 58.27 230 | 57.43 227 | 49.99 236 | 67.20 233 | 62.42 237 | 79.87 237 | 74.68 238 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
ambc | | | | 74.33 226 | | 66.84 234 | 84.26 229 | 84.17 207 | | 93.39 203 | 58.99 212 | 45.93 233 | 18.06 243 | 70.61 227 | 93.94 162 | 86.62 223 | 92.61 231 | 98.13 204 |
|
PMMVS2 | | | 65.18 228 | 68.25 230 | 61.59 228 | 61.37 235 | 79.72 234 | 59.18 239 | 61.80 228 | 64.72 236 | 37.33 234 | 53.82 231 | 35.59 237 | 54.46 235 | 73.94 230 | 80.52 227 | 95.40 227 | 89.43 227 |
|
EMVS | | | 55.14 232 | 55.29 234 | 54.97 231 | 60.87 236 | 57.52 240 | 38.58 242 | 63.57 226 | 64.54 237 | 23.36 242 | 36.96 236 | 27.99 239 | 60.69 229 | 51.17 236 | 66.61 235 | 82.73 236 | 82.25 233 |
|
E-PMN | | | 55.33 231 | 55.79 233 | 54.81 232 | 59.81 237 | 57.23 241 | 38.83 241 | 63.59 225 | 64.06 238 | 24.66 241 | 35.33 237 | 26.40 240 | 58.69 230 | 55.41 235 | 70.54 233 | 83.26 234 | 81.56 234 |
|
no-one | | | 52.34 233 | 53.36 236 | 51.14 233 | 57.63 238 | 69.39 237 | 35.07 244 | 61.58 231 | 44.14 240 | 37.06 235 | 34.80 238 | 26.36 241 | 32.65 238 | 50.68 237 | 70.83 232 | 82.88 235 | 77.30 237 |
|
MVE | | 58.81 19 | 52.07 234 | 55.15 235 | 48.48 235 | 42.45 239 | 62.35 239 | 36.41 243 | 54.70 234 | 49.88 239 | 27.65 240 | 29.98 239 | 18.08 242 | 54.87 234 | 65.93 234 | 77.26 230 | 74.79 238 | 82.59 232 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
testmvs | | | 61.76 230 | 72.90 228 | 48.76 234 | 21.21 240 | 68.61 238 | 66.11 232 | 37.38 237 | 94.83 172 | 33.06 239 | 64.31 226 | 29.72 238 | 86.08 211 | 74.44 229 | 78.71 228 | 48.74 239 | 99.65 167 |
|
test123 | | | 48.14 235 | 58.11 232 | 36.51 236 | 8.71 241 | 56.81 242 | 59.55 234 | 24.08 238 | 77.50 233 | 14.41 243 | 49.20 232 | 11.94 244 | 80.98 216 | 41.62 238 | 69.81 234 | 31.32 241 | 99.90 133 |
|
GG-mvs-BLEND | | | 69.85 227 | 99.39 36 | 35.39 237 | 3.67 242 | 99.94 17 | 99.10 38 | 1.69 239 | 99.85 41 | 3.19 244 | 98.13 74 | 99.46 53 | 4.92 239 | 99.23 28 | 99.14 29 | 99.80 50 | 100.00 1 |
|
sosnet-low-res | | | 0.00 236 | 0.00 237 | 0.00 238 | 0.00 243 | 0.00 244 | 0.00 245 | 0.00 240 | 0.00 241 | 0.00 245 | 0.00 240 | 0.00 245 | 0.00 240 | 0.00 239 | 0.00 238 | 0.00 242 | 0.00 239 |
|
sosnet | | | 0.00 236 | 0.00 237 | 0.00 238 | 0.00 243 | 0.00 244 | 0.00 245 | 0.00 240 | 0.00 241 | 0.00 245 | 0.00 240 | 0.00 245 | 0.00 240 | 0.00 239 | 0.00 238 | 0.00 242 | 0.00 239 |
|
MTAPA | | | | | | | | | | | 96.61 10 | | 100.00 1 | | | | | |
|
MTMP | | | | | | | | | | | 97.42 7 | | 100.00 1 | | | | | |
|
Patchmatch-RL test | | | | | | | | 68.01 229 | | | | | | | | | | |
|
NP-MVS | | | | | | | | | | 99.79 50 | | | | | | | | |
|
Patchmtry | | | | | | | 99.00 110 | 95.46 112 | 65.50 218 | | 67.51 169 | | | | | | | |
|
DeepMVS_CX | | | | | | | 97.31 147 | 79.48 220 | 89.65 59 | 98.66 116 | 60.89 208 | 94.40 113 | 66.89 214 | 87.65 206 | 81.69 227 | | 92.76 230 | 94.24 223 |
|