SMA-MVS | | | 97.53 3 | 97.93 3 | 97.07 8 | 99.21 1 | 99.02 6 | 98.08 18 | 96.25 8 | 96.36 8 | 93.57 12 | 96.56 11 | 99.27 3 | 96.78 14 | 97.91 2 | 97.43 3 | 98.51 14 | 98.94 8 |
|
APDe-MVS | | | 97.79 2 | 97.96 2 | 97.60 1 | 99.20 2 | 99.10 4 | 98.88 2 | 96.68 3 | 96.81 3 | 94.64 4 | 97.84 2 | 98.02 9 | 97.24 2 | 97.74 6 | 97.02 10 | 98.97 2 | 99.16 2 |
|
v1.0 | | | 90.03 86 | 83.83 157 | 97.27 5 | 99.12 3 | 99.14 3 | 98.66 3 | 96.80 1 | 95.74 17 | 93.46 15 | 97.72 3 | 99.48 1 | 96.76 15 | 97.77 3 | 96.92 14 | 98.83 5 | 0.00 246 |
|
zzz-MVS | | | 96.98 12 | 96.68 20 | 97.33 3 | 99.09 4 | 98.71 11 | 98.43 7 | 96.01 13 | 96.11 13 | 95.19 3 | 92.89 30 | 97.32 19 | 96.84 10 | 97.20 15 | 96.09 34 | 98.44 25 | 98.46 27 |
|
HPM-MVS++ | | | 97.22 8 | 97.40 9 | 97.01 9 | 99.08 5 | 98.55 22 | 98.19 13 | 96.48 5 | 96.02 15 | 93.28 18 | 96.26 14 | 98.71 6 | 96.76 15 | 97.30 13 | 96.25 31 | 98.30 48 | 98.68 11 |
|
ACMMP_Plus | | | 96.93 13 | 97.27 11 | 96.53 21 | 99.06 6 | 98.95 7 | 98.24 12 | 96.06 12 | 95.66 19 | 90.96 31 | 95.63 21 | 97.71 13 | 96.53 19 | 97.66 8 | 96.68 17 | 98.30 48 | 98.61 16 |
|
PGM-MVS | | | 96.16 21 | 96.33 25 | 95.95 24 | 99.04 7 | 98.63 17 | 98.32 11 | 92.76 38 | 93.42 46 | 90.49 36 | 96.30 13 | 95.31 36 | 96.71 17 | 96.46 31 | 96.02 35 | 98.38 35 | 98.19 36 |
|
APD-MVS | | | 97.12 9 | 97.05 14 | 97.19 6 | 99.04 7 | 98.63 17 | 98.45 6 | 96.54 4 | 94.81 33 | 93.50 13 | 96.10 16 | 97.40 18 | 96.81 11 | 97.05 18 | 96.82 16 | 98.80 6 | 98.56 17 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
NCCC | | | 96.75 16 | 96.67 21 | 96.85 14 | 99.03 9 | 98.44 30 | 98.15 15 | 96.28 7 | 96.32 9 | 92.39 23 | 92.16 32 | 97.55 16 | 96.68 18 | 97.32 11 | 96.65 19 | 98.55 13 | 98.26 32 |
|
CNVR-MVS | | | 97.30 7 | 97.41 8 | 97.18 7 | 99.02 10 | 98.60 19 | 98.15 15 | 96.24 10 | 96.12 12 | 94.10 9 | 95.54 22 | 97.99 10 | 96.99 6 | 97.97 1 | 97.17 6 | 98.57 12 | 98.50 23 |
|
HSP-MVS | | | 97.51 4 | 97.70 5 | 97.29 4 | 99.00 11 | 99.17 2 | 98.61 4 | 96.41 6 | 95.88 16 | 94.34 8 | 97.72 3 | 99.04 5 | 96.93 9 | 97.29 14 | 95.90 37 | 98.45 24 | 98.94 8 |
|
ACMMPR | | | 96.92 14 | 96.96 15 | 96.87 13 | 98.99 12 | 98.78 9 | 98.38 9 | 95.52 21 | 96.57 6 | 92.81 22 | 96.06 17 | 95.90 31 | 97.07 4 | 96.60 28 | 96.34 28 | 98.46 21 | 98.42 28 |
|
HFP-MVS | | | 97.11 10 | 97.19 12 | 97.00 10 | 98.97 13 | 98.73 10 | 98.37 10 | 95.69 18 | 96.60 5 | 93.28 18 | 96.87 6 | 96.64 24 | 97.27 1 | 96.64 26 | 96.33 29 | 98.44 25 | 98.56 17 |
|
SteuartSystems-ACMMP | | | 97.10 11 | 97.49 7 | 96.65 16 | 98.97 13 | 98.95 7 | 98.43 7 | 95.96 14 | 95.12 26 | 91.46 26 | 96.85 7 | 97.60 15 | 96.37 23 | 97.76 4 | 97.16 7 | 98.68 7 | 98.97 7 |
Skip Steuart: Steuart Systems R&D Blog. |
X-MVS | | | 96.07 23 | 96.33 25 | 95.77 27 | 98.94 15 | 98.66 12 | 97.94 22 | 95.41 26 | 95.12 26 | 88.03 49 | 93.00 29 | 96.06 27 | 95.85 25 | 96.65 25 | 96.35 26 | 98.47 19 | 98.48 24 |
|
MP-MVS | | | 96.56 18 | 96.72 19 | 96.37 22 | 98.93 16 | 98.48 26 | 98.04 19 | 95.55 20 | 94.32 37 | 90.95 33 | 95.88 19 | 97.02 21 | 96.29 24 | 96.77 24 | 96.01 36 | 98.47 19 | 98.56 17 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
MCST-MVS | | | 96.83 15 | 97.06 13 | 96.57 17 | 98.88 17 | 98.47 28 | 98.02 20 | 96.16 11 | 95.58 21 | 90.96 31 | 95.78 20 | 97.84 12 | 96.46 21 | 97.00 20 | 96.17 33 | 98.94 4 | 98.55 22 |
|
CP-MVS | | | 96.68 17 | 96.59 23 | 96.77 15 | 98.85 18 | 98.58 20 | 98.18 14 | 95.51 22 | 95.34 23 | 92.94 21 | 95.21 25 | 96.25 26 | 96.79 13 | 96.44 33 | 95.77 39 | 98.35 37 | 98.56 17 |
|
ESAPD | | | 97.83 1 | 98.13 1 | 97.48 2 | 98.83 19 | 99.19 1 | 98.99 1 | 96.70 2 | 96.05 14 | 94.39 6 | 98.30 1 | 99.47 2 | 97.02 5 | 97.75 5 | 97.02 10 | 98.98 1 | 99.10 5 |
|
mPP-MVS | | | | | | 98.76 20 | | | | | | | 95.49 34 | | | | | |
|
CSCG | | | 95.68 27 | 95.46 32 | 95.93 25 | 98.71 21 | 99.07 5 | 97.13 32 | 93.55 33 | 95.48 22 | 93.35 17 | 90.61 41 | 93.82 41 | 95.16 32 | 94.60 74 | 95.57 42 | 97.70 105 | 99.08 6 |
|
DeepC-MVS_fast | | 93.32 1 | 96.48 19 | 96.42 24 | 96.56 18 | 98.70 22 | 98.31 34 | 97.97 21 | 95.76 17 | 96.31 10 | 92.01 25 | 91.43 37 | 95.42 35 | 96.46 21 | 97.65 9 | 97.69 1 | 98.49 18 | 98.12 41 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
AdaColmap | | | 95.02 33 | 93.71 42 | 96.54 20 | 98.51 23 | 97.76 50 | 96.69 36 | 95.94 16 | 93.72 44 | 93.50 13 | 89.01 48 | 90.53 58 | 96.49 20 | 94.51 77 | 93.76 73 | 98.07 79 | 96.69 90 |
|
train_agg | | | 96.15 22 | 96.64 22 | 95.58 31 | 98.44 24 | 98.03 41 | 98.14 17 | 95.40 27 | 93.90 43 | 87.72 53 | 96.26 14 | 98.10 8 | 95.75 27 | 96.25 38 | 95.45 44 | 98.01 86 | 98.47 25 |
|
CDPH-MVS | | | 94.80 37 | 95.50 30 | 93.98 43 | 98.34 25 | 98.06 40 | 97.41 27 | 93.23 35 | 92.81 49 | 82.98 88 | 92.51 31 | 94.82 37 | 93.53 51 | 96.08 41 | 96.30 30 | 98.42 28 | 97.94 47 |
|
MSLP-MVS++ | | | 96.05 24 | 95.63 28 | 96.55 19 | 98.33 26 | 98.17 37 | 96.94 33 | 94.61 30 | 94.70 35 | 94.37 7 | 89.20 47 | 95.96 30 | 96.81 11 | 95.57 47 | 97.33 5 | 98.24 60 | 98.47 25 |
|
ACMMP | | | 95.54 28 | 95.49 31 | 95.61 30 | 98.27 27 | 98.53 24 | 97.16 31 | 94.86 28 | 94.88 32 | 89.34 40 | 95.36 24 | 91.74 49 | 95.50 30 | 95.51 48 | 94.16 61 | 98.50 17 | 98.22 34 |
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 |
3Dnovator+ | | 90.56 5 | 95.06 32 | 94.56 37 | 95.65 29 | 98.11 28 | 98.15 38 | 97.19 30 | 91.59 48 | 95.11 28 | 93.23 20 | 81.99 97 | 94.71 38 | 95.43 31 | 96.48 30 | 96.88 15 | 98.35 37 | 98.63 13 |
|
3Dnovator | | 90.28 7 | 94.70 38 | 94.34 40 | 95.11 32 | 98.06 29 | 98.21 35 | 96.89 34 | 91.03 54 | 94.72 34 | 91.45 27 | 82.87 88 | 93.10 44 | 94.61 36 | 96.24 39 | 97.08 9 | 98.63 10 | 98.16 37 |
|
PLC | | 90.69 4 | 94.32 40 | 92.99 50 | 95.87 26 | 97.91 30 | 96.49 91 | 95.95 47 | 94.12 31 | 94.94 30 | 94.09 10 | 85.90 62 | 90.77 55 | 95.58 29 | 94.52 76 | 93.32 89 | 97.55 113 | 95.00 155 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
EPNet | | | 93.92 43 | 94.40 38 | 93.36 50 | 97.89 31 | 96.55 88 | 96.08 43 | 92.14 41 | 91.65 60 | 89.16 42 | 94.07 27 | 90.17 62 | 87.78 116 | 95.24 50 | 94.97 50 | 97.09 129 | 98.15 38 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
CPTT-MVS | | | 95.54 28 | 95.07 33 | 96.10 23 | 97.88 32 | 97.98 44 | 97.92 23 | 94.86 28 | 94.56 36 | 92.16 24 | 91.01 39 | 95.71 32 | 96.97 8 | 94.56 75 | 93.50 83 | 96.81 167 | 98.14 39 |
|
QAPM | | | 94.13 42 | 94.33 41 | 93.90 44 | 97.82 33 | 98.37 33 | 96.47 38 | 90.89 55 | 92.73 51 | 85.63 70 | 85.35 67 | 93.87 40 | 94.17 42 | 95.71 46 | 95.90 37 | 98.40 32 | 98.42 28 |
|
DeepC-MVS | | 92.10 3 | 95.22 31 | 94.77 35 | 95.75 28 | 97.77 34 | 98.54 23 | 97.63 26 | 95.96 14 | 95.07 29 | 88.85 44 | 85.35 67 | 91.85 48 | 95.82 26 | 96.88 23 | 97.10 8 | 98.44 25 | 98.63 13 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
OpenMVS | | 88.18 11 | 92.51 53 | 91.61 71 | 93.55 49 | 97.74 35 | 98.02 42 | 95.66 50 | 90.46 58 | 89.14 91 | 86.50 62 | 75.80 132 | 90.38 61 | 92.69 61 | 94.99 53 | 95.30 45 | 98.27 55 | 97.63 59 |
|
TSAR-MVS + ACMM | | | 96.19 20 | 97.39 10 | 94.78 34 | 97.70 36 | 98.41 31 | 97.72 25 | 95.49 23 | 96.47 7 | 86.66 61 | 96.35 12 | 97.85 11 | 93.99 44 | 97.19 16 | 96.37 25 | 97.12 127 | 99.13 3 |
|
MAR-MVS | | | 92.71 52 | 92.63 53 | 92.79 60 | 97.70 36 | 97.15 71 | 93.75 84 | 87.98 96 | 90.71 67 | 85.76 69 | 86.28 59 | 86.38 67 | 94.35 39 | 94.95 55 | 95.49 43 | 97.22 121 | 97.44 66 |
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 |
PHI-MVS | | | 95.86 25 | 96.93 18 | 94.61 38 | 97.60 38 | 98.65 16 | 96.49 37 | 93.13 36 | 94.07 40 | 87.91 52 | 97.12 5 | 97.17 20 | 93.90 47 | 96.46 31 | 96.93 13 | 98.64 9 | 98.10 43 |
|
abl_6 | | | | | 94.78 34 | 97.46 39 | 97.99 43 | 95.76 48 | 91.80 45 | 93.72 44 | 91.25 28 | 91.33 38 | 96.47 25 | 94.28 41 | | | 98.14 68 | 97.39 68 |
|
SD-MVS | | | 97.35 5 | 97.73 4 | 96.90 12 | 97.35 40 | 98.66 12 | 97.85 24 | 96.25 8 | 96.86 2 | 94.54 5 | 96.75 9 | 99.13 4 | 96.99 6 | 96.94 21 | 96.58 20 | 98.39 34 | 99.20 1 |
|
MVS_111021_HR | | | 94.84 35 | 95.91 27 | 93.60 48 | 97.35 40 | 98.46 29 | 95.08 56 | 91.19 51 | 94.18 39 | 85.97 64 | 95.38 23 | 92.56 46 | 93.61 50 | 96.61 27 | 96.25 31 | 98.40 32 | 97.92 49 |
|
TSAR-MVS + MP. | | | 97.31 6 | 97.64 6 | 96.92 11 | 97.28 42 | 98.56 21 | 98.61 4 | 95.48 24 | 96.72 4 | 94.03 11 | 96.73 10 | 98.29 7 | 97.15 3 | 97.61 10 | 96.42 23 | 98.96 3 | 99.13 3 |
|
CANet | | | 94.85 34 | 94.92 34 | 94.78 34 | 97.25 43 | 98.52 25 | 97.20 29 | 91.81 44 | 93.25 47 | 91.06 30 | 86.29 58 | 94.46 39 | 92.99 56 | 97.02 19 | 96.68 17 | 98.34 39 | 98.20 35 |
|
OMC-MVS | | | 94.49 39 | 94.36 39 | 94.64 37 | 97.17 44 | 97.73 51 | 95.49 53 | 92.25 40 | 96.18 11 | 90.34 37 | 88.51 49 | 92.88 45 | 94.90 35 | 94.92 57 | 94.17 60 | 97.69 106 | 96.15 116 |
|
MVS_111021_LR | | | 94.84 35 | 95.57 29 | 94.00 41 | 97.11 45 | 97.72 53 | 94.88 59 | 91.16 52 | 95.24 25 | 88.74 45 | 96.03 18 | 91.52 52 | 94.33 40 | 95.96 42 | 95.01 49 | 97.79 97 | 97.49 64 |
|
CNLPA | | | 93.69 45 | 92.50 55 | 95.06 33 | 97.11 45 | 97.36 59 | 93.88 81 | 93.30 34 | 95.64 20 | 93.44 16 | 80.32 104 | 90.73 56 | 94.99 34 | 93.58 99 | 93.33 87 | 97.67 108 | 96.57 100 |
|
LS3D | | | 91.97 60 | 90.98 76 | 93.12 56 | 97.03 47 | 97.09 74 | 95.33 55 | 95.59 19 | 92.47 52 | 79.26 111 | 81.60 100 | 82.77 89 | 94.39 38 | 94.28 80 | 94.23 59 | 97.14 126 | 94.45 160 |
|
TAPA-MVS | | 90.35 6 | 93.69 45 | 93.52 43 | 93.90 44 | 96.89 48 | 97.62 55 | 96.15 41 | 91.67 47 | 94.94 30 | 85.97 64 | 87.72 52 | 91.96 47 | 94.40 37 | 93.76 93 | 93.06 103 | 98.30 48 | 95.58 134 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
DELS-MVS | | | 93.71 44 | 93.47 44 | 94.00 41 | 96.82 49 | 98.39 32 | 96.80 35 | 91.07 53 | 89.51 89 | 89.94 39 | 83.80 84 | 89.29 63 | 90.95 85 | 97.32 11 | 97.65 2 | 98.42 28 | 98.32 31 |
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 |
EPNet_dtu | | | 88.32 114 | 90.61 78 | 85.64 153 | 96.79 50 | 92.27 180 | 92.03 120 | 90.31 59 | 89.05 92 | 65.44 205 | 89.43 45 | 85.90 72 | 74.22 214 | 92.76 113 | 92.09 122 | 95.02 202 | 92.76 184 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
MSDG | | | 90.42 80 | 88.25 104 | 92.94 59 | 96.67 51 | 94.41 117 | 93.96 77 | 92.91 37 | 89.59 88 | 86.26 63 | 76.74 124 | 80.92 103 | 90.43 91 | 92.60 119 | 92.08 123 | 97.44 117 | 91.41 193 |
|
DeepPCF-MVS | | 92.65 2 | 95.50 30 | 96.96 15 | 93.79 47 | 96.44 52 | 98.21 35 | 93.51 90 | 94.08 32 | 96.94 1 | 89.29 41 | 93.08 28 | 96.77 23 | 93.82 49 | 97.68 7 | 97.40 4 | 95.59 192 | 98.65 12 |
|
PCF-MVS | | 90.19 8 | 92.98 49 | 92.07 64 | 94.04 40 | 96.39 53 | 97.87 45 | 96.03 44 | 95.47 25 | 87.16 109 | 85.09 79 | 84.81 76 | 93.21 43 | 93.46 53 | 91.98 131 | 91.98 126 | 97.78 98 | 97.51 63 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
MVS_0304 | | | 94.30 41 | 94.68 36 | 93.86 46 | 96.33 54 | 98.48 26 | 97.41 27 | 91.20 50 | 92.75 50 | 86.96 59 | 86.03 61 | 93.81 42 | 92.64 62 | 96.89 22 | 96.54 22 | 98.61 11 | 98.24 33 |
|
OPM-MVS | | | 91.08 70 | 89.34 89 | 93.11 57 | 96.18 55 | 96.13 103 | 96.39 39 | 92.39 39 | 82.97 153 | 81.74 90 | 82.55 94 | 80.20 105 | 93.97 46 | 94.62 72 | 93.23 91 | 98.00 87 | 95.73 129 |
|
PVSNet_BlendedMVS | | | 92.80 50 | 92.44 57 | 93.23 52 | 96.02 56 | 97.83 48 | 93.74 85 | 90.58 56 | 91.86 57 | 90.69 34 | 85.87 64 | 82.04 96 | 90.01 93 | 96.39 34 | 95.26 46 | 98.34 39 | 97.81 55 |
|
PVSNet_Blended | | | 92.80 50 | 92.44 57 | 93.23 52 | 96.02 56 | 97.83 48 | 93.74 85 | 90.58 56 | 91.86 57 | 90.69 34 | 85.87 64 | 82.04 96 | 90.01 93 | 96.39 34 | 95.26 46 | 98.34 39 | 97.81 55 |
|
XVS | | | | | | 95.68 58 | 98.66 12 | 94.96 57 | | | 88.03 49 | | 96.06 27 | | | | 98.46 21 | |
|
X-MVStestdata | | | | | | 95.68 58 | 98.66 12 | 94.96 57 | | | 88.03 49 | | 96.06 27 | | | | 98.46 21 | |
|
HQP-MVS | | | 92.39 55 | 92.49 56 | 92.29 64 | 95.65 60 | 95.94 105 | 95.64 51 | 92.12 42 | 92.46 53 | 79.65 109 | 91.97 34 | 82.68 90 | 92.92 58 | 93.47 104 | 92.77 109 | 97.74 101 | 98.12 41 |
|
HyFIR lowres test | | | 87.87 117 | 86.42 130 | 89.57 100 | 95.56 61 | 96.99 77 | 92.37 105 | 84.15 142 | 86.64 113 | 77.17 119 | 57.65 222 | 83.97 80 | 91.08 84 | 92.09 129 | 92.44 113 | 97.09 129 | 95.16 152 |
|
ACMM | | 88.76 10 | 91.70 67 | 90.43 79 | 93.19 54 | 95.56 61 | 95.14 109 | 93.35 95 | 91.48 49 | 92.26 54 | 87.12 57 | 84.02 82 | 79.34 108 | 93.99 44 | 94.07 86 | 92.68 111 | 97.62 112 | 95.50 135 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
COLMAP_ROB | | 84.39 15 | 87.61 119 | 86.03 134 | 89.46 102 | 95.54 63 | 94.48 114 | 91.77 124 | 90.14 60 | 87.16 109 | 75.50 125 | 73.41 147 | 76.86 124 | 87.33 123 | 90.05 163 | 89.76 187 | 96.48 174 | 90.46 202 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
LGP-MVS_train | | | 91.83 63 | 92.04 65 | 91.58 71 | 95.46 64 | 96.18 102 | 95.97 46 | 89.85 62 | 90.45 72 | 77.76 115 | 91.92 35 | 80.07 106 | 92.34 66 | 94.27 81 | 93.47 84 | 98.11 72 | 97.90 53 |
|
CHOSEN 1792x2688 | | | 88.57 111 | 87.82 112 | 89.44 103 | 95.46 64 | 96.89 81 | 93.74 85 | 85.87 120 | 89.63 87 | 77.42 118 | 61.38 217 | 83.31 84 | 88.80 113 | 93.44 106 | 93.16 97 | 95.37 197 | 96.95 81 |
|
PVSNet_Blended_VisFu | | | 91.92 61 | 92.39 59 | 91.36 81 | 95.45 66 | 97.85 47 | 92.25 110 | 89.54 74 | 88.53 99 | 87.47 55 | 79.82 106 | 90.53 58 | 85.47 157 | 96.31 37 | 95.16 48 | 97.99 88 | 98.56 17 |
|
PatchMatch-RL | | | 90.30 81 | 88.93 95 | 91.89 66 | 95.41 67 | 95.68 106 | 90.94 128 | 88.67 88 | 89.80 86 | 86.95 60 | 85.90 62 | 72.51 135 | 92.46 63 | 93.56 102 | 92.18 119 | 96.93 151 | 92.89 180 |
|
TSAR-MVS + COLMAP | | | 92.39 55 | 92.31 60 | 92.47 61 | 95.35 68 | 96.46 92 | 96.13 42 | 92.04 43 | 95.33 24 | 80.11 106 | 94.95 26 | 77.35 121 | 94.05 43 | 94.49 78 | 93.08 100 | 97.15 124 | 94.53 158 |
|
ACMP | | 89.13 9 | 92.03 59 | 91.70 70 | 92.41 62 | 94.92 69 | 96.44 94 | 93.95 79 | 89.96 61 | 91.81 59 | 85.48 75 | 90.97 40 | 79.12 109 | 92.42 64 | 93.28 110 | 92.55 112 | 97.76 99 | 97.74 58 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
UA-Net | | | 90.81 74 | 92.58 54 | 88.74 111 | 94.87 70 | 97.44 57 | 92.61 102 | 88.22 92 | 82.35 156 | 78.93 112 | 85.20 70 | 95.61 33 | 79.56 199 | 96.52 29 | 96.57 21 | 98.23 61 | 94.37 161 |
|
IB-MVS | | 85.10 14 | 87.98 115 | 87.97 109 | 87.99 120 | 94.55 71 | 96.86 82 | 84.52 208 | 88.21 93 | 86.48 118 | 88.54 47 | 74.41 141 | 77.74 117 | 74.10 216 | 89.65 169 | 92.85 107 | 98.06 81 | 97.80 57 |
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 |
CANet_DTU | | | 90.74 77 | 92.93 51 | 88.19 115 | 94.36 72 | 96.61 86 | 94.34 66 | 84.66 136 | 90.66 68 | 68.75 183 | 90.41 42 | 86.89 65 | 89.78 95 | 95.46 49 | 94.87 52 | 97.25 120 | 95.62 132 |
|
casdiffmvs1 | | | 93.20 47 | 93.17 47 | 93.25 51 | 94.35 73 | 97.64 54 | 95.59 52 | 87.34 110 | 94.26 38 | 90.22 38 | 89.46 44 | 85.25 75 | 93.90 47 | 92.68 115 | 94.94 51 | 98.11 72 | 97.92 49 |
|
canonicalmvs | | | 93.08 48 | 93.09 48 | 93.07 58 | 94.24 74 | 97.86 46 | 95.45 54 | 87.86 102 | 94.00 42 | 87.47 55 | 88.32 50 | 82.37 94 | 95.13 33 | 93.96 91 | 96.41 24 | 98.27 55 | 98.73 10 |
|
tfpn | | | 88.67 107 | 86.57 128 | 91.12 85 | 94.14 75 | 97.15 71 | 93.51 90 | 89.37 76 | 85.49 132 | 79.91 108 | 75.26 138 | 62.24 209 | 91.39 79 | 95.00 52 | 93.95 69 | 98.41 30 | 96.88 84 |
|
view800 | | | 89.21 104 | 87.44 122 | 91.27 82 | 94.13 76 | 97.18 70 | 93.74 85 | 89.53 75 | 85.60 131 | 80.34 105 | 75.29 136 | 68.89 155 | 91.57 78 | 94.97 54 | 93.36 86 | 98.34 39 | 96.79 87 |
|
UGNet | | | 91.52 68 | 93.41 45 | 89.32 104 | 94.13 76 | 97.15 71 | 91.83 123 | 89.01 83 | 90.62 69 | 85.86 68 | 86.83 54 | 91.73 50 | 77.40 206 | 94.68 71 | 94.43 56 | 97.71 103 | 98.40 30 |
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 |
thres600view7 | | | 89.28 102 | 87.47 121 | 91.39 78 | 94.12 78 | 97.25 66 | 93.94 80 | 89.74 69 | 85.62 130 | 80.63 103 | 75.24 139 | 69.33 154 | 91.66 77 | 94.92 57 | 93.23 91 | 98.27 55 | 96.72 89 |
|
view600 | | | 89.29 101 | 87.48 120 | 91.41 77 | 94.10 79 | 97.21 68 | 93.96 77 | 89.70 72 | 85.67 127 | 80.75 102 | 75.29 136 | 69.35 153 | 91.70 76 | 94.92 57 | 93.23 91 | 98.26 59 | 96.69 90 |
|
IS_MVSNet | | | 91.87 62 | 93.35 46 | 90.14 97 | 94.09 80 | 97.73 51 | 93.09 97 | 88.12 94 | 88.71 95 | 79.98 107 | 84.49 77 | 90.63 57 | 87.49 121 | 97.07 17 | 96.96 12 | 98.07 79 | 97.88 54 |
|
TSAR-MVS + GP. | | | 95.86 25 | 96.95 17 | 94.60 39 | 94.07 81 | 98.11 39 | 96.30 40 | 91.76 46 | 95.67 18 | 91.07 29 | 96.82 8 | 97.69 14 | 95.71 28 | 95.96 42 | 95.75 40 | 98.68 7 | 98.63 13 |
|
thres400 | | | 89.40 96 | 87.58 118 | 91.53 73 | 94.06 82 | 97.21 68 | 94.19 76 | 89.83 63 | 85.69 126 | 81.08 100 | 75.50 134 | 69.76 152 | 91.80 69 | 94.79 69 | 93.51 77 | 98.20 64 | 96.60 98 |
|
ACMH | | 85.51 13 | 87.31 122 | 86.59 127 | 88.14 118 | 93.96 83 | 94.51 113 | 89.00 176 | 87.99 95 | 81.58 158 | 70.15 166 | 78.41 114 | 71.78 140 | 90.60 89 | 91.30 140 | 91.99 125 | 97.17 123 | 96.58 99 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
MS-PatchMatch | | | 87.63 118 | 87.61 116 | 87.65 125 | 93.95 84 | 94.09 121 | 92.60 103 | 81.52 175 | 86.64 113 | 76.41 123 | 73.46 146 | 85.94 71 | 85.01 162 | 92.23 127 | 90.00 180 | 96.43 176 | 90.93 199 |
|
thres200 | | | 89.49 95 | 87.72 113 | 91.55 72 | 93.95 84 | 97.25 66 | 94.34 66 | 89.74 69 | 85.66 128 | 81.18 95 | 76.12 131 | 70.19 151 | 91.80 69 | 94.92 57 | 93.51 77 | 98.27 55 | 96.40 104 |
|
CLD-MVS | | | 92.50 54 | 91.96 66 | 93.13 55 | 93.93 86 | 96.24 100 | 95.69 49 | 88.77 86 | 92.92 48 | 89.01 43 | 88.19 51 | 81.74 100 | 93.13 55 | 93.63 97 | 93.08 100 | 98.23 61 | 97.91 52 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
tfpn111 | | | 90.16 85 | 88.99 94 | 91.52 75 | 93.90 87 | 97.26 63 | 94.31 68 | 89.75 66 | 85.87 120 | 81.10 98 | 84.41 78 | 70.38 146 | 91.76 71 | 94.92 57 | 93.51 77 | 98.29 52 | 96.61 93 |
|
conf200view11 | | | 89.55 93 | 87.86 110 | 91.52 75 | 93.90 87 | 97.26 63 | 94.31 68 | 89.75 66 | 85.87 120 | 81.10 98 | 76.46 126 | 70.38 146 | 91.76 71 | 94.92 57 | 93.51 77 | 98.29 52 | 96.61 93 |
|
thres100view900 | | | 89.36 97 | 87.61 116 | 91.39 78 | 93.90 87 | 96.86 82 | 94.35 65 | 89.66 73 | 85.87 120 | 81.15 96 | 76.46 126 | 70.38 146 | 91.17 81 | 94.09 85 | 93.43 85 | 98.13 69 | 96.16 115 |
|
tfpn200view9 | | | 89.55 93 | 87.86 110 | 91.53 73 | 93.90 87 | 97.26 63 | 94.31 68 | 89.74 69 | 85.87 120 | 81.15 96 | 76.46 126 | 70.38 146 | 91.76 71 | 94.92 57 | 93.51 77 | 98.28 54 | 96.61 93 |
|
conf0.01 | | | 89.34 99 | 87.39 123 | 91.61 70 | 93.88 91 | 97.34 61 | 94.31 68 | 89.82 65 | 85.87 120 | 81.53 92 | 77.93 116 | 66.15 186 | 91.76 71 | 94.90 64 | 93.51 77 | 98.32 44 | 96.05 120 |
|
conf0.002 | | | 89.25 103 | 87.21 124 | 91.62 69 | 93.87 92 | 97.35 60 | 94.31 68 | 89.83 63 | 85.87 120 | 81.62 91 | 78.72 112 | 63.89 203 | 91.76 71 | 94.90 64 | 93.98 68 | 98.33 43 | 95.77 127 |
|
casdiffmvs | | | 92.13 57 | 91.95 67 | 92.34 63 | 93.87 92 | 97.44 57 | 94.36 64 | 86.99 113 | 92.00 55 | 88.04 48 | 87.23 53 | 81.81 99 | 92.73 59 | 93.78 92 | 94.06 66 | 98.03 83 | 97.30 73 |
|
CHOSEN 280x420 | | | 90.77 75 | 92.14 62 | 89.17 106 | 93.86 94 | 92.81 166 | 93.16 96 | 80.22 191 | 90.21 76 | 84.67 81 | 89.89 43 | 91.38 53 | 90.57 90 | 94.94 56 | 92.11 121 | 92.52 214 | 93.65 172 |
|
tfpn1000 | | | 89.30 100 | 89.72 88 | 88.81 109 | 93.83 95 | 96.50 90 | 91.53 127 | 88.74 87 | 91.20 64 | 76.74 121 | 84.96 74 | 75.44 129 | 83.50 178 | 93.63 97 | 92.42 114 | 98.51 14 | 93.88 169 |
|
FC-MVSNet-train | | | 90.55 78 | 90.19 81 | 90.97 87 | 93.78 96 | 95.16 108 | 92.11 117 | 88.85 85 | 87.64 105 | 83.38 86 | 84.36 80 | 78.41 112 | 89.53 96 | 94.69 70 | 93.15 98 | 98.15 67 | 97.92 49 |
|
conf0.05thres1000 | | | 87.90 116 | 85.88 139 | 90.26 92 | 93.74 97 | 96.39 96 | 92.67 101 | 88.94 84 | 80.97 165 | 77.71 117 | 70.15 161 | 68.40 160 | 90.42 92 | 94.46 79 | 93.29 90 | 98.09 75 | 97.49 64 |
|
Vis-MVSNet (Re-imp) | | | 90.54 79 | 92.76 52 | 87.94 121 | 93.73 98 | 96.94 79 | 92.17 115 | 87.91 97 | 88.77 94 | 76.12 124 | 83.68 85 | 90.80 54 | 79.49 200 | 96.34 36 | 96.35 26 | 98.21 63 | 96.46 102 |
|
tfpnview11 | | | 88.80 106 | 89.21 91 | 88.31 114 | 93.70 99 | 96.24 100 | 92.35 106 | 89.11 80 | 89.90 85 | 72.14 142 | 85.12 71 | 73.93 131 | 84.20 169 | 93.75 94 | 92.85 107 | 98.38 35 | 92.68 187 |
|
EPP-MVSNet | | | 92.13 57 | 93.06 49 | 91.05 86 | 93.66 100 | 97.30 62 | 92.18 113 | 87.90 98 | 90.24 75 | 83.63 83 | 86.14 60 | 90.52 60 | 90.76 87 | 94.82 67 | 94.38 57 | 98.18 66 | 97.98 45 |
|
tfpn_n400 | | | 88.58 109 | 88.91 96 | 88.19 115 | 93.63 101 | 96.34 98 | 92.22 111 | 89.04 81 | 87.37 107 | 72.14 142 | 85.12 71 | 73.93 131 | 84.04 174 | 93.65 95 | 93.20 94 | 98.09 75 | 92.77 182 |
|
tfpnconf | | | 88.58 109 | 88.91 96 | 88.19 115 | 93.63 101 | 96.34 98 | 92.22 111 | 89.04 81 | 87.37 107 | 72.14 142 | 85.12 71 | 73.93 131 | 84.04 174 | 93.65 95 | 93.20 94 | 98.09 75 | 92.77 182 |
|
thresconf0.02 | | | 88.86 105 | 88.70 99 | 89.04 107 | 93.59 103 | 96.40 95 | 92.97 99 | 89.75 66 | 90.16 79 | 74.34 128 | 84.41 78 | 71.00 142 | 85.16 159 | 93.32 108 | 93.12 99 | 98.41 30 | 92.52 189 |
|
tfpn_ndepth | | | 89.72 90 | 89.91 86 | 89.49 101 | 93.56 104 | 96.67 85 | 92.34 107 | 89.25 79 | 90.85 65 | 78.68 114 | 84.25 81 | 77.39 120 | 84.84 163 | 93.58 99 | 92.76 110 | 98.30 48 | 93.90 168 |
|
ACMH+ | | 85.75 12 | 87.19 123 | 86.02 135 | 88.56 112 | 93.42 105 | 94.41 117 | 89.91 159 | 87.66 106 | 83.45 150 | 72.25 140 | 76.42 129 | 71.99 139 | 90.78 86 | 89.86 164 | 90.94 139 | 97.32 118 | 95.11 154 |
|
MVS_Test | | | 91.81 64 | 92.19 61 | 91.37 80 | 93.24 106 | 96.95 78 | 94.43 61 | 86.25 115 | 91.45 62 | 83.45 85 | 86.31 57 | 85.15 76 | 92.93 57 | 93.99 87 | 94.71 54 | 97.92 91 | 96.77 88 |
|
diffmvs1 | | | 91.72 66 | 92.13 63 | 91.24 83 | 93.20 107 | 96.92 80 | 94.37 62 | 86.24 116 | 94.05 41 | 84.30 82 | 85.80 66 | 83.64 82 | 92.71 60 | 93.47 104 | 93.92 71 | 96.60 172 | 97.11 77 |
|
MVSTER | | | 91.73 65 | 91.61 71 | 91.86 67 | 93.18 108 | 94.56 111 | 94.37 62 | 87.90 98 | 90.16 79 | 88.69 46 | 89.23 46 | 81.28 102 | 88.92 110 | 95.75 45 | 93.95 69 | 98.12 70 | 96.37 105 |
|
Anonymous202405211 | | | | 88.00 107 | | 93.16 109 | 96.38 97 | 93.58 89 | 89.34 77 | 87.92 104 | | 65.04 204 | 83.03 86 | 92.07 67 | 92.67 116 | 93.33 87 | 96.96 142 | 97.63 59 |
|
tttt0517 | | | 91.01 73 | 91.71 69 | 90.19 95 | 92.98 110 | 97.07 75 | 91.96 122 | 87.63 107 | 90.61 70 | 81.42 94 | 86.76 56 | 82.26 95 | 89.23 102 | 94.86 66 | 93.03 105 | 97.90 92 | 97.36 69 |
|
Effi-MVS+ | | | 89.79 89 | 89.83 87 | 89.74 98 | 92.98 110 | 96.45 93 | 93.48 92 | 84.24 140 | 87.62 106 | 76.45 122 | 81.76 98 | 77.56 119 | 93.48 52 | 94.61 73 | 93.59 76 | 97.82 96 | 97.22 74 |
|
RPSCF | | | 89.68 91 | 89.24 90 | 90.20 94 | 92.97 112 | 92.93 162 | 92.30 108 | 87.69 104 | 90.44 73 | 85.12 78 | 91.68 36 | 85.84 73 | 90.69 88 | 87.34 201 | 86.07 205 | 92.46 215 | 90.37 203 |
|
TDRefinement | | | 84.97 149 | 83.39 164 | 86.81 134 | 92.97 112 | 94.12 120 | 92.18 113 | 87.77 103 | 82.78 154 | 71.31 151 | 68.43 168 | 68.07 162 | 81.10 195 | 89.70 168 | 89.03 196 | 95.55 194 | 91.62 191 |
|
thisisatest0530 | | | 91.04 72 | 91.74 68 | 90.21 93 | 92.93 114 | 97.00 76 | 92.06 118 | 87.63 107 | 90.74 66 | 81.51 93 | 86.81 55 | 82.48 91 | 89.23 102 | 94.81 68 | 93.03 105 | 97.90 92 | 97.33 71 |
|
Anonymous20240521 | | | 91.24 69 | 91.26 74 | 91.22 84 | 92.84 115 | 93.44 140 | 93.82 82 | 86.75 114 | 91.33 63 | 85.61 71 | 84.00 83 | 85.46 74 | 91.27 80 | 92.91 112 | 93.62 75 | 97.02 133 | 98.05 44 |
|
EPMVS | | | 85.77 137 | 86.24 132 | 85.23 160 | 92.76 116 | 93.78 127 | 89.91 159 | 73.60 218 | 90.19 77 | 74.22 129 | 82.18 96 | 78.06 114 | 87.55 119 | 85.61 210 | 85.38 211 | 93.32 207 | 88.48 213 |
|
DWT-MVSNet_training | | | 86.83 125 | 84.44 151 | 89.61 99 | 92.75 117 | 93.82 125 | 91.66 125 | 82.85 157 | 88.57 97 | 87.48 54 | 79.00 109 | 64.24 202 | 88.82 112 | 85.18 211 | 87.50 201 | 94.07 205 | 92.79 181 |
|
diffmvs | | | 90.76 76 | 90.92 77 | 90.57 88 | 92.71 118 | 96.70 84 | 93.37 94 | 86.13 117 | 91.95 56 | 83.12 87 | 85.24 69 | 80.56 104 | 91.17 81 | 92.08 130 | 93.08 100 | 96.95 144 | 96.82 85 |
|
DI_MVS_plusplus_trai | | | 91.05 71 | 90.15 82 | 92.11 65 | 92.67 119 | 96.61 86 | 96.03 44 | 88.44 90 | 90.25 74 | 85.92 66 | 73.73 142 | 84.89 78 | 91.92 68 | 94.17 84 | 94.07 65 | 97.68 107 | 97.31 72 |
|
Anonymous20231211 | | | 89.82 88 | 88.18 105 | 91.74 68 | 92.52 120 | 96.09 104 | 93.38 93 | 89.30 78 | 88.95 93 | 85.90 67 | 64.55 208 | 84.39 79 | 92.41 65 | 92.24 126 | 93.06 103 | 96.93 151 | 97.95 46 |
|
tpmrst | | | 83.72 176 | 83.45 161 | 84.03 181 | 92.21 121 | 91.66 194 | 88.74 179 | 73.58 219 | 88.14 101 | 72.67 137 | 77.37 120 | 72.11 138 | 86.34 134 | 82.94 222 | 82.05 223 | 90.63 226 | 89.86 207 |
|
CostFormer | | | 86.78 127 | 86.05 133 | 87.62 127 | 92.15 122 | 93.20 152 | 91.55 126 | 75.83 209 | 88.11 102 | 85.29 77 | 81.76 98 | 76.22 126 | 87.80 115 | 84.45 216 | 85.21 212 | 93.12 208 | 93.42 175 |
|
Vis-MVSNet | | | 89.36 97 | 91.49 73 | 86.88 133 | 92.10 123 | 97.60 56 | 92.16 116 | 85.89 119 | 84.21 143 | 75.20 126 | 82.58 92 | 87.13 64 | 77.40 206 | 95.90 44 | 95.63 41 | 98.51 14 | 97.36 69 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
IterMVS-LS | | | 88.60 108 | 88.45 100 | 88.78 110 | 92.02 124 | 92.44 178 | 92.00 121 | 83.57 150 | 86.52 116 | 78.90 113 | 78.61 113 | 81.34 101 | 89.12 105 | 90.68 152 | 93.18 96 | 97.10 128 | 96.35 106 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
tpmp4_e23 | | | 85.67 140 | 84.28 153 | 87.30 129 | 91.96 125 | 92.00 189 | 92.06 118 | 76.27 207 | 87.95 103 | 83.59 84 | 76.97 122 | 70.88 143 | 87.52 120 | 84.80 215 | 84.73 214 | 92.40 216 | 92.61 188 |
|
PatchmatchNet | | | 85.70 138 | 86.65 126 | 84.60 172 | 91.79 126 | 93.40 144 | 89.27 170 | 73.62 217 | 90.19 77 | 72.63 138 | 82.74 91 | 81.93 98 | 87.64 117 | 84.99 212 | 84.29 217 | 92.64 212 | 89.00 209 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
tpm cat1 | | | 84.13 168 | 81.99 196 | 86.63 137 | 91.74 127 | 91.50 197 | 90.68 130 | 75.69 210 | 86.12 119 | 85.44 76 | 72.39 151 | 70.72 144 | 85.16 159 | 80.89 228 | 81.56 226 | 91.07 224 | 90.71 200 |
|
USDC | | | 86.73 128 | 85.96 137 | 87.63 126 | 91.64 128 | 93.97 123 | 92.76 100 | 84.58 138 | 88.19 100 | 70.67 159 | 80.10 105 | 67.86 163 | 89.43 97 | 91.81 132 | 89.77 186 | 96.69 171 | 90.05 206 |
|
gg-mvs-nofinetune | | | 81.83 203 | 83.58 159 | 79.80 211 | 91.57 129 | 96.54 89 | 93.79 83 | 68.80 231 | 62.71 234 | 43.01 240 | 55.28 226 | 85.06 77 | 83.65 176 | 96.13 40 | 94.86 53 | 97.98 90 | 94.46 159 |
|
Fast-Effi-MVS+ | | | 88.56 112 | 87.99 108 | 89.22 105 | 91.56 130 | 95.21 107 | 92.29 109 | 82.69 159 | 86.82 111 | 77.73 116 | 76.24 130 | 73.39 134 | 93.36 54 | 94.22 83 | 93.64 74 | 97.65 109 | 96.43 103 |
|
CMPMVS | | 61.19 17 | 79.86 210 | 77.46 218 | 82.66 200 | 91.54 131 | 91.82 192 | 83.25 212 | 81.57 174 | 70.51 227 | 68.64 184 | 59.89 221 | 66.77 175 | 79.63 198 | 84.00 220 | 84.30 216 | 91.34 222 | 84.89 222 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
ADS-MVSNet | | | 84.08 170 | 84.95 146 | 83.05 193 | 91.53 132 | 91.75 193 | 88.16 183 | 70.70 227 | 89.96 84 | 69.51 176 | 78.83 110 | 76.97 123 | 86.29 135 | 84.08 219 | 84.60 215 | 92.13 220 | 88.48 213 |
|
test-LLR | | | 86.88 124 | 88.28 102 | 85.24 159 | 91.22 133 | 92.07 184 | 87.41 189 | 83.62 148 | 84.58 136 | 69.33 177 | 83.00 86 | 82.79 87 | 84.24 167 | 92.26 124 | 89.81 184 | 95.64 190 | 93.44 173 |
|
test0.0.03 1 | | | 85.58 141 | 87.69 115 | 83.11 190 | 91.22 133 | 92.54 173 | 85.60 207 | 83.62 148 | 85.66 128 | 67.84 190 | 82.79 90 | 79.70 107 | 73.51 218 | 91.15 143 | 90.79 141 | 96.88 163 | 91.23 196 |
|
Effi-MVS+-dtu | | | 87.51 120 | 88.13 106 | 86.77 135 | 91.10 135 | 94.90 110 | 90.91 129 | 82.67 160 | 83.47 149 | 71.55 148 | 81.11 103 | 77.04 122 | 89.41 98 | 92.65 118 | 91.68 132 | 95.00 203 | 96.09 118 |
|
RPMNet | | | 84.82 151 | 85.90 138 | 83.56 185 | 91.10 135 | 92.10 182 | 88.73 180 | 71.11 226 | 84.75 134 | 68.79 182 | 73.56 143 | 77.62 118 | 85.33 158 | 90.08 162 | 89.43 192 | 96.32 178 | 93.77 171 |
|
CR-MVSNet | | | 85.48 143 | 86.29 131 | 84.53 174 | 91.08 137 | 92.10 182 | 89.18 172 | 73.30 222 | 84.75 134 | 71.08 154 | 73.12 150 | 77.91 116 | 86.27 136 | 91.48 136 | 90.75 144 | 96.27 179 | 93.94 166 |
|
TinyColmap | | | 84.04 171 | 82.01 195 | 86.42 139 | 90.87 138 | 91.84 191 | 88.89 178 | 84.07 144 | 82.11 157 | 69.89 173 | 71.08 154 | 60.81 218 | 89.04 106 | 90.52 154 | 89.19 194 | 95.76 185 | 88.50 212 |
|
tpm | | | 83.16 188 | 83.64 158 | 82.60 201 | 90.75 139 | 91.05 200 | 88.49 181 | 73.99 215 | 82.36 155 | 67.08 196 | 78.10 115 | 68.79 156 | 84.17 170 | 85.95 209 | 85.96 207 | 91.09 223 | 93.23 177 |
|
dps | | | 85.00 148 | 83.21 172 | 87.08 131 | 90.73 140 | 92.55 172 | 89.34 169 | 75.29 211 | 84.94 133 | 87.01 58 | 79.27 108 | 67.69 164 | 87.27 124 | 84.22 218 | 83.56 218 | 92.83 210 | 90.25 204 |
|
MDTV_nov1_ep13 | | | 86.64 129 | 87.50 119 | 85.65 152 | 90.73 140 | 93.69 131 | 89.96 157 | 78.03 202 | 89.48 90 | 76.85 120 | 84.92 75 | 82.42 93 | 86.14 141 | 86.85 206 | 86.15 204 | 92.17 218 | 88.97 210 |
|
CDS-MVSNet | | | 88.34 113 | 88.71 98 | 87.90 122 | 90.70 142 | 94.54 112 | 92.38 104 | 86.02 118 | 80.37 172 | 79.42 110 | 79.30 107 | 83.43 83 | 82.04 187 | 93.39 107 | 94.01 67 | 96.86 165 | 95.93 122 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
IterMVS | | | 85.25 146 | 86.49 129 | 83.80 182 | 90.42 143 | 90.77 205 | 90.02 155 | 78.04 201 | 84.10 145 | 66.27 201 | 77.28 121 | 78.41 112 | 83.01 179 | 90.88 145 | 89.72 188 | 95.04 201 | 94.24 162 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
Fast-Effi-MVS+-dtu | | | 86.25 130 | 87.70 114 | 84.56 173 | 90.37 144 | 93.70 130 | 90.54 133 | 78.14 200 | 83.50 148 | 65.37 206 | 81.59 101 | 75.83 128 | 86.09 146 | 91.70 134 | 91.70 130 | 96.88 163 | 95.84 126 |
|
FC-MVSNet-test | | | 86.15 132 | 89.10 93 | 82.71 199 | 89.83 145 | 93.18 154 | 87.88 186 | 84.69 135 | 86.54 115 | 62.18 215 | 82.39 95 | 83.31 84 | 74.18 215 | 92.52 121 | 91.86 127 | 97.50 115 | 93.88 169 |
|
GA-MVS | | | 85.08 147 | 85.65 142 | 84.42 175 | 89.77 146 | 94.25 119 | 89.26 171 | 84.62 137 | 81.19 163 | 62.25 214 | 75.72 133 | 68.44 159 | 84.14 171 | 93.57 101 | 91.68 132 | 96.49 173 | 94.71 157 |
|
PMMVS | | | 89.88 87 | 91.19 75 | 88.35 113 | 89.73 147 | 91.97 190 | 90.62 131 | 81.92 170 | 90.57 71 | 80.58 104 | 92.16 32 | 86.85 66 | 91.17 81 | 92.31 123 | 91.35 136 | 96.11 181 | 93.11 179 |
|
tfpnnormal | | | 83.80 175 | 81.26 205 | 86.77 135 | 89.60 148 | 93.26 151 | 89.72 166 | 87.60 109 | 72.78 219 | 70.44 160 | 60.53 220 | 61.15 217 | 85.55 155 | 92.72 114 | 91.44 134 | 97.71 103 | 96.92 82 |
|
CVMVSNet | | | 83.83 174 | 85.53 143 | 81.85 207 | 89.60 148 | 90.92 201 | 87.81 187 | 83.21 154 | 80.11 175 | 60.16 219 | 76.47 125 | 78.57 111 | 76.79 208 | 89.76 165 | 90.13 174 | 93.51 206 | 92.75 185 |
|
testgi | | | 81.94 202 | 84.09 155 | 79.43 212 | 89.53 150 | 90.83 203 | 82.49 215 | 81.75 173 | 80.59 167 | 59.46 221 | 82.82 89 | 65.75 187 | 67.97 220 | 90.10 161 | 89.52 191 | 95.39 196 | 89.03 208 |
|
LTVRE_ROB | | 81.71 16 | 82.44 198 | 81.84 197 | 83.13 189 | 89.01 151 | 92.99 159 | 88.90 177 | 82.32 166 | 66.26 231 | 54.02 229 | 74.68 140 | 59.62 224 | 88.87 111 | 90.71 151 | 92.02 124 | 95.68 189 | 96.62 92 |
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 |
TAMVS | | | 84.94 150 | 84.95 146 | 84.93 169 | 88.82 152 | 93.18 154 | 88.44 182 | 81.28 177 | 77.16 202 | 73.76 133 | 75.43 135 | 76.57 125 | 82.04 187 | 90.59 153 | 90.79 141 | 95.22 199 | 90.94 198 |
|
EG-PatchMatch MVS | | | 81.70 205 | 81.31 204 | 82.15 205 | 88.75 153 | 93.81 126 | 87.14 192 | 78.89 199 | 71.57 223 | 64.12 211 | 61.20 219 | 68.46 158 | 76.73 209 | 91.48 136 | 90.77 143 | 97.28 119 | 91.90 190 |
|
TransMVSNet (Re) | | | 82.67 195 | 80.93 208 | 84.69 171 | 88.71 154 | 91.50 197 | 87.90 185 | 87.15 111 | 71.54 225 | 68.24 187 | 63.69 210 | 64.67 199 | 78.51 203 | 91.65 135 | 90.73 146 | 97.64 110 | 92.73 186 |
|
FMVSNet3 | | | 90.19 84 | 90.06 85 | 90.34 89 | 88.69 155 | 93.85 124 | 94.58 60 | 85.78 121 | 90.03 81 | 85.56 72 | 77.38 117 | 86.13 68 | 89.22 104 | 93.29 109 | 94.36 58 | 98.20 64 | 95.40 140 |
|
GBi-Net | | | 90.21 82 | 90.11 83 | 90.32 90 | 88.66 156 | 93.65 133 | 94.25 73 | 85.78 121 | 90.03 81 | 85.56 72 | 77.38 117 | 86.13 68 | 89.38 99 | 93.97 88 | 94.16 61 | 98.31 45 | 95.47 136 |
|
test1 | | | 90.21 82 | 90.11 83 | 90.32 90 | 88.66 156 | 93.65 133 | 94.25 73 | 85.78 121 | 90.03 81 | 85.56 72 | 77.38 117 | 86.13 68 | 89.38 99 | 93.97 88 | 94.16 61 | 98.31 45 | 95.47 136 |
|
FMVSNet2 | | | 89.61 92 | 89.14 92 | 90.16 96 | 88.66 156 | 93.65 133 | 94.25 73 | 85.44 128 | 88.57 97 | 84.96 80 | 73.53 144 | 83.82 81 | 89.38 99 | 94.23 82 | 94.68 55 | 98.31 45 | 95.47 136 |
|
PatchT | | | 83.86 173 | 85.51 144 | 81.94 206 | 88.41 159 | 91.56 196 | 78.79 223 | 71.57 225 | 84.08 146 | 71.08 154 | 70.62 155 | 76.13 127 | 86.27 136 | 91.48 136 | 90.75 144 | 95.52 195 | 93.94 166 |
|
UniMVSNet (Re) | | | 86.22 131 | 85.46 145 | 87.11 130 | 88.34 160 | 94.42 116 | 89.65 167 | 87.10 112 | 84.39 140 | 74.61 127 | 70.41 159 | 68.10 161 | 85.10 161 | 91.17 142 | 91.79 128 | 97.84 95 | 97.94 47 |
|
NR-MVSNet | | | 85.46 144 | 84.54 150 | 86.52 138 | 88.33 161 | 93.78 127 | 90.45 134 | 87.87 100 | 84.40 138 | 71.61 147 | 70.59 156 | 62.09 212 | 82.79 181 | 91.75 133 | 91.75 129 | 98.10 74 | 97.44 66 |
|
UniMVSNet_NR-MVSNet | | | 86.80 126 | 85.86 140 | 87.89 123 | 88.17 162 | 94.07 122 | 90.15 150 | 88.51 89 | 84.20 144 | 73.45 134 | 72.38 152 | 70.30 150 | 88.95 108 | 90.25 157 | 92.21 118 | 98.12 70 | 97.62 61 |
|
LP | | | 77.28 217 | 76.57 220 | 78.12 215 | 88.17 162 | 88.06 220 | 80.85 220 | 68.35 234 | 80.78 166 | 61.49 217 | 57.59 223 | 61.80 213 | 77.59 205 | 81.45 227 | 82.34 222 | 92.25 217 | 83.96 225 |
|
thisisatest0515 | | | 85.70 138 | 87.00 125 | 84.19 178 | 88.16 164 | 93.67 132 | 84.20 210 | 84.14 143 | 83.39 151 | 72.91 136 | 76.79 123 | 74.75 130 | 78.82 202 | 92.57 120 | 91.26 137 | 96.94 147 | 96.56 101 |
|
pm-mvs1 | | | 84.55 155 | 83.46 160 | 85.82 147 | 88.16 164 | 93.39 145 | 89.05 175 | 85.36 130 | 74.03 217 | 72.43 139 | 65.08 203 | 71.11 141 | 82.30 186 | 93.48 103 | 91.70 130 | 97.64 110 | 95.43 139 |
|
gm-plane-assit | | | 77.65 215 | 78.50 213 | 76.66 217 | 87.96 166 | 85.43 227 | 64.70 236 | 74.50 213 | 64.15 233 | 51.26 231 | 61.32 218 | 58.17 226 | 84.11 172 | 95.16 51 | 93.83 72 | 97.45 116 | 91.41 193 |
|
test-mter | | | 86.09 135 | 88.38 101 | 83.43 187 | 87.89 167 | 92.61 170 | 86.89 194 | 77.11 205 | 84.30 141 | 68.62 185 | 82.57 93 | 82.45 92 | 84.34 166 | 92.40 122 | 90.11 178 | 95.74 186 | 94.21 164 |
|
pmmvs4 | | | 86.00 136 | 84.28 153 | 88.00 119 | 87.80 168 | 92.01 188 | 89.94 158 | 84.91 134 | 86.79 112 | 80.98 101 | 73.41 147 | 66.34 179 | 88.12 114 | 89.31 179 | 88.90 197 | 96.24 180 | 93.20 178 |
|
TESTMET0.1,1 | | | 86.11 134 | 88.28 102 | 83.59 184 | 87.80 168 | 92.07 184 | 87.41 189 | 77.12 204 | 84.58 136 | 69.33 177 | 83.00 86 | 82.79 87 | 84.24 167 | 92.26 124 | 89.81 184 | 95.64 190 | 93.44 173 |
|
DU-MVS | | | 86.12 133 | 84.81 148 | 87.66 124 | 87.77 170 | 93.78 127 | 90.15 150 | 87.87 100 | 84.40 138 | 73.45 134 | 70.59 156 | 64.82 197 | 88.95 108 | 90.14 158 | 92.33 115 | 97.76 99 | 97.62 61 |
|
Baseline_NR-MVSNet | | | 85.28 145 | 83.42 163 | 87.46 128 | 87.77 170 | 90.80 204 | 89.90 161 | 87.69 104 | 83.93 147 | 74.16 130 | 64.72 206 | 66.43 176 | 87.48 122 | 90.14 158 | 90.83 140 | 97.73 102 | 97.11 77 |
|
SixPastTwentyTwo | | | 83.12 190 | 83.44 162 | 82.74 198 | 87.71 172 | 93.11 158 | 82.30 216 | 82.33 165 | 79.24 190 | 64.33 209 | 78.77 111 | 62.75 206 | 84.11 172 | 88.11 194 | 87.89 199 | 95.70 188 | 94.21 164 |
|
TranMVSNet+NR-MVSNet | | | 85.57 142 | 84.41 152 | 86.92 132 | 87.67 173 | 93.34 146 | 90.31 141 | 88.43 91 | 83.07 152 | 70.11 169 | 69.99 163 | 65.28 192 | 86.96 127 | 89.73 166 | 92.27 116 | 98.06 81 | 97.17 76 |
|
v18 | | | 84.21 165 | 82.90 178 | 85.74 150 | 87.63 174 | 89.75 207 | 90.56 132 | 80.82 181 | 81.42 160 | 72.24 141 | 67.16 173 | 67.23 166 | 86.27 136 | 89.25 183 | 90.24 163 | 96.92 156 | 95.27 145 |
|
v16 | | | 84.14 167 | 82.86 180 | 85.64 153 | 87.61 175 | 89.71 209 | 90.36 135 | 80.70 183 | 81.36 161 | 71.99 145 | 66.91 180 | 67.19 167 | 86.23 139 | 89.32 177 | 90.25 160 | 96.94 147 | 95.29 143 |
|
v17 | | | 84.10 169 | 82.83 181 | 85.57 155 | 87.58 176 | 89.72 208 | 90.30 144 | 80.70 183 | 81.00 164 | 71.72 146 | 67.01 175 | 67.24 165 | 86.19 140 | 89.32 177 | 90.25 160 | 96.95 144 | 95.29 143 |
|
WR-MVS | | | 83.14 189 | 83.38 165 | 82.87 195 | 87.55 177 | 93.29 148 | 86.36 199 | 84.21 141 | 80.05 176 | 66.41 200 | 66.91 180 | 66.92 174 | 75.66 212 | 88.96 191 | 90.56 149 | 97.05 131 | 96.96 80 |
|
v1neww | | | 84.65 153 | 83.34 168 | 86.18 142 | 87.53 178 | 93.49 137 | 90.32 137 | 85.17 131 | 80.57 169 | 71.02 157 | 66.93 178 | 67.04 172 | 86.13 143 | 89.26 180 | 90.23 166 | 96.93 151 | 95.88 124 |
|
v7new | | | 84.65 153 | 83.34 168 | 86.18 142 | 87.53 178 | 93.49 137 | 90.32 137 | 85.17 131 | 80.57 169 | 71.02 157 | 66.93 178 | 67.04 172 | 86.13 143 | 89.26 180 | 90.23 166 | 96.93 151 | 95.88 124 |
|
v8 | | | 84.45 160 | 83.30 170 | 85.80 148 | 87.53 178 | 92.95 160 | 90.31 141 | 82.46 164 | 80.46 171 | 71.43 149 | 66.99 176 | 67.16 169 | 86.14 141 | 89.26 180 | 90.22 169 | 96.94 147 | 96.06 119 |
|
v6 | | | 84.67 152 | 83.36 166 | 86.20 140 | 87.53 178 | 93.49 137 | 90.34 136 | 85.16 133 | 80.58 168 | 71.13 153 | 66.97 177 | 67.10 170 | 86.11 145 | 89.25 183 | 90.22 169 | 96.93 151 | 95.89 123 |
|
WR-MVS_H | | | 82.86 194 | 82.66 183 | 83.10 191 | 87.44 182 | 93.33 147 | 85.71 206 | 83.20 155 | 77.36 201 | 68.20 188 | 66.37 187 | 65.23 193 | 76.05 211 | 89.35 174 | 90.13 174 | 97.99 88 | 96.89 83 |
|
divwei89l23v2f112 | | | 84.40 161 | 83.00 176 | 86.02 146 | 87.42 183 | 93.42 141 | 90.28 145 | 85.52 126 | 79.57 182 | 70.11 169 | 66.64 185 | 66.29 182 | 85.91 148 | 89.16 186 | 90.19 171 | 96.90 158 | 95.73 129 |
|
v1141 | | | 84.40 161 | 83.00 176 | 86.03 144 | 87.41 184 | 93.42 141 | 90.28 145 | 85.53 125 | 79.58 181 | 70.12 168 | 66.62 186 | 66.27 183 | 85.94 147 | 89.16 186 | 90.19 171 | 96.89 160 | 95.73 129 |
|
v1 | | | 84.40 161 | 83.01 175 | 86.03 144 | 87.41 184 | 93.42 141 | 90.31 141 | 85.52 126 | 79.51 184 | 70.13 167 | 66.66 184 | 66.40 177 | 85.89 149 | 89.15 188 | 90.19 171 | 96.89 160 | 95.74 128 |
|
v15 | | | 83.67 178 | 82.37 186 | 85.19 161 | 87.39 186 | 89.63 210 | 90.19 148 | 80.43 185 | 79.49 186 | 70.27 162 | 66.37 187 | 66.33 180 | 85.88 150 | 89.34 176 | 90.23 166 | 96.96 142 | 95.22 150 |
|
V14 | | | 83.66 179 | 82.38 185 | 85.16 162 | 87.37 187 | 89.62 211 | 90.15 150 | 80.33 187 | 79.51 184 | 70.26 163 | 66.30 193 | 66.37 178 | 85.87 151 | 89.38 173 | 90.24 163 | 96.98 138 | 95.22 150 |
|
v148 | | | 83.61 180 | 82.10 193 | 85.37 156 | 87.34 188 | 92.94 161 | 87.48 188 | 85.72 124 | 78.92 191 | 73.87 132 | 65.71 200 | 64.69 198 | 81.78 191 | 87.82 195 | 89.35 193 | 96.01 182 | 95.26 146 |
|
v7 | | | 84.37 164 | 83.23 171 | 85.69 151 | 87.34 188 | 93.19 153 | 90.32 137 | 83.10 156 | 79.88 180 | 69.33 177 | 66.33 190 | 65.75 187 | 87.06 125 | 90.83 147 | 90.38 153 | 96.97 139 | 96.26 113 |
|
v11 | | | 83.72 176 | 82.61 184 | 85.02 165 | 87.34 188 | 89.56 214 | 89.89 162 | 79.92 194 | 79.55 183 | 69.21 181 | 66.36 189 | 65.48 190 | 86.84 129 | 91.43 139 | 90.51 152 | 96.92 156 | 95.37 142 |
|
v10 | | | 84.18 166 | 83.17 173 | 85.37 156 | 87.34 188 | 92.68 168 | 90.32 137 | 81.33 176 | 79.93 179 | 69.23 180 | 66.33 190 | 65.74 189 | 87.03 126 | 90.84 146 | 90.38 153 | 96.97 139 | 96.29 111 |
|
V9 | | | 83.61 180 | 82.33 188 | 85.11 163 | 87.34 188 | 89.59 212 | 90.10 153 | 80.25 188 | 79.38 188 | 70.17 165 | 66.15 194 | 66.33 180 | 85.82 153 | 89.41 172 | 90.24 163 | 96.99 137 | 95.23 149 |
|
testpf | | | 74.66 219 | 76.34 221 | 72.71 223 | 87.34 188 | 80.91 231 | 73.15 231 | 60.30 241 | 78.73 193 | 61.68 216 | 69.83 164 | 62.22 210 | 67.48 221 | 76.83 232 | 78.17 233 | 86.28 235 | 87.68 216 |
|
v12 | | | 83.59 182 | 82.32 189 | 85.07 164 | 87.32 194 | 89.57 213 | 89.87 164 | 80.19 192 | 79.46 187 | 70.19 164 | 66.05 195 | 66.23 185 | 85.84 152 | 89.44 171 | 90.26 159 | 97.01 135 | 95.26 146 |
|
v13 | | | 83.55 184 | 82.29 190 | 85.01 166 | 87.31 195 | 89.55 215 | 89.89 162 | 80.13 193 | 79.34 189 | 69.93 172 | 65.92 198 | 66.25 184 | 85.80 154 | 89.45 170 | 90.27 157 | 97.01 135 | 95.25 148 |
|
v2v482 | | | 84.51 156 | 83.05 174 | 86.20 140 | 87.25 196 | 93.28 149 | 90.22 147 | 85.40 129 | 79.94 178 | 69.78 174 | 67.74 171 | 65.15 194 | 87.57 118 | 89.12 189 | 90.55 150 | 96.97 139 | 95.60 133 |
|
CP-MVSNet | | | 83.11 191 | 82.15 192 | 84.23 177 | 87.20 197 | 92.70 167 | 86.42 198 | 83.53 151 | 77.83 199 | 67.67 191 | 66.89 183 | 60.53 220 | 82.47 184 | 89.23 185 | 90.65 148 | 98.08 78 | 97.20 75 |
|
v1144 | | | 84.03 172 | 82.88 179 | 85.37 156 | 87.17 198 | 93.15 157 | 90.18 149 | 83.31 153 | 78.83 192 | 67.85 189 | 65.99 196 | 64.99 195 | 86.79 130 | 90.75 149 | 90.33 156 | 96.90 158 | 96.15 116 |
|
V42 | | | 84.48 158 | 83.36 166 | 85.79 149 | 87.14 199 | 93.28 149 | 90.03 154 | 83.98 145 | 80.30 173 | 71.20 152 | 66.90 182 | 67.17 168 | 85.55 155 | 89.35 174 | 90.27 157 | 96.82 166 | 96.27 112 |
|
pmmvs5 | | | 83.37 186 | 82.68 182 | 84.18 179 | 87.13 200 | 93.18 154 | 86.74 195 | 82.08 168 | 76.48 206 | 67.28 194 | 71.26 153 | 62.70 207 | 84.71 164 | 90.77 148 | 90.12 177 | 97.15 124 | 94.24 162 |
|
FMVSNet1 | | | 87.33 121 | 86.00 136 | 88.89 108 | 87.13 200 | 92.83 165 | 93.08 98 | 84.46 139 | 81.35 162 | 82.20 89 | 66.33 190 | 77.96 115 | 88.96 107 | 93.97 88 | 94.16 61 | 97.54 114 | 95.38 141 |
|
PS-CasMVS | | | 82.53 196 | 81.54 200 | 83.68 183 | 87.08 202 | 92.54 173 | 86.20 200 | 83.46 152 | 76.46 207 | 65.73 204 | 65.71 200 | 59.41 225 | 81.61 192 | 89.06 190 | 90.55 150 | 98.03 83 | 97.07 79 |
|
our_test_3 | | | | | | 86.93 203 | 89.77 206 | 81.61 217 | | | | | | | | | | |
|
PEN-MVS | | | 82.49 197 | 81.58 199 | 83.56 185 | 86.93 203 | 92.05 187 | 86.71 196 | 83.84 146 | 76.94 204 | 64.68 208 | 67.24 172 | 60.11 221 | 81.17 194 | 87.78 196 | 90.70 147 | 98.02 85 | 96.21 114 |
|
v1192 | | | 83.56 183 | 82.35 187 | 84.98 167 | 86.84 205 | 92.84 163 | 90.01 156 | 82.70 158 | 78.54 194 | 66.48 199 | 64.88 205 | 62.91 205 | 86.91 128 | 90.72 150 | 90.25 160 | 96.94 147 | 96.32 108 |
|
v144192 | | | 83.48 185 | 82.23 191 | 84.94 168 | 86.65 206 | 92.84 163 | 89.63 168 | 82.48 163 | 77.87 198 | 67.36 193 | 65.33 202 | 63.50 204 | 86.51 132 | 89.72 167 | 89.99 181 | 97.03 132 | 96.35 106 |
|
DTE-MVSNet | | | 81.76 204 | 81.04 206 | 82.60 201 | 86.63 207 | 91.48 199 | 85.97 202 | 83.70 147 | 76.45 208 | 62.44 213 | 67.16 173 | 59.98 222 | 78.98 201 | 87.15 203 | 89.93 182 | 97.88 94 | 95.12 153 |
|
v1921920 | | | 83.30 187 | 82.09 194 | 84.70 170 | 86.59 208 | 92.67 169 | 89.82 165 | 82.23 167 | 78.32 195 | 65.76 203 | 64.64 207 | 62.35 208 | 86.78 131 | 90.34 156 | 90.02 179 | 97.02 133 | 96.31 110 |
|
v1240 | | | 82.88 193 | 81.66 198 | 84.29 176 | 86.46 209 | 92.52 176 | 89.06 174 | 81.82 172 | 77.16 202 | 65.09 207 | 64.17 209 | 61.50 214 | 86.36 133 | 90.12 160 | 90.13 174 | 96.95 144 | 96.04 121 |
|
anonymousdsp | | | 84.51 156 | 85.85 141 | 82.95 194 | 86.30 210 | 93.51 136 | 85.77 205 | 80.38 186 | 78.25 197 | 63.42 212 | 73.51 145 | 72.20 137 | 84.64 165 | 93.21 111 | 92.16 120 | 97.19 122 | 98.14 39 |
|
pmmvs6 | | | 80.90 207 | 78.77 212 | 83.38 188 | 85.84 211 | 91.61 195 | 86.01 201 | 82.54 162 | 64.17 232 | 70.43 161 | 54.14 230 | 67.06 171 | 80.73 196 | 90.50 155 | 89.17 195 | 94.74 204 | 94.75 156 |
|
MVS-HIRNet | | | 78.16 213 | 77.57 217 | 78.83 213 | 85.83 212 | 87.76 221 | 76.67 224 | 70.22 228 | 75.82 213 | 67.39 192 | 55.61 225 | 70.52 145 | 81.96 189 | 86.67 207 | 85.06 213 | 90.93 225 | 81.58 228 |
|
test20.03 | | | 76.41 218 | 78.49 214 | 73.98 220 | 85.64 213 | 87.50 222 | 75.89 225 | 80.71 182 | 70.84 226 | 51.07 232 | 68.06 170 | 61.40 216 | 54.99 234 | 88.28 193 | 87.20 202 | 95.58 193 | 86.15 218 |
|
v748 | | | 81.57 206 | 80.68 209 | 82.60 201 | 85.55 214 | 92.07 184 | 83.57 211 | 82.06 169 | 74.64 216 | 69.97 171 | 63.11 213 | 61.46 215 | 78.09 204 | 87.30 202 | 89.88 183 | 96.37 177 | 96.32 108 |
|
v7n | | | 82.25 199 | 81.54 200 | 83.07 192 | 85.55 214 | 92.58 171 | 86.68 197 | 81.10 180 | 76.54 205 | 65.97 202 | 62.91 214 | 60.56 219 | 82.36 185 | 91.07 144 | 90.35 155 | 96.77 168 | 96.80 86 |
|
N_pmnet | | | 77.55 216 | 76.68 219 | 78.56 214 | 85.43 216 | 87.30 224 | 78.84 222 | 81.88 171 | 78.30 196 | 60.61 218 | 61.46 216 | 62.15 211 | 74.03 217 | 82.04 223 | 80.69 229 | 90.59 227 | 84.81 223 |
|
Anonymous20231206 | | | 78.09 214 | 78.11 215 | 78.07 216 | 85.19 217 | 89.17 216 | 80.99 218 | 81.24 179 | 75.46 214 | 58.25 223 | 54.78 229 | 59.90 223 | 66.73 224 | 88.94 192 | 88.26 198 | 96.01 182 | 90.25 204 |
|
MDTV_nov1_ep13_2view | | | 80.43 208 | 80.94 207 | 79.84 210 | 84.82 218 | 90.87 202 | 84.23 209 | 73.80 216 | 80.28 174 | 64.33 209 | 70.05 162 | 68.77 157 | 79.67 197 | 84.83 214 | 83.50 219 | 92.17 218 | 88.25 215 |
|
V4 | | | 82.11 200 | 81.49 203 | 82.83 196 | 84.60 219 | 92.53 175 | 85.97 202 | 80.24 189 | 76.35 210 | 66.87 197 | 63.17 211 | 64.55 201 | 82.54 183 | 87.70 197 | 89.55 189 | 96.73 169 | 96.61 93 |
|
v52 | | | 82.11 200 | 81.50 202 | 82.82 197 | 84.59 220 | 92.51 177 | 85.96 204 | 80.24 189 | 76.38 209 | 66.83 198 | 63.12 212 | 64.62 200 | 82.56 182 | 87.70 197 | 89.55 189 | 96.73 169 | 96.61 93 |
|
FPMVS | | | 69.87 227 | 67.10 230 | 73.10 222 | 84.09 221 | 78.35 235 | 79.40 221 | 76.41 206 | 71.92 221 | 57.71 224 | 54.06 231 | 50.04 232 | 56.72 232 | 71.19 236 | 68.70 237 | 84.25 237 | 75.43 233 |
|
EU-MVSNet | | | 78.43 212 | 80.25 210 | 76.30 218 | 83.81 222 | 87.27 225 | 80.99 218 | 79.52 196 | 76.01 211 | 54.12 228 | 70.44 158 | 64.87 196 | 67.40 223 | 86.23 208 | 85.54 210 | 91.95 221 | 91.41 193 |
|
FMVSNet5 | | | 84.47 159 | 84.72 149 | 84.18 179 | 83.30 223 | 88.43 218 | 88.09 184 | 79.42 197 | 84.25 142 | 74.14 131 | 73.15 149 | 78.74 110 | 83.65 176 | 91.19 141 | 91.19 138 | 96.46 175 | 86.07 219 |
|
MIMVSNet | | | 82.97 192 | 84.00 156 | 81.77 208 | 82.23 224 | 92.25 181 | 87.40 191 | 72.73 224 | 81.48 159 | 69.55 175 | 68.79 167 | 72.42 136 | 81.82 190 | 92.23 127 | 92.25 117 | 96.89 160 | 88.61 211 |
|
PM-MVS | | | 80.29 209 | 79.30 211 | 81.45 209 | 81.91 225 | 88.23 219 | 82.61 214 | 79.01 198 | 79.99 177 | 67.15 195 | 69.07 166 | 51.39 230 | 82.92 180 | 87.55 200 | 85.59 208 | 95.08 200 | 93.28 176 |
|
pmmvs-eth3d | | | 79.78 211 | 77.58 216 | 82.34 204 | 81.57 226 | 87.46 223 | 82.92 213 | 81.28 177 | 75.33 215 | 71.34 150 | 61.88 215 | 52.41 229 | 81.59 193 | 87.56 199 | 86.90 203 | 95.36 198 | 91.48 192 |
|
test2356 | | | 73.82 220 | 74.82 223 | 72.66 224 | 81.25 227 | 80.70 232 | 73.47 230 | 75.91 208 | 72.55 220 | 48.73 235 | 68.14 169 | 50.74 231 | 63.96 226 | 84.44 217 | 85.57 209 | 92.63 213 | 81.60 227 |
|
new-patchmatchnet | | | 72.32 224 | 71.09 227 | 73.74 221 | 81.17 228 | 84.86 228 | 72.21 233 | 77.48 203 | 68.32 229 | 54.89 227 | 55.10 227 | 49.31 234 | 63.68 228 | 79.30 229 | 76.46 234 | 93.03 209 | 84.32 224 |
|
testus | | | 73.65 222 | 74.92 222 | 72.17 226 | 80.93 229 | 81.11 230 | 73.02 232 | 75.23 212 | 73.23 218 | 48.77 234 | 69.38 165 | 46.10 239 | 62.28 229 | 84.84 213 | 86.01 206 | 92.77 211 | 83.75 226 |
|
testmv | | | 65.29 229 | 65.25 232 | 65.34 230 | 77.73 230 | 75.55 238 | 58.75 239 | 73.56 220 | 53.22 239 | 38.47 241 | 49.33 232 | 38.30 241 | 53.38 235 | 79.13 230 | 81.65 224 | 90.15 229 | 79.58 230 |
|
test1235678 | | | 65.29 229 | 65.24 233 | 65.34 230 | 77.73 230 | 75.54 239 | 58.75 239 | 73.56 220 | 53.19 240 | 38.47 241 | 49.32 233 | 38.28 242 | 53.38 235 | 79.13 230 | 81.65 224 | 90.15 229 | 79.57 231 |
|
1111 | | | 66.22 228 | 66.42 231 | 65.98 229 | 75.69 232 | 76.42 236 | 58.90 237 | 63.25 236 | 57.86 236 | 48.33 236 | 45.46 236 | 49.13 235 | 61.32 230 | 81.57 225 | 82.80 221 | 88.38 234 | 71.69 238 |
|
.test1245 | | | 48.95 237 | 46.78 239 | 51.48 235 | 75.69 232 | 76.42 236 | 58.90 237 | 63.25 236 | 57.86 236 | 48.33 236 | 45.46 236 | 49.13 235 | 61.32 230 | 81.57 225 | 5.58 243 | 1.40 247 | 11.42 244 |
|
PMVS | | 56.77 18 | 61.27 232 | 58.64 235 | 64.35 232 | 75.66 234 | 54.60 245 | 53.62 243 | 74.23 214 | 53.69 238 | 58.37 222 | 44.27 239 | 49.38 233 | 44.16 239 | 69.51 238 | 65.35 239 | 80.07 239 | 73.66 234 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
new_pmnet | | | 72.29 225 | 73.25 225 | 71.16 228 | 75.35 235 | 81.38 229 | 73.72 229 | 69.27 230 | 75.97 212 | 49.84 233 | 56.27 224 | 56.12 228 | 69.08 219 | 81.73 224 | 80.86 228 | 89.72 232 | 80.44 229 |
|
ambc | | | | 67.96 229 | | 73.69 236 | 79.79 234 | 73.82 228 | | 71.61 222 | 59.80 220 | 46.00 235 | 20.79 246 | 66.15 225 | 86.92 205 | 80.11 231 | 89.13 233 | 90.50 201 |
|
pmmvs3 | | | 71.13 226 | 71.06 228 | 71.21 227 | 73.54 237 | 80.19 233 | 71.69 234 | 64.86 235 | 62.04 235 | 52.10 230 | 54.92 228 | 48.00 237 | 75.03 213 | 83.75 221 | 83.24 220 | 90.04 231 | 85.27 220 |
|
MDA-MVSNet-bldmvs | | | 73.81 221 | 72.56 226 | 75.28 219 | 72.52 238 | 88.87 217 | 74.95 227 | 82.67 160 | 71.57 223 | 55.02 226 | 65.96 197 | 42.84 240 | 76.11 210 | 70.61 237 | 81.47 227 | 90.38 228 | 86.59 217 |
|
test12356 | | | 60.37 233 | 61.08 234 | 59.53 234 | 72.42 239 | 70.09 241 | 57.72 241 | 69.53 229 | 51.31 241 | 36.05 243 | 47.32 234 | 32.04 243 | 36.19 240 | 74.15 235 | 80.35 230 | 85.27 236 | 72.29 236 |
|
tmp_tt | | | | | 50.24 238 | 68.55 240 | 46.86 247 | 48.90 245 | 18.28 244 | 86.51 117 | 68.32 186 | 70.19 160 | 65.33 191 | 26.69 244 | 74.37 234 | 66.80 238 | 70.72 243 | |
|
Gipuma | | | 58.52 234 | 56.17 236 | 61.27 233 | 67.14 241 | 58.06 244 | 52.16 244 | 68.40 233 | 69.00 228 | 45.02 239 | 22.79 242 | 20.57 247 | 55.11 233 | 76.27 233 | 79.33 232 | 79.80 240 | 67.16 239 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
MIMVSNet1 | | | 73.19 223 | 73.70 224 | 72.60 225 | 65.42 242 | 86.69 226 | 75.56 226 | 79.65 195 | 67.87 230 | 55.30 225 | 45.24 238 | 56.41 227 | 63.79 227 | 86.98 204 | 87.66 200 | 95.85 184 | 85.04 221 |
|
no-one | | | 49.70 236 | 49.06 238 | 50.46 237 | 65.32 243 | 67.46 242 | 38.16 246 | 68.73 232 | 34.38 245 | 22.88 245 | 24.40 241 | 22.99 245 | 28.55 243 | 51.41 241 | 70.93 235 | 79.08 241 | 71.81 237 |
|
PMMVS2 | | | 53.68 235 | 55.72 237 | 51.30 236 | 58.84 244 | 67.02 243 | 54.23 242 | 60.97 240 | 47.50 242 | 19.42 246 | 34.81 240 | 31.97 244 | 30.88 242 | 65.84 239 | 69.99 236 | 83.47 238 | 72.92 235 |
|
EMVS | | | 39.04 240 | 34.32 242 | 44.54 240 | 58.25 245 | 39.35 248 | 27.61 248 | 62.55 239 | 35.99 243 | 16.40 248 | 20.04 245 | 14.77 248 | 44.80 237 | 33.12 244 | 44.10 242 | 57.61 245 | 52.89 242 |
|
E-PMN | | | 40.00 238 | 35.74 241 | 44.98 239 | 57.69 246 | 39.15 249 | 28.05 247 | 62.70 238 | 35.52 244 | 17.78 247 | 20.90 243 | 14.36 249 | 44.47 238 | 35.89 243 | 47.86 241 | 59.15 244 | 56.47 241 |
|
MVE | | 39.81 19 | 39.52 239 | 41.58 240 | 37.11 241 | 33.93 247 | 49.06 246 | 26.45 249 | 54.22 242 | 29.46 246 | 24.15 244 | 20.77 244 | 10.60 250 | 34.42 241 | 51.12 242 | 65.27 240 | 49.49 246 | 64.81 240 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
testmvs | | | 4.35 241 | 6.54 243 | 1.79 243 | 0.60 248 | 1.82 250 | 3.06 251 | 0.95 245 | 7.22 247 | 0.88 250 | 12.38 246 | 1.25 251 | 3.87 246 | 6.09 245 | 5.58 243 | 1.40 247 | 11.42 244 |
|
GG-mvs-BLEND | | | 62.84 231 | 90.21 80 | 30.91 242 | 0.57 249 | 94.45 115 | 86.99 193 | 0.34 247 | 88.71 95 | 0.98 249 | 81.55 102 | 91.58 51 | 0.86 247 | 92.66 117 | 91.43 135 | 95.73 187 | 91.11 197 |
|
test123 | | | 3.48 242 | 5.31 244 | 1.34 244 | 0.20 250 | 1.52 251 | 2.17 252 | 0.58 246 | 6.13 248 | 0.31 251 | 9.85 247 | 0.31 252 | 3.90 245 | 2.65 246 | 5.28 245 | 0.87 249 | 11.46 243 |
|
sosnet-low-res | | | 0.00 243 | 0.00 245 | 0.00 245 | 0.00 251 | 0.00 252 | 0.00 253 | 0.00 248 | 0.00 249 | 0.00 252 | 0.00 248 | 0.00 253 | 0.00 248 | 0.00 247 | 0.00 246 | 0.00 250 | 0.00 246 |
|
sosnet | | | 0.00 243 | 0.00 245 | 0.00 245 | 0.00 251 | 0.00 252 | 0.00 253 | 0.00 248 | 0.00 249 | 0.00 252 | 0.00 248 | 0.00 253 | 0.00 248 | 0.00 247 | 0.00 246 | 0.00 250 | 0.00 246 |
|
MTAPA | | | | | | | | | | | 95.36 2 | | 97.46 17 | | | | | |
|
MTMP | | | | | | | | | | | 95.70 1 | | 96.90 22 | | | | | |
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Patchmatch-RL test | | | | | | | | 18.47 250 | | | | | | | | | | |
|
NP-MVS | | | | | | | | | | 91.63 61 | | | | | | | | |
|
Patchmtry | | | | | | | 92.39 179 | 89.18 172 | 73.30 222 | | 71.08 154 | | | | | | | |
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DeepMVS_CX | | | | | | | 71.82 240 | 68.37 235 | 48.05 243 | 77.38 200 | 46.88 238 | 65.77 199 | 47.03 238 | 67.48 221 | 64.27 240 | | 76.89 242 | 76.72 232 |
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