APDe-MVS | | | 97.79 1 | 97.96 2 | 97.60 1 | 99.20 1 | 99.10 3 | 98.88 1 | 96.68 2 | 96.81 3 | 94.64 4 | 97.84 1 | 98.02 8 | 97.24 2 | 97.74 5 | 97.02 10 | 98.97 1 | 99.16 2 |
|
SMA-MVS | | | 97.42 4 | 97.82 3 | 96.95 9 | 99.18 2 | 99.05 5 | 98.10 17 | 96.31 6 | 96.28 10 | 92.94 19 | 95.50 21 | 99.21 2 | 96.69 16 | 97.96 2 | 97.67 2 | 98.50 15 | 99.06 7 |
|
ESAPD | | | 97.65 2 | 97.98 1 | 97.27 4 | 99.12 3 | 99.14 2 | 98.66 2 | 96.80 1 | 95.74 16 | 93.46 13 | 97.72 2 | 99.48 1 | 96.76 13 | 97.77 3 | 96.92 13 | 98.83 4 | 99.07 6 |
|
zzz-MVS | | | 96.98 12 | 96.68 20 | 97.33 2 | 99.09 4 | 98.71 10 | 98.43 6 | 96.01 12 | 96.11 13 | 95.19 3 | 92.89 29 | 97.32 18 | 96.84 9 | 97.20 14 | 96.09 33 | 98.44 24 | 98.46 27 |
|
HPM-MVS++ | | | 97.22 8 | 97.40 9 | 97.01 7 | 99.08 5 | 98.55 21 | 98.19 12 | 96.48 4 | 96.02 14 | 93.28 16 | 96.26 12 | 98.71 5 | 96.76 13 | 97.30 12 | 96.25 30 | 98.30 47 | 98.68 11 |
|
ACMMP_Plus | | | 96.93 13 | 97.27 11 | 96.53 20 | 99.06 6 | 98.95 6 | 98.24 11 | 96.06 11 | 95.66 18 | 90.96 30 | 95.63 19 | 97.71 12 | 96.53 18 | 97.66 7 | 96.68 16 | 98.30 47 | 98.61 16 |
|
PGM-MVS | | | 96.16 21 | 96.33 25 | 95.95 23 | 99.04 7 | 98.63 16 | 98.32 10 | 92.76 37 | 93.42 43 | 90.49 35 | 96.30 11 | 95.31 35 | 96.71 15 | 96.46 30 | 96.02 34 | 98.38 34 | 98.19 36 |
|
APD-MVS | | | 97.12 9 | 97.05 14 | 97.19 5 | 99.04 7 | 98.63 16 | 98.45 5 | 96.54 3 | 94.81 32 | 93.50 11 | 96.10 14 | 97.40 17 | 96.81 10 | 97.05 17 | 96.82 15 | 98.80 5 | 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 13 | 99.03 9 | 98.44 29 | 98.15 14 | 96.28 7 | 96.32 8 | 92.39 22 | 92.16 31 | 97.55 15 | 96.68 17 | 97.32 10 | 96.65 18 | 98.55 12 | 98.26 32 |
|
CNVR-MVS | | | 97.30 7 | 97.41 8 | 97.18 6 | 99.02 10 | 98.60 18 | 98.15 14 | 96.24 9 | 96.12 12 | 94.10 8 | 95.54 20 | 97.99 9 | 96.99 5 | 97.97 1 | 97.17 6 | 98.57 11 | 98.50 23 |
|
HSP-MVS | | | 97.51 3 | 97.70 5 | 97.29 3 | 99.00 11 | 99.17 1 | 98.61 3 | 96.41 5 | 95.88 15 | 94.34 7 | 97.72 2 | 99.04 4 | 96.93 8 | 97.29 13 | 95.90 36 | 98.45 23 | 98.94 9 |
|
ACMMPR | | | 96.92 14 | 96.96 15 | 96.87 12 | 98.99 12 | 98.78 8 | 98.38 8 | 95.52 20 | 96.57 6 | 92.81 21 | 96.06 15 | 95.90 30 | 97.07 4 | 96.60 27 | 96.34 27 | 98.46 20 | 98.42 28 |
|
HFP-MVS | | | 97.11 10 | 97.19 12 | 97.00 8 | 98.97 13 | 98.73 9 | 98.37 9 | 95.69 17 | 96.60 5 | 93.28 16 | 96.87 5 | 96.64 23 | 97.27 1 | 96.64 25 | 96.33 28 | 98.44 24 | 98.56 17 |
|
SteuartSystems-ACMMP | | | 97.10 11 | 97.49 7 | 96.65 15 | 98.97 13 | 98.95 6 | 98.43 6 | 95.96 13 | 95.12 25 | 91.46 25 | 96.85 6 | 97.60 14 | 96.37 22 | 97.76 4 | 97.16 7 | 98.68 6 | 98.97 8 |
Skip Steuart: Steuart Systems R&D Blog. |
X-MVS | | | 96.07 23 | 96.33 25 | 95.77 26 | 98.94 15 | 98.66 11 | 97.94 21 | 95.41 25 | 95.12 25 | 88.03 46 | 93.00 28 | 96.06 26 | 95.85 24 | 96.65 24 | 96.35 25 | 98.47 18 | 98.48 24 |
|
MP-MVS | | | 96.56 18 | 96.72 19 | 96.37 21 | 98.93 16 | 98.48 25 | 98.04 18 | 95.55 19 | 94.32 36 | 90.95 32 | 95.88 17 | 97.02 20 | 96.29 23 | 96.77 23 | 96.01 35 | 98.47 18 | 98.56 17 |
|
MCST-MVS | | | 96.83 15 | 97.06 13 | 96.57 16 | 98.88 17 | 98.47 27 | 98.02 19 | 96.16 10 | 95.58 20 | 90.96 30 | 95.78 18 | 97.84 11 | 96.46 20 | 97.00 19 | 96.17 32 | 98.94 3 | 98.55 22 |
|
CP-MVS | | | 96.68 17 | 96.59 23 | 96.77 14 | 98.85 18 | 98.58 19 | 98.18 13 | 95.51 21 | 95.34 22 | 92.94 19 | 95.21 24 | 96.25 25 | 96.79 12 | 96.44 32 | 95.77 38 | 98.35 36 | 98.56 17 |
|
mPP-MVS | | | | | | 98.76 19 | | | | | | | 95.49 33 | | | | | |
|
CSCG | | | 95.68 27 | 95.46 32 | 95.93 24 | 98.71 20 | 99.07 4 | 97.13 31 | 93.55 32 | 95.48 21 | 93.35 15 | 90.61 40 | 93.82 40 | 95.16 31 | 94.60 71 | 95.57 41 | 97.70 100 | 99.08 5 |
|
DeepC-MVS_fast | | 93.32 1 | 96.48 19 | 96.42 24 | 96.56 17 | 98.70 21 | 98.31 33 | 97.97 20 | 95.76 16 | 96.31 9 | 92.01 24 | 91.43 36 | 95.42 34 | 96.46 20 | 97.65 8 | 97.69 1 | 98.49 17 | 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 19 | 98.51 22 | 97.76 49 | 96.69 35 | 95.94 15 | 93.72 41 | 93.50 11 | 89.01 46 | 90.53 57 | 96.49 19 | 94.51 74 | 93.76 69 | 98.07 77 | 96.69 82 |
|
train_agg | | | 96.15 22 | 96.64 22 | 95.58 30 | 98.44 23 | 98.03 40 | 98.14 16 | 95.40 26 | 93.90 40 | 87.72 50 | 96.26 12 | 98.10 7 | 95.75 26 | 96.25 37 | 95.45 43 | 98.01 83 | 98.47 25 |
|
CDPH-MVS | | | 94.80 37 | 95.50 30 | 93.98 42 | 98.34 24 | 98.06 39 | 97.41 26 | 93.23 34 | 92.81 47 | 82.98 82 | 92.51 30 | 94.82 36 | 93.53 49 | 96.08 40 | 96.30 29 | 98.42 27 | 97.94 45 |
|
MSLP-MVS++ | | | 96.05 24 | 95.63 28 | 96.55 18 | 98.33 25 | 98.17 36 | 96.94 32 | 94.61 29 | 94.70 34 | 94.37 6 | 89.20 45 | 95.96 29 | 96.81 10 | 95.57 46 | 97.33 5 | 98.24 59 | 98.47 25 |
|
ACMMP | | | 95.54 28 | 95.49 31 | 95.61 29 | 98.27 26 | 98.53 23 | 97.16 30 | 94.86 27 | 94.88 31 | 89.34 38 | 95.36 23 | 91.74 48 | 95.50 29 | 95.51 47 | 94.16 59 | 98.50 15 | 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 28 | 98.11 27 | 98.15 37 | 97.19 29 | 91.59 47 | 95.11 27 | 93.23 18 | 81.99 90 | 94.71 37 | 95.43 30 | 96.48 29 | 96.88 14 | 98.35 36 | 98.63 13 |
|
3Dnovator | | 90.28 7 | 94.70 38 | 94.34 40 | 95.11 31 | 98.06 28 | 98.21 34 | 96.89 33 | 91.03 53 | 94.72 33 | 91.45 26 | 82.87 81 | 93.10 43 | 94.61 35 | 96.24 38 | 97.08 9 | 98.63 9 | 98.16 37 |
|
PLC | | 90.69 4 | 94.32 40 | 92.99 49 | 95.87 25 | 97.91 29 | 96.49 84 | 95.95 46 | 94.12 30 | 94.94 29 | 94.09 9 | 85.90 57 | 90.77 54 | 95.58 28 | 94.52 73 | 93.32 83 | 97.55 108 | 95.00 146 |
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 49 | 97.89 30 | 96.55 81 | 96.08 42 | 92.14 40 | 91.65 56 | 89.16 40 | 94.07 26 | 90.17 61 | 87.78 107 | 95.24 49 | 94.97 49 | 97.09 124 | 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 22 | 97.88 31 | 97.98 43 | 97.92 22 | 94.86 27 | 94.56 35 | 92.16 23 | 91.01 38 | 95.71 31 | 96.97 7 | 94.56 72 | 93.50 78 | 96.81 157 | 98.14 39 |
|
QAPM | | | 94.13 42 | 94.33 41 | 93.90 43 | 97.82 32 | 98.37 32 | 96.47 37 | 90.89 54 | 92.73 49 | 85.63 67 | 85.35 61 | 93.87 39 | 94.17 41 | 95.71 45 | 95.90 36 | 98.40 31 | 98.42 28 |
|
DeepC-MVS | | 92.10 3 | 95.22 31 | 94.77 35 | 95.75 27 | 97.77 33 | 98.54 22 | 97.63 25 | 95.96 13 | 95.07 28 | 88.85 42 | 85.35 61 | 91.85 47 | 95.82 25 | 96.88 22 | 97.10 8 | 98.44 24 | 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 52 | 91.61 67 | 93.55 48 | 97.74 34 | 98.02 41 | 95.66 49 | 90.46 57 | 89.14 84 | 86.50 59 | 75.80 124 | 90.38 60 | 92.69 57 | 94.99 52 | 95.30 44 | 98.27 54 | 97.63 56 |
|
TSAR-MVS + ACMM | | | 96.19 20 | 97.39 10 | 94.78 33 | 97.70 35 | 98.41 30 | 97.72 24 | 95.49 22 | 96.47 7 | 86.66 58 | 96.35 10 | 97.85 10 | 93.99 43 | 97.19 15 | 96.37 24 | 97.12 122 | 99.13 3 |
|
MAR-MVS | | | 92.71 51 | 92.63 52 | 92.79 58 | 97.70 35 | 97.15 68 | 93.75 79 | 87.98 93 | 90.71 61 | 85.76 66 | 86.28 54 | 86.38 67 | 94.35 38 | 94.95 54 | 95.49 42 | 97.22 116 | 97.44 62 |
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 37 | 97.60 37 | 98.65 15 | 96.49 36 | 93.13 35 | 94.07 38 | 87.91 49 | 97.12 4 | 97.17 19 | 93.90 46 | 96.46 30 | 96.93 12 | 98.64 8 | 98.10 43 |
|
abl_6 | | | | | 94.78 33 | 97.46 38 | 97.99 42 | 95.76 47 | 91.80 44 | 93.72 41 | 91.25 27 | 91.33 37 | 96.47 24 | 94.28 40 | | | 98.14 67 | 97.39 64 |
|
SD-MVS | | | 97.35 5 | 97.73 4 | 96.90 11 | 97.35 39 | 98.66 11 | 97.85 23 | 96.25 8 | 96.86 2 | 94.54 5 | 96.75 8 | 99.13 3 | 96.99 5 | 96.94 20 | 96.58 19 | 98.39 33 | 99.20 1 |
|
MVS_111021_HR | | | 94.84 35 | 95.91 27 | 93.60 47 | 97.35 39 | 98.46 28 | 95.08 54 | 91.19 50 | 94.18 37 | 85.97 61 | 95.38 22 | 92.56 45 | 93.61 48 | 96.61 26 | 96.25 30 | 98.40 31 | 97.92 47 |
|
TSAR-MVS + MP. | | | 97.31 6 | 97.64 6 | 96.92 10 | 97.28 41 | 98.56 20 | 98.61 3 | 95.48 23 | 96.72 4 | 94.03 10 | 96.73 9 | 98.29 6 | 97.15 3 | 97.61 9 | 96.42 22 | 98.96 2 | 99.13 3 |
|
CANet | | | 94.85 34 | 94.92 34 | 94.78 33 | 97.25 42 | 98.52 24 | 97.20 28 | 91.81 43 | 93.25 44 | 91.06 29 | 86.29 53 | 94.46 38 | 92.99 54 | 97.02 18 | 96.68 16 | 98.34 38 | 98.20 35 |
|
OMC-MVS | | | 94.49 39 | 94.36 39 | 94.64 36 | 97.17 43 | 97.73 50 | 95.49 51 | 92.25 39 | 96.18 11 | 90.34 36 | 88.51 47 | 92.88 44 | 94.90 34 | 94.92 56 | 94.17 58 | 97.69 101 | 96.15 107 |
|
MVS_111021_LR | | | 94.84 35 | 95.57 29 | 94.00 40 | 97.11 44 | 97.72 52 | 94.88 57 | 91.16 51 | 95.24 24 | 88.74 43 | 96.03 16 | 91.52 51 | 94.33 39 | 95.96 41 | 95.01 48 | 97.79 92 | 97.49 60 |
|
CNLPA | | | 93.69 45 | 92.50 54 | 95.06 32 | 97.11 44 | 97.36 56 | 93.88 77 | 93.30 33 | 95.64 19 | 93.44 14 | 80.32 97 | 90.73 55 | 94.99 33 | 93.58 95 | 93.33 82 | 97.67 103 | 96.57 92 |
|
LS3D | | | 91.97 58 | 90.98 71 | 93.12 54 | 97.03 46 | 97.09 71 | 95.33 53 | 95.59 18 | 92.47 50 | 79.26 103 | 81.60 93 | 82.77 84 | 94.39 37 | 94.28 77 | 94.23 57 | 97.14 121 | 94.45 151 |
|
TAPA-MVS | | 90.35 6 | 93.69 45 | 93.52 43 | 93.90 43 | 96.89 47 | 97.62 53 | 96.15 40 | 91.67 46 | 94.94 29 | 85.97 61 | 87.72 50 | 91.96 46 | 94.40 36 | 93.76 89 | 93.06 96 | 98.30 47 | 95.58 125 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
DELS-MVS | | | 93.71 44 | 93.47 44 | 94.00 40 | 96.82 48 | 98.39 31 | 96.80 34 | 91.07 52 | 89.51 82 | 89.94 37 | 83.80 77 | 89.29 63 | 90.95 77 | 97.32 10 | 97.65 3 | 98.42 27 | 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 | | | 88.32 106 | 90.61 72 | 85.64 145 | 96.79 49 | 92.27 170 | 92.03 112 | 90.31 58 | 89.05 85 | 65.44 196 | 89.43 43 | 85.90 72 | 74.22 204 | 92.76 107 | 92.09 113 | 95.02 192 | 92.76 175 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
MSDG | | | 90.42 74 | 88.25 98 | 92.94 57 | 96.67 50 | 94.41 109 | 93.96 73 | 92.91 36 | 89.59 81 | 86.26 60 | 76.74 116 | 80.92 95 | 90.43 83 | 92.60 111 | 92.08 114 | 97.44 112 | 91.41 184 |
|
DeepPCF-MVS | | 92.65 2 | 95.50 30 | 96.96 15 | 93.79 46 | 96.44 51 | 98.21 34 | 93.51 85 | 94.08 31 | 96.94 1 | 89.29 39 | 93.08 27 | 96.77 22 | 93.82 47 | 97.68 6 | 97.40 4 | 95.59 182 | 98.65 12 |
|
PCF-MVS | | 90.19 8 | 92.98 48 | 92.07 62 | 94.04 39 | 96.39 52 | 97.87 44 | 96.03 43 | 95.47 24 | 87.16 100 | 85.09 75 | 84.81 69 | 93.21 42 | 93.46 51 | 91.98 120 | 91.98 117 | 97.78 93 | 97.51 59 |
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 45 | 96.33 53 | 98.48 25 | 97.41 26 | 91.20 49 | 92.75 48 | 86.96 56 | 86.03 56 | 93.81 41 | 92.64 58 | 96.89 21 | 96.54 21 | 98.61 10 | 98.24 33 |
|
OPM-MVS | | | 91.08 67 | 89.34 83 | 93.11 55 | 96.18 54 | 96.13 95 | 96.39 38 | 92.39 38 | 82.97 143 | 81.74 84 | 82.55 87 | 80.20 96 | 93.97 45 | 94.62 69 | 93.23 85 | 98.00 84 | 95.73 120 |
|
PVSNet_BlendedMVS | | | 92.80 49 | 92.44 56 | 93.23 50 | 96.02 55 | 97.83 47 | 93.74 80 | 90.58 55 | 91.86 53 | 90.69 33 | 85.87 59 | 82.04 89 | 90.01 86 | 96.39 33 | 95.26 45 | 98.34 38 | 97.81 52 |
|
PVSNet_Blended | | | 92.80 49 | 92.44 56 | 93.23 50 | 96.02 55 | 97.83 47 | 93.74 80 | 90.58 55 | 91.86 53 | 90.69 33 | 85.87 59 | 82.04 89 | 90.01 86 | 96.39 33 | 95.26 45 | 98.34 38 | 97.81 52 |
|
XVS | | | | | | 95.68 57 | 98.66 11 | 94.96 55 | | | 88.03 46 | | 96.06 26 | | | | 98.46 20 | |
|
X-MVStestdata | | | | | | 95.68 57 | 98.66 11 | 94.96 55 | | | 88.03 46 | | 96.06 26 | | | | 98.46 20 | |
|
HQP-MVS | | | 92.39 54 | 92.49 55 | 92.29 61 | 95.65 59 | 95.94 96 | 95.64 50 | 92.12 41 | 92.46 51 | 79.65 101 | 91.97 33 | 82.68 85 | 92.92 56 | 93.47 100 | 92.77 99 | 97.74 96 | 98.12 41 |
|
HyFIR lowres test | | | 87.87 109 | 86.42 121 | 89.57 92 | 95.56 60 | 96.99 72 | 92.37 98 | 84.15 133 | 86.64 104 | 77.17 111 | 57.65 212 | 83.97 77 | 91.08 76 | 92.09 119 | 92.44 104 | 97.09 124 | 95.16 143 |
|
ACMM | | 88.76 10 | 91.70 64 | 90.43 73 | 93.19 52 | 95.56 60 | 95.14 101 | 93.35 88 | 91.48 48 | 92.26 52 | 87.12 54 | 84.02 76 | 79.34 99 | 93.99 43 | 94.07 83 | 92.68 101 | 97.62 107 | 95.50 126 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
COLMAP_ROB | | 84.39 15 | 87.61 111 | 86.03 125 | 89.46 94 | 95.54 62 | 94.48 106 | 91.77 115 | 90.14 59 | 87.16 100 | 75.50 117 | 73.41 139 | 76.86 115 | 87.33 114 | 90.05 153 | 89.76 177 | 96.48 163 | 90.46 194 |
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 61 | 92.04 63 | 91.58 67 | 95.46 63 | 96.18 94 | 95.97 45 | 89.85 61 | 90.45 65 | 77.76 107 | 91.92 34 | 80.07 97 | 92.34 61 | 94.27 78 | 93.47 79 | 98.11 71 | 97.90 50 |
|
CHOSEN 1792x2688 | | | 88.57 103 | 87.82 104 | 89.44 95 | 95.46 63 | 96.89 75 | 93.74 80 | 85.87 111 | 89.63 80 | 77.42 110 | 61.38 207 | 83.31 80 | 88.80 104 | 93.44 101 | 93.16 91 | 95.37 187 | 96.95 73 |
|
PVSNet_Blended_VisFu | | | 91.92 59 | 92.39 58 | 91.36 77 | 95.45 65 | 97.85 46 | 92.25 103 | 89.54 73 | 88.53 91 | 87.47 52 | 79.82 99 | 90.53 57 | 85.47 148 | 96.31 36 | 95.16 47 | 97.99 85 | 98.56 17 |
|
PatchMatch-RL | | | 90.30 75 | 88.93 89 | 91.89 63 | 95.41 66 | 95.68 97 | 90.94 119 | 88.67 85 | 89.80 79 | 86.95 57 | 85.90 57 | 72.51 125 | 92.46 59 | 93.56 98 | 92.18 110 | 96.93 142 | 92.89 171 |
|
TSAR-MVS + COLMAP | | | 92.39 54 | 92.31 59 | 92.47 59 | 95.35 67 | 96.46 85 | 96.13 41 | 92.04 42 | 95.33 23 | 80.11 98 | 94.95 25 | 77.35 112 | 94.05 42 | 94.49 75 | 93.08 94 | 97.15 119 | 94.53 149 |
|
ACMP | | 89.13 9 | 92.03 57 | 91.70 66 | 92.41 60 | 94.92 68 | 96.44 87 | 93.95 75 | 89.96 60 | 91.81 55 | 85.48 71 | 90.97 39 | 79.12 100 | 92.42 60 | 93.28 105 | 92.55 102 | 97.76 94 | 97.74 55 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
UA-Net | | | 90.81 69 | 92.58 53 | 88.74 103 | 94.87 69 | 97.44 55 | 92.61 95 | 88.22 89 | 82.35 146 | 78.93 104 | 85.20 63 | 95.61 32 | 79.56 190 | 96.52 28 | 96.57 20 | 98.23 60 | 94.37 152 |
|
IB-MVS | | 85.10 14 | 87.98 107 | 87.97 101 | 87.99 112 | 94.55 70 | 96.86 76 | 84.52 199 | 88.21 90 | 86.48 109 | 88.54 45 | 74.41 133 | 77.74 108 | 74.10 206 | 89.65 159 | 92.85 97 | 98.06 79 | 97.80 54 |
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 71 | 92.93 50 | 88.19 107 | 94.36 71 | 96.61 79 | 94.34 62 | 84.66 127 | 90.66 62 | 68.75 174 | 90.41 41 | 86.89 65 | 89.78 88 | 95.46 48 | 94.87 50 | 97.25 115 | 95.62 123 |
|
canonicalmvs | | | 93.08 47 | 93.09 47 | 93.07 56 | 94.24 72 | 97.86 45 | 95.45 52 | 87.86 99 | 94.00 39 | 87.47 52 | 88.32 48 | 82.37 88 | 95.13 32 | 93.96 88 | 96.41 23 | 98.27 54 | 98.73 10 |
|
tfpn | | | 88.67 99 | 86.57 119 | 91.12 79 | 94.14 73 | 97.15 68 | 93.51 85 | 89.37 75 | 85.49 123 | 79.91 100 | 75.26 130 | 62.24 199 | 91.39 73 | 95.00 51 | 93.95 66 | 98.41 29 | 96.88 76 |
|
view800 | | | 89.21 96 | 87.44 114 | 91.27 78 | 94.13 74 | 97.18 67 | 93.74 80 | 89.53 74 | 85.60 122 | 80.34 97 | 75.29 128 | 68.89 145 | 91.57 72 | 94.97 53 | 93.36 81 | 98.34 38 | 96.79 78 |
|
UGNet | | | 91.52 65 | 93.41 45 | 89.32 96 | 94.13 74 | 97.15 68 | 91.83 114 | 89.01 80 | 90.62 63 | 85.86 64 | 86.83 51 | 91.73 49 | 77.40 196 | 94.68 68 | 94.43 54 | 97.71 98 | 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 94 | 87.47 113 | 91.39 74 | 94.12 76 | 97.25 63 | 93.94 76 | 89.74 68 | 85.62 121 | 80.63 95 | 75.24 131 | 69.33 144 | 91.66 71 | 94.92 56 | 93.23 85 | 98.27 54 | 96.72 80 |
|
view600 | | | 89.29 93 | 87.48 112 | 91.41 73 | 94.10 77 | 97.21 65 | 93.96 73 | 89.70 71 | 85.67 118 | 80.75 94 | 75.29 128 | 69.35 143 | 91.70 70 | 94.92 56 | 93.23 85 | 98.26 58 | 96.69 82 |
|
IS_MVSNet | | | 91.87 60 | 93.35 46 | 90.14 89 | 94.09 78 | 97.73 50 | 93.09 90 | 88.12 91 | 88.71 87 | 79.98 99 | 84.49 70 | 90.63 56 | 87.49 112 | 97.07 16 | 96.96 11 | 98.07 77 | 97.88 51 |
|
TSAR-MVS + GP. | | | 95.86 25 | 96.95 17 | 94.60 38 | 94.07 79 | 98.11 38 | 96.30 39 | 91.76 45 | 95.67 17 | 91.07 28 | 96.82 7 | 97.69 13 | 95.71 27 | 95.96 41 | 95.75 39 | 98.68 6 | 98.63 13 |
|
thres400 | | | 89.40 88 | 87.58 110 | 91.53 69 | 94.06 80 | 97.21 65 | 94.19 72 | 89.83 62 | 85.69 117 | 81.08 92 | 75.50 126 | 69.76 142 | 91.80 63 | 94.79 66 | 93.51 72 | 98.20 63 | 96.60 90 |
|
ACMH | | 85.51 13 | 87.31 114 | 86.59 118 | 88.14 110 | 93.96 81 | 94.51 105 | 89.00 167 | 87.99 92 | 81.58 148 | 70.15 157 | 78.41 107 | 71.78 130 | 90.60 81 | 91.30 129 | 91.99 116 | 97.17 118 | 96.58 91 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
MS-PatchMatch | | | 87.63 110 | 87.61 108 | 87.65 117 | 93.95 82 | 94.09 113 | 92.60 96 | 81.52 165 | 86.64 104 | 76.41 115 | 73.46 138 | 85.94 71 | 85.01 153 | 92.23 117 | 90.00 170 | 96.43 165 | 90.93 190 |
|
thres200 | | | 89.49 87 | 87.72 105 | 91.55 68 | 93.95 82 | 97.25 63 | 94.34 62 | 89.74 68 | 85.66 119 | 81.18 87 | 76.12 123 | 70.19 141 | 91.80 63 | 94.92 56 | 93.51 72 | 98.27 54 | 96.40 95 |
|
CLD-MVS | | | 92.50 53 | 91.96 64 | 93.13 53 | 93.93 84 | 96.24 92 | 95.69 48 | 88.77 83 | 92.92 46 | 89.01 41 | 88.19 49 | 81.74 92 | 93.13 53 | 93.63 93 | 93.08 94 | 98.23 60 | 97.91 49 |
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 79 | 88.99 88 | 91.52 71 | 93.90 85 | 97.26 60 | 94.31 64 | 89.75 65 | 85.87 111 | 81.10 90 | 84.41 72 | 70.38 136 | 91.76 65 | 94.92 56 | 93.51 72 | 98.29 51 | 96.61 85 |
|
conf200view11 | | | 89.55 85 | 87.86 102 | 91.52 71 | 93.90 85 | 97.26 60 | 94.31 64 | 89.75 65 | 85.87 111 | 81.10 90 | 76.46 118 | 70.38 136 | 91.76 65 | 94.92 56 | 93.51 72 | 98.29 51 | 96.61 85 |
|
thres100view900 | | | 89.36 89 | 87.61 108 | 91.39 74 | 93.90 85 | 96.86 76 | 94.35 61 | 89.66 72 | 85.87 111 | 81.15 88 | 76.46 118 | 70.38 136 | 91.17 74 | 94.09 82 | 93.43 80 | 98.13 68 | 96.16 106 |
|
tfpn200view9 | | | 89.55 85 | 87.86 102 | 91.53 69 | 93.90 85 | 97.26 60 | 94.31 64 | 89.74 68 | 85.87 111 | 81.15 88 | 76.46 118 | 70.38 136 | 91.76 65 | 94.92 56 | 93.51 72 | 98.28 53 | 96.61 85 |
|
conf0.01 | | | 89.34 91 | 87.39 115 | 91.61 66 | 93.88 89 | 97.34 58 | 94.31 64 | 89.82 64 | 85.87 111 | 81.53 86 | 77.93 109 | 66.15 176 | 91.76 65 | 94.90 63 | 93.51 72 | 98.32 43 | 96.05 111 |
|
conf0.002 | | | 89.25 95 | 87.21 116 | 91.62 65 | 93.87 90 | 97.35 57 | 94.31 64 | 89.83 62 | 85.87 111 | 81.62 85 | 78.72 105 | 63.89 193 | 91.76 65 | 94.90 63 | 93.98 65 | 98.33 42 | 95.77 118 |
|
CHOSEN 280x420 | | | 90.77 70 | 92.14 61 | 89.17 98 | 93.86 91 | 92.81 156 | 93.16 89 | 80.22 181 | 90.21 69 | 84.67 77 | 89.89 42 | 91.38 52 | 90.57 82 | 94.94 55 | 92.11 112 | 92.52 204 | 93.65 163 |
|
tfpn1000 | | | 89.30 92 | 89.72 82 | 88.81 101 | 93.83 92 | 96.50 83 | 91.53 118 | 88.74 84 | 91.20 59 | 76.74 113 | 84.96 67 | 75.44 120 | 83.50 169 | 93.63 93 | 92.42 105 | 98.51 13 | 93.88 160 |
|
FC-MVSNet-train | | | 90.55 72 | 90.19 75 | 90.97 81 | 93.78 93 | 95.16 100 | 92.11 110 | 88.85 82 | 87.64 96 | 83.38 81 | 84.36 74 | 78.41 103 | 89.53 89 | 94.69 67 | 93.15 92 | 98.15 66 | 97.92 47 |
|
conf0.05thres1000 | | | 87.90 108 | 85.88 130 | 90.26 86 | 93.74 94 | 96.39 89 | 92.67 94 | 88.94 81 | 80.97 155 | 77.71 109 | 70.15 153 | 68.40 150 | 90.42 84 | 94.46 76 | 93.29 84 | 98.09 73 | 97.49 60 |
|
Vis-MVSNet (Re-imp) | | | 90.54 73 | 92.76 51 | 87.94 113 | 93.73 95 | 96.94 74 | 92.17 108 | 87.91 94 | 88.77 86 | 76.12 116 | 83.68 78 | 90.80 53 | 79.49 191 | 96.34 35 | 96.35 25 | 98.21 62 | 96.46 93 |
|
tfpnview11 | | | 88.80 98 | 89.21 85 | 88.31 106 | 93.70 96 | 96.24 92 | 92.35 99 | 89.11 77 | 89.90 78 | 72.14 133 | 85.12 64 | 73.93 121 | 84.20 160 | 93.75 90 | 92.85 97 | 98.38 34 | 92.68 178 |
|
EPP-MVSNet | | | 92.13 56 | 93.06 48 | 91.05 80 | 93.66 97 | 97.30 59 | 92.18 106 | 87.90 95 | 90.24 68 | 83.63 78 | 86.14 55 | 90.52 59 | 90.76 79 | 94.82 65 | 94.38 55 | 98.18 65 | 97.98 44 |
|
tfpn_n400 | | | 88.58 101 | 88.91 90 | 88.19 107 | 93.63 98 | 96.34 90 | 92.22 104 | 89.04 78 | 87.37 98 | 72.14 133 | 85.12 64 | 73.93 121 | 84.04 165 | 93.65 91 | 93.20 88 | 98.09 73 | 92.77 173 |
|
tfpnconf | | | 88.58 101 | 88.91 90 | 88.19 107 | 93.63 98 | 96.34 90 | 92.22 104 | 89.04 78 | 87.37 98 | 72.14 133 | 85.12 64 | 73.93 121 | 84.04 165 | 93.65 91 | 93.20 88 | 98.09 73 | 92.77 173 |
|
thresconf0.02 | | | 88.86 97 | 88.70 93 | 89.04 99 | 93.59 100 | 96.40 88 | 92.97 92 | 89.75 65 | 90.16 72 | 74.34 120 | 84.41 72 | 71.00 132 | 85.16 150 | 93.32 103 | 93.12 93 | 98.41 29 | 92.52 180 |
|
tfpn_ndepth | | | 89.72 82 | 89.91 80 | 89.49 93 | 93.56 101 | 96.67 78 | 92.34 100 | 89.25 76 | 90.85 60 | 78.68 106 | 84.25 75 | 77.39 111 | 84.84 154 | 93.58 95 | 92.76 100 | 98.30 47 | 93.90 159 |
|
ACMH+ | | 85.75 12 | 87.19 115 | 86.02 126 | 88.56 104 | 93.42 102 | 94.41 109 | 89.91 150 | 87.66 103 | 83.45 141 | 72.25 131 | 76.42 121 | 71.99 129 | 90.78 78 | 89.86 154 | 90.94 129 | 97.32 113 | 95.11 145 |
|
MVS_Test | | | 91.81 62 | 92.19 60 | 91.37 76 | 93.24 103 | 96.95 73 | 94.43 59 | 86.25 107 | 91.45 58 | 83.45 80 | 86.31 52 | 85.15 74 | 92.93 55 | 93.99 84 | 94.71 52 | 97.92 88 | 96.77 79 |
|
MVSTER | | | 91.73 63 | 91.61 67 | 91.86 64 | 93.18 104 | 94.56 103 | 94.37 60 | 87.90 95 | 90.16 72 | 88.69 44 | 89.23 44 | 81.28 94 | 88.92 101 | 95.75 44 | 93.95 66 | 98.12 69 | 96.37 96 |
|
Effi-MVS+ | | | 89.79 81 | 89.83 81 | 89.74 90 | 92.98 105 | 96.45 86 | 93.48 87 | 84.24 131 | 87.62 97 | 76.45 114 | 81.76 91 | 77.56 110 | 93.48 50 | 94.61 70 | 93.59 71 | 97.82 91 | 97.22 67 |
|
RPSCF | | | 89.68 83 | 89.24 84 | 90.20 87 | 92.97 106 | 92.93 152 | 92.30 101 | 87.69 101 | 90.44 66 | 85.12 74 | 91.68 35 | 85.84 73 | 90.69 80 | 87.34 191 | 86.07 195 | 92.46 205 | 90.37 195 |
|
TDRefinement | | | 84.97 140 | 83.39 154 | 86.81 126 | 92.97 106 | 94.12 112 | 92.18 106 | 87.77 100 | 82.78 144 | 71.31 142 | 68.43 160 | 68.07 152 | 81.10 186 | 89.70 158 | 89.03 186 | 95.55 184 | 91.62 182 |
|
diffmvs | | | 91.35 66 | 91.81 65 | 90.82 82 | 92.80 108 | 95.62 98 | 93.74 80 | 86.04 108 | 93.17 45 | 85.82 65 | 84.48 71 | 89.74 62 | 90.23 85 | 90.49 145 | 92.45 103 | 96.29 168 | 96.72 80 |
|
EPMVS | | | 85.77 129 | 86.24 123 | 85.23 152 | 92.76 109 | 93.78 119 | 89.91 150 | 73.60 209 | 90.19 70 | 74.22 121 | 82.18 89 | 78.06 105 | 87.55 110 | 85.61 200 | 85.38 201 | 93.32 197 | 88.48 205 |
|
DWT-MVSNet_training | | | 86.83 117 | 84.44 142 | 89.61 91 | 92.75 110 | 93.82 117 | 91.66 116 | 82.85 147 | 88.57 89 | 87.48 51 | 79.00 102 | 64.24 192 | 88.82 103 | 85.18 201 | 87.50 191 | 94.07 195 | 92.79 172 |
|
DI_MVS_plusplus_trai | | | 91.05 68 | 90.15 76 | 92.11 62 | 92.67 111 | 96.61 79 | 96.03 43 | 88.44 87 | 90.25 67 | 85.92 63 | 73.73 134 | 84.89 76 | 91.92 62 | 94.17 81 | 94.07 63 | 97.68 102 | 97.31 66 |
|
tpmrst | | | 83.72 167 | 83.45 151 | 84.03 172 | 92.21 112 | 91.66 184 | 88.74 170 | 73.58 210 | 88.14 93 | 72.67 128 | 77.37 113 | 72.11 128 | 86.34 125 | 82.94 212 | 82.05 214 | 90.63 217 | 89.86 199 |
|
CostFormer | | | 86.78 119 | 86.05 124 | 87.62 119 | 92.15 113 | 93.20 142 | 91.55 117 | 75.83 200 | 88.11 94 | 85.29 73 | 81.76 91 | 76.22 117 | 87.80 106 | 84.45 206 | 85.21 202 | 93.12 198 | 93.42 166 |
|
Vis-MVSNet | | | 89.36 89 | 91.49 69 | 86.88 125 | 92.10 114 | 97.60 54 | 92.16 109 | 85.89 110 | 84.21 134 | 75.20 118 | 82.58 85 | 87.13 64 | 77.40 196 | 95.90 43 | 95.63 40 | 98.51 13 | 97.36 65 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
IterMVS-LS | | | 88.60 100 | 88.45 94 | 88.78 102 | 92.02 115 | 92.44 168 | 92.00 113 | 83.57 140 | 86.52 107 | 78.90 105 | 78.61 106 | 81.34 93 | 89.12 96 | 90.68 141 | 93.18 90 | 97.10 123 | 96.35 97 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
tpmp4_e23 | | | 85.67 131 | 84.28 144 | 87.30 121 | 91.96 116 | 92.00 179 | 92.06 111 | 76.27 198 | 87.95 95 | 83.59 79 | 76.97 115 | 70.88 133 | 87.52 111 | 84.80 205 | 84.73 204 | 92.40 206 | 92.61 179 |
|
PatchmatchNet | | | 85.70 130 | 86.65 117 | 84.60 164 | 91.79 117 | 93.40 134 | 89.27 161 | 73.62 208 | 90.19 70 | 72.63 129 | 82.74 84 | 81.93 91 | 87.64 108 | 84.99 202 | 84.29 207 | 92.64 202 | 89.00 201 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
tpm cat1 | | | 84.13 159 | 81.99 186 | 86.63 129 | 91.74 118 | 91.50 187 | 90.68 121 | 75.69 201 | 86.12 110 | 85.44 72 | 72.39 143 | 70.72 134 | 85.16 150 | 80.89 218 | 81.56 217 | 91.07 214 | 90.71 192 |
|
USDC | | | 86.73 120 | 85.96 128 | 87.63 118 | 91.64 119 | 93.97 115 | 92.76 93 | 84.58 129 | 88.19 92 | 70.67 150 | 80.10 98 | 67.86 153 | 89.43 90 | 91.81 121 | 89.77 176 | 96.69 161 | 90.05 198 |
|
gg-mvs-nofinetune | | | 81.83 194 | 83.58 149 | 79.80 202 | 91.57 120 | 96.54 82 | 93.79 78 | 68.80 222 | 62.71 224 | 43.01 232 | 55.28 216 | 85.06 75 | 83.65 167 | 96.13 39 | 94.86 51 | 97.98 87 | 94.46 150 |
|
Fast-Effi-MVS+ | | | 88.56 104 | 87.99 100 | 89.22 97 | 91.56 121 | 95.21 99 | 92.29 102 | 82.69 149 | 86.82 102 | 77.73 108 | 76.24 122 | 73.39 124 | 93.36 52 | 94.22 80 | 93.64 70 | 97.65 104 | 96.43 94 |
|
CMPMVS | | 61.19 17 | 79.86 201 | 77.46 208 | 82.66 191 | 91.54 122 | 91.82 182 | 83.25 202 | 81.57 164 | 70.51 217 | 68.64 175 | 59.89 211 | 66.77 165 | 79.63 189 | 84.00 210 | 84.30 206 | 91.34 212 | 84.89 214 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
ADS-MVSNet | | | 84.08 161 | 84.95 137 | 83.05 184 | 91.53 123 | 91.75 183 | 88.16 174 | 70.70 218 | 89.96 77 | 69.51 167 | 78.83 103 | 76.97 114 | 86.29 126 | 84.08 209 | 84.60 205 | 92.13 210 | 88.48 205 |
|
test-LLR | | | 86.88 116 | 88.28 96 | 85.24 151 | 91.22 124 | 92.07 174 | 87.41 180 | 83.62 138 | 84.58 127 | 69.33 168 | 83.00 79 | 82.79 82 | 84.24 158 | 92.26 115 | 89.81 174 | 95.64 180 | 93.44 164 |
|
test0.0.03 1 | | | 85.58 132 | 87.69 107 | 83.11 181 | 91.22 124 | 92.54 163 | 85.60 198 | 83.62 138 | 85.66 119 | 67.84 181 | 82.79 83 | 79.70 98 | 73.51 208 | 91.15 132 | 90.79 131 | 96.88 153 | 91.23 187 |
|
Effi-MVS+-dtu | | | 87.51 112 | 88.13 99 | 86.77 127 | 91.10 126 | 94.90 102 | 90.91 120 | 82.67 150 | 83.47 140 | 71.55 139 | 81.11 96 | 77.04 113 | 89.41 91 | 92.65 110 | 91.68 123 | 95.00 193 | 96.09 109 |
|
RPMNet | | | 84.82 142 | 85.90 129 | 83.56 176 | 91.10 126 | 92.10 172 | 88.73 171 | 71.11 217 | 84.75 125 | 68.79 173 | 73.56 135 | 77.62 109 | 85.33 149 | 90.08 152 | 89.43 182 | 96.32 167 | 93.77 162 |
|
CR-MVSNet | | | 85.48 134 | 86.29 122 | 84.53 166 | 91.08 128 | 92.10 172 | 89.18 163 | 73.30 213 | 84.75 125 | 71.08 145 | 73.12 142 | 77.91 107 | 86.27 127 | 91.48 125 | 90.75 134 | 96.27 169 | 93.94 157 |
|
TinyColmap | | | 84.04 162 | 82.01 185 | 86.42 131 | 90.87 129 | 91.84 181 | 88.89 169 | 84.07 134 | 82.11 147 | 69.89 164 | 71.08 146 | 60.81 208 | 89.04 97 | 90.52 143 | 89.19 184 | 95.76 175 | 88.50 204 |
|
tpm | | | 83.16 179 | 83.64 148 | 82.60 192 | 90.75 130 | 91.05 190 | 88.49 172 | 73.99 206 | 82.36 145 | 67.08 187 | 78.10 108 | 68.79 146 | 84.17 161 | 85.95 199 | 85.96 197 | 91.09 213 | 93.23 168 |
|
dps | | | 85.00 139 | 83.21 162 | 87.08 123 | 90.73 131 | 92.55 162 | 89.34 160 | 75.29 202 | 84.94 124 | 87.01 55 | 79.27 101 | 67.69 154 | 87.27 115 | 84.22 208 | 83.56 208 | 92.83 200 | 90.25 196 |
|
MDTV_nov1_ep13 | | | 86.64 121 | 87.50 111 | 85.65 144 | 90.73 131 | 93.69 123 | 89.96 148 | 78.03 193 | 89.48 83 | 76.85 112 | 84.92 68 | 82.42 87 | 86.14 132 | 86.85 196 | 86.15 194 | 92.17 208 | 88.97 202 |
|
CDS-MVSNet | | | 88.34 105 | 88.71 92 | 87.90 114 | 90.70 133 | 94.54 104 | 92.38 97 | 86.02 109 | 80.37 162 | 79.42 102 | 79.30 100 | 83.43 79 | 82.04 178 | 93.39 102 | 94.01 64 | 96.86 155 | 95.93 113 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
IterMVS | | | 85.25 137 | 86.49 120 | 83.80 173 | 90.42 134 | 90.77 195 | 90.02 146 | 78.04 192 | 84.10 136 | 66.27 192 | 77.28 114 | 78.41 103 | 83.01 170 | 90.88 134 | 89.72 178 | 95.04 191 | 94.24 153 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
Fast-Effi-MVS+-dtu | | | 86.25 122 | 87.70 106 | 84.56 165 | 90.37 135 | 93.70 122 | 90.54 124 | 78.14 191 | 83.50 139 | 65.37 197 | 81.59 94 | 75.83 119 | 86.09 137 | 91.70 123 | 91.70 121 | 96.88 153 | 95.84 117 |
|
FC-MVSNet-test | | | 86.15 124 | 89.10 87 | 82.71 190 | 89.83 136 | 93.18 144 | 87.88 177 | 84.69 126 | 86.54 106 | 62.18 206 | 82.39 88 | 83.31 80 | 74.18 205 | 92.52 112 | 91.86 118 | 97.50 110 | 93.88 160 |
|
GA-MVS | | | 85.08 138 | 85.65 133 | 84.42 167 | 89.77 137 | 94.25 111 | 89.26 162 | 84.62 128 | 81.19 153 | 62.25 205 | 75.72 125 | 68.44 149 | 84.14 162 | 93.57 97 | 91.68 123 | 96.49 162 | 94.71 148 |
|
PMMVS | | | 89.88 80 | 91.19 70 | 88.35 105 | 89.73 138 | 91.97 180 | 90.62 122 | 81.92 160 | 90.57 64 | 80.58 96 | 92.16 31 | 86.85 66 | 91.17 74 | 92.31 114 | 91.35 127 | 96.11 171 | 93.11 170 |
|
tfpnnormal | | | 83.80 166 | 81.26 195 | 86.77 127 | 89.60 139 | 93.26 141 | 89.72 157 | 87.60 104 | 72.78 209 | 70.44 151 | 60.53 210 | 61.15 207 | 85.55 146 | 92.72 108 | 91.44 125 | 97.71 98 | 96.92 74 |
|
CVMVSNet | | | 83.83 165 | 85.53 134 | 81.85 198 | 89.60 139 | 90.92 191 | 87.81 178 | 83.21 144 | 80.11 165 | 60.16 210 | 76.47 117 | 78.57 102 | 76.79 198 | 89.76 155 | 90.13 164 | 93.51 196 | 92.75 176 |
|
testgi | | | 81.94 193 | 84.09 146 | 79.43 203 | 89.53 141 | 90.83 193 | 82.49 205 | 81.75 163 | 80.59 157 | 59.46 212 | 82.82 82 | 65.75 177 | 67.97 210 | 90.10 151 | 89.52 181 | 95.39 186 | 89.03 200 |
|
LTVRE_ROB | | 81.71 16 | 82.44 189 | 81.84 187 | 83.13 180 | 89.01 142 | 92.99 149 | 88.90 168 | 82.32 156 | 66.26 221 | 54.02 220 | 74.68 132 | 59.62 214 | 88.87 102 | 90.71 140 | 92.02 115 | 95.68 179 | 96.62 84 |
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 | | | 84.94 141 | 84.95 137 | 84.93 161 | 88.82 143 | 93.18 144 | 88.44 173 | 81.28 167 | 77.16 192 | 73.76 125 | 75.43 127 | 76.57 116 | 82.04 178 | 90.59 142 | 90.79 131 | 95.22 189 | 90.94 189 |
|
EG-PatchMatch MVS | | | 81.70 196 | 81.31 194 | 82.15 196 | 88.75 144 | 93.81 118 | 87.14 183 | 78.89 189 | 71.57 213 | 64.12 202 | 61.20 209 | 68.46 148 | 76.73 199 | 91.48 125 | 90.77 133 | 97.28 114 | 91.90 181 |
|
TransMVSNet (Re) | | | 82.67 186 | 80.93 198 | 84.69 163 | 88.71 145 | 91.50 187 | 87.90 176 | 87.15 105 | 71.54 215 | 68.24 178 | 63.69 200 | 64.67 189 | 78.51 193 | 91.65 124 | 90.73 136 | 97.64 105 | 92.73 177 |
|
FMVSNet3 | | | 90.19 78 | 90.06 79 | 90.34 83 | 88.69 146 | 93.85 116 | 94.58 58 | 85.78 112 | 90.03 74 | 85.56 68 | 77.38 110 | 86.13 68 | 89.22 95 | 93.29 104 | 94.36 56 | 98.20 63 | 95.40 131 |
|
GBi-Net | | | 90.21 76 | 90.11 77 | 90.32 84 | 88.66 147 | 93.65 124 | 94.25 69 | 85.78 112 | 90.03 74 | 85.56 68 | 77.38 110 | 86.13 68 | 89.38 92 | 93.97 85 | 94.16 59 | 98.31 44 | 95.47 127 |
|
test1 | | | 90.21 76 | 90.11 77 | 90.32 84 | 88.66 147 | 93.65 124 | 94.25 69 | 85.78 112 | 90.03 74 | 85.56 68 | 77.38 110 | 86.13 68 | 89.38 92 | 93.97 85 | 94.16 59 | 98.31 44 | 95.47 127 |
|
FMVSNet2 | | | 89.61 84 | 89.14 86 | 90.16 88 | 88.66 147 | 93.65 124 | 94.25 69 | 85.44 119 | 88.57 89 | 84.96 76 | 73.53 136 | 83.82 78 | 89.38 92 | 94.23 79 | 94.68 53 | 98.31 44 | 95.47 127 |
|
PatchT | | | 83.86 164 | 85.51 135 | 81.94 197 | 88.41 150 | 91.56 186 | 78.79 212 | 71.57 216 | 84.08 137 | 71.08 145 | 70.62 147 | 76.13 118 | 86.27 127 | 91.48 125 | 90.75 134 | 95.52 185 | 93.94 157 |
|
UniMVSNet (Re) | | | 86.22 123 | 85.46 136 | 87.11 122 | 88.34 151 | 94.42 108 | 89.65 158 | 87.10 106 | 84.39 131 | 74.61 119 | 70.41 151 | 68.10 151 | 85.10 152 | 91.17 131 | 91.79 119 | 97.84 90 | 97.94 45 |
|
NR-MVSNet | | | 85.46 135 | 84.54 141 | 86.52 130 | 88.33 152 | 93.78 119 | 90.45 125 | 87.87 97 | 84.40 129 | 71.61 138 | 70.59 148 | 62.09 202 | 82.79 172 | 91.75 122 | 91.75 120 | 98.10 72 | 97.44 62 |
|
UniMVSNet_NR-MVSNet | | | 86.80 118 | 85.86 131 | 87.89 115 | 88.17 153 | 94.07 114 | 90.15 141 | 88.51 86 | 84.20 135 | 73.45 126 | 72.38 144 | 70.30 140 | 88.95 99 | 90.25 147 | 92.21 109 | 98.12 69 | 97.62 57 |
|
LP | | | 77.28 208 | 76.57 210 | 78.12 206 | 88.17 153 | 88.06 209 | 80.85 209 | 68.35 225 | 80.78 156 | 61.49 208 | 57.59 213 | 61.80 203 | 77.59 195 | 81.45 217 | 82.34 213 | 92.25 207 | 83.96 217 |
|
pm-mvs1 | | | 84.55 146 | 83.46 150 | 85.82 139 | 88.16 155 | 93.39 135 | 89.05 166 | 85.36 121 | 74.03 207 | 72.43 130 | 65.08 195 | 71.11 131 | 82.30 177 | 93.48 99 | 91.70 121 | 97.64 105 | 95.43 130 |
|
gm-plane-assit | | | 77.65 206 | 78.50 203 | 76.66 208 | 87.96 156 | 85.43 216 | 64.70 226 | 74.50 204 | 64.15 223 | 51.26 223 | 61.32 208 | 58.17 216 | 84.11 163 | 95.16 50 | 93.83 68 | 97.45 111 | 91.41 184 |
|
test-mter | | | 86.09 127 | 88.38 95 | 83.43 178 | 87.89 157 | 92.61 160 | 86.89 185 | 77.11 196 | 84.30 132 | 68.62 176 | 82.57 86 | 82.45 86 | 84.34 157 | 92.40 113 | 90.11 168 | 95.74 176 | 94.21 155 |
|
pmmvs4 | | | 86.00 128 | 84.28 144 | 88.00 111 | 87.80 158 | 92.01 178 | 89.94 149 | 84.91 125 | 86.79 103 | 80.98 93 | 73.41 139 | 66.34 169 | 88.12 105 | 89.31 169 | 88.90 187 | 96.24 170 | 93.20 169 |
|
TESTMET0.1,1 | | | 86.11 126 | 88.28 96 | 83.59 175 | 87.80 158 | 92.07 174 | 87.41 180 | 77.12 195 | 84.58 127 | 69.33 168 | 83.00 79 | 82.79 82 | 84.24 158 | 92.26 115 | 89.81 174 | 95.64 180 | 93.44 164 |
|
DU-MVS | | | 86.12 125 | 84.81 139 | 87.66 116 | 87.77 160 | 93.78 119 | 90.15 141 | 87.87 97 | 84.40 129 | 73.45 126 | 70.59 148 | 64.82 187 | 88.95 99 | 90.14 148 | 92.33 106 | 97.76 94 | 97.62 57 |
|
Baseline_NR-MVSNet | | | 85.28 136 | 83.42 153 | 87.46 120 | 87.77 160 | 90.80 194 | 89.90 152 | 87.69 101 | 83.93 138 | 74.16 122 | 64.72 197 | 66.43 166 | 87.48 113 | 90.14 148 | 90.83 130 | 97.73 97 | 97.11 70 |
|
SixPastTwentyTwo | | | 83.12 181 | 83.44 152 | 82.74 189 | 87.71 162 | 93.11 148 | 82.30 206 | 82.33 155 | 79.24 180 | 64.33 200 | 78.77 104 | 62.75 196 | 84.11 163 | 88.11 184 | 87.89 189 | 95.70 178 | 94.21 155 |
|
TranMVSNet+NR-MVSNet | | | 85.57 133 | 84.41 143 | 86.92 124 | 87.67 163 | 93.34 136 | 90.31 132 | 88.43 88 | 83.07 142 | 70.11 160 | 69.99 155 | 65.28 182 | 86.96 118 | 89.73 156 | 92.27 107 | 98.06 79 | 97.17 69 |
|
v18 | | | 84.21 156 | 82.90 168 | 85.74 142 | 87.63 164 | 89.75 196 | 90.56 123 | 80.82 171 | 81.42 150 | 72.24 132 | 67.16 165 | 67.23 156 | 86.27 127 | 89.25 173 | 90.24 153 | 96.92 146 | 95.27 136 |
|
v16 | | | 84.14 158 | 82.86 170 | 85.64 145 | 87.61 165 | 89.71 198 | 90.36 126 | 80.70 173 | 81.36 151 | 71.99 136 | 66.91 172 | 67.19 157 | 86.23 130 | 89.32 167 | 90.25 150 | 96.94 139 | 95.29 134 |
|
v17 | | | 84.10 160 | 82.83 171 | 85.57 147 | 87.58 166 | 89.72 197 | 90.30 135 | 80.70 173 | 81.00 154 | 71.72 137 | 67.01 167 | 67.24 155 | 86.19 131 | 89.32 167 | 90.25 150 | 96.95 137 | 95.29 134 |
|
WR-MVS | | | 83.14 180 | 83.38 155 | 82.87 186 | 87.55 167 | 93.29 138 | 86.36 190 | 84.21 132 | 80.05 166 | 66.41 191 | 66.91 172 | 66.92 164 | 75.66 202 | 88.96 181 | 90.56 139 | 97.05 126 | 96.96 72 |
|
v1neww | | | 84.65 144 | 83.34 158 | 86.18 134 | 87.53 168 | 93.49 128 | 90.32 128 | 85.17 122 | 80.57 159 | 71.02 148 | 66.93 170 | 67.04 162 | 86.13 134 | 89.26 170 | 90.23 156 | 96.93 142 | 95.88 115 |
|
v7new | | | 84.65 144 | 83.34 158 | 86.18 134 | 87.53 168 | 93.49 128 | 90.32 128 | 85.17 122 | 80.57 159 | 71.02 148 | 66.93 170 | 67.04 162 | 86.13 134 | 89.26 170 | 90.23 156 | 96.93 142 | 95.88 115 |
|
v8 | | | 84.45 151 | 83.30 160 | 85.80 140 | 87.53 168 | 92.95 150 | 90.31 132 | 82.46 154 | 80.46 161 | 71.43 140 | 66.99 168 | 67.16 159 | 86.14 132 | 89.26 170 | 90.22 159 | 96.94 139 | 96.06 110 |
|
v6 | | | 84.67 143 | 83.36 156 | 86.20 132 | 87.53 168 | 93.49 128 | 90.34 127 | 85.16 124 | 80.58 158 | 71.13 144 | 66.97 169 | 67.10 160 | 86.11 136 | 89.25 173 | 90.22 159 | 96.93 142 | 95.89 114 |
|
WR-MVS_H | | | 82.86 185 | 82.66 173 | 83.10 182 | 87.44 172 | 93.33 137 | 85.71 197 | 83.20 145 | 77.36 191 | 68.20 179 | 66.37 179 | 65.23 183 | 76.05 201 | 89.35 164 | 90.13 164 | 97.99 85 | 96.89 75 |
|
divwei89l23v2f112 | | | 84.40 152 | 83.00 166 | 86.02 138 | 87.42 173 | 93.42 131 | 90.28 136 | 85.52 117 | 79.57 172 | 70.11 160 | 66.64 177 | 66.29 172 | 85.91 139 | 89.16 176 | 90.19 161 | 96.90 148 | 95.73 120 |
|
v1141 | | | 84.40 152 | 83.00 166 | 86.03 136 | 87.41 174 | 93.42 131 | 90.28 136 | 85.53 116 | 79.58 171 | 70.12 159 | 66.62 178 | 66.27 173 | 85.94 138 | 89.16 176 | 90.19 161 | 96.89 150 | 95.73 120 |
|
v1 | | | 84.40 152 | 83.01 165 | 86.03 136 | 87.41 174 | 93.42 131 | 90.31 132 | 85.52 117 | 79.51 174 | 70.13 158 | 66.66 176 | 66.40 167 | 85.89 140 | 89.15 178 | 90.19 161 | 96.89 150 | 95.74 119 |
|
v15 | | | 83.67 169 | 82.37 176 | 85.19 153 | 87.39 176 | 89.63 199 | 90.19 139 | 80.43 175 | 79.49 176 | 70.27 153 | 66.37 179 | 66.33 170 | 85.88 141 | 89.34 166 | 90.23 156 | 96.96 136 | 95.22 141 |
|
V14 | | | 83.66 170 | 82.38 175 | 85.16 154 | 87.37 177 | 89.62 200 | 90.15 141 | 80.33 177 | 79.51 174 | 70.26 154 | 66.30 185 | 66.37 168 | 85.87 142 | 89.38 163 | 90.24 153 | 96.98 132 | 95.22 141 |
|
v148 | | | 83.61 171 | 82.10 183 | 85.37 148 | 87.34 178 | 92.94 151 | 87.48 179 | 85.72 115 | 78.92 181 | 73.87 124 | 65.71 192 | 64.69 188 | 81.78 182 | 87.82 185 | 89.35 183 | 96.01 172 | 95.26 137 |
|
v7 | | | 84.37 155 | 83.23 161 | 85.69 143 | 87.34 178 | 93.19 143 | 90.32 128 | 83.10 146 | 79.88 170 | 69.33 168 | 66.33 182 | 65.75 177 | 87.06 116 | 90.83 136 | 90.38 143 | 96.97 133 | 96.26 104 |
|
v11 | | | 83.72 167 | 82.61 174 | 85.02 157 | 87.34 178 | 89.56 203 | 89.89 153 | 79.92 184 | 79.55 173 | 69.21 172 | 66.36 181 | 65.48 180 | 86.84 120 | 91.43 128 | 90.51 142 | 96.92 146 | 95.37 133 |
|
v10 | | | 84.18 157 | 83.17 163 | 85.37 148 | 87.34 178 | 92.68 158 | 90.32 128 | 81.33 166 | 79.93 169 | 69.23 171 | 66.33 182 | 65.74 179 | 87.03 117 | 90.84 135 | 90.38 143 | 96.97 133 | 96.29 102 |
|
V9 | | | 83.61 171 | 82.33 178 | 85.11 155 | 87.34 178 | 89.59 201 | 90.10 144 | 80.25 178 | 79.38 178 | 70.17 156 | 66.15 186 | 66.33 170 | 85.82 144 | 89.41 162 | 90.24 153 | 96.99 131 | 95.23 140 |
|
testpf | | | 74.66 210 | 76.34 211 | 72.71 215 | 87.34 178 | 80.91 221 | 73.15 221 | 60.30 232 | 78.73 183 | 61.68 207 | 69.83 156 | 62.22 200 | 67.48 211 | 76.83 223 | 78.17 224 | 86.28 226 | 87.68 208 |
|
v12 | | | 83.59 173 | 82.32 179 | 85.07 156 | 87.32 184 | 89.57 202 | 89.87 155 | 80.19 182 | 79.46 177 | 70.19 155 | 66.05 187 | 66.23 175 | 85.84 143 | 89.44 161 | 90.26 149 | 97.01 129 | 95.26 137 |
|
v13 | | | 83.55 175 | 82.29 180 | 85.01 158 | 87.31 185 | 89.55 204 | 89.89 153 | 80.13 183 | 79.34 179 | 69.93 163 | 65.92 190 | 66.25 174 | 85.80 145 | 89.45 160 | 90.27 147 | 97.01 129 | 95.25 139 |
|
v2v482 | | | 84.51 147 | 83.05 164 | 86.20 132 | 87.25 186 | 93.28 139 | 90.22 138 | 85.40 120 | 79.94 168 | 69.78 165 | 67.74 163 | 65.15 184 | 87.57 109 | 89.12 179 | 90.55 140 | 96.97 133 | 95.60 124 |
|
CP-MVSNet | | | 83.11 182 | 82.15 182 | 84.23 169 | 87.20 187 | 92.70 157 | 86.42 189 | 83.53 141 | 77.83 189 | 67.67 182 | 66.89 175 | 60.53 210 | 82.47 175 | 89.23 175 | 90.65 138 | 98.08 76 | 97.20 68 |
|
v1144 | | | 84.03 163 | 82.88 169 | 85.37 148 | 87.17 188 | 93.15 147 | 90.18 140 | 83.31 143 | 78.83 182 | 67.85 180 | 65.99 188 | 64.99 185 | 86.79 121 | 90.75 138 | 90.33 146 | 96.90 148 | 96.15 107 |
|
V42 | | | 84.48 149 | 83.36 156 | 85.79 141 | 87.14 189 | 93.28 139 | 90.03 145 | 83.98 135 | 80.30 163 | 71.20 143 | 66.90 174 | 67.17 158 | 85.55 146 | 89.35 164 | 90.27 147 | 96.82 156 | 96.27 103 |
|
pmmvs5 | | | 83.37 177 | 82.68 172 | 84.18 170 | 87.13 190 | 93.18 144 | 86.74 186 | 82.08 158 | 76.48 196 | 67.28 185 | 71.26 145 | 62.70 197 | 84.71 155 | 90.77 137 | 90.12 167 | 97.15 119 | 94.24 153 |
|
FMVSNet1 | | | 87.33 113 | 86.00 127 | 88.89 100 | 87.13 190 | 92.83 155 | 93.08 91 | 84.46 130 | 81.35 152 | 82.20 83 | 66.33 182 | 77.96 106 | 88.96 98 | 93.97 85 | 94.16 59 | 97.54 109 | 95.38 132 |
|
PS-CasMVS | | | 82.53 187 | 81.54 190 | 83.68 174 | 87.08 192 | 92.54 163 | 86.20 191 | 83.46 142 | 76.46 197 | 65.73 195 | 65.71 192 | 59.41 215 | 81.61 183 | 89.06 180 | 90.55 140 | 98.03 81 | 97.07 71 |
|
PEN-MVS | | | 82.49 188 | 81.58 189 | 83.56 176 | 86.93 193 | 92.05 177 | 86.71 187 | 83.84 136 | 76.94 194 | 64.68 199 | 67.24 164 | 60.11 211 | 81.17 185 | 87.78 186 | 90.70 137 | 98.02 82 | 96.21 105 |
|
v1192 | | | 83.56 174 | 82.35 177 | 84.98 159 | 86.84 194 | 92.84 153 | 90.01 147 | 82.70 148 | 78.54 184 | 66.48 190 | 64.88 196 | 62.91 195 | 86.91 119 | 90.72 139 | 90.25 150 | 96.94 139 | 96.32 99 |
|
v144192 | | | 83.48 176 | 82.23 181 | 84.94 160 | 86.65 195 | 92.84 153 | 89.63 159 | 82.48 153 | 77.87 188 | 67.36 184 | 65.33 194 | 63.50 194 | 86.51 123 | 89.72 157 | 89.99 171 | 97.03 127 | 96.35 97 |
|
DTE-MVSNet | | | 81.76 195 | 81.04 196 | 82.60 192 | 86.63 196 | 91.48 189 | 85.97 193 | 83.70 137 | 76.45 198 | 62.44 204 | 67.16 165 | 59.98 212 | 78.98 192 | 87.15 193 | 89.93 172 | 97.88 89 | 95.12 144 |
|
v1921920 | | | 83.30 178 | 82.09 184 | 84.70 162 | 86.59 197 | 92.67 159 | 89.82 156 | 82.23 157 | 78.32 185 | 65.76 194 | 64.64 198 | 62.35 198 | 86.78 122 | 90.34 146 | 90.02 169 | 97.02 128 | 96.31 101 |
|
v1240 | | | 82.88 184 | 81.66 188 | 84.29 168 | 86.46 198 | 92.52 166 | 89.06 165 | 81.82 162 | 77.16 192 | 65.09 198 | 64.17 199 | 61.50 204 | 86.36 124 | 90.12 150 | 90.13 164 | 96.95 137 | 96.04 112 |
|
anonymousdsp | | | 84.51 147 | 85.85 132 | 82.95 185 | 86.30 199 | 93.51 127 | 85.77 196 | 80.38 176 | 78.25 187 | 63.42 203 | 73.51 137 | 72.20 127 | 84.64 156 | 93.21 106 | 92.16 111 | 97.19 117 | 98.14 39 |
|
pmmvs6 | | | 80.90 198 | 78.77 202 | 83.38 179 | 85.84 200 | 91.61 185 | 86.01 192 | 82.54 152 | 64.17 222 | 70.43 152 | 54.14 220 | 67.06 161 | 80.73 187 | 90.50 144 | 89.17 185 | 94.74 194 | 94.75 147 |
|
MVS-HIRNet | | | 78.16 204 | 77.57 207 | 78.83 204 | 85.83 201 | 87.76 210 | 76.67 213 | 70.22 219 | 75.82 203 | 67.39 183 | 55.61 215 | 70.52 135 | 81.96 180 | 86.67 197 | 85.06 203 | 90.93 216 | 81.58 220 |
|
test20.03 | | | 76.41 209 | 78.49 204 | 73.98 211 | 85.64 202 | 87.50 211 | 75.89 214 | 80.71 172 | 70.84 216 | 51.07 224 | 68.06 162 | 61.40 206 | 54.99 225 | 88.28 183 | 87.20 192 | 95.58 183 | 86.15 210 |
|
v748 | | | 81.57 197 | 80.68 199 | 82.60 192 | 85.55 203 | 92.07 174 | 83.57 201 | 82.06 159 | 74.64 206 | 69.97 162 | 63.11 203 | 61.46 205 | 78.09 194 | 87.30 192 | 89.88 173 | 96.37 166 | 96.32 99 |
|
v7n | | | 82.25 190 | 81.54 190 | 83.07 183 | 85.55 203 | 92.58 161 | 86.68 188 | 81.10 170 | 76.54 195 | 65.97 193 | 62.91 204 | 60.56 209 | 82.36 176 | 91.07 133 | 90.35 145 | 96.77 158 | 96.80 77 |
|
N_pmnet | | | 77.55 207 | 76.68 209 | 78.56 205 | 85.43 205 | 87.30 213 | 78.84 211 | 81.88 161 | 78.30 186 | 60.61 209 | 61.46 206 | 62.15 201 | 74.03 207 | 82.04 213 | 80.69 220 | 90.59 218 | 84.81 215 |
|
Anonymous20231206 | | | 78.09 205 | 78.11 205 | 78.07 207 | 85.19 206 | 89.17 205 | 80.99 207 | 81.24 169 | 75.46 204 | 58.25 214 | 54.78 219 | 59.90 213 | 66.73 214 | 88.94 182 | 88.26 188 | 96.01 172 | 90.25 196 |
|
MDTV_nov1_ep13_2view | | | 80.43 199 | 80.94 197 | 79.84 201 | 84.82 207 | 90.87 192 | 84.23 200 | 73.80 207 | 80.28 164 | 64.33 200 | 70.05 154 | 68.77 147 | 79.67 188 | 84.83 204 | 83.50 209 | 92.17 208 | 88.25 207 |
|
V4 | | | 82.11 191 | 81.49 193 | 82.83 187 | 84.60 208 | 92.53 165 | 85.97 193 | 80.24 179 | 76.35 200 | 66.87 188 | 63.17 201 | 64.55 191 | 82.54 174 | 87.70 187 | 89.55 179 | 96.73 159 | 96.61 85 |
|
v52 | | | 82.11 191 | 81.50 192 | 82.82 188 | 84.59 209 | 92.51 167 | 85.96 195 | 80.24 179 | 76.38 199 | 66.83 189 | 63.12 202 | 64.62 190 | 82.56 173 | 87.70 187 | 89.55 179 | 96.73 159 | 96.61 85 |
|
FPMVS | | | 69.87 218 | 67.10 221 | 73.10 213 | 84.09 210 | 78.35 225 | 79.40 210 | 76.41 197 | 71.92 211 | 57.71 215 | 54.06 221 | 50.04 222 | 56.72 223 | 71.19 227 | 68.70 228 | 84.25 228 | 75.43 225 |
|
EU-MVSNet | | | 78.43 203 | 80.25 200 | 76.30 209 | 83.81 211 | 87.27 214 | 80.99 207 | 79.52 186 | 76.01 201 | 54.12 219 | 70.44 150 | 64.87 186 | 67.40 213 | 86.23 198 | 85.54 200 | 91.95 211 | 91.41 184 |
|
FMVSNet5 | | | 84.47 150 | 84.72 140 | 84.18 170 | 83.30 212 | 88.43 207 | 88.09 175 | 79.42 187 | 84.25 133 | 74.14 123 | 73.15 141 | 78.74 101 | 83.65 167 | 91.19 130 | 91.19 128 | 96.46 164 | 86.07 211 |
|
MIMVSNet | | | 82.97 183 | 84.00 147 | 81.77 199 | 82.23 213 | 92.25 171 | 87.40 182 | 72.73 215 | 81.48 149 | 69.55 166 | 68.79 159 | 72.42 126 | 81.82 181 | 92.23 117 | 92.25 108 | 96.89 150 | 88.61 203 |
|
PM-MVS | | | 80.29 200 | 79.30 201 | 81.45 200 | 81.91 214 | 88.23 208 | 82.61 204 | 79.01 188 | 79.99 167 | 67.15 186 | 69.07 158 | 51.39 220 | 82.92 171 | 87.55 190 | 85.59 198 | 95.08 190 | 93.28 167 |
|
pmmvs-eth3d | | | 79.78 202 | 77.58 206 | 82.34 195 | 81.57 215 | 87.46 212 | 82.92 203 | 81.28 167 | 75.33 205 | 71.34 141 | 61.88 205 | 52.41 219 | 81.59 184 | 87.56 189 | 86.90 193 | 95.36 188 | 91.48 183 |
|
test2356 | | | 73.82 211 | 74.82 213 | 72.66 216 | 81.25 216 | 80.70 222 | 73.47 220 | 75.91 199 | 72.55 210 | 48.73 227 | 68.14 161 | 50.74 221 | 63.96 216 | 84.44 207 | 85.57 199 | 92.63 203 | 81.60 219 |
|
new-patchmatchnet | | | 72.32 215 | 71.09 217 | 73.74 212 | 81.17 217 | 84.86 217 | 72.21 223 | 77.48 194 | 68.32 219 | 54.89 218 | 55.10 217 | 49.31 224 | 63.68 218 | 79.30 220 | 76.46 225 | 93.03 199 | 84.32 216 |
|
testus | | | 73.65 213 | 74.92 212 | 72.17 218 | 80.93 218 | 81.11 220 | 73.02 222 | 75.23 203 | 73.23 208 | 48.77 226 | 69.38 157 | 46.10 229 | 62.28 220 | 84.84 203 | 86.01 196 | 92.77 201 | 83.75 218 |
|
Anonymous20231211 | | | 69.76 219 | 67.18 220 | 72.76 214 | 78.31 219 | 83.47 218 | 74.12 217 | 78.37 190 | 51.44 231 | 52.48 221 | 36.04 230 | 45.46 230 | 62.33 219 | 80.49 219 | 82.43 212 | 90.96 215 | 90.93 190 |
|
testmv | | | 65.29 221 | 65.25 223 | 65.34 222 | 77.73 220 | 75.55 228 | 58.75 229 | 73.56 211 | 53.22 229 | 38.47 233 | 49.33 222 | 38.30 232 | 53.38 226 | 79.13 221 | 81.65 215 | 90.15 220 | 79.58 222 |
|
test1235678 | | | 65.29 221 | 65.24 224 | 65.34 222 | 77.73 220 | 75.54 229 | 58.75 229 | 73.56 211 | 53.19 230 | 38.47 233 | 49.32 223 | 38.28 233 | 53.38 226 | 79.13 221 | 81.65 215 | 90.15 220 | 79.57 223 |
|
1111 | | | 66.22 220 | 66.42 222 | 65.98 221 | 75.69 222 | 76.42 226 | 58.90 227 | 63.25 227 | 57.86 226 | 48.33 228 | 45.46 226 | 49.13 225 | 61.32 221 | 81.57 215 | 82.80 211 | 88.38 225 | 71.69 230 |
|
.test1245 | | | 48.95 229 | 46.78 230 | 51.48 227 | 75.69 222 | 76.42 226 | 58.90 227 | 63.25 227 | 57.86 226 | 48.33 228 | 45.46 226 | 49.13 225 | 61.32 221 | 81.57 215 | 5.58 234 | 1.40 238 | 11.42 236 |
|
PMVS | | 56.77 18 | 61.27 224 | 58.64 226 | 64.35 224 | 75.66 224 | 54.60 235 | 53.62 233 | 74.23 205 | 53.69 228 | 58.37 213 | 44.27 229 | 49.38 223 | 44.16 230 | 69.51 229 | 65.35 230 | 80.07 230 | 73.66 226 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
new_pmnet | | | 72.29 216 | 73.25 215 | 71.16 220 | 75.35 225 | 81.38 219 | 73.72 219 | 69.27 221 | 75.97 202 | 49.84 225 | 56.27 214 | 56.12 218 | 69.08 209 | 81.73 214 | 80.86 219 | 89.72 223 | 80.44 221 |
|
ambc | | | | 67.96 219 | | 73.69 226 | 79.79 224 | 73.82 218 | | 71.61 212 | 59.80 211 | 46.00 225 | 20.79 237 | 66.15 215 | 86.92 195 | 80.11 222 | 89.13 224 | 90.50 193 |
|
pmmvs3 | | | 71.13 217 | 71.06 218 | 71.21 219 | 73.54 227 | 80.19 223 | 71.69 224 | 64.86 226 | 62.04 225 | 52.10 222 | 54.92 218 | 48.00 227 | 75.03 203 | 83.75 211 | 83.24 210 | 90.04 222 | 85.27 212 |
|
MDA-MVSNet-bldmvs | | | 73.81 212 | 72.56 216 | 75.28 210 | 72.52 228 | 88.87 206 | 74.95 216 | 82.67 150 | 71.57 213 | 55.02 217 | 65.96 189 | 42.84 231 | 76.11 200 | 70.61 228 | 81.47 218 | 90.38 219 | 86.59 209 |
|
test12356 | | | 60.37 225 | 61.08 225 | 59.53 226 | 72.42 229 | 70.09 231 | 57.72 231 | 69.53 220 | 51.31 232 | 36.05 235 | 47.32 224 | 32.04 234 | 36.19 231 | 74.15 226 | 80.35 221 | 85.27 227 | 72.29 228 |
|
tmp_tt | | | | | 50.24 230 | 68.55 230 | 46.86 237 | 48.90 235 | 18.28 235 | 86.51 108 | 68.32 177 | 70.19 152 | 65.33 181 | 26.69 235 | 74.37 225 | 66.80 229 | 70.72 234 | |
|
Gipuma | | | 58.52 226 | 56.17 227 | 61.27 225 | 67.14 231 | 58.06 234 | 52.16 234 | 68.40 224 | 69.00 218 | 45.02 231 | 22.79 233 | 20.57 238 | 55.11 224 | 76.27 224 | 79.33 223 | 79.80 231 | 67.16 231 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
MIMVSNet1 | | | 73.19 214 | 73.70 214 | 72.60 217 | 65.42 232 | 86.69 215 | 75.56 215 | 79.65 185 | 67.87 220 | 55.30 216 | 45.24 228 | 56.41 217 | 63.79 217 | 86.98 194 | 87.66 190 | 95.85 174 | 85.04 213 |
|
no-one | | | 49.70 228 | 49.06 229 | 50.46 229 | 65.32 233 | 67.46 232 | 38.16 236 | 68.73 223 | 34.38 236 | 22.88 237 | 24.40 232 | 22.99 236 | 28.55 234 | 51.41 232 | 70.93 226 | 79.08 232 | 71.81 229 |
|
PMMVS2 | | | 53.68 227 | 55.72 228 | 51.30 228 | 58.84 234 | 67.02 233 | 54.23 232 | 60.97 231 | 47.50 233 | 19.42 238 | 34.81 231 | 31.97 235 | 30.88 233 | 65.84 230 | 69.99 227 | 83.47 229 | 72.92 227 |
|
EMVS | | | 39.04 232 | 34.32 233 | 44.54 232 | 58.25 235 | 39.35 238 | 27.61 238 | 62.55 230 | 35.99 234 | 16.40 240 | 20.04 236 | 14.77 239 | 44.80 228 | 33.12 235 | 44.10 233 | 57.61 236 | 52.89 234 |
|
E-PMN | | | 40.00 230 | 35.74 232 | 44.98 231 | 57.69 236 | 39.15 239 | 28.05 237 | 62.70 229 | 35.52 235 | 17.78 239 | 20.90 234 | 14.36 240 | 44.47 229 | 35.89 234 | 47.86 232 | 59.15 235 | 56.47 233 |
|
MVE | | 39.81 19 | 39.52 231 | 41.58 231 | 37.11 233 | 33.93 237 | 49.06 236 | 26.45 239 | 54.22 233 | 29.46 237 | 24.15 236 | 20.77 235 | 10.60 241 | 34.42 232 | 51.12 233 | 65.27 231 | 49.49 237 | 64.81 232 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
testmvs | | | 4.35 233 | 6.54 234 | 1.79 235 | 0.60 238 | 1.82 240 | 3.06 241 | 0.95 236 | 7.22 238 | 0.88 242 | 12.38 237 | 1.25 242 | 3.87 237 | 6.09 236 | 5.58 234 | 1.40 238 | 11.42 236 |
|
GG-mvs-BLEND | | | 62.84 223 | 90.21 74 | 30.91 234 | 0.57 239 | 94.45 107 | 86.99 184 | 0.34 238 | 88.71 87 | 0.98 241 | 81.55 95 | 91.58 50 | 0.86 238 | 92.66 109 | 91.43 126 | 95.73 177 | 91.11 188 |
|
test123 | | | 3.48 234 | 5.31 235 | 1.34 236 | 0.20 240 | 1.52 241 | 2.17 242 | 0.58 237 | 6.13 239 | 0.31 243 | 9.85 238 | 0.31 243 | 3.90 236 | 2.65 237 | 5.28 236 | 0.87 240 | 11.46 235 |
|
sosnet-low-res | | | 0.00 235 | 0.00 236 | 0.00 237 | 0.00 241 | 0.00 242 | 0.00 243 | 0.00 239 | 0.00 240 | 0.00 244 | 0.00 239 | 0.00 244 | 0.00 239 | 0.00 238 | 0.00 237 | 0.00 241 | 0.00 238 |
|
sosnet | | | 0.00 235 | 0.00 236 | 0.00 237 | 0.00 241 | 0.00 242 | 0.00 243 | 0.00 239 | 0.00 240 | 0.00 244 | 0.00 239 | 0.00 244 | 0.00 239 | 0.00 238 | 0.00 237 | 0.00 241 | 0.00 238 |
|
MTAPA | | | | | | | | | | | 95.36 2 | | 97.46 16 | | | | | |
|
MTMP | | | | | | | | | | | 95.70 1 | | 96.90 21 | | | | | |
|
Patchmatch-RL test | | | | | | | | 18.47 240 | | | | | | | | | | |
|
NP-MVS | | | | | | | | | | 91.63 57 | | | | | | | | |
|
Patchmtry | | | | | | | 92.39 169 | 89.18 163 | 73.30 213 | | 71.08 145 | | | | | | | |
|
DeepMVS_CX | | | | | | | 71.82 230 | 68.37 225 | 48.05 234 | 77.38 190 | 46.88 230 | 65.77 191 | 47.03 228 | 67.48 211 | 64.27 231 | | 76.89 233 | 76.72 224 |
|