ESAPD | | | 95.11 1 | 95.65 1 | 94.48 1 | 97.96 3 | 98.62 1 | 96.45 1 | 92.82 2 | 96.24 3 | 90.25 5 | 96.16 2 | 93.09 1 | 93.32 3 | 93.93 13 | 92.02 19 | 96.07 19 | 99.50 3 |
|
APDe-MVS | | | 94.31 4 | 94.30 7 | 94.33 2 | 97.57 7 | 98.06 7 | 95.79 2 | 91.98 5 | 95.50 6 | 92.19 1 | 95.25 3 | 87.97 14 | 92.93 4 | 93.01 20 | 91.02 35 | 95.52 28 | 99.29 5 |
|
CNVR-MVS | | | 94.53 3 | 94.85 4 | 94.15 3 | 98.03 2 | 98.59 2 | 95.56 3 | 92.91 1 | 94.86 8 | 88.46 11 | 91.32 17 | 90.83 5 | 94.03 2 | 95.20 3 | 94.16 4 | 95.89 24 | 99.01 12 |
|
HSP-MVS | | | 94.69 2 | 95.39 2 | 93.88 4 | 96.78 14 | 98.11 5 | 94.75 7 | 90.91 9 | 96.89 2 | 89.12 10 | 96.98 1 | 89.47 9 | 94.76 1 | 95.24 2 | 93.29 10 | 96.98 7 | 97.73 30 |
|
APD-MVS | | | 93.47 8 | 93.44 13 | 93.50 5 | 97.06 10 | 97.09 22 | 95.27 6 | 91.47 6 | 95.71 5 | 89.57 7 | 93.66 6 | 86.28 19 | 92.81 5 | 92.06 27 | 90.70 37 | 94.83 43 | 98.60 18 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
MCST-MVS | | | 94.10 5 | 94.77 5 | 93.31 6 | 98.31 1 | 98.34 3 | 95.43 4 | 92.54 3 | 94.41 13 | 83.05 27 | 91.38 15 | 90.97 4 | 92.24 9 | 95.05 5 | 94.02 5 | 98.31 1 | 99.20 7 |
|
NCCC | | | 93.59 7 | 94.00 9 | 93.10 7 | 97.90 5 | 97.93 9 | 95.40 5 | 92.39 4 | 94.47 12 | 84.94 18 | 91.21 18 | 89.32 10 | 92.53 6 | 93.90 14 | 92.98 12 | 95.44 30 | 98.22 24 |
|
HPM-MVS++ | | | 94.04 6 | 94.96 3 | 92.96 8 | 97.93 4 | 97.71 13 | 94.65 9 | 91.01 8 | 95.91 4 | 87.43 13 | 93.52 8 | 92.63 2 | 92.29 8 | 94.22 12 | 92.34 16 | 94.47 47 | 98.37 22 |
|
TSAR-MVS + MP. | | | 93.07 11 | 93.53 12 | 92.53 9 | 94.23 41 | 97.54 17 | 94.75 7 | 89.87 13 | 95.26 7 | 89.20 9 | 93.16 9 | 88.19 13 | 92.15 10 | 91.79 31 | 89.65 48 | 94.99 39 | 99.16 8 |
|
SD-MVS | | | 93.36 9 | 94.33 6 | 92.22 10 | 94.68 38 | 97.89 11 | 94.56 10 | 90.89 10 | 94.80 9 | 90.04 6 | 93.53 7 | 90.14 7 | 89.78 17 | 92.74 22 | 92.17 17 | 93.35 98 | 99.07 10 |
|
SMA-MVS | | | 93.14 10 | 93.96 10 | 92.17 11 | 97.64 6 | 97.82 12 | 94.28 14 | 90.32 11 | 94.72 10 | 85.70 17 | 87.64 25 | 90.68 6 | 91.15 13 | 94.28 11 | 93.86 7 | 93.97 56 | 98.72 16 |
|
zzz-MVS | | | 91.59 17 | 91.12 23 | 92.13 12 | 96.76 15 | 96.68 30 | 93.39 19 | 88.00 20 | 93.63 16 | 90.76 4 | 83.97 32 | 85.33 23 | 89.89 16 | 91.60 33 | 89.65 48 | 94.00 54 | 96.97 52 |
|
MSLP-MVS++ | | | 90.33 24 | 88.82 34 | 92.10 13 | 96.52 21 | 95.93 42 | 94.35 12 | 86.26 28 | 88.37 47 | 89.24 8 | 75.94 44 | 82.60 35 | 89.71 18 | 89.45 54 | 92.17 17 | 96.51 14 | 97.24 42 |
|
DeepC-MVS_fast | | 86.59 2 | 91.69 16 | 91.39 22 | 92.05 14 | 97.43 8 | 96.92 27 | 94.05 15 | 90.23 12 | 93.31 20 | 83.19 25 | 77.91 39 | 84.23 28 | 92.42 7 | 94.62 8 | 94.83 2 | 95.00 38 | 97.88 27 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
HFP-MVS | | | 92.02 14 | 92.13 19 | 91.89 15 | 97.16 9 | 96.46 35 | 93.57 18 | 87.60 21 | 93.79 15 | 88.17 12 | 93.15 10 | 83.94 32 | 91.19 12 | 90.81 40 | 89.83 43 | 93.66 71 | 96.94 54 |
|
AdaColmap | | | 88.46 35 | 85.75 53 | 91.62 16 | 96.25 24 | 95.35 53 | 90.71 39 | 91.08 7 | 90.22 35 | 86.17 14 | 74.33 48 | 73.67 70 | 92.00 11 | 86.31 94 | 85.82 86 | 93.52 81 | 94.53 92 |
|
ACMMP_Plus | | | 92.16 13 | 92.91 17 | 91.28 17 | 96.95 11 | 97.36 18 | 93.66 17 | 89.23 17 | 93.33 17 | 83.71 22 | 90.53 19 | 86.84 16 | 90.39 14 | 93.30 18 | 91.56 28 | 93.74 66 | 97.43 37 |
|
SteuartSystems-ACMMP | | | 92.31 12 | 93.31 14 | 91.15 18 | 96.88 12 | 97.36 18 | 93.95 16 | 89.44 15 | 92.62 22 | 83.20 24 | 94.34 5 | 85.55 21 | 88.95 24 | 93.07 19 | 91.90 23 | 94.51 45 | 98.30 23 |
Skip Steuart: Steuart Systems R&D Blog. |
ACMMPR | | | 91.15 19 | 91.44 21 | 90.81 19 | 96.61 17 | 96.25 39 | 93.09 20 | 87.08 23 | 93.32 19 | 84.78 19 | 92.08 13 | 82.10 38 | 89.71 18 | 90.24 44 | 89.82 44 | 93.61 76 | 96.30 68 |
|
CP-MVS | | | 90.57 23 | 90.68 25 | 90.44 20 | 96.13 25 | 95.90 46 | 92.77 25 | 86.86 27 | 92.12 25 | 84.19 20 | 89.18 23 | 82.37 36 | 89.43 22 | 89.65 52 | 88.43 57 | 93.27 102 | 97.13 46 |
|
3Dnovator | | 80.58 8 | 88.20 37 | 86.53 46 | 90.15 21 | 96.86 13 | 96.46 35 | 91.97 32 | 83.06 46 | 85.16 57 | 83.66 23 | 62.28 91 | 82.15 37 | 88.98 23 | 90.99 38 | 92.65 14 | 96.38 18 | 96.03 72 |
|
MP-MVS | | | 90.81 22 | 91.45 20 | 90.06 22 | 96.59 18 | 96.33 38 | 92.46 29 | 87.19 22 | 90.27 34 | 82.54 31 | 91.38 15 | 84.88 25 | 88.27 31 | 90.58 42 | 89.30 53 | 93.30 100 | 97.44 35 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
train_agg | | | 91.99 15 | 93.71 11 | 89.98 23 | 96.42 23 | 97.03 24 | 94.31 13 | 89.05 18 | 93.33 17 | 77.75 42 | 95.06 4 | 88.27 12 | 88.38 30 | 92.02 28 | 91.41 30 | 94.00 54 | 98.84 15 |
|
CSCG | | | 89.81 28 | 89.69 30 | 89.96 24 | 96.55 19 | 97.90 10 | 92.89 23 | 87.06 24 | 88.74 45 | 86.17 14 | 78.24 38 | 86.53 18 | 84.75 53 | 87.82 76 | 90.59 38 | 92.32 145 | 98.01 26 |
|
3Dnovator+ | | 81.14 5 | 88.59 33 | 87.49 40 | 89.88 25 | 95.83 29 | 96.45 37 | 91.94 33 | 82.41 52 | 87.09 51 | 85.94 16 | 62.80 88 | 85.37 22 | 89.46 20 | 91.51 34 | 91.89 25 | 93.72 68 | 97.30 40 |
|
DeepC-MVS | | 84.14 3 | 88.80 31 | 88.03 38 | 89.71 26 | 94.83 36 | 96.56 31 | 92.57 27 | 89.38 16 | 89.25 42 | 79.59 38 | 70.02 60 | 77.05 56 | 88.24 32 | 92.44 24 | 92.79 13 | 93.65 74 | 98.10 25 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
abl_6 | | | | | 89.54 27 | 95.55 33 | 97.59 15 | 89.01 50 | 85.00 34 | 94.67 11 | 83.04 28 | 84.70 31 | 91.47 3 | 89.46 20 | | | 95.20 35 | 98.63 17 |
|
CANet | | | 89.98 25 | 90.42 29 | 89.47 28 | 94.13 42 | 98.05 8 | 91.76 34 | 83.27 43 | 90.87 31 | 81.90 34 | 72.32 51 | 84.82 26 | 88.42 28 | 94.52 9 | 93.78 8 | 97.34 4 | 98.58 20 |
|
PGM-MVS | | | 89.97 26 | 90.64 27 | 89.18 29 | 96.53 20 | 95.90 46 | 93.06 21 | 82.48 51 | 90.04 36 | 80.37 36 | 92.75 11 | 80.96 43 | 88.93 25 | 89.88 49 | 89.08 54 | 93.69 70 | 95.86 74 |
|
TSAR-MVS + GP. | | | 91.29 18 | 93.11 16 | 89.18 29 | 87.81 85 | 96.21 41 | 92.51 28 | 83.83 40 | 94.24 14 | 83.77 21 | 91.87 14 | 89.62 8 | 90.07 15 | 90.40 43 | 90.31 39 | 97.09 6 | 99.10 9 |
|
DELS-MVS | | | 87.75 40 | 86.92 44 | 88.71 31 | 94.69 37 | 97.34 21 | 92.78 24 | 84.50 37 | 77.87 77 | 81.94 33 | 67.17 65 | 75.49 62 | 82.84 63 | 95.38 1 | 95.93 1 | 95.55 27 | 99.27 6 |
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 |
X-MVS | | | 89.73 29 | 90.65 26 | 88.66 32 | 96.44 22 | 95.93 42 | 92.26 31 | 86.98 25 | 90.73 32 | 76.32 48 | 89.56 22 | 82.05 39 | 86.51 41 | 89.98 47 | 89.60 50 | 93.43 93 | 96.72 62 |
|
CPTT-MVS | | | 88.17 38 | 87.84 39 | 88.55 33 | 93.33 44 | 93.75 64 | 92.33 30 | 84.75 35 | 89.87 38 | 81.72 35 | 83.93 33 | 81.12 42 | 88.45 27 | 85.42 104 | 84.07 105 | 90.72 179 | 96.72 62 |
|
TSAR-MVS + ACMM | | | 90.98 21 | 93.18 15 | 88.42 34 | 95.69 30 | 96.73 29 | 94.52 11 | 86.97 26 | 92.99 21 | 76.32 48 | 92.31 12 | 86.64 17 | 84.40 57 | 92.97 21 | 92.02 19 | 92.62 139 | 98.59 19 |
|
PLC | | 81.02 6 | 84.81 55 | 81.81 76 | 88.31 35 | 93.77 43 | 90.35 101 | 88.80 51 | 84.47 38 | 86.76 52 | 82.17 32 | 66.56 67 | 71.01 80 | 88.41 29 | 85.48 102 | 84.28 103 | 92.26 147 | 88.21 169 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
ACMMP | | | 88.48 34 | 88.71 35 | 88.22 36 | 94.61 39 | 95.53 50 | 90.64 41 | 85.60 32 | 90.97 29 | 78.62 40 | 89.88 21 | 74.20 67 | 86.29 42 | 88.16 74 | 86.37 77 | 93.57 78 | 95.86 74 |
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 |
QAPM | | | 87.06 43 | 86.46 47 | 87.75 37 | 96.63 16 | 97.09 22 | 91.71 35 | 82.62 49 | 80.58 69 | 71.28 70 | 66.04 70 | 84.24 27 | 87.01 37 | 89.93 48 | 89.91 42 | 97.26 5 | 97.44 35 |
|
CNLPA | | | 84.72 56 | 82.14 72 | 87.73 38 | 92.85 47 | 93.83 63 | 84.70 85 | 85.07 33 | 90.90 30 | 83.16 26 | 56.28 115 | 71.53 76 | 88.14 33 | 84.19 113 | 84.00 108 | 92.48 142 | 94.26 97 |
|
EPNet | | | 89.30 30 | 90.89 24 | 87.44 39 | 95.67 31 | 96.81 28 | 91.13 37 | 83.12 45 | 91.14 28 | 76.31 52 | 87.60 26 | 80.40 46 | 84.45 55 | 92.13 26 | 91.12 34 | 93.96 59 | 97.01 50 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
DeepPCF-MVS | | 86.71 1 | 91.00 20 | 94.05 8 | 87.43 40 | 95.58 32 | 98.17 4 | 86.22 67 | 88.59 19 | 97.01 1 | 76.77 47 | 85.11 30 | 88.90 11 | 87.29 35 | 95.02 6 | 94.69 3 | 90.15 187 | 99.48 4 |
|
MVS_0304 | | | 88.43 36 | 89.46 31 | 87.21 41 | 91.85 54 | 97.60 14 | 92.62 26 | 81.10 58 | 87.16 50 | 73.80 57 | 72.19 53 | 83.36 34 | 87.03 36 | 94.64 7 | 93.67 9 | 96.88 8 | 97.64 32 |
|
OpenMVS | | 77.91 11 | 85.09 52 | 83.42 62 | 87.03 42 | 96.12 26 | 96.55 33 | 89.36 47 | 81.59 56 | 79.19 72 | 75.20 54 | 55.84 119 | 79.04 49 | 84.45 55 | 88.47 66 | 89.35 52 | 95.48 29 | 95.48 81 |
|
PVSNet_BlendedMVS | | | 86.98 44 | 87.05 42 | 86.90 43 | 93.03 45 | 96.98 25 | 86.57 63 | 81.82 54 | 89.78 39 | 82.78 29 | 71.54 54 | 66.07 94 | 80.73 78 | 93.46 16 | 91.97 21 | 96.45 15 | 99.53 1 |
|
PVSNet_Blended | | | 86.98 44 | 87.05 42 | 86.90 43 | 93.03 45 | 96.98 25 | 86.57 63 | 81.82 54 | 89.78 39 | 82.78 29 | 71.54 54 | 66.07 94 | 80.73 78 | 93.46 16 | 91.97 21 | 96.45 15 | 99.53 1 |
|
CLD-MVS | | | 85.43 51 | 84.24 59 | 86.83 45 | 87.69 88 | 93.16 72 | 90.01 44 | 82.72 48 | 87.17 49 | 79.28 39 | 71.43 57 | 65.81 96 | 86.02 43 | 87.33 81 | 86.96 70 | 95.25 34 | 97.83 29 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
CDPH-MVS | | | 88.76 32 | 90.43 28 | 86.81 46 | 96.04 27 | 96.53 34 | 92.95 22 | 85.95 30 | 90.36 33 | 67.93 81 | 85.80 29 | 80.69 44 | 83.82 58 | 90.81 40 | 91.85 26 | 94.18 50 | 96.99 51 |
|
PCF-MVS | | 82.38 4 | 85.52 50 | 84.41 58 | 86.81 46 | 91.51 56 | 96.23 40 | 90.27 42 | 89.81 14 | 77.87 77 | 70.67 71 | 69.20 62 | 77.86 50 | 85.55 45 | 85.92 99 | 86.38 76 | 93.03 120 | 97.43 37 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
MVS_111021_HR | | | 87.82 39 | 88.84 33 | 86.62 48 | 94.42 40 | 97.36 18 | 88.21 54 | 83.26 44 | 83.42 60 | 72.52 65 | 82.63 34 | 76.93 57 | 84.95 49 | 91.93 29 | 91.15 33 | 96.39 17 | 98.49 21 |
|
OMC-MVS | | | 86.38 46 | 86.21 50 | 86.57 49 | 92.30 50 | 94.35 59 | 87.60 57 | 83.51 42 | 92.32 24 | 77.37 45 | 72.27 52 | 77.83 51 | 86.59 40 | 87.62 79 | 85.95 83 | 92.08 149 | 93.11 121 |
|
PHI-MVS | | | 89.88 27 | 92.75 18 | 86.52 50 | 94.97 35 | 97.57 16 | 89.99 45 | 84.56 36 | 92.52 23 | 69.72 76 | 90.35 20 | 87.11 15 | 84.89 50 | 91.82 30 | 92.37 15 | 95.02 37 | 97.51 33 |
|
MVS_111021_LR | | | 87.58 42 | 88.67 36 | 86.31 51 | 92.58 48 | 95.89 48 | 86.20 68 | 82.49 50 | 89.08 44 | 77.47 44 | 86.20 28 | 74.22 66 | 85.49 46 | 90.03 46 | 88.52 55 | 93.66 71 | 96.74 61 |
|
MVSTER | | | 87.68 41 | 89.12 32 | 86.01 52 | 88.11 82 | 90.05 106 | 89.28 48 | 77.05 81 | 91.37 26 | 79.97 37 | 76.70 42 | 85.25 24 | 84.89 50 | 93.53 15 | 91.41 30 | 96.73 10 | 95.55 80 |
|
canonicalmvs | | | 85.93 48 | 86.26 49 | 85.54 53 | 88.94 68 | 95.44 51 | 89.56 46 | 76.01 88 | 87.83 48 | 77.70 43 | 76.43 43 | 68.66 89 | 87.80 34 | 87.02 83 | 91.51 29 | 93.25 106 | 96.95 53 |
|
TAPA-MVS | | 80.99 7 | 84.83 54 | 84.42 57 | 85.31 54 | 91.89 53 | 93.73 65 | 88.53 53 | 82.80 47 | 89.99 37 | 69.78 75 | 71.53 56 | 75.03 63 | 85.47 47 | 86.26 95 | 84.54 100 | 93.39 96 | 89.90 144 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
OPM-MVS | | | 81.34 68 | 78.18 96 | 85.02 55 | 91.27 58 | 91.78 91 | 90.66 40 | 83.62 41 | 62.39 142 | 65.91 82 | 63.35 85 | 64.33 101 | 85.03 48 | 87.77 77 | 85.88 85 | 93.66 71 | 91.75 134 |
|
MAR-MVS | | | 85.65 49 | 86.30 48 | 84.88 56 | 95.51 34 | 95.89 48 | 86.50 65 | 76.71 82 | 89.23 43 | 68.59 78 | 70.93 58 | 74.49 64 | 88.55 26 | 89.40 55 | 90.30 40 | 93.42 94 | 93.88 111 |
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 |
HQP-MVS | | | 86.17 47 | 87.35 41 | 84.80 57 | 91.41 57 | 92.37 85 | 91.05 38 | 84.35 39 | 88.52 46 | 64.21 85 | 87.05 27 | 68.91 87 | 84.80 52 | 89.12 57 | 88.16 61 | 92.96 123 | 97.31 39 |
|
DI_MVS_plusplus_trai | | | 83.32 60 | 82.53 70 | 84.25 58 | 86.26 105 | 93.66 66 | 90.23 43 | 77.16 80 | 77.05 84 | 74.06 56 | 53.74 128 | 74.33 65 | 83.61 60 | 91.40 36 | 89.82 44 | 94.17 51 | 97.73 30 |
|
MVS_Test | | | 84.60 57 | 85.13 55 | 83.99 59 | 88.17 80 | 95.27 54 | 88.21 54 | 73.15 108 | 84.31 59 | 70.55 73 | 68.67 63 | 68.78 88 | 86.99 38 | 91.71 32 | 91.90 23 | 96.84 9 | 95.27 85 |
|
TSAR-MVS + COLMAP | | | 84.93 53 | 85.79 52 | 83.92 60 | 90.90 59 | 93.57 67 | 89.25 49 | 82.00 53 | 91.29 27 | 61.66 90 | 88.25 24 | 59.46 115 | 86.71 39 | 89.79 50 | 87.09 67 | 93.01 121 | 91.09 137 |
|
MSDG | | | 78.11 99 | 73.17 134 | 83.86 61 | 91.78 55 | 86.83 135 | 85.25 76 | 86.02 29 | 72.84 102 | 69.69 77 | 51.43 135 | 54.00 133 | 77.61 92 | 81.95 135 | 82.27 129 | 92.83 135 | 82.91 195 |
|
ACMM | | 78.09 10 | 80.91 70 | 78.39 94 | 83.86 61 | 89.61 66 | 87.71 124 | 85.16 78 | 80.67 59 | 79.04 73 | 74.18 55 | 63.82 83 | 60.84 109 | 82.59 64 | 84.33 112 | 83.59 111 | 90.96 174 | 89.39 152 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
ACMP | | 79.58 9 | 82.23 64 | 81.82 75 | 82.71 63 | 88.15 81 | 90.95 98 | 85.23 77 | 78.52 63 | 81.70 66 | 72.52 65 | 78.41 37 | 60.63 110 | 80.48 80 | 82.88 123 | 83.44 113 | 91.37 167 | 94.70 89 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
diffmvs | | | 83.81 58 | 84.78 56 | 82.69 64 | 86.06 109 | 94.03 60 | 86.46 66 | 72.43 115 | 85.71 55 | 75.29 53 | 65.48 75 | 79.49 48 | 81.39 68 | 85.55 101 | 86.98 68 | 94.48 46 | 96.20 70 |
|
thres100view900 | | | 79.83 80 | 77.79 100 | 82.21 65 | 88.42 74 | 93.54 68 | 87.07 58 | 81.11 57 | 70.15 110 | 61.01 97 | 56.65 108 | 51.22 136 | 81.78 67 | 89.77 51 | 85.95 83 | 93.84 62 | 97.26 41 |
|
LS3D | | | 78.72 90 | 75.79 116 | 82.15 66 | 91.91 52 | 89.39 115 | 83.66 90 | 85.88 31 | 76.81 85 | 59.22 108 | 57.67 105 | 58.53 119 | 83.72 59 | 82.07 131 | 81.63 140 | 88.50 198 | 84.39 185 |
|
Anonymous20240521 | | | 77.15 111 | 73.02 135 | 81.97 67 | 80.91 133 | 89.15 117 | 82.61 98 | 77.71 74 | 66.55 122 | 78.02 41 | 49.06 147 | 39.81 202 | 78.63 89 | 84.70 109 | 86.17 80 | 93.97 56 | 95.71 78 |
|
FMVSNet3 | | | 81.93 67 | 81.98 73 | 81.88 68 | 79.49 141 | 87.02 130 | 88.15 56 | 72.57 111 | 83.02 62 | 72.63 62 | 56.55 111 | 73.48 71 | 82.34 66 | 91.49 35 | 91.20 32 | 96.07 19 | 91.13 136 |
|
DWT-MVSNet_training | | | 82.66 61 | 83.34 65 | 81.87 69 | 88.71 70 | 92.63 77 | 82.07 101 | 72.21 117 | 86.37 53 | 72.64 60 | 64.51 79 | 71.44 78 | 80.35 81 | 84.43 111 | 87.73 63 | 95.27 31 | 96.25 69 |
|
LGP-MVS_train | | | 82.12 66 | 82.57 69 | 81.59 70 | 89.26 67 | 90.23 103 | 88.76 52 | 78.05 67 | 81.26 67 | 61.64 91 | 79.52 36 | 62.11 105 | 79.59 85 | 85.20 105 | 84.68 99 | 92.27 146 | 95.02 87 |
|
CHOSEN 1792x2688 | | | 80.23 78 | 79.16 88 | 81.48 71 | 91.97 51 | 96.56 31 | 86.18 69 | 75.40 96 | 76.17 87 | 61.32 94 | 37.43 207 | 61.08 108 | 76.52 100 | 92.35 25 | 91.64 27 | 97.46 3 | 98.86 13 |
|
PatchMatch-RL | | | 78.75 89 | 76.47 110 | 81.41 72 | 88.53 73 | 91.10 96 | 78.09 148 | 77.51 77 | 77.33 81 | 71.98 67 | 64.38 81 | 48.10 152 | 82.55 65 | 84.06 114 | 82.35 127 | 89.78 189 | 87.97 171 |
|
conf0.002 | | | 80.80 72 | 80.30 82 | 81.38 73 | 88.59 71 | 93.19 71 | 85.12 79 | 78.10 65 | 70.15 110 | 61.55 92 | 63.30 86 | 62.66 104 | 81.11 69 | 88.74 63 | 86.94 71 | 93.79 64 | 97.15 44 |
|
conf0.01 | | | 80.10 79 | 79.04 90 | 81.34 74 | 88.56 72 | 93.09 73 | 85.12 79 | 78.08 66 | 70.15 110 | 61.43 93 | 60.90 97 | 58.54 118 | 81.11 69 | 88.66 64 | 84.80 94 | 93.74 66 | 97.14 45 |
|
PVSNet_Blended_VisFu | | | 82.55 62 | 83.70 61 | 81.21 75 | 89.66 63 | 95.15 56 | 82.41 99 | 77.36 78 | 72.53 104 | 73.64 58 | 61.15 96 | 77.19 55 | 70.35 149 | 91.31 37 | 89.72 47 | 93.84 62 | 98.85 14 |
|
tfpn111 | | | 80.42 77 | 79.77 87 | 81.18 76 | 88.42 74 | 92.55 81 | 85.12 79 | 77.94 69 | 70.15 110 | 61.00 99 | 74.56 45 | 51.22 136 | 81.11 69 | 88.23 68 | 84.80 94 | 93.50 86 | 96.90 57 |
|
conf200view11 | | | 79.04 87 | 77.21 102 | 81.18 76 | 88.42 74 | 92.55 81 | 85.12 79 | 77.94 69 | 70.15 110 | 61.00 99 | 56.65 108 | 51.22 136 | 81.11 69 | 88.23 68 | 84.80 94 | 93.50 86 | 96.90 57 |
|
tfpn200view9 | | | 79.05 86 | 77.21 102 | 81.18 76 | 88.42 74 | 92.55 81 | 85.12 79 | 77.94 69 | 70.15 110 | 61.01 97 | 56.65 108 | 51.22 136 | 81.11 69 | 88.23 68 | 84.80 94 | 93.50 86 | 96.90 57 |
|
FC-MVSNet-train | | | 79.54 82 | 78.20 95 | 81.09 79 | 86.55 98 | 88.63 120 | 79.96 113 | 78.53 62 | 70.90 108 | 68.24 79 | 65.87 71 | 56.45 127 | 80.29 82 | 86.20 97 | 84.08 104 | 92.97 122 | 95.31 84 |
|
GBi-Net | | | 80.72 73 | 80.49 80 | 81.00 80 | 78.18 145 | 86.19 148 | 86.73 59 | 72.57 111 | 83.02 62 | 72.63 62 | 56.55 111 | 73.48 71 | 80.99 74 | 86.57 88 | 86.83 72 | 94.89 40 | 90.77 138 |
|
test1 | | | 80.72 73 | 80.49 80 | 81.00 80 | 78.18 145 | 86.19 148 | 86.73 59 | 72.57 111 | 83.02 62 | 72.63 62 | 56.55 111 | 73.48 71 | 80.99 74 | 86.57 88 | 86.83 72 | 94.89 40 | 90.77 138 |
|
thres200 | | | 78.69 91 | 76.71 106 | 80.99 82 | 88.35 78 | 92.56 79 | 86.03 70 | 77.94 69 | 66.27 123 | 60.66 101 | 56.08 116 | 51.11 140 | 79.45 86 | 88.23 68 | 85.54 89 | 93.52 81 | 97.20 43 |
|
thres400 | | | 78.39 95 | 76.39 111 | 80.73 83 | 88.02 83 | 92.94 74 | 84.77 84 | 78.88 60 | 65.20 131 | 59.70 105 | 55.20 121 | 50.85 141 | 79.45 86 | 88.81 60 | 84.81 93 | 93.57 78 | 96.91 56 |
|
FMVSNet2 | | | 79.24 84 | 78.14 97 | 80.53 84 | 78.18 145 | 86.19 148 | 86.73 59 | 71.91 121 | 72.97 101 | 70.48 74 | 50.63 138 | 66.56 93 | 80.99 74 | 90.10 45 | 89.77 46 | 94.89 40 | 90.77 138 |
|
view600 | | | 77.68 102 | 75.68 117 | 80.01 85 | 87.72 86 | 92.57 78 | 83.79 88 | 77.95 68 | 64.41 134 | 58.72 110 | 54.32 126 | 50.54 142 | 78.25 90 | 88.23 68 | 83.13 118 | 93.64 75 | 96.59 66 |
|
thres600view7 | | | 77.66 103 | 75.67 118 | 79.98 86 | 87.71 87 | 92.56 79 | 83.79 88 | 77.94 69 | 64.41 134 | 58.69 111 | 54.32 126 | 50.54 142 | 78.23 91 | 88.23 68 | 83.06 120 | 93.52 81 | 96.55 67 |
|
HyFIR lowres test | | | 78.08 100 | 76.81 104 | 79.56 87 | 90.77 60 | 94.64 58 | 82.97 93 | 69.85 138 | 69.81 116 | 59.53 106 | 33.52 212 | 64.66 98 | 78.97 88 | 88.77 62 | 88.38 58 | 95.27 31 | 97.86 28 |
|
CANet_DTU | | | 83.33 59 | 86.59 45 | 79.53 88 | 88.88 69 | 94.87 57 | 86.63 62 | 68.85 145 | 85.45 56 | 50.54 156 | 77.86 40 | 69.94 84 | 85.62 44 | 92.63 23 | 90.88 36 | 96.63 11 | 94.46 93 |
|
Effi-MVS+ | | | 79.80 81 | 80.04 83 | 79.52 89 | 85.53 111 | 93.31 70 | 85.28 75 | 70.68 133 | 74.15 94 | 58.79 109 | 62.03 93 | 60.51 111 | 83.37 61 | 88.41 67 | 86.09 82 | 93.49 89 | 95.80 76 |
|
CostFormer | | | 80.72 73 | 81.81 76 | 79.44 90 | 86.50 100 | 91.65 93 | 84.31 87 | 59.84 200 | 80.86 68 | 72.69 59 | 62.46 90 | 73.74 68 | 79.93 83 | 82.58 126 | 84.50 101 | 93.37 97 | 96.90 57 |
|
view800 | | | 77.22 108 | 75.35 119 | 79.41 91 | 87.42 90 | 92.21 87 | 82.94 95 | 77.19 79 | 63.67 138 | 57.78 112 | 53.68 129 | 50.19 144 | 77.32 93 | 87.70 78 | 83.84 109 | 93.79 64 | 96.19 71 |
|
tfpn | | | 77.45 105 | 76.23 113 | 78.87 92 | 87.15 92 | 91.90 90 | 82.17 100 | 76.59 83 | 62.98 140 | 56.93 114 | 53.08 132 | 57.31 124 | 76.41 102 | 87.26 82 | 85.20 90 | 93.95 60 | 95.89 73 |
|
tpmp4_e23 | | | 78.57 92 | 78.48 93 | 78.68 93 | 85.38 113 | 89.14 118 | 84.69 86 | 60.32 198 | 78.81 75 | 70.65 72 | 57.89 103 | 65.54 97 | 79.63 84 | 80.09 150 | 83.24 116 | 91.41 166 | 94.63 91 |
|
MS-PatchMatch | | | 77.47 104 | 76.48 109 | 78.63 94 | 89.89 62 | 90.42 100 | 85.42 74 | 69.53 140 | 70.79 109 | 60.43 103 | 50.05 140 | 70.62 83 | 70.66 142 | 86.71 87 | 82.54 124 | 95.86 26 | 84.23 186 |
|
IB-MVS | | 74.10 12 | 78.52 93 | 78.51 92 | 78.52 95 | 90.15 61 | 95.39 52 | 71.95 187 | 77.53 76 | 74.95 91 | 77.25 46 | 58.93 101 | 55.92 128 | 58.37 189 | 79.01 170 | 87.89 62 | 95.88 25 | 97.47 34 |
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 |
EPP-MVSNet | | | 80.82 71 | 82.79 67 | 78.52 95 | 86.31 104 | 92.37 85 | 79.83 115 | 74.51 101 | 73.79 99 | 64.46 84 | 67.01 66 | 80.63 45 | 74.33 110 | 85.63 100 | 84.35 102 | 91.68 159 | 95.79 77 |
|
PMMVS | | | 82.26 63 | 85.48 54 | 78.51 97 | 85.92 110 | 91.92 89 | 78.30 144 | 70.77 131 | 86.30 54 | 61.11 95 | 82.46 35 | 70.88 81 | 84.70 54 | 88.05 75 | 84.78 98 | 90.24 186 | 93.98 103 |
|
Fast-Effi-MVS+ | | | 77.37 106 | 76.68 107 | 78.17 98 | 82.84 122 | 89.94 107 | 81.47 104 | 68.01 153 | 72.99 100 | 60.26 104 | 55.07 122 | 53.20 134 | 82.99 62 | 86.47 93 | 86.12 81 | 93.46 91 | 92.98 124 |
|
UGNet | | | 80.71 76 | 83.09 66 | 77.93 99 | 87.02 93 | 92.71 75 | 80.28 111 | 76.53 84 | 73.83 98 | 71.35 69 | 70.07 59 | 73.71 69 | 58.93 187 | 87.39 80 | 86.97 69 | 93.48 90 | 96.94 54 |
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 |
CHOSEN 280x420 | | | 82.15 65 | 85.87 51 | 77.80 100 | 86.54 99 | 93.42 69 | 81.74 102 | 59.96 199 | 78.99 74 | 63.99 86 | 74.50 47 | 83.95 31 | 80.99 74 | 89.53 53 | 85.01 91 | 93.56 80 | 95.71 78 |
|
tpm cat1 | | | 76.93 112 | 76.19 114 | 77.79 101 | 85.08 115 | 88.58 121 | 82.96 94 | 59.33 201 | 75.72 89 | 72.64 60 | 51.25 136 | 64.41 100 | 75.74 105 | 77.90 179 | 80.10 175 | 90.97 173 | 95.35 82 |
|
tfpn_ndepth | | | 78.22 98 | 78.84 91 | 77.49 102 | 88.32 79 | 90.95 98 | 80.79 106 | 76.31 86 | 74.24 93 | 59.50 107 | 69.52 61 | 60.02 114 | 67.11 162 | 85.06 106 | 82.95 122 | 92.94 128 | 89.18 157 |
|
conf0.05thres1000 | | | 74.20 126 | 71.44 143 | 77.43 103 | 86.09 108 | 89.85 110 | 80.82 105 | 75.79 91 | 53.51 188 | 54.71 120 | 44.37 165 | 49.78 145 | 74.67 107 | 85.02 107 | 83.47 112 | 92.49 141 | 94.10 100 |
|
IterMVS-LS | | | 76.80 114 | 76.33 112 | 77.35 104 | 84.07 119 | 84.11 169 | 81.54 103 | 68.52 147 | 66.17 124 | 61.74 89 | 57.84 104 | 64.31 102 | 74.88 106 | 83.48 121 | 86.21 79 | 93.34 99 | 92.16 129 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
IS_MVSNet | | | 80.92 69 | 84.14 60 | 77.16 105 | 87.43 89 | 93.90 62 | 80.44 107 | 74.64 100 | 75.05 90 | 61.10 96 | 65.59 72 | 76.89 58 | 67.39 160 | 90.88 39 | 90.05 41 | 91.95 153 | 96.62 65 |
|
thresconf0.02 | | | 78.87 88 | 80.50 79 | 76.96 106 | 87.88 84 | 91.71 92 | 82.90 97 | 78.51 64 | 67.91 119 | 50.85 149 | 74.56 45 | 69.93 85 | 67.32 161 | 86.86 86 | 85.65 87 | 94.32 49 | 86.89 177 |
|
FMVSNet1 | | | 74.26 125 | 71.95 140 | 76.95 107 | 74.28 193 | 83.94 171 | 83.61 91 | 69.99 136 | 57.08 164 | 65.08 83 | 42.39 188 | 57.41 123 | 76.98 96 | 86.57 88 | 86.83 72 | 91.77 158 | 89.42 150 |
|
dps | | | 75.76 117 | 75.02 122 | 76.63 108 | 84.51 117 | 88.12 122 | 77.51 155 | 58.33 203 | 75.91 88 | 71.98 67 | 57.37 106 | 57.85 121 | 76.81 99 | 77.89 180 | 78.40 184 | 90.63 180 | 89.63 147 |
|
CDS-MVSNet | | | 76.57 115 | 76.78 105 | 76.32 109 | 80.94 132 | 89.75 111 | 82.94 95 | 72.64 110 | 59.01 159 | 62.95 88 | 58.60 102 | 62.67 103 | 66.91 164 | 86.26 95 | 87.20 64 | 91.57 161 | 93.97 104 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
ACMH+ | | 72.14 13 | 72.38 138 | 69.34 162 | 75.93 110 | 85.21 114 | 84.89 164 | 76.96 162 | 76.04 87 | 59.76 152 | 51.63 137 | 50.37 139 | 48.69 149 | 76.90 98 | 76.06 188 | 78.69 180 | 88.85 196 | 86.90 176 |
|
ACMH | | 71.22 14 | 72.65 136 | 70.13 151 | 75.59 111 | 86.19 106 | 86.14 151 | 75.76 173 | 77.63 75 | 54.79 181 | 46.16 182 | 53.28 131 | 47.28 154 | 77.24 95 | 78.91 172 | 81.18 161 | 90.57 181 | 89.33 153 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
pmmvs4 | | | 73.38 132 | 71.53 142 | 75.55 112 | 75.95 173 | 85.24 161 | 77.25 158 | 71.59 128 | 71.03 107 | 63.10 87 | 49.09 146 | 44.22 177 | 73.73 114 | 82.04 132 | 80.18 173 | 91.68 159 | 88.89 163 |
|
RPSCF | | | 74.27 124 | 73.24 133 | 75.48 113 | 81.01 131 | 80.18 193 | 76.24 167 | 72.37 116 | 74.84 92 | 68.24 79 | 72.47 50 | 67.39 91 | 73.89 111 | 71.05 207 | 69.38 216 | 81.14 223 | 77.37 206 |
|
UA-Net | | | 78.30 96 | 80.92 78 | 75.25 114 | 87.42 90 | 92.48 84 | 79.54 127 | 75.49 95 | 60.47 148 | 60.52 102 | 68.44 64 | 84.08 30 | 57.54 190 | 88.54 65 | 88.45 56 | 90.96 174 | 83.97 190 |
|
test-LLR | | | 79.52 83 | 83.42 62 | 74.97 115 | 81.79 125 | 91.26 94 | 76.17 168 | 70.57 134 | 77.71 79 | 52.14 134 | 66.26 68 | 77.47 53 | 73.10 116 | 87.02 83 | 87.16 65 | 96.05 22 | 97.02 48 |
|
EPMVS | | | 77.16 110 | 79.08 89 | 74.92 116 | 86.73 94 | 91.98 88 | 78.62 140 | 55.44 210 | 79.43 70 | 56.59 116 | 61.24 95 | 70.73 82 | 76.97 97 | 80.59 146 | 81.43 152 | 95.15 36 | 88.17 170 |
|
USDC | | | 73.43 131 | 72.31 138 | 74.73 117 | 80.86 134 | 86.21 146 | 80.42 108 | 71.83 123 | 71.69 106 | 46.94 177 | 59.60 100 | 42.58 189 | 76.47 101 | 82.66 125 | 81.22 159 | 91.88 155 | 82.24 200 |
|
tpmrst | | | 76.27 116 | 77.65 101 | 74.66 118 | 86.13 107 | 89.53 114 | 79.31 132 | 54.91 211 | 77.19 83 | 56.27 117 | 55.87 118 | 64.58 99 | 77.25 94 | 80.85 144 | 80.21 172 | 94.07 52 | 95.32 83 |
|
tfpnview11 | | | 74.85 119 | 75.06 121 | 74.61 119 | 86.58 97 | 89.54 113 | 79.98 112 | 75.81 90 | 64.95 133 | 47.47 173 | 64.85 76 | 54.72 129 | 63.86 171 | 84.54 110 | 82.20 131 | 93.97 56 | 84.64 182 |
|
Effi-MVS+-dtu | | | 74.57 121 | 74.60 126 | 74.53 120 | 81.38 129 | 86.74 137 | 80.39 109 | 67.70 157 | 67.36 121 | 53.06 127 | 59.86 99 | 57.50 122 | 75.84 104 | 80.19 148 | 78.62 182 | 88.79 197 | 91.95 133 |
|
tfpn1000 | | | 75.39 118 | 76.18 115 | 74.47 121 | 86.71 95 | 90.10 105 | 77.57 154 | 74.78 98 | 68.76 118 | 53.33 126 | 63.57 84 | 58.37 120 | 60.84 183 | 83.80 117 | 81.24 157 | 93.58 77 | 87.42 173 |
|
EPNet_dtu | | | 78.49 94 | 81.96 74 | 74.45 122 | 92.57 49 | 88.74 119 | 82.98 92 | 78.83 61 | 83.28 61 | 44.64 194 | 77.40 41 | 67.73 90 | 53.98 199 | 85.44 103 | 84.91 92 | 93.71 69 | 86.22 179 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
tfpn_n400 | | | 74.36 122 | 74.39 128 | 74.32 123 | 86.37 102 | 89.86 108 | 79.71 117 | 75.69 92 | 60.00 150 | 47.47 173 | 64.85 76 | 54.72 129 | 63.70 174 | 83.80 117 | 83.35 114 | 92.96 123 | 84.16 187 |
|
tfpnconf | | | 74.36 122 | 74.39 128 | 74.32 123 | 86.37 102 | 89.86 108 | 79.71 117 | 75.69 92 | 60.00 150 | 47.47 173 | 64.85 76 | 54.72 129 | 63.70 174 | 83.80 117 | 83.35 114 | 92.96 123 | 84.16 187 |
|
TESTMET0.1,1 | | | 79.15 85 | 83.42 62 | 74.18 125 | 79.81 139 | 91.26 94 | 76.17 168 | 67.83 156 | 77.71 79 | 52.14 134 | 66.26 68 | 77.47 53 | 73.10 116 | 87.02 83 | 87.16 65 | 96.05 22 | 97.02 48 |
|
v6 | | | 72.04 144 | 70.26 147 | 74.11 126 | 76.46 161 | 87.06 127 | 79.60 119 | 71.75 124 | 59.48 154 | 52.69 131 | 44.61 158 | 45.79 160 | 71.01 137 | 79.57 157 | 81.45 150 | 93.16 112 | 93.85 114 |
|
v1neww | | | 72.02 145 | 70.23 149 | 74.10 127 | 76.45 162 | 87.06 127 | 79.59 122 | 71.75 124 | 59.35 155 | 52.60 132 | 44.59 160 | 45.74 161 | 71.06 134 | 79.57 157 | 81.46 148 | 93.16 112 | 93.84 115 |
|
v7new | | | 72.02 145 | 70.23 149 | 74.10 127 | 76.45 162 | 87.06 127 | 79.59 122 | 71.75 124 | 59.35 155 | 52.60 132 | 44.59 160 | 45.74 161 | 71.06 134 | 79.57 157 | 81.46 148 | 93.16 112 | 93.84 115 |
|
Vis-MVSNet | | | 77.24 107 | 79.99 86 | 74.02 129 | 84.62 116 | 93.92 61 | 80.33 110 | 72.55 114 | 62.58 141 | 55.25 119 | 64.45 80 | 69.49 86 | 57.00 191 | 88.78 61 | 88.21 60 | 94.36 48 | 92.54 126 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
v2v482 | | | 71.73 147 | 69.80 154 | 73.99 130 | 75.88 180 | 86.66 139 | 79.58 125 | 71.90 122 | 57.58 163 | 50.41 157 | 45.35 155 | 43.24 185 | 73.05 118 | 79.69 152 | 82.18 132 | 93.08 119 | 93.87 112 |
|
MDTV_nov1_ep13 | | | 77.20 109 | 80.04 83 | 73.90 131 | 82.22 123 | 90.14 104 | 79.25 133 | 61.52 192 | 78.63 76 | 56.98 113 | 65.52 74 | 72.80 74 | 73.05 118 | 80.93 143 | 83.20 117 | 90.36 183 | 89.05 159 |
|
COLMAP_ROB | | 66.31 15 | 69.91 168 | 66.61 182 | 73.76 132 | 86.44 101 | 82.76 176 | 76.59 164 | 76.46 85 | 63.82 137 | 50.92 148 | 45.60 154 | 49.13 147 | 65.87 167 | 74.96 193 | 74.45 204 | 86.30 210 | 75.57 210 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
UniMVSNet_NR-MVSNet | | | 73.11 133 | 72.59 136 | 73.71 133 | 76.90 154 | 86.58 141 | 77.01 159 | 75.82 89 | 65.59 127 | 48.82 166 | 50.97 137 | 48.42 150 | 71.61 126 | 79.19 167 | 83.03 121 | 92.11 148 | 94.37 94 |
|
v1141 | | | 71.53 151 | 69.69 156 | 73.68 134 | 76.08 167 | 86.86 132 | 79.59 122 | 72.07 119 | 57.01 165 | 50.78 151 | 44.23 169 | 44.70 169 | 70.68 141 | 79.61 155 | 81.52 143 | 92.89 129 | 93.92 107 |
|
divwei89l23v2f112 | | | 71.53 151 | 69.69 156 | 73.68 134 | 76.09 166 | 86.86 132 | 79.60 119 | 72.08 118 | 56.96 167 | 50.78 151 | 44.24 168 | 44.70 169 | 70.65 143 | 79.62 153 | 81.53 141 | 92.89 129 | 93.93 105 |
|
v1 | | | 71.54 150 | 69.71 155 | 73.66 136 | 76.08 167 | 86.88 131 | 79.60 119 | 72.06 120 | 57.00 166 | 50.75 153 | 44.23 169 | 44.79 166 | 70.61 144 | 79.62 153 | 81.52 143 | 92.88 132 | 93.93 105 |
|
v18 | | | 71.13 156 | 68.98 164 | 73.63 137 | 76.66 158 | 79.78 195 | 79.95 114 | 65.98 168 | 61.34 144 | 54.71 120 | 44.75 157 | 46.06 156 | 71.27 130 | 79.59 156 | 81.51 146 | 93.21 108 | 89.81 145 |
|
tfpnnormal | | | 69.29 176 | 65.58 184 | 73.62 138 | 79.87 138 | 84.82 165 | 76.97 161 | 75.12 97 | 45.29 213 | 49.03 164 | 35.57 210 | 37.20 211 | 68.02 156 | 82.70 124 | 81.24 157 | 92.69 136 | 92.20 128 |
|
v16 | | | 70.93 159 | 68.76 168 | 73.47 139 | 76.60 159 | 79.66 197 | 79.57 126 | 65.81 171 | 60.85 145 | 54.44 123 | 44.50 164 | 45.90 158 | 71.15 131 | 79.50 161 | 81.39 154 | 93.27 102 | 89.51 149 |
|
v8 | | | 71.42 155 | 69.69 156 | 73.43 140 | 76.45 162 | 85.12 163 | 79.53 128 | 67.47 160 | 59.34 157 | 52.90 128 | 44.60 159 | 45.82 159 | 71.05 136 | 79.56 160 | 81.45 150 | 93.17 110 | 91.96 132 |
|
v17 | | | 70.82 161 | 68.69 169 | 73.31 141 | 76.53 160 | 79.67 196 | 79.45 129 | 65.80 172 | 60.32 149 | 53.75 124 | 44.51 163 | 45.92 157 | 71.09 133 | 79.49 162 | 81.38 155 | 93.26 105 | 89.54 148 |
|
V42 | | | 71.58 149 | 70.11 152 | 73.30 142 | 75.66 186 | 86.68 138 | 79.17 135 | 69.92 137 | 59.29 158 | 52.80 129 | 44.36 166 | 45.66 163 | 68.83 154 | 79.48 163 | 81.49 147 | 93.44 92 | 93.82 117 |
|
v7 | | | 71.49 153 | 69.98 153 | 73.25 143 | 75.89 178 | 86.45 142 | 79.44 130 | 69.29 143 | 58.07 161 | 50.08 159 | 43.87 176 | 43.67 179 | 71.94 122 | 82.03 134 | 81.70 136 | 92.88 132 | 94.04 101 |
|
DU-MVS | | | 72.19 139 | 71.35 144 | 73.17 144 | 75.95 173 | 86.02 153 | 77.01 159 | 74.42 103 | 65.39 129 | 48.82 166 | 49.10 144 | 42.81 187 | 71.61 126 | 78.67 173 | 83.10 119 | 91.22 169 | 94.37 94 |
|
Vis-MVSNet (Re-imp) | | | 78.28 97 | 82.68 68 | 73.16 145 | 86.64 96 | 92.68 76 | 78.07 149 | 74.48 102 | 74.05 95 | 53.47 125 | 64.22 82 | 76.52 59 | 54.28 195 | 88.96 59 | 88.29 59 | 92.03 151 | 94.00 102 |
|
PatchmatchNet | | | 76.85 113 | 80.03 85 | 73.15 146 | 84.08 118 | 91.04 97 | 77.76 153 | 55.85 209 | 79.43 70 | 52.74 130 | 62.08 92 | 76.02 60 | 74.56 108 | 79.92 151 | 81.41 153 | 93.92 61 | 90.29 143 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
v10 | | | 70.97 158 | 69.44 159 | 72.75 147 | 75.90 177 | 84.58 168 | 79.43 131 | 66.45 165 | 58.07 161 | 49.93 160 | 43.87 176 | 43.68 178 | 71.91 123 | 82.04 132 | 81.70 136 | 92.89 129 | 92.11 131 |
|
v1144 | | | 70.93 159 | 69.42 161 | 72.70 148 | 75.48 187 | 86.26 144 | 79.22 134 | 69.39 142 | 55.61 178 | 48.05 171 | 43.47 182 | 42.55 190 | 71.51 128 | 82.11 130 | 81.74 135 | 92.56 140 | 94.17 99 |
|
Baseline_NR-MVSNet | | | 70.61 162 | 68.87 166 | 72.65 149 | 75.95 173 | 80.49 191 | 75.92 171 | 74.75 99 | 65.10 132 | 48.78 168 | 41.28 196 | 44.28 176 | 68.45 155 | 78.67 173 | 79.64 176 | 92.04 150 | 92.62 125 |
|
test-mter | | | 77.90 101 | 82.44 71 | 72.60 150 | 78.52 143 | 90.24 102 | 73.85 180 | 65.31 174 | 76.37 86 | 51.29 138 | 65.58 73 | 75.94 61 | 71.36 129 | 85.98 98 | 86.26 78 | 95.26 33 | 96.71 64 |
|
GA-MVS | | | 73.62 127 | 74.52 127 | 72.58 151 | 79.93 137 | 89.29 116 | 78.02 150 | 71.67 127 | 60.79 147 | 42.68 198 | 54.41 125 | 49.07 148 | 70.07 152 | 89.39 56 | 86.55 75 | 93.13 117 | 92.12 130 |
|
v15 | | | 70.00 167 | 67.82 176 | 72.55 152 | 76.06 169 | 79.37 200 | 79.10 136 | 65.30 175 | 56.89 168 | 51.18 140 | 43.96 175 | 44.76 167 | 70.52 146 | 79.40 164 | 81.22 159 | 93.13 117 | 89.14 158 |
|
v148 | | | 70.34 163 | 68.46 171 | 72.54 153 | 76.04 170 | 86.38 143 | 74.83 175 | 72.73 109 | 55.88 177 | 55.26 118 | 43.32 185 | 43.49 180 | 64.52 169 | 76.93 186 | 80.11 174 | 91.85 156 | 93.11 121 |
|
V14 | | | 69.91 168 | 67.71 178 | 72.47 154 | 76.01 171 | 79.30 201 | 78.92 137 | 65.17 176 | 56.74 169 | 51.08 145 | 43.82 178 | 44.73 168 | 70.44 148 | 79.31 165 | 81.14 164 | 93.20 109 | 88.91 162 |
|
TAMVS | | | 72.06 143 | 71.76 141 | 72.41 155 | 76.68 157 | 88.12 122 | 74.82 176 | 68.09 152 | 53.52 187 | 56.91 115 | 52.94 134 | 56.93 126 | 66.91 164 | 81.37 140 | 82.44 126 | 91.07 171 | 86.99 175 |
|
V9 | | | 69.79 172 | 67.57 179 | 72.38 156 | 75.95 173 | 79.21 202 | 78.72 139 | 65.06 177 | 56.51 171 | 51.06 146 | 43.66 179 | 44.70 169 | 70.28 150 | 79.22 166 | 81.06 167 | 93.24 107 | 88.67 166 |
|
v12 | | | 69.66 173 | 67.45 180 | 72.23 157 | 75.89 178 | 79.13 204 | 78.29 145 | 64.96 180 | 56.40 172 | 50.75 153 | 43.53 181 | 44.60 172 | 70.21 151 | 79.11 168 | 80.99 168 | 93.27 102 | 88.41 167 |
|
v11 | | | 69.84 171 | 67.85 175 | 72.17 158 | 75.78 184 | 79.15 203 | 78.20 147 | 64.76 182 | 56.10 175 | 49.50 161 | 43.54 180 | 43.36 183 | 71.62 125 | 82.21 129 | 81.52 143 | 93.17 110 | 89.05 159 |
|
TranMVSNet+NR-MVSNet | | | 71.12 157 | 70.24 148 | 72.15 159 | 76.01 171 | 84.80 166 | 76.55 165 | 75.65 94 | 61.99 143 | 45.29 187 | 48.42 149 | 43.07 186 | 67.55 158 | 78.28 176 | 82.83 123 | 91.85 156 | 92.29 127 |
|
v13 | | | 69.55 175 | 67.33 181 | 72.14 160 | 75.83 181 | 79.04 205 | 78.22 146 | 64.85 181 | 56.16 174 | 50.60 155 | 43.43 183 | 44.56 173 | 70.05 153 | 79.01 170 | 80.92 170 | 93.28 101 | 88.22 168 |
|
v1192 | | | 70.32 164 | 68.77 167 | 72.12 161 | 74.76 189 | 85.62 156 | 78.73 138 | 68.53 146 | 55.08 180 | 46.34 181 | 42.39 188 | 40.67 197 | 71.90 124 | 82.27 128 | 81.53 141 | 92.43 144 | 93.86 113 |
|
UniMVSNet (Re) | | | 72.12 140 | 72.28 139 | 71.93 162 | 76.77 155 | 87.38 126 | 75.73 174 | 73.51 107 | 65.76 125 | 50.24 158 | 48.65 148 | 46.49 155 | 63.85 172 | 80.10 149 | 82.47 125 | 91.49 164 | 95.13 86 |
|
tpm | | | 73.50 129 | 74.85 123 | 71.93 162 | 83.19 121 | 86.84 134 | 78.61 141 | 55.91 208 | 65.64 126 | 48.90 165 | 56.30 114 | 61.09 107 | 72.31 120 | 79.10 169 | 80.61 171 | 92.68 137 | 94.35 96 |
|
v144192 | | | 70.10 165 | 68.55 170 | 71.90 164 | 74.55 190 | 85.67 155 | 77.81 151 | 68.22 151 | 54.65 182 | 46.91 178 | 42.76 186 | 41.27 196 | 70.95 138 | 80.48 147 | 81.11 166 | 92.96 123 | 93.90 110 |
|
NR-MVSNet | | | 71.47 154 | 71.11 145 | 71.90 164 | 77.73 150 | 86.02 153 | 76.88 163 | 74.42 103 | 65.39 129 | 46.09 184 | 49.10 144 | 39.87 201 | 64.27 170 | 81.40 139 | 82.24 130 | 91.99 152 | 93.75 118 |
|
FMVSNet5 | | | 72.83 134 | 73.89 131 | 71.59 166 | 67.42 213 | 76.28 209 | 75.88 172 | 63.74 184 | 77.27 82 | 54.59 122 | 53.32 130 | 71.48 77 | 73.85 112 | 81.95 135 | 81.69 138 | 94.06 53 | 75.20 212 |
|
v1921920 | | | 69.85 170 | 68.38 172 | 71.58 167 | 74.35 191 | 85.39 159 | 77.78 152 | 67.88 155 | 54.64 183 | 45.39 186 | 42.11 191 | 39.97 200 | 71.10 132 | 81.68 138 | 81.17 163 | 92.96 123 | 93.69 120 |
|
Fast-Effi-MVS+-dtu | | | 73.56 128 | 75.32 120 | 71.50 168 | 80.35 135 | 86.83 135 | 79.72 116 | 58.07 204 | 67.64 120 | 44.83 191 | 60.28 98 | 54.07 132 | 73.59 115 | 81.90 137 | 82.30 128 | 92.46 143 | 94.18 98 |
|
pm-mvs1 | | | 69.62 174 | 68.07 174 | 71.44 169 | 77.21 152 | 85.32 160 | 76.11 170 | 71.05 129 | 46.55 211 | 51.17 141 | 41.83 193 | 48.20 151 | 61.81 180 | 84.00 115 | 81.14 164 | 91.28 168 | 89.42 150 |
|
TinyColmap | | | 67.16 182 | 63.51 200 | 71.42 170 | 77.94 148 | 79.54 199 | 72.80 183 | 69.78 139 | 56.58 170 | 45.52 185 | 44.53 162 | 33.53 220 | 74.45 109 | 76.91 187 | 77.06 192 | 88.03 203 | 76.41 207 |
|
CMPMVS | | 50.59 17 | 66.74 185 | 62.72 204 | 71.42 170 | 85.40 112 | 89.72 112 | 72.69 184 | 70.72 132 | 51.24 194 | 51.75 136 | 38.91 203 | 44.40 174 | 63.74 173 | 70.84 208 | 71.52 208 | 84.19 215 | 72.45 217 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
CR-MVSNet | | | 74.84 120 | 77.91 98 | 71.26 172 | 81.77 127 | 85.52 157 | 78.32 142 | 54.14 213 | 74.05 95 | 51.09 142 | 50.00 141 | 71.38 79 | 70.77 139 | 86.48 91 | 84.03 106 | 91.46 165 | 93.92 107 |
|
TDRefinement | | | 67.82 180 | 64.91 190 | 71.22 173 | 82.08 124 | 81.45 184 | 77.42 157 | 73.79 106 | 59.62 153 | 48.35 170 | 42.35 190 | 42.40 191 | 60.87 182 | 74.69 194 | 74.64 203 | 84.83 214 | 79.20 204 |
|
v1240 | | | 69.28 177 | 67.82 176 | 71.00 174 | 74.09 194 | 85.13 162 | 76.54 166 | 67.28 162 | 53.17 189 | 44.70 192 | 41.55 195 | 39.38 203 | 70.51 147 | 81.29 141 | 81.18 161 | 92.88 132 | 93.02 123 |
|
pmmvs5 | | | 70.01 166 | 69.31 163 | 70.82 175 | 75.80 183 | 86.26 144 | 72.94 182 | 67.91 154 | 53.84 186 | 47.22 176 | 47.31 151 | 41.47 195 | 67.61 157 | 83.93 116 | 81.93 134 | 93.42 94 | 90.42 142 |
|
IterMVS | | | 72.43 137 | 74.05 130 | 70.55 176 | 80.34 136 | 81.17 188 | 77.44 156 | 61.00 194 | 63.57 139 | 46.82 179 | 55.88 117 | 59.09 117 | 65.03 168 | 83.15 122 | 83.83 110 | 92.67 138 | 91.65 135 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
ADS-MVSNet | | | 72.11 141 | 73.72 132 | 70.24 177 | 81.24 130 | 86.59 140 | 74.75 177 | 50.56 220 | 72.58 103 | 49.17 163 | 55.40 120 | 61.46 106 | 73.80 113 | 76.01 189 | 78.14 185 | 91.93 154 | 85.86 180 |
|
TransMVSNet (Re) | | | 66.87 184 | 64.30 195 | 69.88 178 | 78.32 144 | 81.35 187 | 73.88 179 | 74.34 105 | 43.19 217 | 45.20 189 | 40.12 197 | 42.37 192 | 55.97 193 | 80.85 144 | 79.15 177 | 91.56 162 | 83.06 194 |
|
gg-mvs-nofinetune | | | 72.10 142 | 74.79 124 | 68.97 179 | 83.31 120 | 95.22 55 | 85.66 73 | 48.77 223 | 35.68 223 | 22.17 232 | 30.49 216 | 77.73 52 | 76.37 103 | 94.30 10 | 93.03 11 | 97.55 2 | 97.05 47 |
|
RPMNet | | | 73.46 130 | 77.85 99 | 68.34 180 | 81.71 128 | 85.52 157 | 73.83 181 | 50.54 221 | 74.05 95 | 46.10 183 | 53.03 133 | 71.91 75 | 66.31 166 | 83.55 120 | 82.18 132 | 91.55 163 | 94.71 88 |
|
test0.0.03 1 | | | 71.70 148 | 74.68 125 | 68.23 181 | 81.79 125 | 83.81 172 | 68.64 193 | 70.57 134 | 68.81 117 | 43.47 195 | 62.77 89 | 60.09 113 | 51.77 205 | 82.48 127 | 81.67 139 | 93.16 112 | 83.13 193 |
|
PatchT | | | 72.66 135 | 76.58 108 | 68.09 182 | 79.02 142 | 86.09 152 | 59.81 211 | 51.78 219 | 72.00 105 | 51.09 142 | 46.84 152 | 66.70 92 | 70.77 139 | 86.48 91 | 84.03 106 | 96.07 19 | 93.92 107 |
|
MIMVSNet | | | 68.66 179 | 69.43 160 | 67.76 183 | 64.92 218 | 84.68 167 | 74.16 178 | 54.10 215 | 60.85 145 | 51.27 139 | 39.47 200 | 49.48 146 | 67.48 159 | 84.86 108 | 85.57 88 | 94.63 44 | 81.10 201 |
|
v7n | | | 66.43 186 | 65.51 185 | 67.51 184 | 71.63 206 | 83.10 174 | 70.89 190 | 65.02 178 | 50.13 201 | 44.68 193 | 39.59 199 | 38.77 205 | 62.57 178 | 77.59 183 | 78.91 178 | 90.29 185 | 90.44 141 |
|
EG-PatchMatch MVS | | | 66.23 187 | 65.20 187 | 67.43 185 | 77.74 149 | 86.20 147 | 72.51 185 | 63.68 185 | 43.95 215 | 43.44 196 | 36.22 209 | 45.43 165 | 54.04 197 | 81.00 142 | 80.95 169 | 93.15 116 | 82.67 198 |
|
pmmvs6 | | | 64.24 198 | 61.77 208 | 67.12 186 | 72.39 200 | 81.39 186 | 71.33 188 | 65.95 170 | 36.05 222 | 48.48 169 | 30.55 215 | 43.45 182 | 58.75 188 | 77.88 181 | 76.36 198 | 85.83 211 | 86.70 178 |
|
pmmvs-eth3d | | | 64.24 198 | 61.96 206 | 66.90 187 | 66.35 215 | 76.04 211 | 66.09 200 | 66.31 166 | 52.59 190 | 50.94 147 | 37.61 205 | 32.79 222 | 62.43 179 | 75.78 190 | 75.48 200 | 89.27 194 | 83.39 192 |
|
CVMVSNet | | | 68.95 178 | 70.79 146 | 66.79 188 | 79.69 140 | 83.75 173 | 72.05 186 | 70.90 130 | 56.20 173 | 36.30 209 | 54.94 124 | 59.22 116 | 54.03 198 | 78.33 175 | 78.65 181 | 87.77 204 | 84.44 184 |
|
v748 | | | 65.00 190 | 63.86 199 | 66.33 189 | 71.85 204 | 82.15 182 | 66.80 196 | 65.64 173 | 48.50 207 | 47.98 172 | 39.62 198 | 39.20 204 | 56.44 192 | 71.25 205 | 77.53 189 | 89.29 193 | 88.74 165 |
|
v52 | | | 65.34 188 | 64.59 192 | 66.21 190 | 69.63 211 | 82.41 180 | 69.22 191 | 62.80 188 | 49.63 202 | 45.15 190 | 39.31 202 | 41.85 193 | 60.68 184 | 72.61 198 | 77.02 194 | 89.75 191 | 89.33 153 |
|
V4 | | | 65.34 188 | 64.59 192 | 66.21 190 | 69.64 210 | 82.42 179 | 69.22 191 | 62.80 188 | 49.60 203 | 45.21 188 | 39.33 201 | 41.82 194 | 60.66 185 | 72.61 198 | 77.03 193 | 89.76 190 | 89.32 155 |
|
anonymousdsp | | | 67.61 181 | 68.94 165 | 66.04 192 | 71.44 207 | 83.97 170 | 66.45 198 | 63.53 186 | 50.54 198 | 42.42 199 | 49.39 142 | 45.63 164 | 62.84 177 | 77.99 178 | 81.34 156 | 89.59 192 | 93.75 118 |
|
LTVRE_ROB | | 63.07 16 | 64.49 196 | 63.16 203 | 66.04 192 | 77.47 151 | 82.64 178 | 70.98 189 | 65.02 178 | 34.01 226 | 29.61 219 | 49.12 143 | 35.58 216 | 70.57 145 | 75.10 192 | 78.45 183 | 82.60 218 | 87.24 174 |
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 |
MVS-HIRNet | | | 64.63 195 | 64.03 198 | 65.33 194 | 75.01 188 | 82.84 175 | 58.54 215 | 52.10 218 | 55.42 179 | 49.29 162 | 29.83 219 | 43.48 181 | 66.97 163 | 78.28 176 | 78.81 179 | 90.07 188 | 79.52 203 |
|
PM-MVS | | | 63.52 203 | 62.51 205 | 64.70 195 | 64.79 220 | 76.08 210 | 65.07 202 | 62.08 190 | 58.13 160 | 46.56 180 | 44.98 156 | 31.31 223 | 62.89 176 | 72.58 200 | 69.93 215 | 86.81 208 | 84.55 183 |
|
CP-MVSNet | | | 64.84 193 | 64.97 188 | 64.69 196 | 72.09 201 | 81.04 189 | 66.66 197 | 67.53 159 | 52.45 191 | 37.40 205 | 44.00 174 | 38.37 207 | 53.54 200 | 72.26 202 | 76.93 195 | 90.94 176 | 89.75 146 |
|
MDTV_nov1_ep13_2view | | | 64.72 194 | 64.94 189 | 64.46 197 | 71.14 208 | 81.94 183 | 67.53 194 | 54.54 212 | 55.92 176 | 43.29 197 | 44.02 173 | 43.27 184 | 59.87 186 | 71.85 204 | 74.77 202 | 90.36 183 | 82.82 196 |
|
PEN-MVS | | | 64.35 197 | 64.29 196 | 64.42 198 | 72.67 197 | 79.83 194 | 66.97 195 | 68.24 150 | 51.21 195 | 35.29 212 | 44.09 171 | 38.51 206 | 52.36 203 | 71.06 206 | 77.65 188 | 90.99 172 | 87.68 172 |
|
PS-CasMVS | | | 64.22 200 | 64.19 197 | 64.25 199 | 71.86 203 | 80.67 190 | 66.42 199 | 67.43 161 | 50.64 197 | 36.48 207 | 42.60 187 | 37.46 210 | 52.56 202 | 71.98 203 | 76.69 197 | 90.76 177 | 89.29 156 |
|
SixPastTwentyTwo | | | 63.75 202 | 63.42 201 | 64.13 200 | 72.91 196 | 80.34 192 | 61.29 208 | 63.90 183 | 49.58 204 | 40.42 202 | 54.99 123 | 37.13 212 | 60.90 181 | 68.46 212 | 70.80 211 | 85.37 213 | 82.65 199 |
|
WR-MVS | | | 64.98 191 | 66.59 183 | 63.09 201 | 74.34 192 | 82.68 177 | 64.98 204 | 69.17 144 | 54.42 184 | 36.18 210 | 44.32 167 | 44.35 175 | 44.65 208 | 73.60 195 | 77.83 186 | 89.21 195 | 88.96 161 |
|
DTE-MVSNet | | | 63.26 204 | 63.41 202 | 63.08 202 | 72.59 198 | 78.56 206 | 65.03 203 | 68.28 149 | 50.53 199 | 32.38 216 | 44.03 172 | 37.79 209 | 49.48 206 | 70.83 209 | 76.73 196 | 90.73 178 | 85.42 181 |
|
WR-MVS_H | | | 64.14 201 | 65.36 186 | 62.71 203 | 72.47 199 | 82.33 181 | 65.13 201 | 66.99 163 | 51.81 193 | 36.47 208 | 43.33 184 | 42.77 188 | 43.99 210 | 72.41 201 | 75.99 199 | 91.20 170 | 88.86 164 |
|
N_pmnet | | | 60.52 208 | 58.83 212 | 62.50 204 | 68.97 212 | 75.61 212 | 59.72 213 | 66.47 164 | 51.90 192 | 41.26 200 | 35.42 211 | 35.63 215 | 52.25 204 | 67.07 216 | 70.08 214 | 86.35 209 | 76.10 208 |
|
Anonymous20231206 | | | 62.05 207 | 61.83 207 | 62.30 205 | 72.09 201 | 77.84 208 | 63.10 206 | 67.62 158 | 50.20 200 | 36.68 206 | 29.59 220 | 37.05 213 | 43.90 211 | 77.33 185 | 77.31 190 | 90.41 182 | 83.49 191 |
|
LP | | | 59.72 209 | 58.23 213 | 61.44 206 | 75.67 185 | 74.97 213 | 61.05 209 | 48.34 224 | 54.02 185 | 40.82 201 | 31.61 214 | 36.92 214 | 54.69 194 | 67.52 214 | 71.18 210 | 88.08 202 | 71.42 220 |
|
testgi | | | 63.11 205 | 64.88 191 | 61.05 207 | 75.83 181 | 78.51 207 | 60.42 210 | 66.20 167 | 48.77 206 | 34.56 214 | 56.96 107 | 40.35 198 | 40.95 219 | 77.46 184 | 77.22 191 | 88.37 201 | 74.86 214 |
|
gm-plane-assit | | | 64.86 192 | 68.15 173 | 61.02 208 | 76.44 165 | 68.29 219 | 41.60 228 | 53.37 216 | 34.68 225 | 26.19 229 | 33.22 213 | 57.09 125 | 71.97 121 | 95.12 4 | 93.97 6 | 96.54 13 | 94.66 90 |
|
FC-MVSNet-test | | | 67.04 183 | 72.47 137 | 60.70 209 | 76.92 153 | 81.41 185 | 61.52 207 | 69.45 141 | 65.58 128 | 26.74 227 | 61.79 94 | 60.40 112 | 41.17 218 | 77.60 182 | 77.78 187 | 88.41 199 | 82.70 197 |
|
MDA-MVSNet-bldmvs | | | 54.99 215 | 52.66 217 | 57.71 210 | 52.74 231 | 74.87 214 | 55.61 216 | 68.41 148 | 43.65 216 | 32.54 215 | 37.93 204 | 22.11 231 | 54.11 196 | 48.85 229 | 67.34 218 | 82.85 217 | 73.88 216 |
|
test2356 | | | 58.43 212 | 59.52 210 | 57.16 211 | 66.71 214 | 68.00 220 | 54.69 217 | 60.91 196 | 49.22 205 | 28.63 222 | 41.86 192 | 33.68 219 | 44.36 209 | 72.98 196 | 75.47 201 | 87.69 205 | 75.40 211 |
|
test20.03 | | | 57.93 213 | 59.22 211 | 56.44 212 | 71.84 205 | 73.78 215 | 53.55 219 | 65.96 169 | 43.02 218 | 28.46 223 | 37.50 206 | 38.17 208 | 30.41 227 | 75.25 191 | 74.42 205 | 88.41 199 | 72.37 218 |
|
EU-MVSNet | | | 58.73 211 | 60.92 209 | 56.17 213 | 66.17 216 | 72.39 216 | 58.85 214 | 61.24 193 | 48.47 208 | 27.91 224 | 46.70 153 | 40.06 199 | 39.07 220 | 68.27 213 | 70.34 213 | 83.77 216 | 80.23 202 |
|
FPMVS | | | 50.25 220 | 45.67 225 | 55.58 214 | 70.48 209 | 60.12 226 | 59.78 212 | 59.33 201 | 46.66 210 | 37.94 203 | 30.22 217 | 27.51 226 | 35.94 223 | 50.98 228 | 47.90 228 | 70.02 229 | 56.31 226 |
|
testus | | | 55.91 214 | 56.38 214 | 55.37 215 | 65.15 217 | 65.88 223 | 50.07 223 | 60.92 195 | 45.62 212 | 26.99 226 | 41.74 194 | 24.43 229 | 42.08 215 | 69.50 211 | 73.60 206 | 86.97 207 | 73.91 215 |
|
new-patchmatchnet | | | 53.91 216 | 52.69 216 | 55.33 216 | 64.83 219 | 70.90 217 | 52.24 221 | 61.75 191 | 41.09 219 | 30.82 217 | 29.90 218 | 28.22 225 | 36.69 222 | 61.52 222 | 65.08 221 | 85.64 212 | 72.14 219 |
|
testpf | | | 59.38 210 | 64.51 194 | 53.40 217 | 76.71 156 | 66.40 221 | 50.18 222 | 38.98 234 | 64.13 136 | 35.10 213 | 47.91 150 | 51.41 135 | 43.16 212 | 66.37 217 | 71.23 209 | 76.25 226 | 84.14 189 |
|
pmmvs3 | | | 52.59 218 | 52.43 218 | 52.78 218 | 54.53 229 | 64.49 225 | 50.07 223 | 46.89 227 | 35.31 224 | 30.19 218 | 27.27 222 | 26.96 227 | 53.02 201 | 67.28 215 | 70.54 212 | 81.96 219 | 75.20 212 |
|
Anonymous20231211 | | | 49.72 221 | 47.45 222 | 52.38 219 | 60.54 223 | 66.16 222 | 52.47 220 | 60.87 197 | 25.32 232 | 25.16 230 | 15.98 231 | 23.66 230 | 37.00 221 | 61.01 224 | 64.41 223 | 78.25 224 | 75.60 209 |
|
MIMVSNet1 | | | 52.76 217 | 53.95 215 | 51.38 220 | 41.96 235 | 70.79 218 | 53.56 218 | 63.03 187 | 39.36 220 | 27.83 225 | 22.73 229 | 33.07 221 | 34.47 224 | 70.49 210 | 72.69 207 | 87.41 206 | 68.51 221 |
|
new_pmnet | | | 50.32 219 | 51.36 219 | 49.11 221 | 49.19 232 | 64.89 224 | 48.66 226 | 47.99 226 | 47.55 209 | 26.27 228 | 29.51 221 | 28.66 224 | 44.89 207 | 61.12 223 | 62.74 225 | 77.66 225 | 65.03 224 |
|
1111 | | | 48.34 222 | 47.93 221 | 48.83 222 | 58.14 225 | 59.33 228 | 37.54 229 | 43.85 228 | 31.76 227 | 29.36 220 | 23.26 226 | 34.58 217 | 42.20 213 | 65.15 218 | 68.72 217 | 81.86 220 | 52.66 229 |
|
testmv | | | 46.89 223 | 46.37 223 | 47.48 223 | 60.96 221 | 58.36 230 | 36.71 231 | 56.94 205 | 27.16 230 | 17.93 234 | 23.94 224 | 18.84 233 | 31.06 225 | 61.55 220 | 66.72 219 | 81.28 221 | 68.05 222 |
|
test1235678 | | | 46.88 224 | 46.36 224 | 47.48 223 | 60.96 221 | 58.35 231 | 36.71 231 | 56.94 205 | 27.15 231 | 17.93 234 | 23.93 225 | 18.82 234 | 31.06 225 | 61.55 220 | 66.71 220 | 81.27 222 | 68.04 223 |
|
PMVS | | 36.83 18 | 40.62 226 | 36.39 227 | 45.56 225 | 58.40 224 | 33.20 236 | 32.62 235 | 56.02 207 | 28.25 229 | 37.92 204 | 22.29 230 | 26.15 228 | 25.29 229 | 48.49 230 | 43.82 231 | 63.13 232 | 52.53 230 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
test12356 | | | 41.15 225 | 41.46 226 | 40.78 226 | 53.10 230 | 49.87 232 | 33.37 234 | 52.25 217 | 25.12 233 | 15.64 236 | 22.76 228 | 15.01 235 | 15.81 232 | 52.97 226 | 64.54 222 | 74.50 228 | 59.96 225 |
|
tmp_tt | | | | | 39.78 227 | 56.31 227 | 31.71 238 | 35.84 233 | 15.08 236 | 82.57 65 | 50.83 150 | 63.07 87 | 47.51 153 | 15.28 233 | 52.23 227 | 44.24 230 | 65.35 231 | |
|
Gipuma | | | 35.20 227 | 33.96 228 | 36.65 228 | 43.30 234 | 32.51 237 | 26.96 237 | 48.31 225 | 38.87 221 | 20.08 233 | 8.08 234 | 7.41 239 | 26.44 228 | 53.60 225 | 58.43 226 | 54.81 234 | 38.79 233 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
.test1245 | | | 33.05 228 | 31.21 230 | 35.20 229 | 58.14 225 | 59.33 228 | 37.54 229 | 43.85 228 | 31.76 227 | 29.36 220 | 23.26 226 | 34.58 217 | 42.20 213 | 65.15 218 | 0.77 235 | 0.11 239 | 3.62 237 |
|
no-one | | | 32.08 230 | 31.09 231 | 33.23 230 | 46.10 233 | 46.90 234 | 20.80 238 | 49.13 222 | 16.27 235 | 7.85 238 | 10.62 233 | 10.68 237 | 13.65 235 | 31.50 233 | 51.31 227 | 61.83 233 | 50.38 231 |
|
GG-mvs-BLEND | | | 62.08 206 | 88.31 37 | 31.46 231 | 0.16 240 | 98.10 6 | 91.57 36 | 0.09 237 | 85.07 58 | 0.21 242 | 73.90 49 | 83.74 33 | 0.19 239 | 88.98 58 | 89.39 51 | 96.58 12 | 99.02 11 |
|
PMMVS2 | | | 32.52 229 | 33.92 229 | 30.88 232 | 34.15 238 | 44.70 235 | 27.79 236 | 39.69 233 | 22.21 234 | 4.31 241 | 15.73 232 | 14.13 236 | 12.45 236 | 40.11 231 | 47.00 229 | 66.88 230 | 53.54 227 |
|
E-PMN | | | 21.42 231 | 17.56 233 | 25.94 233 | 36.25 237 | 19.02 240 | 11.56 239 | 43.72 231 | 15.25 237 | 6.99 239 | 8.04 235 | 4.53 241 | 21.77 231 | 16.13 235 | 26.16 233 | 35.34 236 | 33.77 234 |
|
EMVS | | | 20.61 233 | 16.32 234 | 25.62 234 | 36.41 236 | 18.93 241 | 11.51 240 | 43.75 230 | 15.65 236 | 6.53 240 | 7.56 237 | 4.68 240 | 22.03 230 | 14.56 236 | 23.10 234 | 33.51 237 | 29.77 235 |
|
MVE | | 25.07 19 | 21.25 232 | 23.51 232 | 18.62 235 | 15.07 239 | 29.77 239 | 10.67 241 | 34.60 235 | 12.51 238 | 9.46 237 | 7.84 236 | 3.82 242 | 14.38 234 | 27.45 234 | 42.42 232 | 27.56 238 | 40.74 232 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
testmvs | | | 0.76 234 | 1.23 235 | 0.21 236 | 0.05 241 | 0.21 242 | 0.38 243 | 0.09 237 | 0.94 239 | 0.05 243 | 2.13 239 | 0.08 243 | 0.60 238 | 0.82 237 | 0.77 235 | 0.11 239 | 3.62 237 |
|
test123 | | | 0.67 235 | 1.11 236 | 0.16 237 | 0.01 242 | 0.14 243 | 0.20 244 | 0.04 239 | 0.77 240 | 0.02 244 | 2.15 238 | 0.02 244 | 0.61 237 | 0.23 238 | 0.72 237 | 0.07 241 | 3.76 236 |
|
sosnet-low-res | | | 0.00 236 | 0.00 237 | 0.00 238 | 0.00 243 | 0.00 244 | 0.00 245 | 0.00 240 | 0.00 241 | 0.00 245 | 0.00 240 | 0.00 245 | 0.00 240 | 0.00 239 | 0.00 238 | 0.00 242 | 0.00 239 |
|
sosnet | | | 0.00 236 | 0.00 237 | 0.00 238 | 0.00 243 | 0.00 244 | 0.00 245 | 0.00 240 | 0.00 241 | 0.00 245 | 0.00 240 | 0.00 245 | 0.00 240 | 0.00 239 | 0.00 238 | 0.00 242 | 0.00 239 |
|
our_test_3 | | | | | | 73.80 195 | 79.57 198 | 64.47 205 | | | | | | | | | | |
|
ambc | | | | 50.35 220 | | 55.61 228 | 59.93 227 | 48.73 225 | | 44.08 214 | 35.81 211 | 24.01 223 | 10.64 238 | 41.57 217 | 72.83 197 | 63.35 224 | 74.99 227 | 77.61 205 |
|
MTAPA | | | | | | | | | | | 91.14 2 | | 85.84 20 | | | | | |
|
MTMP | | | | | | | | | | | 90.95 3 | | 84.13 29 | | | | | |
|
Patchmatch-RL test | | | | | | | | 8.17 242 | | | | | | | | | | |
|
XVS | | | | | | 89.65 64 | 95.93 42 | 85.97 71 | | | 76.32 48 | | 82.05 39 | | | | 93.51 84 | |
|
X-MVStestdata | | | | | | 89.65 64 | 95.93 42 | 85.97 71 | | | 76.32 48 | | 82.05 39 | | | | 93.51 84 | |
|
mPP-MVS | | | | | | 95.90 28 | | | | | | | 80.22 47 | | | | | |
|
NP-MVS | | | | | | | | | | 89.55 41 | | | | | | | | |
|
Patchmtry | | | | | | | 87.41 125 | 78.32 142 | 54.14 213 | | 51.09 142 | | | | | | | |
|
DeepMVS_CX | | | | | | | 48.96 233 | 43.77 227 | 40.58 232 | 50.93 196 | 24.67 231 | 36.95 208 | 20.18 232 | 41.60 216 | 38.92 232 | | 52.37 235 | 53.31 228 |
|