SMA-MVS | | | 87.48 3 | 90.13 4 | 84.39 5 | 91.76 2 | 90.70 5 | 90.63 4 | 75.36 9 | 90.51 3 | 79.89 10 | 85.65 15 | 88.82 4 | 77.90 14 | 90.00 1 | 89.77 1 | 90.82 7 | 95.49 1 |
|
ESAPD | | | 88.46 1 | 91.07 1 | 85.41 1 | 91.73 3 | 92.08 1 | 91.91 2 | 76.73 1 | 90.14 4 | 80.33 8 | 92.75 1 | 90.44 1 | 80.73 3 | 88.97 5 | 87.63 9 | 91.01 6 | 95.48 2 |
|
APDe-MVS | | | 88.00 2 | 90.50 2 | 85.08 2 | 90.95 6 | 91.58 4 | 92.03 1 | 75.53 8 | 91.15 1 | 80.10 9 | 92.27 3 | 88.34 7 | 80.80 2 | 88.00 11 | 86.99 15 | 91.09 4 | 95.16 3 |
|
CSCG | | | 85.28 18 | 87.68 15 | 82.49 21 | 89.95 20 | 91.99 2 | 88.82 20 | 71.20 32 | 86.41 18 | 79.63 12 | 79.26 25 | 88.36 6 | 73.94 35 | 86.64 28 | 86.67 21 | 91.40 2 | 94.41 4 |
|
DeepPCF-MVS | | 79.04 1 | 85.30 17 | 88.93 8 | 81.06 27 | 88.77 30 | 90.48 6 | 85.46 41 | 73.08 23 | 90.97 2 | 73.77 32 | 84.81 18 | 85.95 15 | 77.43 19 | 88.22 8 | 87.73 7 | 87.85 67 | 94.34 5 |
|
CNVR-MVS | | | 86.36 10 | 88.19 13 | 84.23 7 | 91.33 5 | 89.84 10 | 90.34 7 | 75.56 6 | 87.36 14 | 78.97 13 | 81.19 24 | 86.76 12 | 78.74 7 | 89.30 3 | 88.58 2 | 90.45 22 | 94.33 6 |
|
ACMMP_Plus | | | 86.52 9 | 89.01 7 | 83.62 13 | 90.28 15 | 90.09 9 | 90.32 8 | 74.05 16 | 88.32 10 | 79.74 11 | 87.04 11 | 85.59 18 | 76.97 25 | 89.35 2 | 88.44 4 | 90.35 25 | 94.27 7 |
|
SteuartSystems-ACMMP | | | 85.99 12 | 88.31 12 | 83.27 17 | 90.73 8 | 89.84 10 | 90.27 9 | 74.31 11 | 84.56 26 | 75.88 25 | 87.32 10 | 85.04 19 | 77.31 20 | 89.01 4 | 88.46 3 | 91.14 3 | 93.96 8 |
Skip Steuart: Steuart Systems R&D Blog. |
HSP-MVS | | | 87.45 4 | 90.22 3 | 84.22 8 | 90.00 19 | 91.80 3 | 90.59 5 | 75.80 4 | 89.93 5 | 78.35 16 | 92.54 2 | 89.18 3 | 80.89 1 | 87.99 12 | 86.29 26 | 89.70 36 | 93.85 9 |
|
TSAR-MVS + MP. | | | 86.88 7 | 89.23 6 | 84.14 9 | 89.78 22 | 88.67 27 | 90.59 5 | 73.46 22 | 88.99 7 | 80.52 7 | 91.26 4 | 88.65 5 | 79.91 5 | 86.96 26 | 86.22 27 | 90.59 14 | 93.83 10 |
|
APD-MVS | | | 86.84 8 | 88.91 10 | 84.41 4 | 90.66 9 | 90.10 8 | 90.78 3 | 75.64 5 | 87.38 13 | 78.72 14 | 90.68 6 | 86.82 11 | 80.15 4 | 87.13 21 | 86.45 24 | 90.51 16 | 93.83 10 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
SD-MVS | | | 86.96 6 | 89.45 5 | 84.05 11 | 90.13 16 | 89.23 18 | 89.77 13 | 74.59 10 | 89.17 6 | 80.70 5 | 89.93 7 | 89.67 2 | 78.47 8 | 87.57 16 | 86.79 18 | 90.67 13 | 93.76 12 |
|
TSAR-MVS + ACMM | | | 85.10 20 | 88.81 11 | 80.77 30 | 89.55 24 | 88.53 29 | 88.59 23 | 72.55 25 | 87.39 12 | 71.90 38 | 90.95 5 | 87.55 9 | 74.57 30 | 87.08 23 | 86.54 22 | 87.47 72 | 93.67 13 |
|
DeepC-MVS | | 78.47 2 | 84.81 22 | 86.03 25 | 83.37 15 | 89.29 27 | 90.38 7 | 88.61 22 | 76.50 2 | 86.25 19 | 77.22 20 | 75.12 35 | 80.28 39 | 77.59 18 | 88.39 7 | 88.17 6 | 91.02 5 | 93.66 14 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
MP-MVS | | | 85.50 15 | 87.40 17 | 83.28 16 | 90.65 10 | 89.51 15 | 89.16 19 | 74.11 15 | 83.70 29 | 78.06 18 | 85.54 16 | 84.89 22 | 77.31 20 | 87.40 18 | 87.14 14 | 90.41 23 | 93.65 15 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
HPM-MVS++ | | | 87.09 5 | 88.92 9 | 84.95 3 | 92.61 1 | 87.91 35 | 90.23 10 | 76.06 3 | 88.85 8 | 81.20 4 | 87.33 9 | 87.93 8 | 79.47 6 | 88.59 6 | 88.23 5 | 90.15 29 | 93.60 16 |
|
NCCC | | | 85.34 16 | 86.59 21 | 83.88 12 | 91.48 4 | 88.88 21 | 89.79 12 | 75.54 7 | 86.67 17 | 77.94 19 | 76.55 31 | 84.99 20 | 78.07 12 | 88.04 9 | 87.68 8 | 90.46 21 | 93.31 17 |
|
MCST-MVS | | | 85.13 19 | 86.62 20 | 83.39 14 | 90.55 12 | 89.82 12 | 89.29 17 | 73.89 19 | 84.38 27 | 76.03 24 | 79.01 27 | 85.90 16 | 78.47 8 | 87.81 13 | 86.11 29 | 92.11 1 | 93.29 18 |
|
CP-MVS | | | 84.74 23 | 86.43 23 | 82.77 20 | 89.48 25 | 88.13 34 | 88.64 21 | 73.93 18 | 84.92 21 | 76.77 22 | 81.94 22 | 83.50 25 | 77.29 22 | 86.92 27 | 86.49 23 | 90.49 17 | 93.14 19 |
|
HFP-MVS | | | 86.15 11 | 87.95 14 | 84.06 10 | 90.80 7 | 89.20 19 | 89.62 15 | 74.26 12 | 87.52 11 | 80.63 6 | 86.82 12 | 84.19 24 | 78.22 10 | 87.58 15 | 87.19 13 | 90.81 8 | 93.13 20 |
|
ACMMPR | | | 85.52 14 | 87.53 16 | 83.17 18 | 90.13 16 | 89.27 16 | 89.30 16 | 73.97 17 | 86.89 16 | 77.14 21 | 86.09 13 | 83.18 27 | 77.74 16 | 87.42 17 | 87.20 12 | 90.77 9 | 92.63 21 |
|
zzz-MVS | | | 85.71 13 | 86.88 19 | 84.34 6 | 90.54 13 | 87.11 39 | 89.77 13 | 74.17 14 | 88.54 9 | 83.08 2 | 78.60 28 | 86.10 14 | 78.11 11 | 87.80 14 | 87.46 11 | 90.35 25 | 92.56 22 |
|
TSAR-MVS + GP. | | | 83.69 26 | 86.58 22 | 80.32 31 | 85.14 49 | 86.96 40 | 84.91 45 | 70.25 36 | 84.71 25 | 73.91 31 | 85.16 17 | 85.63 17 | 77.92 13 | 85.44 36 | 85.71 32 | 89.77 33 | 92.45 23 |
|
DeepC-MVS_fast | | 78.24 3 | 84.27 25 | 85.50 27 | 82.85 19 | 90.46 14 | 89.24 17 | 87.83 28 | 74.24 13 | 84.88 22 | 76.23 23 | 75.26 34 | 81.05 37 | 77.62 17 | 88.02 10 | 87.62 10 | 90.69 12 | 92.41 24 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
X-MVS | | | 83.23 28 | 85.20 29 | 80.92 29 | 89.71 23 | 88.68 24 | 88.21 27 | 73.60 20 | 82.57 33 | 71.81 41 | 77.07 29 | 81.92 31 | 71.72 50 | 86.98 25 | 86.86 16 | 90.47 18 | 92.36 25 |
|
CPTT-MVS | | | 81.77 33 | 83.10 34 | 80.21 32 | 85.93 45 | 86.45 45 | 87.72 29 | 70.98 33 | 82.54 34 | 71.53 44 | 74.23 40 | 81.49 34 | 76.31 27 | 82.85 57 | 81.87 53 | 88.79 52 | 92.26 26 |
|
ACMMP | | | 83.42 27 | 85.27 28 | 81.26 26 | 88.47 31 | 88.49 30 | 88.31 26 | 72.09 27 | 83.42 30 | 72.77 36 | 82.65 20 | 78.22 43 | 75.18 29 | 86.24 33 | 85.76 31 | 90.74 10 | 92.13 27 |
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 |
train_agg | | | 84.86 21 | 87.21 18 | 82.11 23 | 90.59 11 | 85.47 50 | 89.81 11 | 73.55 21 | 83.95 28 | 73.30 33 | 89.84 8 | 87.23 10 | 75.61 28 | 86.47 30 | 85.46 34 | 89.78 32 | 92.06 28 |
|
PGM-MVS | | | 84.42 24 | 86.29 24 | 82.23 22 | 90.04 18 | 88.82 23 | 89.23 18 | 71.74 30 | 82.82 32 | 74.61 28 | 84.41 19 | 82.09 29 | 77.03 24 | 87.13 21 | 86.73 20 | 90.73 11 | 92.06 28 |
|
HQP-MVS | | | 81.19 36 | 83.27 33 | 78.76 41 | 87.40 35 | 85.45 51 | 86.95 30 | 70.47 35 | 81.31 37 | 66.91 59 | 79.24 26 | 76.63 47 | 71.67 51 | 84.43 43 | 83.78 44 | 89.19 45 | 92.05 30 |
|
PHI-MVS | | | 82.36 31 | 85.89 26 | 78.24 44 | 86.40 42 | 89.52 14 | 85.52 39 | 69.52 43 | 82.38 35 | 65.67 61 | 81.35 23 | 82.36 28 | 73.07 40 | 87.31 20 | 86.76 19 | 89.24 43 | 91.56 31 |
|
3Dnovator+ | | 75.73 4 | 82.40 30 | 82.76 35 | 81.97 24 | 88.02 32 | 89.67 13 | 86.60 32 | 71.48 31 | 81.28 38 | 78.18 17 | 64.78 70 | 77.96 45 | 77.13 23 | 87.32 19 | 86.83 17 | 90.41 23 | 91.48 32 |
|
MSLP-MVS++ | | | 82.09 32 | 82.66 36 | 81.42 25 | 87.03 38 | 87.22 38 | 85.82 37 | 70.04 37 | 80.30 39 | 78.66 15 | 68.67 57 | 81.04 38 | 77.81 15 | 85.19 39 | 84.88 39 | 89.19 45 | 91.31 33 |
|
CDPH-MVS | | | 82.64 29 | 85.03 30 | 79.86 34 | 89.41 26 | 88.31 31 | 88.32 25 | 71.84 29 | 80.11 40 | 67.47 56 | 82.09 21 | 81.44 35 | 71.85 48 | 85.89 35 | 86.15 28 | 90.24 27 | 91.25 34 |
|
PCF-MVS | | 73.28 6 | 79.42 44 | 80.41 47 | 78.26 43 | 84.88 55 | 88.17 32 | 86.08 34 | 69.85 38 | 75.23 52 | 68.43 51 | 68.03 60 | 78.38 42 | 71.76 49 | 81.26 71 | 80.65 72 | 88.56 55 | 91.18 35 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
MVS_0304 | | | 81.73 34 | 83.86 31 | 79.26 37 | 86.22 44 | 89.18 20 | 86.41 33 | 67.15 57 | 75.28 50 | 70.75 48 | 74.59 37 | 83.49 26 | 74.42 32 | 87.05 24 | 86.34 25 | 90.58 15 | 91.08 36 |
|
PVSNet_Blended_VisFu | | | 76.57 56 | 77.90 55 | 75.02 57 | 80.56 70 | 86.58 44 | 79.24 66 | 66.18 61 | 64.81 79 | 68.18 53 | 65.61 66 | 71.45 63 | 67.05 68 | 84.16 44 | 81.80 54 | 88.90 49 | 90.92 37 |
|
LGP-MVS_train | | | 79.83 39 | 81.22 42 | 78.22 45 | 86.28 43 | 85.36 53 | 86.76 31 | 69.59 41 | 77.34 45 | 65.14 63 | 75.68 33 | 70.79 66 | 71.37 53 | 84.60 41 | 84.01 42 | 90.18 28 | 90.74 38 |
|
abl_6 | | | | | 79.05 38 | 87.27 36 | 88.85 22 | 83.62 50 | 68.25 49 | 81.68 36 | 72.94 35 | 73.79 41 | 84.45 23 | 72.55 43 | | | 89.66 38 | 90.64 39 |
|
canonicalmvs | | | 79.16 47 | 82.37 38 | 75.41 55 | 82.33 62 | 86.38 46 | 80.80 57 | 63.18 81 | 82.90 31 | 67.34 57 | 72.79 43 | 76.07 49 | 69.62 59 | 83.46 53 | 84.41 41 | 89.20 44 | 90.60 40 |
|
ACMP | | 73.23 7 | 79.79 40 | 80.53 45 | 78.94 39 | 85.61 47 | 85.68 48 | 85.61 38 | 69.59 41 | 77.33 46 | 71.00 47 | 74.45 38 | 69.16 75 | 71.88 46 | 83.15 54 | 83.37 47 | 89.92 31 | 90.57 41 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
CANet | | | 81.62 35 | 83.41 32 | 79.53 36 | 87.06 37 | 88.59 28 | 85.47 40 | 67.96 53 | 76.59 48 | 74.05 29 | 74.69 36 | 81.98 30 | 72.98 41 | 86.14 34 | 85.47 33 | 89.68 37 | 90.42 42 |
|
QAPM | | | 78.47 51 | 80.22 49 | 76.43 52 | 85.03 51 | 86.75 43 | 80.62 58 | 66.00 64 | 73.77 57 | 65.35 62 | 65.54 68 | 78.02 44 | 72.69 42 | 83.71 48 | 83.36 48 | 88.87 51 | 90.41 43 |
|
DELS-MVS | | | 79.15 48 | 81.07 43 | 76.91 50 | 83.54 56 | 87.31 37 | 84.45 46 | 64.92 71 | 69.98 61 | 69.34 50 | 71.62 47 | 76.26 48 | 69.84 58 | 86.57 29 | 85.90 30 | 89.39 41 | 89.88 44 |
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 |
OPM-MVS | | | 79.68 43 | 79.28 52 | 80.15 33 | 87.99 33 | 86.77 42 | 88.52 24 | 72.72 24 | 64.55 82 | 67.65 55 | 67.87 61 | 74.33 56 | 74.31 33 | 86.37 32 | 85.25 36 | 89.73 35 | 89.81 45 |
|
MVS_111021_HR | | | 80.13 38 | 81.46 40 | 78.58 42 | 85.77 46 | 85.17 54 | 83.45 51 | 69.28 44 | 74.08 56 | 70.31 49 | 74.31 39 | 75.26 53 | 73.13 39 | 86.46 31 | 85.15 37 | 89.53 39 | 89.81 45 |
|
anonymousdsp | | | 65.28 153 | 67.98 138 | 62.13 169 | 58.73 216 | 73.98 173 | 67.10 176 | 50.69 198 | 48.41 201 | 47.66 153 | 54.27 136 | 52.75 167 | 61.45 119 | 76.71 147 | 80.20 77 | 87.13 82 | 89.53 47 |
|
3Dnovator | | 73.76 5 | 79.75 41 | 80.52 46 | 78.84 40 | 84.94 54 | 87.35 36 | 84.43 47 | 65.54 67 | 78.29 44 | 73.97 30 | 63.00 76 | 75.62 52 | 74.07 34 | 85.00 40 | 85.34 35 | 90.11 30 | 89.04 48 |
|
EPP-MVSNet | | | 74.00 66 | 77.41 61 | 70.02 98 | 80.53 71 | 83.91 60 | 74.99 117 | 62.68 98 | 65.06 77 | 49.77 143 | 68.68 56 | 72.09 62 | 63.06 107 | 82.49 59 | 80.73 64 | 89.12 47 | 88.91 49 |
|
OMC-MVS | | | 80.26 37 | 82.59 37 | 77.54 47 | 83.04 57 | 85.54 49 | 83.25 52 | 65.05 70 | 87.32 15 | 72.42 37 | 72.04 45 | 78.97 41 | 73.30 38 | 83.86 46 | 81.60 56 | 88.15 58 | 88.83 50 |
|
UGNet | | | 72.78 71 | 77.67 57 | 67.07 135 | 71.65 172 | 83.24 65 | 75.20 111 | 63.62 78 | 64.93 78 | 56.72 101 | 71.82 46 | 73.30 57 | 49.02 181 | 81.02 74 | 80.70 70 | 86.22 117 | 88.67 51 |
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 |
EPNet | | | 79.08 49 | 80.62 44 | 77.28 48 | 88.90 29 | 83.17 67 | 83.65 49 | 72.41 26 | 74.41 53 | 67.15 58 | 76.78 30 | 74.37 55 | 64.43 101 | 83.70 49 | 83.69 45 | 87.15 78 | 88.19 52 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
Vis-MVSNet | | | 72.77 72 | 77.20 63 | 67.59 122 | 74.19 148 | 84.01 59 | 76.61 107 | 61.69 114 | 60.62 107 | 50.61 137 | 70.25 51 | 71.31 65 | 55.57 160 | 83.85 47 | 82.28 50 | 86.90 92 | 88.08 53 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
MVS_111021_LR | | | 78.13 53 | 79.85 51 | 76.13 53 | 81.12 66 | 81.50 74 | 80.28 59 | 65.25 68 | 76.09 49 | 71.32 46 | 76.49 32 | 72.87 60 | 72.21 44 | 82.79 58 | 81.29 58 | 86.59 113 | 87.91 54 |
|
ACMM | | 72.26 8 | 78.86 50 | 78.13 54 | 79.71 35 | 86.89 39 | 83.40 64 | 86.02 35 | 70.50 34 | 75.28 50 | 71.49 45 | 63.01 75 | 69.26 74 | 73.57 37 | 84.11 45 | 83.98 43 | 89.76 34 | 87.84 55 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
OpenMVS | | 70.44 10 | 76.15 59 | 76.82 66 | 75.37 56 | 85.01 52 | 84.79 56 | 78.99 71 | 62.07 108 | 71.27 60 | 67.88 54 | 57.91 100 | 72.36 61 | 70.15 57 | 82.23 60 | 81.41 57 | 88.12 60 | 87.78 56 |
|
MAR-MVS | | | 79.21 46 | 80.32 48 | 77.92 46 | 87.46 34 | 88.15 33 | 83.95 48 | 67.48 56 | 74.28 54 | 68.25 52 | 64.70 71 | 77.04 46 | 72.17 45 | 85.42 37 | 85.00 38 | 88.22 56 | 87.62 57 |
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 |
Effi-MVS+ | | | 75.28 62 | 76.20 67 | 74.20 63 | 81.15 65 | 83.24 65 | 81.11 55 | 63.13 83 | 66.37 68 | 60.27 77 | 64.30 72 | 68.88 79 | 70.93 56 | 81.56 64 | 81.69 55 | 88.61 53 | 87.35 58 |
|
IB-MVS | | 66.94 12 | 71.21 79 | 71.66 84 | 70.68 80 | 79.18 77 | 82.83 69 | 72.61 146 | 61.77 112 | 59.66 113 | 63.44 70 | 53.26 154 | 59.65 104 | 59.16 130 | 76.78 146 | 82.11 52 | 87.90 64 | 87.33 59 |
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 |
CNLPA | | | 77.20 55 | 77.54 58 | 76.80 51 | 82.63 59 | 84.31 58 | 79.77 62 | 64.64 72 | 85.17 20 | 73.18 34 | 56.37 108 | 69.81 71 | 74.53 31 | 81.12 73 | 78.69 91 | 86.04 128 | 87.29 60 |
|
PVSNet_BlendedMVS | | | 76.21 57 | 77.52 59 | 74.69 60 | 79.46 75 | 83.79 61 | 77.50 100 | 64.34 75 | 69.88 62 | 71.88 39 | 68.54 58 | 70.42 68 | 67.05 68 | 83.48 51 | 79.63 80 | 87.89 65 | 86.87 61 |
|
PVSNet_Blended | | | 76.21 57 | 77.52 59 | 74.69 60 | 79.46 75 | 83.79 61 | 77.50 100 | 64.34 75 | 69.88 62 | 71.88 39 | 68.54 58 | 70.42 68 | 67.05 68 | 83.48 51 | 79.63 80 | 87.89 65 | 86.87 61 |
|
AdaColmap | | | 79.74 42 | 78.62 53 | 81.05 28 | 89.23 28 | 86.06 47 | 84.95 44 | 71.96 28 | 79.39 43 | 75.51 26 | 63.16 74 | 68.84 80 | 76.51 26 | 83.55 50 | 82.85 49 | 88.13 59 | 86.46 63 |
|
IS_MVSNet | | | 73.33 67 | 77.34 62 | 68.65 111 | 81.29 64 | 83.47 63 | 74.45 120 | 63.58 79 | 65.75 74 | 48.49 146 | 67.11 65 | 70.61 67 | 54.63 165 | 84.51 42 | 83.58 46 | 89.48 40 | 86.34 64 |
|
TSAR-MVS + COLMAP | | | 78.34 52 | 81.64 39 | 74.48 62 | 80.13 73 | 85.01 55 | 81.73 53 | 65.93 66 | 84.75 24 | 61.68 72 | 85.79 14 | 66.27 86 | 71.39 52 | 82.91 56 | 80.78 63 | 86.01 129 | 85.98 65 |
|
DI_MVS_plusplus_trai | | | 75.13 63 | 76.12 68 | 73.96 64 | 78.18 83 | 81.55 73 | 80.97 56 | 62.54 102 | 68.59 65 | 65.13 64 | 61.43 77 | 74.81 54 | 69.32 61 | 81.01 75 | 79.59 82 | 87.64 70 | 85.89 66 |
|
v144192 | | | 69.34 111 | 68.68 127 | 70.12 96 | 74.06 151 | 80.54 88 | 78.08 96 | 60.54 127 | 54.99 169 | 54.13 114 | 52.92 161 | 52.80 166 | 66.73 76 | 77.13 140 | 76.72 138 | 87.15 78 | 85.63 67 |
|
v1921920 | | | 69.03 114 | 68.32 133 | 69.86 99 | 74.03 152 | 80.37 93 | 77.55 98 | 60.25 135 | 54.62 170 | 53.59 119 | 52.36 172 | 51.50 181 | 66.75 75 | 77.17 139 | 76.69 143 | 86.96 91 | 85.56 68 |
|
v52 | | | 65.23 154 | 66.24 157 | 64.06 157 | 61.94 206 | 76.42 148 | 72.06 151 | 54.30 180 | 49.94 195 | 50.04 140 | 47.41 192 | 52.42 168 | 60.23 127 | 75.71 154 | 76.22 149 | 85.78 137 | 85.56 68 |
|
V4 | | | 65.23 154 | 66.23 158 | 64.06 157 | 61.94 206 | 76.42 148 | 72.05 152 | 54.31 179 | 49.91 197 | 50.06 139 | 47.42 191 | 52.40 169 | 60.24 126 | 75.71 154 | 76.22 149 | 85.78 137 | 85.56 68 |
|
v1192 | | | 69.50 108 | 68.83 120 | 70.29 93 | 74.49 146 | 80.92 82 | 78.55 86 | 60.54 127 | 55.04 167 | 54.21 113 | 52.79 165 | 52.33 170 | 66.92 73 | 77.88 118 | 77.35 119 | 87.04 88 | 85.51 71 |
|
MVS_Test | | | 75.37 61 | 77.13 64 | 73.31 66 | 79.07 78 | 81.32 76 | 79.98 60 | 60.12 141 | 69.72 64 | 64.11 67 | 70.53 49 | 73.22 58 | 68.90 62 | 80.14 91 | 79.48 86 | 87.67 69 | 85.50 72 |
|
Effi-MVS+-dtu | | | 71.82 75 | 71.86 83 | 71.78 69 | 78.77 79 | 80.47 92 | 78.55 86 | 61.67 115 | 60.68 105 | 55.49 108 | 58.48 93 | 65.48 88 | 68.85 63 | 76.92 143 | 75.55 159 | 87.35 74 | 85.46 73 |
|
CLD-MVS | | | 79.35 45 | 81.23 41 | 77.16 49 | 85.01 52 | 86.92 41 | 85.87 36 | 60.89 122 | 80.07 42 | 75.35 27 | 72.96 42 | 73.21 59 | 68.43 66 | 85.41 38 | 84.63 40 | 87.41 73 | 85.44 74 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
v1240 | | | 68.64 119 | 67.89 140 | 69.51 103 | 73.89 154 | 80.26 96 | 76.73 105 | 59.97 143 | 53.43 179 | 53.08 122 | 51.82 175 | 50.84 184 | 66.62 77 | 76.79 145 | 76.77 126 | 86.78 105 | 85.34 75 |
|
MVSTER | | | 72.06 74 | 74.24 72 | 69.51 103 | 70.39 180 | 75.97 155 | 76.91 104 | 57.36 170 | 64.64 81 | 61.39 74 | 68.86 54 | 63.76 92 | 63.46 104 | 81.44 65 | 79.70 79 | 87.56 71 | 85.31 76 |
|
TAPA-MVS | | 71.42 9 | 77.69 54 | 80.05 50 | 74.94 58 | 80.68 69 | 84.52 57 | 81.36 54 | 63.14 82 | 84.77 23 | 64.82 65 | 68.72 55 | 75.91 51 | 71.86 47 | 81.62 62 | 79.55 84 | 87.80 68 | 85.24 77 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
IterMVS-LS | | | 71.69 76 | 72.82 79 | 70.37 92 | 77.54 99 | 76.34 151 | 75.13 115 | 60.46 129 | 61.53 101 | 57.57 90 | 64.89 69 | 67.33 83 | 66.04 93 | 77.09 142 | 77.37 118 | 85.48 145 | 85.18 78 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
v1144 | | | 69.93 100 | 69.36 112 | 70.61 82 | 74.89 126 | 80.93 80 | 79.11 69 | 60.64 124 | 55.97 158 | 55.31 110 | 53.85 147 | 54.14 145 | 66.54 78 | 78.10 115 | 77.44 116 | 87.14 81 | 85.09 79 |
|
v10 | | | 70.22 91 | 69.76 101 | 70.74 77 | 74.79 133 | 80.30 95 | 79.22 67 | 59.81 144 | 57.71 129 | 56.58 104 | 54.22 142 | 55.31 134 | 66.95 71 | 78.28 113 | 77.47 114 | 87.12 85 | 85.07 80 |
|
v7n | | | 67.05 146 | 66.94 151 | 67.17 132 | 72.35 165 | 78.97 102 | 73.26 144 | 58.88 153 | 51.16 191 | 50.90 134 | 48.21 187 | 50.11 188 | 60.96 120 | 77.70 121 | 77.38 117 | 86.68 110 | 85.05 81 |
|
v7 | | | 70.33 89 | 69.87 97 | 70.88 71 | 74.79 133 | 81.04 79 | 79.22 67 | 60.57 126 | 57.70 130 | 56.65 103 | 54.23 140 | 55.29 136 | 66.95 71 | 78.28 113 | 77.47 114 | 87.12 85 | 85.05 81 |
|
V42 | | | 68.76 118 | 69.63 102 | 67.74 118 | 64.93 202 | 78.01 118 | 78.30 92 | 56.48 174 | 58.65 122 | 56.30 105 | 54.26 138 | 57.03 119 | 64.85 100 | 77.47 131 | 77.01 123 | 85.60 143 | 84.96 83 |
|
UniMVSNet (Re) | | | 69.53 106 | 71.90 82 | 66.76 141 | 76.42 107 | 80.93 80 | 72.59 147 | 68.03 52 | 61.75 99 | 41.68 182 | 58.34 97 | 57.23 118 | 53.27 172 | 79.53 100 | 80.62 73 | 88.57 54 | 84.90 84 |
|
Fast-Effi-MVS+ | | | 73.11 70 | 73.66 73 | 72.48 68 | 77.72 97 | 80.88 83 | 78.55 86 | 58.83 160 | 65.19 76 | 60.36 76 | 59.98 84 | 62.42 97 | 71.22 54 | 81.66 61 | 80.61 74 | 88.20 57 | 84.88 85 |
|
CANet_DTU | | | 73.29 68 | 76.96 65 | 69.00 107 | 77.04 104 | 82.06 72 | 79.49 65 | 56.30 175 | 67.85 66 | 53.29 121 | 71.12 48 | 70.37 70 | 61.81 117 | 81.59 63 | 80.96 61 | 86.09 123 | 84.73 86 |
|
diffmvs | | | 73.13 69 | 75.65 69 | 70.19 95 | 74.07 150 | 77.17 132 | 78.24 94 | 57.45 168 | 72.44 59 | 64.02 68 | 69.05 53 | 75.92 50 | 64.86 99 | 75.18 159 | 75.27 162 | 82.47 171 | 84.53 87 |
|
FC-MVSNet-train | | | 72.60 73 | 75.07 71 | 69.71 102 | 81.10 67 | 78.79 111 | 73.74 137 | 65.23 69 | 66.10 71 | 53.34 120 | 70.36 50 | 63.40 94 | 56.92 145 | 81.44 65 | 80.96 61 | 87.93 63 | 84.46 88 |
|
ACMH | | 65.37 14 | 70.71 82 | 70.00 93 | 71.54 70 | 82.51 61 | 82.47 71 | 77.78 97 | 68.13 50 | 56.19 155 | 46.06 163 | 54.30 135 | 51.20 182 | 68.68 64 | 80.66 77 | 80.72 65 | 86.07 124 | 84.45 89 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
v748 | | | 65.12 156 | 65.24 170 | 64.98 151 | 69.77 183 | 76.45 147 | 69.47 162 | 57.06 172 | 49.93 196 | 50.70 135 | 47.87 190 | 49.50 192 | 57.14 142 | 73.64 169 | 75.18 163 | 85.75 139 | 84.14 90 |
|
UniMVSNet_NR-MVSNet | | | 70.59 83 | 72.19 81 | 68.72 109 | 77.72 97 | 80.72 84 | 73.81 135 | 69.65 40 | 61.99 95 | 43.23 174 | 60.54 80 | 57.50 110 | 58.57 131 | 79.56 99 | 81.07 60 | 89.34 42 | 83.97 91 |
|
DU-MVS | | | 69.63 101 | 70.91 87 | 68.13 115 | 75.99 114 | 79.54 98 | 73.81 135 | 69.20 45 | 61.20 103 | 43.23 174 | 58.52 91 | 53.50 152 | 58.57 131 | 79.22 103 | 80.45 75 | 87.97 62 | 83.97 91 |
|
ACMH+ | | 66.54 13 | 71.36 78 | 70.09 92 | 72.85 67 | 82.59 60 | 81.13 78 | 78.56 85 | 68.04 51 | 61.55 100 | 52.52 127 | 51.50 176 | 54.14 145 | 68.56 65 | 78.85 107 | 79.50 85 | 86.82 99 | 83.94 93 |
|
v8 | | | 70.23 90 | 69.86 99 | 70.67 81 | 74.69 140 | 79.82 97 | 78.79 79 | 59.18 150 | 58.80 119 | 58.20 85 | 55.00 122 | 57.33 113 | 66.31 87 | 77.51 129 | 76.71 141 | 86.82 99 | 83.88 94 |
|
v6 | | | 70.35 86 | 69.94 94 | 70.83 72 | 74.68 141 | 80.62 85 | 78.81 76 | 60.16 139 | 58.81 118 | 58.17 86 | 55.01 121 | 57.31 115 | 66.32 86 | 77.53 126 | 76.73 137 | 86.82 99 | 83.62 95 |
|
v1neww | | | 70.34 87 | 69.93 95 | 70.82 73 | 74.68 141 | 80.61 86 | 78.80 77 | 60.17 136 | 58.74 120 | 58.10 87 | 55.00 122 | 57.28 116 | 66.33 84 | 77.53 126 | 76.74 133 | 86.82 99 | 83.61 96 |
|
v7new | | | 70.34 87 | 69.93 95 | 70.82 73 | 74.68 141 | 80.61 86 | 78.80 77 | 60.17 136 | 58.74 120 | 58.10 87 | 55.00 122 | 57.28 116 | 66.33 84 | 77.53 126 | 76.74 133 | 86.82 99 | 83.61 96 |
|
v1 | | | 69.97 97 | 69.45 108 | 70.59 83 | 74.78 135 | 80.51 89 | 78.84 74 | 60.30 131 | 56.98 134 | 56.81 98 | 54.69 130 | 56.29 126 | 65.91 96 | 77.37 133 | 76.71 141 | 86.89 94 | 83.59 98 |
|
NR-MVSNet | | | 68.79 117 | 70.56 89 | 66.71 143 | 77.48 100 | 79.54 98 | 73.52 140 | 69.20 45 | 61.20 103 | 39.76 185 | 58.52 91 | 50.11 188 | 51.37 176 | 80.26 89 | 80.71 69 | 88.97 48 | 83.59 98 |
|
divwei89l23v2f112 | | | 69.97 97 | 69.44 109 | 70.58 85 | 74.78 135 | 80.50 90 | 78.85 72 | 60.30 131 | 56.97 136 | 56.75 99 | 54.67 132 | 56.27 127 | 65.92 95 | 77.37 133 | 76.72 138 | 86.88 95 | 83.58 100 |
|
v1141 | | | 69.96 99 | 69.44 109 | 70.58 85 | 74.78 135 | 80.50 90 | 78.85 72 | 60.30 131 | 56.95 137 | 56.74 100 | 54.68 131 | 56.26 128 | 65.93 94 | 77.38 132 | 76.72 138 | 86.88 95 | 83.57 101 |
|
v2v482 | | | 70.05 95 | 69.46 106 | 70.74 77 | 74.62 145 | 80.32 94 | 79.00 70 | 60.62 125 | 57.41 131 | 56.89 96 | 55.43 115 | 55.14 137 | 66.39 80 | 77.25 138 | 77.14 121 | 86.90 92 | 83.57 101 |
|
CHOSEN 1792x2688 | | | 69.20 113 | 69.26 113 | 69.13 105 | 76.86 105 | 78.93 105 | 77.27 102 | 60.12 141 | 61.86 97 | 54.42 112 | 42.54 201 | 61.61 98 | 66.91 74 | 78.55 110 | 78.14 103 | 79.23 186 | 83.23 103 |
|
v11 | | | 69.37 110 | 68.65 128 | 70.20 94 | 74.87 129 | 76.97 141 | 78.29 93 | 58.55 164 | 56.38 152 | 56.04 106 | 54.02 144 | 54.98 138 | 66.47 79 | 78.30 112 | 76.91 124 | 86.97 90 | 83.02 104 |
|
v13 | | | 69.52 107 | 68.76 124 | 70.41 90 | 74.88 127 | 77.02 140 | 78.52 90 | 58.86 154 | 56.61 149 | 56.91 95 | 54.00 145 | 56.17 130 | 66.11 92 | 77.93 116 | 76.74 133 | 87.21 76 | 82.83 105 |
|
v17 | | | 70.03 96 | 69.43 111 | 70.72 79 | 74.75 138 | 77.09 133 | 78.78 81 | 58.85 156 | 59.53 115 | 58.72 83 | 54.87 127 | 57.39 112 | 66.38 81 | 77.60 125 | 76.75 131 | 86.83 98 | 82.80 106 |
|
v12 | | | 69.54 105 | 68.79 122 | 70.41 90 | 74.88 127 | 77.03 138 | 78.54 89 | 58.85 156 | 56.71 141 | 56.87 97 | 54.13 143 | 56.23 129 | 66.15 88 | 77.89 117 | 76.74 133 | 87.17 77 | 82.80 106 |
|
v16 | | | 70.07 94 | 69.46 106 | 70.79 75 | 74.74 139 | 77.08 134 | 78.79 79 | 58.86 154 | 59.75 112 | 59.15 80 | 54.87 127 | 57.33 113 | 66.38 81 | 77.61 124 | 76.77 126 | 86.81 104 | 82.79 108 |
|
V9 | | | 69.58 104 | 68.83 120 | 70.46 87 | 74.85 130 | 77.04 136 | 78.65 84 | 58.85 156 | 56.83 140 | 57.12 93 | 54.26 138 | 56.31 124 | 66.14 90 | 77.83 119 | 76.76 128 | 87.13 82 | 82.79 108 |
|
v18 | | | 70.10 93 | 69.52 104 | 70.77 76 | 74.66 144 | 77.06 135 | 78.84 74 | 58.84 159 | 60.01 111 | 59.23 79 | 55.06 120 | 57.47 111 | 66.34 83 | 77.50 130 | 76.75 131 | 86.71 106 | 82.77 110 |
|
V14 | | | 69.59 103 | 68.86 119 | 70.45 89 | 74.83 131 | 77.04 136 | 78.70 83 | 58.83 160 | 56.95 137 | 57.08 94 | 54.41 134 | 56.34 123 | 66.15 88 | 77.77 120 | 76.76 128 | 87.08 87 | 82.74 111 |
|
TranMVSNet+NR-MVSNet | | | 69.25 112 | 70.81 88 | 67.43 123 | 77.23 103 | 79.46 100 | 73.48 141 | 69.66 39 | 60.43 108 | 39.56 186 | 58.82 90 | 53.48 154 | 55.74 158 | 79.59 97 | 81.21 59 | 88.89 50 | 82.70 112 |
|
v15 | | | 69.61 102 | 68.88 118 | 70.46 87 | 74.81 132 | 77.03 138 | 78.75 82 | 58.83 160 | 57.06 133 | 57.18 92 | 54.55 133 | 56.37 122 | 66.13 91 | 77.70 121 | 76.76 128 | 87.03 89 | 82.69 113 |
|
Baseline_NR-MVSNet | | | 67.53 140 | 68.77 123 | 66.09 145 | 75.99 114 | 74.75 169 | 72.43 148 | 68.41 48 | 61.33 102 | 38.33 190 | 51.31 177 | 54.13 147 | 56.03 154 | 79.22 103 | 78.19 101 | 85.37 146 | 82.45 114 |
|
LTVRE_ROB | | 59.44 16 | 61.82 189 | 62.64 190 | 60.87 178 | 72.83 164 | 77.19 131 | 64.37 192 | 58.97 151 | 33.56 227 | 28.00 210 | 52.59 170 | 42.21 211 | 63.93 103 | 74.52 161 | 76.28 146 | 77.15 193 | 82.13 115 |
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 |
Anonymous20240521 | | | 62.94 174 | 66.99 150 | 58.22 190 | 74.13 149 | 76.58 145 | 59.13 208 | 61.72 113 | 52.53 182 | 32.20 204 | 52.87 162 | 54.34 143 | 36.44 207 | 73.90 166 | 76.66 144 | 85.71 141 | 82.02 116 |
|
GBi-Net | | | 70.78 80 | 73.37 76 | 67.76 116 | 72.95 160 | 78.00 119 | 75.15 112 | 62.72 93 | 64.13 83 | 51.44 129 | 58.37 94 | 69.02 76 | 57.59 137 | 81.33 68 | 80.72 65 | 86.70 107 | 82.02 116 |
|
test1 | | | 70.78 80 | 73.37 76 | 67.76 116 | 72.95 160 | 78.00 119 | 75.15 112 | 62.72 93 | 64.13 83 | 51.44 129 | 58.37 94 | 69.02 76 | 57.59 137 | 81.33 68 | 80.72 65 | 86.70 107 | 82.02 116 |
|
FMVSNet2 | | | 70.39 85 | 72.67 80 | 67.72 119 | 72.95 160 | 78.00 119 | 75.15 112 | 62.69 97 | 63.29 88 | 51.25 133 | 55.64 112 | 68.49 82 | 57.59 137 | 80.91 76 | 80.35 76 | 86.70 107 | 82.02 116 |
|
CP-MVSNet | | | 62.68 176 | 65.49 169 | 59.40 187 | 71.84 168 | 75.34 159 | 62.87 198 | 67.04 58 | 52.64 181 | 27.19 211 | 53.38 151 | 48.15 197 | 41.40 200 | 71.26 182 | 75.68 155 | 86.07 124 | 82.00 120 |
|
FMVSNet1 | | | 68.84 116 | 70.47 91 | 66.94 137 | 71.35 177 | 77.68 127 | 74.71 119 | 62.35 107 | 56.93 139 | 49.94 142 | 50.01 182 | 64.59 90 | 57.07 143 | 81.33 68 | 80.72 65 | 86.25 116 | 82.00 120 |
|
PS-CasMVS | | | 62.38 182 | 65.06 173 | 59.25 188 | 71.73 169 | 75.21 166 | 62.77 199 | 66.99 59 | 51.94 188 | 26.96 212 | 52.00 174 | 47.52 200 | 41.06 201 | 71.16 185 | 75.60 158 | 85.97 133 | 81.97 122 |
|
UA-Net | | | 74.47 64 | 77.80 56 | 70.59 83 | 85.33 48 | 85.40 52 | 73.54 139 | 65.98 65 | 60.65 106 | 56.00 107 | 72.11 44 | 79.15 40 | 54.63 165 | 83.13 55 | 82.25 51 | 88.04 61 | 81.92 123 |
|
FMVSNet3 | | | 70.49 84 | 72.90 78 | 67.67 120 | 72.88 163 | 77.98 122 | 74.96 118 | 62.72 93 | 64.13 83 | 51.44 129 | 58.37 94 | 69.02 76 | 57.43 140 | 79.43 101 | 79.57 83 | 86.59 113 | 81.81 124 |
|
PLC | | 68.99 11 | 75.68 60 | 75.31 70 | 76.12 54 | 82.94 58 | 81.26 77 | 79.94 61 | 66.10 62 | 77.15 47 | 66.86 60 | 59.13 89 | 68.53 81 | 73.73 36 | 80.38 83 | 79.04 88 | 87.13 82 | 81.68 125 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
Fast-Effi-MVS+-dtu | | | 68.34 121 | 69.47 105 | 67.01 136 | 75.15 122 | 77.97 124 | 77.12 103 | 55.40 178 | 57.87 124 | 46.68 160 | 56.17 111 | 60.39 100 | 62.36 110 | 76.32 151 | 76.25 148 | 85.35 147 | 81.34 126 |
|
v148 | | | 67.85 130 | 67.53 142 | 68.23 113 | 73.25 158 | 77.57 130 | 74.26 131 | 57.36 170 | 55.70 160 | 57.45 91 | 53.53 149 | 55.42 133 | 61.96 113 | 75.23 158 | 73.92 169 | 85.08 150 | 81.32 127 |
|
EG-PatchMatch MVS | | | 67.24 144 | 66.94 151 | 67.60 121 | 78.73 80 | 81.35 75 | 73.28 143 | 59.49 146 | 46.89 206 | 51.42 132 | 43.65 198 | 53.49 153 | 55.50 161 | 81.38 67 | 80.66 71 | 87.15 78 | 81.17 128 |
|
WR-MVS | | | 63.03 172 | 67.40 145 | 57.92 192 | 75.14 123 | 77.60 129 | 60.56 203 | 66.10 62 | 54.11 175 | 23.88 214 | 53.94 146 | 53.58 150 | 34.50 210 | 73.93 165 | 77.71 109 | 87.35 74 | 80.94 129 |
|
WR-MVS_H | | | 61.83 188 | 65.87 165 | 57.12 195 | 71.72 170 | 76.87 142 | 61.45 201 | 66.19 60 | 51.97 187 | 22.92 221 | 53.13 158 | 52.30 172 | 33.80 211 | 71.03 186 | 75.00 165 | 86.65 111 | 80.78 130 |
|
PEN-MVS | | | 62.96 173 | 65.77 166 | 59.70 184 | 73.98 153 | 75.45 158 | 63.39 196 | 67.61 55 | 52.49 183 | 25.49 213 | 53.39 150 | 49.12 193 | 40.85 202 | 71.94 179 | 77.26 120 | 86.86 97 | 80.72 131 |
|
conf0.05thres1000 | | | 66.26 149 | 66.77 153 | 65.66 147 | 77.45 101 | 78.10 117 | 71.85 153 | 62.44 106 | 51.47 190 | 43.00 177 | 47.92 189 | 51.66 180 | 53.40 170 | 79.71 95 | 77.97 104 | 85.82 136 | 80.56 132 |
|
GA-MVS | | | 68.14 123 | 69.17 115 | 66.93 138 | 73.77 155 | 78.50 115 | 74.45 120 | 58.28 165 | 55.11 166 | 48.44 147 | 60.08 82 | 53.99 148 | 61.50 118 | 78.43 111 | 77.57 112 | 85.13 149 | 80.54 133 |
|
tfpn111 | | | 68.38 120 | 69.23 114 | 67.39 125 | 77.83 89 | 78.93 105 | 74.28 125 | 62.81 86 | 56.64 143 | 46.70 156 | 56.24 109 | 53.47 155 | 56.59 146 | 80.41 78 | 78.43 93 | 86.11 120 | 80.53 134 |
|
conf200view11 | | | 68.11 124 | 68.72 125 | 67.39 125 | 77.83 89 | 78.93 105 | 74.28 125 | 62.81 86 | 56.64 143 | 46.70 156 | 52.65 167 | 53.47 155 | 56.59 146 | 80.41 78 | 78.43 93 | 86.11 120 | 80.53 134 |
|
tfpn200view9 | | | 68.11 124 | 68.72 125 | 67.40 124 | 77.83 89 | 78.93 105 | 74.28 125 | 62.81 86 | 56.64 143 | 46.82 154 | 52.65 167 | 53.47 155 | 56.59 146 | 80.41 78 | 78.43 93 | 86.11 120 | 80.52 136 |
|
thres400 | | | 67.95 128 | 68.62 129 | 67.17 132 | 77.90 84 | 78.59 114 | 74.27 130 | 62.72 93 | 56.34 154 | 45.77 165 | 53.00 159 | 53.35 160 | 56.46 151 | 80.21 90 | 78.43 93 | 85.91 135 | 80.43 137 |
|
thres600view7 | | | 67.68 134 | 68.43 131 | 66.80 139 | 77.90 84 | 78.86 109 | 73.84 134 | 62.75 91 | 56.07 156 | 44.70 171 | 52.85 164 | 52.81 165 | 55.58 159 | 80.41 78 | 77.77 107 | 86.05 126 | 80.28 138 |
|
LS3D | | | 74.08 65 | 73.39 75 | 74.88 59 | 85.05 50 | 82.62 70 | 79.71 63 | 68.66 47 | 72.82 58 | 58.80 82 | 57.61 101 | 61.31 99 | 71.07 55 | 80.32 87 | 78.87 90 | 86.00 131 | 80.18 139 |
|
HyFIR lowres test | | | 69.47 109 | 68.94 117 | 70.09 97 | 76.77 106 | 82.93 68 | 76.63 106 | 60.17 136 | 59.00 117 | 54.03 115 | 40.54 207 | 65.23 89 | 67.89 67 | 76.54 150 | 78.30 99 | 85.03 151 | 80.07 140 |
|
view600 | | | 67.63 138 | 68.36 132 | 66.77 140 | 77.84 88 | 78.66 112 | 73.74 137 | 62.62 100 | 56.04 157 | 44.98 168 | 52.86 163 | 52.83 164 | 55.48 162 | 80.36 84 | 77.75 108 | 85.95 134 | 80.02 141 |
|
pm-mvs1 | | | 65.62 151 | 67.42 144 | 63.53 162 | 73.66 156 | 76.39 150 | 69.66 159 | 60.87 123 | 49.73 198 | 43.97 173 | 51.24 178 | 57.00 120 | 48.16 182 | 79.89 93 | 77.84 106 | 84.85 157 | 79.82 142 |
|
view800 | | | 67.35 143 | 68.22 135 | 66.35 144 | 77.83 89 | 78.62 113 | 72.97 145 | 62.58 101 | 55.71 159 | 44.13 172 | 52.69 166 | 52.24 174 | 54.58 167 | 80.27 88 | 78.19 101 | 86.01 129 | 79.79 143 |
|
thres200 | | | 67.98 127 | 68.55 130 | 67.30 130 | 77.89 86 | 78.86 109 | 74.18 132 | 62.75 91 | 56.35 153 | 46.48 161 | 52.98 160 | 53.54 151 | 56.46 151 | 80.41 78 | 77.97 104 | 86.05 126 | 79.78 144 |
|
conf0.01 | | | 67.72 133 | 67.99 137 | 67.39 125 | 77.82 94 | 78.94 103 | 74.28 125 | 62.81 86 | 56.64 143 | 46.70 156 | 53.33 152 | 48.59 195 | 56.59 146 | 80.34 85 | 78.43 93 | 86.16 119 | 79.67 145 |
|
CostFormer | | | 68.92 115 | 69.58 103 | 68.15 114 | 75.98 116 | 76.17 154 | 78.22 95 | 51.86 191 | 65.80 73 | 61.56 73 | 63.57 73 | 62.83 95 | 61.85 115 | 70.40 195 | 68.67 193 | 79.42 184 | 79.62 146 |
|
tfpn | | | 66.58 147 | 67.18 147 | 65.88 146 | 77.82 94 | 78.45 116 | 72.07 150 | 62.52 103 | 55.35 163 | 43.21 176 | 52.54 171 | 46.12 204 | 53.68 168 | 80.02 92 | 78.23 100 | 85.99 132 | 79.55 147 |
|
tfpnnormal | | | 64.27 165 | 63.64 184 | 65.02 150 | 75.84 117 | 75.61 157 | 71.24 156 | 62.52 103 | 47.79 203 | 42.97 178 | 42.65 200 | 44.49 208 | 52.66 174 | 78.77 108 | 76.86 125 | 84.88 155 | 79.29 148 |
|
conf0.002 | | | 67.52 141 | 67.64 141 | 67.39 125 | 77.80 96 | 78.94 103 | 74.28 125 | 62.81 86 | 56.64 143 | 46.70 156 | 53.65 148 | 46.28 203 | 56.59 146 | 80.33 86 | 78.37 98 | 86.17 118 | 79.23 149 |
|
thres100view900 | | | 67.60 139 | 68.02 136 | 67.12 134 | 77.83 89 | 77.75 126 | 73.90 133 | 62.52 103 | 56.64 143 | 46.82 154 | 52.65 167 | 53.47 155 | 55.92 155 | 78.77 108 | 77.62 111 | 85.72 140 | 79.23 149 |
|
pmmvs6 | | | 62.41 180 | 62.88 187 | 61.87 172 | 71.38 176 | 75.18 167 | 67.76 170 | 59.45 148 | 41.64 214 | 42.52 181 | 37.33 209 | 52.91 163 | 46.87 187 | 77.67 123 | 76.26 147 | 83.23 168 | 79.18 151 |
|
CVMVSNet | | | 62.55 177 | 65.89 164 | 58.64 189 | 66.95 194 | 69.15 188 | 66.49 183 | 56.29 176 | 52.46 184 | 32.70 202 | 59.27 88 | 58.21 109 | 50.09 178 | 71.77 180 | 71.39 180 | 79.31 185 | 78.99 152 |
|
IterMVS | | | 66.36 148 | 68.30 134 | 64.10 156 | 69.48 187 | 74.61 170 | 73.41 142 | 50.79 197 | 57.30 132 | 48.28 148 | 60.64 79 | 59.92 103 | 60.85 124 | 74.14 164 | 72.66 175 | 81.80 173 | 78.82 153 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
DTE-MVSNet | | | 61.85 186 | 64.96 176 | 58.22 190 | 74.32 147 | 74.39 171 | 61.01 202 | 67.85 54 | 51.76 189 | 21.91 224 | 53.28 153 | 48.17 196 | 37.74 205 | 72.22 176 | 76.44 145 | 86.52 115 | 78.49 154 |
|
SixPastTwentyTwo | | | 61.84 187 | 62.45 192 | 61.12 176 | 69.20 188 | 72.20 177 | 62.03 200 | 57.40 169 | 46.54 207 | 38.03 192 | 57.14 106 | 41.72 212 | 58.12 135 | 69.67 197 | 71.58 179 | 81.94 172 | 78.30 155 |
|
TDRefinement | | | 66.09 150 | 65.03 175 | 67.31 129 | 69.73 184 | 76.75 143 | 75.33 108 | 64.55 74 | 60.28 109 | 49.72 144 | 45.63 196 | 42.83 210 | 60.46 125 | 75.75 153 | 75.95 154 | 84.08 163 | 78.04 156 |
|
MS-PatchMatch | | | 70.17 92 | 70.49 90 | 69.79 100 | 80.98 68 | 77.97 124 | 77.51 99 | 58.95 152 | 62.33 93 | 55.22 111 | 53.14 157 | 65.90 87 | 62.03 112 | 79.08 105 | 77.11 122 | 84.08 163 | 77.91 157 |
|
pmmvs4 | | | 67.89 129 | 67.39 146 | 68.48 112 | 71.60 174 | 73.57 174 | 74.45 120 | 60.98 121 | 64.65 80 | 57.97 89 | 54.95 125 | 51.73 179 | 61.88 114 | 73.78 167 | 75.11 164 | 83.99 165 | 77.91 157 |
|
PM-MVS | | | 60.48 193 | 60.94 200 | 59.94 182 | 58.85 215 | 66.83 197 | 64.27 193 | 51.39 194 | 55.03 168 | 48.03 149 | 50.00 184 | 40.79 214 | 58.26 134 | 69.20 200 | 67.13 203 | 78.84 187 | 77.60 159 |
|
EPNet_dtu | | | 68.08 126 | 71.00 86 | 64.67 154 | 79.64 74 | 68.62 191 | 75.05 116 | 63.30 80 | 66.36 69 | 45.27 167 | 67.40 63 | 66.84 85 | 43.64 195 | 75.37 157 | 74.98 166 | 81.15 176 | 77.44 160 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
CDS-MVSNet | | | 67.65 136 | 69.83 100 | 65.09 149 | 75.39 121 | 76.55 146 | 74.42 123 | 63.75 77 | 53.55 178 | 49.37 145 | 59.41 87 | 62.45 96 | 44.44 193 | 79.71 95 | 79.82 78 | 83.17 169 | 77.36 161 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
gg-mvs-nofinetune | | | 62.55 177 | 65.05 174 | 59.62 185 | 78.72 81 | 77.61 128 | 70.83 157 | 53.63 181 | 39.71 218 | 22.04 223 | 36.36 211 | 64.32 91 | 47.53 183 | 81.16 72 | 79.03 89 | 85.00 152 | 77.17 162 |
|
RPSCF | | | 67.64 137 | 71.25 85 | 63.43 164 | 61.86 208 | 70.73 183 | 67.26 174 | 50.86 196 | 74.20 55 | 58.91 81 | 67.49 62 | 69.33 73 | 64.10 102 | 71.41 181 | 68.45 197 | 77.61 190 | 77.17 162 |
|
DWT-MVSNet_training | | | 67.24 144 | 65.96 162 | 68.74 108 | 76.15 112 | 74.36 172 | 74.37 124 | 56.66 173 | 61.82 98 | 60.51 75 | 58.23 99 | 49.76 190 | 65.07 98 | 70.04 196 | 70.39 183 | 79.70 183 | 77.11 164 |
|
TransMVSNet (Re) | | | 64.74 161 | 65.66 167 | 63.66 161 | 77.40 102 | 75.33 160 | 69.86 158 | 62.67 99 | 47.63 204 | 41.21 183 | 50.01 182 | 52.33 170 | 45.31 192 | 79.57 98 | 77.69 110 | 85.49 144 | 77.07 165 |
|
tpmp4_e23 | | | 68.32 122 | 67.08 149 | 69.76 101 | 77.86 87 | 75.22 165 | 78.37 91 | 56.17 177 | 66.06 72 | 64.27 66 | 57.15 105 | 54.89 139 | 63.40 105 | 70.97 188 | 68.29 198 | 78.46 188 | 77.00 166 |
|
MSDG | | | 71.52 77 | 69.87 97 | 73.44 65 | 82.21 63 | 79.35 101 | 79.52 64 | 64.59 73 | 66.15 70 | 61.87 71 | 53.21 156 | 56.09 131 | 65.85 97 | 78.94 106 | 78.50 92 | 86.60 112 | 76.85 167 |
|
Vis-MVSNet (Re-imp) | | | 67.83 131 | 73.52 74 | 61.19 175 | 78.37 82 | 76.72 144 | 66.80 179 | 62.96 84 | 65.50 75 | 34.17 201 | 67.19 64 | 69.68 72 | 39.20 204 | 79.39 102 | 79.44 87 | 85.68 142 | 76.73 168 |
|
test-mter | | | 60.84 192 | 64.62 178 | 56.42 197 | 55.99 223 | 64.18 203 | 65.39 186 | 34.23 230 | 54.39 173 | 46.21 162 | 57.40 104 | 59.49 105 | 55.86 156 | 71.02 187 | 69.65 186 | 80.87 179 | 76.20 169 |
|
pmmvs-eth3d | | | 63.52 171 | 62.44 193 | 64.77 153 | 66.82 196 | 70.12 185 | 69.41 163 | 59.48 147 | 54.34 174 | 52.71 123 | 46.24 195 | 44.35 209 | 56.93 144 | 72.37 172 | 73.77 170 | 83.30 167 | 75.91 170 |
|
CMPMVS | | 47.78 17 | 62.49 179 | 62.52 191 | 62.46 167 | 70.01 182 | 70.66 184 | 62.97 197 | 51.84 192 | 51.98 186 | 56.71 102 | 42.87 199 | 53.62 149 | 57.80 136 | 72.23 175 | 70.37 184 | 75.45 201 | 75.91 170 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
ambc | | | | 53.42 210 | | 64.99 201 | 63.36 209 | 49.96 220 | | 47.07 205 | 37.12 193 | 28.97 221 | 16.36 236 | 41.82 198 | 75.10 160 | 67.34 199 | 71.55 213 | 75.72 172 |
|
CR-MVSNet | | | 64.83 159 | 65.54 168 | 64.01 159 | 70.64 179 | 69.41 186 | 65.97 184 | 52.74 186 | 57.81 126 | 52.65 124 | 54.27 136 | 56.31 124 | 60.92 121 | 72.20 177 | 73.09 173 | 81.12 177 | 75.69 173 |
|
PatchT | | | 61.97 185 | 64.04 181 | 59.55 186 | 60.49 210 | 67.40 194 | 56.54 211 | 48.65 205 | 56.69 142 | 52.65 124 | 51.10 179 | 52.14 175 | 60.92 121 | 72.20 177 | 73.09 173 | 78.03 189 | 75.69 173 |
|
RPMNet | | | 61.71 190 | 62.88 187 | 60.34 180 | 69.51 186 | 69.41 186 | 63.48 195 | 49.23 201 | 57.81 126 | 45.64 166 | 50.51 180 | 50.12 187 | 53.13 173 | 68.17 204 | 68.49 196 | 81.07 178 | 75.62 175 |
|
USDC | | | 67.36 142 | 67.90 139 | 66.74 142 | 71.72 170 | 75.23 163 | 71.58 154 | 60.28 134 | 67.45 67 | 50.54 138 | 60.93 78 | 45.20 207 | 62.08 111 | 76.56 149 | 74.50 167 | 84.25 162 | 75.38 176 |
|
COLMAP_ROB | | 62.73 15 | 67.66 135 | 66.76 154 | 68.70 110 | 80.49 72 | 77.98 122 | 75.29 110 | 62.95 85 | 63.62 86 | 49.96 141 | 47.32 194 | 50.72 185 | 58.57 131 | 76.87 144 | 75.50 160 | 84.94 154 | 75.33 177 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
EU-MVSNet | | | 54.63 205 | 58.69 203 | 49.90 212 | 56.99 219 | 62.70 213 | 56.41 212 | 50.64 199 | 45.95 209 | 23.14 218 | 50.42 181 | 46.51 202 | 36.63 206 | 65.51 208 | 64.85 207 | 75.57 198 | 74.91 178 |
|
tpm cat1 | | | 65.41 152 | 63.81 183 | 67.28 131 | 75.61 120 | 72.88 175 | 75.32 109 | 52.85 185 | 62.97 90 | 63.66 69 | 53.24 155 | 53.29 162 | 61.83 116 | 65.54 207 | 64.14 210 | 74.43 204 | 74.60 179 |
|
pmmvs5 | | | 62.37 183 | 64.04 181 | 60.42 179 | 65.03 200 | 71.67 180 | 67.17 175 | 52.70 188 | 50.30 192 | 44.80 169 | 54.23 140 | 51.19 183 | 49.37 180 | 72.88 171 | 73.48 172 | 83.45 166 | 74.55 180 |
|
test-LLR | | | 64.42 162 | 64.36 179 | 64.49 155 | 75.02 124 | 63.93 205 | 66.61 181 | 61.96 109 | 54.41 171 | 47.77 150 | 57.46 102 | 60.25 101 | 55.20 163 | 70.80 189 | 69.33 188 | 80.40 180 | 74.38 181 |
|
TESTMET0.1,1 | | | 61.10 191 | 64.36 179 | 57.29 194 | 57.53 218 | 63.93 205 | 66.61 181 | 36.22 228 | 54.41 171 | 47.77 150 | 57.46 102 | 60.25 101 | 55.20 163 | 70.80 189 | 69.33 188 | 80.40 180 | 74.38 181 |
|
tpm | | | 62.41 180 | 63.15 185 | 61.55 174 | 72.24 166 | 63.79 207 | 71.31 155 | 46.12 213 | 57.82 125 | 55.33 109 | 59.90 85 | 54.74 140 | 53.63 169 | 67.24 205 | 64.29 208 | 70.65 215 | 74.25 183 |
|
PMMVS | | | 65.06 158 | 69.17 115 | 60.26 181 | 55.25 225 | 63.43 208 | 66.71 180 | 43.01 222 | 62.41 92 | 50.64 136 | 69.44 52 | 67.04 84 | 63.29 106 | 74.36 163 | 73.54 171 | 82.68 170 | 73.99 184 |
|
tfpn_ndepth | | | 65.09 157 | 67.12 148 | 62.73 166 | 75.75 119 | 76.23 152 | 68.00 168 | 60.36 130 | 58.16 123 | 40.27 184 | 54.89 126 | 54.22 144 | 46.80 188 | 76.69 148 | 75.66 156 | 85.19 148 | 73.98 185 |
|
Anonymous20231211 | | | 51.46 212 | 50.59 214 | 52.46 209 | 67.30 192 | 66.70 198 | 55.00 213 | 59.22 149 | 29.96 229 | 17.62 229 | 19.11 231 | 28.74 229 | 35.72 208 | 66.42 206 | 69.52 187 | 79.92 182 | 73.71 186 |
|
PatchMatch-RL | | | 67.78 132 | 66.65 155 | 69.10 106 | 73.01 159 | 72.69 176 | 68.49 166 | 61.85 111 | 62.93 91 | 60.20 78 | 56.83 107 | 50.42 186 | 69.52 60 | 75.62 156 | 74.46 168 | 81.51 174 | 73.62 187 |
|
CHOSEN 280x420 | | | 58.70 198 | 61.88 196 | 54.98 202 | 55.45 224 | 50.55 228 | 64.92 189 | 40.36 224 | 55.21 164 | 38.13 191 | 48.31 186 | 63.76 92 | 63.03 108 | 73.73 168 | 68.58 195 | 68.00 220 | 73.04 188 |
|
tfpn1000 | | | 63.81 170 | 66.31 156 | 60.90 177 | 75.76 118 | 75.74 156 | 65.14 188 | 60.14 140 | 56.47 150 | 35.99 198 | 55.11 119 | 52.30 172 | 43.42 196 | 76.21 152 | 75.34 161 | 84.97 153 | 73.01 189 |
|
tfpn_n400 | | | 64.23 166 | 66.05 160 | 62.12 170 | 76.20 110 | 75.24 161 | 67.43 172 | 61.15 118 | 54.04 176 | 36.38 195 | 55.35 116 | 51.89 176 | 46.94 185 | 77.31 135 | 76.15 151 | 84.59 159 | 72.36 190 |
|
tfpnconf | | | 64.23 166 | 66.05 160 | 62.12 170 | 76.20 110 | 75.24 161 | 67.43 172 | 61.15 118 | 54.04 176 | 36.38 195 | 55.35 116 | 51.89 176 | 46.94 185 | 77.31 135 | 76.15 151 | 84.59 159 | 72.36 190 |
|
gm-plane-assit | | | 57.00 201 | 57.62 207 | 56.28 198 | 76.10 113 | 62.43 215 | 47.62 224 | 46.57 211 | 33.84 226 | 23.24 217 | 37.52 208 | 40.19 215 | 59.61 129 | 79.81 94 | 77.55 113 | 84.55 161 | 72.03 192 |
|
tfpnview11 | | | 64.33 164 | 66.17 159 | 62.18 168 | 76.25 109 | 75.23 163 | 67.45 171 | 61.16 117 | 55.50 161 | 36.38 195 | 55.35 116 | 51.89 176 | 46.96 184 | 77.28 137 | 76.10 153 | 84.86 156 | 71.85 193 |
|
thresconf0.02 | | | 64.77 160 | 65.90 163 | 63.44 163 | 76.37 108 | 75.17 168 | 69.51 161 | 61.28 116 | 56.98 134 | 39.01 188 | 56.24 109 | 48.68 194 | 49.78 179 | 77.13 140 | 75.61 157 | 84.71 158 | 71.53 194 |
|
TinyColmap | | | 62.84 175 | 61.03 199 | 64.96 152 | 69.61 185 | 71.69 179 | 68.48 167 | 59.76 145 | 55.41 162 | 47.69 152 | 47.33 193 | 34.20 220 | 62.76 109 | 74.52 161 | 72.59 176 | 81.44 175 | 71.47 195 |
|
dps | | | 64.00 169 | 62.99 186 | 65.18 148 | 73.29 157 | 72.07 178 | 68.98 165 | 53.07 184 | 57.74 128 | 58.41 84 | 55.55 114 | 47.74 199 | 60.89 123 | 69.53 198 | 67.14 202 | 76.44 196 | 71.19 196 |
|
tpmrst | | | 62.00 184 | 62.35 194 | 61.58 173 | 71.62 173 | 64.14 204 | 69.07 164 | 48.22 209 | 62.21 94 | 53.93 116 | 58.26 98 | 55.30 135 | 55.81 157 | 63.22 212 | 62.62 213 | 70.85 214 | 70.70 197 |
|
GG-mvs-BLEND | | | 46.86 219 | 67.51 143 | 22.75 232 | 0.05 240 | 76.21 153 | 64.69 190 | 0.04 237 | 61.90 96 | 0.09 242 | 55.57 113 | 71.32 64 | 0.08 237 | 70.54 191 | 67.19 201 | 71.58 212 | 69.86 198 |
|
MDTV_nov1_ep13 | | | 64.37 163 | 65.24 170 | 63.37 165 | 68.94 189 | 70.81 182 | 72.40 149 | 50.29 200 | 60.10 110 | 53.91 117 | 60.07 83 | 59.15 106 | 57.21 141 | 69.43 199 | 67.30 200 | 77.47 191 | 69.78 199 |
|
MDTV_nov1_ep13_2view | | | 60.16 194 | 60.51 201 | 59.75 183 | 65.39 199 | 69.05 189 | 68.00 168 | 48.29 207 | 51.99 185 | 45.95 164 | 48.01 188 | 49.64 191 | 53.39 171 | 68.83 201 | 66.52 204 | 77.47 191 | 69.55 200 |
|
FC-MVSNet-test | | | 56.90 202 | 65.20 172 | 47.21 214 | 66.98 193 | 63.20 210 | 49.11 222 | 58.60 163 | 59.38 116 | 11.50 234 | 65.60 67 | 56.68 121 | 24.66 225 | 71.17 184 | 71.36 181 | 72.38 210 | 69.02 201 |
|
Anonymous20231206 | | | 56.36 203 | 57.80 206 | 54.67 203 | 70.08 181 | 66.39 199 | 60.46 204 | 57.54 167 | 49.50 200 | 29.30 207 | 33.86 217 | 46.64 201 | 35.18 209 | 70.44 193 | 68.88 192 | 75.47 200 | 68.88 202 |
|
PatchmatchNet | | | 64.21 168 | 64.65 177 | 63.69 160 | 71.29 178 | 68.66 190 | 69.63 160 | 51.70 193 | 63.04 89 | 53.77 118 | 59.83 86 | 58.34 108 | 60.23 127 | 68.54 202 | 66.06 205 | 75.56 199 | 68.08 203 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
MDA-MVSNet-bldmvs | | | 53.37 210 | 53.01 212 | 53.79 207 | 43.67 233 | 67.95 193 | 59.69 206 | 57.92 166 | 43.69 210 | 32.41 203 | 41.47 202 | 27.89 230 | 52.38 175 | 56.97 226 | 65.99 206 | 76.68 194 | 67.13 204 |
|
TAMVS | | | 59.58 196 | 62.81 189 | 55.81 199 | 66.03 198 | 65.64 202 | 63.86 194 | 48.74 204 | 49.95 194 | 37.07 194 | 54.77 129 | 58.54 107 | 44.44 193 | 72.29 174 | 71.79 177 | 74.70 203 | 66.66 205 |
|
MIMVSNet | | | 58.52 199 | 61.34 198 | 55.22 201 | 60.76 209 | 67.01 196 | 66.81 178 | 49.02 203 | 56.43 151 | 38.90 189 | 40.59 206 | 54.54 142 | 40.57 203 | 73.16 170 | 71.65 178 | 75.30 202 | 66.00 206 |
|
testpf | | | 47.41 215 | 48.47 221 | 46.18 215 | 66.30 197 | 50.67 227 | 48.15 223 | 42.60 223 | 37.10 222 | 28.75 208 | 40.97 203 | 39.01 217 | 30.82 214 | 52.95 229 | 53.74 228 | 60.46 227 | 64.87 207 |
|
test0.0.03 1 | | | 58.80 197 | 61.58 197 | 55.56 200 | 75.02 124 | 68.45 192 | 59.58 207 | 61.96 109 | 52.74 180 | 29.57 206 | 49.75 185 | 54.56 141 | 31.46 213 | 71.19 183 | 69.77 185 | 75.75 197 | 64.57 208 |
|
FMVSNet5 | | | 57.24 200 | 60.02 202 | 53.99 205 | 56.45 220 | 62.74 212 | 65.27 187 | 47.03 210 | 55.14 165 | 39.55 187 | 40.88 204 | 53.42 159 | 41.83 197 | 72.35 173 | 71.10 182 | 73.79 206 | 64.50 209 |
|
EPMVS | | | 60.00 195 | 61.97 195 | 57.71 193 | 68.46 190 | 63.17 211 | 64.54 191 | 48.23 208 | 63.30 87 | 44.72 170 | 60.19 81 | 56.05 132 | 50.85 177 | 65.27 209 | 62.02 215 | 69.44 217 | 63.81 210 |
|
pmmvs3 | | | 47.65 214 | 49.08 218 | 45.99 216 | 44.61 230 | 54.79 223 | 50.04 219 | 31.95 233 | 33.91 225 | 29.90 205 | 30.37 219 | 33.53 221 | 46.31 190 | 63.50 211 | 63.67 211 | 73.14 209 | 63.77 211 |
|
test20.03 | | | 53.93 208 | 56.28 208 | 51.19 210 | 72.19 167 | 65.83 200 | 53.20 216 | 61.08 120 | 42.74 212 | 22.08 222 | 37.07 210 | 45.76 206 | 24.29 226 | 70.44 193 | 69.04 190 | 74.31 205 | 63.05 212 |
|
testgi | | | 54.39 207 | 57.86 205 | 50.35 211 | 71.59 175 | 67.24 195 | 54.95 214 | 53.25 183 | 43.36 211 | 23.78 215 | 44.64 197 | 47.87 198 | 24.96 222 | 70.45 192 | 68.66 194 | 73.60 207 | 62.78 213 |
|
MIMVSNet1 | | | 49.27 213 | 53.25 211 | 44.62 218 | 44.61 230 | 61.52 216 | 53.61 215 | 52.18 189 | 41.62 215 | 18.68 226 | 28.14 225 | 41.58 213 | 25.50 220 | 68.46 203 | 69.04 190 | 73.15 208 | 62.37 214 |
|
LP | | | 53.62 209 | 53.43 209 | 53.83 206 | 58.51 217 | 62.59 214 | 57.31 210 | 46.04 214 | 47.86 202 | 42.69 180 | 36.08 213 | 36.86 218 | 46.53 189 | 64.38 210 | 64.25 209 | 71.92 211 | 62.00 215 |
|
new-patchmatchnet | | | 46.97 218 | 49.47 217 | 44.05 220 | 62.82 204 | 56.55 219 | 45.35 225 | 52.01 190 | 42.47 213 | 17.04 230 | 35.73 215 | 35.21 219 | 21.84 231 | 61.27 217 | 54.83 226 | 65.26 225 | 60.26 216 |
|
MVS-HIRNet | | | 54.41 206 | 52.10 213 | 57.11 196 | 58.99 214 | 56.10 220 | 49.68 221 | 49.10 202 | 46.18 208 | 52.15 128 | 33.18 218 | 46.11 205 | 56.10 153 | 63.19 213 | 59.70 221 | 76.64 195 | 60.25 217 |
|
ADS-MVSNet | | | 55.94 204 | 58.01 204 | 53.54 208 | 62.48 205 | 58.48 217 | 59.12 209 | 46.20 212 | 59.65 114 | 42.88 179 | 52.34 173 | 53.31 161 | 46.31 190 | 62.00 216 | 60.02 220 | 64.23 226 | 60.24 218 |
|
FPMVS | | | 51.87 211 | 50.00 216 | 54.07 204 | 66.83 195 | 57.25 218 | 60.25 205 | 50.91 195 | 50.25 193 | 34.36 200 | 36.04 214 | 32.02 222 | 41.49 199 | 58.98 224 | 56.07 224 | 70.56 216 | 59.36 219 |
|
no-one | | | 36.35 227 | 37.59 228 | 34.91 226 | 46.13 228 | 49.89 229 | 27.99 235 | 43.56 221 | 20.91 235 | 7.03 237 | 14.64 233 | 15.50 237 | 18.92 232 | 42.95 230 | 60.20 219 | 65.84 224 | 59.03 220 |
|
testus | | | 45.61 221 | 49.06 219 | 41.59 222 | 56.13 222 | 55.28 221 | 43.51 226 | 39.64 226 | 37.74 220 | 18.23 227 | 35.52 216 | 31.28 223 | 24.69 224 | 62.46 215 | 62.90 212 | 67.33 221 | 58.26 221 |
|
PMVS | | 39.38 18 | 46.06 220 | 43.30 225 | 49.28 213 | 62.93 203 | 38.75 234 | 41.88 227 | 53.50 182 | 33.33 228 | 35.46 199 | 28.90 222 | 31.01 225 | 33.04 212 | 58.61 225 | 54.63 227 | 68.86 218 | 57.88 222 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
testmv | | | 42.58 223 | 44.36 222 | 40.49 223 | 54.63 226 | 52.76 224 | 41.21 230 | 44.37 219 | 28.83 230 | 12.87 231 | 27.16 226 | 25.03 231 | 23.01 227 | 60.83 218 | 61.13 216 | 66.88 222 | 54.81 223 |
|
test1235678 | | | 42.57 224 | 44.36 222 | 40.49 223 | 54.63 226 | 52.75 225 | 41.21 230 | 44.37 219 | 28.82 231 | 12.87 231 | 27.15 227 | 25.01 232 | 23.01 227 | 60.83 218 | 61.13 216 | 66.88 222 | 54.81 223 |
|
test2356 | | | 47.20 217 | 48.62 220 | 45.54 217 | 56.38 221 | 54.89 222 | 50.62 218 | 45.08 217 | 38.65 219 | 23.40 216 | 36.23 212 | 31.10 224 | 29.31 216 | 62.76 214 | 62.49 214 | 68.48 219 | 54.23 225 |
|
N_pmnet | | | 47.35 216 | 50.13 215 | 44.11 219 | 59.98 211 | 51.64 226 | 51.86 217 | 44.80 218 | 49.58 199 | 20.76 225 | 40.65 205 | 40.05 216 | 29.64 215 | 59.84 222 | 55.15 225 | 57.63 228 | 54.00 226 |
|
new_pmnet | | | 38.40 225 | 42.64 226 | 33.44 227 | 37.54 236 | 45.00 232 | 36.60 232 | 32.72 232 | 40.27 216 | 12.72 233 | 29.89 220 | 28.90 228 | 24.78 223 | 53.17 228 | 52.90 229 | 56.31 229 | 48.34 227 |
|
1111 | | | 43.08 222 | 44.02 224 | 41.98 221 | 59.22 212 | 49.27 230 | 41.48 228 | 45.63 215 | 35.01 223 | 23.06 219 | 28.60 223 | 30.15 226 | 27.22 217 | 60.42 220 | 57.97 222 | 55.27 231 | 46.74 228 |
|
test12356 | | | 35.10 228 | 38.50 227 | 31.13 229 | 44.14 232 | 43.70 233 | 32.27 233 | 34.42 229 | 26.51 233 | 9.47 235 | 25.22 229 | 20.34 233 | 10.86 234 | 53.47 227 | 56.15 223 | 55.59 230 | 44.11 229 |
|
Gipuma | | | 36.38 226 | 35.80 229 | 37.07 225 | 45.76 229 | 33.90 235 | 29.81 234 | 48.47 206 | 39.91 217 | 18.02 228 | 8.00 237 | 8.14 239 | 25.14 221 | 59.29 223 | 61.02 218 | 55.19 232 | 40.31 230 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
MVE | | 19.12 19 | 20.47 233 | 23.27 232 | 17.20 234 | 12.66 239 | 25.41 237 | 10.52 240 | 34.14 231 | 14.79 238 | 6.53 240 | 8.79 236 | 4.68 240 | 16.64 233 | 29.49 233 | 41.63 230 | 22.73 237 | 38.11 231 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
PMMVS2 | | | 25.60 230 | 29.75 230 | 20.76 233 | 28.00 237 | 30.93 236 | 23.10 236 | 29.18 234 | 23.14 234 | 1.46 241 | 18.23 232 | 16.54 235 | 5.08 235 | 40.22 231 | 41.40 231 | 37.76 233 | 37.79 232 |
|
DeepMVS_CX | | | | | | | 18.74 240 | 18.55 237 | 8.02 235 | 26.96 232 | 7.33 236 | 23.81 230 | 13.05 238 | 25.99 219 | 25.17 234 | | 22.45 238 | 36.25 233 |
|
E-PMN | | | 21.77 231 | 18.24 233 | 25.89 230 | 40.22 234 | 19.58 238 | 12.46 239 | 39.87 225 | 18.68 237 | 6.71 238 | 9.57 234 | 4.31 242 | 22.36 230 | 19.89 235 | 27.28 233 | 33.73 234 | 28.34 234 |
|
EMVS | | | 20.98 232 | 17.15 234 | 25.44 231 | 39.51 235 | 19.37 239 | 12.66 238 | 39.59 227 | 19.10 236 | 6.62 239 | 9.27 235 | 4.40 241 | 22.43 229 | 17.99 236 | 24.40 234 | 31.81 235 | 25.53 235 |
|
test123 | | | 0.09 234 | 0.14 236 | 0.02 236 | 0.00 242 | 0.02 242 | 0.02 244 | 0.01 238 | 0.09 240 | 0.00 244 | 0.30 238 | 0.00 244 | 0.08 237 | 0.03 238 | 0.09 237 | 0.01 239 | 0.45 236 |
|
.test1245 | | | 30.81 229 | 29.14 231 | 32.77 228 | 59.22 212 | 49.27 230 | 41.48 228 | 45.63 215 | 35.01 223 | 23.06 219 | 28.60 223 | 30.15 226 | 27.22 217 | 60.42 220 | 0.10 235 | 0.01 239 | 0.43 237 |
|
testmvs | | | 0.09 234 | 0.15 235 | 0.02 236 | 0.01 241 | 0.02 242 | 0.05 243 | 0.01 238 | 0.11 239 | 0.01 243 | 0.26 239 | 0.01 243 | 0.06 239 | 0.10 237 | 0.10 235 | 0.01 239 | 0.43 237 |
|
sosnet-low-res | | | 0.00 236 | 0.00 237 | 0.00 238 | 0.00 242 | 0.00 244 | 0.00 245 | 0.00 240 | 0.00 241 | 0.00 244 | 0.00 240 | 0.00 244 | 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 | 0.00 242 | 0.00 244 | 0.00 245 | 0.00 240 | 0.00 241 | 0.00 244 | 0.00 240 | 0.00 244 | 0.00 240 | 0.00 239 | 0.00 238 | 0.00 242 | 0.00 239 |
|
our_test_3 | | | | | | 67.93 191 | 70.99 181 | 66.89 177 | | | | | | | | | | |
|
MTAPA | | | | | | | | | | | 83.48 1 | | 86.45 13 | | | | | |
|
MTMP | | | | | | | | | | | 82.66 3 | | 84.91 21 | | | | | |
|
Patchmatch-RL test | | | | | | | | 2.85 242 | | | | | | | | | | |
|
tmp_tt | | | | | 14.50 235 | 14.68 238 | 7.17 241 | 10.46 241 | 2.21 236 | 37.73 221 | 28.71 209 | 25.26 228 | 16.98 234 | 4.37 236 | 31.49 232 | 29.77 232 | 26.56 236 | |
|
XVS | | | | | | 86.63 40 | 88.68 24 | 85.00 42 | | | 71.81 41 | | 81.92 31 | | | | 90.47 18 | |
|
X-MVStestdata | | | | | | 86.63 40 | 88.68 24 | 85.00 42 | | | 71.81 41 | | 81.92 31 | | | | 90.47 18 | |
|
mPP-MVS | | | | | | 89.90 21 | | | | | | | 81.29 36 | | | | | |
|
NP-MVS | | | | | | | | | | 80.10 41 | | | | | | | | |
|
Patchmtry | | | | | | | 65.80 201 | 65.97 184 | 52.74 186 | | 52.65 124 | | | | | | | |
|