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