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 | 73.54 138 | 65.98 64 | 60.65 105 | 56.00 106 | 72.11 43 | 79.15 39 | 54.63 164 | 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 86 | 78.87 89 | 86.00 130 | 80.18 137 |
|
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 | 62.07 107 | 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 | 60.89 120 | 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 82 | 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 | 86.04 127 | 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 | 51.50 174 | 54.14 143 | 68.56 64 | 78.85 106 | 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 | 56.19 154 | 46.06 162 | 54.30 134 | 51.20 180 | 68.68 63 | 80.66 76 | 80.72 64 | 86.07 123 | 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 | 53.21 155 | 56.09 130 | 65.85 96 | 78.94 105 | 78.50 91 | 86.60 111 | 76.85 165 |
|
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 | 54.63 164 | 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 | 78.79 110 | 73.74 136 | 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 | 77.97 123 | 77.51 98 | 58.95 150 | 62.33 92 | 55.22 110 | 53.14 156 | 65.90 86 | 62.03 111 | 79.08 104 | 77.11 121 | 84.08 161 | 77.91 155 |
|
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 97 | 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 | 67.66 134 | 66.76 152 | 68.70 109 | 80.49 71 | 77.98 121 | 75.29 109 | 62.95 84 | 63.62 85 | 49.96 140 | 47.32 192 | 50.72 183 | 58.57 130 | 76.87 143 | 75.50 158 | 84.94 152 | 75.33 175 |
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 | 86.01 128 | 85.98 64 |
|
EPNet_dtu | | | 68.08 125 | 71.00 85 | 64.67 153 | 79.64 73 | 68.62 188 | 75.05 115 | 63.30 79 | 66.36 68 | 45.27 166 | 67.40 62 | 66.84 84 | 43.64 194 | 75.37 156 | 74.98 164 | 81.15 174 | 77.44 158 |
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 | 72.61 145 | 61.77 111 | 59.66 112 | 63.44 69 | 53.26 153 | 59.65 103 | 59.16 129 | 76.78 145 | 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 | 60.12 139 | 69.72 63 | 64.11 66 | 70.53 48 | 73.22 57 | 68.90 61 | 80.14 90 | 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 | 61.67 113 | 60.68 104 | 55.49 107 | 58.48 92 | 65.48 87 | 68.85 62 | 76.92 142 | 75.55 157 | 87.35 73 | 85.46 72 |
|
EG-PatchMatch MVS | | | 67.24 143 | 66.94 149 | 67.60 120 | 78.73 79 | 81.35 74 | 73.28 142 | 59.49 144 | 46.89 204 | 51.42 131 | 43.65 196 | 53.49 151 | 55.50 160 | 81.38 66 | 80.66 70 | 87.15 77 | 81.17 126 |
|
gg-mvs-nofinetune | | | 62.55 175 | 65.05 172 | 59.62 184 | 78.72 80 | 77.61 127 | 70.83 156 | 53.63 179 | 39.71 216 | 22.04 221 | 36.36 209 | 64.32 90 | 47.53 182 | 81.16 71 | 79.03 88 | 85.00 150 | 77.17 160 |
|
Vis-MVSNet (Re-imp) | | | 67.83 130 | 73.52 73 | 61.19 174 | 78.37 81 | 76.72 143 | 66.80 177 | 62.96 83 | 65.50 74 | 34.17 200 | 67.19 63 | 69.68 71 | 39.20 203 | 79.39 101 | 79.44 86 | 85.68 140 | 76.73 166 |
|
DI_MVS_plusplus_trai | | | 75.13 62 | 76.12 67 | 73.96 63 | 78.18 82 | 81.55 72 | 80.97 55 | 62.54 101 | 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 | | | 67.68 133 | 68.43 130 | 66.80 138 | 77.90 83 | 78.86 108 | 73.84 133 | 62.75 90 | 56.07 155 | 44.70 170 | 52.85 162 | 52.81 163 | 55.58 158 | 80.41 77 | 77.77 106 | 86.05 125 | 80.28 136 |
|
thres400 | | | 67.95 127 | 68.62 128 | 67.17 131 | 77.90 83 | 78.59 113 | 74.27 129 | 62.72 92 | 56.34 153 | 45.77 164 | 53.00 158 | 53.35 158 | 56.46 150 | 80.21 89 | 78.43 92 | 85.91 134 | 80.43 135 |
|
thres200 | | | 67.98 126 | 68.55 129 | 67.30 129 | 77.89 85 | 78.86 108 | 74.18 131 | 62.75 90 | 56.35 152 | 46.48 160 | 52.98 159 | 53.54 149 | 56.46 150 | 80.41 77 | 77.97 103 | 86.05 125 | 79.78 142 |
|
tpmp4_e23 | | | 68.32 121 | 67.08 148 | 69.76 100 | 77.86 86 | 75.22 163 | 78.37 90 | 56.17 175 | 66.06 71 | 64.27 65 | 57.15 104 | 54.89 138 | 63.40 104 | 70.97 186 | 68.29 196 | 78.46 186 | 77.00 164 |
|
view600 | | | 67.63 137 | 68.36 131 | 66.77 139 | 77.84 87 | 78.66 111 | 73.74 136 | 62.62 99 | 56.04 156 | 44.98 167 | 52.86 161 | 52.83 162 | 55.48 161 | 80.36 83 | 77.75 107 | 85.95 133 | 80.02 139 |
|
tfpn111 | | | 68.38 119 | 69.23 113 | 67.39 124 | 77.83 88 | 78.93 104 | 74.28 124 | 62.81 85 | 56.64 142 | 46.70 155 | 56.24 108 | 53.47 153 | 56.59 145 | 80.41 77 | 78.43 92 | 86.11 119 | 80.53 132 |
|
conf200view11 | | | 68.11 123 | 68.72 124 | 67.39 124 | 77.83 88 | 78.93 104 | 74.28 124 | 62.81 85 | 56.64 142 | 46.70 155 | 52.65 165 | 53.47 153 | 56.59 145 | 80.41 77 | 78.43 92 | 86.11 119 | 80.53 132 |
|
thres100view900 | | | 67.60 138 | 68.02 135 | 67.12 133 | 77.83 88 | 77.75 125 | 73.90 132 | 62.52 102 | 56.64 142 | 46.82 153 | 52.65 165 | 53.47 153 | 55.92 154 | 78.77 107 | 77.62 110 | 85.72 139 | 79.23 147 |
|
tfpn200view9 | | | 68.11 123 | 68.72 124 | 67.40 123 | 77.83 88 | 78.93 104 | 74.28 124 | 62.81 85 | 56.64 142 | 46.82 153 | 52.65 165 | 53.47 153 | 56.59 145 | 80.41 77 | 78.43 92 | 86.11 119 | 80.52 134 |
|
view800 | | | 67.35 142 | 68.22 134 | 66.35 143 | 77.83 88 | 78.62 112 | 72.97 144 | 62.58 100 | 55.71 158 | 44.13 171 | 52.69 164 | 52.24 172 | 54.58 166 | 80.27 87 | 78.19 100 | 86.01 128 | 79.79 141 |
|
conf0.01 | | | 67.72 132 | 67.99 136 | 67.39 124 | 77.82 93 | 78.94 102 | 74.28 124 | 62.81 85 | 56.64 142 | 46.70 155 | 53.33 151 | 48.59 193 | 56.59 145 | 80.34 84 | 78.43 92 | 86.16 118 | 79.67 143 |
|
tfpn | | | 66.58 146 | 67.18 146 | 65.88 145 | 77.82 93 | 78.45 115 | 72.07 149 | 62.52 102 | 55.35 162 | 43.21 175 | 52.54 169 | 46.12 202 | 53.68 167 | 80.02 91 | 78.23 99 | 85.99 131 | 79.55 145 |
|
conf0.002 | | | 67.52 140 | 67.64 140 | 67.39 124 | 77.80 95 | 78.94 102 | 74.28 124 | 62.81 85 | 56.64 142 | 46.70 155 | 53.65 147 | 46.28 201 | 56.59 145 | 80.33 85 | 78.37 97 | 86.17 117 | 79.23 147 |
|
Fast-Effi-MVS+ | | | 73.11 69 | 73.66 72 | 72.48 67 | 77.72 96 | 80.88 82 | 78.55 85 | 58.83 158 | 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 96 | 80.72 83 | 73.81 134 | 69.65 39 | 61.99 94 | 43.23 173 | 60.54 79 | 57.50 109 | 58.57 130 | 79.56 98 | 81.07 59 | 89.34 41 | 83.97 90 |
|
IterMVS-LS | | | 71.69 75 | 72.82 78 | 70.37 91 | 77.54 98 | 76.34 149 | 75.13 114 | 60.46 127 | 61.53 100 | 57.57 89 | 64.89 68 | 67.33 82 | 66.04 92 | 77.09 141 | 77.37 117 | 85.48 143 | 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 | 66.71 142 | 77.48 99 | 79.54 97 | 73.52 139 | 69.20 44 | 61.20 102 | 39.76 184 | 58.52 90 | 50.11 186 | 51.37 175 | 80.26 88 | 80.71 68 | 88.97 47 | 83.59 97 |
|
conf0.05thres1000 | | | 66.26 148 | 66.77 151 | 65.66 146 | 77.45 100 | 78.10 116 | 71.85 152 | 62.44 105 | 51.47 188 | 43.00 176 | 47.92 187 | 51.66 178 | 53.40 169 | 79.71 94 | 77.97 103 | 85.82 135 | 80.56 130 |
|
TransMVSNet (Re) | | | 64.74 160 | 65.66 165 | 63.66 160 | 77.40 101 | 75.33 158 | 69.86 157 | 62.67 98 | 47.63 202 | 41.21 182 | 50.01 180 | 52.33 168 | 45.31 191 | 79.57 97 | 77.69 109 | 85.49 142 | 77.07 163 |
|
TranMVSNet+NR-MVSNet | | | 69.25 111 | 70.81 87 | 67.43 122 | 77.23 102 | 79.46 99 | 73.48 140 | 69.66 38 | 60.43 107 | 39.56 185 | 58.82 89 | 53.48 152 | 55.74 157 | 79.59 96 | 81.21 58 | 88.89 49 | 82.70 111 |
|
CANet_DTU | | | 73.29 67 | 76.96 64 | 69.00 106 | 77.04 103 | 82.06 71 | 79.49 64 | 56.30 173 | 67.85 65 | 53.29 120 | 71.12 47 | 70.37 69 | 61.81 116 | 81.59 62 | 80.96 60 | 86.09 122 | 84.73 85 |
|
CHOSEN 1792x2688 | | | 69.20 112 | 69.26 112 | 69.13 104 | 76.86 104 | 78.93 104 | 77.27 101 | 60.12 139 | 61.86 96 | 54.42 111 | 42.54 199 | 61.61 97 | 66.91 73 | 78.55 109 | 78.14 102 | 79.23 184 | 83.23 102 |
|
HyFIR lowres test | | | 69.47 108 | 68.94 116 | 70.09 96 | 76.77 105 | 82.93 67 | 76.63 105 | 60.17 134 | 59.00 116 | 54.03 114 | 40.54 205 | 65.23 88 | 67.89 66 | 76.54 149 | 78.30 98 | 85.03 149 | 80.07 138 |
|
UniMVSNet (Re) | | | 69.53 105 | 71.90 81 | 66.76 140 | 76.42 106 | 80.93 79 | 72.59 146 | 68.03 51 | 61.75 98 | 41.68 181 | 58.34 96 | 57.23 117 | 53.27 171 | 79.53 99 | 80.62 72 | 88.57 53 | 84.90 83 |
|
thresconf0.02 | | | 64.77 159 | 65.90 161 | 63.44 162 | 76.37 107 | 75.17 166 | 69.51 160 | 61.28 114 | 56.98 133 | 39.01 187 | 56.24 108 | 48.68 192 | 49.78 178 | 77.13 139 | 75.61 155 | 84.71 156 | 71.53 192 |
|
tfpnview11 | | | 64.33 163 | 66.17 157 | 62.18 167 | 76.25 108 | 75.23 161 | 67.45 170 | 61.16 115 | 55.50 160 | 36.38 194 | 55.35 115 | 51.89 174 | 46.96 183 | 77.28 136 | 76.10 151 | 84.86 154 | 71.85 191 |
|
tfpn_n400 | | | 64.23 165 | 66.05 158 | 62.12 169 | 76.20 109 | 75.24 159 | 67.43 171 | 61.15 116 | 54.04 175 | 36.38 194 | 55.35 115 | 51.89 174 | 46.94 184 | 77.31 134 | 76.15 149 | 84.59 157 | 72.36 188 |
|
tfpnconf | | | 64.23 165 | 66.05 158 | 62.12 169 | 76.20 109 | 75.24 159 | 67.43 171 | 61.15 116 | 54.04 175 | 36.38 194 | 55.35 115 | 51.89 174 | 46.94 184 | 77.31 134 | 76.15 149 | 84.59 157 | 72.36 188 |
|
DWT-MVSNet_training | | | 67.24 143 | 65.96 160 | 68.74 107 | 76.15 111 | 74.36 170 | 74.37 123 | 56.66 171 | 61.82 97 | 60.51 74 | 58.23 98 | 49.76 188 | 65.07 97 | 70.04 194 | 70.39 181 | 79.70 181 | 77.11 162 |
|
gm-plane-assit | | | 57.00 199 | 57.62 205 | 56.28 196 | 76.10 112 | 62.43 212 | 47.62 221 | 46.57 209 | 33.84 224 | 23.24 215 | 37.52 206 | 40.19 213 | 59.61 128 | 79.81 93 | 77.55 112 | 84.55 159 | 72.03 190 |
|
DU-MVS | | | 69.63 100 | 70.91 86 | 68.13 114 | 75.99 113 | 79.54 97 | 73.81 134 | 69.20 44 | 61.20 102 | 43.23 173 | 58.52 90 | 53.50 150 | 58.57 130 | 79.22 102 | 80.45 74 | 87.97 61 | 83.97 90 |
|
Baseline_NR-MVSNet | | | 67.53 139 | 68.77 122 | 66.09 144 | 75.99 113 | 74.75 167 | 72.43 147 | 68.41 47 | 61.33 101 | 38.33 189 | 51.31 175 | 54.13 145 | 56.03 153 | 79.22 102 | 78.19 100 | 85.37 144 | 82.45 113 |
|
CostFormer | | | 68.92 114 | 69.58 102 | 68.15 113 | 75.98 115 | 76.17 152 | 78.22 94 | 51.86 189 | 65.80 72 | 61.56 72 | 63.57 72 | 62.83 94 | 61.85 114 | 70.40 193 | 68.67 191 | 79.42 182 | 79.62 144 |
|
tfpnnormal | | | 64.27 164 | 63.64 182 | 65.02 149 | 75.84 116 | 75.61 155 | 71.24 155 | 62.52 102 | 47.79 201 | 42.97 177 | 42.65 198 | 44.49 206 | 52.66 173 | 78.77 107 | 76.86 124 | 84.88 153 | 79.29 146 |
|
tfpn1000 | | | 63.81 169 | 66.31 154 | 60.90 176 | 75.76 117 | 75.74 154 | 65.14 186 | 60.14 138 | 56.47 149 | 35.99 197 | 55.11 118 | 52.30 170 | 43.42 195 | 76.21 151 | 75.34 159 | 84.97 151 | 73.01 187 |
|
tfpn_ndepth | | | 65.09 156 | 67.12 147 | 62.73 165 | 75.75 118 | 76.23 150 | 68.00 167 | 60.36 128 | 58.16 122 | 40.27 183 | 54.89 125 | 54.22 142 | 46.80 187 | 76.69 147 | 75.66 154 | 85.19 146 | 73.98 183 |
|
tpm cat1 | | | 65.41 151 | 63.81 181 | 67.28 130 | 75.61 119 | 72.88 173 | 75.32 108 | 52.85 183 | 62.97 89 | 63.66 68 | 53.24 154 | 53.29 160 | 61.83 115 | 65.54 205 | 64.14 208 | 74.43 202 | 74.60 177 |
|
CDS-MVSNet | | | 67.65 135 | 69.83 99 | 65.09 148 | 75.39 120 | 76.55 144 | 74.42 122 | 63.75 76 | 53.55 177 | 49.37 144 | 59.41 86 | 62.45 95 | 44.44 192 | 79.71 94 | 79.82 77 | 83.17 167 | 77.36 159 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
Fast-Effi-MVS+-dtu | | | 68.34 120 | 69.47 104 | 67.01 135 | 75.15 121 | 77.97 123 | 77.12 102 | 55.40 176 | 57.87 123 | 46.68 159 | 56.17 110 | 60.39 99 | 62.36 109 | 76.32 150 | 76.25 146 | 85.35 145 | 81.34 124 |
|
WR-MVS | | | 63.03 171 | 67.40 144 | 57.92 190 | 75.14 122 | 77.60 128 | 60.56 201 | 66.10 61 | 54.11 174 | 23.88 212 | 53.94 145 | 53.58 148 | 34.50 208 | 73.93 164 | 77.71 108 | 87.35 73 | 80.94 127 |
|
test-LLR | | | 64.42 161 | 64.36 177 | 64.49 154 | 75.02 123 | 63.93 202 | 66.61 179 | 61.96 108 | 54.41 170 | 47.77 149 | 57.46 101 | 60.25 100 | 55.20 162 | 70.80 187 | 69.33 186 | 80.40 178 | 74.38 179 |
|
test0.0.03 1 | | | 58.80 195 | 61.58 195 | 55.56 198 | 75.02 123 | 68.45 189 | 59.58 205 | 61.96 108 | 52.74 179 | 29.57 204 | 49.75 183 | 54.56 140 | 31.46 211 | 71.19 181 | 69.77 183 | 75.75 195 | 64.57 206 |
|
v1144 | | | 69.93 99 | 69.36 111 | 70.61 81 | 74.89 125 | 80.93 79 | 79.11 68 | 60.64 122 | 55.97 157 | 55.31 109 | 53.85 146 | 54.14 143 | 66.54 77 | 78.10 114 | 77.44 115 | 87.14 80 | 85.09 78 |
|
v13 | | | 69.52 106 | 68.76 123 | 70.41 89 | 74.88 126 | 77.02 139 | 78.52 89 | 58.86 152 | 56.61 148 | 56.91 94 | 54.00 144 | 56.17 129 | 66.11 91 | 77.93 115 | 76.74 132 | 87.21 75 | 82.83 104 |
|
v12 | | | 69.54 104 | 68.79 121 | 70.41 89 | 74.88 126 | 77.03 137 | 78.54 88 | 58.85 154 | 56.71 140 | 56.87 96 | 54.13 142 | 56.23 128 | 66.15 87 | 77.89 116 | 76.74 132 | 87.17 76 | 82.80 105 |
|
v11 | | | 69.37 109 | 68.65 127 | 70.20 93 | 74.87 128 | 76.97 140 | 78.29 92 | 58.55 162 | 56.38 151 | 56.04 105 | 54.02 143 | 54.98 137 | 66.47 78 | 78.30 111 | 76.91 123 | 86.97 89 | 83.02 103 |
|
V9 | | | 69.58 103 | 68.83 119 | 70.46 86 | 74.85 129 | 77.04 135 | 78.65 83 | 58.85 154 | 56.83 139 | 57.12 92 | 54.26 137 | 56.31 123 | 66.14 89 | 77.83 118 | 76.76 127 | 87.13 81 | 82.79 107 |
|
V14 | | | 69.59 102 | 68.86 118 | 70.45 88 | 74.83 130 | 77.04 135 | 78.70 82 | 58.83 158 | 56.95 136 | 57.08 93 | 54.41 133 | 56.34 122 | 66.15 87 | 77.77 119 | 76.76 127 | 87.08 86 | 82.74 110 |
|
v15 | | | 69.61 101 | 68.88 117 | 70.46 86 | 74.81 131 | 77.03 137 | 78.75 81 | 58.83 158 | 57.06 132 | 57.18 91 | 54.55 132 | 56.37 121 | 66.13 90 | 77.70 120 | 76.76 127 | 87.03 88 | 82.69 112 |
|
v7 | | | 70.33 88 | 69.87 96 | 70.88 70 | 74.79 132 | 81.04 78 | 79.22 66 | 60.57 124 | 57.70 129 | 56.65 102 | 54.23 139 | 55.29 135 | 66.95 70 | 78.28 112 | 77.47 113 | 87.12 84 | 85.05 80 |
|
v10 | | | 70.22 90 | 69.76 100 | 70.74 76 | 74.79 132 | 80.30 94 | 79.22 66 | 59.81 142 | 57.71 128 | 56.58 103 | 54.22 141 | 55.31 133 | 66.95 70 | 78.28 112 | 77.47 113 | 87.12 84 | 85.07 79 |
|
v1141 | | | 69.96 98 | 69.44 108 | 70.58 84 | 74.78 134 | 80.50 89 | 78.85 71 | 60.30 129 | 56.95 136 | 56.74 99 | 54.68 130 | 56.26 127 | 65.93 93 | 77.38 131 | 76.72 137 | 86.88 94 | 83.57 100 |
|
divwei89l23v2f112 | | | 69.97 96 | 69.44 108 | 70.58 84 | 74.78 134 | 80.50 89 | 78.85 71 | 60.30 129 | 56.97 135 | 56.75 98 | 54.67 131 | 56.27 126 | 65.92 94 | 77.37 132 | 76.72 137 | 86.88 94 | 83.58 99 |
|
v1 | | | 69.97 96 | 69.45 107 | 70.59 82 | 74.78 134 | 80.51 88 | 78.84 73 | 60.30 129 | 56.98 133 | 56.81 97 | 54.69 129 | 56.29 125 | 65.91 95 | 77.37 132 | 76.71 140 | 86.89 93 | 83.59 97 |
|
v17 | | | 70.03 95 | 69.43 110 | 70.72 78 | 74.75 137 | 77.09 132 | 78.78 80 | 58.85 154 | 59.53 114 | 58.72 82 | 54.87 126 | 57.39 111 | 66.38 80 | 77.60 124 | 76.75 130 | 86.83 97 | 82.80 105 |
|
v16 | | | 70.07 93 | 69.46 105 | 70.79 74 | 74.74 138 | 77.08 133 | 78.79 78 | 58.86 152 | 59.75 111 | 59.15 79 | 54.87 126 | 57.33 112 | 66.38 80 | 77.61 123 | 76.77 125 | 86.81 103 | 82.79 107 |
|
v8 | | | 70.23 89 | 69.86 98 | 70.67 80 | 74.69 139 | 79.82 96 | 78.79 78 | 59.18 148 | 58.80 118 | 58.20 84 | 55.00 121 | 57.33 112 | 66.31 86 | 77.51 128 | 76.71 140 | 86.82 98 | 83.88 93 |
|
v1neww | | | 70.34 86 | 69.93 94 | 70.82 72 | 74.68 140 | 80.61 85 | 78.80 76 | 60.17 134 | 58.74 119 | 58.10 86 | 55.00 121 | 57.28 115 | 66.33 83 | 77.53 125 | 76.74 132 | 86.82 98 | 83.61 95 |
|
v7new | | | 70.34 86 | 69.93 94 | 70.82 72 | 74.68 140 | 80.61 85 | 78.80 76 | 60.17 134 | 58.74 119 | 58.10 86 | 55.00 121 | 57.28 115 | 66.33 83 | 77.53 125 | 76.74 132 | 86.82 98 | 83.61 95 |
|
v6 | | | 70.35 85 | 69.94 93 | 70.83 71 | 74.68 140 | 80.62 84 | 78.81 75 | 60.16 137 | 58.81 117 | 58.17 85 | 55.01 120 | 57.31 114 | 66.32 85 | 77.53 125 | 76.73 136 | 86.82 98 | 83.62 94 |
|
v18 | | | 70.10 92 | 69.52 103 | 70.77 75 | 74.66 143 | 77.06 134 | 78.84 73 | 58.84 157 | 60.01 110 | 59.23 78 | 55.06 119 | 57.47 110 | 66.34 82 | 77.50 129 | 76.75 130 | 86.71 105 | 82.77 109 |
|
v2v482 | | | 70.05 94 | 69.46 105 | 70.74 76 | 74.62 144 | 80.32 93 | 79.00 69 | 60.62 123 | 57.41 130 | 56.89 95 | 55.43 114 | 55.14 136 | 66.39 79 | 77.25 137 | 77.14 120 | 86.90 91 | 83.57 100 |
|
v1192 | | | 69.50 107 | 68.83 119 | 70.29 92 | 74.49 145 | 80.92 81 | 78.55 85 | 60.54 125 | 55.04 166 | 54.21 112 | 52.79 163 | 52.33 168 | 66.92 72 | 77.88 117 | 77.35 118 | 87.04 87 | 85.51 70 |
|
DTE-MVSNet | | | 61.85 184 | 64.96 174 | 58.22 189 | 74.32 146 | 74.39 169 | 61.01 200 | 67.85 53 | 51.76 187 | 21.91 222 | 53.28 152 | 48.17 194 | 37.74 204 | 72.22 174 | 76.44 143 | 86.52 114 | 78.49 152 |
|
Vis-MVSNet | | | 72.77 71 | 77.20 62 | 67.59 121 | 74.19 147 | 84.01 58 | 76.61 106 | 61.69 112 | 60.62 106 | 50.61 136 | 70.25 50 | 71.31 64 | 55.57 159 | 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 | 74.07 148 | 77.17 131 | 78.24 93 | 57.45 166 | 72.44 58 | 64.02 67 | 69.05 52 | 75.92 49 | 64.86 98 | 75.18 158 | 75.27 160 | 82.47 169 | 84.53 86 |
|
v144192 | | | 69.34 110 | 68.68 126 | 70.12 95 | 74.06 149 | 80.54 87 | 78.08 95 | 60.54 125 | 54.99 168 | 54.13 113 | 52.92 160 | 52.80 164 | 66.73 75 | 77.13 139 | 76.72 137 | 87.15 77 | 85.63 66 |
|
v1921920 | | | 69.03 113 | 68.32 132 | 69.86 98 | 74.03 150 | 80.37 92 | 77.55 97 | 60.25 133 | 54.62 169 | 53.59 118 | 52.36 170 | 51.50 179 | 66.75 74 | 77.17 138 | 76.69 142 | 86.96 90 | 85.56 67 |
|
PEN-MVS | | | 62.96 172 | 65.77 164 | 59.70 183 | 73.98 151 | 75.45 156 | 63.39 194 | 67.61 54 | 52.49 181 | 25.49 211 | 53.39 149 | 49.12 191 | 40.85 201 | 71.94 177 | 77.26 119 | 86.86 96 | 80.72 129 |
|
v1240 | | | 68.64 118 | 67.89 139 | 69.51 102 | 73.89 152 | 80.26 95 | 76.73 104 | 59.97 141 | 53.43 178 | 53.08 121 | 51.82 173 | 50.84 182 | 66.62 76 | 76.79 144 | 76.77 125 | 86.78 104 | 85.34 74 |
|
GA-MVS | | | 68.14 122 | 69.17 114 | 66.93 137 | 73.77 153 | 78.50 114 | 74.45 119 | 58.28 163 | 55.11 165 | 48.44 146 | 60.08 81 | 53.99 146 | 61.50 117 | 78.43 110 | 77.57 111 | 85.13 147 | 80.54 131 |
|
pm-mvs1 | | | 65.62 150 | 67.42 143 | 63.53 161 | 73.66 154 | 76.39 148 | 69.66 158 | 60.87 121 | 49.73 196 | 43.97 172 | 51.24 176 | 57.00 119 | 48.16 181 | 79.89 92 | 77.84 105 | 84.85 155 | 79.82 140 |
|
dps | | | 64.00 168 | 62.99 184 | 65.18 147 | 73.29 155 | 72.07 176 | 68.98 164 | 53.07 182 | 57.74 127 | 58.41 83 | 55.55 113 | 47.74 197 | 60.89 122 | 69.53 196 | 67.14 200 | 76.44 194 | 71.19 194 |
|
v148 | | | 67.85 129 | 67.53 141 | 68.23 112 | 73.25 156 | 77.57 129 | 74.26 130 | 57.36 168 | 55.70 159 | 57.45 90 | 53.53 148 | 55.42 132 | 61.96 112 | 75.23 157 | 73.92 167 | 85.08 148 | 81.32 125 |
|
PatchMatch-RL | | | 67.78 131 | 66.65 153 | 69.10 105 | 73.01 157 | 72.69 174 | 68.49 165 | 61.85 110 | 62.93 90 | 60.20 77 | 56.83 106 | 50.42 184 | 69.52 59 | 75.62 155 | 74.46 166 | 81.51 172 | 73.62 185 |
|
GBi-Net | | | 70.78 79 | 73.37 75 | 67.76 115 | 72.95 158 | 78.00 118 | 75.15 111 | 62.72 92 | 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 | 72.95 158 | 78.00 118 | 75.15 111 | 62.72 92 | 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 | 72.95 158 | 78.00 118 | 75.15 111 | 62.69 96 | 63.29 87 | 51.25 132 | 55.64 111 | 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 | 72.88 161 | 77.98 121 | 74.96 117 | 62.72 92 | 64.13 82 | 51.44 128 | 58.37 93 | 69.02 75 | 57.43 139 | 79.43 100 | 79.57 82 | 86.59 112 | 81.81 122 |
|
LTVRE_ROB | | 59.44 16 | 61.82 187 | 62.64 188 | 60.87 177 | 72.83 162 | 77.19 130 | 64.37 190 | 58.97 149 | 33.56 225 | 28.00 208 | 52.59 168 | 42.21 209 | 63.93 102 | 74.52 160 | 76.28 144 | 77.15 191 | 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 | | | 67.05 145 | 66.94 149 | 67.17 131 | 72.35 163 | 78.97 101 | 73.26 143 | 58.88 151 | 51.16 189 | 50.90 133 | 48.21 185 | 50.11 186 | 60.96 119 | 77.70 120 | 77.38 116 | 86.68 109 | 85.05 80 |
|
tpm | | | 62.41 178 | 63.15 183 | 61.55 173 | 72.24 164 | 63.79 204 | 71.31 154 | 46.12 211 | 57.82 124 | 55.33 108 | 59.90 84 | 54.74 139 | 53.63 168 | 67.24 203 | 64.29 206 | 70.65 213 | 74.25 181 |
|
test20.03 | | | 53.93 206 | 56.28 206 | 51.19 208 | 72.19 165 | 65.83 197 | 53.20 213 | 61.08 118 | 42.74 210 | 22.08 220 | 37.07 208 | 45.76 204 | 24.29 224 | 70.44 191 | 69.04 188 | 74.31 203 | 63.05 210 |
|
CP-MVSNet | | | 62.68 174 | 65.49 167 | 59.40 186 | 71.84 166 | 75.34 157 | 62.87 196 | 67.04 57 | 52.64 180 | 27.19 209 | 53.38 150 | 48.15 195 | 41.40 199 | 71.26 180 | 75.68 153 | 86.07 123 | 82.00 118 |
|
PS-CasMVS | | | 62.38 180 | 65.06 171 | 59.25 187 | 71.73 167 | 75.21 164 | 62.77 197 | 66.99 58 | 51.94 186 | 26.96 210 | 52.00 172 | 47.52 198 | 41.06 200 | 71.16 183 | 75.60 156 | 85.97 132 | 81.97 120 |
|
WR-MVS_H | | | 61.83 186 | 65.87 163 | 57.12 193 | 71.72 168 | 76.87 141 | 61.45 199 | 66.19 59 | 51.97 185 | 22.92 219 | 53.13 157 | 52.30 170 | 33.80 209 | 71.03 184 | 75.00 163 | 86.65 110 | 80.78 128 |
|
USDC | | | 67.36 141 | 67.90 138 | 66.74 141 | 71.72 168 | 75.23 161 | 71.58 153 | 60.28 132 | 67.45 66 | 50.54 137 | 60.93 77 | 45.20 205 | 62.08 110 | 76.56 148 | 74.50 165 | 84.25 160 | 75.38 174 |
|
UGNet | | | 72.78 70 | 77.67 56 | 67.07 134 | 71.65 170 | 83.24 64 | 75.20 110 | 63.62 77 | 64.93 77 | 56.72 100 | 71.82 45 | 73.30 56 | 49.02 180 | 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 | | | 62.00 182 | 62.35 192 | 61.58 172 | 71.62 171 | 64.14 201 | 69.07 163 | 48.22 207 | 62.21 93 | 53.93 115 | 58.26 97 | 55.30 134 | 55.81 156 | 63.22 210 | 62.62 211 | 70.85 212 | 70.70 195 |
|
pmmvs4 | | | 67.89 128 | 67.39 145 | 68.48 111 | 71.60 172 | 73.57 172 | 74.45 119 | 60.98 119 | 64.65 79 | 57.97 88 | 54.95 124 | 51.73 177 | 61.88 113 | 73.78 165 | 75.11 162 | 83.99 163 | 77.91 155 |
|
testgi | | | 54.39 205 | 57.86 203 | 50.35 209 | 71.59 173 | 67.24 192 | 54.95 211 | 53.25 181 | 43.36 209 | 23.78 213 | 44.64 195 | 47.87 196 | 24.96 220 | 70.45 190 | 68.66 192 | 73.60 205 | 62.78 211 |
|
pmmvs6 | | | 62.41 178 | 62.88 185 | 61.87 171 | 71.38 174 | 75.18 165 | 67.76 169 | 59.45 146 | 41.64 212 | 42.52 180 | 37.33 207 | 52.91 161 | 46.87 186 | 77.67 122 | 76.26 145 | 83.23 166 | 79.18 149 |
|
FMVSNet1 | | | 68.84 115 | 70.47 90 | 66.94 136 | 71.35 175 | 77.68 126 | 74.71 118 | 62.35 106 | 56.93 138 | 49.94 141 | 50.01 180 | 64.59 89 | 57.07 142 | 81.33 67 | 80.72 64 | 86.25 115 | 82.00 118 |
|
PatchmatchNet | | | 64.21 167 | 64.65 175 | 63.69 159 | 71.29 176 | 68.66 187 | 69.63 159 | 51.70 191 | 63.04 88 | 53.77 117 | 59.83 85 | 58.34 107 | 60.23 126 | 68.54 200 | 66.06 203 | 75.56 197 | 68.08 201 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
CR-MVSNet | | | 64.83 158 | 65.54 166 | 64.01 158 | 70.64 177 | 69.41 183 | 65.97 182 | 52.74 184 | 57.81 125 | 52.65 123 | 54.27 135 | 56.31 123 | 60.92 120 | 72.20 175 | 73.09 171 | 81.12 175 | 75.69 171 |
|
MVSTER | | | 72.06 73 | 74.24 71 | 69.51 102 | 70.39 178 | 75.97 153 | 76.91 103 | 57.36 168 | 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 | | | 56.36 201 | 57.80 204 | 54.67 201 | 70.08 179 | 66.39 196 | 60.46 202 | 57.54 165 | 49.50 198 | 29.30 205 | 33.86 215 | 46.64 199 | 35.18 207 | 70.44 191 | 68.88 190 | 75.47 198 | 68.88 200 |
|
CMPMVS | | 47.78 17 | 62.49 177 | 62.52 189 | 62.46 166 | 70.01 180 | 70.66 181 | 62.97 195 | 51.84 190 | 51.98 184 | 56.71 101 | 42.87 197 | 53.62 147 | 57.80 135 | 72.23 173 | 70.37 182 | 75.45 199 | 75.91 168 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
v748 | | | 65.12 155 | 65.24 168 | 64.98 150 | 69.77 181 | 76.45 145 | 69.47 161 | 57.06 170 | 49.93 194 | 50.70 134 | 47.87 188 | 49.50 190 | 57.14 141 | 73.64 167 | 75.18 161 | 85.75 138 | 84.14 89 |
|
TDRefinement | | | 66.09 149 | 65.03 173 | 67.31 128 | 69.73 182 | 76.75 142 | 75.33 107 | 64.55 73 | 60.28 108 | 49.72 143 | 45.63 194 | 42.83 208 | 60.46 124 | 75.75 152 | 75.95 152 | 84.08 161 | 78.04 154 |
|
TinyColmap | | | 62.84 173 | 61.03 197 | 64.96 151 | 69.61 183 | 71.69 177 | 68.48 166 | 59.76 143 | 55.41 161 | 47.69 151 | 47.33 191 | 34.20 218 | 62.76 108 | 74.52 160 | 72.59 174 | 81.44 173 | 71.47 193 |
|
RPMNet | | | 61.71 188 | 62.88 185 | 60.34 179 | 69.51 184 | 69.41 183 | 63.48 193 | 49.23 199 | 57.81 125 | 45.64 165 | 50.51 178 | 50.12 185 | 53.13 172 | 68.17 202 | 68.49 194 | 81.07 176 | 75.62 173 |
|
IterMVS | | | 66.36 147 | 68.30 133 | 64.10 155 | 69.48 185 | 74.61 168 | 73.41 141 | 50.79 195 | 57.30 131 | 48.28 147 | 60.64 78 | 59.92 102 | 60.85 123 | 74.14 163 | 72.66 173 | 81.80 171 | 78.82 151 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
SixPastTwentyTwo | | | 61.84 185 | 62.45 190 | 61.12 175 | 69.20 186 | 72.20 175 | 62.03 198 | 57.40 167 | 46.54 205 | 38.03 191 | 57.14 105 | 41.72 210 | 58.12 134 | 69.67 195 | 71.58 177 | 81.94 170 | 78.30 153 |
|
MDTV_nov1_ep13 | | | 64.37 162 | 65.24 168 | 63.37 164 | 68.94 187 | 70.81 179 | 72.40 148 | 50.29 198 | 60.10 109 | 53.91 116 | 60.07 82 | 59.15 105 | 57.21 140 | 69.43 197 | 67.30 198 | 77.47 189 | 69.78 197 |
|
EPMVS | | | 60.00 193 | 61.97 193 | 57.71 191 | 68.46 188 | 63.17 208 | 64.54 189 | 48.23 206 | 63.30 86 | 44.72 169 | 60.19 80 | 56.05 131 | 50.85 176 | 65.27 207 | 62.02 213 | 69.44 215 | 63.81 208 |
|
Anonymous20231211 | | | 51.46 210 | 50.59 212 | 52.46 207 | 67.30 189 | 66.70 195 | 55.00 210 | 59.22 147 | 29.96 227 | 17.62 227 | 19.11 229 | 28.74 227 | 35.72 206 | 66.42 204 | 69.52 185 | 79.92 180 | 73.71 184 |
|
FC-MVSNet-test | | | 56.90 200 | 65.20 170 | 47.21 212 | 66.98 190 | 63.20 207 | 49.11 219 | 58.60 161 | 59.38 115 | 11.50 232 | 65.60 66 | 56.68 120 | 24.66 223 | 71.17 182 | 71.36 179 | 72.38 208 | 69.02 199 |
|
CVMVSNet | | | 62.55 175 | 65.89 162 | 58.64 188 | 66.95 191 | 69.15 185 | 66.49 181 | 56.29 174 | 52.46 182 | 32.70 201 | 59.27 87 | 58.21 108 | 50.09 177 | 71.77 178 | 71.39 178 | 79.31 183 | 78.99 150 |
|
FPMVS | | | 51.87 209 | 50.00 214 | 54.07 202 | 66.83 192 | 57.25 215 | 60.25 203 | 50.91 193 | 50.25 191 | 34.36 199 | 36.04 212 | 32.02 220 | 41.49 198 | 58.98 222 | 56.07 222 | 70.56 214 | 59.36 217 |
|
pmmvs-eth3d | | | 63.52 170 | 62.44 191 | 64.77 152 | 66.82 193 | 70.12 182 | 69.41 162 | 59.48 145 | 54.34 173 | 52.71 122 | 46.24 193 | 44.35 207 | 56.93 143 | 72.37 170 | 73.77 168 | 83.30 165 | 75.91 168 |
|
testpf | | | 47.41 213 | 48.47 219 | 46.18 213 | 66.30 194 | 50.67 224 | 48.15 220 | 42.60 221 | 37.10 220 | 28.75 206 | 40.97 201 | 39.01 215 | 30.82 212 | 52.95 227 | 53.74 226 | 60.46 225 | 64.87 205 |
|
TAMVS | | | 59.58 194 | 62.81 187 | 55.81 197 | 66.03 195 | 65.64 199 | 63.86 192 | 48.74 202 | 49.95 192 | 37.07 193 | 54.77 128 | 58.54 106 | 44.44 192 | 72.29 172 | 71.79 175 | 74.70 201 | 66.66 203 |
|
MDTV_nov1_ep13_2view | | | 60.16 192 | 60.51 199 | 59.75 182 | 65.39 196 | 69.05 186 | 68.00 167 | 48.29 205 | 51.99 183 | 45.95 163 | 48.01 186 | 49.64 189 | 53.39 170 | 68.83 199 | 66.52 202 | 77.47 189 | 69.55 198 |
|
pmmvs5 | | | 62.37 181 | 64.04 179 | 60.42 178 | 65.03 197 | 71.67 178 | 67.17 174 | 52.70 186 | 50.30 190 | 44.80 168 | 54.23 139 | 51.19 181 | 49.37 179 | 72.88 169 | 73.48 170 | 83.45 164 | 74.55 178 |
|
ambc | | | | 53.42 208 | | 64.99 198 | 63.36 206 | 49.96 217 | | 47.07 203 | 37.12 192 | 28.97 219 | 16.36 234 | 41.82 197 | 75.10 159 | 67.34 197 | 71.55 211 | 75.72 170 |
|
V42 | | | 68.76 117 | 69.63 101 | 67.74 117 | 64.93 199 | 78.01 117 | 78.30 91 | 56.48 172 | 58.65 121 | 56.30 104 | 54.26 137 | 57.03 118 | 64.85 99 | 77.47 130 | 77.01 122 | 85.60 141 | 84.96 82 |
|
PMVS | | 39.38 18 | 46.06 218 | 43.30 223 | 49.28 211 | 62.93 200 | 38.75 231 | 41.88 224 | 53.50 180 | 33.33 226 | 35.46 198 | 28.90 220 | 31.01 223 | 33.04 210 | 58.61 223 | 54.63 225 | 68.86 216 | 57.88 220 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
new-patchmatchnet | | | 46.97 216 | 49.47 215 | 44.05 218 | 62.82 201 | 56.55 216 | 45.35 222 | 52.01 188 | 42.47 211 | 17.04 228 | 35.73 213 | 35.21 217 | 21.84 229 | 61.27 215 | 54.83 224 | 65.26 223 | 60.26 214 |
|
ADS-MVSNet | | | 55.94 202 | 58.01 202 | 53.54 206 | 62.48 202 | 58.48 214 | 59.12 206 | 46.20 210 | 59.65 113 | 42.88 178 | 52.34 171 | 53.31 159 | 46.31 189 | 62.00 214 | 60.02 218 | 64.23 224 | 60.24 216 |
|
v52 | | | 65.23 153 | 66.24 155 | 64.06 156 | 61.94 203 | 76.42 146 | 72.06 150 | 54.30 178 | 49.94 193 | 50.04 139 | 47.41 190 | 52.42 166 | 60.23 126 | 75.71 153 | 76.22 147 | 85.78 136 | 85.56 67 |
|
V4 | | | 65.23 153 | 66.23 156 | 64.06 156 | 61.94 203 | 76.42 146 | 72.05 151 | 54.31 177 | 49.91 195 | 50.06 138 | 47.42 189 | 52.40 167 | 60.24 125 | 75.71 153 | 76.22 147 | 85.78 136 | 85.56 67 |
|
RPSCF | | | 67.64 136 | 71.25 84 | 63.43 163 | 61.86 205 | 70.73 180 | 67.26 173 | 50.86 194 | 74.20 54 | 58.91 80 | 67.49 61 | 69.33 72 | 64.10 101 | 71.41 179 | 68.45 195 | 77.61 188 | 77.17 160 |
|
MIMVSNet | | | 58.52 197 | 61.34 196 | 55.22 199 | 60.76 206 | 67.01 193 | 66.81 176 | 49.02 201 | 56.43 150 | 38.90 188 | 40.59 204 | 54.54 141 | 40.57 202 | 73.16 168 | 71.65 176 | 75.30 200 | 66.00 204 |
|
PatchT | | | 61.97 183 | 64.04 179 | 59.55 185 | 60.49 207 | 67.40 191 | 56.54 208 | 48.65 203 | 56.69 141 | 52.65 123 | 51.10 177 | 52.14 173 | 60.92 120 | 72.20 175 | 73.09 171 | 78.03 187 | 75.69 171 |
|
N_pmnet | | | 47.35 214 | 50.13 213 | 44.11 217 | 59.98 208 | 51.64 223 | 51.86 214 | 44.80 216 | 49.58 197 | 20.76 223 | 40.65 203 | 40.05 214 | 29.64 213 | 59.84 220 | 55.15 223 | 57.63 226 | 54.00 224 |
|
1111 | | | 43.08 220 | 44.02 222 | 41.98 219 | 59.22 209 | 49.27 227 | 41.48 225 | 45.63 213 | 35.01 221 | 23.06 217 | 28.60 221 | 30.15 224 | 27.22 215 | 60.42 218 | 57.97 220 | 55.27 229 | 46.74 226 |
|
.test1245 | | | 30.81 227 | 29.14 229 | 32.77 226 | 59.22 209 | 49.27 227 | 41.48 225 | 45.63 213 | 35.01 221 | 23.06 217 | 28.60 221 | 30.15 224 | 27.22 215 | 60.42 218 | 0.10 233 | 0.01 237 | 0.43 235 |
|
MVS-HIRNet | | | 54.41 204 | 52.10 211 | 57.11 194 | 58.99 211 | 56.10 217 | 49.68 218 | 49.10 200 | 46.18 206 | 52.15 127 | 33.18 216 | 46.11 203 | 56.10 152 | 63.19 211 | 59.70 219 | 76.64 193 | 60.25 215 |
|
PM-MVS | | | 60.48 191 | 60.94 198 | 59.94 181 | 58.85 212 | 66.83 194 | 64.27 191 | 51.39 192 | 55.03 167 | 48.03 148 | 50.00 182 | 40.79 212 | 58.26 133 | 69.20 198 | 67.13 201 | 78.84 185 | 77.60 157 |
|
anonymousdsp | | | 65.28 152 | 67.98 137 | 62.13 168 | 58.73 213 | 73.98 171 | 67.10 175 | 50.69 196 | 48.41 199 | 47.66 152 | 54.27 135 | 52.75 165 | 61.45 118 | 76.71 146 | 80.20 76 | 87.13 81 | 89.53 46 |
|
LP | | | 53.62 207 | 53.43 207 | 53.83 204 | 58.51 214 | 62.59 211 | 57.31 207 | 46.04 212 | 47.86 200 | 42.69 179 | 36.08 211 | 36.86 216 | 46.53 188 | 64.38 208 | 64.25 207 | 71.92 209 | 62.00 213 |
|
TESTMET0.1,1 | | | 61.10 189 | 64.36 177 | 57.29 192 | 57.53 215 | 63.93 202 | 66.61 179 | 36.22 226 | 54.41 170 | 47.77 149 | 57.46 101 | 60.25 100 | 55.20 162 | 70.80 187 | 69.33 186 | 80.40 178 | 74.38 179 |
|
EU-MVSNet | | | 54.63 203 | 58.69 201 | 49.90 210 | 56.99 216 | 62.70 210 | 56.41 209 | 50.64 197 | 45.95 207 | 23.14 216 | 50.42 179 | 46.51 200 | 36.63 205 | 65.51 206 | 64.85 205 | 75.57 196 | 74.91 176 |
|
FMVSNet5 | | | 57.24 198 | 60.02 200 | 53.99 203 | 56.45 217 | 62.74 209 | 65.27 185 | 47.03 208 | 55.14 164 | 39.55 186 | 40.88 202 | 53.42 157 | 41.83 196 | 72.35 171 | 71.10 180 | 73.79 204 | 64.50 207 |
|
test2356 | | | 47.20 215 | 48.62 218 | 45.54 215 | 56.38 218 | 54.89 219 | 50.62 215 | 45.08 215 | 38.65 217 | 23.40 214 | 36.23 210 | 31.10 222 | 29.31 214 | 62.76 212 | 62.49 212 | 68.48 217 | 54.23 223 |
|
testus | | | 45.61 219 | 49.06 217 | 41.59 220 | 56.13 219 | 55.28 218 | 43.51 223 | 39.64 224 | 37.74 218 | 18.23 225 | 35.52 214 | 31.28 221 | 24.69 222 | 62.46 213 | 62.90 210 | 67.33 219 | 58.26 219 |
|
test-mter | | | 60.84 190 | 64.62 176 | 56.42 195 | 55.99 220 | 64.18 200 | 65.39 184 | 34.23 228 | 54.39 172 | 46.21 161 | 57.40 103 | 59.49 104 | 55.86 155 | 71.02 185 | 69.65 184 | 80.87 177 | 76.20 167 |
|
CHOSEN 280x420 | | | 58.70 196 | 61.88 194 | 54.98 200 | 55.45 221 | 50.55 225 | 64.92 187 | 40.36 222 | 55.21 163 | 38.13 190 | 48.31 184 | 63.76 91 | 63.03 107 | 73.73 166 | 68.58 193 | 68.00 218 | 73.04 186 |
|
PMMVS | | | 65.06 157 | 69.17 114 | 60.26 180 | 55.25 222 | 63.43 205 | 66.71 178 | 43.01 220 | 62.41 91 | 50.64 135 | 69.44 51 | 67.04 83 | 63.29 105 | 74.36 162 | 73.54 169 | 82.68 168 | 73.99 182 |
|
testmv | | | 42.58 221 | 44.36 220 | 40.49 221 | 54.63 223 | 52.76 221 | 41.21 227 | 44.37 217 | 28.83 228 | 12.87 229 | 27.16 224 | 25.03 229 | 23.01 225 | 60.83 216 | 61.13 214 | 66.88 220 | 54.81 221 |
|
test1235678 | | | 42.57 222 | 44.36 220 | 40.49 221 | 54.63 223 | 52.75 222 | 41.21 227 | 44.37 217 | 28.82 229 | 12.87 229 | 27.15 225 | 25.01 230 | 23.01 225 | 60.83 216 | 61.13 214 | 66.88 220 | 54.81 221 |
|
no-one | | | 36.35 225 | 37.59 226 | 34.91 224 | 46.13 225 | 49.89 226 | 27.99 232 | 43.56 219 | 20.91 233 | 7.03 235 | 14.64 231 | 15.50 235 | 18.92 230 | 42.95 228 | 60.20 217 | 65.84 222 | 59.03 218 |
|
Gipuma | | | 36.38 224 | 35.80 227 | 37.07 223 | 45.76 226 | 33.90 232 | 29.81 231 | 48.47 204 | 39.91 215 | 18.02 226 | 8.00 235 | 8.14 237 | 25.14 219 | 59.29 221 | 61.02 216 | 55.19 230 | 40.31 228 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
pmmvs3 | | | 47.65 212 | 49.08 216 | 45.99 214 | 44.61 227 | 54.79 220 | 50.04 216 | 31.95 231 | 33.91 223 | 29.90 203 | 30.37 217 | 33.53 219 | 46.31 189 | 63.50 209 | 63.67 209 | 73.14 207 | 63.77 209 |
|
MIMVSNet1 | | | 49.27 211 | 53.25 209 | 44.62 216 | 44.61 227 | 61.52 213 | 53.61 212 | 52.18 187 | 41.62 213 | 18.68 224 | 28.14 223 | 41.58 211 | 25.50 218 | 68.46 201 | 69.04 188 | 73.15 206 | 62.37 212 |
|
test12356 | | | 35.10 226 | 38.50 225 | 31.13 227 | 44.14 229 | 43.70 230 | 32.27 230 | 34.42 227 | 26.51 231 | 9.47 233 | 25.22 227 | 20.34 231 | 10.86 232 | 53.47 225 | 56.15 221 | 55.59 228 | 44.11 227 |
|
MDA-MVSNet-bldmvs | | | 53.37 208 | 53.01 210 | 53.79 205 | 43.67 230 | 67.95 190 | 59.69 204 | 57.92 164 | 43.69 208 | 32.41 202 | 41.47 200 | 27.89 228 | 52.38 174 | 56.97 224 | 65.99 204 | 76.68 192 | 67.13 202 |
|
E-PMN | | | 21.77 229 | 18.24 231 | 25.89 228 | 40.22 231 | 19.58 235 | 12.46 236 | 39.87 223 | 18.68 235 | 6.71 236 | 9.57 232 | 4.31 240 | 22.36 228 | 19.89 233 | 27.28 231 | 33.73 232 | 28.34 232 |
|
EMVS | | | 20.98 230 | 17.15 232 | 25.44 229 | 39.51 232 | 19.37 236 | 12.66 235 | 39.59 225 | 19.10 234 | 6.62 237 | 9.27 233 | 4.40 239 | 22.43 227 | 17.99 234 | 24.40 232 | 31.81 233 | 25.53 233 |
|
new_pmnet | | | 38.40 223 | 42.64 224 | 33.44 225 | 37.54 233 | 45.00 229 | 36.60 229 | 32.72 230 | 40.27 214 | 12.72 231 | 29.89 218 | 28.90 226 | 24.78 221 | 53.17 226 | 52.90 227 | 56.31 227 | 48.34 225 |
|
PMMVS2 | | | 25.60 228 | 29.75 228 | 20.76 231 | 28.00 234 | 30.93 233 | 23.10 233 | 29.18 232 | 23.14 232 | 1.46 239 | 18.23 230 | 16.54 233 | 5.08 233 | 40.22 229 | 41.40 229 | 37.76 231 | 37.79 230 |
|
tmp_tt | | | | | 14.50 233 | 14.68 235 | 7.17 238 | 10.46 238 | 2.21 234 | 37.73 219 | 28.71 207 | 25.26 226 | 16.98 232 | 4.37 234 | 31.49 230 | 29.77 230 | 26.56 234 | |
|
MVE | | 19.12 19 | 20.47 231 | 23.27 230 | 17.20 232 | 12.66 236 | 25.41 234 | 10.52 237 | 34.14 229 | 14.79 236 | 6.53 238 | 8.79 234 | 4.68 238 | 16.64 231 | 29.49 231 | 41.63 228 | 22.73 235 | 38.11 229 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
GG-mvs-BLEND | | | 46.86 217 | 67.51 142 | 22.75 230 | 0.05 237 | 76.21 151 | 64.69 188 | 0.04 235 | 61.90 95 | 0.09 240 | 55.57 112 | 71.32 63 | 0.08 235 | 70.54 189 | 67.19 199 | 71.58 210 | 69.86 196 |
|
testmvs | | | 0.09 232 | 0.15 233 | 0.02 234 | 0.01 238 | 0.02 239 | 0.05 240 | 0.01 236 | 0.11 237 | 0.01 241 | 0.26 237 | 0.01 241 | 0.06 237 | 0.10 235 | 0.10 233 | 0.01 237 | 0.43 235 |
|
sosnet-low-res | | | 0.00 234 | 0.00 235 | 0.00 236 | 0.00 239 | 0.00 241 | 0.00 242 | 0.00 238 | 0.00 239 | 0.00 242 | 0.00 238 | 0.00 242 | 0.00 238 | 0.00 237 | 0.00 236 | 0.00 240 | 0.00 237 |
|
sosnet | | | 0.00 234 | 0.00 235 | 0.00 236 | 0.00 239 | 0.00 241 | 0.00 242 | 0.00 238 | 0.00 239 | 0.00 242 | 0.00 238 | 0.00 242 | 0.00 238 | 0.00 237 | 0.00 236 | 0.00 240 | 0.00 237 |
|
test123 | | | 0.09 232 | 0.14 234 | 0.02 234 | 0.00 239 | 0.02 239 | 0.02 241 | 0.01 236 | 0.09 238 | 0.00 242 | 0.30 236 | 0.00 242 | 0.08 235 | 0.03 236 | 0.09 235 | 0.01 237 | 0.45 234 |
|
MTAPA | | | | | | | | | | | 83.48 1 | | 86.45 12 | | | | | |
|
MTMP | | | | | | | | | | | 82.66 3 | | 84.91 20 | | | | | |
|
Patchmatch-RL test | | | | | | | | 2.85 239 | | | | | | | | | | |
|
NP-MVS | | | | | | | | | | 80.10 40 | | | | | | | | |
|
Patchmtry | | | | | | | 65.80 198 | 65.97 182 | 52.74 184 | | 52.65 123 | | | | | | | |
|
DeepMVS_CX | | | | | | | 18.74 237 | 18.55 234 | 8.02 233 | 26.96 230 | 7.33 234 | 23.81 228 | 13.05 236 | 25.99 217 | 25.17 232 | | 22.45 236 | 36.25 231 |
|