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