ESAPD | | | 95.35 1 | 95.97 1 | 94.63 2 | 97.35 5 | 97.95 1 | 97.09 2 | 93.48 1 | 93.91 8 | 90.13 11 | 96.41 2 | 95.14 1 | 92.88 4 | 95.64 2 | 94.53 7 | 96.86 2 | 98.21 1 |
|
APDe-MVS | | | 95.23 2 | 95.69 2 | 94.70 1 | 97.12 10 | 97.81 3 | 97.19 1 | 92.83 2 | 95.06 2 | 90.98 5 | 96.47 1 | 92.77 8 | 93.38 1 | 95.34 5 | 94.21 11 | 96.68 4 | 98.17 2 |
|
HSP-MVS | | | 94.83 3 | 95.37 3 | 94.21 5 | 96.82 19 | 97.94 2 | 96.69 3 | 92.37 7 | 93.97 7 | 90.29 9 | 96.16 3 | 93.71 3 | 92.70 5 | 94.80 12 | 93.13 31 | 96.37 8 | 97.90 5 |
|
HPM-MVS++ | | | 94.60 4 | 94.91 6 | 94.24 4 | 97.86 1 | 96.53 27 | 96.14 6 | 92.51 4 | 93.87 10 | 90.76 7 | 93.45 13 | 93.84 2 | 92.62 6 | 95.11 8 | 94.08 14 | 95.58 39 | 97.48 10 |
|
SD-MVS | | | 94.53 5 | 95.22 4 | 93.73 10 | 95.69 30 | 97.03 9 | 95.77 16 | 91.95 8 | 94.41 3 | 91.35 4 | 94.97 4 | 93.34 5 | 91.80 15 | 94.72 15 | 93.99 15 | 95.82 26 | 98.07 3 |
|
TSAR-MVS + MP. | | | 94.48 6 | 94.97 5 | 93.90 8 | 95.53 31 | 97.01 10 | 96.69 3 | 90.71 17 | 94.24 4 | 90.92 6 | 94.97 4 | 92.19 10 | 93.03 2 | 94.83 11 | 93.60 21 | 96.51 7 | 97.97 4 |
|
APD-MVS | | | 94.37 7 | 94.47 11 | 94.26 3 | 97.18 8 | 96.99 11 | 96.53 5 | 92.68 3 | 92.45 19 | 89.96 12 | 94.53 7 | 91.63 14 | 92.89 3 | 94.58 17 | 93.82 18 | 96.31 11 | 97.26 13 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
CNVR-MVS | | | 94.37 7 | 94.65 7 | 94.04 7 | 97.29 6 | 97.11 7 | 96.00 8 | 92.43 6 | 93.45 11 | 89.85 14 | 90.92 20 | 93.04 6 | 92.59 7 | 95.77 1 | 94.82 3 | 96.11 15 | 97.42 12 |
|
SteuartSystems-ACMMP | | | 94.06 9 | 94.65 7 | 93.38 14 | 96.97 15 | 97.36 5 | 96.12 7 | 91.78 9 | 92.05 23 | 87.34 25 | 94.42 8 | 90.87 18 | 91.87 14 | 95.47 4 | 94.59 6 | 96.21 13 | 97.77 7 |
Skip Steuart: Steuart Systems R&D Blog. |
HFP-MVS | | | 94.02 10 | 94.22 13 | 93.78 9 | 97.25 7 | 96.85 15 | 95.81 14 | 90.94 16 | 94.12 5 | 90.29 9 | 94.09 10 | 89.98 24 | 92.52 8 | 93.94 25 | 93.49 26 | 95.87 21 | 97.10 18 |
|
ACMMP_Plus | | | 93.94 11 | 94.49 10 | 93.30 15 | 97.03 13 | 97.31 6 | 95.96 9 | 91.30 13 | 93.41 13 | 88.55 19 | 93.00 14 | 90.33 21 | 91.43 21 | 95.53 3 | 94.41 9 | 95.53 41 | 97.47 11 |
|
MCST-MVS | | | 93.81 12 | 94.06 14 | 93.53 12 | 96.79 20 | 96.85 15 | 95.95 10 | 91.69 11 | 92.20 21 | 87.17 27 | 90.83 22 | 93.41 4 | 91.96 12 | 94.49 19 | 93.50 24 | 97.61 1 | 97.12 17 |
|
MPTG | | | 93.80 13 | 93.45 21 | 94.20 6 | 97.53 2 | 96.43 31 | 95.88 13 | 91.12 15 | 94.09 6 | 92.74 3 | 87.68 28 | 90.77 19 | 92.04 11 | 94.74 14 | 93.56 23 | 95.91 19 | 96.85 22 |
|
ACMMPR | | | 93.72 14 | 93.94 15 | 93.48 13 | 97.07 11 | 96.93 12 | 95.78 15 | 90.66 19 | 93.88 9 | 89.24 16 | 93.53 12 | 89.08 31 | 92.24 9 | 93.89 27 | 93.50 24 | 95.88 20 | 96.73 26 |
|
NCCC | | | 93.69 15 | 93.66 18 | 93.72 11 | 97.37 4 | 96.66 24 | 95.93 12 | 92.50 5 | 93.40 14 | 88.35 20 | 87.36 30 | 92.33 9 | 92.18 10 | 94.89 10 | 94.09 13 | 96.00 16 | 96.91 21 |
|
MP-MVS | | | 93.35 16 | 93.59 19 | 93.08 18 | 97.39 3 | 96.82 17 | 95.38 19 | 90.71 17 | 90.82 30 | 88.07 22 | 92.83 16 | 90.29 22 | 91.32 22 | 94.03 22 | 93.19 30 | 95.61 37 | 97.16 15 |
|
CP-MVS | | | 93.25 17 | 93.26 22 | 93.24 16 | 96.84 18 | 96.51 28 | 95.52 18 | 90.61 20 | 92.37 20 | 88.88 17 | 90.91 21 | 89.52 27 | 91.91 13 | 93.64 29 | 92.78 37 | 95.69 32 | 97.09 19 |
|
DeepC-MVS_fast | | 88.76 1 | 93.10 18 | 93.02 25 | 93.19 17 | 97.13 9 | 96.51 28 | 95.35 20 | 91.19 14 | 93.14 16 | 88.14 21 | 85.26 36 | 89.49 28 | 91.45 18 | 95.17 6 | 95.07 1 | 95.85 24 | 96.48 29 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
TSAR-MVS + ACMM | | | 92.97 19 | 94.51 9 | 91.16 31 | 95.88 28 | 96.59 25 | 95.09 23 | 90.45 23 | 93.42 12 | 83.01 47 | 94.68 6 | 90.74 20 | 88.74 34 | 94.75 13 | 93.78 19 | 93.82 138 | 97.63 8 |
|
train_agg | | | 92.87 20 | 93.53 20 | 92.09 25 | 96.88 17 | 95.38 43 | 95.94 11 | 90.59 21 | 90.65 32 | 83.65 45 | 94.31 9 | 91.87 13 | 90.30 26 | 93.38 31 | 92.42 38 | 95.17 57 | 96.73 26 |
|
PGM-MVS | | | 92.76 21 | 93.03 24 | 92.45 23 | 97.03 13 | 96.67 23 | 95.73 17 | 87.92 35 | 90.15 37 | 86.53 31 | 92.97 15 | 88.33 37 | 91.69 16 | 93.62 30 | 93.03 32 | 95.83 25 | 96.41 32 |
|
CSCG | | | 92.76 21 | 93.16 23 | 92.29 24 | 96.30 22 | 97.74 4 | 94.67 27 | 88.98 29 | 92.46 18 | 89.73 15 | 86.67 32 | 92.15 11 | 88.69 35 | 92.26 45 | 92.92 35 | 95.40 45 | 97.89 6 |
|
TSAR-MVS + GP. | | | 92.71 23 | 93.91 16 | 91.30 29 | 91.96 65 | 96.00 36 | 93.43 35 | 87.94 34 | 92.53 17 | 86.27 35 | 93.57 11 | 91.94 12 | 91.44 20 | 93.29 32 | 92.89 36 | 96.78 3 | 97.15 16 |
|
DeepPCF-MVS | | 88.51 2 | 92.64 24 | 94.42 12 | 90.56 35 | 94.84 37 | 96.92 13 | 91.31 55 | 89.61 25 | 95.16 1 | 84.55 40 | 89.91 24 | 91.45 15 | 90.15 28 | 95.12 7 | 94.81 4 | 92.90 159 | 97.58 9 |
|
X-MVS | | | 92.36 25 | 92.75 26 | 91.90 27 | 96.89 16 | 96.70 20 | 95.25 21 | 90.48 22 | 91.50 28 | 83.95 42 | 88.20 26 | 88.82 33 | 89.11 31 | 93.75 28 | 93.43 27 | 95.75 31 | 96.83 24 |
|
DeepC-MVS | | 87.86 3 | 92.26 26 | 91.86 29 | 92.73 20 | 96.18 23 | 96.87 14 | 95.19 22 | 91.76 10 | 92.17 22 | 86.58 30 | 81.79 43 | 85.85 43 | 90.88 24 | 94.57 18 | 94.61 5 | 95.80 27 | 97.18 14 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
PHI-MVS | | | 92.05 27 | 93.74 17 | 90.08 38 | 94.96 34 | 97.06 8 | 93.11 39 | 87.71 37 | 90.71 31 | 80.78 57 | 92.40 17 | 91.03 16 | 87.68 45 | 94.32 21 | 94.48 8 | 96.21 13 | 96.16 35 |
|
ACMMP | | | 92.03 28 | 92.16 27 | 91.87 28 | 95.88 28 | 96.55 26 | 94.47 29 | 89.49 26 | 91.71 26 | 85.26 36 | 91.52 19 | 84.48 48 | 90.21 27 | 92.82 40 | 91.63 44 | 95.92 18 | 96.42 31 |
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 |
MSLP-MVS++ | | | 92.02 29 | 91.40 31 | 92.75 19 | 96.01 26 | 95.88 39 | 93.73 34 | 89.00 27 | 89.89 38 | 90.31 8 | 81.28 48 | 88.85 32 | 91.45 18 | 92.88 39 | 94.24 10 | 96.00 16 | 96.76 25 |
|
3Dnovator+ | | 86.06 4 | 91.60 30 | 90.86 36 | 92.47 22 | 96.00 27 | 96.50 30 | 94.70 26 | 87.83 36 | 90.49 33 | 89.92 13 | 74.68 73 | 89.35 29 | 90.66 25 | 94.02 23 | 94.14 12 | 95.67 34 | 96.85 22 |
|
CPTT-MVS | | | 91.39 31 | 90.95 34 | 91.91 26 | 95.06 32 | 95.24 45 | 95.02 24 | 88.98 29 | 91.02 29 | 86.71 29 | 84.89 38 | 88.58 36 | 91.60 17 | 90.82 75 | 89.67 78 | 94.08 116 | 96.45 30 |
|
CANet | | | 91.33 32 | 91.46 30 | 91.18 30 | 95.01 33 | 96.71 19 | 93.77 32 | 87.39 39 | 87.72 45 | 87.26 26 | 81.77 44 | 89.73 25 | 87.32 50 | 94.43 20 | 93.86 17 | 96.31 11 | 96.02 38 |
|
CDPH-MVS | | | 91.14 33 | 92.01 28 | 90.11 37 | 96.18 23 | 96.18 34 | 94.89 25 | 88.80 31 | 88.76 42 | 77.88 72 | 89.18 25 | 87.71 40 | 87.29 51 | 93.13 34 | 93.31 29 | 95.62 36 | 95.84 40 |
|
MVS_0304 | | | 90.88 34 | 91.35 32 | 90.34 36 | 93.91 45 | 96.79 18 | 94.49 28 | 86.54 43 | 86.57 49 | 82.85 48 | 81.68 46 | 89.70 26 | 87.57 47 | 94.64 16 | 93.93 16 | 96.67 5 | 96.15 36 |
|
MVS_111021_HR | | | 90.56 35 | 91.29 33 | 89.70 43 | 94.71 39 | 95.63 41 | 91.81 51 | 86.38 44 | 87.53 46 | 81.29 54 | 87.96 27 | 85.43 45 | 87.69 44 | 93.90 26 | 92.93 34 | 96.33 9 | 95.69 43 |
|
3Dnovator | | 85.17 5 | 90.48 36 | 89.90 40 | 91.16 31 | 94.88 36 | 95.74 40 | 93.82 31 | 85.36 50 | 89.28 39 | 87.81 23 | 74.34 75 | 87.40 41 | 88.56 36 | 93.07 35 | 93.74 20 | 96.53 6 | 95.71 42 |
|
AdaColmap | | | 90.29 37 | 88.38 49 | 92.53 21 | 96.10 25 | 95.19 46 | 92.98 40 | 91.40 12 | 89.08 41 | 88.65 18 | 78.35 59 | 81.44 61 | 91.30 23 | 90.81 76 | 90.21 64 | 94.72 84 | 93.59 69 |
|
OMC-MVS | | | 90.23 38 | 90.40 37 | 90.03 39 | 93.45 50 | 95.29 44 | 91.89 50 | 86.34 45 | 93.25 15 | 84.94 39 | 81.72 45 | 86.65 42 | 88.90 32 | 91.69 52 | 90.27 63 | 94.65 89 | 93.95 65 |
|
MVS_111021_LR | | | 90.14 39 | 90.89 35 | 89.26 47 | 93.23 52 | 94.05 60 | 90.43 59 | 84.65 55 | 90.16 36 | 84.52 41 | 90.14 23 | 83.80 52 | 87.99 41 | 92.50 43 | 90.92 52 | 94.74 82 | 94.70 57 |
|
DELS-MVS | | | 89.71 40 | 89.68 41 | 89.74 41 | 93.75 47 | 96.22 33 | 93.76 33 | 85.84 46 | 82.53 62 | 85.05 38 | 78.96 56 | 84.24 49 | 84.25 64 | 94.91 9 | 94.91 2 | 95.78 30 | 96.02 38 |
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 |
EPNet | | | 89.60 41 | 89.91 39 | 89.24 48 | 96.45 21 | 93.61 65 | 92.95 41 | 88.03 33 | 85.74 53 | 83.36 46 | 87.29 31 | 83.05 55 | 80.98 78 | 92.22 46 | 91.85 42 | 93.69 145 | 95.58 46 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
QAPM | | | 89.49 42 | 89.58 42 | 89.38 46 | 94.73 38 | 95.94 37 | 92.35 43 | 85.00 53 | 85.69 54 | 80.03 61 | 76.97 65 | 87.81 39 | 87.87 42 | 92.18 49 | 92.10 40 | 96.33 9 | 96.40 33 |
|
canonicalmvs | | | 89.36 43 | 89.92 38 | 88.70 52 | 91.38 66 | 95.92 38 | 91.81 51 | 82.61 85 | 90.37 34 | 82.73 50 | 82.09 41 | 79.28 74 | 88.30 39 | 91.17 59 | 93.59 22 | 95.36 48 | 97.04 20 |
|
HQP-MVS | | | 89.13 44 | 89.58 42 | 88.60 54 | 93.53 49 | 93.67 63 | 93.29 37 | 87.58 38 | 88.53 43 | 75.50 76 | 87.60 29 | 80.32 65 | 87.07 52 | 90.66 81 | 89.95 71 | 94.62 92 | 96.35 34 |
|
TAPA-MVS | | 84.37 7 | 88.91 45 | 88.93 45 | 88.89 49 | 93.00 56 | 94.85 51 | 92.00 47 | 84.84 54 | 91.68 27 | 80.05 60 | 79.77 52 | 84.56 47 | 88.17 40 | 90.11 86 | 89.00 96 | 95.30 52 | 92.57 94 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
PCF-MVS | | 84.60 6 | 88.66 46 | 87.75 58 | 89.73 42 | 93.06 55 | 96.02 35 | 93.22 38 | 90.00 24 | 82.44 64 | 80.02 62 | 77.96 60 | 85.16 46 | 87.36 49 | 88.54 105 | 88.54 101 | 94.72 84 | 95.61 45 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
CLD-MVS | | | 88.66 46 | 88.52 47 | 88.82 50 | 91.37 67 | 94.22 57 | 92.82 42 | 82.08 93 | 88.27 44 | 85.14 37 | 81.86 42 | 78.53 76 | 85.93 58 | 91.17 59 | 90.61 58 | 95.55 40 | 95.00 50 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
PLC | | 83.76 9 | 88.61 48 | 86.83 63 | 90.70 33 | 94.22 42 | 92.63 85 | 91.50 53 | 87.19 40 | 89.16 40 | 86.87 28 | 75.51 70 | 80.87 62 | 89.98 29 | 90.01 87 | 89.20 90 | 94.41 106 | 90.45 147 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
TSAR-MVS + COLMAP | | | 88.40 49 | 89.09 44 | 87.60 60 | 92.72 60 | 93.92 62 | 92.21 44 | 85.57 49 | 91.73 25 | 73.72 84 | 91.75 18 | 73.22 97 | 87.64 46 | 91.49 53 | 89.71 77 | 93.73 144 | 91.82 116 |
|
CNLPA | | | 88.40 49 | 87.00 61 | 90.03 39 | 93.73 48 | 94.28 56 | 89.56 68 | 85.81 47 | 91.87 24 | 87.55 24 | 69.53 107 | 81.49 60 | 89.23 30 | 89.45 94 | 88.59 100 | 94.31 110 | 93.82 67 |
|
MAR-MVS | | | 88.39 51 | 88.44 48 | 88.33 57 | 94.90 35 | 95.06 48 | 90.51 58 | 83.59 65 | 85.27 55 | 79.07 65 | 77.13 63 | 82.89 56 | 87.70 43 | 92.19 48 | 92.32 39 | 94.23 111 | 94.20 63 |
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 |
ACMP | | 83.90 8 | 88.32 52 | 88.06 52 | 88.62 53 | 92.18 63 | 93.98 61 | 91.28 56 | 85.24 51 | 86.69 48 | 81.23 55 | 85.62 34 | 75.13 86 | 87.01 53 | 89.83 89 | 89.77 76 | 94.79 78 | 95.43 49 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
LGP-MVS_train | | | 88.25 53 | 88.55 46 | 87.89 58 | 92.84 59 | 93.66 64 | 93.35 36 | 85.22 52 | 85.77 52 | 74.03 83 | 86.60 33 | 76.29 83 | 86.62 55 | 91.20 57 | 90.58 60 | 95.29 53 | 95.75 41 |
|
PVSNet_BlendedMVS | | | 88.19 54 | 88.00 53 | 88.42 55 | 92.71 61 | 94.82 52 | 89.08 74 | 83.81 61 | 84.91 56 | 86.38 32 | 79.14 54 | 78.11 77 | 82.66 67 | 93.05 36 | 91.10 47 | 95.86 22 | 94.86 53 |
|
PVSNet_Blended | | | 88.19 54 | 88.00 53 | 88.42 55 | 92.71 61 | 94.82 52 | 89.08 74 | 83.81 61 | 84.91 56 | 86.38 32 | 79.14 54 | 78.11 77 | 82.66 67 | 93.05 36 | 91.10 47 | 95.86 22 | 94.86 53 |
|
OpenMVS | | 82.53 11 | 87.71 56 | 86.84 62 | 88.73 51 | 94.42 41 | 95.06 48 | 91.02 57 | 83.49 68 | 82.50 63 | 82.24 52 | 67.62 118 | 85.48 44 | 85.56 59 | 91.19 58 | 91.30 46 | 95.67 34 | 94.75 55 |
|
ACMM | | 83.27 10 | 87.68 57 | 86.09 68 | 89.54 45 | 93.26 51 | 92.19 88 | 91.43 54 | 86.74 42 | 86.02 51 | 82.85 48 | 75.63 69 | 75.14 85 | 88.41 37 | 90.68 80 | 89.99 68 | 94.59 93 | 92.97 78 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
OPM-MVS | | | 87.56 58 | 85.80 69 | 89.62 44 | 93.90 46 | 94.09 59 | 94.12 30 | 88.18 32 | 75.40 112 | 77.30 75 | 76.41 66 | 77.93 79 | 88.79 33 | 92.20 47 | 90.82 53 | 95.40 45 | 93.72 68 |
|
PVSNet_Blended_VisFu | | | 87.40 59 | 87.80 55 | 86.92 63 | 92.86 57 | 95.40 42 | 88.56 86 | 83.45 71 | 79.55 87 | 82.26 51 | 74.49 74 | 84.03 50 | 79.24 128 | 92.97 38 | 91.53 45 | 95.15 59 | 96.65 28 |
|
MVS_Test | | | 86.93 60 | 87.24 60 | 86.56 64 | 90.10 94 | 93.47 67 | 90.31 60 | 80.12 112 | 83.55 60 | 78.12 68 | 79.58 53 | 79.80 69 | 85.45 60 | 90.17 85 | 90.59 59 | 95.29 53 | 93.53 70 |
|
EPP-MVSNet | | | 86.55 61 | 87.76 57 | 85.15 68 | 90.52 79 | 94.41 55 | 87.24 105 | 82.32 90 | 81.79 67 | 73.60 85 | 78.57 58 | 82.41 57 | 82.07 71 | 91.23 55 | 90.39 62 | 95.14 60 | 95.48 47 |
|
DI_MVS_plusplus_trai | | | 86.41 62 | 85.54 70 | 87.42 61 | 89.24 102 | 93.13 73 | 92.16 46 | 82.65 83 | 82.30 65 | 80.75 58 | 68.30 114 | 80.41 64 | 85.01 61 | 90.56 82 | 90.07 66 | 94.70 86 | 94.01 64 |
|
IS_MVSNet | | | 86.18 63 | 88.18 51 | 83.85 90 | 91.02 70 | 94.72 54 | 87.48 97 | 82.46 87 | 81.05 74 | 70.28 95 | 76.98 64 | 82.20 59 | 76.65 143 | 93.97 24 | 93.38 28 | 95.18 56 | 94.97 51 |
|
UA-Net | | | 86.07 64 | 87.78 56 | 84.06 86 | 92.85 58 | 95.11 47 | 87.73 93 | 84.38 56 | 73.22 138 | 73.18 87 | 79.99 51 | 89.22 30 | 71.47 176 | 93.22 33 | 93.03 32 | 94.76 81 | 90.69 142 |
|
MVSTER | | | 86.03 65 | 86.12 67 | 85.93 65 | 88.62 105 | 89.93 125 | 89.33 70 | 79.91 115 | 81.87 66 | 81.35 53 | 81.07 49 | 74.91 87 | 80.66 84 | 92.13 50 | 90.10 65 | 95.68 33 | 92.80 84 |
|
LS3D | | | 85.96 66 | 84.37 76 | 87.81 59 | 94.13 43 | 93.27 70 | 90.26 61 | 89.00 27 | 84.91 56 | 72.84 89 | 71.74 93 | 72.47 99 | 87.45 48 | 89.53 93 | 89.09 93 | 93.20 154 | 89.60 149 |
|
UGNet | | | 85.90 67 | 88.23 50 | 83.18 98 | 88.96 103 | 94.10 58 | 87.52 96 | 83.60 64 | 81.66 68 | 77.90 71 | 80.76 50 | 83.19 54 | 66.70 195 | 91.13 70 | 90.71 57 | 94.39 107 | 96.06 37 |
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 |
diffmvs | | | 85.70 68 | 86.35 66 | 84.95 70 | 87.75 110 | 90.96 101 | 89.09 73 | 78.56 135 | 86.50 50 | 80.44 59 | 77.86 61 | 83.93 51 | 81.64 73 | 85.52 151 | 86.79 124 | 92.21 166 | 92.87 81 |
|
CANet_DTU | | | 85.43 69 | 87.72 59 | 82.76 102 | 90.95 73 | 93.01 79 | 89.99 62 | 75.46 171 | 82.67 61 | 64.91 146 | 83.14 39 | 80.09 66 | 80.68 83 | 92.03 51 | 91.03 49 | 94.57 95 | 92.08 108 |
|
Effi-MVS+ | | | 85.33 70 | 85.08 72 | 85.63 67 | 89.69 101 | 93.42 68 | 89.90 63 | 80.31 110 | 79.32 88 | 72.48 91 | 73.52 85 | 74.03 90 | 86.55 56 | 90.99 72 | 89.98 69 | 94.83 76 | 94.27 62 |
|
FC-MVSNet-train | | | 85.18 71 | 85.31 71 | 85.03 69 | 90.67 74 | 91.62 94 | 87.66 94 | 83.61 63 | 79.75 84 | 74.37 82 | 78.69 57 | 71.21 103 | 78.91 130 | 91.23 55 | 89.96 70 | 94.96 67 | 94.69 58 |
|
GBi-Net | | | 84.51 72 | 84.80 73 | 84.17 83 | 84.20 149 | 89.95 122 | 89.70 65 | 80.37 106 | 81.17 70 | 75.50 76 | 69.63 102 | 79.69 71 | 79.75 113 | 90.73 77 | 90.72 54 | 95.52 42 | 91.71 121 |
|
test1 | | | 84.51 72 | 84.80 73 | 84.17 83 | 84.20 149 | 89.95 122 | 89.70 65 | 80.37 106 | 81.17 70 | 75.50 76 | 69.63 102 | 79.69 71 | 79.75 113 | 90.73 77 | 90.72 54 | 95.52 42 | 91.71 121 |
|
FMVSNet3 | | | 84.44 74 | 84.64 75 | 84.21 82 | 84.32 148 | 90.13 120 | 89.85 64 | 80.37 106 | 81.17 70 | 75.50 76 | 69.63 102 | 79.69 71 | 79.62 116 | 89.72 91 | 90.52 61 | 95.59 38 | 91.58 127 |
|
Vis-MVSNet | | | 84.38 75 | 86.68 65 | 81.70 118 | 87.65 114 | 94.89 50 | 88.14 88 | 80.90 104 | 74.48 122 | 68.23 114 | 77.53 62 | 80.72 63 | 69.98 181 | 92.68 41 | 91.90 41 | 95.33 51 | 94.58 59 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
FMVSNet2 | | | 83.87 76 | 83.73 79 | 84.05 88 | 84.20 149 | 89.95 122 | 89.70 65 | 80.21 111 | 79.17 90 | 74.89 80 | 65.91 124 | 77.49 80 | 79.75 113 | 90.87 74 | 91.00 51 | 95.52 42 | 91.71 121 |
|
MSDG | | | 83.87 76 | 81.02 101 | 87.19 62 | 92.17 64 | 89.80 128 | 89.15 71 | 85.72 48 | 80.61 79 | 79.24 64 | 66.66 122 | 68.75 111 | 82.69 66 | 87.95 111 | 87.44 114 | 94.19 112 | 85.92 183 |
|
Fast-Effi-MVS+ | | | 83.77 78 | 82.98 81 | 84.69 71 | 87.98 108 | 91.87 91 | 88.10 90 | 77.70 145 | 78.10 96 | 73.04 88 | 69.13 109 | 68.51 112 | 86.66 54 | 90.49 83 | 89.85 73 | 94.67 88 | 92.88 80 |
|
Vis-MVSNet (Re-imp) | | | 83.65 79 | 86.81 64 | 79.96 153 | 90.46 82 | 92.71 84 | 84.84 152 | 82.00 94 | 80.93 76 | 62.44 163 | 76.29 67 | 82.32 58 | 65.54 198 | 92.29 44 | 91.66 43 | 94.49 101 | 91.47 128 |
|
tfpn111 | | | 83.51 80 | 82.68 83 | 84.47 76 | 90.30 86 | 93.09 74 | 89.05 76 | 82.72 77 | 75.14 113 | 69.49 103 | 74.24 76 | 63.13 132 | 80.38 91 | 91.15 64 | 89.51 80 | 94.91 69 | 92.50 100 |
|
RPSCF | | | 83.46 81 | 83.36 80 | 83.59 94 | 87.75 110 | 87.35 161 | 84.82 153 | 79.46 126 | 83.84 59 | 78.12 68 | 82.69 40 | 79.87 67 | 82.60 69 | 82.47 189 | 81.13 193 | 88.78 195 | 86.13 181 |
|
PatchMatch-RL | | | 83.34 82 | 81.36 95 | 85.65 66 | 90.33 85 | 89.52 135 | 84.36 156 | 81.82 96 | 80.87 78 | 79.29 63 | 74.04 78 | 62.85 139 | 86.05 57 | 88.40 107 | 87.04 121 | 92.04 168 | 86.77 174 |
|
IterMVS-LS | | | 83.28 83 | 82.95 82 | 83.65 92 | 88.39 107 | 88.63 149 | 86.80 124 | 78.64 134 | 76.56 103 | 73.43 86 | 72.52 92 | 75.35 84 | 80.81 81 | 86.43 134 | 88.51 102 | 93.84 137 | 92.66 88 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
tfpn200view9 | | | 82.86 84 | 81.46 92 | 84.48 75 | 90.30 86 | 93.09 74 | 89.05 76 | 82.71 79 | 75.14 113 | 69.56 100 | 65.72 126 | 63.13 132 | 80.38 91 | 91.15 64 | 89.51 80 | 94.91 69 | 92.50 100 |
|
conf200view11 | | | 82.85 85 | 81.46 92 | 84.47 76 | 90.30 86 | 93.09 74 | 89.05 76 | 82.72 77 | 75.14 113 | 69.49 103 | 65.72 126 | 63.13 132 | 80.38 91 | 91.15 64 | 89.51 80 | 94.91 69 | 92.50 100 |
|
thres200 | | | 82.77 86 | 81.25 97 | 84.54 72 | 90.38 83 | 93.05 77 | 89.13 72 | 82.67 81 | 74.40 123 | 69.53 102 | 65.69 129 | 63.03 137 | 80.63 85 | 91.15 64 | 89.42 85 | 94.88 73 | 92.04 110 |
|
thres400 | | | 82.68 87 | 81.15 98 | 84.47 76 | 90.52 79 | 92.89 83 | 88.95 82 | 82.71 79 | 74.33 124 | 69.22 107 | 65.31 131 | 62.61 141 | 80.63 85 | 90.96 73 | 89.50 84 | 94.79 78 | 92.45 105 |
|
conf0.01 | | | 82.64 88 | 81.02 101 | 84.53 74 | 90.30 86 | 93.22 72 | 89.05 76 | 82.75 75 | 75.14 113 | 69.69 99 | 67.15 120 | 59.19 181 | 80.38 91 | 91.16 62 | 89.51 80 | 95.00 65 | 91.76 119 |
|
IB-MVS | | 79.09 12 | 82.60 89 | 82.19 86 | 83.07 99 | 91.08 69 | 93.55 66 | 80.90 184 | 81.35 100 | 76.56 103 | 80.87 56 | 64.81 140 | 69.97 106 | 68.87 185 | 85.64 144 | 90.06 67 | 95.36 48 | 94.74 56 |
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 |
thres100view900 | | | 82.55 90 | 81.01 104 | 84.34 79 | 90.30 86 | 92.27 86 | 89.04 81 | 82.77 74 | 75.14 113 | 69.56 100 | 65.72 126 | 63.13 132 | 79.62 116 | 89.97 88 | 89.26 88 | 94.73 83 | 91.61 126 |
|
conf0.002 | | | 82.54 91 | 80.83 109 | 84.54 72 | 90.28 91 | 93.24 71 | 89.05 76 | 82.75 75 | 75.14 113 | 69.75 98 | 67.99 115 | 57.12 192 | 80.38 91 | 91.16 62 | 89.79 74 | 95.02 63 | 91.36 130 |
|
thres600view7 | | | 82.53 92 | 81.02 101 | 84.28 80 | 90.61 75 | 93.05 77 | 88.57 84 | 82.67 81 | 74.12 128 | 68.56 112 | 65.09 135 | 62.13 152 | 80.40 90 | 91.15 64 | 89.02 95 | 94.88 73 | 92.59 91 |
|
view600 | | | 82.51 93 | 81.00 105 | 84.27 81 | 90.56 78 | 92.95 81 | 88.57 84 | 82.57 86 | 74.16 127 | 68.70 111 | 65.13 134 | 62.15 151 | 80.36 96 | 91.15 64 | 88.98 97 | 94.87 75 | 92.48 103 |
|
view800 | | | 82.38 94 | 80.93 106 | 84.06 86 | 90.59 77 | 92.96 80 | 88.11 89 | 82.44 88 | 73.92 129 | 68.10 115 | 65.07 136 | 61.64 154 | 80.10 102 | 91.17 59 | 89.24 89 | 95.01 64 | 92.56 95 |
|
CHOSEN 1792x2688 | | | 82.16 95 | 80.91 108 | 83.61 93 | 91.14 68 | 92.01 90 | 89.55 69 | 79.15 130 | 79.87 83 | 70.29 94 | 52.51 206 | 72.56 98 | 81.39 74 | 88.87 100 | 88.17 106 | 90.15 187 | 92.37 106 |
|
Effi-MVS+-dtu | | | 82.05 96 | 81.76 88 | 82.38 104 | 87.72 112 | 90.56 106 | 86.90 122 | 78.05 141 | 73.85 132 | 66.85 123 | 71.29 95 | 71.90 101 | 82.00 72 | 86.64 129 | 85.48 165 | 92.76 161 | 92.58 93 |
|
EPNet_dtu | | | 81.98 97 | 83.82 78 | 79.83 155 | 94.10 44 | 85.97 179 | 87.29 102 | 84.08 60 | 80.61 79 | 59.96 184 | 81.62 47 | 77.19 81 | 62.91 202 | 87.21 115 | 86.38 135 | 90.66 183 | 87.77 165 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
UniMVSNet_NR-MVSNet | | | 81.87 98 | 81.33 96 | 82.50 103 | 85.31 136 | 91.30 97 | 85.70 138 | 84.25 57 | 75.89 107 | 64.21 149 | 66.95 121 | 64.65 125 | 80.22 98 | 87.07 118 | 89.18 91 | 95.27 55 | 94.29 60 |
|
ACMH | | 78.52 14 | 81.86 99 | 80.45 114 | 83.51 95 | 90.51 81 | 91.22 98 | 85.62 141 | 84.23 58 | 70.29 161 | 62.21 164 | 69.04 111 | 64.05 130 | 84.48 63 | 87.57 113 | 88.45 103 | 94.01 121 | 92.54 98 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
ACMH+ | | 79.08 13 | 81.84 100 | 80.06 117 | 83.91 89 | 89.92 99 | 90.62 105 | 86.21 133 | 83.48 70 | 73.88 131 | 65.75 137 | 66.38 123 | 65.30 122 | 84.63 62 | 85.90 139 | 87.25 118 | 93.45 149 | 91.13 132 |
|
tfpn | | | 81.79 101 | 80.06 117 | 83.82 91 | 90.61 75 | 92.91 82 | 87.62 95 | 82.34 89 | 73.66 135 | 67.46 118 | 64.99 137 | 55.50 200 | 79.77 112 | 91.12 71 | 89.62 79 | 95.14 60 | 92.59 91 |
|
MS-PatchMatch | | | 81.79 101 | 81.44 94 | 82.19 107 | 90.35 84 | 89.29 139 | 88.08 91 | 75.36 172 | 77.60 98 | 69.00 108 | 64.37 143 | 78.87 75 | 77.14 142 | 88.03 110 | 85.70 161 | 93.19 155 | 86.24 180 |
|
tfpn_ndepth | | | 81.77 103 | 82.29 85 | 81.15 133 | 89.79 100 | 91.71 93 | 85.49 144 | 81.63 99 | 79.17 90 | 64.76 147 | 73.04 87 | 68.14 116 | 70.62 179 | 88.72 101 | 87.88 110 | 94.63 91 | 87.38 167 |
|
PMMVS | | | 81.65 104 | 84.05 77 | 78.86 160 | 78.56 206 | 82.63 200 | 83.10 164 | 67.22 203 | 81.39 69 | 70.11 97 | 84.91 37 | 79.74 70 | 82.12 70 | 87.31 114 | 85.70 161 | 92.03 169 | 86.67 177 |
|
FMVSNet1 | | | 81.64 105 | 80.61 113 | 82.84 101 | 82.36 190 | 89.20 141 | 88.67 83 | 79.58 124 | 70.79 155 | 72.63 90 | 58.95 186 | 72.26 100 | 79.34 127 | 90.73 77 | 90.72 54 | 94.47 102 | 91.62 125 |
|
CDS-MVSNet | | | 81.63 106 | 82.09 87 | 81.09 135 | 87.21 119 | 90.28 116 | 87.46 99 | 80.33 109 | 69.06 174 | 70.66 92 | 71.30 94 | 73.87 91 | 67.99 188 | 89.58 92 | 89.87 72 | 92.87 160 | 90.69 142 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
HyFIR lowres test | | | 81.62 107 | 79.45 128 | 84.14 85 | 91.00 71 | 93.38 69 | 88.27 87 | 78.19 139 | 76.28 105 | 70.18 96 | 48.78 210 | 73.69 93 | 83.52 65 | 87.05 119 | 87.83 112 | 93.68 146 | 89.15 152 |
|
UniMVSNet (Re) | | | 81.22 108 | 81.08 100 | 81.39 125 | 85.35 135 | 91.76 92 | 84.93 151 | 82.88 73 | 76.13 106 | 65.02 145 | 64.94 138 | 63.09 136 | 75.17 150 | 87.71 112 | 89.04 94 | 94.97 66 | 94.88 52 |
|
DU-MVS | | | 81.20 109 | 80.30 115 | 82.25 105 | 84.98 143 | 90.94 103 | 85.70 138 | 83.58 66 | 75.74 109 | 64.21 149 | 65.30 132 | 59.60 178 | 80.22 98 | 86.89 122 | 89.31 86 | 94.77 80 | 94.29 60 |
|
thresconf0.02 | | | 81.14 110 | 80.93 106 | 81.39 125 | 90.01 98 | 91.31 96 | 86.79 125 | 82.28 91 | 76.97 102 | 61.46 175 | 74.24 76 | 62.08 153 | 72.98 168 | 88.70 102 | 87.90 108 | 94.81 77 | 85.28 186 |
|
tfpn1000 | | | 81.03 111 | 81.70 89 | 80.25 151 | 90.18 92 | 91.35 95 | 83.96 159 | 81.15 103 | 78.00 97 | 62.11 166 | 73.37 86 | 65.75 119 | 69.17 184 | 88.68 103 | 87.44 114 | 94.93 68 | 87.29 169 |
|
conf0.05thres1000 | | | 81.00 112 | 79.12 129 | 83.20 97 | 90.14 93 | 92.15 89 | 87.05 117 | 82.09 92 | 68.11 180 | 66.19 128 | 59.67 180 | 61.10 165 | 79.05 129 | 90.47 84 | 89.11 92 | 94.68 87 | 93.22 72 |
|
CostFormer | | | 80.94 113 | 80.21 116 | 81.79 113 | 87.69 113 | 88.58 150 | 87.47 98 | 70.66 189 | 80.02 81 | 77.88 72 | 73.03 88 | 71.40 102 | 78.24 134 | 79.96 199 | 79.63 195 | 88.82 194 | 88.84 153 |
|
tfpnview11 | | | 80.84 114 | 81.10 99 | 80.54 145 | 90.10 94 | 90.96 101 | 85.44 145 | 81.84 95 | 75.77 108 | 59.27 188 | 73.54 82 | 64.40 126 | 71.69 173 | 89.16 96 | 87.97 107 | 94.91 69 | 85.92 183 |
|
USDC | | | 80.69 115 | 79.89 121 | 81.62 121 | 86.48 124 | 89.11 144 | 86.53 129 | 78.86 131 | 81.15 73 | 63.48 156 | 72.98 89 | 59.12 184 | 81.16 76 | 87.10 117 | 85.01 169 | 93.23 153 | 84.77 191 |
|
tfpn_n400 | | | 80.63 116 | 80.79 110 | 80.43 148 | 90.02 96 | 91.08 99 | 85.34 147 | 81.79 97 | 72.93 141 | 59.27 188 | 73.54 82 | 64.40 126 | 71.61 174 | 89.05 98 | 88.21 104 | 94.56 96 | 86.32 178 |
|
tfpnconf | | | 80.63 116 | 80.79 110 | 80.43 148 | 90.02 96 | 91.08 99 | 85.34 147 | 81.79 97 | 72.93 141 | 59.27 188 | 73.54 82 | 64.40 126 | 71.61 174 | 89.05 98 | 88.21 104 | 94.56 96 | 86.32 178 |
|
TranMVSNet+NR-MVSNet | | | 80.52 118 | 79.84 122 | 81.33 128 | 84.92 145 | 90.39 110 | 85.53 143 | 84.22 59 | 74.27 125 | 60.68 182 | 64.93 139 | 59.96 173 | 77.48 139 | 86.75 127 | 89.28 87 | 95.12 62 | 93.29 71 |
|
DWT-MVSNet_training | | | 80.51 119 | 78.05 148 | 83.39 96 | 88.64 104 | 88.33 155 | 86.11 135 | 76.33 156 | 79.65 85 | 78.64 67 | 69.62 105 | 58.89 186 | 80.82 79 | 80.50 196 | 82.03 191 | 89.77 190 | 87.36 168 |
|
COLMAP_ROB | | 76.78 15 | 80.50 120 | 78.49 134 | 82.85 100 | 90.96 72 | 89.65 133 | 86.20 134 | 83.40 72 | 77.15 100 | 66.54 124 | 62.27 148 | 65.62 121 | 77.89 137 | 85.23 163 | 84.70 173 | 92.11 167 | 84.83 190 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
CHOSEN 280x420 | | | 80.28 121 | 81.66 90 | 78.67 162 | 82.92 183 | 79.24 212 | 85.36 146 | 66.79 205 | 78.11 95 | 70.32 93 | 75.03 72 | 79.87 67 | 81.09 77 | 89.07 97 | 83.16 182 | 85.54 211 | 87.17 171 |
|
NR-MVSNet | | | 80.25 122 | 79.98 120 | 80.56 144 | 85.20 138 | 90.94 103 | 85.65 140 | 83.58 66 | 75.74 109 | 61.36 177 | 65.30 132 | 56.75 194 | 72.38 169 | 88.46 106 | 88.80 98 | 95.16 58 | 93.87 66 |
|
v6 | | | 80.11 123 | 78.47 135 | 82.01 108 | 83.97 156 | 90.49 107 | 87.19 110 | 79.67 120 | 71.59 148 | 67.51 117 | 61.26 152 | 62.46 146 | 79.81 111 | 85.49 153 | 86.18 148 | 93.89 131 | 91.86 113 |
|
v1neww | | | 80.09 124 | 78.45 137 | 82.00 109 | 83.97 156 | 90.49 107 | 87.18 111 | 79.67 120 | 71.49 149 | 67.44 119 | 61.24 154 | 62.41 147 | 79.83 108 | 85.49 153 | 86.19 145 | 93.88 133 | 91.86 113 |
|
v7new | | | 80.09 124 | 78.45 137 | 82.00 109 | 83.97 156 | 90.49 107 | 87.18 111 | 79.67 120 | 71.49 149 | 67.44 119 | 61.24 154 | 62.41 147 | 79.83 108 | 85.49 153 | 86.19 145 | 93.88 133 | 91.86 113 |
|
pmmvs4 | | | 79.99 126 | 78.08 144 | 82.22 106 | 83.04 180 | 87.16 164 | 84.95 150 | 78.80 133 | 78.64 93 | 74.53 81 | 64.61 141 | 59.41 179 | 79.45 126 | 84.13 175 | 84.54 175 | 92.53 163 | 88.08 160 |
|
Fast-Effi-MVS+-dtu | | | 79.95 127 | 80.69 112 | 79.08 158 | 86.36 125 | 89.14 143 | 85.85 136 | 72.28 183 | 72.85 143 | 59.32 187 | 70.43 100 | 68.42 113 | 77.57 138 | 86.14 136 | 86.44 134 | 93.11 156 | 91.39 129 |
|
v8 | | | 79.90 128 | 78.39 140 | 81.66 120 | 83.97 156 | 89.81 127 | 87.16 114 | 77.40 147 | 71.49 149 | 67.71 116 | 61.24 154 | 62.49 144 | 79.83 108 | 85.48 157 | 86.17 149 | 93.89 131 | 92.02 112 |
|
v2v482 | | | 79.84 129 | 78.07 145 | 81.90 112 | 83.75 169 | 90.21 119 | 87.17 113 | 79.85 119 | 70.65 156 | 65.93 136 | 61.93 149 | 60.07 172 | 80.82 79 | 85.25 162 | 86.71 125 | 93.88 133 | 91.70 124 |
|
Baseline_NR-MVSNet | | | 79.84 129 | 78.37 141 | 81.55 123 | 84.98 143 | 86.66 168 | 85.06 149 | 83.49 68 | 75.57 111 | 63.31 157 | 58.22 190 | 60.97 166 | 78.00 136 | 86.89 122 | 87.13 119 | 94.47 102 | 93.15 74 |
|
tpmp4_e23 | | | 79.82 131 | 77.96 153 | 82.00 109 | 87.59 115 | 86.93 165 | 87.81 92 | 72.21 184 | 79.99 82 | 78.02 70 | 67.83 117 | 64.77 123 | 78.74 131 | 79.99 198 | 78.90 198 | 87.65 200 | 87.29 169 |
|
v7 | | | 79.79 132 | 78.28 142 | 81.54 124 | 83.73 170 | 90.34 115 | 87.27 103 | 78.27 138 | 70.50 158 | 65.59 138 | 60.59 169 | 60.47 168 | 80.46 88 | 86.90 121 | 86.63 128 | 93.92 127 | 92.56 95 |
|
v1 | | | 79.76 133 | 78.06 147 | 81.74 116 | 83.89 163 | 90.38 111 | 87.20 107 | 79.88 118 | 70.23 162 | 66.17 133 | 60.92 162 | 61.56 155 | 79.50 124 | 85.37 158 | 86.17 149 | 93.81 139 | 91.77 117 |
|
v1141 | | | 79.75 134 | 78.04 149 | 81.75 114 | 83.89 163 | 90.37 112 | 87.20 107 | 79.89 117 | 70.23 162 | 66.18 130 | 60.92 162 | 61.48 159 | 79.54 120 | 85.36 159 | 86.17 149 | 93.81 139 | 91.76 119 |
|
divwei89l23v2f112 | | | 79.75 134 | 78.04 149 | 81.75 114 | 83.90 160 | 90.37 112 | 87.21 106 | 79.90 116 | 70.20 164 | 66.18 130 | 60.92 162 | 61.48 159 | 79.52 123 | 85.36 159 | 86.17 149 | 93.81 139 | 91.77 117 |
|
v18 | | | 79.71 136 | 77.98 152 | 81.73 117 | 84.02 155 | 86.67 167 | 87.37 100 | 76.35 155 | 72.61 144 | 68.86 109 | 61.35 151 | 62.65 140 | 79.94 104 | 85.49 153 | 86.21 140 | 93.85 136 | 90.92 135 |
|
v16 | | | 79.65 137 | 77.91 154 | 81.69 119 | 84.04 153 | 86.65 170 | 87.20 107 | 76.32 157 | 72.41 145 | 68.71 110 | 61.13 159 | 62.52 143 | 79.93 105 | 85.55 149 | 86.22 138 | 93.92 127 | 90.91 136 |
|
v10 | | | 79.62 138 | 78.19 143 | 81.28 129 | 83.73 170 | 89.69 132 | 87.27 103 | 76.86 151 | 70.50 158 | 65.46 139 | 60.58 171 | 60.47 168 | 80.44 89 | 86.91 120 | 86.63 128 | 93.93 125 | 92.55 97 |
|
v17 | | | 79.59 139 | 77.88 155 | 81.60 122 | 84.03 154 | 86.66 168 | 87.13 116 | 76.31 158 | 72.09 146 | 68.29 113 | 61.15 158 | 62.57 142 | 79.90 106 | 85.55 149 | 86.20 143 | 93.93 125 | 90.93 134 |
|
V42 | | | 79.59 139 | 78.43 139 | 80.94 139 | 82.79 186 | 89.71 131 | 86.66 126 | 76.73 153 | 71.38 152 | 67.42 121 | 61.01 160 | 62.30 149 | 78.39 133 | 85.56 148 | 86.48 132 | 93.65 147 | 92.60 90 |
|
GA-MVS | | | 79.52 141 | 79.71 125 | 79.30 157 | 85.68 131 | 90.36 114 | 84.55 154 | 78.44 136 | 70.47 160 | 57.87 195 | 68.52 113 | 61.38 163 | 76.21 145 | 89.40 95 | 87.89 109 | 93.04 158 | 89.96 148 |
|
test-LLR | | | 79.47 142 | 79.84 122 | 79.03 159 | 87.47 116 | 82.40 203 | 81.24 179 | 78.05 141 | 73.72 133 | 62.69 160 | 73.76 79 | 74.42 88 | 73.49 163 | 84.61 171 | 82.99 184 | 91.25 177 | 87.01 172 |
|
v1144 | | | 79.38 143 | 77.83 156 | 81.18 131 | 83.62 172 | 90.23 117 | 87.15 115 | 78.35 137 | 69.13 173 | 64.02 153 | 60.20 177 | 59.41 179 | 80.14 101 | 86.78 125 | 86.57 130 | 93.81 139 | 92.53 99 |
|
MDTV_nov1_ep13 | | | 79.14 144 | 79.49 127 | 78.74 161 | 85.40 134 | 86.89 166 | 84.32 158 | 70.29 191 | 78.85 92 | 69.42 105 | 75.37 71 | 73.29 96 | 75.64 148 | 80.61 195 | 79.48 197 | 87.36 201 | 81.91 199 |
|
v15 | | | 79.13 145 | 77.37 160 | 81.19 130 | 83.90 160 | 86.56 172 | 87.01 118 | 76.15 162 | 70.20 164 | 66.48 125 | 60.71 167 | 61.55 156 | 79.60 118 | 85.59 147 | 86.19 145 | 93.98 123 | 90.80 141 |
|
V14 | | | 79.11 146 | 77.35 162 | 81.16 132 | 83.90 160 | 86.54 173 | 86.94 119 | 76.10 164 | 70.14 166 | 66.41 127 | 60.59 169 | 61.54 157 | 79.59 119 | 85.64 144 | 86.20 143 | 94.04 119 | 90.82 139 |
|
V9 | | | 79.08 147 | 77.32 164 | 81.14 134 | 83.89 163 | 86.52 174 | 86.85 123 | 76.06 165 | 70.02 167 | 66.42 126 | 60.44 172 | 61.52 158 | 79.54 120 | 85.68 143 | 86.21 140 | 94.08 116 | 90.83 138 |
|
TDRefinement | | | 79.05 148 | 77.05 170 | 81.39 125 | 88.45 106 | 89.00 146 | 86.92 120 | 82.65 83 | 74.21 126 | 64.41 148 | 59.17 183 | 59.16 182 | 74.52 156 | 85.23 163 | 85.09 168 | 91.37 175 | 87.51 166 |
|
v12 | | | 79.03 149 | 77.28 165 | 81.06 136 | 83.88 167 | 86.49 175 | 86.62 127 | 76.02 166 | 69.99 168 | 66.18 130 | 60.34 175 | 61.44 161 | 79.54 120 | 85.70 142 | 86.21 140 | 94.11 115 | 90.82 139 |
|
v11 | | | 79.02 150 | 77.36 161 | 80.95 138 | 83.89 163 | 86.48 176 | 86.53 129 | 75.77 170 | 69.69 170 | 65.21 144 | 60.36 174 | 60.24 171 | 80.32 97 | 87.20 116 | 86.54 131 | 93.96 124 | 91.02 133 |
|
v13 | | | 78.99 151 | 77.25 167 | 81.02 137 | 83.87 168 | 86.47 177 | 86.60 128 | 75.96 168 | 69.87 169 | 66.07 134 | 60.25 176 | 61.41 162 | 79.49 125 | 85.72 141 | 86.22 138 | 94.14 114 | 90.84 137 |
|
v1192 | | | 78.94 152 | 77.33 163 | 80.82 140 | 83.25 176 | 89.90 126 | 86.91 121 | 77.72 144 | 68.63 177 | 62.61 162 | 59.17 183 | 57.53 190 | 80.62 87 | 86.89 122 | 86.47 133 | 93.79 143 | 92.75 87 |
|
v144192 | | | 78.81 153 | 77.22 168 | 80.67 142 | 82.95 181 | 89.79 129 | 86.40 131 | 77.42 146 | 68.26 179 | 63.13 158 | 59.50 181 | 58.13 188 | 80.08 103 | 85.93 138 | 86.08 154 | 94.06 118 | 92.83 83 |
|
IterMVS | | | 78.79 154 | 79.71 125 | 77.71 167 | 85.26 137 | 85.91 180 | 84.54 155 | 69.84 195 | 73.38 137 | 61.25 178 | 70.53 99 | 70.35 104 | 74.43 157 | 85.21 165 | 83.80 179 | 90.95 181 | 88.77 154 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
CR-MVSNet | | | 78.71 155 | 78.86 131 | 78.55 163 | 85.85 130 | 85.15 188 | 82.30 171 | 68.23 198 | 74.71 120 | 65.37 141 | 64.39 142 | 69.59 108 | 77.18 140 | 85.10 167 | 84.87 170 | 92.34 165 | 88.21 158 |
|
PatchmatchNet | | | 78.67 156 | 78.85 132 | 78.46 164 | 86.85 123 | 86.03 178 | 83.77 161 | 68.11 200 | 80.88 77 | 66.19 128 | 72.90 90 | 73.40 95 | 78.06 135 | 79.25 203 | 77.71 205 | 87.75 199 | 81.75 200 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
v148 | | | 78.59 157 | 76.84 173 | 80.62 143 | 83.61 173 | 89.16 142 | 83.65 162 | 79.24 129 | 69.38 172 | 69.34 106 | 59.88 179 | 60.41 170 | 75.19 149 | 83.81 177 | 84.63 174 | 92.70 162 | 90.63 144 |
|
v1921920 | | | 78.57 158 | 76.99 171 | 80.41 150 | 82.93 182 | 89.63 134 | 86.38 132 | 77.14 149 | 68.31 178 | 61.80 170 | 58.89 187 | 56.79 193 | 80.19 100 | 86.50 133 | 86.05 156 | 94.02 120 | 92.76 86 |
|
pm-mvs1 | | | 78.51 159 | 77.75 158 | 79.40 156 | 84.83 146 | 89.30 138 | 83.55 163 | 79.38 127 | 62.64 202 | 63.68 155 | 58.73 188 | 64.68 124 | 70.78 178 | 89.79 90 | 87.84 111 | 94.17 113 | 91.28 131 |
|
v1240 | | | 78.15 160 | 76.53 174 | 80.04 152 | 82.85 185 | 89.48 137 | 85.61 142 | 76.77 152 | 67.05 182 | 61.18 180 | 58.37 189 | 56.16 198 | 79.89 107 | 86.11 137 | 86.08 154 | 93.92 127 | 92.47 104 |
|
dps | | | 78.02 161 | 75.94 182 | 80.44 147 | 86.06 127 | 86.62 171 | 82.58 166 | 69.98 193 | 75.14 113 | 77.76 74 | 69.08 110 | 59.93 174 | 78.47 132 | 79.47 201 | 77.96 202 | 87.78 198 | 83.40 195 |
|
anonymousdsp | | | 77.94 162 | 79.00 130 | 76.71 178 | 79.03 204 | 87.83 158 | 79.58 189 | 72.87 182 | 65.80 191 | 58.86 194 | 65.82 125 | 62.48 145 | 75.99 146 | 86.77 126 | 88.66 99 | 93.92 127 | 95.68 44 |
|
test-mter | | | 77.79 163 | 80.02 119 | 75.18 189 | 81.18 198 | 82.85 198 | 80.52 187 | 62.03 220 | 73.62 136 | 62.16 165 | 73.55 81 | 73.83 92 | 73.81 161 | 84.67 170 | 83.34 181 | 91.37 175 | 88.31 157 |
|
TESTMET0.1,1 | | | 77.78 164 | 79.84 122 | 75.38 188 | 80.86 199 | 82.40 203 | 81.24 179 | 62.72 219 | 73.72 133 | 62.69 160 | 73.76 79 | 74.42 88 | 73.49 163 | 84.61 171 | 82.99 184 | 91.25 177 | 87.01 172 |
|
tpm cat1 | | | 77.78 164 | 75.28 191 | 80.70 141 | 87.14 120 | 85.84 181 | 85.81 137 | 70.40 190 | 77.44 99 | 78.80 66 | 63.72 144 | 64.01 131 | 76.55 144 | 75.60 213 | 75.21 213 | 85.51 212 | 85.12 188 |
|
EPMVS | | | 77.53 166 | 78.07 145 | 76.90 177 | 86.89 122 | 84.91 191 | 82.18 174 | 66.64 206 | 81.00 75 | 64.11 152 | 72.75 91 | 69.68 107 | 74.42 158 | 79.36 202 | 78.13 201 | 87.14 204 | 80.68 205 |
|
tfpnnormal | | | 77.46 167 | 74.86 194 | 80.49 146 | 86.34 126 | 88.92 147 | 84.33 157 | 81.26 101 | 61.39 206 | 61.70 172 | 51.99 207 | 53.66 207 | 74.84 153 | 88.63 104 | 87.38 117 | 94.50 100 | 92.08 108 |
|
v7n | | | 77.22 168 | 76.23 177 | 78.38 165 | 81.89 193 | 89.10 145 | 82.24 173 | 76.36 154 | 65.96 190 | 61.21 179 | 56.56 193 | 55.79 199 | 75.07 152 | 86.55 130 | 86.68 126 | 93.52 148 | 92.95 79 |
|
RPMNet | | | 77.07 169 | 77.63 159 | 76.42 180 | 85.56 133 | 85.15 188 | 81.37 176 | 65.27 212 | 74.71 120 | 60.29 183 | 63.71 145 | 66.59 118 | 73.64 162 | 82.71 186 | 82.12 189 | 92.38 164 | 88.39 156 |
|
pmmvs5 | | | 76.93 170 | 76.33 176 | 77.62 168 | 81.97 192 | 88.40 154 | 81.32 178 | 74.35 176 | 65.42 196 | 61.42 176 | 63.07 146 | 57.95 189 | 73.23 166 | 85.60 146 | 85.35 167 | 93.41 150 | 88.55 155 |
|
TinyColmap | | | 76.73 171 | 73.95 197 | 79.96 153 | 85.16 140 | 85.64 184 | 82.34 170 | 78.19 139 | 70.63 157 | 62.06 167 | 60.69 168 | 49.61 214 | 80.81 81 | 85.12 166 | 83.69 180 | 91.22 179 | 82.27 198 |
|
CVMVSNet | | | 76.70 172 | 78.46 136 | 74.64 194 | 83.34 175 | 84.48 192 | 81.83 175 | 74.58 173 | 68.88 175 | 51.23 207 | 69.77 101 | 70.05 105 | 67.49 191 | 84.27 174 | 83.81 178 | 89.38 192 | 87.96 162 |
|
WR-MVS | | | 76.63 173 | 78.02 151 | 75.02 190 | 84.14 152 | 89.76 130 | 78.34 197 | 80.64 105 | 69.56 171 | 52.32 203 | 61.26 152 | 61.24 164 | 60.66 203 | 84.45 173 | 87.07 120 | 93.99 122 | 92.77 85 |
|
TransMVSNet (Re) | | | 76.57 174 | 75.16 192 | 78.22 166 | 85.60 132 | 87.24 162 | 82.46 167 | 81.23 102 | 59.80 210 | 59.05 193 | 57.07 192 | 59.14 183 | 66.60 196 | 88.09 109 | 86.82 122 | 94.37 108 | 87.95 163 |
|
v52 | | | 76.55 175 | 75.89 183 | 77.31 172 | 79.94 203 | 88.49 152 | 81.07 182 | 73.62 179 | 65.49 194 | 61.66 173 | 56.29 196 | 58.90 185 | 74.30 159 | 83.47 181 | 85.62 163 | 93.28 151 | 92.99 76 |
|
V4 | | | 76.55 175 | 75.89 183 | 77.32 171 | 79.95 202 | 88.50 151 | 81.07 182 | 73.62 179 | 65.47 195 | 61.71 171 | 56.31 195 | 58.87 187 | 74.28 160 | 83.48 180 | 85.62 163 | 93.28 151 | 92.98 77 |
|
tpmrst | | | 76.55 175 | 75.99 181 | 77.20 173 | 87.32 118 | 83.05 196 | 82.86 165 | 65.62 210 | 78.61 94 | 67.22 122 | 69.19 108 | 65.71 120 | 75.87 147 | 76.75 210 | 75.33 212 | 84.31 216 | 83.28 196 |
|
FC-MVSNet-test | | | 76.53 178 | 81.62 91 | 70.58 203 | 84.99 142 | 85.73 182 | 74.81 204 | 78.85 132 | 77.00 101 | 39.13 227 | 75.90 68 | 73.50 94 | 54.08 210 | 86.54 131 | 85.99 157 | 91.65 171 | 86.68 175 |
|
PatchT | | | 76.42 179 | 77.81 157 | 74.80 192 | 78.46 207 | 84.30 193 | 71.82 210 | 65.03 214 | 73.89 130 | 65.37 141 | 61.58 150 | 66.70 117 | 77.18 140 | 85.10 167 | 84.87 170 | 90.94 182 | 88.21 158 |
|
TAMVS | | | 76.42 179 | 77.16 169 | 75.56 186 | 83.05 179 | 85.55 185 | 80.58 186 | 71.43 186 | 65.40 197 | 61.04 181 | 67.27 119 | 69.22 110 | 67.99 188 | 84.88 169 | 84.78 172 | 89.28 193 | 83.01 197 |
|
EG-PatchMatch MVS | | | 76.40 181 | 75.47 189 | 77.48 169 | 85.86 129 | 90.22 118 | 82.45 168 | 73.96 178 | 59.64 211 | 59.60 186 | 52.75 205 | 62.20 150 | 68.44 187 | 88.23 108 | 87.50 113 | 94.55 98 | 87.78 164 |
|
CP-MVSNet | | | 76.36 182 | 76.41 175 | 76.32 182 | 82.73 187 | 88.64 148 | 79.39 190 | 79.62 123 | 67.21 181 | 53.70 199 | 60.72 166 | 55.22 202 | 67.91 190 | 83.52 179 | 86.34 136 | 94.55 98 | 93.19 73 |
|
tpm | | | 76.30 183 | 76.05 180 | 76.59 179 | 86.97 121 | 83.01 197 | 83.83 160 | 67.06 204 | 71.83 147 | 63.87 154 | 69.56 106 | 62.88 138 | 73.41 165 | 79.79 200 | 78.59 199 | 84.41 215 | 86.68 175 |
|
v748 | | | 76.17 184 | 75.10 193 | 77.43 170 | 81.60 194 | 88.01 156 | 79.02 194 | 76.28 159 | 64.47 198 | 64.14 151 | 56.55 194 | 56.26 197 | 70.40 180 | 82.50 188 | 85.77 159 | 93.11 156 | 92.15 107 |
|
test0.0.03 1 | | | 76.03 185 | 78.51 133 | 73.12 200 | 87.47 116 | 85.13 190 | 76.32 201 | 78.05 141 | 73.19 140 | 50.98 208 | 70.64 97 | 69.28 109 | 55.53 206 | 85.33 161 | 84.38 176 | 90.39 185 | 81.63 201 |
|
PEN-MVS | | | 76.02 186 | 76.07 178 | 75.95 185 | 83.17 178 | 87.97 157 | 79.65 188 | 80.07 114 | 66.57 186 | 51.45 205 | 60.94 161 | 55.47 201 | 66.81 194 | 82.72 185 | 86.80 123 | 94.59 93 | 92.03 111 |
|
SixPastTwentyTwo | | | 76.02 186 | 75.72 186 | 76.36 181 | 83.38 174 | 87.54 159 | 75.50 203 | 76.22 160 | 65.50 193 | 57.05 196 | 70.64 97 | 53.97 206 | 74.54 155 | 80.96 194 | 82.12 189 | 91.44 173 | 89.35 151 |
|
PS-CasMVS | | | 75.90 188 | 75.86 185 | 75.96 184 | 82.59 188 | 88.46 153 | 79.23 193 | 79.56 125 | 66.00 189 | 52.77 201 | 59.48 182 | 54.35 205 | 67.14 193 | 83.37 182 | 86.23 137 | 94.47 102 | 93.10 75 |
|
WR-MVS_H | | | 75.84 189 | 76.93 172 | 74.57 195 | 82.86 184 | 89.50 136 | 78.34 197 | 79.36 128 | 66.90 184 | 52.51 202 | 60.20 177 | 59.71 175 | 59.73 204 | 83.61 178 | 85.77 159 | 94.65 89 | 92.84 82 |
|
LTVRE_ROB | | 74.41 16 | 75.78 190 | 74.72 195 | 77.02 176 | 85.88 128 | 89.22 140 | 82.44 169 | 77.17 148 | 50.57 222 | 45.45 215 | 65.44 130 | 52.29 210 | 81.25 75 | 85.50 152 | 87.42 116 | 89.94 189 | 92.62 89 |
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 |
gg-mvs-nofinetune | | | 75.64 191 | 77.26 166 | 73.76 196 | 87.92 109 | 92.20 87 | 87.32 101 | 64.67 215 | 51.92 221 | 35.35 228 | 46.44 214 | 77.05 82 | 71.97 170 | 92.64 42 | 91.02 50 | 95.34 50 | 89.53 150 |
|
FMVSNet5 | | | 75.50 192 | 76.07 178 | 74.83 191 | 76.16 212 | 81.19 206 | 81.34 177 | 70.21 192 | 73.20 139 | 61.59 174 | 58.97 185 | 68.33 114 | 68.50 186 | 85.87 140 | 85.85 158 | 91.18 180 | 79.11 209 |
|
DTE-MVSNet | | | 75.14 193 | 75.44 190 | 74.80 192 | 83.18 177 | 87.19 163 | 78.25 199 | 80.11 113 | 66.05 188 | 48.31 211 | 60.88 165 | 54.67 203 | 64.54 200 | 82.57 187 | 86.17 149 | 94.43 105 | 90.53 146 |
|
pmmvs6 | | | 74.83 194 | 72.89 200 | 77.09 174 | 82.11 191 | 87.50 160 | 80.88 185 | 76.97 150 | 52.79 220 | 61.91 169 | 46.66 213 | 60.49 167 | 69.28 183 | 86.74 128 | 85.46 166 | 91.39 174 | 90.56 145 |
|
MIMVSNet | | | 74.69 195 | 75.60 188 | 73.62 197 | 76.02 214 | 85.31 187 | 81.21 181 | 67.43 201 | 71.02 154 | 59.07 192 | 54.48 198 | 64.07 129 | 66.14 197 | 86.52 132 | 86.64 127 | 91.83 170 | 81.17 203 |
|
ADS-MVSNet | | | 74.53 196 | 75.69 187 | 73.17 199 | 81.57 196 | 80.71 208 | 79.27 192 | 63.03 218 | 79.27 89 | 59.94 185 | 67.86 116 | 68.32 115 | 71.08 177 | 77.33 207 | 76.83 208 | 84.12 218 | 79.53 206 |
|
pmmvs-eth3d | | | 74.32 197 | 71.96 202 | 77.08 175 | 77.33 210 | 82.71 199 | 78.41 196 | 76.02 166 | 66.65 185 | 65.98 135 | 54.23 201 | 49.02 216 | 73.14 167 | 82.37 190 | 82.69 186 | 91.61 172 | 86.05 182 |
|
PM-MVS | | | 74.17 198 | 73.10 198 | 75.41 187 | 76.07 213 | 82.53 201 | 77.56 200 | 71.69 185 | 71.04 153 | 61.92 168 | 61.23 157 | 47.30 217 | 74.82 154 | 81.78 192 | 79.80 194 | 90.42 184 | 88.05 161 |
|
CMPMVS | | 56.49 17 | 73.84 199 | 71.73 203 | 76.31 183 | 85.20 138 | 85.67 183 | 75.80 202 | 73.23 181 | 62.26 203 | 65.40 140 | 53.40 204 | 59.70 176 | 71.77 172 | 80.25 197 | 79.56 196 | 86.45 207 | 81.28 202 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
MDTV_nov1_ep13_2view | | | 73.21 200 | 72.91 199 | 73.56 198 | 80.01 200 | 84.28 194 | 78.62 195 | 66.43 207 | 68.64 176 | 59.12 191 | 60.39 173 | 59.69 177 | 69.81 182 | 78.82 205 | 77.43 207 | 87.36 201 | 81.11 204 |
|
testgi | | | 71.92 201 | 74.20 196 | 69.27 206 | 84.58 147 | 83.06 195 | 73.40 206 | 74.39 175 | 64.04 200 | 46.17 214 | 68.90 112 | 57.15 191 | 48.89 217 | 84.07 176 | 83.08 183 | 88.18 197 | 79.09 210 |
|
Anonymous20231206 | | | 70.80 202 | 70.59 205 | 71.04 202 | 81.60 194 | 82.49 202 | 74.64 205 | 75.87 169 | 64.17 199 | 49.27 209 | 44.85 217 | 53.59 208 | 54.68 209 | 83.07 183 | 82.34 188 | 90.17 186 | 83.65 194 |
|
gm-plane-assit | | | 70.29 203 | 70.65 204 | 69.88 204 | 85.03 141 | 78.50 213 | 58.41 224 | 65.47 211 | 50.39 223 | 40.88 220 | 49.60 209 | 50.11 213 | 75.14 151 | 91.43 54 | 89.78 75 | 94.32 109 | 84.73 192 |
|
EU-MVSNet | | | 69.98 204 | 72.30 201 | 67.28 209 | 75.67 215 | 79.39 210 | 73.12 207 | 69.94 194 | 63.59 201 | 42.80 218 | 62.93 147 | 56.71 195 | 55.07 208 | 79.13 204 | 78.55 200 | 87.06 205 | 85.82 185 |
|
MVS-HIRNet | | | 68.83 205 | 66.39 209 | 71.68 201 | 77.58 208 | 75.52 216 | 66.45 216 | 65.05 213 | 62.16 204 | 62.84 159 | 44.76 218 | 56.60 196 | 71.96 171 | 78.04 206 | 75.06 214 | 86.18 209 | 72.56 218 |
|
LP | | | 68.35 206 | 67.23 207 | 69.67 205 | 77.49 209 | 79.38 211 | 72.84 209 | 61.37 221 | 66.94 183 | 55.08 197 | 47.00 212 | 50.35 212 | 65.16 199 | 75.61 212 | 76.03 209 | 86.08 210 | 75.28 215 |
|
test20.03 | | | 68.31 207 | 70.05 206 | 66.28 211 | 82.41 189 | 80.84 207 | 67.35 215 | 76.11 163 | 58.44 213 | 40.80 221 | 53.77 202 | 54.54 204 | 42.28 223 | 83.07 183 | 81.96 192 | 88.73 196 | 77.76 212 |
|
N_pmnet | | | 66.85 208 | 66.63 208 | 67.11 210 | 78.73 205 | 74.66 217 | 70.53 211 | 71.07 187 | 66.46 187 | 46.54 213 | 51.68 208 | 51.91 211 | 55.48 207 | 74.68 215 | 72.38 219 | 80.29 223 | 74.65 216 |
|
MDA-MVSNet-bldmvs | | | 66.22 209 | 64.49 212 | 68.24 207 | 61.67 227 | 82.11 205 | 70.07 212 | 76.16 161 | 59.14 212 | 47.94 212 | 54.35 200 | 35.82 229 | 67.33 192 | 64.94 227 | 75.68 211 | 86.30 208 | 79.36 207 |
|
MIMVSNet1 | | | 65.00 210 | 66.24 210 | 63.55 215 | 58.41 231 | 80.01 209 | 69.00 213 | 74.03 177 | 55.81 218 | 41.88 219 | 36.81 226 | 49.48 215 | 47.89 218 | 81.32 193 | 82.40 187 | 90.08 188 | 77.88 211 |
|
test2356 | | | 63.96 211 | 64.10 214 | 63.78 214 | 74.71 216 | 71.55 220 | 65.83 217 | 67.38 202 | 57.11 215 | 40.41 222 | 53.58 203 | 41.13 223 | 49.35 216 | 77.00 209 | 77.57 206 | 85.01 214 | 70.79 219 |
|
testpf | | | 63.91 212 | 65.23 211 | 62.38 216 | 81.32 197 | 69.95 223 | 62.71 222 | 54.16 228 | 61.29 207 | 48.73 210 | 57.31 191 | 52.50 209 | 50.97 213 | 67.50 223 | 68.86 224 | 76.36 226 | 79.21 208 |
|
new-patchmatchnet | | | 63.80 213 | 63.31 215 | 64.37 213 | 76.49 211 | 75.99 215 | 63.73 219 | 70.99 188 | 57.27 214 | 43.08 217 | 45.86 215 | 43.80 218 | 45.13 222 | 73.20 218 | 70.68 223 | 86.80 206 | 76.34 214 |
|
FPMVS | | | 63.63 214 | 60.08 220 | 67.78 208 | 80.01 200 | 71.50 221 | 72.88 208 | 69.41 197 | 61.82 205 | 53.11 200 | 45.12 216 | 42.11 221 | 50.86 214 | 66.69 224 | 63.84 226 | 80.41 222 | 69.46 223 |
|
testus | | | 63.31 215 | 64.48 213 | 61.94 218 | 73.99 218 | 71.99 219 | 63.56 221 | 63.25 217 | 57.01 216 | 39.41 226 | 54.38 199 | 38.73 227 | 46.24 221 | 77.01 208 | 77.93 203 | 85.20 213 | 74.29 217 |
|
Anonymous20231211 | | | 62.95 216 | 60.42 219 | 65.89 212 | 74.22 217 | 78.37 214 | 67.66 214 | 74.47 174 | 40.37 230 | 39.59 225 | 27.51 229 | 38.26 228 | 52.13 211 | 75.39 214 | 77.89 204 | 87.28 203 | 85.16 187 |
|
pmmvs3 | | | 61.89 217 | 61.74 217 | 62.06 217 | 64.30 225 | 70.83 222 | 64.22 218 | 52.14 230 | 48.78 224 | 44.47 216 | 41.67 220 | 41.70 222 | 63.03 201 | 76.06 211 | 76.02 210 | 84.18 217 | 77.14 213 |
|
new_pmnet | | | 59.28 218 | 61.47 218 | 56.73 223 | 61.66 228 | 68.29 224 | 59.57 223 | 54.91 226 | 60.83 208 | 34.38 229 | 44.66 219 | 43.65 219 | 49.90 215 | 71.66 221 | 71.56 222 | 79.94 224 | 69.67 222 |
|
GG-mvs-BLEND | | | 57.56 219 | 82.61 84 | 28.34 232 | 0.22 237 | 90.10 121 | 79.37 191 | 0.14 236 | 79.56 86 | 0.40 240 | 71.25 96 | 83.40 53 | 0.30 237 | 86.27 135 | 83.87 177 | 89.59 191 | 83.83 193 |
|
1111 | | | 57.32 220 | 57.20 221 | 57.46 220 | 71.89 222 | 67.50 227 | 52.34 225 | 58.78 223 | 46.57 225 | 39.69 223 | 37.38 224 | 38.78 225 | 46.37 219 | 74.15 216 | 74.36 218 | 75.70 227 | 61.66 226 |
|
testmv | | | 56.62 221 | 56.41 222 | 56.86 221 | 71.92 220 | 67.58 225 | 52.17 227 | 65.69 208 | 40.60 228 | 28.53 231 | 37.90 222 | 31.52 230 | 40.10 225 | 72.64 219 | 74.73 216 | 82.78 220 | 69.91 220 |
|
test1235678 | | | 56.61 222 | 56.40 223 | 56.86 221 | 71.92 220 | 67.58 225 | 52.17 227 | 65.69 208 | 40.58 229 | 28.52 232 | 37.89 223 | 31.49 231 | 40.10 225 | 72.64 219 | 74.72 217 | 82.78 220 | 69.90 221 |
|
PMVS | | 50.48 18 | 55.81 223 | 51.93 224 | 60.33 219 | 72.90 219 | 49.34 232 | 48.78 229 | 69.51 196 | 43.49 227 | 54.25 198 | 36.26 227 | 41.04 224 | 39.71 227 | 65.07 226 | 60.70 227 | 76.85 225 | 67.58 224 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
test12356 | | | 50.02 224 | 51.22 225 | 48.61 225 | 63.00 226 | 60.15 230 | 47.60 231 | 56.49 225 | 38.02 231 | 24.74 234 | 36.14 228 | 25.93 233 | 24.79 230 | 66.19 225 | 71.68 221 | 75.07 228 | 60.44 228 |
|
Gipuma | | | 49.17 225 | 47.05 226 | 51.65 224 | 59.67 230 | 48.39 233 | 41.98 232 | 63.47 216 | 55.64 219 | 33.33 230 | 14.90 232 | 13.78 237 | 41.34 224 | 69.31 222 | 72.30 220 | 70.11 230 | 55.00 230 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
no-one | | | 44.14 226 | 43.91 228 | 44.40 227 | 59.91 229 | 61.10 229 | 34.07 234 | 60.09 222 | 27.71 233 | 14.44 236 | 19.11 231 | 19.28 235 | 23.90 232 | 47.36 231 | 66.69 225 | 73.98 229 | 66.11 225 |
|
PMMVS2 | | | 41.68 227 | 44.74 227 | 38.10 228 | 46.97 234 | 52.32 231 | 40.63 233 | 48.08 231 | 35.51 232 | 7.36 239 | 26.86 230 | 24.64 234 | 16.72 233 | 55.24 229 | 59.03 228 | 68.85 231 | 59.59 229 |
|
.test1245 | | | 41.43 228 | 38.48 229 | 44.88 226 | 71.89 222 | 67.50 227 | 52.34 225 | 58.78 223 | 46.57 225 | 39.69 223 | 37.38 224 | 38.78 225 | 46.37 219 | 74.15 216 | 1.18 233 | 0.20 237 | 3.76 235 |
|
E-PMN | | | 31.40 229 | 26.80 231 | 36.78 229 | 51.39 233 | 29.96 236 | 20.20 236 | 54.17 227 | 25.93 235 | 12.75 237 | 14.73 233 | 8.58 239 | 34.10 229 | 27.36 233 | 37.83 231 | 48.07 234 | 43.18 232 |
|
MVE | | 30.17 19 | 30.88 230 | 33.52 230 | 27.80 233 | 23.78 236 | 39.16 235 | 18.69 238 | 46.90 232 | 21.88 236 | 15.39 235 | 14.37 234 | 7.31 240 | 24.41 231 | 41.63 232 | 56.22 229 | 37.64 236 | 54.07 231 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
EMVS | | | 30.49 231 | 25.44 232 | 36.39 230 | 51.47 232 | 29.89 237 | 20.17 237 | 54.00 229 | 26.49 234 | 12.02 238 | 13.94 235 | 8.84 238 | 34.37 228 | 25.04 234 | 34.37 232 | 46.29 235 | 39.53 233 |
|
testmvs | | | 1.03 232 | 1.63 233 | 0.34 234 | 0.09 238 | 0.35 239 | 0.61 240 | 0.16 235 | 1.49 237 | 0.10 241 | 3.15 236 | 0.15 241 | 0.86 236 | 1.32 235 | 1.18 233 | 0.20 237 | 3.76 235 |
|
test123 | | | 0.87 233 | 1.40 234 | 0.25 235 | 0.03 239 | 0.25 240 | 0.35 241 | 0.08 237 | 1.21 238 | 0.05 242 | 2.84 237 | 0.03 242 | 0.89 235 | 0.43 236 | 1.16 235 | 0.13 239 | 3.87 234 |
|
sosnet-low-res | | | 0.00 234 | 0.00 235 | 0.00 236 | 0.00 240 | 0.00 241 | 0.00 242 | 0.00 238 | 0.00 239 | 0.00 243 | 0.00 238 | 0.00 243 | 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 | 0.00 240 | 0.00 241 | 0.00 242 | 0.00 238 | 0.00 239 | 0.00 243 | 0.00 238 | 0.00 243 | 0.00 238 | 0.00 237 | 0.00 236 | 0.00 240 | 0.00 237 |
|
ambc | | | | 61.92 216 | | 70.98 224 | 73.54 218 | 63.64 220 | | 60.06 209 | 52.23 204 | 38.44 221 | 19.17 236 | 57.12 205 | 82.33 191 | 75.03 215 | 83.21 219 | 84.89 189 |
|
MTAPA | | | | | | | | | | | 92.97 2 | | 91.03 16 | | | | | |
|
MTMP | | | | | | | | | | | 93.14 1 | | 90.21 23 | | | | | |
|
Patchmatch-RL test | | | | | | | | 8.55 239 | | | | | | | | | | |
|
tmp_tt | | | | | 32.73 231 | 43.96 235 | 21.15 238 | 26.71 235 | 8.99 234 | 65.67 192 | 51.39 206 | 56.01 197 | 42.64 220 | 11.76 234 | 56.60 228 | 50.81 230 | 53.55 233 | |
|
XVS | | | | | | 93.11 53 | 96.70 20 | 91.91 48 | | | 83.95 42 | | 88.82 33 | | | | 95.79 28 | |
|
X-MVStestdata | | | | | | 93.11 53 | 96.70 20 | 91.91 48 | | | 83.95 42 | | 88.82 33 | | | | 95.79 28 | |
|
abl_6 | | | | | 90.66 34 | 94.65 40 | 96.27 32 | 92.21 44 | 86.94 41 | 90.23 35 | 86.38 32 | 85.50 35 | 92.96 7 | 88.37 38 | | | 95.40 45 | 95.46 48 |
|
mPP-MVS | | | | | | 97.06 12 | | | | | | | 88.08 38 | | | | | |
|
NP-MVS | | | | | | | | | | 87.47 47 | | | | | | | | |
|
Patchmtry | | | | | | | 85.54 186 | 82.30 171 | 68.23 198 | | 65.37 141 | | | | | | | |
|
DeepMVS_CX | | | | | | | 48.31 234 | 48.03 230 | 26.08 233 | 56.42 217 | 25.77 233 | 47.51 211 | 31.31 232 | 51.30 212 | 48.49 230 | | 53.61 232 | 61.52 227 |
|