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