ESAPD | | | 78.19 1 | 83.74 1 | 71.72 1 | 79.01 1 | 81.38 1 | 83.23 2 | 58.63 2 | 83.92 4 | 62.44 11 | 87.06 2 | 85.82 1 | 64.54 3 | 79.39 4 | 77.99 7 | 82.44 16 | 90.61 1 |
|
APDe-MVS | | | 77.58 2 | 82.93 2 | 71.35 2 | 77.86 2 | 80.55 2 | 83.38 1 | 57.61 6 | 85.57 1 | 61.11 14 | 86.10 4 | 82.98 3 | 64.76 2 | 78.29 11 | 76.78 18 | 83.40 5 | 90.20 2 |
|
HSP-MVS | | | 76.78 3 | 82.44 3 | 70.19 7 | 75.26 10 | 80.22 3 | 80.59 7 | 57.85 5 | 84.79 3 | 60.84 15 | 88.54 1 | 83.43 2 | 66.24 1 | 78.21 14 | 76.47 20 | 80.34 37 | 85.43 26 |
|
ACMMP_Plus | | | 76.15 4 | 81.17 4 | 70.30 5 | 74.09 14 | 79.47 5 | 81.59 5 | 57.09 9 | 81.38 6 | 63.89 5 | 79.02 9 | 80.48 12 | 62.24 13 | 80.05 3 | 79.12 3 | 82.94 9 | 88.64 4 |
|
HPM-MVS++ | | | 76.01 5 | 80.47 7 | 70.81 3 | 76.60 4 | 74.96 30 | 80.18 11 | 58.36 3 | 81.96 5 | 63.50 6 | 78.80 10 | 82.53 6 | 64.40 4 | 78.74 7 | 78.84 4 | 81.81 26 | 87.46 12 |
|
APD-MVS | | | 75.80 6 | 80.90 6 | 69.86 11 | 75.42 9 | 78.48 11 | 81.43 6 | 57.44 7 | 80.45 10 | 59.32 21 | 85.28 5 | 80.82 11 | 63.96 5 | 76.89 24 | 76.08 23 | 81.58 32 | 88.30 7 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
CNVR-MVS | | | 75.62 7 | 79.91 9 | 70.61 4 | 75.76 6 | 78.82 9 | 81.66 4 | 57.12 8 | 79.77 12 | 63.04 7 | 70.69 19 | 81.15 9 | 62.99 7 | 80.23 2 | 79.54 2 | 83.11 6 | 89.16 3 |
|
SteuartSystems-ACMMP | | | 75.23 8 | 79.60 10 | 70.13 8 | 76.81 3 | 78.92 7 | 81.74 3 | 57.99 4 | 75.30 24 | 59.83 20 | 75.69 13 | 78.45 18 | 60.48 24 | 80.58 1 | 79.77 1 | 83.94 3 | 88.52 5 |
Skip Steuart: Steuart Systems R&D Blog. |
TSAR-MVS + MP. | | | 75.22 9 | 80.06 8 | 69.56 12 | 74.61 12 | 72.74 44 | 80.59 7 | 55.70 19 | 80.80 8 | 62.65 9 | 86.25 3 | 82.92 4 | 62.07 15 | 76.89 24 | 75.66 26 | 81.77 28 | 85.19 28 |
|
HFP-MVS | | | 74.87 10 | 78.86 15 | 70.21 6 | 73.99 15 | 77.91 13 | 80.36 10 | 56.63 11 | 78.41 15 | 64.27 3 | 74.54 15 | 77.75 22 | 62.96 8 | 78.70 8 | 77.82 9 | 83.02 7 | 86.91 15 |
|
CSCG | | | 74.68 11 | 79.22 11 | 69.40 13 | 75.69 8 | 80.01 4 | 79.12 18 | 52.83 35 | 79.34 13 | 63.99 4 | 70.49 20 | 82.02 7 | 60.35 26 | 77.48 21 | 77.22 15 | 84.38 1 | 87.97 10 |
|
SD-MVS | | | 74.43 12 | 78.87 13 | 69.26 15 | 74.39 13 | 73.70 40 | 79.06 19 | 55.24 21 | 81.04 7 | 62.71 8 | 80.18 8 | 82.61 5 | 61.70 17 | 75.43 35 | 73.92 38 | 82.44 16 | 85.22 27 |
|
MP-MVS | | | 74.31 13 | 78.87 13 | 68.99 16 | 73.49 17 | 78.56 10 | 79.25 17 | 56.51 12 | 75.33 22 | 60.69 17 | 75.30 14 | 79.12 17 | 61.81 16 | 77.78 18 | 77.93 8 | 82.18 22 | 88.06 9 |
|
NCCC | | | 74.27 14 | 77.83 20 | 70.13 8 | 75.70 7 | 77.41 17 | 80.51 9 | 57.09 9 | 78.25 16 | 62.28 12 | 65.54 32 | 78.26 19 | 62.18 14 | 79.13 5 | 78.51 5 | 83.01 8 | 87.68 11 |
|
MPTG | | | 74.25 15 | 77.97 19 | 69.91 10 | 73.43 18 | 74.06 38 | 79.69 13 | 56.44 13 | 80.74 9 | 64.98 2 | 68.72 25 | 79.98 14 | 62.92 9 | 78.24 13 | 77.77 11 | 81.99 24 | 86.30 17 |
|
DeepPCF-MVS | | 66.49 1 | 74.25 15 | 80.97 5 | 66.41 26 | 67.75 45 | 78.87 8 | 75.61 33 | 54.16 27 | 84.86 2 | 58.22 27 | 77.94 11 | 81.01 10 | 62.52 11 | 78.34 9 | 77.38 12 | 80.16 40 | 88.40 6 |
|
train_agg | | | 73.89 17 | 78.25 17 | 68.80 18 | 75.25 11 | 72.27 46 | 79.75 12 | 56.05 16 | 74.87 27 | 58.97 22 | 81.83 7 | 79.76 15 | 61.05 21 | 77.39 22 | 76.01 24 | 81.71 29 | 85.61 24 |
|
DeepC-MVS | | 66.32 2 | 73.85 18 | 78.10 18 | 68.90 17 | 67.92 43 | 79.31 6 | 78.16 23 | 59.28 1 | 78.24 17 | 61.13 13 | 67.36 31 | 76.10 26 | 63.40 6 | 79.11 6 | 78.41 6 | 83.52 4 | 88.16 8 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
ACMMPR | | | 73.79 19 | 78.41 16 | 68.40 19 | 72.35 22 | 77.79 14 | 79.32 15 | 56.38 14 | 77.67 19 | 58.30 26 | 74.16 16 | 76.66 23 | 61.40 18 | 78.32 10 | 77.80 10 | 82.68 13 | 86.51 16 |
|
MCST-MVS | | | 73.67 20 | 77.39 21 | 69.33 14 | 76.26 5 | 78.19 12 | 78.77 20 | 54.54 24 | 75.33 22 | 59.99 19 | 67.96 27 | 79.23 16 | 62.43 12 | 78.00 15 | 75.71 25 | 84.02 2 | 87.30 13 |
|
PGM-MVS | | | 72.89 21 | 77.13 22 | 67.94 20 | 72.47 21 | 77.25 18 | 79.27 16 | 54.63 23 | 73.71 29 | 57.95 28 | 72.38 17 | 75.33 28 | 60.75 22 | 78.25 12 | 77.36 14 | 82.57 15 | 85.62 23 |
|
CP-MVS | | | 72.63 22 | 76.95 23 | 67.59 21 | 70.67 29 | 75.53 28 | 77.95 25 | 56.01 17 | 75.65 21 | 58.82 23 | 69.16 24 | 76.48 24 | 60.46 25 | 77.66 19 | 77.20 16 | 81.65 30 | 86.97 14 |
|
TSAR-MVS + ACMM | | | 72.56 23 | 79.07 12 | 64.96 35 | 73.24 19 | 73.16 43 | 78.50 21 | 48.80 58 | 79.34 13 | 55.32 35 | 85.04 6 | 81.49 8 | 58.57 32 | 75.06 38 | 73.75 39 | 75.35 102 | 85.61 24 |
|
DeepC-MVS_fast | | 65.08 3 | 72.00 24 | 76.11 24 | 67.21 23 | 68.93 39 | 77.46 15 | 76.54 29 | 54.35 25 | 74.92 26 | 58.64 25 | 65.18 33 | 74.04 36 | 62.62 10 | 77.92 16 | 77.02 17 | 82.16 23 | 86.21 18 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
ACMMP | | | 71.57 25 | 75.84 25 | 66.59 25 | 70.30 33 | 76.85 23 | 78.46 22 | 53.95 28 | 73.52 30 | 55.56 33 | 70.13 21 | 71.36 41 | 58.55 33 | 77.00 23 | 76.23 22 | 82.71 12 | 85.81 22 |
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 |
CDPH-MVS | | | 71.47 26 | 75.82 26 | 66.41 26 | 72.97 20 | 77.15 19 | 78.14 24 | 54.71 22 | 69.88 42 | 53.07 50 | 70.98 18 | 74.83 30 | 56.95 43 | 76.22 28 | 76.57 19 | 82.62 14 | 85.09 29 |
|
X-MVS | | | 71.18 27 | 75.66 27 | 65.96 30 | 71.71 24 | 76.96 20 | 77.26 27 | 55.88 18 | 72.75 32 | 54.48 43 | 64.39 36 | 74.47 31 | 54.19 55 | 77.84 17 | 77.37 13 | 82.21 20 | 85.85 21 |
|
HQP-MVS | | | 70.88 28 | 75.02 28 | 66.05 29 | 71.69 25 | 74.47 35 | 77.51 26 | 53.17 32 | 72.89 31 | 54.88 39 | 70.03 22 | 70.48 43 | 57.26 39 | 76.02 30 | 75.01 30 | 81.78 27 | 86.21 18 |
|
TSAR-MVS + GP. | | | 69.71 29 | 73.92 31 | 64.80 37 | 68.27 41 | 70.56 51 | 71.90 45 | 50.75 45 | 71.38 36 | 57.46 30 | 68.68 26 | 75.42 27 | 60.10 27 | 73.47 43 | 73.99 37 | 80.32 38 | 83.97 33 |
|
3Dnovator+ | | 62.63 4 | 69.51 30 | 72.62 34 | 65.88 31 | 68.21 42 | 76.47 24 | 73.50 43 | 52.74 36 | 70.85 38 | 58.65 24 | 55.97 63 | 69.95 44 | 61.11 20 | 76.80 26 | 75.09 27 | 81.09 35 | 83.23 39 |
|
MVS_0304 | | | 69.49 31 | 73.96 30 | 64.28 40 | 67.92 43 | 76.13 26 | 74.90 36 | 47.60 60 | 63.29 52 | 54.09 47 | 67.44 30 | 76.35 25 | 59.53 29 | 75.81 32 | 75.03 28 | 81.62 31 | 83.70 36 |
|
OPM-MVS | | | 69.33 32 | 71.05 40 | 67.32 22 | 72.34 23 | 75.70 27 | 79.57 14 | 56.34 15 | 55.21 63 | 53.81 48 | 59.51 52 | 68.96 46 | 59.67 28 | 77.61 20 | 76.44 21 | 82.19 21 | 83.88 35 |
|
PHI-MVS | | | 69.27 33 | 74.84 29 | 62.76 45 | 66.83 47 | 74.83 31 | 73.88 41 | 49.32 54 | 70.61 39 | 50.93 54 | 69.62 23 | 74.84 29 | 57.25 40 | 75.53 34 | 74.32 35 | 78.35 54 | 84.17 32 |
|
LGP-MVS_train | | | 68.87 34 | 72.03 36 | 65.18 34 | 69.33 37 | 74.03 39 | 76.67 28 | 53.88 29 | 68.46 43 | 52.05 53 | 63.21 38 | 63.89 58 | 56.31 45 | 75.99 31 | 74.43 34 | 82.83 11 | 84.18 31 |
|
CANet | | | 68.77 35 | 73.01 32 | 63.83 41 | 68.30 40 | 75.19 29 | 73.73 42 | 47.90 59 | 63.86 49 | 54.84 40 | 67.51 29 | 74.36 34 | 57.62 36 | 74.22 41 | 73.57 42 | 80.56 36 | 82.36 40 |
|
CPTT-MVS | | | 68.76 36 | 73.01 32 | 63.81 42 | 65.42 54 | 73.66 41 | 76.39 31 | 52.08 37 | 72.61 33 | 50.33 56 | 60.73 49 | 72.65 39 | 59.43 30 | 73.32 44 | 72.12 44 | 79.19 48 | 85.99 20 |
|
ACMP | | 61.42 5 | 68.72 37 | 71.37 38 | 65.64 32 | 69.06 38 | 74.45 36 | 75.88 32 | 53.30 31 | 68.10 44 | 55.74 32 | 61.53 48 | 62.29 63 | 56.97 42 | 74.70 39 | 74.23 36 | 82.88 10 | 84.31 30 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
MSLP-MVS++ | | | 68.17 38 | 70.72 43 | 65.19 33 | 69.41 36 | 70.64 50 | 74.99 35 | 45.76 66 | 70.20 41 | 60.17 18 | 56.42 61 | 73.01 37 | 61.14 19 | 72.80 46 | 70.54 50 | 79.70 42 | 81.42 45 |
|
MAR-MVS | | | 68.04 39 | 70.74 42 | 64.90 36 | 71.68 26 | 76.33 25 | 74.63 38 | 50.48 49 | 63.81 50 | 55.52 34 | 54.88 68 | 69.90 45 | 57.39 38 | 75.42 36 | 74.79 32 | 79.71 41 | 80.03 49 |
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 |
AdaColmap | | | 67.89 40 | 68.85 50 | 66.77 24 | 73.73 16 | 74.30 37 | 75.28 34 | 53.58 30 | 70.24 40 | 57.59 29 | 51.19 82 | 59.19 75 | 60.74 23 | 75.33 37 | 73.72 40 | 79.69 44 | 77.96 59 |
|
MVS_111021_HR | | | 67.62 41 | 70.39 44 | 64.39 38 | 69.77 35 | 70.45 52 | 71.44 48 | 51.72 41 | 60.77 57 | 55.06 37 | 62.14 45 | 66.40 55 | 58.13 35 | 76.13 29 | 74.79 32 | 80.19 39 | 82.04 43 |
|
ACMM | | 60.30 7 | 67.58 42 | 68.82 51 | 66.13 28 | 70.59 30 | 72.01 48 | 76.54 29 | 54.26 26 | 65.64 48 | 54.78 41 | 50.35 84 | 61.72 66 | 58.74 31 | 75.79 33 | 75.03 28 | 81.88 25 | 81.17 46 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
PCF-MVS | | 59.98 8 | 67.32 43 | 71.04 41 | 62.97 44 | 64.77 56 | 74.49 34 | 74.78 37 | 49.54 52 | 67.44 45 | 54.39 46 | 58.35 56 | 72.81 38 | 55.79 51 | 71.54 51 | 69.24 58 | 78.57 50 | 83.41 37 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
CLD-MVS | | | 67.02 44 | 71.57 37 | 61.71 46 | 71.01 28 | 74.81 32 | 71.62 46 | 38.91 165 | 71.86 35 | 60.70 16 | 64.97 34 | 67.88 52 | 51.88 93 | 76.77 27 | 74.98 31 | 76.11 94 | 69.75 119 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
3Dnovator | | 60.86 6 | 66.99 45 | 70.32 45 | 63.11 43 | 66.63 48 | 74.52 33 | 71.56 47 | 45.76 66 | 67.37 46 | 55.00 38 | 54.31 72 | 68.19 50 | 58.49 34 | 73.97 42 | 73.63 41 | 81.22 34 | 80.23 48 |
|
DELS-MVS | | | 65.87 46 | 70.30 46 | 60.71 47 | 64.05 63 | 72.68 45 | 70.90 49 | 45.43 70 | 57.49 60 | 49.05 60 | 64.43 35 | 68.66 47 | 55.11 53 | 74.31 40 | 73.02 43 | 79.70 42 | 81.51 44 |
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 |
canonicalmvs | | | 65.62 47 | 72.06 35 | 58.11 56 | 63.94 64 | 71.05 49 | 64.49 97 | 43.18 115 | 74.08 28 | 47.35 63 | 64.17 37 | 71.97 40 | 51.17 96 | 71.87 49 | 70.74 48 | 78.51 52 | 80.56 47 |
|
QAPM | | | 65.27 48 | 69.49 49 | 60.35 48 | 65.43 53 | 72.20 47 | 65.69 88 | 47.23 61 | 63.46 51 | 49.14 59 | 53.56 73 | 71.04 42 | 57.01 41 | 72.60 47 | 71.41 47 | 77.62 58 | 82.14 42 |
|
OMC-MVS | | | 65.16 49 | 71.35 39 | 57.94 60 | 52.95 158 | 68.82 56 | 69.00 50 | 38.28 172 | 79.89 11 | 55.20 36 | 62.76 41 | 68.31 49 | 56.14 48 | 71.30 53 | 68.70 63 | 76.06 96 | 79.67 50 |
|
EPNet | | | 65.14 50 | 69.54 48 | 60.00 50 | 66.61 49 | 67.67 65 | 67.53 54 | 55.32 20 | 62.67 54 | 46.22 70 | 67.74 28 | 65.93 56 | 48.07 109 | 72.17 48 | 72.12 44 | 76.28 86 | 78.47 56 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
PVSNet_Blended_VisFu | | | 63.65 51 | 66.92 52 | 59.83 51 | 60.03 74 | 73.44 42 | 66.33 79 | 48.95 56 | 52.20 77 | 50.81 55 | 56.07 62 | 60.25 71 | 53.56 60 | 73.23 45 | 70.01 55 | 79.30 46 | 83.24 38 |
|
Effi-MVS+ | | | 63.28 52 | 65.96 57 | 60.17 49 | 64.26 60 | 68.06 60 | 68.78 51 | 45.71 68 | 54.08 67 | 46.64 66 | 55.92 64 | 63.13 61 | 55.94 49 | 70.38 60 | 71.43 46 | 79.68 45 | 78.70 54 |
|
MVS_111021_LR | | | 63.05 53 | 66.43 54 | 59.10 53 | 61.33 68 | 63.77 101 | 65.87 86 | 43.58 104 | 60.20 58 | 53.70 49 | 62.09 46 | 62.38 62 | 55.84 50 | 70.24 61 | 68.08 67 | 74.30 107 | 78.28 58 |
|
OpenMVS | | 57.13 9 | 62.81 54 | 65.75 58 | 59.39 52 | 66.47 50 | 69.52 54 | 64.26 99 | 43.07 120 | 61.34 56 | 50.19 57 | 47.29 118 | 64.41 57 | 54.60 54 | 70.18 62 | 68.62 65 | 77.73 56 | 78.89 53 |
|
CNLPA | | | 62.78 55 | 66.31 55 | 58.65 54 | 58.47 82 | 68.41 59 | 65.98 85 | 41.22 150 | 78.02 18 | 56.04 31 | 46.65 121 | 59.50 74 | 57.50 37 | 69.67 64 | 65.27 121 | 72.70 135 | 76.67 68 |
|
TSAR-MVS + COLMAP | | | 62.65 56 | 69.90 47 | 54.19 99 | 46.31 190 | 66.73 75 | 65.49 90 | 41.36 148 | 76.57 20 | 46.31 69 | 76.80 12 | 56.68 83 | 53.27 68 | 69.50 65 | 66.65 84 | 72.40 141 | 76.36 76 |
|
MVS_Test | | | 62.40 57 | 66.23 56 | 57.94 60 | 59.77 77 | 64.77 97 | 66.50 78 | 41.76 140 | 57.26 61 | 49.33 58 | 62.68 42 | 67.47 54 | 53.50 63 | 68.57 73 | 66.25 91 | 76.77 71 | 76.58 71 |
|
DI_MVS_plusplus_trai | | | 61.88 58 | 65.17 62 | 58.06 57 | 60.05 73 | 65.26 92 | 66.03 83 | 44.22 82 | 55.75 62 | 46.73 65 | 54.64 70 | 68.12 51 | 54.13 57 | 69.13 66 | 66.66 83 | 77.18 64 | 76.61 69 |
|
PVSNet_BlendedMVS | | | 61.63 59 | 64.82 63 | 57.91 62 | 57.21 123 | 67.55 66 | 63.47 103 | 46.08 64 | 54.72 64 | 52.46 51 | 58.59 54 | 60.73 68 | 51.82 94 | 70.46 58 | 65.20 123 | 76.44 83 | 76.50 74 |
|
PVSNet_Blended | | | 61.63 59 | 64.82 63 | 57.91 62 | 57.21 123 | 67.55 66 | 63.47 103 | 46.08 64 | 54.72 64 | 52.46 51 | 58.59 54 | 60.73 68 | 51.82 94 | 70.46 58 | 65.20 123 | 76.44 83 | 76.50 74 |
|
TAPA-MVS | | 54.74 10 | 60.85 61 | 66.61 53 | 54.12 100 | 47.38 186 | 65.33 90 | 65.35 91 | 36.51 183 | 75.16 25 | 48.82 61 | 54.70 69 | 63.51 59 | 53.31 67 | 68.36 74 | 64.97 126 | 73.37 120 | 74.27 104 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
Fast-Effi-MVS+ | | | 60.36 62 | 63.35 69 | 56.87 82 | 58.70 79 | 65.86 88 | 65.08 92 | 37.11 178 | 53.00 74 | 45.36 79 | 52.12 78 | 56.07 88 | 56.27 46 | 71.28 54 | 69.42 57 | 78.71 49 | 75.69 80 |
|
Effi-MVS+-dtu | | | 60.34 63 | 62.32 72 | 58.03 59 | 64.31 58 | 67.44 68 | 65.99 84 | 42.26 137 | 49.55 85 | 42.00 105 | 48.92 96 | 59.79 73 | 56.27 46 | 68.07 85 | 67.03 76 | 77.35 63 | 75.45 82 |
|
LS3D | | | 60.20 64 | 61.70 73 | 58.45 55 | 64.18 61 | 67.77 62 | 67.19 56 | 48.84 57 | 61.67 55 | 41.27 108 | 45.89 132 | 51.81 114 | 54.18 56 | 68.78 68 | 66.50 89 | 75.03 103 | 69.48 125 |
|
diffmvs | | | 59.53 65 | 64.04 68 | 54.26 98 | 55.09 138 | 59.86 141 | 64.80 94 | 39.55 164 | 58.39 59 | 46.21 71 | 60.48 50 | 67.82 53 | 49.27 101 | 63.53 150 | 63.32 146 | 70.64 156 | 74.89 86 |
|
EPP-MVSNet | | | 59.39 66 | 65.45 60 | 52.32 113 | 60.96 70 | 67.70 64 | 58.42 121 | 44.75 77 | 49.71 84 | 27.23 168 | 59.03 53 | 62.20 64 | 43.34 131 | 70.71 57 | 69.13 59 | 79.25 47 | 79.63 51 |
|
ACMH+ | | 53.71 12 | 59.26 67 | 60.28 85 | 58.06 57 | 64.17 62 | 68.46 58 | 67.51 55 | 50.93 44 | 52.46 76 | 35.83 129 | 40.83 178 | 45.12 156 | 52.32 89 | 69.88 63 | 69.00 61 | 77.59 60 | 76.21 77 |
|
PLC | | 52.09 14 | 59.21 68 | 62.47 71 | 55.41 95 | 53.24 157 | 64.84 96 | 64.47 98 | 40.41 158 | 65.92 47 | 44.53 92 | 46.19 129 | 55.69 89 | 55.33 52 | 68.24 78 | 65.30 120 | 74.50 105 | 71.09 111 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
v7 | | | 59.19 69 | 60.62 78 | 57.53 65 | 57.96 85 | 67.19 71 | 67.09 59 | 44.28 81 | 46.84 114 | 45.45 77 | 48.19 110 | 51.06 116 | 53.62 59 | 67.84 91 | 66.59 86 | 76.79 68 | 76.60 70 |
|
v10 | | | 59.17 70 | 60.60 79 | 57.50 66 | 57.95 86 | 66.73 75 | 67.09 59 | 44.11 83 | 46.85 113 | 45.42 78 | 48.18 112 | 51.07 115 | 53.63 58 | 67.84 91 | 66.59 86 | 76.79 68 | 76.92 66 |
|
v6 | | | 58.89 71 | 60.54 81 | 56.96 71 | 57.34 110 | 66.13 84 | 66.71 70 | 42.84 122 | 47.85 108 | 45.80 73 | 49.04 90 | 52.95 99 | 52.79 73 | 67.53 101 | 65.59 112 | 76.26 87 | 74.73 88 |
|
CANet_DTU | | | 58.88 72 | 64.68 65 | 52.12 114 | 55.77 131 | 66.75 74 | 63.92 100 | 37.04 179 | 53.32 70 | 37.45 123 | 59.81 51 | 61.81 65 | 44.43 125 | 68.25 76 | 67.47 74 | 74.12 110 | 75.33 83 |
|
v1144 | | | 58.88 72 | 60.16 92 | 57.39 67 | 58.03 84 | 67.26 69 | 67.14 58 | 44.46 80 | 45.17 136 | 44.33 93 | 47.81 115 | 49.92 125 | 53.20 69 | 67.77 96 | 66.62 85 | 77.15 65 | 76.58 71 |
|
v1neww | | | 58.88 72 | 60.54 81 | 56.94 72 | 57.33 112 | 66.13 84 | 66.70 72 | 42.84 122 | 47.84 109 | 45.74 75 | 49.02 92 | 52.93 100 | 52.78 74 | 67.53 101 | 65.59 112 | 76.26 87 | 74.73 88 |
|
v7new | | | 58.88 72 | 60.54 81 | 56.94 72 | 57.33 112 | 66.13 84 | 66.70 72 | 42.84 122 | 47.84 109 | 45.74 75 | 49.02 92 | 52.93 100 | 52.78 74 | 67.53 101 | 65.59 112 | 76.26 87 | 74.73 88 |
|
v8 | | | 58.88 72 | 60.57 80 | 56.92 76 | 57.35 108 | 65.69 89 | 66.69 74 | 42.64 130 | 47.89 107 | 45.77 74 | 49.04 90 | 52.98 98 | 52.77 76 | 67.51 104 | 65.57 116 | 76.26 87 | 75.30 84 |
|
v16 | | | 58.71 77 | 60.20 88 | 56.97 70 | 57.35 108 | 63.36 109 | 66.67 75 | 42.49 132 | 48.69 100 | 46.36 68 | 48.87 98 | 52.92 102 | 52.82 72 | 67.57 99 | 65.58 115 | 76.15 93 | 74.38 100 |
|
v17 | | | 58.69 78 | 60.19 91 | 56.94 72 | 57.38 103 | 63.37 108 | 66.67 75 | 42.47 134 | 48.52 104 | 46.10 72 | 48.90 97 | 53.00 97 | 52.84 70 | 67.58 98 | 65.60 111 | 76.19 91 | 74.38 100 |
|
v2v482 | | | 58.69 78 | 60.12 95 | 57.03 69 | 57.16 125 | 66.05 87 | 67.17 57 | 43.52 106 | 46.33 119 | 45.19 80 | 49.46 87 | 51.02 117 | 52.51 84 | 67.30 111 | 66.03 94 | 76.61 79 | 74.62 95 |
|
v18 | | | 58.68 80 | 60.20 88 | 56.90 79 | 57.26 121 | 63.28 110 | 66.58 77 | 42.42 135 | 48.86 94 | 46.37 67 | 49.01 94 | 53.05 96 | 52.74 77 | 67.40 109 | 65.52 117 | 76.02 98 | 74.28 103 |
|
v1141 | | | 58.56 81 | 60.05 97 | 56.81 85 | 57.36 105 | 66.18 82 | 66.80 67 | 43.11 117 | 45.87 130 | 44.60 89 | 48.71 100 | 51.83 112 | 52.38 86 | 67.46 105 | 65.64 109 | 76.63 76 | 74.66 91 |
|
divwei89l23v2f112 | | | 58.56 81 | 60.05 97 | 56.81 85 | 57.36 105 | 66.18 82 | 66.80 67 | 43.11 117 | 45.89 129 | 44.60 89 | 48.71 100 | 51.84 111 | 52.38 86 | 67.45 107 | 65.65 106 | 76.63 76 | 74.66 91 |
|
v1 | | | 58.56 81 | 60.06 96 | 56.83 84 | 57.36 105 | 66.19 81 | 66.80 67 | 43.10 119 | 45.87 130 | 44.68 87 | 48.73 99 | 51.83 112 | 52.38 86 | 67.45 107 | 65.65 106 | 76.63 76 | 74.66 91 |
|
v1192 | | | 58.51 84 | 59.66 105 | 57.17 68 | 57.82 87 | 67.72 63 | 66.21 82 | 44.83 76 | 44.15 143 | 43.49 96 | 46.68 120 | 47.94 129 | 53.55 61 | 67.39 110 | 66.51 88 | 77.13 66 | 77.20 64 |
|
UA-Net | | | 58.50 85 | 64.68 65 | 51.30 116 | 66.97 46 | 67.13 72 | 53.68 156 | 45.65 69 | 49.51 87 | 31.58 143 | 62.91 39 | 68.47 48 | 35.85 166 | 68.20 79 | 67.28 75 | 74.03 111 | 69.24 129 |
|
Vis-MVSNet | | | 58.48 86 | 65.70 59 | 50.06 123 | 53.40 156 | 67.20 70 | 60.24 117 | 43.32 112 | 48.83 95 | 30.23 149 | 62.38 44 | 61.61 67 | 40.35 142 | 71.03 56 | 69.77 56 | 72.82 128 | 79.11 52 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
MSDG | | | 58.46 87 | 58.97 115 | 57.85 64 | 66.27 52 | 66.23 80 | 67.72 52 | 42.33 136 | 53.43 69 | 43.68 95 | 43.39 152 | 45.35 153 | 49.75 99 | 68.66 71 | 67.77 70 | 77.38 62 | 67.96 132 |
|
V9 | | | 58.45 88 | 59.75 100 | 56.92 76 | 57.51 96 | 63.49 104 | 66.86 62 | 42.73 127 | 46.07 125 | 45.05 82 | 48.45 105 | 51.99 108 | 52.66 80 | 68.04 89 | 65.75 101 | 76.72 73 | 74.50 97 |
|
v13 | | | 58.44 89 | 59.72 104 | 56.94 72 | 57.55 90 | 63.51 102 | 66.86 62 | 42.81 125 | 45.90 128 | 44.98 84 | 48.17 113 | 51.87 110 | 52.68 78 | 68.20 79 | 65.78 99 | 76.78 70 | 74.63 94 |
|
v12 | | | 58.44 89 | 59.74 103 | 56.92 76 | 57.54 92 | 63.50 103 | 66.84 65 | 42.77 126 | 45.96 126 | 44.95 85 | 48.31 106 | 51.94 109 | 52.67 79 | 68.14 82 | 65.75 101 | 76.75 72 | 74.55 96 |
|
V14 | | | 58.44 89 | 59.75 100 | 56.90 79 | 57.48 98 | 63.46 105 | 66.85 64 | 42.68 128 | 46.16 122 | 45.03 83 | 48.57 103 | 52.04 107 | 52.65 81 | 67.93 90 | 65.72 104 | 76.69 74 | 74.40 99 |
|
v15 | | | 58.43 92 | 59.75 100 | 56.88 81 | 57.45 99 | 63.44 106 | 66.84 65 | 42.65 129 | 46.24 121 | 45.07 81 | 48.68 102 | 52.07 106 | 52.63 82 | 67.84 91 | 65.70 105 | 76.65 75 | 74.31 102 |
|
FC-MVSNet-train | | | 58.40 93 | 63.15 70 | 52.85 109 | 64.29 59 | 61.84 118 | 55.98 138 | 46.47 62 | 53.06 72 | 34.96 132 | 61.95 47 | 56.37 86 | 39.49 144 | 68.67 70 | 68.36 66 | 75.92 99 | 71.81 108 |
|
IB-MVS | | 54.11 11 | 58.36 94 | 60.70 77 | 55.62 93 | 58.67 80 | 68.02 61 | 61.56 106 | 43.15 116 | 46.09 123 | 44.06 94 | 44.24 144 | 50.99 119 | 48.71 104 | 66.70 120 | 70.33 51 | 77.60 59 | 78.50 55 |
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 |
IterMVS-LS | | | 58.30 95 | 61.39 74 | 54.71 97 | 59.92 76 | 58.40 158 | 59.42 118 | 43.64 101 | 48.71 98 | 40.25 112 | 57.53 59 | 58.55 77 | 52.15 91 | 65.42 143 | 65.34 119 | 72.85 126 | 75.77 78 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
ACMH | | 52.42 13 | 58.24 96 | 59.56 108 | 56.70 87 | 66.34 51 | 69.59 53 | 66.71 70 | 49.12 55 | 46.08 124 | 28.90 156 | 42.67 166 | 41.20 185 | 52.60 83 | 71.39 52 | 70.28 52 | 76.51 81 | 75.72 79 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
v144192 | | | 58.23 97 | 59.40 111 | 56.87 82 | 57.56 89 | 66.89 73 | 65.70 87 | 45.01 75 | 44.06 144 | 42.88 98 | 46.61 122 | 48.09 128 | 53.49 64 | 66.94 116 | 65.90 97 | 76.61 79 | 77.29 62 |
|
MS-PatchMatch | | | 58.19 98 | 60.20 88 | 55.85 92 | 65.17 55 | 64.16 99 | 64.82 93 | 41.48 147 | 50.95 79 | 42.17 104 | 45.38 137 | 56.42 84 | 48.08 108 | 68.30 75 | 66.70 82 | 73.39 119 | 69.46 127 |
|
v11 | | | 58.19 98 | 59.47 109 | 56.70 87 | 57.54 92 | 63.42 107 | 66.28 81 | 42.49 132 | 45.62 134 | 44.59 91 | 48.16 114 | 50.78 120 | 52.84 70 | 67.80 95 | 65.76 100 | 76.49 82 | 74.76 87 |
|
IS_MVSNet | | | 57.95 100 | 64.26 67 | 50.60 118 | 61.62 67 | 65.25 93 | 57.18 127 | 45.42 71 | 50.79 80 | 26.49 170 | 57.81 58 | 60.05 72 | 34.51 170 | 71.24 55 | 70.20 54 | 78.36 53 | 74.44 98 |
|
v1921920 | | | 57.89 101 | 59.02 114 | 56.58 89 | 57.55 90 | 66.66 78 | 64.72 96 | 44.70 78 | 43.55 147 | 42.73 100 | 46.17 130 | 46.93 143 | 53.51 62 | 66.78 119 | 65.75 101 | 76.29 85 | 77.28 63 |
|
v1240 | | | 57.55 102 | 58.63 117 | 56.29 91 | 57.30 118 | 66.48 79 | 63.77 101 | 44.56 79 | 42.77 163 | 42.48 102 | 45.64 135 | 46.28 148 | 53.46 65 | 66.32 127 | 65.80 98 | 76.16 92 | 77.13 65 |
|
MVSTER | | | 57.19 103 | 61.11 76 | 52.62 111 | 50.82 174 | 58.79 154 | 61.55 107 | 37.86 175 | 48.81 96 | 41.31 107 | 57.43 60 | 52.10 105 | 48.60 105 | 68.19 81 | 66.75 81 | 75.56 100 | 75.68 81 |
|
UGNet | | | 57.03 104 | 65.25 61 | 47.44 155 | 46.54 189 | 66.73 75 | 56.30 134 | 43.28 113 | 50.06 82 | 32.99 136 | 62.57 43 | 63.26 60 | 33.31 176 | 68.25 76 | 67.58 72 | 72.20 145 | 78.29 57 |
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 |
EG-PatchMatch MVS | | | 56.98 105 | 58.24 121 | 55.50 94 | 64.66 57 | 68.62 57 | 61.48 108 | 43.63 103 | 38.44 196 | 41.44 106 | 38.05 188 | 46.18 150 | 43.95 126 | 71.71 50 | 70.61 49 | 77.87 55 | 74.08 105 |
|
V42 | | | 56.97 106 | 60.14 93 | 53.28 104 | 48.16 181 | 62.78 115 | 66.30 80 | 37.93 174 | 47.44 111 | 42.68 101 | 48.19 110 | 52.59 104 | 51.90 92 | 67.46 105 | 65.94 96 | 72.72 129 | 76.55 73 |
|
UniMVSNet_NR-MVSNet | | | 56.94 107 | 61.14 75 | 52.05 115 | 60.02 75 | 65.21 94 | 57.44 125 | 52.93 34 | 49.37 88 | 24.31 179 | 54.62 71 | 50.54 121 | 39.04 146 | 68.69 69 | 68.84 62 | 78.53 51 | 70.72 112 |
|
HyFIR lowres test | | | 56.87 108 | 58.60 118 | 54.84 96 | 56.62 128 | 69.27 55 | 64.77 95 | 42.21 138 | 45.66 133 | 37.50 122 | 33.08 197 | 57.47 82 | 53.33 66 | 65.46 142 | 67.94 68 | 74.60 104 | 71.35 110 |
|
tpmp4_e23 | | | 56.84 109 | 57.14 128 | 56.49 90 | 62.45 65 | 62.05 116 | 67.57 53 | 41.56 145 | 54.17 66 | 48.57 62 | 49.18 88 | 46.54 146 | 50.44 98 | 61.93 163 | 58.82 177 | 68.34 170 | 67.28 137 |
|
CostFormer | | | 56.57 110 | 59.13 113 | 53.60 101 | 57.52 95 | 61.12 128 | 66.94 61 | 35.95 185 | 53.44 68 | 44.68 87 | 55.87 65 | 54.44 91 | 48.21 107 | 60.37 171 | 58.33 180 | 68.27 172 | 70.33 117 |
|
Fast-Effi-MVS+-dtu | | | 56.30 111 | 59.29 112 | 52.82 110 | 58.64 81 | 64.89 95 | 65.56 89 | 32.89 203 | 45.80 132 | 35.04 131 | 45.89 132 | 54.14 92 | 49.41 100 | 67.16 113 | 66.45 90 | 75.37 101 | 70.69 114 |
|
TranMVSNet+NR-MVSNet | | | 55.87 112 | 60.14 93 | 50.88 117 | 59.46 78 | 63.82 100 | 57.93 123 | 52.98 33 | 48.94 93 | 20.52 189 | 52.87 75 | 47.33 137 | 36.81 163 | 69.12 67 | 69.03 60 | 77.56 61 | 69.89 118 |
|
CHOSEN 1792x2688 | | | 55.85 113 | 58.01 122 | 53.33 103 | 57.26 121 | 62.82 114 | 63.29 105 | 41.55 146 | 46.65 116 | 38.34 117 | 34.55 195 | 53.50 93 | 52.43 85 | 67.10 114 | 67.56 73 | 67.13 177 | 73.92 106 |
|
v7n | | | 55.67 114 | 57.46 127 | 53.59 102 | 56.06 129 | 65.29 91 | 61.06 111 | 43.26 114 | 40.17 183 | 37.99 119 | 40.79 179 | 45.27 155 | 47.09 113 | 67.67 97 | 66.21 92 | 76.08 95 | 76.82 67 |
|
GA-MVS | | | 55.67 114 | 58.33 119 | 52.58 112 | 55.23 136 | 63.09 111 | 61.08 110 | 40.15 160 | 42.95 154 | 37.02 125 | 52.61 76 | 47.68 132 | 47.51 111 | 65.92 135 | 65.35 118 | 74.49 106 | 70.68 115 |
|
v148 | | | 55.58 116 | 57.61 126 | 53.20 106 | 54.59 147 | 61.86 117 | 61.18 109 | 38.70 170 | 44.30 142 | 42.25 103 | 47.53 116 | 50.24 124 | 48.73 103 | 65.15 144 | 62.61 156 | 73.79 113 | 71.61 109 |
|
DU-MVS | | | 55.41 117 | 59.59 106 | 50.54 120 | 54.60 145 | 62.97 112 | 57.44 125 | 51.80 39 | 48.62 102 | 24.31 179 | 51.99 79 | 47.00 142 | 39.04 146 | 68.11 83 | 67.75 71 | 76.03 97 | 70.72 112 |
|
NR-MVSNet | | | 55.35 118 | 59.46 110 | 50.56 119 | 61.33 68 | 62.97 112 | 57.91 124 | 51.80 39 | 48.62 102 | 20.59 188 | 51.99 79 | 44.73 164 | 34.10 173 | 68.58 72 | 68.64 64 | 77.66 57 | 70.67 116 |
|
GBi-Net | | | 55.20 119 | 60.25 86 | 49.31 127 | 52.42 160 | 61.44 122 | 57.03 128 | 44.04 86 | 49.18 90 | 30.47 145 | 48.28 107 | 58.19 78 | 38.22 149 | 68.05 86 | 66.96 77 | 73.69 115 | 69.65 120 |
|
test1 | | | 55.20 119 | 60.25 86 | 49.31 127 | 52.42 160 | 61.44 122 | 57.03 128 | 44.04 86 | 49.18 90 | 30.47 145 | 48.28 107 | 58.19 78 | 38.22 149 | 68.05 86 | 66.96 77 | 73.69 115 | 69.65 120 |
|
UniMVSNet (Re) | | | 55.15 121 | 60.39 84 | 49.03 133 | 55.31 133 | 64.59 98 | 55.77 139 | 50.63 46 | 48.66 101 | 20.95 187 | 51.47 81 | 50.40 122 | 34.41 172 | 67.81 94 | 67.89 69 | 77.11 67 | 71.88 107 |
|
FMVSNet2 | | | 55.04 122 | 59.95 99 | 49.31 127 | 52.42 160 | 61.44 122 | 57.03 128 | 44.08 85 | 49.55 85 | 30.40 148 | 46.89 119 | 58.84 76 | 38.22 149 | 67.07 115 | 66.21 92 | 73.69 115 | 69.65 120 |
|
FMVSNet3 | | | 54.78 123 | 59.58 107 | 49.17 130 | 52.37 163 | 61.31 126 | 56.72 132 | 44.04 86 | 49.18 90 | 30.47 145 | 48.28 107 | 58.19 78 | 38.09 152 | 65.48 141 | 65.20 123 | 73.31 121 | 69.45 128 |
|
pmmvs4 | | | 54.66 124 | 56.07 133 | 53.00 108 | 54.63 144 | 57.08 165 | 60.43 116 | 44.10 84 | 51.69 78 | 40.55 110 | 46.55 125 | 44.79 163 | 45.95 120 | 62.54 155 | 63.66 141 | 72.36 143 | 66.20 147 |
|
FMVSNet1 | | | 54.08 125 | 58.68 116 | 48.71 141 | 50.90 173 | 61.35 125 | 56.73 131 | 43.94 90 | 45.91 127 | 29.32 155 | 42.72 165 | 56.26 87 | 37.70 153 | 68.05 86 | 66.96 77 | 73.69 115 | 69.50 124 |
|
DWT-MVSNet_training | | | 53.80 126 | 54.31 151 | 53.21 105 | 57.65 88 | 59.04 152 | 60.65 112 | 40.11 161 | 46.35 118 | 42.77 99 | 49.07 89 | 41.07 186 | 51.06 97 | 58.62 181 | 58.96 176 | 67.00 180 | 67.06 138 |
|
v52 | | | 53.60 127 | 56.74 131 | 49.93 124 | 45.54 193 | 61.64 120 | 60.65 112 | 36.99 180 | 38.75 192 | 36.32 127 | 39.64 183 | 47.13 139 | 47.05 114 | 66.89 117 | 65.65 106 | 73.04 124 | 77.48 60 |
|
V4 | | | 53.60 127 | 56.73 132 | 49.93 124 | 45.54 193 | 61.64 120 | 60.65 112 | 36.99 180 | 38.74 194 | 36.33 126 | 39.64 183 | 47.12 140 | 47.05 114 | 66.89 117 | 65.64 109 | 73.04 124 | 77.48 60 |
|
Baseline_NR-MVSNet | | | 53.50 129 | 57.89 123 | 48.37 144 | 54.60 145 | 59.25 150 | 56.10 135 | 51.84 38 | 49.32 89 | 17.92 199 | 45.38 137 | 47.68 132 | 36.93 162 | 68.11 83 | 65.95 95 | 72.84 127 | 69.57 123 |
|
IterMVS | | | 53.45 130 | 57.12 129 | 49.17 130 | 49.23 178 | 60.93 129 | 59.05 120 | 34.63 189 | 44.53 138 | 33.22 135 | 51.09 83 | 51.01 118 | 48.38 106 | 62.43 156 | 60.79 166 | 70.54 158 | 69.05 130 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
tpm cat1 | | | 53.30 131 | 53.41 159 | 53.17 107 | 58.16 83 | 59.15 151 | 63.73 102 | 38.27 173 | 50.73 81 | 46.98 64 | 45.57 136 | 44.00 171 | 49.20 102 | 55.90 199 | 54.02 198 | 62.65 192 | 64.50 166 |
|
v748 | | | 52.93 132 | 55.29 141 | 50.19 122 | 51.90 167 | 61.31 126 | 56.54 133 | 40.05 162 | 39.12 190 | 34.82 134 | 39.93 182 | 43.83 172 | 43.66 127 | 64.26 148 | 63.32 146 | 74.15 109 | 75.28 85 |
|
anonymousdsp | | | 52.84 133 | 57.78 124 | 47.06 156 | 40.24 209 | 58.95 153 | 53.70 155 | 33.54 198 | 36.51 203 | 32.69 138 | 43.88 146 | 45.40 152 | 47.97 110 | 67.17 112 | 70.28 52 | 74.22 108 | 82.29 41 |
|
tfpn200view9 | | | 52.53 134 | 55.51 135 | 49.06 132 | 57.31 114 | 60.24 131 | 55.42 143 | 43.77 92 | 42.85 157 | 27.81 160 | 43.00 161 | 45.06 158 | 37.32 155 | 66.38 122 | 64.54 128 | 72.71 132 | 66.54 140 |
|
conf200view11 | | | 52.51 135 | 55.51 135 | 49.01 134 | 57.31 114 | 60.24 131 | 55.42 143 | 43.77 92 | 42.85 157 | 27.51 162 | 43.00 161 | 45.06 158 | 37.32 155 | 66.38 122 | 64.54 128 | 72.71 132 | 66.54 140 |
|
tfpn111 | | | 52.44 136 | 55.38 138 | 49.01 134 | 57.31 114 | 60.24 131 | 55.42 143 | 43.77 92 | 42.85 157 | 27.51 162 | 42.03 172 | 45.06 158 | 37.32 155 | 66.38 122 | 64.54 128 | 72.71 132 | 66.54 140 |
|
CDS-MVSNet | | | 52.42 137 | 57.06 130 | 47.02 157 | 53.92 154 | 58.30 160 | 55.50 141 | 46.47 62 | 42.52 166 | 29.38 154 | 49.50 86 | 52.85 103 | 28.49 191 | 66.70 120 | 66.89 80 | 68.34 170 | 62.63 175 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
thres200 | | | 52.39 138 | 55.37 140 | 48.90 138 | 57.39 102 | 60.18 134 | 55.60 140 | 43.73 98 | 42.93 155 | 27.41 166 | 43.35 153 | 45.09 157 | 36.61 164 | 66.36 125 | 63.92 140 | 72.66 136 | 65.78 153 |
|
thres400 | | | 52.38 139 | 55.51 135 | 48.74 140 | 57.49 97 | 60.10 138 | 55.45 142 | 43.54 105 | 42.90 156 | 26.72 169 | 43.34 154 | 45.03 162 | 36.61 164 | 66.20 132 | 64.53 132 | 72.66 136 | 66.43 143 |
|
EPNet_dtu | | | 52.05 140 | 58.26 120 | 44.81 168 | 54.10 152 | 50.09 192 | 52.01 164 | 40.82 155 | 53.03 73 | 27.41 166 | 54.90 67 | 57.96 81 | 26.72 196 | 62.97 152 | 62.70 155 | 67.78 174 | 66.19 148 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
thres100view900 | | | 52.04 141 | 54.81 147 | 48.80 139 | 57.31 114 | 59.33 146 | 55.30 148 | 42.92 121 | 42.85 157 | 27.81 160 | 43.00 161 | 45.06 158 | 36.99 161 | 64.74 146 | 63.51 143 | 72.47 140 | 65.21 160 |
|
conf0.01 | | | 52.02 142 | 54.62 148 | 49.00 136 | 57.30 118 | 60.17 136 | 55.42 143 | 43.76 95 | 42.85 157 | 27.49 164 | 43.12 158 | 39.71 194 | 37.32 155 | 66.26 130 | 64.54 128 | 72.72 129 | 65.66 155 |
|
COLMAP_ROB | | 46.52 15 | 51.99 143 | 54.86 146 | 48.63 142 | 49.13 179 | 61.73 119 | 60.53 115 | 36.57 182 | 53.14 71 | 32.95 137 | 37.10 189 | 38.68 198 | 40.49 141 | 65.72 138 | 63.08 149 | 72.11 146 | 64.60 165 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
view600 | | | 51.96 144 | 55.13 143 | 48.27 146 | 57.41 101 | 60.05 139 | 54.74 150 | 43.64 101 | 42.57 165 | 25.88 172 | 43.11 159 | 44.48 167 | 35.34 167 | 66.27 128 | 63.61 142 | 72.61 139 | 65.80 152 |
|
TransMVSNet (Re) | | | 51.92 145 | 55.38 138 | 47.88 151 | 60.95 71 | 59.90 140 | 53.95 153 | 45.14 73 | 39.47 187 | 24.85 176 | 43.87 147 | 46.51 147 | 29.15 187 | 67.55 100 | 65.23 122 | 73.26 123 | 65.16 161 |
|
thres600view7 | | | 51.91 146 | 55.14 142 | 48.14 147 | 57.43 100 | 60.18 134 | 54.60 151 | 43.73 98 | 42.61 164 | 25.20 174 | 43.10 160 | 44.47 168 | 35.19 168 | 66.36 125 | 63.28 148 | 72.66 136 | 66.01 150 |
|
conf0.002 | | | 51.76 147 | 54.13 153 | 49.00 136 | 57.28 120 | 60.15 137 | 55.42 143 | 43.75 97 | 42.85 157 | 27.49 164 | 43.13 157 | 37.12 206 | 37.32 155 | 66.23 131 | 64.17 135 | 72.72 129 | 65.24 159 |
|
view800 | | | 51.55 148 | 54.89 145 | 47.66 154 | 57.37 104 | 59.77 143 | 53.62 157 | 43.72 100 | 42.22 167 | 24.94 175 | 42.80 164 | 43.81 173 | 33.94 174 | 66.09 133 | 64.38 134 | 72.39 142 | 65.14 162 |
|
pmmvs-eth3d | | | 51.33 149 | 52.25 169 | 50.26 121 | 50.82 174 | 54.65 175 | 56.03 137 | 43.45 111 | 43.51 148 | 37.20 124 | 39.20 186 | 39.04 197 | 42.28 134 | 61.85 164 | 62.78 153 | 71.78 150 | 64.72 164 |
|
USDC | | | 51.11 150 | 53.71 155 | 48.08 149 | 44.76 196 | 55.99 168 | 53.01 161 | 40.90 152 | 52.49 75 | 36.14 128 | 44.67 142 | 33.66 210 | 43.27 132 | 63.23 151 | 61.10 162 | 70.39 159 | 64.82 163 |
|
pm-mvs1 | | | 51.02 151 | 55.55 134 | 45.73 163 | 54.16 151 | 58.52 156 | 50.92 166 | 42.56 131 | 40.32 182 | 25.67 173 | 43.66 149 | 50.34 123 | 30.06 185 | 65.85 136 | 63.97 139 | 70.99 155 | 66.21 146 |
|
conf0.05thres1000 | | | 50.64 152 | 53.84 154 | 46.92 159 | 57.02 126 | 59.29 148 | 52.29 163 | 43.80 91 | 39.84 186 | 23.81 182 | 39.26 185 | 43.14 176 | 32.52 180 | 65.74 137 | 64.04 136 | 72.05 147 | 65.53 156 |
|
tfpn | | | 50.58 153 | 53.65 157 | 47.00 158 | 57.34 110 | 59.31 147 | 52.41 162 | 43.76 95 | 41.81 171 | 23.86 181 | 42.49 167 | 37.80 201 | 32.63 179 | 65.68 140 | 64.02 138 | 71.99 148 | 64.41 167 |
|
CR-MVSNet | | | 50.47 154 | 52.61 164 | 47.98 150 | 49.03 180 | 52.94 180 | 48.27 175 | 38.86 167 | 44.41 139 | 39.59 114 | 44.34 143 | 44.65 166 | 46.63 117 | 58.97 176 | 60.31 169 | 65.48 183 | 62.66 173 |
|
dps | | | 50.42 155 | 51.20 181 | 49.51 126 | 55.88 130 | 56.07 167 | 53.73 154 | 38.89 166 | 43.66 145 | 40.36 111 | 45.66 134 | 37.63 203 | 45.23 122 | 59.05 174 | 56.18 183 | 62.94 191 | 60.16 184 |
|
Vis-MVSNet (Re-imp) | | | 50.37 156 | 57.73 125 | 41.80 187 | 57.53 94 | 54.35 176 | 45.70 193 | 45.24 72 | 49.80 83 | 13.43 207 | 58.23 57 | 56.42 84 | 20.11 208 | 62.96 153 | 63.36 145 | 68.76 169 | 58.96 189 |
|
MDTV_nov1_ep13 | | | 50.32 157 | 52.43 167 | 47.86 152 | 49.87 177 | 54.70 174 | 58.10 122 | 34.29 191 | 45.59 135 | 37.71 120 | 47.44 117 | 47.42 136 | 41.86 136 | 58.07 184 | 55.21 191 | 65.34 185 | 58.56 190 |
|
tfpnnormal | | | 50.16 158 | 52.19 170 | 47.78 153 | 56.86 127 | 58.37 159 | 54.15 152 | 44.01 89 | 38.35 198 | 25.94 171 | 36.10 191 | 37.89 200 | 34.50 171 | 65.93 134 | 63.42 144 | 71.26 153 | 65.28 158 |
|
PatchMatch-RL | | | 50.11 159 | 51.56 174 | 48.43 143 | 46.23 191 | 51.94 185 | 50.21 168 | 38.62 171 | 46.62 117 | 37.51 121 | 42.43 168 | 39.38 195 | 52.24 90 | 60.98 167 | 59.56 173 | 65.76 182 | 60.01 186 |
|
PatchmatchNet | | | 49.92 160 | 51.29 177 | 48.32 145 | 51.83 168 | 51.86 186 | 53.38 160 | 37.63 177 | 47.90 106 | 40.83 109 | 48.54 104 | 45.30 154 | 45.19 123 | 56.86 188 | 53.99 200 | 61.08 196 | 54.57 201 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
TDRefinement | | | 49.31 161 | 52.44 166 | 45.67 164 | 30.44 224 | 59.42 145 | 59.24 119 | 39.78 163 | 48.76 97 | 31.20 144 | 35.73 192 | 29.90 214 | 42.81 133 | 64.24 149 | 62.59 157 | 70.55 157 | 66.43 143 |
|
test-LLR | | | 49.28 162 | 50.29 185 | 48.10 148 | 55.26 134 | 47.16 200 | 49.52 169 | 43.48 109 | 39.22 188 | 31.98 139 | 43.65 150 | 47.93 130 | 41.29 139 | 56.80 189 | 55.36 189 | 67.08 178 | 61.94 176 |
|
CMPMVS | | 37.70 17 | 49.24 163 | 52.71 163 | 45.19 165 | 45.97 192 | 51.23 188 | 47.44 181 | 29.31 210 | 43.04 153 | 44.69 86 | 34.45 196 | 48.35 127 | 43.64 128 | 62.59 154 | 59.82 172 | 60.08 197 | 69.48 125 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
PEN-MVS | | | 49.21 164 | 54.32 150 | 43.24 179 | 54.33 150 | 59.26 149 | 47.04 184 | 51.37 43 | 41.67 172 | 9.97 217 | 46.22 128 | 41.80 180 | 22.97 205 | 60.52 169 | 64.03 137 | 73.73 114 | 66.75 139 |
|
PMMVS | | | 49.20 165 | 54.28 152 | 43.28 178 | 34.13 218 | 45.70 208 | 48.98 172 | 26.09 221 | 46.31 120 | 34.92 133 | 55.22 66 | 53.47 94 | 47.48 112 | 59.43 173 | 59.04 175 | 68.05 173 | 60.77 181 |
|
gg-mvs-nofinetune | | | 49.07 166 | 52.56 165 | 45.00 167 | 61.99 66 | 59.78 142 | 53.55 159 | 41.63 141 | 31.62 213 | 12.08 209 | 29.56 206 | 53.28 95 | 29.57 186 | 66.27 128 | 64.49 133 | 71.19 154 | 62.92 172 |
|
tpm | | | 48.82 167 | 51.27 178 | 45.96 162 | 54.10 152 | 47.35 199 | 56.05 136 | 30.23 208 | 46.70 115 | 43.21 97 | 52.54 77 | 47.55 135 | 37.28 160 | 54.11 204 | 50.50 209 | 54.90 210 | 60.12 185 |
|
WR-MVS | | | 48.78 168 | 55.06 144 | 41.45 189 | 55.50 132 | 60.40 130 | 43.77 202 | 49.99 51 | 41.92 169 | 8.10 222 | 45.24 140 | 45.56 151 | 17.47 210 | 61.57 165 | 64.60 127 | 73.85 112 | 66.14 149 |
|
CP-MVSNet | | | 48.37 169 | 53.53 158 | 42.34 184 | 51.35 171 | 58.01 161 | 46.56 185 | 50.54 47 | 41.62 173 | 10.61 213 | 46.53 126 | 40.68 190 | 23.18 202 | 58.71 179 | 61.83 158 | 71.81 149 | 67.36 136 |
|
pmmvs6 | | | 48.35 170 | 51.64 172 | 44.51 171 | 51.92 166 | 57.94 162 | 49.44 171 | 42.17 139 | 34.45 206 | 24.62 178 | 28.87 210 | 46.90 144 | 29.07 189 | 64.60 147 | 63.08 149 | 69.83 161 | 65.68 154 |
|
tfpn_ndepth | | | 48.34 171 | 52.27 168 | 43.76 173 | 54.35 149 | 56.46 166 | 47.24 183 | 40.92 151 | 43.45 149 | 21.04 186 | 41.16 177 | 43.22 175 | 28.90 190 | 61.57 165 | 60.65 167 | 70.12 160 | 59.34 187 |
|
PS-CasMVS | | | 48.18 172 | 53.25 162 | 42.27 185 | 51.26 172 | 57.94 162 | 46.51 186 | 50.52 48 | 41.30 176 | 10.56 215 | 45.35 139 | 40.34 192 | 23.04 204 | 58.66 180 | 61.79 159 | 71.74 151 | 67.38 135 |
|
thresconf0.02 | | | 48.17 173 | 51.22 180 | 44.60 170 | 55.14 137 | 55.73 169 | 48.95 173 | 41.35 149 | 43.43 151 | 21.23 185 | 42.03 172 | 37.25 205 | 31.19 182 | 62.33 159 | 60.61 168 | 69.76 162 | 57.17 194 |
|
PatchT | | | 48.08 174 | 51.03 182 | 44.64 169 | 42.96 203 | 50.12 191 | 40.36 210 | 35.09 187 | 43.17 152 | 39.59 114 | 42.00 174 | 39.96 193 | 46.63 117 | 58.97 176 | 60.31 169 | 63.21 190 | 62.66 173 |
|
tpmrst | | | 48.08 174 | 49.88 189 | 45.98 161 | 52.71 159 | 48.11 197 | 53.62 157 | 33.70 196 | 48.70 99 | 39.74 113 | 48.96 95 | 46.23 149 | 40.29 143 | 50.14 213 | 49.28 211 | 55.80 207 | 57.71 192 |
|
DTE-MVSNet | | | 48.03 176 | 53.28 161 | 41.91 186 | 54.64 143 | 57.50 164 | 44.63 200 | 51.66 42 | 41.02 178 | 7.97 223 | 46.26 127 | 40.90 187 | 20.24 207 | 60.45 170 | 62.89 152 | 72.33 144 | 63.97 168 |
|
WR-MVS_H | | | 47.65 177 | 53.67 156 | 40.63 192 | 51.45 169 | 59.74 144 | 44.71 199 | 49.37 53 | 40.69 180 | 7.61 224 | 46.04 131 | 44.34 170 | 17.32 211 | 57.79 185 | 61.18 160 | 73.30 122 | 65.86 151 |
|
MDTV_nov1_ep13_2view | | | 47.62 178 | 49.72 190 | 45.18 166 | 48.05 182 | 53.70 178 | 54.90 149 | 33.80 195 | 39.90 185 | 29.79 152 | 38.85 187 | 41.89 179 | 39.17 145 | 58.99 175 | 55.55 188 | 65.34 185 | 59.17 188 |
|
tfpnview11 | | | 47.58 179 | 51.57 173 | 42.92 180 | 54.94 139 | 55.30 171 | 46.21 187 | 41.58 144 | 42.10 168 | 18.54 194 | 42.25 169 | 41.54 182 | 27.12 193 | 62.29 160 | 61.12 161 | 69.15 164 | 56.40 198 |
|
tfpn_n400 | | | 47.56 180 | 51.56 174 | 42.90 181 | 54.91 140 | 55.28 172 | 46.21 187 | 41.59 142 | 41.51 174 | 18.54 194 | 42.25 169 | 41.54 182 | 27.12 193 | 62.41 157 | 61.02 163 | 69.05 165 | 56.90 196 |
|
tfpnconf | | | 47.56 180 | 51.56 174 | 42.90 181 | 54.91 140 | 55.28 172 | 46.21 187 | 41.59 142 | 41.51 174 | 18.54 194 | 42.25 169 | 41.54 182 | 27.12 193 | 62.41 157 | 61.02 163 | 69.05 165 | 56.90 196 |
|
SixPastTwentyTwo | | | 47.55 182 | 50.25 187 | 44.41 172 | 47.30 187 | 54.31 177 | 47.81 178 | 40.36 159 | 33.76 207 | 19.93 191 | 43.75 148 | 32.77 212 | 42.07 135 | 59.82 172 | 60.94 165 | 68.98 167 | 66.37 145 |
|
TinyColmap | | | 47.08 183 | 47.56 197 | 46.52 160 | 42.35 205 | 53.44 179 | 51.77 165 | 40.70 156 | 43.44 150 | 31.92 141 | 29.78 205 | 23.72 225 | 45.04 124 | 61.99 162 | 59.54 174 | 67.35 176 | 61.03 180 |
|
pmmvs5 | | | 47.07 184 | 51.02 183 | 42.46 183 | 45.18 195 | 51.47 187 | 48.23 177 | 33.09 202 | 38.17 199 | 28.62 158 | 46.60 123 | 43.48 174 | 30.74 183 | 58.28 182 | 58.63 179 | 68.92 168 | 60.48 182 |
|
LTVRE_ROB | | 44.17 16 | 47.06 185 | 50.15 188 | 43.44 176 | 51.39 170 | 58.42 157 | 42.90 204 | 43.51 107 | 22.27 228 | 14.85 205 | 41.94 175 | 34.57 208 | 45.43 121 | 62.28 161 | 62.77 154 | 62.56 193 | 68.83 131 |
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 |
tfpn1000 | | | 46.75 186 | 51.24 179 | 41.51 188 | 54.39 148 | 55.60 170 | 43.85 201 | 40.90 152 | 41.82 170 | 16.71 201 | 41.26 176 | 41.58 181 | 23.96 200 | 60.76 168 | 60.27 171 | 69.26 163 | 57.42 193 |
|
RPMNet | | | 46.41 187 | 48.72 192 | 43.72 174 | 47.77 184 | 52.94 180 | 46.02 192 | 33.92 193 | 44.41 139 | 31.82 142 | 36.89 190 | 37.42 204 | 37.41 154 | 53.88 205 | 54.02 198 | 65.37 184 | 61.47 178 |
|
RPSCF | | | 46.41 187 | 54.42 149 | 37.06 204 | 25.70 232 | 45.14 209 | 45.39 195 | 20.81 226 | 62.79 53 | 35.10 130 | 44.92 141 | 55.60 90 | 43.56 129 | 56.12 196 | 52.45 205 | 51.80 216 | 63.91 169 |
|
CVMVSNet | | | 46.38 189 | 52.01 171 | 39.81 194 | 42.40 204 | 50.26 190 | 46.15 190 | 37.68 176 | 40.03 184 | 15.09 204 | 46.56 124 | 47.56 134 | 33.72 175 | 56.50 193 | 55.65 187 | 63.80 189 | 67.53 133 |
|
TESTMET0.1,1 | | | 46.09 190 | 50.29 185 | 41.18 190 | 36.91 214 | 47.16 200 | 49.52 169 | 20.32 227 | 39.22 188 | 31.98 139 | 43.65 150 | 47.93 130 | 41.29 139 | 56.80 189 | 55.36 189 | 67.08 178 | 61.94 176 |
|
test-mter | | | 45.30 191 | 50.37 184 | 39.38 196 | 33.65 220 | 46.99 202 | 47.59 179 | 18.59 229 | 38.75 192 | 28.00 159 | 43.28 155 | 46.82 145 | 41.50 138 | 57.28 187 | 55.78 186 | 66.93 181 | 63.70 170 |
|
gm-plane-assit | | | 44.74 192 | 45.95 199 | 43.33 177 | 60.88 72 | 46.79 205 | 36.97 214 | 32.24 207 | 24.15 224 | 11.79 210 | 29.26 209 | 32.97 211 | 46.64 116 | 65.09 145 | 62.95 151 | 71.45 152 | 60.42 183 |
|
EPMVS | | | 44.66 193 | 47.86 196 | 40.92 191 | 47.97 183 | 44.70 210 | 47.58 180 | 33.27 199 | 48.11 105 | 29.58 153 | 49.65 85 | 44.38 169 | 34.65 169 | 51.71 208 | 47.90 215 | 52.49 215 | 48.57 215 |
|
PM-MVS | | | 44.55 194 | 48.13 195 | 40.37 193 | 32.85 222 | 46.82 204 | 46.11 191 | 29.28 211 | 40.48 181 | 29.99 150 | 39.98 181 | 34.39 209 | 41.80 137 | 56.08 197 | 53.88 202 | 62.19 194 | 65.31 157 |
|
TAMVS | | | 44.02 195 | 49.18 191 | 37.99 202 | 47.03 188 | 45.97 207 | 45.04 196 | 28.47 213 | 39.11 191 | 20.23 190 | 43.22 156 | 48.52 126 | 28.49 191 | 58.15 183 | 57.95 182 | 58.71 199 | 51.36 206 |
|
MIMVSNet | | | 43.79 196 | 48.53 193 | 38.27 200 | 41.46 206 | 48.97 195 | 50.81 167 | 32.88 204 | 44.55 137 | 22.07 183 | 32.05 198 | 47.15 138 | 24.76 199 | 58.73 178 | 56.09 185 | 57.63 204 | 52.14 204 |
|
test0.0.03 1 | | | 43.15 197 | 46.95 198 | 38.72 199 | 55.26 134 | 50.56 189 | 42.48 205 | 43.48 109 | 38.16 200 | 15.11 203 | 35.07 194 | 44.69 165 | 16.47 213 | 55.95 198 | 54.34 197 | 59.54 198 | 49.87 213 |
|
Anonymous20231206 | | | 42.28 198 | 45.89 200 | 38.07 201 | 51.96 165 | 48.98 194 | 43.66 203 | 38.81 169 | 38.74 194 | 14.32 206 | 26.74 212 | 40.90 187 | 20.94 206 | 56.64 192 | 54.67 195 | 58.71 199 | 54.59 200 |
|
MVS-HIRNet | | | 42.24 199 | 41.15 212 | 43.51 175 | 44.06 202 | 40.74 213 | 35.77 217 | 35.35 186 | 35.38 204 | 38.34 117 | 25.63 214 | 38.55 199 | 43.48 130 | 50.77 210 | 47.03 219 | 64.07 187 | 49.98 211 |
|
MDA-MVSNet-bldmvs | | | 41.36 200 | 43.15 208 | 39.27 198 | 28.74 226 | 52.68 182 | 44.95 198 | 40.84 154 | 32.89 209 | 18.13 198 | 31.61 200 | 22.09 227 | 38.97 148 | 50.45 212 | 56.11 184 | 64.01 188 | 56.23 199 |
|
FMVSNet5 | | | 40.96 201 | 45.81 201 | 35.29 208 | 34.30 217 | 44.55 211 | 47.28 182 | 28.84 212 | 40.76 179 | 21.62 184 | 29.85 204 | 42.44 177 | 24.77 198 | 57.53 186 | 55.00 192 | 54.93 209 | 50.56 209 |
|
CHOSEN 280x420 | | | 40.80 202 | 45.05 204 | 35.84 207 | 32.95 221 | 29.57 228 | 44.98 197 | 23.71 224 | 37.54 201 | 18.42 197 | 31.36 201 | 47.07 141 | 46.41 119 | 56.71 191 | 54.65 196 | 48.55 221 | 58.47 191 |
|
LP | | | 40.79 203 | 41.99 209 | 39.38 196 | 40.98 207 | 46.49 206 | 42.14 206 | 33.66 197 | 35.37 205 | 29.89 151 | 29.30 208 | 27.81 216 | 32.74 177 | 52.55 206 | 52.19 206 | 56.87 205 | 50.23 210 |
|
Anonymous20231211 | | | 40.75 204 | 41.57 210 | 39.80 195 | 54.71 142 | 52.32 184 | 41.42 208 | 45.09 74 | 24.45 223 | 6.80 225 | 14.58 227 | 23.43 226 | 23.08 203 | 56.20 195 | 58.74 178 | 67.68 175 | 61.31 179 |
|
ADS-MVSNet | | | 40.67 205 | 43.38 207 | 37.50 203 | 44.36 198 | 39.79 216 | 42.09 207 | 32.67 205 | 44.34 141 | 28.87 157 | 40.76 180 | 40.37 191 | 30.22 184 | 48.34 223 | 45.87 221 | 46.81 224 | 44.21 219 |
|
EU-MVSNet | | | 40.63 206 | 45.65 202 | 34.78 209 | 39.11 210 | 46.94 203 | 40.02 211 | 34.03 192 | 33.50 208 | 10.37 216 | 35.57 193 | 37.80 201 | 23.65 201 | 51.90 207 | 50.21 210 | 61.49 195 | 63.62 171 |
|
test20.03 | | | 40.38 207 | 44.20 205 | 35.92 206 | 53.73 155 | 49.05 193 | 38.54 212 | 43.49 108 | 32.55 210 | 9.54 218 | 27.88 211 | 39.12 196 | 12.24 224 | 56.28 194 | 54.69 194 | 57.96 203 | 49.83 214 |
|
FC-MVSNet-test | | | 39.65 208 | 48.35 194 | 29.49 215 | 44.43 197 | 39.28 217 | 30.23 224 | 40.44 157 | 43.59 146 | 3.12 235 | 53.00 74 | 42.03 178 | 10.02 231 | 55.09 201 | 54.77 193 | 48.66 220 | 50.71 208 |
|
testgi | | | 38.71 209 | 43.64 206 | 32.95 211 | 52.30 164 | 48.63 196 | 35.59 218 | 35.05 188 | 31.58 214 | 9.03 221 | 30.29 202 | 40.75 189 | 11.19 229 | 55.30 200 | 53.47 203 | 54.53 212 | 45.48 217 |
|
FPMVS | | | 38.36 210 | 40.41 213 | 35.97 205 | 38.92 211 | 39.85 215 | 45.50 194 | 25.79 222 | 41.13 177 | 18.70 193 | 30.10 203 | 24.56 220 | 31.86 181 | 49.42 218 | 46.80 220 | 55.04 208 | 51.03 207 |
|
GG-mvs-BLEND | | | 36.62 211 | 53.39 160 | 17.06 229 | 0.01 237 | 58.61 155 | 48.63 174 | 0.01 235 | 47.13 112 | 0.02 240 | 43.98 145 | 60.64 70 | 0.03 235 | 54.92 203 | 51.47 208 | 53.64 213 | 56.99 195 |
|
MIMVSNet1 | | | 35.51 212 | 41.41 211 | 28.63 217 | 27.53 228 | 43.36 212 | 38.09 213 | 33.82 194 | 32.01 211 | 6.77 226 | 21.63 222 | 35.43 207 | 11.97 226 | 55.05 202 | 53.99 200 | 53.59 214 | 48.36 216 |
|
pmmvs3 | | | 35.10 213 | 38.47 214 | 31.17 213 | 26.37 231 | 40.47 214 | 34.51 220 | 18.09 230 | 24.75 222 | 16.88 200 | 23.05 218 | 26.69 218 | 32.69 178 | 50.73 211 | 51.60 207 | 58.46 202 | 51.98 205 |
|
testpf | | | 34.85 214 | 36.16 219 | 33.31 210 | 47.49 185 | 35.56 224 | 36.85 215 | 32.31 206 | 23.08 225 | 15.63 202 | 29.39 207 | 29.48 215 | 19.62 209 | 41.38 226 | 41.07 225 | 47.95 222 | 53.18 202 |
|
PMVS | | 27.84 18 | 33.81 215 | 35.28 220 | 32.09 212 | 34.13 218 | 24.81 231 | 32.51 221 | 26.48 220 | 26.41 221 | 19.37 192 | 23.76 217 | 24.02 224 | 25.18 197 | 50.78 209 | 47.24 218 | 54.89 211 | 49.95 212 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
test2356 | | | 33.40 216 | 36.53 217 | 29.76 214 | 37.51 213 | 38.39 219 | 34.68 219 | 27.35 215 | 27.88 216 | 10.61 213 | 25.54 215 | 24.44 221 | 17.15 212 | 49.99 215 | 48.32 213 | 51.24 217 | 41.16 223 |
|
new-patchmatchnet | | | 33.24 217 | 37.20 215 | 28.62 218 | 44.32 199 | 38.26 221 | 29.68 227 | 36.05 184 | 31.97 212 | 6.33 227 | 26.59 213 | 27.33 217 | 11.12 230 | 50.08 214 | 41.05 226 | 44.23 225 | 45.15 218 |
|
N_pmnet | | | 32.67 218 | 36.85 216 | 27.79 219 | 40.55 208 | 32.13 227 | 35.80 216 | 26.79 219 | 37.24 202 | 9.10 219 | 32.02 199 | 30.94 213 | 16.30 214 | 47.22 224 | 41.21 224 | 38.21 227 | 37.21 224 |
|
1111 | | | 31.35 219 | 33.52 223 | 28.83 216 | 44.28 200 | 32.44 225 | 31.71 222 | 33.25 200 | 27.87 217 | 10.92 211 | 22.18 220 | 24.05 222 | 15.89 215 | 49.03 221 | 44.09 222 | 36.94 229 | 34.96 225 |
|
testus | | | 31.33 220 | 36.31 218 | 25.52 223 | 37.55 212 | 38.40 218 | 25.87 228 | 23.58 225 | 26.46 220 | 5.97 228 | 24.15 216 | 24.92 219 | 12.44 223 | 49.14 220 | 48.21 214 | 47.73 223 | 42.86 220 |
|
testmv | | | 30.97 221 | 34.42 221 | 26.95 220 | 36.49 215 | 37.38 222 | 29.80 225 | 27.28 216 | 22.34 226 | 4.72 229 | 20.63 224 | 20.64 228 | 13.22 221 | 49.86 217 | 47.74 216 | 50.20 218 | 42.36 221 |
|
test1235678 | | | 30.97 221 | 34.42 221 | 26.95 220 | 36.49 215 | 37.38 222 | 29.79 226 | 27.28 216 | 22.33 227 | 4.72 229 | 20.62 225 | 20.64 228 | 13.22 221 | 49.87 216 | 47.74 216 | 50.20 218 | 42.36 221 |
|
no-one | | | 29.19 223 | 31.89 224 | 26.05 222 | 30.96 223 | 38.33 220 | 21.54 229 | 29.86 209 | 15.84 232 | 3.56 232 | 11.28 231 | 13.03 233 | 14.44 220 | 38.96 227 | 52.83 204 | 55.96 206 | 52.92 203 |
|
Gipuma | | | 25.87 224 | 26.91 227 | 24.66 224 | 28.98 225 | 20.17 232 | 20.46 231 | 34.62 190 | 29.55 215 | 9.10 219 | 4.91 235 | 5.31 237 | 15.76 217 | 49.37 219 | 49.10 212 | 39.03 226 | 29.95 228 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
test12356 | | | 23.91 225 | 28.47 225 | 18.60 226 | 26.80 230 | 28.30 229 | 20.92 230 | 19.76 228 | 19.89 229 | 2.88 237 | 18.48 226 | 16.57 231 | 4.05 232 | 42.34 225 | 41.93 223 | 37.21 228 | 31.75 226 |
|
new_pmnet | | | 23.19 226 | 28.17 226 | 17.37 227 | 17.03 233 | 24.92 230 | 19.66 232 | 16.16 232 | 27.05 219 | 4.42 231 | 20.77 223 | 19.20 230 | 12.19 225 | 37.71 228 | 36.38 227 | 34.77 230 | 31.17 227 |
|
.test1245 | | | 22.44 227 | 22.23 228 | 22.67 225 | 44.28 200 | 32.44 225 | 31.71 222 | 33.25 200 | 27.87 217 | 10.92 211 | 22.18 220 | 24.05 222 | 15.89 215 | 49.03 221 | 0.01 233 | 0.00 237 | 0.06 235 |
|
PMMVS2 | | | 15.84 228 | 19.68 229 | 11.35 231 | 15.74 234 | 16.95 233 | 13.31 233 | 17.64 231 | 16.08 231 | 0.36 239 | 13.12 228 | 11.47 234 | 1.69 234 | 28.82 229 | 27.24 229 | 19.38 233 | 24.09 230 |
|
E-PMN | | | 15.09 229 | 13.19 231 | 17.30 228 | 27.80 227 | 12.62 235 | 7.81 235 | 27.54 214 | 14.62 234 | 3.19 233 | 6.89 232 | 2.52 240 | 15.09 218 | 15.93 231 | 20.22 230 | 22.38 231 | 19.53 231 |
|
EMVS | | | 14.49 230 | 12.45 232 | 16.87 230 | 27.02 229 | 12.56 236 | 8.13 234 | 27.19 218 | 15.05 233 | 3.14 234 | 6.69 233 | 2.67 239 | 15.08 219 | 14.60 233 | 18.05 231 | 20.67 232 | 17.56 233 |
|
MVE | | 12.28 19 | 13.53 231 | 15.72 230 | 10.96 232 | 7.39 235 | 15.71 234 | 6.05 236 | 23.73 223 | 10.29 236 | 3.01 236 | 5.77 234 | 3.41 238 | 11.91 227 | 20.11 230 | 29.79 228 | 13.67 234 | 24.98 229 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
testmvs | | | 0.01 232 | 0.02 233 | 0.00 234 | 0.00 238 | 0.00 239 | 0.01 240 | 0.00 236 | 0.01 237 | 0.00 241 | 0.03 237 | 0.00 241 | 0.01 236 | 0.01 235 | 0.01 233 | 0.00 237 | 0.06 235 |
|
test123 | | | 0.01 232 | 0.02 233 | 0.00 234 | 0.00 238 | 0.00 239 | 0.00 241 | 0.00 236 | 0.01 237 | 0.00 241 | 0.04 236 | 0.00 241 | 0.01 236 | 0.00 236 | 0.01 233 | 0.00 237 | 0.07 234 |
|
sosnet-low-res | | | 0.00 234 | 0.00 235 | 0.00 234 | 0.00 238 | 0.00 239 | 0.00 241 | 0.00 236 | 0.00 239 | 0.00 241 | 0.00 238 | 0.00 241 | 0.00 238 | 0.00 236 | 0.00 236 | 0.00 237 | 0.00 237 |
|
sosnet | | | 0.00 234 | 0.00 235 | 0.00 234 | 0.00 238 | 0.00 239 | 0.00 241 | 0.00 236 | 0.00 239 | 0.00 241 | 0.00 238 | 0.00 241 | 0.00 238 | 0.00 236 | 0.00 236 | 0.00 237 | 0.00 237 |
|
ambc | | | | 45.54 203 | | 50.66 176 | 52.63 183 | 40.99 209 | | 38.36 197 | 24.67 177 | 22.62 219 | 13.94 232 | 29.14 188 | 65.71 139 | 58.06 181 | 58.60 201 | 67.43 134 |
|
MTAPA | | | | | | | | | | | 65.14 1 | | 80.20 13 | | | | | |
|
MTMP | | | | | | | | | | | 62.63 10 | | 78.04 20 | | | | | |
|
Patchmatch-RL test | | | | | | | | 1.04 239 | | | | | | | | | | |
|
tmp_tt | | | | | 5.40 233 | 3.97 236 | 2.35 238 | 3.26 238 | 0.44 234 | 17.56 230 | 12.09 208 | 11.48 230 | 7.14 235 | 1.98 233 | 15.68 232 | 15.49 232 | 10.69 235 | |
|
XVS | | | | | | 70.49 31 | 76.96 20 | 74.36 39 | | | 54.48 43 | | 74.47 31 | | | | 82.24 18 | |
|
X-MVStestdata | | | | | | 70.49 31 | 76.96 20 | 74.36 39 | | | 54.48 43 | | 74.47 31 | | | | 82.24 18 | |
|
abl_6 | | | | | 64.36 39 | 70.08 34 | 77.45 16 | 72.88 44 | 50.15 50 | 71.31 37 | 54.77 42 | 62.79 40 | 77.99 21 | 56.80 44 | | | 81.50 33 | 83.91 34 |
|
mPP-MVS | | | | | | 71.67 27 | | | | | | | 74.36 34 | | | | | |
|
NP-MVS | | | | | | | | | | 72.00 34 | | | | | | | | |
|
Patchmtry | | | | | | | 47.61 198 | 48.27 175 | 38.86 167 | | 39.59 114 | | | | | | | |
|
DeepMVS_CX | | | | | | | 6.95 237 | 5.98 237 | 2.25 233 | 11.73 235 | 2.07 238 | 11.85 229 | 5.43 236 | 11.75 228 | 11.40 234 | | 8.10 236 | 18.38 232 |
|