HSP-MVS | | | 86.82 1 | 89.95 1 | 83.16 4 | 89.38 20 | 91.60 11 | 85.63 10 | 74.15 6 | 94.20 1 | 75.52 13 | 94.99 1 | 83.21 4 | 85.96 1 | 87.67 3 | 85.88 5 | 88.32 24 | 92.13 44 |
|
APDe-MVS | | | 86.37 2 | 88.41 3 | 84.00 3 | 91.43 7 | 91.83 9 | 88.34 1 | 74.67 4 | 91.19 3 | 81.76 1 | 91.13 2 | 81.94 10 | 80.07 4 | 83.38 20 | 82.58 27 | 87.69 35 | 96.78 6 |
|
CNVR-MVS | | | 85.96 3 | 87.58 5 | 84.06 2 | 92.58 3 | 92.40 5 | 87.62 3 | 77.77 2 | 88.44 8 | 75.93 11 | 79.49 19 | 81.97 9 | 81.65 3 | 87.04 5 | 86.58 3 | 88.79 14 | 97.18 3 |
|
MCST-MVS | | | 85.75 4 | 86.99 7 | 84.31 1 | 94.07 1 | 92.80 2 | 88.15 2 | 79.10 1 | 85.66 16 | 70.72 23 | 76.50 26 | 80.45 12 | 82.17 2 | 88.35 1 | 87.49 2 | 91.63 2 | 97.65 1 |
|
HPM-MVS++ | | | 85.64 5 | 88.43 2 | 82.39 6 | 92.65 2 | 90.24 19 | 85.83 8 | 74.21 5 | 90.68 5 | 75.63 12 | 86.77 8 | 84.15 2 | 78.68 8 | 86.33 6 | 85.26 8 | 87.32 42 | 95.60 12 |
|
APD-MVS | | | 84.83 6 | 87.00 6 | 82.30 7 | 89.61 18 | 89.21 27 | 86.51 6 | 73.64 10 | 90.98 4 | 77.99 6 | 89.89 4 | 80.04 15 | 79.18 6 | 82.00 36 | 81.37 39 | 86.88 51 | 95.49 14 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
TSAR-MVS + MP. | | | 84.39 7 | 86.58 10 | 81.83 8 | 88.09 32 | 86.47 51 | 85.63 10 | 73.62 11 | 90.13 6 | 79.24 3 | 89.67 5 | 82.99 5 | 77.72 10 | 81.22 42 | 80.92 47 | 86.68 54 | 94.66 21 |
|
SD-MVS | | | 84.31 8 | 86.96 8 | 81.22 9 | 88.98 24 | 88.68 30 | 85.65 9 | 73.85 9 | 89.09 7 | 79.63 2 | 87.34 7 | 84.84 1 | 73.71 26 | 82.66 26 | 81.60 36 | 85.48 103 | 94.51 22 |
|
NCCC | | | 84.16 9 | 85.46 14 | 82.64 5 | 92.34 4 | 90.57 16 | 86.57 5 | 76.51 3 | 86.85 13 | 72.91 16 | 77.20 25 | 78.69 18 | 79.09 7 | 84.64 14 | 84.88 13 | 88.44 22 | 95.41 15 |
|
ACMMP_Plus | | | 83.54 10 | 86.37 11 | 80.25 14 | 89.57 19 | 90.10 21 | 85.27 13 | 71.66 16 | 87.38 9 | 73.08 15 | 84.23 12 | 80.16 13 | 75.31 17 | 84.85 12 | 83.64 19 | 86.57 55 | 94.21 27 |
|
train_agg | | | 83.35 11 | 86.93 9 | 79.17 20 | 89.70 16 | 88.41 33 | 85.60 12 | 72.89 14 | 86.31 14 | 66.58 34 | 90.48 3 | 82.24 8 | 73.06 30 | 83.10 22 | 82.64 26 | 87.21 47 | 95.30 17 |
|
DeepPCF-MVS | | 76.94 1 | 83.08 12 | 87.77 4 | 77.60 28 | 90.11 12 | 90.96 13 | 78.48 47 | 72.63 15 | 93.10 2 | 65.84 35 | 80.67 17 | 81.55 11 | 74.80 21 | 85.94 8 | 85.39 7 | 83.75 146 | 96.77 7 |
|
CSCG | | | 82.90 13 | 84.52 16 | 81.02 11 | 91.85 5 | 93.43 1 | 87.14 4 | 74.01 8 | 81.96 26 | 76.14 9 | 70.84 30 | 82.49 6 | 69.71 48 | 82.32 32 | 85.18 10 | 87.26 44 | 95.40 16 |
|
SteuartSystems-ACMMP | | | 82.51 14 | 85.35 15 | 79.20 19 | 90.25 10 | 89.39 26 | 84.79 14 | 70.95 18 | 82.86 22 | 68.32 31 | 86.44 9 | 77.19 19 | 73.07 29 | 83.63 19 | 83.64 19 | 87.82 32 | 94.34 24 |
Skip Steuart: Steuart Systems R&D Blog. |
HFP-MVS | | | 82.48 15 | 84.12 17 | 80.56 12 | 90.15 11 | 87.55 43 | 84.28 16 | 69.67 27 | 85.22 17 | 77.95 7 | 84.69 11 | 75.94 22 | 75.04 19 | 81.85 37 | 81.17 42 | 86.30 60 | 92.40 42 |
|
TSAR-MVS + GP. | | | 82.27 16 | 85.98 12 | 77.94 26 | 80.72 64 | 88.25 36 | 81.12 36 | 67.71 38 | 87.10 10 | 73.31 14 | 85.23 10 | 83.68 3 | 76.64 13 | 80.43 49 | 81.47 38 | 88.15 30 | 95.66 11 |
|
DeepC-MVS_fast | | 75.41 2 | 81.69 17 | 82.10 26 | 81.20 10 | 91.04 9 | 87.81 41 | 83.42 20 | 74.04 7 | 83.77 20 | 71.09 21 | 66.88 38 | 72.44 30 | 79.48 5 | 85.08 10 | 84.97 12 | 88.12 31 | 93.78 30 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
MPTG | | | 81.65 18 | 83.10 20 | 79.97 16 | 88.14 31 | 87.62 42 | 83.96 19 | 69.90 24 | 86.92 11 | 77.67 8 | 72.47 29 | 78.74 17 | 74.13 25 | 81.59 40 | 81.15 43 | 86.01 71 | 93.19 35 |
|
TSAR-MVS + ACMM | | | 81.59 19 | 85.84 13 | 76.63 32 | 89.82 15 | 86.53 50 | 86.32 7 | 66.72 43 | 85.96 15 | 65.43 36 | 88.98 6 | 82.29 7 | 67.57 65 | 82.06 35 | 81.33 40 | 83.93 144 | 93.75 31 |
|
MP-MVS | | | 80.94 20 | 83.49 19 | 77.96 25 | 88.48 25 | 88.16 37 | 82.82 25 | 69.34 29 | 80.79 32 | 69.67 27 | 82.35 14 | 77.13 20 | 71.60 39 | 80.97 46 | 80.96 46 | 85.87 84 | 94.06 28 |
|
CANet | | | 80.90 21 | 82.93 22 | 78.53 24 | 86.83 38 | 92.26 6 | 81.19 35 | 66.95 41 | 81.60 29 | 69.90 26 | 66.93 37 | 74.80 24 | 76.79 12 | 84.68 13 | 84.77 14 | 89.50 8 | 95.50 13 |
|
ACMMPR | | | 80.62 22 | 82.98 21 | 77.87 27 | 88.41 26 | 87.05 45 | 83.02 22 | 69.18 30 | 83.91 19 | 68.35 30 | 82.89 13 | 73.64 27 | 72.16 35 | 80.78 47 | 81.13 44 | 86.10 65 | 91.43 51 |
|
DeepC-MVS | | 74.46 3 | 80.30 23 | 81.05 29 | 79.42 17 | 87.42 34 | 88.50 32 | 83.23 21 | 73.27 12 | 82.78 23 | 71.01 22 | 62.86 46 | 69.93 44 | 74.80 21 | 84.30 15 | 84.20 16 | 86.79 53 | 94.77 19 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
DELS-MVS | | | 79.49 24 | 79.84 33 | 79.08 21 | 88.26 30 | 92.49 3 | 84.12 18 | 70.63 20 | 65.27 69 | 69.60 29 | 61.29 51 | 66.50 51 | 72.75 31 | 88.07 2 | 88.03 1 | 89.13 11 | 97.22 2 |
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 |
CP-MVS | | | 79.44 25 | 81.51 28 | 77.02 31 | 86.95 36 | 85.96 57 | 82.00 27 | 68.44 35 | 81.82 27 | 67.39 32 | 77.43 23 | 73.68 26 | 71.62 38 | 79.56 54 | 79.58 52 | 85.73 93 | 92.51 41 |
|
MVS_0304 | | | 79.43 26 | 82.20 24 | 76.20 35 | 84.22 45 | 91.79 10 | 81.82 30 | 63.81 62 | 76.83 43 | 61.71 46 | 66.37 39 | 75.52 23 | 76.38 15 | 85.54 9 | 85.03 11 | 89.28 10 | 94.32 25 |
|
PHI-MVS | | | 79.43 26 | 84.06 18 | 74.04 48 | 86.15 40 | 91.57 12 | 80.85 39 | 68.90 33 | 82.22 25 | 51.81 76 | 78.10 21 | 74.28 25 | 70.39 45 | 84.01 18 | 84.00 17 | 86.14 64 | 94.24 26 |
|
PGM-MVS | | | 79.42 28 | 81.84 27 | 76.60 33 | 88.38 28 | 86.69 48 | 82.97 24 | 65.75 49 | 80.39 33 | 64.94 37 | 81.95 16 | 72.11 35 | 71.41 40 | 80.45 48 | 80.55 50 | 86.18 62 | 90.76 59 |
|
CDPH-MVS | | | 79.39 29 | 82.13 25 | 76.19 36 | 89.22 23 | 88.34 34 | 84.20 17 | 71.00 17 | 79.67 36 | 56.97 65 | 77.77 22 | 72.24 34 | 68.50 58 | 81.33 41 | 82.74 24 | 87.23 45 | 92.84 38 |
|
EPNet | | | 79.28 30 | 82.25 23 | 75.83 38 | 88.31 29 | 90.14 20 | 79.43 45 | 68.07 36 | 81.76 28 | 61.26 48 | 77.26 24 | 70.08 43 | 70.06 46 | 82.43 30 | 82.00 31 | 87.82 32 | 92.09 45 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
MSLP-MVS++ | | | 78.57 31 | 77.33 42 | 80.02 15 | 88.39 27 | 84.79 63 | 84.62 15 | 66.17 47 | 75.96 45 | 78.40 4 | 61.59 49 | 71.47 37 | 73.54 28 | 78.43 62 | 78.88 57 | 88.97 12 | 90.18 65 |
|
3Dnovator | | 70.49 5 | 78.42 32 | 76.77 47 | 80.35 13 | 91.43 7 | 90.27 18 | 81.84 29 | 70.79 19 | 72.10 49 | 71.95 17 | 50.02 80 | 67.86 49 | 77.47 11 | 82.89 23 | 84.24 15 | 88.61 18 | 89.99 66 |
|
HQP-MVS | | | 78.26 33 | 80.91 30 | 75.17 43 | 85.67 42 | 84.33 66 | 83.01 23 | 69.38 28 | 79.88 35 | 55.83 66 | 79.85 18 | 64.90 56 | 70.81 42 | 82.46 28 | 81.78 33 | 86.30 60 | 93.18 36 |
|
X-MVS | | | 78.16 34 | 80.55 31 | 75.38 41 | 87.99 33 | 86.27 53 | 81.05 37 | 68.98 31 | 78.33 38 | 61.07 50 | 75.25 27 | 72.27 31 | 67.52 66 | 80.03 51 | 80.52 51 | 85.66 100 | 91.20 53 |
|
3Dnovator+ | | 70.16 6 | 77.87 35 | 77.29 43 | 78.55 23 | 89.25 22 | 88.32 35 | 80.09 41 | 67.95 37 | 74.89 48 | 71.83 19 | 52.05 74 | 70.68 40 | 76.27 16 | 82.27 33 | 82.04 29 | 85.92 77 | 90.77 58 |
|
canonicalmvs | | | 77.65 36 | 79.59 34 | 75.39 40 | 81.52 56 | 89.83 25 | 81.32 34 | 60.74 95 | 80.05 34 | 66.72 33 | 68.43 34 | 65.09 54 | 74.72 23 | 78.87 58 | 82.73 25 | 87.32 42 | 92.16 43 |
|
ACMMP | | | 77.61 37 | 79.59 34 | 75.30 42 | 85.87 41 | 85.58 58 | 81.42 32 | 67.38 40 | 79.38 37 | 62.61 42 | 78.53 20 | 65.79 53 | 68.80 56 | 78.56 61 | 78.50 61 | 85.75 89 | 90.80 57 |
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 |
QAPM | | | 77.50 38 | 77.43 41 | 77.59 29 | 91.52 6 | 92.00 8 | 81.41 33 | 70.63 20 | 66.22 62 | 58.05 62 | 54.70 64 | 71.79 36 | 74.49 24 | 82.46 28 | 82.04 29 | 89.46 9 | 92.79 40 |
|
MVS_111021_HR | | | 77.42 39 | 78.40 38 | 76.28 34 | 86.95 36 | 90.68 14 | 77.41 54 | 70.56 23 | 66.21 63 | 62.48 44 | 66.17 40 | 63.98 58 | 72.08 36 | 82.87 24 | 83.15 22 | 88.24 27 | 95.71 10 |
|
CLD-MVS | | | 77.36 40 | 77.29 43 | 77.45 30 | 82.21 52 | 88.11 38 | 81.92 28 | 68.96 32 | 77.97 40 | 69.62 28 | 62.08 47 | 59.44 74 | 73.57 27 | 81.75 38 | 81.27 41 | 88.41 23 | 90.39 62 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
MAR-MVS | | | 77.19 41 | 78.37 39 | 75.81 39 | 89.87 14 | 90.58 15 | 79.33 46 | 65.56 51 | 77.62 42 | 58.33 60 | 59.24 57 | 67.98 47 | 74.83 20 | 82.37 31 | 83.12 23 | 86.95 50 | 87.67 98 |
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 |
MVSTER | | | 76.92 42 | 79.92 32 | 73.42 51 | 74.98 97 | 82.97 71 | 78.15 48 | 63.41 65 | 78.02 39 | 64.41 39 | 67.54 35 | 72.80 29 | 71.05 41 | 83.29 21 | 83.73 18 | 88.53 21 | 91.12 54 |
|
PVSNet_BlendedMVS | | | 76.84 43 | 78.47 36 | 74.95 44 | 82.37 50 | 89.90 23 | 75.45 65 | 65.45 52 | 74.99 46 | 70.66 24 | 63.07 44 | 58.27 80 | 67.60 63 | 84.24 16 | 81.70 34 | 88.18 28 | 97.10 4 |
|
PVSNet_Blended | | | 76.84 43 | 78.47 36 | 74.95 44 | 82.37 50 | 89.90 23 | 75.45 65 | 65.45 52 | 74.99 46 | 70.66 24 | 63.07 44 | 58.27 80 | 67.60 63 | 84.24 16 | 81.70 34 | 88.18 28 | 97.10 4 |
|
AdaColmap | | | 76.23 45 | 73.55 59 | 79.35 18 | 89.38 20 | 85.00 62 | 79.99 43 | 73.04 13 | 76.60 44 | 71.17 20 | 55.18 62 | 57.99 82 | 77.87 9 | 76.82 73 | 76.82 71 | 84.67 129 | 86.45 106 |
|
PCF-MVS | | 70.85 4 | 75.73 46 | 76.55 50 | 74.78 47 | 83.67 46 | 88.04 40 | 81.47 31 | 70.62 22 | 69.24 59 | 57.52 63 | 60.59 54 | 69.18 45 | 70.65 43 | 77.11 70 | 77.65 68 | 84.75 127 | 94.01 29 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
CPTT-MVS | | | 75.43 47 | 77.13 45 | 73.44 50 | 81.43 57 | 82.55 75 | 80.96 38 | 64.35 57 | 77.95 41 | 61.39 47 | 69.20 33 | 70.94 39 | 69.38 53 | 73.89 105 | 73.32 131 | 83.14 157 | 92.06 46 |
|
MVS_Test | | | 75.22 48 | 76.69 48 | 73.51 49 | 79.30 69 | 88.82 29 | 80.06 42 | 58.74 105 | 69.77 56 | 57.50 64 | 59.78 56 | 61.35 69 | 75.31 17 | 82.07 34 | 83.60 21 | 90.13 5 | 91.41 52 |
|
OpenMVS | | 67.62 8 | 74.92 49 | 73.91 57 | 76.09 37 | 90.10 13 | 90.38 17 | 78.01 49 | 66.35 45 | 66.09 64 | 62.80 41 | 46.33 105 | 64.55 57 | 71.77 37 | 79.92 52 | 80.88 48 | 87.52 38 | 89.20 73 |
|
MVS_111021_LR | | | 74.26 50 | 75.95 51 | 72.27 56 | 79.43 68 | 85.04 61 | 72.71 77 | 65.27 54 | 70.92 53 | 63.58 40 | 69.32 32 | 60.31 72 | 69.43 51 | 77.01 71 | 77.15 69 | 83.22 153 | 91.93 49 |
|
OMC-MVS | | | 74.03 51 | 75.82 52 | 71.95 58 | 79.56 66 | 80.98 93 | 75.35 67 | 63.21 66 | 84.48 18 | 61.83 45 | 61.54 50 | 66.89 50 | 69.41 52 | 76.60 75 | 74.07 120 | 82.34 166 | 86.15 110 |
|
DI_MVS_plusplus_trai | | | 73.94 52 | 74.85 55 | 72.88 53 | 76.57 87 | 86.80 46 | 80.41 40 | 61.47 85 | 62.35 73 | 59.44 59 | 47.91 88 | 68.12 46 | 72.24 34 | 82.84 25 | 81.50 37 | 87.15 48 | 94.42 23 |
|
diffmvs | | | 73.50 53 | 75.66 53 | 70.97 61 | 74.96 99 | 86.71 47 | 77.16 56 | 57.42 125 | 71.12 52 | 60.43 55 | 57.20 59 | 70.40 42 | 68.79 57 | 76.11 81 | 76.05 79 | 87.10 49 | 92.06 46 |
|
TSAR-MVS + COLMAP | | | 73.09 54 | 76.86 46 | 68.71 72 | 74.97 98 | 82.49 76 | 74.51 72 | 61.83 81 | 83.16 21 | 49.31 86 | 82.22 15 | 51.62 105 | 68.94 55 | 78.76 60 | 75.52 87 | 82.67 161 | 84.23 124 |
|
CANet_DTU | | | 72.84 55 | 76.63 49 | 68.43 75 | 76.81 85 | 86.62 49 | 75.54 64 | 54.71 153 | 72.06 50 | 43.54 110 | 67.11 36 | 58.46 77 | 72.40 33 | 81.13 45 | 80.82 49 | 87.57 37 | 90.21 64 |
|
DWT-MVSNet_training | | | 72.81 56 | 73.98 56 | 71.45 60 | 81.26 58 | 86.37 52 | 72.08 80 | 59.82 102 | 69.13 60 | 58.15 61 | 54.71 63 | 61.33 71 | 67.81 62 | 76.86 72 | 78.63 58 | 89.59 6 | 90.86 56 |
|
OPM-MVS | | | 72.74 57 | 70.93 72 | 74.85 46 | 85.30 43 | 84.34 65 | 82.82 25 | 69.79 25 | 49.96 113 | 55.39 70 | 54.09 69 | 60.14 73 | 70.04 47 | 80.38 50 | 79.43 53 | 85.74 92 | 88.20 95 |
|
CHOSEN 1792x2688 | | | 72.55 58 | 71.98 64 | 73.22 52 | 86.57 39 | 92.41 4 | 75.63 61 | 66.77 42 | 62.08 74 | 52.32 73 | 30.27 196 | 50.74 108 | 66.14 68 | 86.22 7 | 85.41 6 | 91.90 1 | 96.75 8 |
|
CostFormer | | | 72.18 59 | 73.90 58 | 70.18 65 | 79.47 67 | 86.19 56 | 76.94 57 | 48.62 186 | 66.07 65 | 60.40 56 | 54.14 68 | 65.82 52 | 67.98 60 | 75.84 84 | 76.41 75 | 87.67 36 | 92.83 39 |
|
ACMP | | 68.86 7 | 72.15 60 | 72.25 63 | 72.03 57 | 80.96 60 | 80.87 95 | 77.93 50 | 64.13 59 | 69.29 57 | 60.79 53 | 64.04 42 | 53.54 99 | 63.91 77 | 73.74 109 | 75.27 88 | 84.45 134 | 88.98 76 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
LGP-MVS_train | | | 72.02 61 | 73.18 62 | 70.67 63 | 82.13 53 | 80.26 110 | 79.58 44 | 63.04 68 | 70.09 54 | 51.98 74 | 65.06 41 | 55.62 92 | 62.49 85 | 75.97 83 | 76.32 76 | 84.80 126 | 88.93 77 |
|
PVSNet_Blended_VisFu | | | 71.76 62 | 73.54 60 | 69.69 66 | 79.01 70 | 87.16 44 | 72.05 81 | 61.80 82 | 56.46 91 | 59.66 58 | 53.88 70 | 62.48 61 | 59.08 125 | 81.17 43 | 78.90 56 | 86.53 57 | 94.74 20 |
|
TAPA-MVS | | 67.10 9 | 71.45 63 | 73.47 61 | 69.10 70 | 77.04 82 | 80.78 96 | 73.81 75 | 62.10 77 | 80.80 31 | 51.28 77 | 60.91 52 | 63.80 60 | 67.98 60 | 74.59 94 | 72.42 145 | 82.37 165 | 80.97 152 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
CNLPA | | | 71.37 64 | 70.27 75 | 72.66 55 | 80.79 63 | 81.33 89 | 71.07 107 | 65.75 49 | 82.36 24 | 64.80 38 | 42.46 121 | 56.49 85 | 72.70 32 | 73.00 117 | 70.52 166 | 80.84 177 | 85.76 115 |
|
Effi-MVS+ | | | 70.42 65 | 71.23 70 | 69.47 67 | 78.04 74 | 85.24 60 | 75.57 63 | 58.88 104 | 59.56 80 | 48.47 87 | 52.73 73 | 54.94 94 | 69.69 49 | 78.34 64 | 77.06 70 | 86.18 62 | 90.73 60 |
|
ACMM | | 66.70 10 | 70.42 65 | 68.49 84 | 72.67 54 | 82.85 47 | 77.76 135 | 77.70 52 | 64.76 56 | 64.61 70 | 60.74 54 | 49.29 82 | 53.97 98 | 65.86 69 | 74.97 91 | 75.57 86 | 84.13 142 | 83.29 132 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
FMVSNet3 | | | 70.41 67 | 71.89 66 | 68.68 73 | 70.89 124 | 79.42 119 | 75.63 61 | 60.97 91 | 65.32 66 | 51.06 78 | 47.37 94 | 62.05 63 | 64.90 73 | 82.49 27 | 82.27 28 | 88.64 17 | 84.34 123 |
|
PMMVS | | | 70.37 68 | 75.06 54 | 64.90 92 | 71.46 118 | 81.88 79 | 64.10 149 | 55.64 141 | 71.31 51 | 46.69 93 | 70.69 31 | 58.56 75 | 69.53 50 | 79.03 57 | 75.63 84 | 81.96 169 | 88.32 94 |
|
MS-PatchMatch | | | 70.34 69 | 69.00 80 | 71.91 59 | 85.20 44 | 85.35 59 | 77.84 51 | 61.77 83 | 58.01 85 | 55.40 69 | 41.26 128 | 58.34 79 | 61.69 88 | 81.70 39 | 78.29 62 | 89.56 7 | 80.02 157 |
|
tpmp4_e23 | | | 69.38 70 | 69.47 78 | 69.28 69 | 78.20 73 | 82.35 77 | 75.92 58 | 49.20 184 | 64.15 71 | 59.96 57 | 47.93 87 | 55.77 90 | 68.06 59 | 73.05 116 | 74.53 99 | 84.34 136 | 88.50 93 |
|
GBi-Net | | | 69.21 71 | 70.40 73 | 67.81 78 | 69.49 129 | 78.65 124 | 74.54 68 | 60.97 91 | 65.32 66 | 51.06 78 | 47.37 94 | 62.05 63 | 63.43 79 | 77.49 66 | 78.22 63 | 87.37 39 | 83.73 127 |
|
test1 | | | 69.21 71 | 70.40 73 | 67.81 78 | 69.49 129 | 78.65 124 | 74.54 68 | 60.97 91 | 65.32 66 | 51.06 78 | 47.37 94 | 62.05 63 | 63.43 79 | 77.49 66 | 78.22 63 | 87.37 39 | 83.73 127 |
|
IB-MVS | | 64.48 11 | 69.02 73 | 68.97 81 | 69.09 71 | 81.75 55 | 89.01 28 | 64.50 147 | 64.91 55 | 56.65 89 | 62.59 43 | 47.89 89 | 45.23 119 | 51.99 148 | 69.18 169 | 81.88 32 | 88.77 15 | 92.93 37 |
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 |
FC-MVSNet-train | | | 68.83 74 | 68.29 85 | 69.47 67 | 78.35 72 | 79.94 111 | 64.72 146 | 66.38 44 | 54.96 100 | 54.51 71 | 56.75 60 | 47.91 114 | 66.91 67 | 75.57 88 | 75.75 82 | 85.92 77 | 87.12 101 |
|
PLC | | 64.00 12 | 68.54 75 | 66.66 95 | 70.74 62 | 80.28 65 | 74.88 158 | 72.64 78 | 63.70 64 | 69.26 58 | 55.71 67 | 47.24 97 | 55.31 93 | 70.42 44 | 72.05 143 | 70.67 164 | 81.66 171 | 77.19 166 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
HyFIR lowres test | | | 68.39 76 | 68.28 86 | 68.52 74 | 80.85 61 | 88.11 38 | 71.08 106 | 58.09 110 | 54.87 102 | 47.80 90 | 27.55 201 | 55.80 89 | 64.97 72 | 79.11 56 | 79.14 55 | 88.31 25 | 93.35 32 |
|
test-LLR | | | 68.23 77 | 71.61 68 | 64.28 110 | 71.37 119 | 81.32 90 | 63.98 153 | 61.03 88 | 58.62 82 | 42.96 120 | 52.74 71 | 61.65 67 | 57.74 131 | 75.64 86 | 78.09 66 | 88.61 18 | 93.21 33 |
|
FMVSNet2 | | | 68.06 78 | 68.57 83 | 67.45 81 | 69.49 129 | 78.65 124 | 74.54 68 | 60.23 101 | 56.29 92 | 49.64 85 | 42.13 124 | 57.08 84 | 63.43 79 | 81.15 44 | 80.99 45 | 87.37 39 | 83.73 127 |
|
Fast-Effi-MVS+ | | | 67.59 79 | 67.56 90 | 67.62 80 | 73.67 104 | 81.14 92 | 71.12 104 | 54.79 152 | 58.88 81 | 50.61 82 | 46.70 103 | 47.05 115 | 69.12 54 | 76.06 82 | 76.44 74 | 86.43 58 | 86.65 104 |
|
EPP-MVSNet | | | 67.58 80 | 71.10 71 | 63.48 119 | 75.71 93 | 83.35 70 | 66.85 136 | 57.83 113 | 53.02 107 | 41.15 135 | 55.82 61 | 67.89 48 | 56.01 137 | 74.40 96 | 72.92 140 | 83.33 151 | 90.30 63 |
|
UGNet | | | 67.57 81 | 71.69 67 | 62.76 130 | 69.88 127 | 82.58 74 | 66.43 140 | 58.64 106 | 54.71 103 | 51.87 75 | 61.74 48 | 62.01 66 | 45.46 176 | 74.78 93 | 74.99 90 | 84.24 138 | 91.02 55 |
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 |
tpm cat1 | | | 67.47 82 | 67.05 94 | 67.98 77 | 76.63 86 | 81.51 87 | 74.49 73 | 47.65 191 | 61.18 76 | 61.12 49 | 42.51 120 | 53.02 102 | 64.74 75 | 70.11 161 | 71.50 152 | 83.22 153 | 89.49 69 |
|
TESTMET0.1,1 | | | 67.38 83 | 71.61 68 | 62.45 134 | 66.05 167 | 81.32 90 | 63.98 153 | 55.36 145 | 58.62 82 | 42.96 120 | 52.74 71 | 61.65 67 | 57.74 131 | 75.64 86 | 78.09 66 | 88.61 18 | 93.21 33 |
|
IS_MVSNet | | | 67.29 84 | 71.98 64 | 61.82 140 | 76.92 83 | 84.32 67 | 65.90 144 | 58.22 108 | 55.75 96 | 39.22 143 | 54.51 66 | 62.47 62 | 45.99 173 | 78.83 59 | 78.52 60 | 84.70 128 | 89.47 70 |
|
tpmrst | | | 67.15 85 | 68.12 88 | 66.03 88 | 76.21 89 | 80.98 93 | 71.27 100 | 45.05 198 | 60.69 78 | 50.63 81 | 46.95 102 | 54.15 97 | 65.30 70 | 71.80 146 | 71.77 149 | 87.72 34 | 90.48 61 |
|
thres100view900 | | | 67.14 86 | 66.09 99 | 68.38 76 | 77.70 76 | 83.84 69 | 74.52 71 | 66.33 46 | 49.16 118 | 43.40 114 | 43.24 111 | 41.34 129 | 62.59 84 | 79.31 55 | 75.92 81 | 85.73 93 | 89.81 67 |
|
conf0.002 | | | 67.12 87 | 67.13 93 | 67.11 83 | 77.95 75 | 82.11 78 | 71.71 90 | 63.06 67 | 49.16 118 | 43.43 112 | 47.76 91 | 48.79 111 | 61.42 90 | 76.61 74 | 76.55 73 | 85.07 113 | 88.92 79 |
|
EPMVS | | | 66.21 88 | 67.49 91 | 64.73 97 | 75.81 92 | 84.20 68 | 68.94 124 | 44.37 202 | 61.55 75 | 48.07 89 | 49.21 84 | 54.87 95 | 62.88 82 | 71.82 145 | 71.40 156 | 88.28 26 | 79.37 160 |
|
EPNet_dtu | | | 66.17 89 | 70.13 76 | 61.54 142 | 81.04 59 | 77.39 139 | 68.87 125 | 62.50 76 | 69.78 55 | 33.51 176 | 63.77 43 | 56.22 86 | 37.65 193 | 72.20 139 | 72.18 147 | 85.69 96 | 79.38 159 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
IterMVS-LS | | | 66.08 90 | 66.56 97 | 65.51 89 | 73.67 104 | 74.88 158 | 70.89 110 | 53.55 160 | 50.42 111 | 48.32 88 | 50.59 78 | 55.66 91 | 61.83 87 | 73.93 104 | 74.42 106 | 84.82 125 | 86.01 112 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
tfpn200view9 | | | 65.90 91 | 64.96 104 | 67.00 84 | 77.70 76 | 81.58 84 | 71.71 90 | 62.94 72 | 49.16 118 | 43.40 114 | 43.24 111 | 41.34 129 | 61.42 90 | 76.24 77 | 74.63 95 | 84.84 121 | 88.52 90 |
|
conf200view11 | | | 65.89 92 | 64.96 104 | 66.98 85 | 77.70 76 | 81.58 84 | 71.71 90 | 62.94 72 | 49.16 118 | 43.28 116 | 43.24 111 | 41.34 129 | 61.42 90 | 76.24 77 | 74.63 95 | 84.84 121 | 88.52 90 |
|
thres200 | | | 65.58 93 | 64.74 107 | 66.56 86 | 77.52 80 | 81.61 82 | 73.44 76 | 62.95 70 | 46.23 137 | 42.45 130 | 42.76 115 | 41.18 133 | 58.12 129 | 76.24 77 | 75.59 85 | 84.89 118 | 89.58 68 |
|
MSDG | | | 65.57 94 | 61.57 143 | 70.24 64 | 82.02 54 | 76.47 147 | 74.46 74 | 68.73 34 | 56.52 90 | 50.33 83 | 38.47 155 | 41.10 135 | 62.42 86 | 72.12 141 | 72.94 139 | 83.47 149 | 73.37 182 |
|
Vis-MVSNet | | | 65.53 95 | 69.83 77 | 60.52 146 | 70.80 125 | 84.59 64 | 66.37 142 | 55.47 144 | 48.40 124 | 40.62 139 | 57.67 58 | 58.43 78 | 45.37 177 | 77.49 66 | 76.24 77 | 84.47 133 | 85.99 113 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
PatchmatchNet | | | 65.43 96 | 67.71 89 | 62.78 129 | 73.49 106 | 82.83 72 | 66.42 141 | 45.40 197 | 60.40 79 | 45.27 99 | 49.22 83 | 57.60 83 | 60.01 109 | 70.61 153 | 71.38 159 | 86.08 67 | 81.91 147 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
MDTV_nov1_ep13 | | | 65.21 97 | 67.28 92 | 62.79 128 | 70.91 123 | 81.72 81 | 69.28 123 | 49.50 181 | 58.08 84 | 43.94 109 | 50.50 79 | 56.02 87 | 58.86 126 | 70.72 152 | 73.37 129 | 84.24 138 | 80.52 153 |
|
thres400 | | | 65.18 98 | 64.44 109 | 66.04 87 | 76.40 88 | 82.63 73 | 71.52 98 | 64.27 58 | 44.93 145 | 40.69 138 | 41.86 125 | 40.79 142 | 58.12 129 | 77.67 65 | 74.64 94 | 85.26 106 | 88.56 89 |
|
tpm | | | 64.85 99 | 66.02 100 | 63.48 119 | 74.52 101 | 78.38 127 | 70.98 108 | 44.99 200 | 51.61 109 | 43.28 116 | 47.66 92 | 53.18 100 | 60.57 100 | 70.58 155 | 71.30 161 | 86.54 56 | 89.45 71 |
|
UA-Net | | | 64.62 100 | 68.23 87 | 60.42 147 | 77.53 79 | 81.38 88 | 60.08 175 | 57.47 121 | 47.01 129 | 44.75 105 | 60.68 53 | 71.32 38 | 41.84 184 | 73.27 111 | 72.25 146 | 80.83 178 | 71.68 190 |
|
Effi-MVS+-dtu | | | 64.58 101 | 64.08 110 | 65.16 90 | 73.04 109 | 75.17 157 | 70.68 112 | 56.23 135 | 54.12 105 | 44.71 106 | 47.42 93 | 51.10 106 | 63.82 78 | 68.08 173 | 66.32 186 | 82.47 164 | 86.38 108 |
|
GA-MVS | | | 64.55 102 | 65.76 102 | 63.12 125 | 69.68 128 | 81.56 86 | 69.59 120 | 58.16 109 | 45.23 143 | 35.58 167 | 47.01 101 | 41.82 128 | 59.41 120 | 79.62 53 | 78.54 59 | 86.32 59 | 86.56 105 |
|
LS3D | | | 64.54 103 | 62.14 136 | 67.34 82 | 80.85 61 | 75.79 153 | 69.99 116 | 65.87 48 | 60.77 77 | 44.35 107 | 42.43 122 | 45.95 118 | 65.01 71 | 69.88 164 | 68.69 174 | 77.97 195 | 71.43 192 |
|
CDS-MVSNet | | | 64.22 104 | 65.89 101 | 62.28 136 | 70.05 126 | 80.59 105 | 69.91 118 | 57.98 111 | 43.53 155 | 46.58 94 | 48.22 86 | 50.76 107 | 46.45 170 | 75.68 85 | 76.08 78 | 82.70 160 | 86.34 109 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
v6 | | | 64.09 105 | 63.40 115 | 64.90 92 | 68.28 140 | 80.78 96 | 71.85 84 | 57.64 117 | 46.73 132 | 45.18 101 | 39.40 141 | 40.89 139 | 60.54 103 | 72.86 122 | 74.40 107 | 85.92 77 | 88.72 85 |
|
v1neww | | | 64.08 106 | 63.38 116 | 64.89 94 | 68.27 142 | 80.77 98 | 71.84 85 | 57.65 115 | 46.66 134 | 45.10 102 | 39.40 141 | 40.86 140 | 60.57 100 | 72.86 122 | 74.40 107 | 85.92 77 | 88.71 86 |
|
v7new | | | 64.08 106 | 63.38 116 | 64.89 94 | 68.27 142 | 80.77 98 | 71.84 85 | 57.65 115 | 46.66 134 | 45.10 102 | 39.40 141 | 40.86 140 | 60.57 100 | 72.86 122 | 74.40 107 | 85.92 77 | 88.71 86 |
|
dps | | | 64.08 106 | 63.22 119 | 65.08 91 | 75.27 96 | 79.65 115 | 66.68 138 | 46.63 196 | 56.94 87 | 55.67 68 | 43.96 107 | 43.63 125 | 64.00 76 | 69.50 168 | 69.82 169 | 82.25 167 | 79.02 161 |
|
test-mter | | | 64.06 109 | 69.24 79 | 58.01 161 | 59.07 195 | 77.40 138 | 59.13 179 | 48.11 189 | 55.64 97 | 39.18 144 | 51.56 75 | 58.54 76 | 55.38 139 | 73.52 110 | 76.00 80 | 87.22 46 | 92.05 48 |
|
thresconf0.02 | | | 63.92 110 | 65.18 103 | 62.46 133 | 75.91 91 | 80.65 104 | 67.51 133 | 63.86 61 | 45.00 144 | 33.32 177 | 51.38 76 | 51.68 104 | 48.34 160 | 75.49 89 | 75.13 89 | 85.84 88 | 76.91 168 |
|
view600 | | | 63.91 111 | 63.27 118 | 64.66 99 | 75.57 94 | 81.73 80 | 69.71 119 | 63.04 68 | 43.97 148 | 39.18 144 | 41.09 129 | 40.24 150 | 55.38 139 | 76.28 76 | 72.04 148 | 85.08 112 | 87.52 99 |
|
thres600view7 | | | 63.77 112 | 63.14 120 | 64.51 101 | 75.49 95 | 81.61 82 | 69.59 120 | 62.95 70 | 43.96 149 | 38.90 146 | 41.09 129 | 40.24 150 | 55.25 141 | 76.24 77 | 71.54 151 | 84.89 118 | 87.30 100 |
|
v2v482 | | | 63.68 113 | 62.85 125 | 64.65 100 | 68.01 150 | 80.46 107 | 71.90 82 | 57.60 118 | 44.26 146 | 42.82 128 | 39.80 139 | 38.62 166 | 61.56 89 | 73.06 114 | 74.86 92 | 86.03 70 | 88.90 80 |
|
v7 | | | 63.61 114 | 63.02 122 | 64.29 109 | 67.88 154 | 80.32 108 | 71.60 96 | 56.63 131 | 45.37 141 | 42.84 125 | 38.54 153 | 38.91 164 | 61.05 95 | 74.39 97 | 74.52 100 | 85.75 89 | 89.10 75 |
|
v1 | | | 63.49 115 | 62.77 128 | 64.32 106 | 68.13 144 | 80.70 101 | 71.70 94 | 57.43 122 | 43.69 152 | 42.89 124 | 39.03 147 | 39.77 154 | 59.93 112 | 72.93 119 | 74.48 104 | 85.86 85 | 88.77 81 |
|
v1141 | | | 63.48 116 | 62.75 130 | 64.32 106 | 68.13 144 | 80.69 102 | 71.69 95 | 57.43 122 | 43.66 154 | 42.83 127 | 39.02 148 | 39.74 156 | 59.95 110 | 72.94 118 | 74.49 102 | 85.86 85 | 88.75 83 |
|
divwei89l23v2f112 | | | 63.48 116 | 62.76 129 | 64.32 106 | 68.13 144 | 80.68 103 | 71.71 90 | 57.43 122 | 43.69 152 | 42.84 125 | 39.01 149 | 39.75 155 | 59.94 111 | 72.93 119 | 74.49 102 | 85.86 85 | 88.75 83 |
|
FMVSNet1 | | | 63.48 116 | 63.07 121 | 63.97 112 | 65.31 173 | 76.37 149 | 71.77 89 | 57.90 112 | 43.32 157 | 45.66 97 | 35.06 182 | 49.43 110 | 58.57 127 | 77.49 66 | 78.22 63 | 84.59 131 | 81.60 150 |
|
v8 | | | 63.44 119 | 62.58 132 | 64.43 103 | 68.28 140 | 78.07 130 | 71.82 87 | 54.85 150 | 46.70 133 | 45.20 100 | 39.40 141 | 40.91 138 | 60.54 103 | 72.85 126 | 74.39 112 | 85.92 77 | 85.76 115 |
|
v18 | | | 63.31 120 | 62.02 138 | 64.81 96 | 68.48 136 | 73.38 167 | 72.14 79 | 54.28 155 | 48.99 123 | 47.21 91 | 39.56 140 | 41.20 132 | 60.80 97 | 72.89 121 | 74.46 105 | 85.96 76 | 83.64 130 |
|
pmmvs4 | | | 63.14 121 | 62.46 133 | 63.94 113 | 66.03 168 | 76.40 148 | 66.82 137 | 57.60 118 | 56.74 88 | 50.26 84 | 40.81 133 | 37.51 170 | 59.26 123 | 71.75 147 | 71.48 153 | 83.68 147 | 82.53 140 |
|
v16 | | | 63.12 122 | 61.78 140 | 64.68 98 | 68.45 137 | 73.29 168 | 71.86 83 | 54.12 156 | 48.36 125 | 47.00 92 | 39.30 145 | 41.01 136 | 60.67 98 | 72.83 127 | 74.40 107 | 86.01 71 | 83.24 134 |
|
Fast-Effi-MVS+-dtu | | | 63.05 123 | 64.72 108 | 61.11 144 | 71.21 122 | 76.81 146 | 70.72 111 | 43.13 206 | 52.51 108 | 35.34 168 | 46.55 104 | 46.36 116 | 61.40 93 | 71.57 148 | 71.44 154 | 84.84 121 | 87.79 97 |
|
view800 | | | 63.02 124 | 62.69 131 | 63.39 121 | 74.79 100 | 80.76 100 | 67.83 129 | 61.93 80 | 43.16 159 | 37.78 155 | 40.43 134 | 39.73 157 | 53.16 146 | 75.01 90 | 73.32 131 | 84.87 120 | 86.43 107 |
|
v1144 | | | 63.00 125 | 62.39 134 | 63.70 115 | 67.72 157 | 80.27 109 | 71.23 102 | 56.40 132 | 42.51 163 | 40.81 137 | 38.12 162 | 37.73 168 | 60.42 106 | 74.46 95 | 74.55 98 | 85.64 101 | 89.12 74 |
|
v10 | | | 63.00 125 | 62.22 135 | 63.90 114 | 67.88 154 | 77.78 134 | 71.59 97 | 54.34 154 | 45.37 141 | 42.76 129 | 38.53 154 | 38.93 163 | 61.05 95 | 74.39 97 | 74.52 100 | 85.75 89 | 86.04 111 |
|
v17 | | | 62.99 127 | 61.70 141 | 64.51 101 | 68.40 138 | 73.28 169 | 71.80 88 | 54.11 157 | 47.87 126 | 46.14 95 | 39.29 146 | 41.01 136 | 60.60 99 | 72.81 128 | 74.39 112 | 85.99 74 | 83.25 133 |
|
tfpn_ndepth | | | 62.95 128 | 63.75 112 | 62.02 138 | 76.89 84 | 79.48 118 | 64.09 150 | 60.98 90 | 49.48 115 | 38.73 147 | 49.92 81 | 44.79 120 | 47.37 165 | 71.91 144 | 71.66 150 | 84.07 143 | 79.00 162 |
|
V42 | | | 62.86 129 | 62.97 123 | 62.74 131 | 60.84 188 | 78.99 122 | 71.46 99 | 57.13 129 | 46.85 130 | 44.28 108 | 38.87 150 | 40.73 144 | 57.63 133 | 72.60 134 | 74.14 117 | 85.09 111 | 88.63 88 |
|
tfpn | | | 62.54 130 | 62.79 127 | 62.25 137 | 74.16 102 | 79.86 113 | 66.07 143 | 60.97 91 | 42.43 164 | 36.41 159 | 39.88 138 | 43.76 124 | 51.25 153 | 73.85 106 | 74.17 116 | 84.67 129 | 85.57 118 |
|
gg-mvs-nofinetune | | | 62.34 131 | 66.19 98 | 57.86 165 | 76.15 90 | 88.61 31 | 71.18 103 | 41.24 215 | 25.74 216 | 13.16 219 | 22.91 211 | 63.97 59 | 54.52 143 | 85.06 11 | 85.25 9 | 90.92 3 | 91.78 50 |
|
CR-MVSNet | | | 62.31 132 | 64.75 106 | 59.47 153 | 68.63 135 | 71.29 184 | 67.53 131 | 43.18 204 | 55.83 94 | 41.40 132 | 41.04 131 | 55.85 88 | 57.29 134 | 72.76 129 | 73.27 134 | 78.77 192 | 83.23 135 |
|
UniMVSNet_NR-MVSNet | | | 62.30 133 | 63.51 114 | 60.89 145 | 69.48 132 | 77.83 133 | 64.07 151 | 63.94 60 | 50.03 112 | 31.17 182 | 44.82 106 | 41.12 134 | 51.37 150 | 71.02 150 | 74.81 93 | 85.30 105 | 84.95 119 |
|
v1192 | | | 62.25 134 | 61.64 142 | 62.96 126 | 66.88 162 | 79.72 114 | 69.96 117 | 55.77 139 | 41.58 170 | 39.42 141 | 37.05 168 | 35.96 181 | 60.50 105 | 74.30 102 | 74.09 119 | 85.24 107 | 88.76 82 |
|
Vis-MVSNet (Re-imp) | | | 62.25 134 | 68.74 82 | 54.68 180 | 73.70 103 | 78.74 123 | 56.51 186 | 57.49 120 | 55.22 98 | 26.86 194 | 54.56 65 | 61.35 69 | 31.06 196 | 73.10 113 | 74.90 91 | 82.49 163 | 83.31 131 |
|
CHOSEN 280x420 | | | 62.23 136 | 66.57 96 | 57.17 170 | 59.88 192 | 68.92 189 | 61.20 169 | 42.28 208 | 54.17 104 | 39.57 140 | 47.78 90 | 64.97 55 | 62.68 83 | 73.85 106 | 69.52 171 | 77.43 196 | 86.75 103 |
|
PatchMatch-RL | | | 62.22 137 | 60.69 150 | 64.01 111 | 68.74 134 | 75.75 154 | 59.27 178 | 60.35 97 | 56.09 93 | 53.80 72 | 47.06 100 | 36.45 176 | 64.80 74 | 68.22 172 | 67.22 181 | 77.10 197 | 74.02 177 |
|
v15 | | | 62.07 138 | 60.70 149 | 63.67 116 | 68.09 147 | 73.00 170 | 71.27 100 | 53.41 161 | 43.70 151 | 43.43 112 | 38.77 151 | 39.83 152 | 59.87 113 | 72.74 131 | 74.25 114 | 85.98 75 | 82.61 139 |
|
v144192 | | | 62.05 139 | 61.46 144 | 62.73 132 | 66.59 165 | 79.87 112 | 69.30 122 | 55.88 137 | 41.50 171 | 39.41 142 | 37.23 166 | 36.45 176 | 59.62 116 | 72.69 132 | 73.51 126 | 85.61 102 | 88.93 77 |
|
v148 | | | 62.00 140 | 61.19 146 | 62.96 126 | 67.46 160 | 79.49 117 | 67.87 128 | 57.66 114 | 42.30 165 | 45.02 104 | 38.20 160 | 38.89 165 | 54.77 142 | 69.83 165 | 72.60 144 | 84.96 114 | 87.01 102 |
|
V14 | | | 61.96 141 | 60.56 151 | 63.59 117 | 68.06 148 | 72.93 173 | 71.10 105 | 53.33 163 | 43.47 156 | 43.28 116 | 38.59 152 | 39.78 153 | 59.76 115 | 72.65 133 | 74.19 115 | 86.01 71 | 82.32 144 |
|
IterMVS | | | 61.87 142 | 63.55 113 | 59.90 149 | 67.29 161 | 72.20 179 | 67.34 134 | 48.56 187 | 47.48 128 | 37.86 154 | 47.07 99 | 48.27 112 | 54.08 144 | 72.12 141 | 73.71 124 | 84.30 137 | 83.99 125 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
V9 | | | 61.85 143 | 60.42 154 | 63.51 118 | 68.02 149 | 72.85 174 | 70.91 109 | 53.24 164 | 43.25 158 | 43.27 119 | 38.41 156 | 39.73 157 | 59.60 117 | 72.55 135 | 74.13 118 | 86.04 69 | 82.04 146 |
|
v11 | | | 61.74 144 | 60.47 153 | 63.22 124 | 67.83 156 | 72.72 176 | 70.31 115 | 52.95 170 | 42.75 162 | 41.89 131 | 38.16 161 | 38.49 167 | 60.40 107 | 74.35 99 | 74.40 107 | 85.92 77 | 82.39 143 |
|
v12 | | | 61.70 145 | 60.27 156 | 63.38 122 | 68.00 151 | 72.76 175 | 70.63 113 | 53.14 166 | 43.01 160 | 42.95 123 | 38.25 158 | 39.64 159 | 59.48 119 | 72.47 137 | 74.05 121 | 86.06 68 | 81.71 149 |
|
v1921920 | | | 61.66 146 | 61.10 147 | 62.31 135 | 66.32 166 | 79.57 116 | 68.41 127 | 55.49 143 | 41.03 172 | 38.69 148 | 36.64 174 | 35.27 187 | 59.60 117 | 73.23 112 | 73.41 128 | 85.37 104 | 88.51 92 |
|
v13 | | | 61.60 147 | 60.13 159 | 63.31 123 | 67.95 153 | 72.67 177 | 70.51 114 | 53.05 167 | 42.80 161 | 42.96 120 | 38.10 163 | 39.57 160 | 59.31 122 | 72.36 138 | 73.98 123 | 86.10 65 | 81.40 151 |
|
ACMH | | 59.42 14 | 61.59 148 | 59.22 169 | 64.36 105 | 78.92 71 | 78.26 128 | 67.65 130 | 67.48 39 | 39.81 176 | 30.98 184 | 38.25 158 | 34.59 189 | 61.37 94 | 70.55 156 | 73.47 127 | 79.74 186 | 79.59 158 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
ACMH+ | | 60.36 13 | 61.16 149 | 58.38 171 | 64.42 104 | 77.37 81 | 74.35 163 | 68.45 126 | 62.81 75 | 45.86 139 | 38.48 149 | 35.71 177 | 37.35 171 | 59.81 114 | 67.24 175 | 69.80 170 | 79.58 187 | 78.32 164 |
|
v1240 | | | 61.09 150 | 60.55 152 | 61.72 141 | 65.92 170 | 79.28 120 | 67.16 135 | 54.91 149 | 39.79 177 | 38.10 151 | 36.08 176 | 34.64 188 | 59.15 124 | 72.86 122 | 73.36 130 | 85.10 109 | 87.84 96 |
|
NR-MVSNet | | | 61.08 151 | 62.09 137 | 59.90 149 | 71.96 117 | 75.87 151 | 63.60 157 | 61.96 78 | 49.31 116 | 27.95 191 | 42.76 115 | 33.85 193 | 48.82 159 | 74.35 99 | 74.05 121 | 85.13 108 | 84.45 121 |
|
DU-MVS | | | 60.87 152 | 61.82 139 | 59.76 151 | 66.69 163 | 75.87 151 | 64.07 151 | 61.96 78 | 49.31 116 | 31.17 182 | 42.76 115 | 36.95 173 | 51.37 150 | 69.67 166 | 73.20 137 | 83.30 152 | 84.95 119 |
|
UniMVSNet (Re) | | | 60.62 153 | 62.93 124 | 57.92 162 | 67.64 158 | 77.90 132 | 61.75 166 | 61.24 87 | 49.83 114 | 29.80 186 | 42.57 118 | 40.62 148 | 43.36 180 | 70.49 158 | 73.27 134 | 83.76 145 | 85.81 114 |
|
PatchT | | | 60.46 154 | 63.85 111 | 56.51 172 | 65.95 169 | 75.68 155 | 47.34 199 | 41.39 211 | 53.89 106 | 41.40 132 | 37.84 164 | 50.30 109 | 57.29 134 | 72.76 129 | 73.27 134 | 85.67 97 | 83.23 135 |
|
TranMVSNet+NR-MVSNet | | | 60.38 155 | 61.30 145 | 59.30 154 | 68.34 139 | 75.57 156 | 63.38 160 | 63.78 63 | 46.74 131 | 27.73 192 | 42.56 119 | 36.84 174 | 47.66 163 | 70.36 159 | 74.59 97 | 84.91 117 | 82.46 141 |
|
conf0.05thres1000 | | | 60.33 156 | 59.42 166 | 61.40 143 | 73.15 108 | 78.25 129 | 65.29 145 | 60.30 98 | 36.61 188 | 35.75 165 | 33.25 184 | 39.23 161 | 50.35 156 | 72.18 140 | 72.67 143 | 83.57 148 | 83.74 126 |
|
pmmvs5 | | | 59.72 157 | 60.24 157 | 59.11 156 | 62.77 182 | 77.33 140 | 63.17 161 | 54.00 158 | 40.21 175 | 37.23 156 | 40.41 135 | 35.99 180 | 51.75 149 | 72.55 135 | 72.74 142 | 85.72 95 | 82.45 142 |
|
USDC | | | 59.69 158 | 60.03 160 | 59.28 155 | 64.04 177 | 71.84 182 | 63.15 162 | 55.36 145 | 54.90 101 | 35.02 171 | 48.34 85 | 29.79 204 | 58.16 128 | 70.60 154 | 71.33 160 | 79.99 184 | 73.42 181 |
|
Baseline_NR-MVSNet | | | 59.47 159 | 60.28 155 | 58.54 159 | 66.69 163 | 73.90 164 | 61.63 167 | 62.90 74 | 49.15 122 | 26.87 193 | 35.18 181 | 37.62 169 | 48.20 161 | 69.67 166 | 73.61 125 | 84.92 115 | 82.82 138 |
|
pm-mvs1 | | | 59.21 160 | 59.58 165 | 58.77 158 | 67.97 152 | 77.07 145 | 64.12 148 | 57.20 127 | 34.73 196 | 36.86 157 | 35.34 179 | 40.54 149 | 43.34 181 | 74.32 101 | 73.30 133 | 83.13 158 | 81.77 148 |
|
tfpnnormal | | | 58.97 161 | 56.48 179 | 61.89 139 | 71.27 121 | 76.21 150 | 66.65 139 | 61.76 84 | 32.90 202 | 36.41 159 | 27.83 200 | 29.14 205 | 50.64 155 | 73.06 114 | 73.05 138 | 84.58 132 | 83.15 137 |
|
tfpnview11 | | | 58.92 162 | 59.60 164 | 58.13 160 | 72.99 110 | 77.11 143 | 60.48 170 | 60.37 96 | 42.10 167 | 29.10 188 | 43.45 108 | 40.72 145 | 41.67 185 | 70.53 157 | 70.43 167 | 84.17 141 | 72.85 184 |
|
FMVSNet5 | | | 58.86 163 | 60.24 157 | 57.25 169 | 52.66 212 | 66.25 195 | 63.77 156 | 52.86 171 | 57.85 86 | 37.92 153 | 36.12 175 | 52.22 103 | 51.37 150 | 70.88 151 | 71.43 155 | 84.92 115 | 66.91 200 |
|
TAMVS | | | 58.86 163 | 60.91 148 | 56.47 173 | 62.38 184 | 77.57 136 | 58.97 180 | 52.98 168 | 38.76 182 | 36.17 162 | 42.26 123 | 47.94 113 | 46.45 170 | 70.23 160 | 70.79 163 | 81.86 170 | 78.82 163 |
|
EG-PatchMatch MVS | | | 58.73 165 | 58.03 174 | 59.55 152 | 72.32 114 | 80.49 106 | 63.44 159 | 55.55 142 | 32.49 203 | 38.31 150 | 28.87 198 | 37.22 172 | 42.84 182 | 74.30 102 | 75.70 83 | 84.84 121 | 77.14 167 |
|
tfpn_n400 | | | 58.64 166 | 59.27 167 | 57.89 163 | 72.83 111 | 77.26 141 | 60.35 171 | 60.29 99 | 39.77 178 | 29.10 188 | 43.45 108 | 40.72 145 | 41.61 186 | 70.06 162 | 71.39 157 | 83.17 155 | 72.26 187 |
|
tfpnconf | | | 58.64 166 | 59.27 167 | 57.89 163 | 72.83 111 | 77.26 141 | 60.35 171 | 60.29 99 | 39.77 178 | 29.10 188 | 43.45 108 | 40.72 145 | 41.61 186 | 70.06 162 | 71.39 157 | 83.17 155 | 72.26 187 |
|
RPMNet | | | 58.63 168 | 62.80 126 | 53.76 186 | 67.59 159 | 71.29 184 | 54.60 188 | 38.13 219 | 55.83 94 | 35.70 166 | 41.58 127 | 53.04 101 | 47.89 162 | 66.10 177 | 67.38 179 | 78.65 194 | 84.40 122 |
|
ADS-MVSNet | | | 58.40 169 | 59.16 170 | 57.52 167 | 65.80 171 | 74.57 162 | 60.26 173 | 40.17 216 | 50.51 110 | 38.01 152 | 40.11 137 | 44.72 121 | 59.36 121 | 64.91 181 | 66.55 184 | 81.53 172 | 72.72 186 |
|
tfpn1000 | | | 58.35 170 | 59.96 161 | 56.47 173 | 72.78 113 | 77.51 137 | 56.66 185 | 59.16 103 | 43.74 150 | 29.76 187 | 42.79 114 | 42.49 126 | 37.04 194 | 68.92 170 | 68.98 172 | 83.45 150 | 75.25 172 |
|
TransMVSNet (Re) | | | 57.83 171 | 56.90 177 | 58.91 157 | 72.26 115 | 74.69 161 | 63.57 158 | 61.42 86 | 32.30 204 | 32.65 179 | 33.97 183 | 35.96 181 | 39.17 191 | 73.84 108 | 72.84 141 | 84.37 135 | 74.69 175 |
|
MIMVSNet | | | 57.78 172 | 59.71 163 | 55.53 177 | 54.79 203 | 77.10 144 | 63.89 155 | 45.02 199 | 46.59 136 | 36.79 158 | 28.36 199 | 40.77 143 | 45.84 174 | 74.97 91 | 76.58 72 | 86.87 52 | 73.60 180 |
|
CMPMVS | | 43.63 17 | 57.67 173 | 55.43 180 | 60.28 148 | 72.01 116 | 79.00 121 | 62.77 163 | 53.23 165 | 41.77 169 | 45.42 98 | 30.74 195 | 39.03 162 | 53.01 147 | 64.81 183 | 64.65 192 | 75.26 203 | 68.03 198 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
test0.0.03 1 | | | 57.35 174 | 59.89 162 | 54.38 182 | 71.37 119 | 73.45 166 | 52.71 190 | 61.03 88 | 46.11 138 | 26.33 195 | 41.73 126 | 44.08 122 | 29.72 199 | 71.43 149 | 70.90 162 | 85.10 109 | 71.56 191 |
|
v7n | | | 57.04 175 | 56.64 178 | 57.52 167 | 62.85 181 | 74.75 160 | 61.76 165 | 51.80 174 | 35.58 195 | 36.02 164 | 32.33 188 | 33.61 194 | 50.16 157 | 67.73 174 | 70.34 168 | 82.51 162 | 82.12 145 |
|
pmmvs-eth3d | | | 55.20 176 | 53.95 187 | 56.65 171 | 57.34 201 | 67.77 191 | 57.54 183 | 53.74 159 | 40.93 173 | 41.09 136 | 31.19 194 | 29.10 206 | 49.07 158 | 65.54 178 | 67.28 180 | 81.14 175 | 75.81 169 |
|
v748 | | | 55.19 177 | 54.63 183 | 55.85 175 | 61.44 187 | 72.97 172 | 58.72 181 | 51.62 175 | 34.48 198 | 36.39 161 | 32.09 189 | 33.05 195 | 45.48 175 | 61.85 193 | 67.87 177 | 81.45 173 | 80.08 156 |
|
COLMAP_ROB | | 51.17 15 | 55.13 178 | 52.90 191 | 57.73 166 | 73.47 107 | 67.21 193 | 62.13 164 | 55.82 138 | 47.83 127 | 34.39 172 | 31.60 193 | 34.24 190 | 44.90 178 | 63.88 190 | 62.52 200 | 75.67 201 | 63.02 209 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
RPSCF | | | 55.07 179 | 58.06 173 | 51.57 188 | 48.87 219 | 58.95 209 | 53.68 189 | 41.26 214 | 62.42 72 | 45.88 96 | 54.38 67 | 54.26 96 | 53.75 145 | 57.15 202 | 53.53 218 | 66.01 217 | 65.75 203 |
|
gm-plane-assit | | | 54.99 180 | 57.99 175 | 51.49 190 | 69.27 133 | 54.42 215 | 32.32 219 | 42.59 207 | 21.18 222 | 13.71 217 | 23.61 207 | 43.84 123 | 60.21 108 | 87.09 4 | 86.55 4 | 90.81 4 | 89.28 72 |
|
anonymousdsp | | | 54.99 180 | 57.24 176 | 52.36 187 | 53.82 209 | 71.75 183 | 51.49 191 | 48.14 188 | 33.74 200 | 33.66 175 | 38.34 157 | 36.13 179 | 47.54 164 | 64.53 185 | 70.60 165 | 79.53 188 | 85.59 117 |
|
CVMVSNet | | | 54.92 182 | 58.16 172 | 51.13 191 | 62.61 183 | 68.44 190 | 55.45 187 | 52.38 172 | 42.28 166 | 21.45 202 | 47.10 98 | 46.10 117 | 37.96 192 | 64.42 186 | 63.81 194 | 76.92 199 | 75.01 174 |
|
v52 | | | 54.79 183 | 55.15 181 | 54.36 184 | 54.07 207 | 72.13 180 | 59.84 176 | 49.39 182 | 34.50 197 | 35.08 170 | 31.63 192 | 35.74 183 | 47.21 168 | 63.90 188 | 67.92 175 | 80.59 180 | 80.23 154 |
|
V4 | | | 54.78 184 | 55.14 182 | 54.37 183 | 54.07 207 | 72.13 180 | 59.83 177 | 49.39 182 | 34.46 199 | 35.11 169 | 31.64 191 | 35.72 184 | 47.22 167 | 63.90 188 | 67.92 175 | 80.59 180 | 80.23 154 |
|
GG-mvs-BLEND | | | 54.54 185 | 77.58 40 | 27.67 222 | 0.03 234 | 90.09 22 | 77.20 55 | 0.02 232 | 66.83 61 | 0.05 237 | 59.90 55 | 73.33 28 | 0.04 232 | 78.40 63 | 79.30 54 | 88.65 16 | 95.20 18 |
|
MDTV_nov1_ep13_2view | | | 54.47 186 | 54.61 184 | 54.30 185 | 60.50 189 | 73.82 165 | 57.92 182 | 43.38 203 | 39.43 181 | 32.51 180 | 33.23 185 | 34.05 191 | 47.26 166 | 62.36 191 | 66.21 187 | 84.24 138 | 73.19 183 |
|
pmmvs6 | | | 54.20 187 | 53.54 188 | 54.97 178 | 63.22 180 | 72.98 171 | 60.17 174 | 52.32 173 | 26.77 215 | 34.30 173 | 23.29 210 | 36.23 178 | 40.33 189 | 68.77 171 | 68.76 173 | 79.47 189 | 78.00 165 |
|
MVS-HIRNet | | | 53.86 188 | 53.02 189 | 54.85 179 | 60.30 191 | 72.36 178 | 44.63 207 | 42.20 209 | 39.45 180 | 43.47 111 | 21.66 214 | 34.00 192 | 55.47 138 | 65.42 179 | 67.16 182 | 83.02 159 | 71.08 193 |
|
TDRefinement | | | 52.70 189 | 51.02 197 | 54.66 181 | 57.41 200 | 65.06 199 | 61.47 168 | 54.94 147 | 44.03 147 | 33.93 174 | 30.13 197 | 27.57 207 | 46.17 172 | 61.86 192 | 62.48 201 | 74.01 207 | 66.06 202 |
|
TinyColmap | | | 52.66 190 | 50.09 200 | 55.65 176 | 59.72 193 | 64.02 203 | 57.15 184 | 52.96 169 | 40.28 174 | 32.51 180 | 32.42 187 | 20.97 219 | 56.65 136 | 63.95 187 | 65.15 191 | 74.91 204 | 63.87 206 |
|
Anonymous20231206 | | | 52.23 191 | 52.80 192 | 51.56 189 | 64.70 176 | 69.41 187 | 51.01 192 | 58.60 107 | 36.63 187 | 22.44 201 | 21.80 213 | 31.42 200 | 30.52 197 | 66.79 176 | 67.83 178 | 82.10 168 | 75.73 170 |
|
PEN-MVS | | | 51.04 192 | 52.94 190 | 48.82 195 | 61.45 186 | 66.00 196 | 48.68 196 | 57.20 127 | 36.87 186 | 15.36 213 | 36.98 169 | 32.72 196 | 28.77 203 | 57.63 201 | 66.37 185 | 81.44 174 | 74.00 178 |
|
WR-MVS | | | 51.02 193 | 54.56 185 | 46.90 201 | 63.84 178 | 69.23 188 | 44.78 206 | 56.38 133 | 38.19 183 | 14.19 215 | 37.38 165 | 36.82 175 | 22.39 213 | 60.14 196 | 66.20 188 | 79.81 185 | 73.95 179 |
|
CP-MVSNet | | | 50.57 194 | 52.60 194 | 48.21 198 | 58.77 197 | 65.82 197 | 48.17 197 | 56.29 134 | 37.41 184 | 16.59 210 | 37.14 167 | 31.95 198 | 29.21 200 | 56.60 204 | 63.71 195 | 80.22 182 | 75.56 171 |
|
PS-CasMVS | | | 50.17 195 | 52.02 195 | 48.02 199 | 58.60 198 | 65.54 198 | 48.04 198 | 56.19 136 | 36.42 190 | 16.42 212 | 35.68 178 | 31.33 201 | 28.85 202 | 56.42 206 | 63.54 196 | 80.01 183 | 75.18 173 |
|
PM-MVS | | | 50.11 196 | 50.38 199 | 49.80 193 | 47.23 221 | 62.08 207 | 50.91 193 | 44.84 201 | 41.90 168 | 36.10 163 | 35.22 180 | 26.05 213 | 46.83 169 | 57.64 200 | 55.42 217 | 72.90 208 | 74.32 176 |
|
DTE-MVSNet | | | 49.82 197 | 51.92 196 | 47.37 200 | 61.75 185 | 64.38 201 | 45.89 205 | 57.33 126 | 36.11 191 | 12.79 220 | 36.87 170 | 31.93 199 | 25.73 208 | 58.01 198 | 65.22 190 | 80.75 179 | 70.93 194 |
|
WR-MVS_H | | | 49.62 198 | 52.63 193 | 46.11 204 | 58.80 196 | 67.58 192 | 46.14 204 | 54.94 147 | 36.51 189 | 13.63 218 | 36.75 172 | 35.67 185 | 22.10 214 | 56.43 205 | 62.76 198 | 81.06 176 | 72.73 185 |
|
LTVRE_ROB | | 47.26 16 | 49.41 199 | 49.91 201 | 48.82 195 | 64.76 175 | 69.79 186 | 49.05 194 | 47.12 193 | 20.36 224 | 16.52 211 | 36.65 173 | 26.96 208 | 50.76 154 | 60.47 195 | 63.16 197 | 64.73 218 | 72.00 189 |
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 |
SixPastTwentyTwo | | | 49.11 200 | 49.22 202 | 48.99 194 | 58.54 199 | 64.14 202 | 47.18 200 | 47.75 190 | 31.15 206 | 24.42 197 | 41.01 132 | 26.55 209 | 44.04 179 | 54.76 212 | 58.70 207 | 71.99 211 | 68.21 196 |
|
testgi | | | 48.51 201 | 50.53 198 | 46.16 203 | 64.78 174 | 67.15 194 | 41.54 210 | 54.81 151 | 29.12 210 | 17.03 208 | 32.07 190 | 31.98 197 | 20.15 217 | 65.26 180 | 67.00 183 | 78.67 193 | 61.10 214 |
|
LP | | | 48.21 202 | 46.65 208 | 50.03 192 | 60.39 190 | 63.86 204 | 48.73 195 | 38.71 218 | 35.60 194 | 32.99 178 | 23.31 209 | 24.95 215 | 40.07 190 | 57.73 199 | 61.56 202 | 79.29 190 | 59.51 215 |
|
N_pmnet | | | 47.67 203 | 47.00 207 | 48.45 197 | 54.72 204 | 62.78 205 | 46.95 201 | 51.25 176 | 36.01 192 | 26.09 196 | 26.59 204 | 25.93 214 | 35.50 195 | 55.67 208 | 59.01 205 | 76.22 200 | 63.04 208 |
|
FC-MVSNet-test | | | 47.24 204 | 54.37 186 | 38.93 214 | 59.49 194 | 58.25 211 | 34.48 218 | 53.36 162 | 45.66 140 | 6.66 229 | 50.62 77 | 42.02 127 | 16.62 223 | 58.39 197 | 61.21 203 | 62.99 219 | 64.40 205 |
|
test20.03 | | | 47.23 205 | 48.69 203 | 45.53 205 | 63.28 179 | 64.39 200 | 41.01 212 | 56.93 130 | 29.16 209 | 15.21 214 | 23.90 206 | 30.76 203 | 17.51 222 | 64.63 184 | 65.26 189 | 79.21 191 | 62.71 210 |
|
test2356 | | | 46.29 206 | 47.37 205 | 45.03 206 | 54.38 205 | 57.99 212 | 42.03 209 | 50.32 178 | 30.78 207 | 16.65 209 | 27.40 202 | 23.70 216 | 29.86 198 | 61.20 194 | 64.31 193 | 76.93 198 | 66.22 201 |
|
EU-MVSNet | | | 44.84 207 | 47.85 204 | 41.32 211 | 49.26 216 | 56.59 214 | 43.07 208 | 47.64 192 | 33.03 201 | 13.82 216 | 36.78 171 | 30.99 202 | 24.37 211 | 53.80 213 | 55.57 216 | 69.78 213 | 68.21 196 |
|
MDA-MVSNet-bldmvs | | | 44.15 208 | 42.27 214 | 46.34 202 | 38.34 224 | 62.31 206 | 46.28 202 | 55.74 140 | 29.83 208 | 20.98 203 | 27.11 203 | 16.45 226 | 41.98 183 | 41.11 224 | 57.47 209 | 74.72 205 | 61.65 213 |
|
testpf | | | 43.39 209 | 47.17 206 | 38.98 213 | 65.58 172 | 47.38 223 | 36.09 216 | 31.67 226 | 36.97 185 | 19.47 205 | 33.01 186 | 35.62 186 | 23.61 212 | 50.86 218 | 56.08 214 | 57.48 223 | 70.27 195 |
|
testus | | | 42.30 210 | 43.69 209 | 40.67 212 | 53.21 210 | 53.50 216 | 31.81 220 | 49.96 179 | 27.06 213 | 11.55 222 | 25.67 205 | 19.00 222 | 25.20 209 | 55.34 209 | 62.59 199 | 72.31 210 | 62.69 211 |
|
new-patchmatchnet | | | 42.21 211 | 42.97 211 | 41.33 210 | 53.05 211 | 59.89 208 | 39.38 213 | 49.61 180 | 28.26 212 | 12.10 221 | 22.17 212 | 21.54 218 | 19.22 218 | 50.96 217 | 56.04 215 | 74.61 206 | 61.92 212 |
|
pmmvs3 | | | 41.86 212 | 42.29 213 | 41.36 209 | 39.80 222 | 52.66 217 | 38.93 215 | 35.85 225 | 23.40 219 | 20.22 204 | 19.30 215 | 20.84 220 | 40.56 188 | 55.98 207 | 58.79 206 | 72.80 209 | 65.03 204 |
|
MIMVSNet1 | | | 40.84 213 | 43.46 210 | 37.79 216 | 32.14 226 | 58.92 210 | 39.24 214 | 50.83 177 | 27.00 214 | 11.29 223 | 16.76 224 | 26.53 210 | 17.75 221 | 57.14 203 | 61.12 204 | 75.46 202 | 56.78 218 |
|
Anonymous20231211 | | | 40.44 214 | 39.25 215 | 41.84 208 | 54.29 206 | 57.29 213 | 41.10 211 | 49.06 185 | 17.67 227 | 10.15 224 | 10.63 226 | 16.79 225 | 25.15 210 | 52.14 214 | 56.70 212 | 71.30 212 | 63.51 207 |
|
FPMVS | | | 39.11 215 | 36.39 219 | 42.28 207 | 55.97 202 | 45.94 224 | 46.23 203 | 41.57 210 | 35.73 193 | 22.61 199 | 23.46 208 | 19.82 221 | 28.32 206 | 43.57 220 | 40.67 223 | 58.96 221 | 45.54 220 |
|
1111 | | | 38.93 216 | 38.98 216 | 38.86 215 | 50.10 214 | 50.42 218 | 29.52 221 | 38.00 220 | 22.67 220 | 17.99 206 | 17.40 217 | 26.26 211 | 28.72 204 | 54.86 210 | 58.20 208 | 68.82 216 | 43.08 223 |
|
testmv | | | 37.40 217 | 37.95 217 | 36.76 217 | 48.97 217 | 49.33 221 | 28.65 224 | 46.74 194 | 18.34 225 | 7.68 227 | 16.80 222 | 14.47 227 | 19.18 219 | 51.72 215 | 56.93 210 | 69.36 214 | 58.09 216 |
|
test1235678 | | | 37.40 217 | 37.94 218 | 36.76 217 | 48.97 217 | 49.30 222 | 28.65 224 | 46.73 195 | 18.33 226 | 7.68 227 | 16.79 223 | 14.46 228 | 19.18 219 | 51.72 215 | 56.92 211 | 69.36 214 | 58.07 217 |
|
new_pmnet | | | 33.19 219 | 35.52 220 | 30.47 220 | 27.55 230 | 45.31 225 | 29.29 223 | 30.92 227 | 29.00 211 | 9.88 226 | 18.77 216 | 17.64 224 | 26.77 207 | 44.07 219 | 45.98 221 | 58.41 222 | 47.87 219 |
|
PMVS | | 27.44 18 | 32.08 220 | 29.07 222 | 35.60 219 | 48.33 220 | 24.79 229 | 26.97 226 | 41.34 212 | 20.45 223 | 22.50 200 | 17.11 221 | 18.64 223 | 20.44 216 | 41.99 223 | 38.06 224 | 54.02 226 | 42.44 224 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
test12356 | | | 29.92 221 | 31.49 221 | 28.08 221 | 38.46 223 | 37.74 227 | 21.36 227 | 40.17 216 | 16.83 228 | 5.61 231 | 15.66 225 | 11.48 229 | 6.60 229 | 42.01 222 | 51.23 219 | 56.29 224 | 45.52 221 |
|
no-one | | | 26.96 222 | 26.51 223 | 27.49 223 | 37.87 225 | 39.14 226 | 17.12 229 | 41.31 213 | 12.02 230 | 3.68 233 | 8.04 228 | 8.42 232 | 10.67 227 | 28.11 226 | 45.96 222 | 54.27 225 | 43.89 222 |
|
.test1245 | | | 25.86 223 | 24.56 225 | 27.39 224 | 50.10 214 | 50.42 218 | 29.52 221 | 38.00 220 | 22.67 220 | 17.99 206 | 17.40 217 | 26.26 211 | 28.72 204 | 54.86 210 | 0.05 230 | 0.01 234 | 0.24 232 |
|
Gipuma | | | 24.91 224 | 24.61 224 | 25.26 225 | 31.47 227 | 21.59 230 | 18.06 228 | 37.53 222 | 25.43 217 | 10.03 225 | 4.18 232 | 4.25 234 | 14.85 224 | 43.20 221 | 47.03 220 | 39.62 228 | 26.55 228 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
PMMVS2 | | | 20.45 225 | 22.31 226 | 18.27 228 | 20.52 231 | 26.73 228 | 14.85 231 | 28.43 229 | 13.69 229 | 0.79 236 | 10.35 227 | 9.10 230 | 3.83 231 | 27.64 227 | 32.87 225 | 41.17 227 | 35.81 225 |
|
E-PMN | | | 15.08 226 | 11.65 228 | 19.08 226 | 28.73 228 | 12.31 233 | 6.95 234 | 36.87 224 | 10.71 232 | 3.63 234 | 5.13 229 | 2.22 237 | 13.81 226 | 11.34 230 | 18.50 228 | 24.49 230 | 21.32 229 |
|
EMVS | | | 14.40 227 | 10.71 229 | 18.70 227 | 28.15 229 | 12.09 234 | 7.06 233 | 36.89 223 | 11.00 231 | 3.56 235 | 4.95 230 | 2.27 236 | 13.91 225 | 10.13 231 | 16.06 229 | 22.63 231 | 18.51 230 |
|
MVE | | 15.98 19 | 14.37 228 | 16.36 227 | 12.04 230 | 7.72 233 | 20.24 231 | 5.90 235 | 29.05 228 | 8.28 233 | 3.92 232 | 4.72 231 | 2.42 235 | 9.57 228 | 18.89 229 | 31.46 226 | 16.07 233 | 28.53 227 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
testmvs | | | 0.05 229 | 0.08 230 | 0.01 231 | 0.00 235 | 0.01 236 | 0.03 237 | 0.01 233 | 0.05 234 | 0.00 238 | 0.14 234 | 0.01 238 | 0.03 234 | 0.05 232 | 0.05 230 | 0.01 234 | 0.24 232 |
|
test123 | | | 0.05 229 | 0.08 230 | 0.01 231 | 0.00 235 | 0.01 236 | 0.01 238 | 0.00 234 | 0.05 234 | 0.00 238 | 0.16 233 | 0.00 239 | 0.04 232 | 0.02 233 | 0.05 230 | 0.00 236 | 0.26 231 |
|
ESAPD | | | 0.00 231 | 0.00 232 | 0.00 233 | 0.00 235 | 0.00 238 | 0.00 239 | 0.00 234 | 0.00 236 | 0.00 238 | 0.00 235 | 0.00 239 | 0.00 235 | 0.00 234 | 0.00 233 | 0.00 236 | 0.00 234 |
|
sosnet-low-res | | | 0.00 231 | 0.00 232 | 0.00 233 | 0.00 235 | 0.00 238 | 0.00 239 | 0.00 234 | 0.00 236 | 0.00 238 | 0.00 235 | 0.00 239 | 0.00 235 | 0.00 234 | 0.00 233 | 0.00 236 | 0.00 234 |
|
sosnet | | | 0.00 231 | 0.00 232 | 0.00 233 | 0.00 235 | 0.00 238 | 0.00 239 | 0.00 234 | 0.00 236 | 0.00 238 | 0.00 235 | 0.00 239 | 0.00 235 | 0.00 234 | 0.00 233 | 0.00 236 | 0.00 234 |
|
ambc | | | | 42.30 212 | | 50.36 213 | 49.51 220 | 35.47 217 | | 32.04 205 | 23.53 198 | 17.36 219 | 8.95 231 | 29.06 201 | 64.88 182 | 56.26 213 | 61.29 220 | 67.12 199 |
|
MTAPA | | | | | | | | | | | 78.32 5 | | 79.42 16 | | | | | |
|
MTMP | | | | | | | | | | | 76.04 10 | | 76.65 21 | | | | | |
|
Patchmatch-RL test | | | | | | | | 2.17 236 | | | | | | | | | | |
|
tmp_tt | | | | | 16.09 229 | 13.07 232 | 8.12 235 | 13.61 232 | 2.08 231 | 55.09 99 | 30.10 185 | 40.26 136 | 22.83 217 | 5.35 230 | 29.91 225 | 25.25 227 | 32.33 229 | |
|
XVS | | | | | | 82.43 48 | 86.27 53 | 75.70 59 | | | 61.07 50 | | 72.27 31 | | | | 85.67 97 | |
|
X-MVStestdata | | | | | | 82.43 48 | 86.27 53 | 75.70 59 | | | 61.07 50 | | 72.27 31 | | | | 85.67 97 | |
|
abl_6 | | | | | 79.06 22 | 89.68 17 | 92.14 7 | 77.70 52 | 69.68 26 | 86.87 12 | 71.88 18 | 74.29 28 | 80.06 14 | 76.56 14 | | | 88.84 13 | 95.82 9 |
|
mPP-MVS | | | | | | 86.96 35 | | | | | | | 70.61 41 | | | | | |
|
NP-MVS | | | | | | | | | | 81.60 29 | | | | | | | | |
|
Patchmtry | | | | | | | 78.06 131 | 67.53 131 | 43.18 204 | | 41.40 132 | | | | | | | |
|
DeepMVS_CX | | | | | | | 19.81 232 | 17.01 230 | 10.02 230 | 23.61 218 | 5.85 230 | 17.21 220 | 8.03 233 | 21.13 215 | 22.60 228 | | 21.42 232 | 30.01 226 |
|