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