CNVR-MVS | | | 98.73 1 | 99.17 4 | 98.22 1 | 99.47 1 | 99.85 2 | 99.57 2 | 96.23 1 | 99.30 9 | 94.90 5 | 98.65 10 | 98.93 14 | 99.36 1 | 99.46 3 | 98.21 10 | 99.81 6 | 99.80 36 |
|
HSP-MVS | | | 98.70 2 | 99.28 1 | 98.03 3 | 99.21 12 | 99.82 4 | 99.17 17 | 96.09 9 | 99.54 2 | 94.79 6 | 98.79 6 | 99.55 5 | 99.05 4 | 99.54 1 | 98.19 13 | 99.84 3 | 99.52 66 |
|
ESAPD | | | 98.61 3 | 99.15 5 | 97.97 5 | 99.36 5 | 99.80 5 | 99.56 3 | 96.18 2 | 99.26 10 | 93.88 12 | 98.64 11 | 99.98 1 | 99.04 5 | 98.89 8 | 97.49 29 | 99.79 9 | 99.98 3 |
|
APDe-MVS | | | 98.60 4 | 98.97 7 | 98.18 2 | 99.38 4 | 99.78 10 | 99.35 9 | 96.14 5 | 99.24 12 | 95.66 3 | 98.19 17 | 99.01 12 | 98.66 13 | 98.77 11 | 97.80 22 | 99.86 2 | 99.97 5 |
|
NCCC | | | 98.41 5 | 99.18 2 | 97.52 11 | 99.36 5 | 99.84 3 | 99.55 4 | 96.08 11 | 99.33 8 | 91.77 20 | 98.79 6 | 99.46 7 | 98.59 15 | 99.15 6 | 98.07 18 | 99.73 12 | 99.64 51 |
|
SD-MVS | | | 98.33 6 | 99.01 6 | 97.54 10 | 97.17 45 | 99.77 11 | 99.14 19 | 96.09 9 | 99.34 7 | 94.06 11 | 97.91 22 | 99.89 3 | 99.18 3 | 97.99 24 | 98.21 10 | 99.63 23 | 99.95 9 |
|
APD-MVS | | | 98.28 7 | 98.69 12 | 97.80 6 | 99.31 9 | 99.62 24 | 99.31 13 | 96.15 4 | 99.19 14 | 93.60 13 | 97.28 25 | 98.35 21 | 98.72 12 | 98.27 17 | 98.22 9 | 99.73 12 | 99.89 23 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
SMA-MVS | | | 98.26 8 | 98.97 7 | 97.44 12 | 99.42 2 | 99.79 6 | 99.33 12 | 96.12 6 | 99.25 11 | 91.26 21 | 96.72 27 | 99.96 2 | 98.95 7 | 98.81 10 | 98.52 7 | 99.56 43 | 99.72 43 |
|
MCST-MVS | | | 98.20 9 | 99.18 2 | 97.06 18 | 99.27 11 | 99.87 1 | 99.37 7 | 96.11 7 | 99.37 5 | 89.29 29 | 98.76 8 | 99.50 6 | 98.37 21 | 99.23 5 | 97.64 25 | 99.95 1 | 99.87 29 |
|
HPM-MVS++ | | | 98.16 10 | 98.87 11 | 97.32 14 | 99.39 3 | 99.70 16 | 99.18 16 | 96.10 8 | 99.09 16 | 91.14 23 | 98.02 20 | 99.89 3 | 98.44 19 | 98.75 12 | 97.03 42 | 99.67 18 | 99.63 55 |
|
MSLP-MVS++ | | | 98.12 11 | 98.23 24 | 97.99 4 | 99.28 10 | 99.72 13 | 99.59 1 | 95.27 23 | 98.61 26 | 94.79 6 | 96.11 30 | 97.79 30 | 99.27 2 | 96.62 53 | 98.96 4 | 99.77 10 | 99.80 36 |
|
HFP-MVS | | | 98.02 12 | 98.55 16 | 97.40 13 | 99.11 16 | 99.69 17 | 99.41 5 | 95.41 21 | 98.79 24 | 91.86 19 | 98.61 12 | 98.16 23 | 99.02 6 | 97.87 28 | 97.40 31 | 99.60 28 | 99.35 77 |
|
TSAR-MVS + MP. | | | 97.98 13 | 98.62 15 | 97.23 16 | 97.08 46 | 99.55 30 | 99.17 17 | 95.69 16 | 99.40 4 | 93.04 15 | 96.68 28 | 98.96 13 | 98.58 16 | 98.82 9 | 96.95 44 | 99.81 6 | 99.96 6 |
|
zzz-MVS | | | 97.93 14 | 98.05 28 | 97.80 6 | 99.20 13 | 99.64 20 | 99.40 6 | 95.76 14 | 98.01 45 | 94.31 10 | 96.54 29 | 98.49 20 | 98.58 16 | 98.22 20 | 96.23 53 | 99.54 52 | 99.23 84 |
|
SteuartSystems-ACMMP | | | 97.86 15 | 98.91 9 | 96.64 22 | 98.89 22 | 99.79 6 | 99.34 10 | 95.20 25 | 98.48 28 | 89.91 27 | 98.58 13 | 98.69 16 | 96.84 40 | 98.92 7 | 98.16 15 | 99.66 19 | 99.74 39 |
Skip Steuart: Steuart Systems R&D Blog. |
CP-MVS | | | 97.81 16 | 98.26 23 | 97.28 15 | 99.00 19 | 99.65 19 | 99.10 20 | 95.32 22 | 98.38 34 | 92.21 18 | 98.33 15 | 97.74 31 | 98.50 18 | 97.66 35 | 96.55 52 | 99.57 39 | 99.48 71 |
|
ACMMPR | | | 97.78 17 | 98.28 21 | 97.20 17 | 99.03 18 | 99.68 18 | 99.37 7 | 95.24 24 | 98.86 23 | 91.16 22 | 97.86 23 | 97.26 33 | 98.79 10 | 97.64 37 | 97.40 31 | 99.60 28 | 99.25 83 |
|
DeepC-MVS_fast | | 95.01 1 | 97.67 18 | 98.22 25 | 97.02 19 | 99.00 19 | 99.79 6 | 99.10 20 | 95.82 13 | 99.05 17 | 89.53 28 | 93.54 44 | 96.77 36 | 98.83 8 | 99.34 4 | 99.44 1 | 99.82 4 | 99.63 55 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
AdaColmap | | | 97.54 19 | 97.35 34 | 97.77 8 | 99.17 14 | 99.55 30 | 98.57 26 | 95.76 14 | 99.04 18 | 94.66 8 | 97.94 21 | 94.39 49 | 98.82 9 | 96.21 59 | 94.78 72 | 99.62 25 | 99.52 66 |
|
ACMMP_Plus | | | 97.51 20 | 98.27 22 | 96.63 23 | 99.34 7 | 99.72 13 | 99.25 14 | 95.94 12 | 98.11 39 | 87.10 43 | 96.98 26 | 98.50 19 | 98.61 14 | 98.58 14 | 96.83 47 | 99.56 43 | 99.14 91 |
|
MP-MVS | | | 97.46 21 | 98.30 20 | 96.48 24 | 98.93 21 | 99.43 40 | 99.20 15 | 95.42 20 | 98.43 30 | 87.60 40 | 98.19 17 | 98.01 29 | 98.09 23 | 98.05 23 | 96.67 50 | 99.64 21 | 99.35 77 |
|
train_agg | | | 97.42 22 | 98.88 10 | 95.71 28 | 98.46 29 | 99.60 27 | 99.05 22 | 95.16 26 | 99.10 15 | 84.38 55 | 98.47 14 | 98.85 15 | 97.61 27 | 98.54 15 | 97.66 24 | 99.62 25 | 99.93 15 |
|
CPTT-MVS | | | 97.32 23 | 97.60 33 | 96.99 20 | 98.29 32 | 99.31 51 | 99.04 23 | 94.67 30 | 97.99 46 | 93.12 14 | 98.03 19 | 98.26 22 | 98.77 11 | 96.08 63 | 94.26 80 | 98.07 185 | 99.27 82 |
|
X-MVS | | | 97.20 24 | 98.42 19 | 95.77 26 | 99.04 17 | 99.64 20 | 98.95 25 | 95.10 28 | 98.16 37 | 83.97 59 | 98.27 16 | 98.08 26 | 97.95 24 | 97.89 25 | 97.46 30 | 99.58 34 | 99.47 72 |
|
PHI-MVS | | | 97.09 25 | 98.69 12 | 95.22 33 | 97.99 38 | 99.59 29 | 97.56 39 | 92.16 34 | 98.41 32 | 87.11 42 | 98.70 9 | 99.42 8 | 96.95 36 | 96.88 50 | 98.16 15 | 99.56 43 | 99.70 45 |
|
PGM-MVS | | | 97.03 26 | 98.14 27 | 95.73 27 | 99.34 7 | 99.61 26 | 99.34 10 | 89.99 40 | 97.70 49 | 87.67 39 | 99.44 2 | 96.45 39 | 98.44 19 | 97.65 36 | 97.09 39 | 99.58 34 | 99.06 100 |
|
PLC | | 94.37 2 | 97.03 26 | 96.54 37 | 97.60 9 | 98.84 23 | 98.64 70 | 98.17 31 | 94.99 29 | 99.01 19 | 96.80 1 | 93.21 48 | 95.64 41 | 97.36 29 | 96.37 56 | 94.79 71 | 99.41 81 | 98.12 136 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
TSAR-MVS + ACMM | | | 96.90 28 | 98.64 14 | 94.88 35 | 98.12 36 | 99.47 35 | 99.01 24 | 95.43 19 | 99.23 13 | 81.98 77 | 95.95 31 | 99.16 11 | 95.13 63 | 98.61 13 | 98.11 17 | 99.58 34 | 99.93 15 |
|
TSAR-MVS + GP. | | | 96.47 29 | 98.45 18 | 94.17 40 | 92.12 77 | 99.29 52 | 97.76 35 | 88.05 51 | 99.36 6 | 90.26 26 | 97.82 24 | 99.21 9 | 97.21 32 | 96.78 52 | 96.74 48 | 99.63 23 | 99.94 12 |
|
EPNet | | | 96.23 30 | 97.89 30 | 94.29 38 | 97.62 41 | 99.44 39 | 97.14 47 | 88.63 47 | 98.16 37 | 88.14 35 | 99.46 1 | 94.15 50 | 94.61 71 | 97.20 43 | 97.23 35 | 99.57 39 | 99.59 60 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
CNLPA | | | 96.14 31 | 95.43 46 | 96.98 21 | 98.55 26 | 99.41 44 | 95.91 53 | 95.15 27 | 99.00 20 | 95.71 2 | 84.21 101 | 94.55 47 | 97.25 31 | 95.50 88 | 96.23 53 | 99.28 97 | 99.09 99 |
|
MVS_111021_LR | | | 96.07 32 | 97.94 29 | 93.88 43 | 97.86 39 | 99.43 40 | 95.70 56 | 89.65 43 | 98.73 25 | 84.86 53 | 99.38 3 | 94.08 51 | 95.78 60 | 97.81 31 | 96.73 49 | 99.43 79 | 99.42 74 |
|
ACMMP | | | 96.05 33 | 96.70 36 | 95.29 32 | 98.01 37 | 99.43 40 | 97.60 38 | 94.33 32 | 97.62 52 | 86.17 46 | 98.92 4 | 92.81 58 | 96.10 53 | 95.67 77 | 93.33 100 | 99.55 49 | 99.12 94 |
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 |
3Dnovator+ | | 90.72 7 | 95.99 34 | 96.42 39 | 95.50 30 | 98.18 34 | 99.33 50 | 97.44 41 | 87.73 56 | 97.93 47 | 92.36 17 | 84.67 94 | 97.33 32 | 97.55 28 | 97.32 40 | 98.47 8 | 99.72 16 | 99.88 24 |
|
DeepPCF-MVS | | 94.02 3 | 95.92 35 | 98.47 17 | 92.95 52 | 97.57 42 | 99.79 6 | 91.45 111 | 94.42 31 | 99.76 1 | 86.48 45 | 92.88 50 | 98.12 25 | 92.62 89 | 99.49 2 | 99.32 2 | 95.15 214 | 99.95 9 |
|
CDPH-MVS | | | 95.90 36 | 97.77 32 | 93.72 46 | 98.28 33 | 99.43 40 | 98.40 27 | 91.30 38 | 98.34 35 | 78.62 99 | 94.80 36 | 95.74 40 | 96.11 52 | 97.86 29 | 98.67 6 | 99.59 30 | 99.56 63 |
|
CSCG | | | 95.77 37 | 95.35 48 | 96.26 25 | 99.13 15 | 99.60 27 | 98.14 32 | 91.89 37 | 96.57 65 | 92.61 16 | 89.65 61 | 91.74 65 | 96.96 34 | 93.69 118 | 96.58 51 | 98.86 127 | 99.63 55 |
|
OMC-MVS | | | 95.75 38 | 95.84 43 | 95.64 29 | 98.52 28 | 99.34 49 | 97.15 46 | 92.02 36 | 98.94 22 | 90.45 25 | 88.31 64 | 94.64 45 | 96.35 48 | 96.02 66 | 95.99 62 | 99.34 90 | 97.65 145 |
|
MVS_111021_HR | | | 95.70 39 | 98.16 26 | 92.83 53 | 97.57 42 | 99.77 11 | 94.78 69 | 88.05 51 | 98.61 26 | 82.29 70 | 98.85 5 | 94.66 44 | 94.63 70 | 97.80 32 | 97.63 26 | 99.64 21 | 99.79 38 |
|
3Dnovator | | 90.31 8 | 95.67 40 | 96.16 41 | 95.11 34 | 98.59 25 | 99.37 48 | 97.50 40 | 87.98 53 | 98.02 44 | 89.09 30 | 85.36 90 | 94.62 46 | 97.66 25 | 97.10 46 | 98.90 5 | 99.82 4 | 99.73 41 |
|
CANet | | | 95.40 41 | 96.27 40 | 94.40 37 | 96.25 51 | 99.62 24 | 98.37 28 | 88.59 48 | 98.09 40 | 87.58 41 | 84.57 96 | 95.54 43 | 95.87 58 | 98.12 21 | 98.03 20 | 99.73 12 | 99.90 21 |
|
QAPM | | | 95.17 42 | 96.05 42 | 94.14 41 | 98.55 26 | 99.49 33 | 97.41 42 | 87.88 54 | 97.72 48 | 84.21 57 | 84.59 95 | 95.60 42 | 97.21 32 | 97.10 46 | 98.19 13 | 99.57 39 | 99.65 49 |
|
MVSTER | | | 94.75 43 | 96.50 38 | 92.70 56 | 90.91 104 | 94.51 136 | 97.37 44 | 83.37 91 | 98.40 33 | 89.04 31 | 93.23 47 | 97.04 35 | 95.91 56 | 97.73 33 | 95.59 66 | 99.61 27 | 99.01 101 |
|
TAPA-MVS | | 92.04 6 | 94.72 44 | 95.13 50 | 94.24 39 | 97.72 40 | 99.17 55 | 97.61 37 | 92.16 34 | 97.66 51 | 81.99 76 | 87.84 71 | 93.94 52 | 96.50 46 | 95.74 74 | 94.27 79 | 99.46 74 | 97.31 153 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
DeepC-MVS | | 92.23 5 | 94.53 45 | 94.26 59 | 94.86 36 | 96.73 48 | 99.50 32 | 97.85 34 | 95.45 18 | 96.22 73 | 82.73 66 | 80.68 111 | 88.02 76 | 96.92 37 | 97.49 39 | 98.20 12 | 99.47 63 | 99.69 47 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
CHOSEN 280x420 | | | 94.51 46 | 97.78 31 | 90.70 74 | 95.54 57 | 99.49 33 | 94.14 79 | 74.91 153 | 98.43 30 | 85.32 51 | 94.78 37 | 99.19 10 | 94.95 67 | 97.02 48 | 96.18 57 | 99.35 86 | 99.36 76 |
|
MVS_0304 | | | 94.35 47 | 95.66 45 | 92.83 53 | 94.82 59 | 99.46 37 | 98.19 30 | 87.75 55 | 97.32 57 | 81.83 79 | 83.50 103 | 93.19 56 | 94.71 69 | 98.24 19 | 98.07 18 | 99.68 17 | 99.83 32 |
|
MAR-MVS | | | 94.18 48 | 95.12 51 | 93.09 51 | 98.40 31 | 99.17 55 | 94.20 78 | 81.92 99 | 98.47 29 | 86.52 44 | 90.92 57 | 84.21 93 | 98.12 22 | 95.88 69 | 97.59 27 | 99.40 82 | 99.58 62 |
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 |
PCF-MVS | | 92.56 4 | 93.95 49 | 93.82 63 | 94.10 42 | 96.07 53 | 99.25 53 | 96.82 49 | 95.51 17 | 92.00 119 | 81.51 80 | 82.97 106 | 93.88 54 | 95.63 62 | 94.24 106 | 94.71 74 | 99.09 109 | 99.70 45 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
DELS-MVS | | | 93.82 50 | 93.82 63 | 93.81 45 | 96.34 50 | 99.47 35 | 97.26 45 | 88.53 49 | 92.13 117 | 87.80 38 | 79.67 113 | 88.01 77 | 93.14 81 | 98.28 16 | 99.22 3 | 99.80 8 | 99.98 3 |
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 |
OpenMVS | | 88.43 11 | 93.49 51 | 93.62 66 | 93.34 47 | 98.46 29 | 99.39 45 | 97.00 48 | 87.66 58 | 95.37 81 | 81.21 81 | 75.96 128 | 91.58 66 | 96.21 51 | 96.37 56 | 97.10 38 | 99.52 53 | 99.54 65 |
|
PVSNet_BlendedMVS | | | 93.30 52 | 93.46 71 | 93.10 49 | 95.60 55 | 99.38 46 | 93.59 85 | 88.70 45 | 98.09 40 | 88.10 36 | 86.96 77 | 75.02 122 | 93.08 82 | 97.89 25 | 96.90 45 | 99.56 43 | 100.00 1 |
|
PVSNet_Blended | | | 93.30 52 | 93.46 71 | 93.10 49 | 95.60 55 | 99.38 46 | 93.59 85 | 88.70 45 | 98.09 40 | 88.10 36 | 86.96 77 | 75.02 122 | 93.08 82 | 97.89 25 | 96.90 45 | 99.56 43 | 100.00 1 |
|
PMMVS | | | 93.05 54 | 95.40 47 | 90.31 80 | 91.41 93 | 97.54 95 | 92.62 96 | 83.25 93 | 98.08 43 | 79.44 96 | 95.18 34 | 88.52 75 | 96.43 47 | 95.70 75 | 93.88 85 | 98.68 155 | 98.91 103 |
|
conf0.002 | | | 92.80 55 | 93.55 70 | 91.93 59 | 91.66 79 | 98.85 58 | 95.03 63 | 86.42 63 | 93.24 104 | 82.20 73 | 92.98 49 | 79.35 113 | 96.80 41 | 95.83 70 | 94.67 76 | 99.48 59 | 99.91 19 |
|
diffmvs | | | 92.73 56 | 94.75 53 | 90.37 78 | 90.81 108 | 98.11 80 | 94.69 72 | 80.93 110 | 96.91 62 | 82.50 69 | 85.28 92 | 92.99 57 | 93.84 77 | 94.67 103 | 96.19 56 | 99.44 78 | 99.12 94 |
|
LS3D | | | 92.70 57 | 92.23 87 | 93.26 48 | 96.24 52 | 98.72 62 | 97.93 33 | 96.17 3 | 96.41 66 | 72.46 112 | 81.39 109 | 80.76 105 | 97.66 25 | 95.69 76 | 95.62 65 | 99.07 111 | 97.02 162 |
|
IS_MVSNet | | | 92.67 58 | 94.99 52 | 89.96 84 | 91.17 97 | 98.54 74 | 92.77 91 | 84.00 87 | 92.72 113 | 81.90 78 | 85.67 88 | 92.47 60 | 90.39 108 | 97.82 30 | 97.81 21 | 99.51 54 | 99.91 19 |
|
TSAR-MVS + COLMAP | | | 92.56 59 | 92.44 84 | 92.71 55 | 94.61 61 | 97.69 90 | 97.69 36 | 91.09 39 | 98.96 21 | 76.71 101 | 94.68 38 | 69.41 148 | 96.91 38 | 95.80 72 | 94.18 81 | 99.26 98 | 96.33 176 |
|
canonicalmvs | | | 92.54 60 | 93.28 73 | 91.68 62 | 91.44 92 | 98.24 79 | 95.45 61 | 81.84 103 | 95.98 77 | 84.85 54 | 90.69 59 | 78.53 114 | 96.96 34 | 92.97 124 | 97.06 40 | 99.57 39 | 99.47 72 |
|
PatchMatch-RL | | | 92.54 60 | 92.82 80 | 92.21 57 | 96.57 49 | 98.74 61 | 91.85 107 | 86.30 66 | 96.23 72 | 85.18 52 | 95.21 33 | 73.58 126 | 94.22 75 | 95.40 91 | 93.08 104 | 99.14 104 | 97.49 151 |
|
MVS_Test | | | 92.42 62 | 94.43 54 | 90.08 83 | 90.69 109 | 98.26 78 | 94.78 69 | 80.81 112 | 97.27 58 | 78.76 98 | 87.06 75 | 84.25 92 | 95.84 59 | 97.67 34 | 97.56 28 | 99.59 30 | 98.93 102 |
|
conf0.01 | | | 92.41 63 | 92.86 79 | 91.90 60 | 91.65 80 | 98.84 59 | 95.03 63 | 86.38 65 | 93.24 104 | 82.03 75 | 91.90 56 | 77.54 116 | 96.80 41 | 95.78 73 | 92.82 112 | 99.48 59 | 99.90 21 |
|
EPP-MVSNet | | | 92.29 64 | 94.35 58 | 89.88 85 | 90.36 113 | 97.69 90 | 90.89 115 | 83.31 92 | 93.39 103 | 83.47 63 | 85.56 89 | 93.92 53 | 91.93 96 | 95.49 89 | 94.77 73 | 99.34 90 | 99.62 58 |
|
tfpn_ndepth | | | 92.26 65 | 93.84 62 | 90.42 77 | 91.45 91 | 97.91 86 | 92.73 92 | 85.80 75 | 96.69 64 | 82.22 71 | 91.92 55 | 83.42 95 | 90.76 106 | 95.51 87 | 93.28 101 | 99.58 34 | 98.14 132 |
|
thresconf0.02 | | | 92.16 66 | 95.16 49 | 88.67 96 | 91.10 98 | 97.63 92 | 92.93 89 | 86.58 62 | 96.29 70 | 73.55 108 | 94.67 39 | 88.63 73 | 88.29 125 | 96.14 62 | 95.40 67 | 99.58 34 | 97.33 152 |
|
DWT-MVSNet_training | | | 92.09 67 | 93.58 69 | 90.35 79 | 91.27 94 | 97.94 85 | 92.05 102 | 78.82 125 | 97.40 55 | 88.83 33 | 87.91 66 | 86.76 85 | 91.99 95 | 90.03 143 | 95.25 68 | 99.13 106 | 99.73 41 |
|
tfpn111 | | | 91.99 68 | 92.28 86 | 91.65 63 | 91.61 81 | 98.69 64 | 95.03 63 | 86.17 67 | 93.24 104 | 80.82 83 | 94.67 39 | 71.15 134 | 96.80 41 | 95.53 81 | 92.82 112 | 99.47 63 | 99.88 24 |
|
HQP-MVS | | | 91.94 69 | 93.03 76 | 90.66 76 | 93.69 63 | 96.48 110 | 95.92 52 | 89.73 41 | 97.33 56 | 72.65 110 | 95.37 32 | 73.56 127 | 92.75 88 | 94.85 100 | 94.12 82 | 99.23 101 | 99.51 68 |
|
MSDG | | | 91.93 70 | 90.28 115 | 93.85 44 | 97.36 44 | 97.12 101 | 95.88 54 | 94.07 33 | 94.52 91 | 84.13 58 | 76.74 123 | 80.89 104 | 92.54 90 | 93.97 114 | 93.61 95 | 99.14 104 | 95.10 187 |
|
UGNet | | | 91.71 71 | 94.43 54 | 88.53 97 | 92.72 73 | 98.00 83 | 90.22 122 | 84.81 85 | 94.45 92 | 83.05 64 | 87.65 73 | 92.74 59 | 81.04 178 | 94.51 105 | 94.45 77 | 99.32 95 | 99.21 88 |
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 |
thres100view900 | | | 91.69 72 | 91.52 93 | 91.88 61 | 91.61 81 | 98.89 57 | 95.49 59 | 86.96 60 | 93.24 104 | 80.82 83 | 87.90 67 | 71.15 134 | 96.88 39 | 96.00 67 | 93.51 97 | 99.51 54 | 99.95 9 |
|
CLD-MVS | | | 91.67 73 | 91.30 99 | 92.10 58 | 91.25 96 | 96.59 107 | 95.93 51 | 87.25 59 | 96.86 63 | 85.55 50 | 87.08 74 | 73.01 128 | 93.26 80 | 93.07 122 | 92.84 109 | 99.34 90 | 99.68 48 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
tfpn1000 | | | 91.48 74 | 93.17 75 | 89.51 89 | 91.27 94 | 97.71 89 | 92.08 101 | 85.28 82 | 96.13 74 | 80.20 89 | 90.77 58 | 82.52 98 | 88.64 121 | 95.17 96 | 92.35 122 | 99.56 43 | 97.52 150 |
|
conf200view11 | | | 91.47 75 | 91.31 96 | 91.65 63 | 91.61 81 | 98.69 64 | 95.03 63 | 86.17 67 | 93.24 104 | 80.82 83 | 87.90 67 | 71.15 134 | 96.80 41 | 95.53 81 | 92.82 112 | 99.47 63 | 99.88 24 |
|
tfpn200view9 | | | 91.47 75 | 91.31 96 | 91.65 63 | 91.61 81 | 98.69 64 | 95.03 63 | 86.17 67 | 93.24 104 | 80.82 83 | 87.90 67 | 71.15 134 | 96.80 41 | 95.53 81 | 92.82 112 | 99.47 63 | 99.88 24 |
|
CANet_DTU | | | 91.36 77 | 95.75 44 | 86.23 110 | 92.31 76 | 98.71 63 | 95.60 58 | 78.41 129 | 98.20 36 | 56.48 188 | 94.38 42 | 87.96 78 | 95.11 64 | 96.89 49 | 96.07 58 | 99.48 59 | 98.01 140 |
|
thres200 | | | 91.36 77 | 91.19 101 | 91.55 66 | 91.60 85 | 98.69 64 | 94.98 68 | 86.17 67 | 92.16 116 | 80.76 87 | 87.66 72 | 71.15 134 | 96.35 48 | 95.53 81 | 93.23 103 | 99.47 63 | 99.92 18 |
|
tfpn | | | 91.26 79 | 91.55 92 | 90.92 73 | 91.47 90 | 98.50 76 | 93.85 84 | 85.72 76 | 91.40 127 | 79.30 97 | 84.78 93 | 77.33 117 | 95.70 61 | 95.29 93 | 93.73 87 | 99.47 63 | 99.82 34 |
|
FMVSNet3 | | | 91.25 80 | 92.13 89 | 90.21 81 | 85.64 143 | 93.14 145 | 95.29 62 | 80.09 113 | 96.40 67 | 85.74 47 | 77.13 117 | 86.81 82 | 94.98 66 | 97.19 44 | 97.11 37 | 99.55 49 | 97.13 157 |
|
thres400 | | | 91.24 81 | 91.01 106 | 91.50 68 | 91.56 86 | 98.77 60 | 94.66 73 | 86.41 64 | 91.87 121 | 80.56 88 | 87.05 76 | 71.01 139 | 96.35 48 | 95.67 77 | 92.82 112 | 99.48 59 | 99.88 24 |
|
PVSNet_Blended_VisFu | | | 91.20 82 | 92.89 78 | 89.23 92 | 93.41 66 | 98.61 72 | 89.80 123 | 85.39 81 | 92.84 111 | 82.80 65 | 74.21 133 | 91.38 68 | 84.64 142 | 97.22 42 | 96.04 61 | 99.34 90 | 99.93 15 |
|
DI_MVS_plusplus_trai | | | 91.11 83 | 91.47 94 | 90.68 75 | 90.01 115 | 97.77 87 | 95.87 55 | 83.56 90 | 94.72 88 | 82.12 74 | 68.46 150 | 87.46 79 | 93.07 84 | 96.46 55 | 95.73 64 | 99.47 63 | 99.71 44 |
|
Vis-MVSNet (Re-imp) | | | 91.05 84 | 94.43 54 | 87.11 103 | 91.05 100 | 97.99 84 | 92.53 97 | 83.82 89 | 92.71 114 | 76.28 102 | 84.50 97 | 92.43 61 | 79.52 184 | 97.24 41 | 97.68 23 | 99.43 79 | 98.45 117 |
|
view600 | | | 90.97 85 | 90.70 108 | 91.30 69 | 91.53 87 | 98.69 64 | 94.33 74 | 86.17 67 | 91.75 123 | 80.19 90 | 86.06 85 | 70.90 140 | 96.10 53 | 95.53 81 | 92.08 125 | 99.47 63 | 99.86 30 |
|
thres600view7 | | | 90.97 85 | 90.70 108 | 91.30 69 | 91.53 87 | 98.69 64 | 94.33 74 | 86.17 67 | 91.75 123 | 80.19 90 | 86.06 85 | 70.90 140 | 96.10 53 | 95.53 81 | 92.08 125 | 99.47 63 | 99.86 30 |
|
view800 | | | 90.79 87 | 90.54 112 | 91.09 72 | 91.50 89 | 98.58 73 | 94.09 80 | 85.92 74 | 91.57 126 | 79.68 93 | 85.29 91 | 70.72 143 | 95.91 56 | 95.40 91 | 92.39 121 | 99.47 63 | 99.83 32 |
|
ACMP | | 89.80 9 | 90.72 88 | 91.15 102 | 90.21 81 | 92.55 74 | 96.52 109 | 92.63 95 | 85.71 77 | 94.65 89 | 81.06 82 | 93.32 45 | 70.56 144 | 90.52 107 | 92.68 128 | 91.05 134 | 98.76 136 | 99.31 81 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
ACMM | | 89.40 10 | 90.58 89 | 90.02 118 | 91.23 71 | 93.30 68 | 94.75 132 | 90.69 118 | 88.22 50 | 95.20 82 | 82.70 67 | 88.54 63 | 71.40 133 | 93.48 79 | 93.64 119 | 90.94 135 | 98.99 119 | 95.72 184 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
GBi-Net | | | 90.49 90 | 91.12 104 | 89.75 87 | 84.99 146 | 92.73 148 | 93.94 81 | 80.09 113 | 96.40 67 | 85.74 47 | 77.13 117 | 86.81 82 | 94.42 72 | 94.12 108 | 93.73 87 | 99.35 86 | 96.90 166 |
|
test1 | | | 90.49 90 | 91.12 104 | 89.75 87 | 84.99 146 | 92.73 148 | 93.94 81 | 80.09 113 | 96.40 67 | 85.74 47 | 77.13 117 | 86.81 82 | 94.42 72 | 94.12 108 | 93.73 87 | 99.35 86 | 96.90 166 |
|
tfpnview11 | | | 90.36 92 | 92.74 81 | 87.59 99 | 90.93 103 | 97.30 100 | 92.28 99 | 85.63 78 | 95.88 78 | 70.44 118 | 92.30 52 | 79.50 110 | 86.76 135 | 95.26 95 | 92.83 111 | 99.51 54 | 96.09 177 |
|
LGP-MVS_train | | | 90.34 93 | 91.63 91 | 88.83 95 | 93.31 67 | 96.14 114 | 95.49 59 | 85.24 83 | 93.91 96 | 68.71 126 | 93.96 43 | 71.63 131 | 91.12 103 | 93.82 116 | 92.79 118 | 99.07 111 | 99.16 90 |
|
tfpn_n400 | | | 90.13 94 | 92.47 82 | 87.40 100 | 90.89 105 | 97.37 98 | 92.05 102 | 85.47 79 | 93.43 101 | 70.44 118 | 92.30 52 | 79.50 110 | 86.50 136 | 94.84 101 | 93.93 83 | 99.07 111 | 95.91 180 |
|
tfpnconf | | | 90.13 94 | 92.47 82 | 87.40 100 | 90.89 105 | 97.37 98 | 92.05 102 | 85.47 79 | 93.43 101 | 70.44 118 | 92.30 52 | 79.50 110 | 86.50 136 | 94.84 101 | 93.93 83 | 99.07 111 | 95.91 180 |
|
EPNet_dtu | | | 89.82 96 | 94.18 60 | 84.74 121 | 96.87 47 | 95.54 125 | 92.65 94 | 86.91 61 | 96.99 60 | 54.17 201 | 92.41 51 | 88.54 74 | 78.35 190 | 96.15 61 | 96.05 60 | 99.47 63 | 93.60 195 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
RPSCF | | | 89.81 97 | 89.75 120 | 89.88 85 | 93.22 70 | 93.99 139 | 94.78 69 | 85.23 84 | 94.01 95 | 82.52 68 | 95.00 35 | 87.23 80 | 92.01 94 | 85.16 205 | 83.48 213 | 91.54 220 | 89.38 211 |
|
MDTV_nov1_ep13 | | | 89.63 98 | 94.38 57 | 84.09 127 | 88.76 127 | 97.53 96 | 89.37 131 | 68.46 197 | 96.95 61 | 70.27 122 | 87.88 70 | 93.67 55 | 91.04 104 | 93.12 120 | 93.83 86 | 96.62 206 | 97.68 144 |
|
UA-Net | | | 89.56 99 | 93.03 76 | 85.52 117 | 92.46 75 | 97.55 94 | 91.92 106 | 81.91 100 | 85.24 153 | 71.39 114 | 83.57 102 | 96.56 38 | 76.01 199 | 96.81 51 | 97.04 41 | 99.46 74 | 94.41 190 |
|
FMVSNet2 | | | 89.51 100 | 89.63 121 | 89.38 90 | 84.99 146 | 92.73 148 | 93.94 81 | 79.28 119 | 93.73 98 | 84.28 56 | 69.36 149 | 82.32 99 | 94.42 72 | 96.16 60 | 96.22 55 | 99.35 86 | 96.90 166 |
|
CostFormer | | | 89.42 101 | 91.67 90 | 86.80 106 | 89.99 116 | 96.33 112 | 90.75 116 | 64.79 204 | 95.17 83 | 83.62 62 | 86.20 83 | 82.15 100 | 92.96 85 | 89.22 157 | 92.94 105 | 98.68 155 | 99.65 49 |
|
FC-MVSNet-train | | | 89.37 102 | 89.62 122 | 89.08 94 | 90.48 111 | 94.16 138 | 89.45 127 | 83.99 88 | 91.09 128 | 80.09 92 | 82.84 107 | 74.52 125 | 91.44 100 | 93.79 117 | 91.57 131 | 99.01 117 | 99.35 77 |
|
OPM-MVS | | | 89.33 103 | 87.45 135 | 91.53 67 | 94.49 62 | 96.20 113 | 96.47 50 | 89.72 42 | 82.77 164 | 75.43 103 | 80.53 112 | 70.86 142 | 93.80 78 | 94.00 112 | 91.85 129 | 99.29 96 | 95.91 180 |
|
test-LLR | | | 89.31 104 | 93.60 67 | 84.30 124 | 88.08 130 | 96.98 102 | 88.10 135 | 78.00 132 | 94.83 85 | 62.43 144 | 84.29 99 | 90.96 69 | 89.70 113 | 95.63 79 | 92.86 107 | 99.51 54 | 99.64 51 |
|
EPMVS | | | 89.31 104 | 93.70 65 | 84.18 126 | 91.10 98 | 98.10 81 | 89.17 132 | 62.71 208 | 96.24 71 | 70.21 123 | 86.46 81 | 92.37 62 | 92.79 86 | 91.95 134 | 93.59 96 | 99.10 108 | 97.19 154 |
|
Effi-MVS+ | | | 88.96 106 | 91.13 103 | 86.43 108 | 89.12 123 | 97.62 93 | 93.15 87 | 75.52 148 | 93.90 97 | 66.40 130 | 86.23 82 | 70.51 145 | 95.03 65 | 95.89 68 | 94.28 78 | 99.37 83 | 99.51 68 |
|
test0.0.03 1 | | | 88.71 107 | 92.22 88 | 84.63 122 | 88.08 130 | 94.71 134 | 85.91 169 | 78.00 132 | 95.54 80 | 72.96 109 | 86.10 84 | 85.88 87 | 83.59 152 | 92.95 126 | 93.24 102 | 99.25 100 | 97.09 158 |
|
PatchmatchNet | | | 88.67 108 | 94.10 61 | 82.34 142 | 89.38 121 | 97.72 88 | 87.24 142 | 62.18 213 | 97.00 59 | 64.79 135 | 87.97 65 | 94.43 48 | 91.55 98 | 91.21 138 | 92.77 119 | 98.90 123 | 97.60 147 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
dps | | | 88.66 109 | 90.19 116 | 86.88 105 | 89.94 117 | 96.48 110 | 89.56 125 | 64.08 206 | 94.12 94 | 89.00 32 | 83.39 104 | 82.56 97 | 90.16 111 | 86.81 191 | 89.26 153 | 98.53 171 | 98.71 108 |
|
TESTMET0.1,1 | | | 88.63 110 | 93.60 67 | 82.84 139 | 84.07 152 | 96.98 102 | 88.10 135 | 73.22 169 | 94.83 85 | 62.43 144 | 84.29 99 | 90.96 69 | 89.70 113 | 95.63 79 | 92.86 107 | 99.51 54 | 99.64 51 |
|
CHOSEN 1792x2688 | | | 88.63 110 | 89.01 126 | 88.19 98 | 94.83 58 | 99.21 54 | 92.66 93 | 79.85 116 | 92.40 115 | 72.18 113 | 56.38 202 | 80.22 106 | 90.24 109 | 97.64 37 | 97.28 34 | 99.37 83 | 99.94 12 |
|
CDS-MVSNet | | | 88.59 112 | 90.13 117 | 86.79 107 | 86.98 137 | 95.43 126 | 92.03 105 | 81.33 108 | 85.54 150 | 74.51 106 | 77.07 120 | 85.14 89 | 87.03 133 | 93.90 115 | 95.18 69 | 98.88 125 | 98.67 110 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
IB-MVS | | 84.67 14 | 88.34 113 | 90.61 111 | 85.70 114 | 92.99 72 | 98.62 71 | 78.85 202 | 86.07 73 | 94.35 93 | 88.64 34 | 85.99 87 | 75.69 120 | 68.09 212 | 88.21 160 | 91.43 132 | 99.55 49 | 99.96 6 |
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 |
conf0.05thres1000 | | | 88.28 114 | 87.54 134 | 89.15 93 | 91.00 102 | 97.50 97 | 92.18 100 | 84.70 86 | 85.15 155 | 73.91 107 | 73.77 135 | 70.50 147 | 94.01 76 | 93.99 113 | 92.21 123 | 99.11 107 | 99.64 51 |
|
test-mter | | | 88.25 115 | 93.27 74 | 82.38 141 | 83.89 153 | 96.86 105 | 87.10 146 | 72.80 171 | 94.58 90 | 61.85 149 | 83.21 105 | 90.65 71 | 89.18 116 | 95.43 90 | 92.58 120 | 99.46 74 | 99.61 59 |
|
COLMAP_ROB | | 84.42 15 | 88.24 116 | 87.32 136 | 89.32 91 | 95.83 54 | 95.82 118 | 92.81 90 | 87.68 57 | 92.09 118 | 72.64 111 | 72.34 141 | 79.96 108 | 88.79 118 | 89.54 152 | 89.46 149 | 98.16 182 | 92.00 201 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
tpmp4_e23 | | | 88.10 117 | 90.02 118 | 85.86 112 | 89.94 117 | 95.73 122 | 91.83 108 | 64.92 202 | 94.79 87 | 78.25 100 | 81.03 110 | 78.34 115 | 92.33 92 | 88.10 162 | 92.82 112 | 97.90 192 | 99.34 80 |
|
IterMVS-LS | | | 87.95 118 | 89.40 124 | 86.26 109 | 88.79 126 | 90.93 181 | 91.23 113 | 76.05 145 | 90.87 129 | 71.07 116 | 75.51 130 | 81.18 103 | 91.21 102 | 94.11 111 | 95.01 70 | 99.20 103 | 98.23 130 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
HyFIR lowres test | | | 87.86 119 | 88.25 130 | 87.40 100 | 94.67 60 | 98.54 74 | 90.33 121 | 76.51 144 | 89.60 136 | 70.89 117 | 51.43 217 | 85.69 88 | 92.79 86 | 96.59 54 | 95.96 63 | 99.22 102 | 99.94 12 |
|
Vis-MVSNet | | | 87.60 120 | 91.31 96 | 83.27 134 | 89.14 122 | 98.04 82 | 90.35 120 | 79.42 117 | 87.23 140 | 66.92 129 | 79.10 116 | 84.63 91 | 74.34 205 | 95.81 71 | 96.06 59 | 99.46 74 | 98.32 126 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
RPMNet | | | 87.35 121 | 92.41 85 | 81.45 146 | 88.85 125 | 96.06 115 | 89.42 130 | 59.59 222 | 93.57 99 | 61.81 150 | 76.48 126 | 91.48 67 | 90.18 110 | 96.32 58 | 93.37 99 | 98.87 126 | 99.59 60 |
|
tpm cat1 | | | 87.34 122 | 88.52 129 | 85.95 111 | 89.83 119 | 95.80 119 | 90.73 117 | 64.91 203 | 92.99 110 | 82.21 72 | 71.19 147 | 82.68 96 | 90.13 112 | 86.38 195 | 90.87 137 | 97.90 192 | 99.74 39 |
|
MS-PatchMatch | | | 87.19 123 | 88.59 128 | 85.55 116 | 93.15 71 | 96.58 108 | 92.35 98 | 74.19 161 | 91.97 120 | 70.33 121 | 71.42 145 | 85.89 86 | 84.28 145 | 93.12 120 | 89.16 155 | 99.00 118 | 91.99 202 |
|
Effi-MVS+-dtu | | | 87.18 124 | 90.48 113 | 83.32 133 | 86.51 139 | 95.76 121 | 91.16 114 | 74.28 160 | 90.44 133 | 61.31 153 | 86.72 80 | 72.68 129 | 91.25 101 | 95.01 98 | 93.64 91 | 95.45 213 | 99.12 94 |
|
FMVSNet5 | | | 87.06 125 | 89.52 123 | 84.20 125 | 79.92 203 | 86.57 203 | 87.11 145 | 72.37 173 | 96.06 75 | 75.41 104 | 84.33 98 | 91.76 64 | 91.60 97 | 91.51 136 | 91.22 133 | 98.77 133 | 85.16 218 |
|
Fast-Effi-MVS+-dtu | | | 86.94 126 | 91.27 100 | 81.89 143 | 86.27 140 | 95.06 127 | 90.68 119 | 68.93 194 | 91.76 122 | 57.18 186 | 89.56 62 | 75.85 119 | 89.19 115 | 94.56 104 | 92.84 109 | 99.07 111 | 99.23 84 |
|
Fast-Effi-MVS+ | | | 86.94 126 | 87.88 132 | 85.84 113 | 86.99 136 | 95.80 119 | 91.24 112 | 73.48 167 | 92.75 112 | 69.22 124 | 72.70 139 | 65.71 155 | 94.84 68 | 94.98 99 | 94.71 74 | 99.26 98 | 98.48 116 |
|
tpmrst | | | 86.78 128 | 90.29 114 | 82.69 140 | 90.55 110 | 96.95 104 | 88.49 134 | 62.58 209 | 95.09 84 | 63.52 141 | 76.67 125 | 84.00 94 | 92.05 93 | 87.93 164 | 91.89 128 | 98.98 120 | 99.50 70 |
|
CR-MVSNet | | | 86.73 129 | 91.47 94 | 81.20 152 | 88.56 128 | 96.06 115 | 89.43 128 | 61.37 216 | 93.57 99 | 60.81 155 | 72.89 138 | 88.85 72 | 88.13 127 | 96.03 64 | 93.64 91 | 98.89 124 | 99.22 86 |
|
ADS-MVSNet | | | 86.68 130 | 90.79 107 | 81.88 144 | 90.38 112 | 96.81 106 | 86.90 147 | 60.50 220 | 96.01 76 | 63.93 138 | 81.67 108 | 84.72 90 | 90.78 105 | 87.03 177 | 91.67 130 | 98.77 133 | 97.63 146 |
|
FMVSNet1 | | | 85.85 131 | 84.91 145 | 86.96 104 | 82.70 158 | 91.39 175 | 91.54 110 | 77.45 136 | 85.29 152 | 79.56 95 | 60.70 165 | 72.68 129 | 92.37 91 | 94.12 108 | 93.73 87 | 98.12 183 | 96.44 173 |
|
FC-MVSNet-test | | | 85.51 132 | 89.08 125 | 81.35 147 | 85.31 145 | 93.35 141 | 87.65 137 | 77.55 135 | 90.01 134 | 64.07 137 | 79.63 114 | 81.83 102 | 74.94 202 | 92.08 131 | 90.83 139 | 98.55 168 | 95.81 183 |
|
ACMH | | 85.22 13 | 85.40 133 | 85.73 142 | 85.02 119 | 91.76 78 | 94.46 137 | 84.97 180 | 81.54 106 | 85.18 154 | 65.22 134 | 76.92 122 | 64.22 156 | 88.58 122 | 90.17 141 | 90.25 145 | 98.03 186 | 98.90 104 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
TAMVS | | | 85.35 134 | 86.00 141 | 84.59 123 | 84.97 149 | 95.57 124 | 88.98 133 | 77.29 139 | 81.44 175 | 71.36 115 | 71.48 144 | 75.00 124 | 87.03 133 | 91.92 135 | 92.21 123 | 97.92 191 | 94.40 191 |
|
ACMH+ | | 85.62 12 | 85.27 135 | 84.96 144 | 85.64 115 | 90.84 107 | 94.78 131 | 87.46 139 | 81.30 109 | 86.94 141 | 67.35 128 | 74.56 132 | 64.09 157 | 88.70 119 | 88.14 161 | 89.00 156 | 98.22 181 | 97.19 154 |
|
USDC | | | 85.11 136 | 85.35 143 | 84.83 120 | 89.45 120 | 94.93 130 | 92.98 88 | 77.30 138 | 90.53 131 | 61.80 151 | 76.69 124 | 59.62 166 | 88.90 117 | 92.78 127 | 90.79 141 | 98.53 171 | 92.12 199 |
|
IterMVS | | | 85.02 137 | 88.98 127 | 80.41 162 | 87.03 135 | 90.34 191 | 89.78 124 | 69.45 190 | 89.77 135 | 54.04 202 | 73.71 136 | 82.05 101 | 83.44 157 | 95.11 97 | 93.64 91 | 98.75 141 | 98.22 131 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
PatchT | | | 84.89 138 | 90.67 110 | 78.13 192 | 87.83 133 | 94.99 129 | 72.46 214 | 60.22 221 | 91.74 125 | 60.81 155 | 72.16 142 | 86.95 81 | 88.13 127 | 96.03 64 | 93.64 91 | 99.36 85 | 99.22 86 |
|
pmmvs4 | | | 84.88 139 | 84.67 146 | 85.13 118 | 82.80 157 | 92.37 153 | 87.29 140 | 79.08 120 | 90.51 132 | 74.94 105 | 70.37 148 | 62.49 159 | 88.17 126 | 92.01 133 | 88.51 162 | 98.49 173 | 96.44 173 |
|
CVMVSNet | | | 84.01 140 | 86.91 137 | 80.61 159 | 88.39 129 | 93.29 142 | 86.06 159 | 82.29 97 | 83.13 161 | 54.29 198 | 72.68 140 | 79.59 109 | 75.11 201 | 91.23 137 | 92.91 106 | 97.54 199 | 95.58 185 |
|
tpm | | | 83.97 141 | 87.97 131 | 79.31 181 | 87.35 134 | 93.21 144 | 86.00 164 | 61.90 214 | 90.69 130 | 54.01 203 | 79.42 115 | 75.61 121 | 88.65 120 | 87.18 172 | 90.48 143 | 97.95 190 | 99.21 88 |
|
GA-MVS | | | 83.83 142 | 86.63 138 | 80.58 160 | 85.40 144 | 94.73 133 | 87.27 141 | 78.76 127 | 86.49 143 | 49.57 211 | 74.21 133 | 67.67 151 | 83.38 159 | 95.28 94 | 90.92 136 | 99.08 110 | 97.09 158 |
|
UniMVSNet_NR-MVSNet | | | 83.83 142 | 83.70 149 | 83.98 128 | 81.41 171 | 92.56 152 | 86.54 151 | 82.96 94 | 85.98 146 | 66.27 131 | 66.16 156 | 63.63 158 | 87.78 130 | 87.65 167 | 90.81 140 | 98.94 121 | 99.13 92 |
|
UniMVSNet (Re) | | | 83.28 144 | 83.16 150 | 83.42 132 | 81.93 161 | 93.12 146 | 86.27 153 | 80.83 111 | 85.88 147 | 68.23 127 | 64.56 159 | 60.58 161 | 84.25 146 | 89.13 158 | 89.44 151 | 99.04 116 | 99.40 75 |
|
TinyColmap | | | 83.03 145 | 82.24 154 | 83.95 129 | 88.88 124 | 93.22 143 | 89.48 126 | 76.89 141 | 87.53 139 | 62.12 146 | 68.46 150 | 55.03 201 | 88.43 124 | 90.87 139 | 89.65 147 | 97.89 194 | 90.91 206 |
|
testgi | | | 82.88 146 | 86.14 140 | 79.08 185 | 86.05 141 | 92.20 160 | 81.23 199 | 74.77 156 | 88.70 137 | 57.63 184 | 86.73 79 | 61.53 160 | 76.83 197 | 90.33 140 | 89.43 152 | 97.99 187 | 94.05 192 |
|
DU-MVS | | | 82.87 147 | 82.16 155 | 83.70 131 | 80.77 190 | 92.24 157 | 86.54 151 | 81.91 100 | 86.41 144 | 66.27 131 | 63.95 160 | 55.66 199 | 87.78 130 | 86.83 188 | 90.86 138 | 98.94 121 | 99.13 92 |
|
MIMVSNet | | | 82.87 147 | 86.17 139 | 79.02 186 | 77.23 214 | 92.88 147 | 84.88 181 | 60.62 219 | 86.72 142 | 64.16 136 | 73.58 137 | 71.48 132 | 88.51 123 | 94.14 107 | 93.50 98 | 98.72 146 | 90.87 207 |
|
NR-MVSNet | | | 82.37 149 | 81.95 157 | 82.85 138 | 82.56 160 | 92.24 157 | 87.49 138 | 81.91 100 | 86.41 144 | 65.51 133 | 63.95 160 | 52.93 211 | 80.80 180 | 89.41 154 | 89.61 148 | 98.85 128 | 99.10 98 |
|
Baseline_NR-MVSNet | | | 82.08 150 | 80.64 167 | 83.77 130 | 80.77 190 | 88.50 198 | 86.88 148 | 81.71 104 | 85.58 149 | 68.80 125 | 58.20 194 | 57.75 178 | 86.16 138 | 86.83 188 | 88.68 159 | 98.33 178 | 98.90 104 |
|
TranMVSNet+NR-MVSNet | | | 82.07 151 | 81.36 160 | 82.90 137 | 80.43 196 | 91.39 175 | 87.16 144 | 82.75 95 | 84.28 159 | 62.98 142 | 62.28 164 | 56.01 196 | 85.30 141 | 86.06 198 | 90.69 142 | 98.80 129 | 98.80 106 |
|
pm-mvs1 | | | 81.68 152 | 81.70 158 | 81.65 145 | 82.61 159 | 92.26 156 | 85.54 176 | 78.95 121 | 76.29 207 | 63.81 139 | 58.43 193 | 66.33 154 | 80.63 181 | 92.30 129 | 89.93 146 | 98.37 177 | 96.39 175 |
|
testpf | | | 81.62 153 | 87.82 133 | 74.38 207 | 85.88 142 | 89.26 196 | 74.45 212 | 48.92 232 | 95.87 79 | 60.31 163 | 76.95 121 | 80.17 107 | 80.07 183 | 85.72 202 | 88.77 158 | 96.67 205 | 98.01 140 |
|
TDRefinement | | | 81.49 154 | 80.08 176 | 83.13 136 | 91.02 101 | 94.53 135 | 91.66 109 | 82.43 96 | 81.70 172 | 62.12 146 | 62.30 163 | 59.32 167 | 73.93 206 | 87.31 170 | 85.29 205 | 97.61 196 | 90.14 208 |
|
anonymousdsp | | | 81.29 155 | 84.52 148 | 77.52 194 | 79.83 204 | 92.62 151 | 82.61 192 | 70.88 180 | 80.76 179 | 50.82 208 | 68.35 152 | 68.76 149 | 82.45 176 | 93.00 123 | 89.45 150 | 98.55 168 | 98.69 109 |
|
gg-mvs-nofinetune | | | 81.27 156 | 84.65 147 | 77.32 195 | 87.96 132 | 98.48 77 | 95.64 57 | 56.36 227 | 59.35 225 | 32.80 232 | 47.96 220 | 92.11 63 | 91.49 99 | 98.12 21 | 97.00 43 | 99.65 20 | 99.56 63 |
|
tfpnnormal | | | 81.11 157 | 79.33 189 | 83.19 135 | 84.23 150 | 92.29 155 | 86.76 149 | 82.27 98 | 72.67 213 | 62.02 148 | 56.10 204 | 53.86 209 | 85.35 140 | 92.06 132 | 89.23 154 | 98.49 173 | 99.11 97 |
|
v6 | | | 81.06 158 | 80.87 162 | 81.28 148 | 81.47 170 | 92.12 164 | 86.14 155 | 78.42 128 | 81.99 170 | 59.68 167 | 60.14 169 | 58.36 174 | 83.22 165 | 86.99 181 | 88.14 177 | 98.76 136 | 98.32 126 |
|
v1neww | | | 81.04 159 | 80.86 163 | 81.25 149 | 81.48 168 | 92.14 162 | 86.06 159 | 78.41 129 | 82.02 168 | 59.43 169 | 60.09 173 | 58.30 176 | 83.37 160 | 87.02 179 | 88.15 175 | 98.76 136 | 98.33 124 |
|
v7new | | | 81.04 159 | 80.86 163 | 81.25 149 | 81.48 168 | 92.14 162 | 86.06 159 | 78.41 129 | 82.02 168 | 59.43 169 | 60.09 173 | 58.30 176 | 83.37 160 | 87.02 179 | 88.15 175 | 98.76 136 | 98.33 124 |
|
V42 | | | 80.88 161 | 80.74 165 | 81.05 153 | 81.21 175 | 92.01 168 | 85.96 165 | 77.75 134 | 81.62 173 | 59.73 166 | 59.93 175 | 58.35 175 | 82.98 167 | 86.90 185 | 88.06 186 | 98.69 153 | 98.32 126 |
|
v2v482 | | | 80.86 162 | 80.52 171 | 81.25 149 | 80.79 189 | 91.85 169 | 85.68 174 | 78.78 126 | 81.05 176 | 58.09 181 | 60.46 166 | 56.08 194 | 85.45 139 | 87.27 171 | 88.53 161 | 98.73 145 | 98.38 120 |
|
v7 | | | 80.74 163 | 80.95 161 | 80.50 161 | 81.23 173 | 91.58 172 | 86.12 156 | 74.83 154 | 82.30 167 | 57.64 183 | 58.74 189 | 57.45 182 | 84.48 143 | 89.75 147 | 88.27 167 | 98.72 146 | 98.57 113 |
|
v1141 | | | 80.70 164 | 80.42 172 | 81.02 155 | 81.14 176 | 92.03 166 | 85.94 167 | 78.92 123 | 80.59 183 | 58.40 179 | 59.32 180 | 57.41 185 | 82.97 168 | 87.10 173 | 88.16 173 | 98.72 146 | 98.37 121 |
|
divwei89l23v2f112 | | | 80.69 165 | 80.42 172 | 81.02 155 | 81.13 177 | 92.04 165 | 85.95 166 | 78.92 123 | 80.45 185 | 58.43 177 | 59.34 179 | 57.46 181 | 82.92 169 | 87.09 174 | 88.16 173 | 98.75 141 | 98.36 123 |
|
v1 | | | 80.69 165 | 80.38 174 | 81.05 153 | 81.13 177 | 92.02 167 | 86.02 163 | 78.93 122 | 80.32 191 | 58.65 173 | 59.29 181 | 57.45 182 | 82.83 172 | 87.07 175 | 88.14 177 | 98.74 144 | 98.37 121 |
|
v8 | | | 80.61 167 | 80.61 169 | 80.62 158 | 81.51 166 | 91.00 180 | 86.06 159 | 74.07 163 | 81.78 171 | 59.93 165 | 60.10 172 | 58.42 173 | 83.35 162 | 86.99 181 | 88.11 182 | 98.79 130 | 97.83 143 |
|
pmmvs5 | | | 80.48 168 | 81.43 159 | 79.36 179 | 81.50 167 | 92.24 157 | 82.07 195 | 74.08 162 | 78.10 198 | 55.86 191 | 67.72 153 | 54.35 206 | 83.91 151 | 92.97 124 | 88.65 160 | 98.77 133 | 96.01 178 |
|
v10 | | | 80.38 169 | 80.73 166 | 79.96 169 | 81.22 174 | 90.40 190 | 86.11 157 | 71.63 175 | 82.42 166 | 57.65 182 | 58.74 189 | 57.47 180 | 84.44 144 | 89.75 147 | 88.28 166 | 98.71 150 | 98.06 139 |
|
v1144 | | | 80.36 170 | 80.63 168 | 80.05 167 | 80.86 188 | 91.56 173 | 85.78 173 | 75.22 150 | 80.73 180 | 55.83 192 | 58.51 192 | 56.99 192 | 83.93 150 | 89.79 146 | 88.25 168 | 98.68 155 | 98.56 114 |
|
SixPastTwentyTwo | | | 80.28 171 | 82.06 156 | 78.21 191 | 81.89 162 | 92.35 154 | 77.72 204 | 74.48 157 | 83.04 163 | 54.22 199 | 76.06 127 | 56.40 193 | 83.55 153 | 86.83 188 | 84.83 208 | 97.38 200 | 94.93 188 |
|
v18 | | | 80.16 172 | 80.01 180 | 80.34 164 | 81.72 163 | 85.71 205 | 86.58 150 | 70.68 181 | 83.23 160 | 60.78 159 | 60.39 167 | 58.50 172 | 83.49 154 | 87.03 177 | 88.19 171 | 98.79 130 | 97.06 160 |
|
v16 | | | 80.03 173 | 79.95 181 | 80.13 166 | 81.64 164 | 85.63 207 | 86.17 154 | 70.42 184 | 83.12 162 | 60.34 162 | 60.11 170 | 58.61 170 | 83.45 156 | 86.98 183 | 88.12 181 | 98.75 141 | 97.05 161 |
|
v17 | | | 79.95 174 | 79.87 182 | 80.05 167 | 81.55 165 | 85.65 206 | 86.10 158 | 70.44 183 | 82.59 165 | 60.02 164 | 60.26 168 | 58.53 171 | 83.41 158 | 86.98 183 | 88.09 183 | 98.76 136 | 97.02 162 |
|
CP-MVSNet | | | 79.90 175 | 79.49 186 | 80.38 163 | 80.72 192 | 90.83 182 | 82.98 189 | 75.17 151 | 79.70 193 | 61.39 152 | 59.74 176 | 51.98 214 | 83.31 163 | 87.37 169 | 88.38 164 | 98.71 150 | 98.45 117 |
|
v1192 | | | 79.84 176 | 80.05 179 | 79.61 172 | 80.49 195 | 91.04 179 | 85.56 175 | 74.37 159 | 80.73 180 | 54.35 197 | 57.07 199 | 54.54 205 | 84.23 147 | 89.94 144 | 88.38 164 | 98.63 162 | 98.61 111 |
|
WR-MVS_H | | | 79.76 177 | 80.07 177 | 79.40 177 | 81.25 172 | 91.73 171 | 82.77 190 | 74.82 155 | 79.02 197 | 62.55 143 | 59.41 178 | 57.32 188 | 76.27 198 | 87.61 168 | 87.30 197 | 98.78 132 | 98.09 137 |
|
WR-MVS | | | 79.67 178 | 80.25 175 | 79.00 187 | 80.65 193 | 91.16 177 | 83.31 187 | 76.57 143 | 80.97 177 | 60.50 161 | 59.20 182 | 58.66 169 | 74.38 204 | 85.85 200 | 87.76 192 | 98.61 163 | 98.14 132 |
|
v148 | | | 79.66 179 | 79.13 194 | 80.27 165 | 81.02 181 | 91.76 170 | 81.90 196 | 79.32 118 | 79.24 195 | 63.79 140 | 58.07 196 | 54.34 207 | 77.17 195 | 84.42 207 | 87.52 196 | 98.40 175 | 98.59 112 |
|
LTVRE_ROB | | 79.45 16 | 79.66 179 | 80.55 170 | 78.61 189 | 83.01 156 | 92.19 161 | 87.18 143 | 73.69 166 | 71.70 216 | 43.22 222 | 71.22 146 | 50.85 215 | 87.82 129 | 89.47 153 | 90.43 144 | 96.75 203 | 98.00 142 |
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 |
v144192 | | | 79.61 181 | 79.77 183 | 79.41 176 | 80.28 197 | 91.06 178 | 84.87 182 | 73.86 164 | 79.65 194 | 55.38 193 | 57.76 197 | 55.20 200 | 83.46 155 | 88.42 159 | 87.89 189 | 98.61 163 | 98.42 119 |
|
v1921920 | | | 79.55 182 | 79.77 183 | 79.30 182 | 80.24 198 | 90.77 183 | 85.37 179 | 73.75 165 | 80.38 188 | 53.78 204 | 56.89 201 | 54.18 208 | 84.05 148 | 89.55 151 | 88.13 180 | 98.59 165 | 98.52 115 |
|
v11 | | | 79.54 183 | 79.71 185 | 79.35 180 | 80.96 183 | 85.36 214 | 85.81 172 | 69.10 193 | 81.49 174 | 57.63 184 | 58.90 187 | 57.07 191 | 83.94 149 | 90.09 142 | 88.08 185 | 98.66 160 | 96.97 164 |
|
TransMVSNet (Re) | | | 79.51 184 | 78.36 199 | 80.84 157 | 83.17 154 | 89.72 193 | 84.22 185 | 81.45 107 | 73.98 211 | 60.79 158 | 57.20 198 | 56.05 195 | 77.11 196 | 89.88 145 | 88.86 157 | 98.30 180 | 92.83 197 |
|
v15 | | | 79.35 185 | 79.20 191 | 79.54 174 | 81.08 180 | 85.48 208 | 85.92 168 | 70.02 186 | 80.60 182 | 58.63 174 | 59.14 184 | 57.40 186 | 82.87 171 | 86.89 186 | 87.95 187 | 98.70 152 | 96.92 165 |
|
MVS-HIRNet | | | 79.34 186 | 82.56 151 | 75.57 202 | 84.11 151 | 95.02 128 | 75.03 211 | 57.28 225 | 85.50 151 | 55.88 190 | 53.00 214 | 70.51 145 | 83.05 166 | 92.12 130 | 91.96 127 | 98.09 184 | 89.83 210 |
|
V14 | | | 79.33 187 | 79.18 192 | 79.51 175 | 81.00 182 | 85.46 210 | 85.88 170 | 69.79 187 | 80.52 184 | 58.76 172 | 59.16 183 | 57.52 179 | 82.91 170 | 86.86 187 | 87.90 188 | 98.72 146 | 96.87 170 |
|
V9 | | | 79.23 188 | 79.09 195 | 79.39 178 | 80.95 185 | 85.40 211 | 85.85 171 | 69.63 188 | 80.42 186 | 58.45 176 | 58.94 186 | 57.42 184 | 82.77 173 | 86.79 192 | 87.85 190 | 98.69 153 | 96.83 171 |
|
v12 | | | 79.16 189 | 79.04 196 | 79.30 182 | 80.88 186 | 85.37 213 | 85.45 178 | 69.52 189 | 80.39 187 | 58.57 175 | 58.90 187 | 57.17 190 | 82.68 175 | 86.76 193 | 87.82 191 | 98.68 155 | 96.88 169 |
|
v13 | | | 79.09 190 | 78.98 197 | 79.22 184 | 80.88 186 | 85.34 215 | 85.50 177 | 69.40 191 | 80.36 189 | 58.14 180 | 58.62 191 | 57.30 189 | 82.70 174 | 86.72 194 | 87.75 193 | 98.67 159 | 96.76 172 |
|
PS-CasMVS | | | 79.06 191 | 78.58 198 | 79.63 171 | 80.59 194 | 90.55 187 | 82.54 193 | 75.04 152 | 77.76 199 | 58.84 171 | 58.16 195 | 50.11 219 | 82.09 177 | 87.05 176 | 88.18 172 | 98.66 160 | 98.27 129 |
|
v1240 | | | 78.97 192 | 79.27 190 | 78.63 188 | 80.04 199 | 90.61 185 | 84.25 184 | 72.95 170 | 79.22 196 | 52.70 206 | 56.22 203 | 52.88 213 | 83.28 164 | 89.60 150 | 88.20 170 | 98.56 167 | 98.14 132 |
|
MDTV_nov1_ep13_2view | | | 78.83 193 | 82.35 152 | 74.73 206 | 78.65 207 | 91.51 174 | 79.18 201 | 62.52 210 | 84.51 157 | 52.51 207 | 67.49 154 | 67.29 152 | 78.90 188 | 85.52 203 | 86.34 201 | 96.62 206 | 93.76 193 |
|
PEN-MVS | | | 78.80 194 | 78.13 201 | 79.58 173 | 80.03 200 | 89.67 194 | 83.61 186 | 75.83 146 | 77.71 201 | 58.41 178 | 60.11 170 | 50.00 220 | 81.02 179 | 84.08 208 | 88.14 177 | 98.59 165 | 97.18 156 |
|
EG-PatchMatch MVS | | | 78.32 195 | 79.42 188 | 77.03 199 | 83.03 155 | 93.77 140 | 84.47 183 | 69.26 192 | 75.85 208 | 53.69 205 | 55.68 207 | 60.23 164 | 73.20 207 | 89.69 149 | 88.22 169 | 98.55 168 | 92.54 198 |
|
DTE-MVSNet | | | 77.92 196 | 77.42 205 | 78.51 190 | 79.34 205 | 89.00 197 | 83.05 188 | 75.60 147 | 76.89 203 | 56.58 187 | 59.63 177 | 50.31 217 | 78.09 193 | 82.57 215 | 87.56 195 | 98.38 176 | 95.95 179 |
|
v7n | | | 77.71 197 | 78.25 200 | 77.09 198 | 78.49 208 | 90.55 187 | 82.15 194 | 71.11 179 | 76.79 204 | 54.18 200 | 55.63 208 | 50.20 218 | 78.28 191 | 89.36 156 | 87.15 198 | 98.33 178 | 98.07 138 |
|
v52 | | | 77.69 198 | 78.04 202 | 77.29 196 | 77.79 213 | 90.54 189 | 81.76 197 | 71.62 177 | 76.52 205 | 55.34 195 | 55.70 206 | 55.91 197 | 79.27 186 | 84.02 209 | 86.03 202 | 97.96 189 | 97.56 148 |
|
V4 | | | 77.67 199 | 78.01 203 | 77.28 197 | 77.82 212 | 90.56 186 | 81.70 198 | 71.63 175 | 76.33 206 | 55.38 193 | 55.74 205 | 55.83 198 | 79.20 187 | 84.02 209 | 86.01 203 | 97.97 188 | 97.55 149 |
|
gm-plane-assit | | | 77.20 200 | 82.26 153 | 71.30 210 | 81.10 179 | 82.00 220 | 54.33 227 | 64.41 205 | 63.80 224 | 40.93 225 | 59.04 185 | 76.57 118 | 87.30 132 | 98.26 18 | 97.36 33 | 99.74 11 | 98.76 107 |
|
LP | | | 77.20 200 | 79.14 193 | 74.92 205 | 86.71 138 | 90.62 184 | 77.97 203 | 57.87 224 | 85.88 147 | 50.75 209 | 55.29 209 | 66.34 153 | 79.39 185 | 80.75 216 | 85.03 206 | 96.86 202 | 90.09 209 |
|
N_pmnet | | | 76.83 202 | 77.97 204 | 75.50 203 | 80.96 183 | 88.23 200 | 72.81 213 | 76.83 142 | 80.87 178 | 50.55 210 | 56.94 200 | 60.09 165 | 75.70 200 | 83.28 213 | 84.23 210 | 96.14 210 | 92.12 199 |
|
pmmvs6 | | | 76.79 203 | 75.69 211 | 78.09 193 | 79.95 202 | 89.57 195 | 80.92 200 | 74.46 158 | 64.79 222 | 60.74 160 | 45.71 223 | 60.55 162 | 78.37 189 | 88.04 163 | 86.00 204 | 94.07 216 | 95.15 186 |
|
CMPMVS | | 58.73 17 | 76.78 204 | 74.27 212 | 79.70 170 | 93.26 69 | 95.58 123 | 82.74 191 | 77.44 137 | 71.46 219 | 56.29 189 | 53.58 213 | 59.13 168 | 77.33 194 | 79.20 217 | 79.71 218 | 91.14 223 | 81.24 221 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
EU-MVSNet | | | 76.76 205 | 79.47 187 | 73.60 208 | 79.99 201 | 87.47 201 | 77.39 205 | 75.43 149 | 77.62 202 | 47.83 214 | 64.78 158 | 60.44 163 | 64.80 213 | 86.28 196 | 86.53 200 | 96.17 209 | 93.19 196 |
|
v748 | | | 76.68 206 | 76.82 208 | 76.51 200 | 78.70 206 | 90.06 192 | 77.12 206 | 73.40 168 | 73.32 212 | 59.57 168 | 55.00 211 | 50.71 216 | 72.48 208 | 83.71 212 | 86.78 199 | 97.81 195 | 98.13 135 |
|
PM-MVS | | | 75.81 207 | 76.11 210 | 75.46 204 | 73.81 215 | 85.48 208 | 76.42 208 | 70.57 182 | 80.05 192 | 54.75 196 | 62.33 162 | 39.56 227 | 80.59 182 | 87.71 166 | 82.81 214 | 96.61 208 | 94.81 189 |
|
pmmvs-eth3d | | | 75.17 208 | 74.09 213 | 76.43 201 | 72.92 218 | 84.49 216 | 76.61 207 | 72.42 172 | 74.33 209 | 61.28 154 | 54.71 212 | 39.42 228 | 78.20 192 | 87.77 165 | 84.25 209 | 97.17 201 | 93.63 194 |
|
Anonymous20231206 | | | 74.59 209 | 77.00 207 | 71.78 209 | 77.89 211 | 87.45 202 | 75.14 210 | 72.29 174 | 77.76 199 | 46.65 216 | 52.14 215 | 52.93 211 | 61.10 218 | 89.37 155 | 88.09 183 | 97.59 197 | 91.30 204 |
|
test2356 | | | 74.04 210 | 80.07 177 | 67.01 218 | 73.77 216 | 80.65 221 | 67.82 220 | 66.87 200 | 84.93 156 | 37.70 229 | 75.45 131 | 57.40 186 | 60.26 219 | 86.28 196 | 88.47 163 | 95.64 212 | 87.33 215 |
|
test20.03 | | | 72.81 211 | 76.24 209 | 68.80 213 | 78.31 209 | 85.40 211 | 71.04 215 | 71.20 178 | 71.85 215 | 43.40 221 | 65.31 157 | 54.71 204 | 51.27 224 | 85.92 199 | 84.18 211 | 97.58 198 | 86.35 217 |
|
testus | | | 72.50 212 | 77.19 206 | 67.04 216 | 73.69 217 | 80.06 222 | 67.65 221 | 68.24 198 | 84.46 158 | 37.48 231 | 75.90 129 | 40.07 226 | 59.40 220 | 85.45 204 | 87.69 194 | 95.76 211 | 86.70 216 |
|
new_pmnet | | | 71.86 213 | 73.67 214 | 69.75 212 | 72.56 220 | 84.20 217 | 70.95 217 | 66.81 201 | 80.34 190 | 43.62 220 | 51.60 216 | 53.81 210 | 71.24 210 | 82.91 214 | 80.93 215 | 93.35 218 | 81.92 220 |
|
MDA-MVSNet-bldmvs | | | 69.61 214 | 70.36 216 | 68.74 214 | 62.88 229 | 88.50 198 | 65.40 224 | 77.01 140 | 71.60 218 | 43.93 217 | 66.71 155 | 35.33 230 | 72.47 209 | 61.01 229 | 80.63 216 | 90.73 224 | 88.75 213 |
|
pmmvs3 | | | 69.04 215 | 70.75 215 | 67.04 216 | 66.83 224 | 78.54 223 | 64.99 225 | 60.92 218 | 64.67 223 | 40.61 226 | 55.08 210 | 40.29 225 | 74.89 203 | 83.76 211 | 84.01 212 | 93.98 217 | 88.88 212 |
|
MIMVSNet1 | | | 68.63 216 | 70.24 217 | 66.76 219 | 56.86 232 | 83.26 218 | 67.93 219 | 70.26 185 | 68.05 220 | 46.80 215 | 40.44 225 | 48.15 221 | 62.01 216 | 84.96 206 | 84.86 207 | 96.69 204 | 81.93 219 |
|
GG-mvs-BLEND | | | 67.99 217 | 97.35 34 | 33.72 233 | 1.22 239 | 99.72 13 | 98.30 29 | 0.57 238 | 97.61 54 | 1.18 242 | 93.26 46 | 96.63 37 | 1.74 238 | 97.15 45 | 97.14 36 | 99.34 90 | 99.96 6 |
|
new-patchmatchnet | | | 67.66 218 | 68.07 218 | 67.18 215 | 72.85 219 | 82.86 219 | 63.09 226 | 68.61 196 | 66.60 221 | 42.64 224 | 49.28 218 | 38.68 229 | 61.21 217 | 75.84 220 | 75.22 224 | 94.67 215 | 88.00 214 |
|
Anonymous20231211 | | | 63.52 219 | 62.24 223 | 65.02 221 | 68.68 221 | 78.21 224 | 65.79 223 | 68.17 199 | 49.86 232 | 42.89 223 | 29.67 232 | 34.65 231 | 55.41 222 | 75.07 221 | 76.98 222 | 89.18 226 | 91.26 205 |
|
FPMVS | | | 63.27 220 | 61.31 224 | 65.57 220 | 78.25 210 | 74.42 227 | 75.23 209 | 68.92 195 | 72.33 214 | 43.87 218 | 49.01 219 | 43.94 223 | 48.64 226 | 61.15 228 | 58.81 230 | 78.51 232 | 69.49 230 |
|
1111 | | | 61.69 221 | 63.75 220 | 59.29 222 | 64.35 225 | 70.45 228 | 48.44 231 | 48.86 233 | 55.76 226 | 39.40 227 | 39.25 226 | 54.73 202 | 62.55 214 | 77.84 218 | 80.37 217 | 92.16 219 | 67.84 231 |
|
testmv | | | 60.16 222 | 62.42 221 | 57.53 223 | 67.85 222 | 69.87 230 | 48.47 229 | 62.44 211 | 54.75 228 | 29.08 233 | 46.99 221 | 31.77 232 | 45.97 227 | 74.85 222 | 79.08 220 | 91.39 221 | 79.62 223 |
|
test1235678 | | | 60.16 222 | 62.41 222 | 57.53 223 | 67.85 222 | 69.86 231 | 48.47 229 | 62.43 212 | 54.73 229 | 29.08 233 | 46.99 221 | 31.76 233 | 45.97 227 | 74.84 223 | 79.07 221 | 91.39 221 | 79.61 224 |
|
test12356 | | | 57.24 224 | 59.66 225 | 54.43 226 | 64.26 227 | 66.14 232 | 49.96 228 | 61.73 215 | 54.37 230 | 28.80 235 | 44.89 224 | 25.68 235 | 32.36 232 | 70.23 226 | 79.19 219 | 89.46 225 | 77.11 225 |
|
Gipuma | | | 54.59 225 | 53.98 227 | 55.30 225 | 59.03 231 | 52.63 235 | 47.17 234 | 56.08 228 | 71.68 217 | 37.54 230 | 20.90 234 | 19.00 236 | 52.33 223 | 71.69 225 | 75.20 225 | 79.64 231 | 66.79 232 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
PMVS | | 49.05 18 | 51.88 226 | 50.56 229 | 53.42 227 | 64.21 228 | 43.30 237 | 42.64 235 | 62.93 207 | 50.56 231 | 43.72 219 | 37.44 228 | 42.95 224 | 35.05 231 | 58.76 231 | 54.58 231 | 71.95 234 | 66.33 233 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
.test1245 | | | 51.60 227 | 57.21 226 | 45.06 229 | 64.35 225 | 70.45 228 | 48.44 231 | 48.86 233 | 55.76 226 | 39.40 227 | 39.25 226 | 54.73 202 | 62.55 214 | 77.84 218 | 27.11 234 | 6.75 238 | 75.30 228 |
|
PMMVS2 | | | 50.69 228 | 52.33 228 | 48.78 228 | 51.24 234 | 64.81 233 | 47.91 233 | 53.79 231 | 44.95 233 | 21.75 236 | 29.98 231 | 25.90 234 | 31.98 234 | 59.95 230 | 65.37 227 | 86.00 229 | 75.36 227 |
|
no-one | | | 41.64 229 | 41.19 230 | 42.16 230 | 52.35 233 | 58.34 234 | 27.46 237 | 57.21 226 | 28.41 238 | 21.09 237 | 19.65 235 | 17.04 237 | 21.39 237 | 39.74 233 | 61.20 229 | 73.45 233 | 63.95 235 |
|
E-PMN | | | 37.15 230 | 34.82 232 | 39.86 231 | 47.53 236 | 35.42 239 | 23.79 238 | 55.26 229 | 35.18 236 | 14.12 239 | 17.38 238 | 14.13 239 | 39.73 230 | 32.24 234 | 46.98 232 | 58.76 235 | 62.39 236 |
|
EMVS | | | 36.45 231 | 33.63 233 | 39.74 232 | 48.47 235 | 35.73 238 | 23.59 239 | 55.11 230 | 35.61 235 | 12.88 240 | 17.49 236 | 14.62 238 | 41.04 229 | 29.33 235 | 43.00 233 | 57.32 236 | 59.62 237 |
|
MVE | | 42.40 19 | 36.00 232 | 38.65 231 | 32.92 234 | 29.16 237 | 46.17 236 | 22.61 240 | 44.21 235 | 26.44 239 | 18.88 238 | 17.41 237 | 9.36 241 | 32.29 233 | 45.75 232 | 61.38 228 | 50.35 237 | 64.03 234 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
testmvs | | | 21.55 233 | 30.91 234 | 10.62 235 | 2.78 238 | 11.66 240 | 18.51 241 | 4.82 236 | 38.21 234 | 4.06 241 | 36.35 229 | 4.47 242 | 26.81 235 | 23.27 236 | 27.11 234 | 6.75 238 | 75.30 228 |
|
test123 | | | 16.81 234 | 24.80 235 | 7.48 236 | 0.82 240 | 8.38 241 | 11.92 242 | 2.60 237 | 28.96 237 | 1.12 243 | 28.39 233 | 1.26 243 | 24.51 236 | 8.93 237 | 22.19 236 | 3.90 240 | 75.49 226 |
|
sosnet-low-res | | | 0.00 235 | 0.00 236 | 0.00 237 | 0.00 241 | 0.00 242 | 0.00 243 | 0.00 239 | 0.00 240 | 0.00 244 | 0.00 239 | 0.00 244 | 0.00 239 | 0.00 238 | 0.00 237 | 0.00 241 | 0.00 238 |
|
sosnet | | | 0.00 235 | 0.00 236 | 0.00 237 | 0.00 241 | 0.00 242 | 0.00 243 | 0.00 239 | 0.00 240 | 0.00 244 | 0.00 239 | 0.00 244 | 0.00 239 | 0.00 238 | 0.00 237 | 0.00 241 | 0.00 238 |
|
ambc | | | | 64.61 219 | | 61.80 230 | 75.31 226 | 71.00 216 | | 74.16 210 | 48.83 212 | 36.02 230 | 13.22 240 | 58.66 221 | 85.80 201 | 76.26 223 | 88.01 227 | 91.53 203 |
|
MTAPA | | | | | | | | | | | 94.58 9 | | 98.56 18 | | | | | |
|
MTMP | | | | | | | | | | | 95.24 4 | | 98.13 24 | | | | | |
|
Patchmatch-RL test | | | | | | | | 37.05 236 | | | | | | | | | | |
|
tmp_tt | | | | | 71.24 211 | 90.29 114 | 76.39 225 | 65.81 222 | 59.43 223 | 97.62 52 | 79.65 94 | 90.60 60 | 68.71 150 | 49.71 225 | 72.71 224 | 65.70 226 | 82.54 230 | |
|
XVS | | | | | | 93.63 64 | 99.64 20 | 94.32 76 | | | 83.97 59 | | 98.08 26 | | | | 99.59 30 | |
|
X-MVStestdata | | | | | | 93.63 64 | 99.64 20 | 94.32 76 | | | 83.97 59 | | 98.08 26 | | | | 99.59 30 | |
|
abl_6 | | | | | 95.40 31 | 98.18 34 | 99.45 38 | 97.39 43 | 89.27 44 | 99.48 3 | 90.52 24 | 94.52 41 | 98.63 17 | 97.32 30 | | | 99.73 12 | 99.82 34 |
|
mPP-MVS | | | | | | 98.66 24 | | | | | | | 97.11 34 | | | | | |
|
NP-MVS | | | | | | | | | | 97.69 50 | | | | | | | | |
|
Patchmtry | | | | | | | 95.86 117 | 89.43 128 | 61.37 216 | | 60.81 155 | | | | | | | |
|
DeepMVS_CX | | | | | | | 85.88 204 | 69.83 218 | 81.56 105 | 87.99 138 | 48.22 213 | 71.85 143 | 45.52 222 | 68.67 211 | 63.21 227 | | 86.64 228 | 80.03 222 |
|