HPM-MVS++ | | | 87.09 5 | 88.92 9 | 84.95 4 | 92.61 1 | 87.91 36 | 90.23 11 | 76.06 3 | 88.85 9 | 81.20 5 | 87.33 10 | 87.93 9 | 79.47 7 | 88.59 7 | 88.23 5 | 90.15 30 | 93.60 16 |
|
v1.0 | | | 81.11 37 | 77.43 64 | 85.41 2 | 91.73 2 | 92.08 2 | 91.91 3 | 76.73 1 | 90.14 5 | 80.33 10 | 92.75 2 | 90.44 2 | 80.73 4 | 88.97 5 | 87.63 9 | 91.01 6 | 0.00 243 |
|
SMA-MVS | | | 87.56 3 | 90.17 4 | 84.52 5 | 91.71 3 | 90.57 6 | 90.77 5 | 75.19 10 | 90.67 3 | 80.50 9 | 86.59 14 | 88.86 5 | 78.09 13 | 89.92 1 | 89.41 1 | 90.84 8 | 95.19 2 |
|
NCCC | | | 85.34 16 | 86.59 21 | 83.88 13 | 91.48 4 | 88.88 22 | 89.79 13 | 75.54 8 | 86.67 18 | 77.94 20 | 76.55 32 | 84.99 21 | 78.07 14 | 88.04 10 | 87.68 8 | 90.46 22 | 93.31 17 |
|
CNVR-MVS | | | 86.36 10 | 88.19 13 | 84.23 8 | 91.33 5 | 89.84 11 | 90.34 8 | 75.56 7 | 87.36 15 | 78.97 14 | 81.19 25 | 86.76 13 | 78.74 8 | 89.30 3 | 88.58 2 | 90.45 23 | 94.33 6 |
|
APDe-MVS | | | 88.00 2 | 90.50 2 | 85.08 3 | 90.95 6 | 91.58 5 | 92.03 1 | 75.53 9 | 91.15 1 | 80.10 11 | 92.27 4 | 88.34 8 | 80.80 3 | 88.00 12 | 86.99 16 | 91.09 4 | 95.16 3 |
|
ESAPD | | | 88.63 1 | 91.29 1 | 85.53 1 | 90.87 7 | 92.20 1 | 91.98 2 | 76.00 4 | 90.55 4 | 82.09 4 | 93.85 1 | 90.75 1 | 81.25 1 | 88.62 6 | 87.59 11 | 90.96 7 | 95.48 1 |
|
HFP-MVS | | | 86.15 11 | 87.95 14 | 84.06 11 | 90.80 8 | 89.20 20 | 89.62 16 | 74.26 13 | 87.52 12 | 80.63 7 | 86.82 13 | 84.19 25 | 78.22 11 | 87.58 16 | 87.19 14 | 90.81 9 | 93.13 20 |
|
SteuartSystems-ACMMP | | | 85.99 12 | 88.31 12 | 83.27 18 | 90.73 9 | 89.84 11 | 90.27 10 | 74.31 12 | 84.56 27 | 75.88 26 | 87.32 11 | 85.04 20 | 77.31 21 | 89.01 4 | 88.46 3 | 91.14 3 | 93.96 8 |
Skip Steuart: Steuart Systems R&D Blog. |
APD-MVS | | | 86.84 8 | 88.91 10 | 84.41 6 | 90.66 10 | 90.10 9 | 90.78 4 | 75.64 6 | 87.38 14 | 78.72 15 | 90.68 7 | 86.82 12 | 80.15 5 | 87.13 22 | 86.45 25 | 90.51 17 | 93.83 10 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
MP-MVS | | | 85.50 15 | 87.40 17 | 83.28 17 | 90.65 11 | 89.51 16 | 89.16 20 | 74.11 16 | 83.70 30 | 78.06 19 | 85.54 17 | 84.89 23 | 77.31 21 | 87.40 19 | 87.14 15 | 90.41 24 | 93.65 15 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
train_agg | | | 84.86 21 | 87.21 18 | 82.11 24 | 90.59 12 | 85.47 53 | 89.81 12 | 73.55 22 | 83.95 29 | 73.30 34 | 89.84 9 | 87.23 11 | 75.61 29 | 86.47 31 | 85.46 35 | 89.78 33 | 92.06 28 |
|
MCST-MVS | | | 85.13 19 | 86.62 20 | 83.39 15 | 90.55 13 | 89.82 13 | 89.29 18 | 73.89 20 | 84.38 28 | 76.03 25 | 79.01 28 | 85.90 17 | 78.47 9 | 87.81 14 | 86.11 30 | 92.11 1 | 93.29 18 |
|
zzz-MVS | | | 85.71 13 | 86.88 19 | 84.34 7 | 90.54 14 | 87.11 40 | 89.77 14 | 74.17 15 | 88.54 10 | 83.08 2 | 78.60 29 | 86.10 15 | 78.11 12 | 87.80 15 | 87.46 12 | 90.35 26 | 92.56 22 |
|
DeepC-MVS_fast | | 78.24 3 | 84.27 25 | 85.50 27 | 82.85 20 | 90.46 15 | 89.24 18 | 87.83 29 | 74.24 14 | 84.88 23 | 76.23 24 | 75.26 35 | 81.05 38 | 77.62 18 | 88.02 11 | 87.62 10 | 90.69 13 | 92.41 24 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
ACMMP_Plus | | | 86.52 9 | 89.01 7 | 83.62 14 | 90.28 16 | 90.09 10 | 90.32 9 | 74.05 17 | 88.32 11 | 79.74 12 | 87.04 12 | 85.59 19 | 76.97 26 | 89.35 2 | 88.44 4 | 90.35 26 | 94.27 7 |
|
SD-MVS | | | 86.96 6 | 89.45 5 | 84.05 12 | 90.13 17 | 89.23 19 | 89.77 14 | 74.59 11 | 89.17 7 | 80.70 6 | 89.93 8 | 89.67 3 | 78.47 9 | 87.57 17 | 86.79 19 | 90.67 14 | 93.76 12 |
|
ACMMPR | | | 85.52 14 | 87.53 16 | 83.17 19 | 90.13 17 | 89.27 17 | 89.30 17 | 73.97 18 | 86.89 17 | 77.14 22 | 86.09 15 | 83.18 28 | 77.74 17 | 87.42 18 | 87.20 13 | 90.77 10 | 92.63 21 |
|
PGM-MVS | | | 84.42 24 | 86.29 24 | 82.23 23 | 90.04 19 | 88.82 24 | 89.23 19 | 71.74 31 | 82.82 33 | 74.61 29 | 84.41 20 | 82.09 30 | 77.03 25 | 87.13 22 | 86.73 21 | 90.73 12 | 92.06 28 |
|
HSP-MVS | | | 87.45 4 | 90.22 3 | 84.22 9 | 90.00 20 | 91.80 4 | 90.59 6 | 75.80 5 | 89.93 6 | 78.35 17 | 92.54 3 | 89.18 4 | 80.89 2 | 87.99 13 | 86.29 27 | 89.70 37 | 93.85 9 |
|
CSCG | | | 85.28 18 | 87.68 15 | 82.49 22 | 89.95 21 | 91.99 3 | 88.82 21 | 71.20 33 | 86.41 19 | 79.63 13 | 79.26 26 | 88.36 7 | 73.94 36 | 86.64 29 | 86.67 22 | 91.40 2 | 94.41 4 |
|
mPP-MVS | | | | | | 89.90 22 | | | | | | | 81.29 37 | | | | | |
|
TSAR-MVS + MP. | | | 86.88 7 | 89.23 6 | 84.14 10 | 89.78 23 | 88.67 28 | 90.59 6 | 73.46 23 | 88.99 8 | 80.52 8 | 91.26 5 | 88.65 6 | 79.91 6 | 86.96 27 | 86.22 28 | 90.59 15 | 93.83 10 |
|
X-MVS | | | 83.23 28 | 85.20 29 | 80.92 30 | 89.71 24 | 88.68 25 | 88.21 28 | 73.60 21 | 82.57 34 | 71.81 43 | 77.07 30 | 81.92 32 | 71.72 53 | 86.98 26 | 86.86 17 | 90.47 19 | 92.36 25 |
|
TSAR-MVS + ACMM | | | 85.10 20 | 88.81 11 | 80.77 31 | 89.55 25 | 88.53 30 | 88.59 24 | 72.55 26 | 87.39 13 | 71.90 40 | 90.95 6 | 87.55 10 | 74.57 31 | 87.08 24 | 86.54 23 | 87.47 75 | 93.67 13 |
|
CP-MVS | | | 84.74 23 | 86.43 23 | 82.77 21 | 89.48 26 | 88.13 35 | 88.64 22 | 73.93 19 | 84.92 22 | 76.77 23 | 81.94 23 | 83.50 26 | 77.29 23 | 86.92 28 | 86.49 24 | 90.49 18 | 93.14 19 |
|
CDPH-MVS | | | 82.64 29 | 85.03 30 | 79.86 35 | 89.41 27 | 88.31 32 | 88.32 26 | 71.84 30 | 80.11 41 | 67.47 59 | 82.09 22 | 81.44 36 | 71.85 51 | 85.89 36 | 86.15 29 | 90.24 28 | 91.25 34 |
|
DeepC-MVS | | 78.47 2 | 84.81 22 | 86.03 25 | 83.37 16 | 89.29 28 | 90.38 8 | 88.61 23 | 76.50 2 | 86.25 20 | 77.22 21 | 75.12 36 | 80.28 40 | 77.59 19 | 88.39 8 | 88.17 6 | 91.02 5 | 93.66 14 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
AdaColmap | | | 79.74 43 | 78.62 56 | 81.05 29 | 89.23 29 | 86.06 50 | 84.95 46 | 71.96 29 | 79.39 44 | 75.51 27 | 63.16 79 | 68.84 86 | 76.51 27 | 83.55 51 | 82.85 50 | 88.13 62 | 86.46 68 |
|
EPNet | | | 79.08 51 | 80.62 45 | 77.28 50 | 88.90 30 | 83.17 70 | 83.65 52 | 72.41 27 | 74.41 56 | 67.15 61 | 76.78 31 | 74.37 57 | 64.43 105 | 83.70 50 | 83.69 46 | 87.15 81 | 88.19 53 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
DeepPCF-MVS | | 79.04 1 | 85.30 17 | 88.93 8 | 81.06 28 | 88.77 31 | 90.48 7 | 85.46 43 | 73.08 24 | 90.97 2 | 73.77 33 | 84.81 19 | 85.95 16 | 77.43 20 | 88.22 9 | 87.73 7 | 87.85 70 | 94.34 5 |
|
ACMMP | | | 83.42 27 | 85.27 28 | 81.26 27 | 88.47 32 | 88.49 31 | 88.31 27 | 72.09 28 | 83.42 31 | 72.77 37 | 82.65 21 | 78.22 44 | 75.18 30 | 86.24 34 | 85.76 32 | 90.74 11 | 92.13 27 |
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+ | | 75.73 4 | 82.40 30 | 82.76 35 | 81.97 25 | 88.02 33 | 89.67 14 | 86.60 33 | 71.48 32 | 81.28 39 | 78.18 18 | 64.78 75 | 77.96 46 | 77.13 24 | 87.32 20 | 86.83 18 | 90.41 24 | 91.48 32 |
|
OPM-MVS | | | 79.68 44 | 79.28 53 | 80.15 34 | 87.99 34 | 86.77 44 | 88.52 25 | 72.72 25 | 64.55 87 | 67.65 58 | 67.87 65 | 74.33 58 | 74.31 34 | 86.37 33 | 85.25 37 | 89.73 36 | 89.81 45 |
|
MAR-MVS | | | 79.21 48 | 80.32 49 | 77.92 47 | 87.46 35 | 88.15 34 | 83.95 51 | 67.48 57 | 74.28 57 | 68.25 55 | 64.70 76 | 77.04 47 | 72.17 47 | 85.42 38 | 85.00 39 | 88.22 59 | 87.62 60 |
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 |
HQP-MVS | | | 81.19 36 | 83.27 33 | 78.76 42 | 87.40 36 | 85.45 54 | 86.95 31 | 70.47 36 | 81.31 38 | 66.91 62 | 79.24 27 | 76.63 49 | 71.67 54 | 84.43 44 | 83.78 45 | 89.19 46 | 92.05 30 |
|
abl_6 | | | | | 79.05 39 | 87.27 37 | 88.85 23 | 83.62 53 | 68.25 50 | 81.68 37 | 72.94 36 | 73.79 42 | 84.45 24 | 72.55 45 | | | 89.66 39 | 90.64 39 |
|
CANet | | | 81.62 35 | 83.41 32 | 79.53 37 | 87.06 38 | 88.59 29 | 85.47 42 | 67.96 54 | 76.59 50 | 74.05 30 | 74.69 37 | 81.98 31 | 72.98 43 | 86.14 35 | 85.47 34 | 89.68 38 | 90.42 42 |
|
MSLP-MVS++ | | | 82.09 32 | 82.66 36 | 81.42 26 | 87.03 39 | 87.22 39 | 85.82 38 | 70.04 38 | 80.30 40 | 78.66 16 | 68.67 61 | 81.04 39 | 77.81 16 | 85.19 40 | 84.88 40 | 89.19 46 | 91.31 33 |
|
ACMM | | 72.26 8 | 78.86 52 | 78.13 57 | 79.71 36 | 86.89 40 | 83.40 67 | 86.02 36 | 70.50 35 | 75.28 53 | 71.49 47 | 63.01 80 | 69.26 80 | 73.57 38 | 84.11 46 | 83.98 44 | 89.76 35 | 87.84 57 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
XVS | | | | | | 86.63 41 | 88.68 25 | 85.00 44 | | | 71.81 43 | | 81.92 32 | | | | 90.47 19 | |
|
X-MVStestdata | | | | | | 86.63 41 | 88.68 25 | 85.00 44 | | | 71.81 43 | | 81.92 32 | | | | 90.47 19 | |
|
PHI-MVS | | | 82.36 31 | 85.89 26 | 78.24 45 | 86.40 43 | 89.52 15 | 85.52 41 | 69.52 44 | 82.38 36 | 65.67 64 | 81.35 24 | 82.36 29 | 73.07 42 | 87.31 21 | 86.76 20 | 89.24 44 | 91.56 31 |
|
LGP-MVS_train | | | 79.83 40 | 81.22 42 | 78.22 46 | 86.28 44 | 85.36 56 | 86.76 32 | 69.59 42 | 77.34 47 | 65.14 66 | 75.68 34 | 70.79 70 | 71.37 56 | 84.60 42 | 84.01 43 | 90.18 29 | 90.74 38 |
|
MVS_0304 | | | 81.73 34 | 83.86 31 | 79.26 38 | 86.22 45 | 89.18 21 | 86.41 34 | 67.15 58 | 75.28 53 | 70.75 50 | 74.59 38 | 83.49 27 | 74.42 33 | 87.05 25 | 86.34 26 | 90.58 16 | 91.08 36 |
|
CPTT-MVS | | | 81.77 33 | 83.10 34 | 80.21 33 | 85.93 46 | 86.45 47 | 87.72 30 | 70.98 34 | 82.54 35 | 71.53 46 | 74.23 41 | 81.49 35 | 76.31 28 | 82.85 58 | 81.87 54 | 88.79 53 | 92.26 26 |
|
MVS_111021_HR | | | 80.13 39 | 81.46 40 | 78.58 43 | 85.77 47 | 85.17 57 | 83.45 54 | 69.28 45 | 74.08 59 | 70.31 51 | 74.31 40 | 75.26 54 | 73.13 41 | 86.46 32 | 85.15 38 | 89.53 40 | 89.81 45 |
|
ACMP | | 73.23 7 | 79.79 41 | 80.53 46 | 78.94 40 | 85.61 48 | 85.68 51 | 85.61 40 | 69.59 42 | 77.33 48 | 71.00 49 | 74.45 39 | 69.16 81 | 71.88 49 | 83.15 55 | 83.37 48 | 89.92 32 | 90.57 41 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
UA-Net | | | 74.47 68 | 77.80 59 | 70.59 90 | 85.33 49 | 85.40 55 | 73.54 146 | 65.98 66 | 60.65 113 | 56.00 113 | 72.11 46 | 79.15 41 | 54.63 172 | 83.13 56 | 82.25 52 | 88.04 64 | 81.92 128 |
|
TSAR-MVS + GP. | | | 83.69 26 | 86.58 22 | 80.32 32 | 85.14 50 | 86.96 42 | 84.91 47 | 70.25 37 | 84.71 26 | 73.91 32 | 85.16 18 | 85.63 18 | 77.92 15 | 85.44 37 | 85.71 33 | 89.77 34 | 92.45 23 |
|
LS3D | | | 74.08 70 | 73.39 80 | 74.88 62 | 85.05 51 | 82.62 74 | 79.71 67 | 68.66 48 | 72.82 61 | 58.80 87 | 57.61 106 | 61.31 106 | 71.07 58 | 80.32 93 | 78.87 96 | 86.00 136 | 80.18 144 |
|
QAPM | | | 78.47 53 | 80.22 50 | 76.43 55 | 85.03 52 | 86.75 45 | 80.62 62 | 66.00 65 | 73.77 60 | 65.35 65 | 65.54 72 | 78.02 45 | 72.69 44 | 83.71 49 | 83.36 49 | 88.87 52 | 90.41 43 |
|
OpenMVS | | 70.44 10 | 76.15 62 | 76.82 70 | 75.37 59 | 85.01 53 | 84.79 59 | 78.99 76 | 62.07 113 | 71.27 63 | 67.88 57 | 57.91 105 | 72.36 64 | 70.15 60 | 82.23 61 | 81.41 58 | 88.12 63 | 87.78 58 |
|
CLD-MVS | | | 79.35 47 | 81.23 41 | 77.16 51 | 85.01 53 | 86.92 43 | 85.87 37 | 60.89 127 | 80.07 43 | 75.35 28 | 72.96 44 | 73.21 61 | 68.43 69 | 85.41 39 | 84.63 41 | 87.41 76 | 85.44 80 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
3Dnovator | | 73.76 5 | 79.75 42 | 80.52 47 | 78.84 41 | 84.94 55 | 87.35 37 | 84.43 50 | 65.54 68 | 78.29 46 | 73.97 31 | 63.00 81 | 75.62 53 | 74.07 35 | 85.00 41 | 85.34 36 | 90.11 31 | 89.04 49 |
|
PCF-MVS | | 73.28 6 | 79.42 46 | 80.41 48 | 78.26 44 | 84.88 56 | 88.17 33 | 86.08 35 | 69.85 39 | 75.23 55 | 68.43 54 | 68.03 64 | 78.38 43 | 71.76 52 | 81.26 73 | 80.65 74 | 88.56 58 | 91.18 35 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
casdiffmvs1 | | | 79.56 45 | 81.02 44 | 77.86 48 | 84.19 57 | 87.00 41 | 85.73 39 | 63.24 84 | 79.22 45 | 72.05 39 | 73.55 43 | 76.93 48 | 73.25 40 | 80.92 79 | 80.20 80 | 88.69 55 | 89.31 48 |
|
DELS-MVS | | | 79.15 50 | 81.07 43 | 76.91 53 | 83.54 58 | 87.31 38 | 84.45 48 | 64.92 73 | 69.98 64 | 69.34 53 | 71.62 49 | 76.26 50 | 69.84 61 | 86.57 30 | 85.90 31 | 89.39 42 | 89.88 44 |
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023 |
OMC-MVS | | | 80.26 38 | 82.59 37 | 77.54 49 | 83.04 59 | 85.54 52 | 83.25 55 | 65.05 72 | 87.32 16 | 72.42 38 | 72.04 47 | 78.97 42 | 73.30 39 | 83.86 47 | 81.60 57 | 88.15 61 | 88.83 51 |
|
PLC | | 68.99 11 | 75.68 64 | 75.31 75 | 76.12 57 | 82.94 60 | 81.26 84 | 79.94 65 | 66.10 63 | 77.15 49 | 66.86 63 | 59.13 94 | 68.53 87 | 73.73 37 | 80.38 89 | 79.04 94 | 87.13 85 | 81.68 130 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
casdiffmvs | | | 77.90 56 | 78.63 55 | 77.06 52 | 82.85 61 | 86.44 48 | 84.45 48 | 64.35 77 | 71.84 62 | 69.93 52 | 70.80 52 | 72.99 62 | 72.00 48 | 80.84 81 | 79.80 84 | 88.76 54 | 87.71 59 |
|
CNLPA | | | 77.20 58 | 77.54 61 | 76.80 54 | 82.63 62 | 84.31 61 | 79.77 66 | 64.64 74 | 85.17 21 | 73.18 35 | 56.37 113 | 69.81 77 | 74.53 32 | 81.12 76 | 78.69 97 | 86.04 133 | 87.29 64 |
|
ACMH+ | | 66.54 13 | 71.36 84 | 70.09 99 | 72.85 70 | 82.59 63 | 81.13 85 | 78.56 90 | 68.04 52 | 61.55 107 | 52.52 133 | 51.50 180 | 54.14 151 | 68.56 68 | 78.85 115 | 79.50 91 | 86.82 102 | 83.94 99 |
|
ACMH | | 65.37 14 | 70.71 88 | 70.00 100 | 71.54 75 | 82.51 64 | 82.47 75 | 77.78 101 | 68.13 51 | 56.19 162 | 46.06 169 | 54.30 140 | 51.20 188 | 68.68 67 | 80.66 83 | 80.72 67 | 86.07 129 | 84.45 95 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
canonicalmvs | | | 79.16 49 | 82.37 38 | 75.41 58 | 82.33 65 | 86.38 49 | 80.80 60 | 63.18 85 | 82.90 32 | 67.34 60 | 72.79 45 | 76.07 51 | 69.62 62 | 83.46 54 | 84.41 42 | 89.20 45 | 90.60 40 |
|
MSDG | | | 71.52 83 | 69.87 104 | 73.44 68 | 82.21 66 | 79.35 108 | 79.52 68 | 64.59 75 | 66.15 75 | 61.87 75 | 53.21 161 | 56.09 138 | 65.85 102 | 78.94 114 | 78.50 99 | 86.60 116 | 76.85 172 |
|
IS_MVSNet | | | 73.33 73 | 77.34 66 | 68.65 117 | 81.29 67 | 83.47 66 | 74.45 127 | 63.58 82 | 65.75 79 | 48.49 152 | 67.11 69 | 70.61 72 | 54.63 172 | 84.51 43 | 83.58 47 | 89.48 41 | 86.34 69 |
|
Effi-MVS+ | | | 75.28 66 | 76.20 72 | 74.20 66 | 81.15 68 | 83.24 68 | 81.11 58 | 63.13 87 | 66.37 73 | 60.27 81 | 64.30 77 | 68.88 85 | 70.93 59 | 81.56 65 | 81.69 56 | 88.61 56 | 87.35 62 |
|
MVS_111021_LR | | | 78.13 55 | 79.85 52 | 76.13 56 | 81.12 69 | 81.50 81 | 80.28 63 | 65.25 70 | 76.09 52 | 71.32 48 | 76.49 33 | 72.87 63 | 72.21 46 | 82.79 59 | 81.29 59 | 86.59 117 | 87.91 56 |
|
FC-MVSNet-train | | | 72.60 78 | 75.07 76 | 69.71 108 | 81.10 70 | 78.79 119 | 73.74 144 | 65.23 71 | 66.10 76 | 53.34 126 | 70.36 54 | 63.40 101 | 56.92 152 | 81.44 66 | 80.96 63 | 87.93 66 | 84.46 94 |
|
MS-PatchMatch | | | 70.17 98 | 70.49 97 | 69.79 106 | 80.98 71 | 77.97 132 | 77.51 103 | 58.95 158 | 62.33 100 | 55.22 117 | 53.14 162 | 65.90 94 | 62.03 119 | 79.08 113 | 77.11 130 | 84.08 170 | 77.91 162 |
|
Anonymous202405211 | | | | 72.16 88 | | 80.85 72 | 81.85 78 | 76.88 111 | 65.40 69 | 62.89 98 | | 46.35 199 | 67.99 89 | 62.05 118 | 81.15 75 | 80.38 78 | 85.97 138 | 84.50 93 |
|
TAPA-MVS | | 71.42 9 | 77.69 57 | 80.05 51 | 74.94 61 | 80.68 73 | 84.52 60 | 81.36 57 | 63.14 86 | 84.77 24 | 64.82 68 | 68.72 59 | 75.91 52 | 71.86 50 | 81.62 63 | 79.55 90 | 87.80 71 | 85.24 83 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
PVSNet_Blended_VisFu | | | 76.57 59 | 77.90 58 | 75.02 60 | 80.56 74 | 86.58 46 | 79.24 71 | 66.18 62 | 64.81 84 | 68.18 56 | 65.61 70 | 71.45 66 | 67.05 72 | 84.16 45 | 81.80 55 | 88.90 50 | 90.92 37 |
|
EPP-MVSNet | | | 74.00 71 | 77.41 65 | 70.02 104 | 80.53 75 | 83.91 63 | 74.99 124 | 62.68 103 | 65.06 82 | 49.77 149 | 68.68 60 | 72.09 65 | 63.06 111 | 82.49 60 | 80.73 66 | 89.12 48 | 88.91 50 |
|
COLMAP_ROB | | 62.73 15 | 67.66 141 | 66.76 160 | 68.70 116 | 80.49 76 | 77.98 130 | 75.29 117 | 62.95 89 | 63.62 92 | 49.96 147 | 47.32 198 | 50.72 191 | 58.57 138 | 76.87 152 | 75.50 167 | 84.94 159 | 75.33 182 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
Anonymous20240521 | | | 73.65 72 | 75.78 74 | 71.16 77 | 80.19 77 | 79.27 109 | 77.45 106 | 61.68 119 | 66.73 72 | 58.72 88 | 65.31 73 | 69.96 76 | 62.19 116 | 81.29 72 | 80.97 62 | 86.74 109 | 86.91 65 |
|
Anonymous20231211 | | | 71.90 80 | 72.48 86 | 71.21 76 | 80.14 78 | 81.53 80 | 76.92 109 | 62.89 90 | 64.46 88 | 58.94 85 | 43.80 203 | 70.98 69 | 62.22 115 | 80.70 82 | 80.19 82 | 86.18 122 | 85.73 72 |
|
TSAR-MVS + COLMAP | | | 78.34 54 | 81.64 39 | 74.48 65 | 80.13 79 | 85.01 58 | 81.73 56 | 65.93 67 | 84.75 25 | 61.68 76 | 85.79 16 | 66.27 93 | 71.39 55 | 82.91 57 | 80.78 65 | 86.01 134 | 85.98 70 |
|
EPNet_dtu | | | 68.08 132 | 71.00 93 | 64.67 160 | 79.64 80 | 68.62 197 | 75.05 123 | 63.30 83 | 66.36 74 | 45.27 173 | 67.40 67 | 66.84 92 | 43.64 202 | 75.37 165 | 74.98 172 | 81.15 182 | 77.44 165 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
PVSNet_BlendedMVS | | | 76.21 60 | 77.52 62 | 74.69 63 | 79.46 81 | 83.79 64 | 77.50 104 | 64.34 78 | 69.88 65 | 71.88 41 | 68.54 62 | 70.42 73 | 67.05 72 | 83.48 52 | 79.63 86 | 87.89 68 | 86.87 66 |
|
PVSNet_Blended | | | 76.21 60 | 77.52 62 | 74.69 63 | 79.46 81 | 83.79 64 | 77.50 104 | 64.34 78 | 69.88 65 | 71.88 41 | 68.54 62 | 70.42 73 | 67.05 72 | 83.48 52 | 79.63 86 | 87.89 68 | 86.87 66 |
|
IB-MVS | | 66.94 12 | 71.21 85 | 71.66 91 | 70.68 87 | 79.18 83 | 82.83 73 | 72.61 153 | 61.77 117 | 59.66 120 | 63.44 72 | 53.26 159 | 59.65 111 | 59.16 137 | 76.78 154 | 82.11 53 | 87.90 67 | 87.33 63 |
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 |
MVS_Test | | | 75.37 65 | 77.13 68 | 73.31 69 | 79.07 84 | 81.32 83 | 79.98 64 | 60.12 148 | 69.72 67 | 64.11 70 | 70.53 53 | 73.22 60 | 68.90 65 | 80.14 98 | 79.48 92 | 87.67 72 | 85.50 78 |
|
Effi-MVS+-dtu | | | 71.82 81 | 71.86 90 | 71.78 73 | 78.77 85 | 80.47 99 | 78.55 91 | 61.67 120 | 60.68 112 | 55.49 114 | 58.48 98 | 65.48 95 | 68.85 66 | 76.92 151 | 75.55 166 | 87.35 77 | 85.46 79 |
|
EG-PatchMatch MVS | | | 67.24 150 | 66.94 157 | 67.60 127 | 78.73 86 | 81.35 82 | 73.28 150 | 59.49 153 | 46.89 212 | 51.42 138 | 43.65 204 | 53.49 159 | 55.50 168 | 81.38 68 | 80.66 73 | 87.15 81 | 81.17 133 |
|
gg-mvs-nofinetune | | | 62.55 182 | 65.05 180 | 59.62 191 | 78.72 87 | 77.61 136 | 70.83 164 | 53.63 186 | 39.71 224 | 22.04 228 | 36.36 217 | 64.32 98 | 47.53 190 | 81.16 74 | 79.03 95 | 85.00 157 | 77.17 167 |
|
Vis-MVSNet (Re-imp) | | | 67.83 137 | 73.52 79 | 61.19 181 | 78.37 88 | 76.72 151 | 66.80 186 | 62.96 88 | 65.50 80 | 34.17 207 | 67.19 68 | 69.68 78 | 39.20 211 | 79.39 109 | 79.44 93 | 85.68 147 | 76.73 173 |
|
DI_MVS_plusplus_trai | | | 75.13 67 | 76.12 73 | 73.96 67 | 78.18 89 | 81.55 79 | 80.97 59 | 62.54 107 | 68.59 69 | 65.13 67 | 61.43 82 | 74.81 56 | 69.32 64 | 81.01 78 | 79.59 88 | 87.64 73 | 85.89 71 |
|
thres600view7 | | | 67.68 140 | 68.43 138 | 66.80 145 | 77.90 90 | 78.86 117 | 73.84 141 | 62.75 96 | 56.07 163 | 44.70 177 | 52.85 168 | 52.81 171 | 55.58 166 | 80.41 84 | 77.77 115 | 86.05 131 | 80.28 143 |
|
thres400 | | | 67.95 134 | 68.62 136 | 67.17 138 | 77.90 90 | 78.59 122 | 74.27 137 | 62.72 98 | 56.34 161 | 45.77 171 | 53.00 164 | 53.35 166 | 56.46 158 | 80.21 97 | 78.43 100 | 85.91 141 | 80.43 142 |
|
thres200 | | | 67.98 133 | 68.55 137 | 67.30 136 | 77.89 92 | 78.86 117 | 74.18 139 | 62.75 96 | 56.35 160 | 46.48 167 | 52.98 165 | 53.54 157 | 56.46 158 | 80.41 84 | 77.97 112 | 86.05 131 | 79.78 149 |
|
tpmp4_e23 | | | 68.32 128 | 67.08 156 | 69.76 107 | 77.86 93 | 75.22 171 | 78.37 96 | 56.17 182 | 66.06 77 | 64.27 69 | 57.15 110 | 54.89 146 | 63.40 109 | 70.97 194 | 68.29 203 | 78.46 193 | 77.00 171 |
|
view600 | | | 67.63 144 | 68.36 139 | 66.77 146 | 77.84 94 | 78.66 120 | 73.74 144 | 62.62 105 | 56.04 164 | 44.98 174 | 52.86 167 | 52.83 170 | 55.48 169 | 80.36 90 | 77.75 116 | 85.95 140 | 80.02 146 |
|
diffmvs1 | | | 76.02 63 | 78.90 54 | 72.65 71 | 77.84 94 | 82.90 72 | 80.67 61 | 60.86 129 | 76.16 51 | 63.35 73 | 71.50 50 | 74.85 55 | 68.35 70 | 80.22 96 | 78.69 97 | 84.87 161 | 88.00 55 |
|
tfpn111 | | | 68.38 126 | 69.23 121 | 67.39 131 | 77.83 96 | 78.93 113 | 74.28 132 | 62.81 91 | 56.64 150 | 46.70 162 | 56.24 114 | 53.47 161 | 56.59 153 | 80.41 84 | 78.43 100 | 86.11 125 | 80.53 139 |
|
conf200view11 | | | 68.11 130 | 68.72 132 | 67.39 131 | 77.83 96 | 78.93 113 | 74.28 132 | 62.81 91 | 56.64 150 | 46.70 162 | 52.65 171 | 53.47 161 | 56.59 153 | 80.41 84 | 78.43 100 | 86.11 125 | 80.53 139 |
|
thres100view900 | | | 67.60 145 | 68.02 143 | 67.12 140 | 77.83 96 | 77.75 134 | 73.90 140 | 62.52 108 | 56.64 150 | 46.82 160 | 52.65 171 | 53.47 161 | 55.92 162 | 78.77 116 | 77.62 119 | 85.72 146 | 79.23 154 |
|
tfpn200view9 | | | 68.11 130 | 68.72 132 | 67.40 130 | 77.83 96 | 78.93 113 | 74.28 132 | 62.81 91 | 56.64 150 | 46.82 160 | 52.65 171 | 53.47 161 | 56.59 153 | 80.41 84 | 78.43 100 | 86.11 125 | 80.52 141 |
|
view800 | | | 67.35 149 | 68.22 142 | 66.35 150 | 77.83 96 | 78.62 121 | 72.97 152 | 62.58 106 | 55.71 166 | 44.13 178 | 52.69 170 | 52.24 180 | 54.58 174 | 80.27 94 | 78.19 108 | 86.01 134 | 79.79 148 |
|
conf0.01 | | | 67.72 139 | 67.99 144 | 67.39 131 | 77.82 101 | 78.94 111 | 74.28 132 | 62.81 91 | 56.64 150 | 46.70 162 | 53.33 157 | 48.59 201 | 56.59 153 | 80.34 91 | 78.43 100 | 86.16 124 | 79.67 150 |
|
tfpn | | | 66.58 153 | 67.18 154 | 65.88 152 | 77.82 101 | 78.45 124 | 72.07 157 | 62.52 108 | 55.35 170 | 43.21 182 | 52.54 175 | 46.12 210 | 53.68 175 | 80.02 99 | 78.23 107 | 85.99 137 | 79.55 152 |
|
conf0.002 | | | 67.52 147 | 67.64 148 | 67.39 131 | 77.80 103 | 78.94 111 | 74.28 132 | 62.81 91 | 56.64 150 | 46.70 162 | 53.65 153 | 46.28 209 | 56.59 153 | 80.33 92 | 78.37 105 | 86.17 123 | 79.23 154 |
|
Fast-Effi-MVS+ | | | 73.11 75 | 73.66 78 | 72.48 72 | 77.72 104 | 80.88 90 | 78.55 91 | 58.83 166 | 65.19 81 | 60.36 80 | 59.98 89 | 62.42 104 | 71.22 57 | 81.66 62 | 80.61 76 | 88.20 60 | 84.88 91 |
|
UniMVSNet_NR-MVSNet | | | 70.59 89 | 72.19 87 | 68.72 115 | 77.72 104 | 80.72 91 | 73.81 142 | 69.65 41 | 61.99 102 | 43.23 180 | 60.54 85 | 57.50 117 | 58.57 138 | 79.56 106 | 81.07 61 | 89.34 43 | 83.97 97 |
|
IterMVS-LS | | | 71.69 82 | 72.82 84 | 70.37 99 | 77.54 106 | 76.34 157 | 75.13 122 | 60.46 136 | 61.53 108 | 57.57 96 | 64.89 74 | 67.33 90 | 66.04 98 | 77.09 150 | 77.37 126 | 85.48 150 | 85.18 84 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
NR-MVSNet | | | 68.79 123 | 70.56 96 | 66.71 149 | 77.48 107 | 79.54 105 | 73.52 147 | 69.20 46 | 61.20 110 | 39.76 191 | 58.52 96 | 50.11 194 | 51.37 183 | 80.26 95 | 80.71 71 | 88.97 49 | 83.59 104 |
|
conf0.05thres1000 | | | 66.26 155 | 66.77 159 | 65.66 153 | 77.45 108 | 78.10 125 | 71.85 160 | 62.44 111 | 51.47 196 | 43.00 183 | 47.92 193 | 51.66 186 | 53.40 177 | 79.71 102 | 77.97 112 | 85.82 142 | 80.56 137 |
|
TransMVSNet (Re) | | | 64.74 167 | 65.66 173 | 63.66 167 | 77.40 109 | 75.33 166 | 69.86 165 | 62.67 104 | 47.63 210 | 41.21 189 | 50.01 186 | 52.33 176 | 45.31 199 | 79.57 105 | 77.69 118 | 85.49 149 | 77.07 170 |
|
TranMVSNet+NR-MVSNet | | | 69.25 118 | 70.81 95 | 67.43 129 | 77.23 110 | 79.46 107 | 73.48 148 | 69.66 40 | 60.43 115 | 39.56 192 | 58.82 95 | 53.48 160 | 55.74 165 | 79.59 104 | 81.21 60 | 88.89 51 | 82.70 118 |
|
diffmvs | | | 74.38 69 | 76.65 71 | 71.74 74 | 77.05 111 | 81.86 77 | 79.30 70 | 60.54 133 | 69.54 68 | 62.16 74 | 69.70 56 | 70.74 71 | 66.73 80 | 79.18 112 | 78.14 110 | 84.63 165 | 87.42 61 |
|
CANet_DTU | | | 73.29 74 | 76.96 69 | 69.00 113 | 77.04 112 | 82.06 76 | 79.49 69 | 56.30 180 | 67.85 70 | 53.29 127 | 71.12 51 | 70.37 75 | 61.81 124 | 81.59 64 | 80.96 63 | 86.09 128 | 84.73 92 |
|
CHOSEN 1792x2688 | | | 69.20 119 | 69.26 120 | 69.13 111 | 76.86 113 | 78.93 113 | 77.27 107 | 60.12 148 | 61.86 104 | 54.42 118 | 42.54 207 | 61.61 105 | 66.91 78 | 78.55 118 | 78.14 110 | 79.23 191 | 83.23 109 |
|
HyFIR lowres test | | | 69.47 115 | 68.94 124 | 70.09 103 | 76.77 114 | 82.93 71 | 76.63 113 | 60.17 143 | 59.00 124 | 54.03 121 | 40.54 213 | 65.23 96 | 67.89 71 | 76.54 158 | 78.30 106 | 85.03 156 | 80.07 145 |
|
UniMVSNet (Re) | | | 69.53 112 | 71.90 89 | 66.76 147 | 76.42 115 | 80.93 87 | 72.59 154 | 68.03 53 | 61.75 106 | 41.68 188 | 58.34 102 | 57.23 125 | 53.27 179 | 79.53 107 | 80.62 75 | 88.57 57 | 84.90 90 |
|
thresconf0.02 | | | 64.77 166 | 65.90 169 | 63.44 169 | 76.37 116 | 75.17 174 | 69.51 168 | 61.28 121 | 56.98 141 | 39.01 194 | 56.24 114 | 48.68 200 | 49.78 186 | 77.13 148 | 75.61 164 | 84.71 164 | 71.53 198 |
|
tfpnview11 | | | 64.33 170 | 66.17 165 | 62.18 174 | 76.25 117 | 75.23 169 | 67.45 178 | 61.16 122 | 55.50 168 | 36.38 201 | 55.35 121 | 51.89 182 | 46.96 191 | 77.28 145 | 76.10 160 | 84.86 162 | 71.85 197 |
|
tfpn_n400 | | | 64.23 172 | 66.05 166 | 62.12 176 | 76.20 118 | 75.24 167 | 67.43 179 | 61.15 123 | 54.04 183 | 36.38 201 | 55.35 121 | 51.89 182 | 46.94 192 | 77.31 143 | 76.15 158 | 84.59 166 | 72.36 194 |
|
tfpnconf | | | 64.23 172 | 66.05 166 | 62.12 176 | 76.20 118 | 75.24 167 | 67.43 179 | 61.15 123 | 54.04 183 | 36.38 201 | 55.35 121 | 51.89 182 | 46.94 192 | 77.31 143 | 76.15 158 | 84.59 166 | 72.36 194 |
|
DWT-MVSNet_training | | | 67.24 150 | 65.96 168 | 68.74 114 | 76.15 120 | 74.36 178 | 74.37 131 | 56.66 178 | 61.82 105 | 60.51 79 | 58.23 104 | 49.76 196 | 65.07 103 | 70.04 202 | 70.39 189 | 79.70 188 | 77.11 169 |
|
gm-plane-assit | | | 57.00 206 | 57.62 213 | 56.28 203 | 76.10 121 | 62.43 220 | 47.62 229 | 46.57 216 | 33.84 232 | 23.24 222 | 37.52 214 | 40.19 221 | 59.61 136 | 79.81 101 | 77.55 121 | 84.55 168 | 72.03 196 |
|
DU-MVS | | | 69.63 107 | 70.91 94 | 68.13 121 | 75.99 122 | 79.54 105 | 73.81 142 | 69.20 46 | 61.20 110 | 43.23 180 | 58.52 96 | 53.50 158 | 58.57 138 | 79.22 110 | 80.45 77 | 87.97 65 | 83.97 97 |
|
Baseline_NR-MVSNet | | | 67.53 146 | 68.77 130 | 66.09 151 | 75.99 122 | 74.75 175 | 72.43 155 | 68.41 49 | 61.33 109 | 38.33 196 | 51.31 181 | 54.13 153 | 56.03 161 | 79.22 110 | 78.19 108 | 85.37 151 | 82.45 120 |
|
CostFormer | | | 68.92 121 | 69.58 110 | 68.15 120 | 75.98 124 | 76.17 160 | 78.22 99 | 51.86 196 | 65.80 78 | 61.56 77 | 63.57 78 | 62.83 102 | 61.85 122 | 70.40 201 | 68.67 198 | 79.42 189 | 79.62 151 |
|
tfpnnormal | | | 64.27 171 | 63.64 190 | 65.02 156 | 75.84 125 | 75.61 163 | 71.24 163 | 62.52 108 | 47.79 209 | 42.97 184 | 42.65 206 | 44.49 214 | 52.66 181 | 78.77 116 | 76.86 133 | 84.88 160 | 79.29 153 |
|
tfpn1000 | | | 63.81 176 | 66.31 162 | 60.90 183 | 75.76 126 | 75.74 162 | 65.14 195 | 60.14 147 | 56.47 157 | 35.99 204 | 55.11 124 | 52.30 178 | 43.42 203 | 76.21 160 | 75.34 168 | 84.97 158 | 73.01 193 |
|
tfpn_ndepth | | | 65.09 163 | 67.12 155 | 62.73 172 | 75.75 127 | 76.23 158 | 68.00 175 | 60.36 137 | 58.16 130 | 40.27 190 | 54.89 131 | 54.22 150 | 46.80 195 | 76.69 156 | 75.66 163 | 85.19 153 | 73.98 190 |
|
tpm cat1 | | | 65.41 158 | 63.81 189 | 67.28 137 | 75.61 128 | 72.88 181 | 75.32 116 | 52.85 190 | 62.97 96 | 63.66 71 | 53.24 160 | 53.29 168 | 61.83 123 | 65.54 212 | 64.14 215 | 74.43 209 | 74.60 184 |
|
CDS-MVSNet | | | 67.65 142 | 69.83 107 | 65.09 155 | 75.39 129 | 76.55 152 | 74.42 130 | 63.75 80 | 53.55 185 | 49.37 151 | 59.41 92 | 62.45 103 | 44.44 200 | 79.71 102 | 79.82 83 | 83.17 176 | 77.36 166 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
Fast-Effi-MVS+-dtu | | | 68.34 127 | 69.47 112 | 67.01 142 | 75.15 130 | 77.97 132 | 77.12 108 | 55.40 183 | 57.87 131 | 46.68 166 | 56.17 116 | 60.39 107 | 62.36 114 | 76.32 159 | 76.25 155 | 85.35 152 | 81.34 131 |
|
WR-MVS | | | 63.03 178 | 67.40 152 | 57.92 197 | 75.14 131 | 77.60 137 | 60.56 210 | 66.10 63 | 54.11 182 | 23.88 219 | 53.94 151 | 53.58 156 | 34.50 215 | 73.93 172 | 77.71 117 | 87.35 77 | 80.94 134 |
|
test-LLR | | | 64.42 168 | 64.36 185 | 64.49 161 | 75.02 132 | 63.93 210 | 66.61 188 | 61.96 114 | 54.41 178 | 47.77 156 | 57.46 107 | 60.25 108 | 55.20 170 | 70.80 195 | 69.33 193 | 80.40 186 | 74.38 186 |
|
test0.0.03 1 | | | 58.80 202 | 61.58 203 | 55.56 205 | 75.02 132 | 68.45 198 | 59.58 214 | 61.96 114 | 52.74 187 | 29.57 211 | 49.75 189 | 54.56 148 | 31.46 218 | 71.19 189 | 69.77 191 | 75.75 202 | 64.57 212 |
|
v1144 | | | 69.93 106 | 69.36 119 | 70.61 89 | 74.89 134 | 80.93 87 | 79.11 74 | 60.64 130 | 55.97 165 | 55.31 116 | 53.85 152 | 54.14 151 | 66.54 83 | 78.10 123 | 77.44 124 | 87.14 84 | 85.09 85 |
|
v13 | | | 69.52 113 | 68.76 131 | 70.41 97 | 74.88 135 | 77.02 147 | 78.52 95 | 58.86 160 | 56.61 156 | 56.91 101 | 54.00 150 | 56.17 137 | 66.11 97 | 77.93 124 | 76.74 141 | 87.21 79 | 82.83 111 |
|
v12 | | | 69.54 111 | 68.79 129 | 70.41 97 | 74.88 135 | 77.03 145 | 78.54 94 | 58.85 162 | 56.71 148 | 56.87 103 | 54.13 148 | 56.23 136 | 66.15 93 | 77.89 125 | 76.74 141 | 87.17 80 | 82.80 112 |
|
v11 | | | 69.37 116 | 68.65 135 | 70.20 101 | 74.87 137 | 76.97 148 | 78.29 98 | 58.55 170 | 56.38 159 | 56.04 112 | 54.02 149 | 54.98 145 | 66.47 84 | 78.30 120 | 76.91 132 | 86.97 93 | 83.02 110 |
|
V9 | | | 69.58 110 | 68.83 127 | 70.46 94 | 74.85 138 | 77.04 143 | 78.65 89 | 58.85 162 | 56.83 147 | 57.12 99 | 54.26 143 | 56.31 131 | 66.14 95 | 77.83 127 | 76.76 136 | 87.13 85 | 82.79 114 |
|
V14 | | | 69.59 109 | 68.86 126 | 70.45 96 | 74.83 139 | 77.04 143 | 78.70 88 | 58.83 166 | 56.95 144 | 57.08 100 | 54.41 139 | 56.34 130 | 66.15 93 | 77.77 128 | 76.76 136 | 87.08 90 | 82.74 117 |
|
v15 | | | 69.61 108 | 68.88 125 | 70.46 94 | 74.81 140 | 77.03 145 | 78.75 87 | 58.83 166 | 57.06 140 | 57.18 98 | 54.55 138 | 56.37 129 | 66.13 96 | 77.70 129 | 76.76 136 | 87.03 92 | 82.69 119 |
|
v7 | | | 70.33 95 | 69.87 104 | 70.88 78 | 74.79 141 | 81.04 86 | 79.22 72 | 60.57 132 | 57.70 137 | 56.65 109 | 54.23 145 | 55.29 143 | 66.95 75 | 78.28 121 | 77.47 122 | 87.12 88 | 85.05 87 |
|
v10 | | | 70.22 97 | 69.76 108 | 70.74 84 | 74.79 141 | 80.30 102 | 79.22 72 | 59.81 151 | 57.71 136 | 56.58 110 | 54.22 147 | 55.31 141 | 66.95 75 | 78.28 121 | 77.47 122 | 87.12 88 | 85.07 86 |
|
v1141 | | | 69.96 105 | 69.44 116 | 70.58 92 | 74.78 143 | 80.50 97 | 78.85 77 | 60.30 138 | 56.95 144 | 56.74 106 | 54.68 136 | 56.26 135 | 65.93 99 | 77.38 140 | 76.72 146 | 86.88 98 | 83.57 107 |
|
divwei89l23v2f112 | | | 69.97 103 | 69.44 116 | 70.58 92 | 74.78 143 | 80.50 97 | 78.85 77 | 60.30 138 | 56.97 143 | 56.75 105 | 54.67 137 | 56.27 134 | 65.92 100 | 77.37 141 | 76.72 146 | 86.88 98 | 83.58 106 |
|
v1 | | | 69.97 103 | 69.45 115 | 70.59 90 | 74.78 143 | 80.51 96 | 78.84 79 | 60.30 138 | 56.98 141 | 56.81 104 | 54.69 135 | 56.29 133 | 65.91 101 | 77.37 141 | 76.71 149 | 86.89 97 | 83.59 104 |
|
v17 | | | 70.03 102 | 69.43 118 | 70.72 86 | 74.75 146 | 77.09 140 | 78.78 86 | 58.85 162 | 59.53 122 | 58.72 88 | 54.87 132 | 57.39 119 | 66.38 86 | 77.60 133 | 76.75 139 | 86.83 101 | 82.80 112 |
|
v16 | | | 70.07 100 | 69.46 113 | 70.79 82 | 74.74 147 | 77.08 141 | 78.79 84 | 58.86 160 | 59.75 119 | 59.15 84 | 54.87 132 | 57.33 120 | 66.38 86 | 77.61 132 | 76.77 134 | 86.81 107 | 82.79 114 |
|
v8 | | | 70.23 96 | 69.86 106 | 70.67 88 | 74.69 148 | 79.82 104 | 78.79 84 | 59.18 156 | 58.80 126 | 58.20 91 | 55.00 127 | 57.33 120 | 66.31 92 | 77.51 137 | 76.71 149 | 86.82 102 | 83.88 100 |
|
v1neww | | | 70.34 93 | 69.93 102 | 70.82 80 | 74.68 149 | 80.61 93 | 78.80 82 | 60.17 143 | 58.74 127 | 58.10 93 | 55.00 127 | 57.28 123 | 66.33 89 | 77.53 134 | 76.74 141 | 86.82 102 | 83.61 102 |
|
v7new | | | 70.34 93 | 69.93 102 | 70.82 80 | 74.68 149 | 80.61 93 | 78.80 82 | 60.17 143 | 58.74 127 | 58.10 93 | 55.00 127 | 57.28 123 | 66.33 89 | 77.53 134 | 76.74 141 | 86.82 102 | 83.61 102 |
|
v6 | | | 70.35 92 | 69.94 101 | 70.83 79 | 74.68 149 | 80.62 92 | 78.81 81 | 60.16 146 | 58.81 125 | 58.17 92 | 55.01 126 | 57.31 122 | 66.32 91 | 77.53 134 | 76.73 145 | 86.82 102 | 83.62 101 |
|
v18 | | | 70.10 99 | 69.52 111 | 70.77 83 | 74.66 152 | 77.06 142 | 78.84 79 | 58.84 165 | 60.01 118 | 59.23 83 | 55.06 125 | 57.47 118 | 66.34 88 | 77.50 138 | 76.75 139 | 86.71 110 | 82.77 116 |
|
v2v482 | | | 70.05 101 | 69.46 113 | 70.74 84 | 74.62 153 | 80.32 101 | 79.00 75 | 60.62 131 | 57.41 138 | 56.89 102 | 55.43 120 | 55.14 144 | 66.39 85 | 77.25 146 | 77.14 129 | 86.90 95 | 83.57 107 |
|
v1192 | | | 69.50 114 | 68.83 127 | 70.29 100 | 74.49 154 | 80.92 89 | 78.55 91 | 60.54 133 | 55.04 174 | 54.21 119 | 52.79 169 | 52.33 176 | 66.92 77 | 77.88 126 | 77.35 127 | 87.04 91 | 85.51 77 |
|
DTE-MVSNet | | | 61.85 191 | 64.96 182 | 58.22 196 | 74.32 155 | 74.39 177 | 61.01 209 | 67.85 55 | 51.76 195 | 21.91 229 | 53.28 158 | 48.17 202 | 37.74 212 | 72.22 182 | 76.44 152 | 86.52 119 | 78.49 159 |
|
Vis-MVSNet | | | 72.77 77 | 77.20 67 | 67.59 128 | 74.19 156 | 84.01 62 | 76.61 114 | 61.69 118 | 60.62 114 | 50.61 143 | 70.25 55 | 71.31 68 | 55.57 167 | 83.85 48 | 82.28 51 | 86.90 95 | 88.08 54 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
v144192 | | | 69.34 117 | 68.68 134 | 70.12 102 | 74.06 157 | 80.54 95 | 78.08 100 | 60.54 133 | 54.99 176 | 54.13 120 | 52.92 166 | 52.80 172 | 66.73 80 | 77.13 148 | 76.72 146 | 87.15 81 | 85.63 73 |
|
v1921920 | | | 69.03 120 | 68.32 140 | 69.86 105 | 74.03 158 | 80.37 100 | 77.55 102 | 60.25 142 | 54.62 177 | 53.59 125 | 52.36 176 | 51.50 187 | 66.75 79 | 77.17 147 | 76.69 151 | 86.96 94 | 85.56 74 |
|
PEN-MVS | | | 62.96 179 | 65.77 172 | 59.70 190 | 73.98 159 | 75.45 164 | 63.39 203 | 67.61 56 | 52.49 189 | 25.49 218 | 53.39 155 | 49.12 199 | 40.85 209 | 71.94 185 | 77.26 128 | 86.86 100 | 80.72 136 |
|
v1240 | | | 68.64 125 | 67.89 147 | 69.51 109 | 73.89 160 | 80.26 103 | 76.73 112 | 59.97 150 | 53.43 186 | 53.08 128 | 51.82 179 | 50.84 190 | 66.62 82 | 76.79 153 | 76.77 134 | 86.78 108 | 85.34 81 |
|
GA-MVS | | | 68.14 129 | 69.17 122 | 66.93 144 | 73.77 161 | 78.50 123 | 74.45 127 | 58.28 171 | 55.11 173 | 48.44 153 | 60.08 87 | 53.99 154 | 61.50 125 | 78.43 119 | 77.57 120 | 85.13 154 | 80.54 138 |
|
pm-mvs1 | | | 65.62 157 | 67.42 151 | 63.53 168 | 73.66 162 | 76.39 156 | 69.66 166 | 60.87 128 | 49.73 204 | 43.97 179 | 51.24 182 | 57.00 127 | 48.16 189 | 79.89 100 | 77.84 114 | 84.85 163 | 79.82 147 |
|
dps | | | 64.00 175 | 62.99 192 | 65.18 154 | 73.29 163 | 72.07 184 | 68.98 172 | 53.07 189 | 57.74 135 | 58.41 90 | 55.55 119 | 47.74 205 | 60.89 130 | 69.53 204 | 67.14 207 | 76.44 201 | 71.19 200 |
|
v148 | | | 67.85 136 | 67.53 149 | 68.23 119 | 73.25 164 | 77.57 138 | 74.26 138 | 57.36 175 | 55.70 167 | 57.45 97 | 53.53 154 | 55.42 140 | 61.96 120 | 75.23 166 | 73.92 175 | 85.08 155 | 81.32 132 |
|
PatchMatch-RL | | | 67.78 138 | 66.65 161 | 69.10 112 | 73.01 165 | 72.69 182 | 68.49 173 | 61.85 116 | 62.93 97 | 60.20 82 | 56.83 112 | 50.42 192 | 69.52 63 | 75.62 164 | 74.46 174 | 81.51 180 | 73.62 191 |
|
GBi-Net | | | 70.78 86 | 73.37 81 | 67.76 122 | 72.95 166 | 78.00 127 | 75.15 119 | 62.72 98 | 64.13 89 | 51.44 135 | 58.37 99 | 69.02 82 | 57.59 144 | 81.33 69 | 80.72 67 | 86.70 111 | 82.02 122 |
|
test1 | | | 70.78 86 | 73.37 81 | 67.76 122 | 72.95 166 | 78.00 127 | 75.15 119 | 62.72 98 | 64.13 89 | 51.44 135 | 58.37 99 | 69.02 82 | 57.59 144 | 81.33 69 | 80.72 67 | 86.70 111 | 82.02 122 |
|
FMVSNet2 | | | 70.39 91 | 72.67 85 | 67.72 125 | 72.95 166 | 78.00 127 | 75.15 119 | 62.69 102 | 63.29 94 | 51.25 139 | 55.64 117 | 68.49 88 | 57.59 144 | 80.91 80 | 80.35 79 | 86.70 111 | 82.02 122 |
|
FMVSNet3 | | | 70.49 90 | 72.90 83 | 67.67 126 | 72.88 169 | 77.98 130 | 74.96 125 | 62.72 98 | 64.13 89 | 51.44 135 | 58.37 99 | 69.02 82 | 57.43 147 | 79.43 108 | 79.57 89 | 86.59 117 | 81.81 129 |
|
LTVRE_ROB | | 59.44 16 | 61.82 194 | 62.64 196 | 60.87 184 | 72.83 170 | 77.19 139 | 64.37 199 | 58.97 157 | 33.56 233 | 28.00 215 | 52.59 174 | 42.21 217 | 63.93 107 | 74.52 168 | 76.28 153 | 77.15 198 | 82.13 121 |
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 |
v7n | | | 67.05 152 | 66.94 157 | 67.17 138 | 72.35 171 | 78.97 110 | 73.26 151 | 58.88 159 | 51.16 197 | 50.90 140 | 48.21 191 | 50.11 194 | 60.96 127 | 77.70 129 | 77.38 125 | 86.68 114 | 85.05 87 |
|
tpm | | | 62.41 185 | 63.15 191 | 61.55 180 | 72.24 172 | 63.79 212 | 71.31 162 | 46.12 218 | 57.82 132 | 55.33 115 | 59.90 90 | 54.74 147 | 53.63 176 | 67.24 211 | 64.29 213 | 70.65 220 | 74.25 188 |
|
test20.03 | | | 53.93 213 | 56.28 214 | 51.19 214 | 72.19 173 | 65.83 205 | 53.20 221 | 61.08 125 | 42.74 218 | 22.08 227 | 37.07 216 | 45.76 212 | 24.29 231 | 70.44 199 | 69.04 195 | 74.31 210 | 63.05 216 |
|
CP-MVSNet | | | 62.68 181 | 65.49 175 | 59.40 193 | 71.84 174 | 75.34 165 | 62.87 205 | 67.04 59 | 52.64 188 | 27.19 216 | 53.38 156 | 48.15 203 | 41.40 207 | 71.26 188 | 75.68 162 | 86.07 129 | 82.00 125 |
|
PS-CasMVS | | | 62.38 187 | 65.06 179 | 59.25 194 | 71.73 175 | 75.21 172 | 62.77 206 | 66.99 60 | 51.94 194 | 26.96 217 | 52.00 178 | 47.52 206 | 41.06 208 | 71.16 191 | 75.60 165 | 85.97 138 | 81.97 127 |
|
WR-MVS_H | | | 61.83 193 | 65.87 171 | 57.12 200 | 71.72 176 | 76.87 149 | 61.45 208 | 66.19 61 | 51.97 193 | 22.92 226 | 53.13 163 | 52.30 178 | 33.80 216 | 71.03 192 | 75.00 171 | 86.65 115 | 80.78 135 |
|
USDC | | | 67.36 148 | 67.90 146 | 66.74 148 | 71.72 176 | 75.23 169 | 71.58 161 | 60.28 141 | 67.45 71 | 50.54 144 | 60.93 83 | 45.20 213 | 62.08 117 | 76.56 157 | 74.50 173 | 84.25 169 | 75.38 181 |
|
UGNet | | | 72.78 76 | 77.67 60 | 67.07 141 | 71.65 178 | 83.24 68 | 75.20 118 | 63.62 81 | 64.93 83 | 56.72 107 | 71.82 48 | 73.30 59 | 49.02 188 | 81.02 77 | 80.70 72 | 86.22 121 | 88.67 52 |
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 |
tpmrst | | | 62.00 189 | 62.35 200 | 61.58 179 | 71.62 179 | 64.14 209 | 69.07 171 | 48.22 214 | 62.21 101 | 53.93 122 | 58.26 103 | 55.30 142 | 55.81 164 | 63.22 217 | 62.62 218 | 70.85 219 | 70.70 201 |
|
pmmvs4 | | | 67.89 135 | 67.39 153 | 68.48 118 | 71.60 180 | 73.57 180 | 74.45 127 | 60.98 126 | 64.65 85 | 57.97 95 | 54.95 130 | 51.73 185 | 61.88 121 | 73.78 173 | 75.11 170 | 83.99 172 | 77.91 162 |
|
testgi | | | 54.39 212 | 57.86 211 | 50.35 215 | 71.59 181 | 67.24 201 | 54.95 219 | 53.25 188 | 43.36 217 | 23.78 220 | 44.64 202 | 47.87 204 | 24.96 227 | 70.45 198 | 68.66 199 | 73.60 212 | 62.78 217 |
|
pmmvs6 | | | 62.41 185 | 62.88 193 | 61.87 178 | 71.38 182 | 75.18 173 | 67.76 177 | 59.45 155 | 41.64 220 | 42.52 187 | 37.33 215 | 52.91 169 | 46.87 194 | 77.67 131 | 76.26 154 | 83.23 175 | 79.18 156 |
|
FMVSNet1 | | | 68.84 122 | 70.47 98 | 66.94 143 | 71.35 183 | 77.68 135 | 74.71 126 | 62.35 112 | 56.93 146 | 49.94 148 | 50.01 186 | 64.59 97 | 57.07 150 | 81.33 69 | 80.72 67 | 86.25 120 | 82.00 125 |
|
PatchmatchNet | | | 64.21 174 | 64.65 183 | 63.69 166 | 71.29 184 | 68.66 196 | 69.63 167 | 51.70 198 | 63.04 95 | 53.77 124 | 59.83 91 | 58.34 115 | 60.23 134 | 68.54 208 | 66.06 210 | 75.56 204 | 68.08 207 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
CR-MVSNet | | | 64.83 165 | 65.54 174 | 64.01 165 | 70.64 185 | 69.41 192 | 65.97 191 | 52.74 191 | 57.81 133 | 52.65 130 | 54.27 141 | 56.31 131 | 60.92 128 | 72.20 183 | 73.09 179 | 81.12 183 | 75.69 178 |
|
MVSTER | | | 72.06 79 | 74.24 77 | 69.51 109 | 70.39 186 | 75.97 161 | 76.91 110 | 57.36 175 | 64.64 86 | 61.39 78 | 68.86 58 | 63.76 99 | 63.46 108 | 81.44 66 | 79.70 85 | 87.56 74 | 85.31 82 |
|
Anonymous20231206 | | | 56.36 208 | 57.80 212 | 54.67 208 | 70.08 187 | 66.39 204 | 60.46 211 | 57.54 173 | 49.50 206 | 29.30 212 | 33.86 223 | 46.64 207 | 35.18 214 | 70.44 199 | 68.88 197 | 75.47 205 | 68.88 206 |
|
CMPMVS | | 47.78 17 | 62.49 184 | 62.52 197 | 62.46 173 | 70.01 188 | 70.66 190 | 62.97 204 | 51.84 197 | 51.98 192 | 56.71 108 | 42.87 205 | 53.62 155 | 57.80 143 | 72.23 181 | 70.37 190 | 75.45 206 | 75.91 175 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
v748 | | | 65.12 162 | 65.24 176 | 64.98 157 | 69.77 189 | 76.45 153 | 69.47 169 | 57.06 177 | 49.93 202 | 50.70 141 | 47.87 194 | 49.50 198 | 57.14 149 | 73.64 175 | 75.18 169 | 85.75 145 | 84.14 96 |
|
TDRefinement | | | 66.09 156 | 65.03 181 | 67.31 135 | 69.73 190 | 76.75 150 | 75.33 115 | 64.55 76 | 60.28 116 | 49.72 150 | 45.63 201 | 42.83 216 | 60.46 132 | 75.75 161 | 75.95 161 | 84.08 170 | 78.04 161 |
|
TinyColmap | | | 62.84 180 | 61.03 205 | 64.96 158 | 69.61 191 | 71.69 185 | 68.48 174 | 59.76 152 | 55.41 169 | 47.69 158 | 47.33 197 | 34.20 226 | 62.76 113 | 74.52 168 | 72.59 182 | 81.44 181 | 71.47 199 |
|
RPMNet | | | 61.71 195 | 62.88 193 | 60.34 186 | 69.51 192 | 69.41 192 | 63.48 202 | 49.23 206 | 57.81 133 | 45.64 172 | 50.51 184 | 50.12 193 | 53.13 180 | 68.17 210 | 68.49 201 | 81.07 184 | 75.62 180 |
|
IterMVS | | | 66.36 154 | 68.30 141 | 64.10 162 | 69.48 193 | 74.61 176 | 73.41 149 | 50.79 202 | 57.30 139 | 48.28 154 | 60.64 84 | 59.92 110 | 60.85 131 | 74.14 171 | 72.66 181 | 81.80 179 | 78.82 158 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
SixPastTwentyTwo | | | 61.84 192 | 62.45 198 | 61.12 182 | 69.20 194 | 72.20 183 | 62.03 207 | 57.40 174 | 46.54 213 | 38.03 198 | 57.14 111 | 41.72 218 | 58.12 142 | 69.67 203 | 71.58 185 | 81.94 178 | 78.30 160 |
|
MDTV_nov1_ep13 | | | 64.37 169 | 65.24 176 | 63.37 171 | 68.94 195 | 70.81 188 | 72.40 156 | 50.29 205 | 60.10 117 | 53.91 123 | 60.07 88 | 59.15 113 | 57.21 148 | 69.43 205 | 67.30 205 | 77.47 196 | 69.78 203 |
|
EPMVS | | | 60.00 200 | 61.97 201 | 57.71 198 | 68.46 196 | 63.17 216 | 64.54 198 | 48.23 213 | 63.30 93 | 44.72 176 | 60.19 86 | 56.05 139 | 50.85 184 | 65.27 214 | 62.02 220 | 69.44 222 | 63.81 214 |
|
our_test_3 | | | | | | 67.93 197 | 70.99 187 | 66.89 184 | | | | | | | | | | |
|
FC-MVSNet-test | | | 56.90 207 | 65.20 178 | 47.21 218 | 66.98 198 | 63.20 215 | 49.11 227 | 58.60 169 | 59.38 123 | 11.50 238 | 65.60 71 | 56.68 128 | 24.66 230 | 71.17 190 | 71.36 187 | 72.38 215 | 69.02 205 |
|
CVMVSNet | | | 62.55 182 | 65.89 170 | 58.64 195 | 66.95 199 | 69.15 194 | 66.49 190 | 56.29 181 | 52.46 190 | 32.70 208 | 59.27 93 | 58.21 116 | 50.09 185 | 71.77 186 | 71.39 186 | 79.31 190 | 78.99 157 |
|
FPMVS | | | 51.87 216 | 50.00 221 | 54.07 209 | 66.83 200 | 57.25 223 | 60.25 212 | 50.91 200 | 50.25 199 | 34.36 206 | 36.04 220 | 32.02 228 | 41.49 206 | 58.98 229 | 56.07 229 | 70.56 221 | 59.36 223 |
|
pmmvs-eth3d | | | 63.52 177 | 62.44 199 | 64.77 159 | 66.82 201 | 70.12 191 | 69.41 170 | 59.48 154 | 54.34 181 | 52.71 129 | 46.24 200 | 44.35 215 | 56.93 151 | 72.37 178 | 73.77 176 | 83.30 174 | 75.91 175 |
|
testpf | | | 47.41 219 | 48.47 226 | 46.18 219 | 66.30 202 | 50.67 232 | 48.15 228 | 42.60 228 | 37.10 228 | 28.75 213 | 40.97 209 | 39.01 223 | 30.82 219 | 52.95 234 | 53.74 233 | 60.46 232 | 64.87 211 |
|
TAMVS | | | 59.58 201 | 62.81 195 | 55.81 204 | 66.03 203 | 65.64 207 | 63.86 201 | 48.74 209 | 49.95 200 | 37.07 200 | 54.77 134 | 58.54 114 | 44.44 200 | 72.29 180 | 71.79 183 | 74.70 208 | 66.66 209 |
|
MDTV_nov1_ep13_2view | | | 60.16 199 | 60.51 207 | 59.75 189 | 65.39 204 | 69.05 195 | 68.00 175 | 48.29 212 | 51.99 191 | 45.95 170 | 48.01 192 | 49.64 197 | 53.39 178 | 68.83 207 | 66.52 209 | 77.47 196 | 69.55 204 |
|
pmmvs5 | | | 62.37 188 | 64.04 187 | 60.42 185 | 65.03 205 | 71.67 186 | 67.17 182 | 52.70 193 | 50.30 198 | 44.80 175 | 54.23 145 | 51.19 189 | 49.37 187 | 72.88 177 | 73.48 178 | 83.45 173 | 74.55 185 |
|
ambc | | | | 53.42 216 | | 64.99 206 | 63.36 214 | 49.96 225 | | 47.07 211 | 37.12 199 | 28.97 227 | 16.36 241 | 41.82 205 | 75.10 167 | 67.34 204 | 71.55 218 | 75.72 177 |
|
V42 | | | 68.76 124 | 69.63 109 | 67.74 124 | 64.93 207 | 78.01 126 | 78.30 97 | 56.48 179 | 58.65 129 | 56.30 111 | 54.26 143 | 57.03 126 | 64.85 104 | 77.47 139 | 77.01 131 | 85.60 148 | 84.96 89 |
|
PMVS | | 39.38 18 | 46.06 224 | 43.30 230 | 49.28 217 | 62.93 208 | 38.75 239 | 41.88 232 | 53.50 187 | 33.33 234 | 35.46 205 | 28.90 228 | 31.01 231 | 33.04 217 | 58.61 230 | 54.63 232 | 68.86 223 | 57.88 226 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
new-patchmatchnet | | | 46.97 222 | 49.47 222 | 44.05 224 | 62.82 209 | 56.55 224 | 45.35 230 | 52.01 195 | 42.47 219 | 17.04 234 | 35.73 221 | 35.21 225 | 21.84 236 | 61.27 222 | 54.83 231 | 65.26 230 | 60.26 220 |
|
ADS-MVSNet | | | 55.94 209 | 58.01 210 | 53.54 213 | 62.48 210 | 58.48 222 | 59.12 215 | 46.20 217 | 59.65 121 | 42.88 185 | 52.34 177 | 53.31 167 | 46.31 197 | 62.00 221 | 60.02 225 | 64.23 231 | 60.24 222 |
|
v52 | | | 65.23 160 | 66.24 163 | 64.06 163 | 61.94 211 | 76.42 154 | 72.06 158 | 54.30 185 | 49.94 201 | 50.04 146 | 47.41 196 | 52.42 174 | 60.23 134 | 75.71 162 | 76.22 156 | 85.78 143 | 85.56 74 |
|
V4 | | | 65.23 160 | 66.23 164 | 64.06 163 | 61.94 211 | 76.42 154 | 72.05 159 | 54.31 184 | 49.91 203 | 50.06 145 | 47.42 195 | 52.40 175 | 60.24 133 | 75.71 162 | 76.22 156 | 85.78 143 | 85.56 74 |
|
RPSCF | | | 67.64 143 | 71.25 92 | 63.43 170 | 61.86 213 | 70.73 189 | 67.26 181 | 50.86 201 | 74.20 58 | 58.91 86 | 67.49 66 | 69.33 79 | 64.10 106 | 71.41 187 | 68.45 202 | 77.61 195 | 77.17 167 |
|
MIMVSNet | | | 58.52 204 | 61.34 204 | 55.22 206 | 60.76 214 | 67.01 202 | 66.81 185 | 49.02 208 | 56.43 158 | 38.90 195 | 40.59 212 | 54.54 149 | 40.57 210 | 73.16 176 | 71.65 184 | 75.30 207 | 66.00 210 |
|
PatchT | | | 61.97 190 | 64.04 187 | 59.55 192 | 60.49 215 | 67.40 200 | 56.54 217 | 48.65 210 | 56.69 149 | 52.65 130 | 51.10 183 | 52.14 181 | 60.92 128 | 72.20 183 | 73.09 179 | 78.03 194 | 75.69 178 |
|
N_pmnet | | | 47.35 220 | 50.13 220 | 44.11 223 | 59.98 216 | 51.64 231 | 51.86 222 | 44.80 223 | 49.58 205 | 20.76 230 | 40.65 211 | 40.05 222 | 29.64 220 | 59.84 227 | 55.15 230 | 57.63 233 | 54.00 230 |
|
1111 | | | 43.08 226 | 44.02 229 | 41.98 225 | 59.22 217 | 49.27 235 | 41.48 233 | 45.63 220 | 35.01 229 | 23.06 224 | 28.60 229 | 30.15 232 | 27.22 222 | 60.42 225 | 57.97 227 | 55.27 236 | 46.74 232 |
|
.test1245 | | | 30.81 233 | 29.14 236 | 32.77 232 | 59.22 217 | 49.27 235 | 41.48 233 | 45.63 220 | 35.01 229 | 23.06 224 | 28.60 229 | 30.15 232 | 27.22 222 | 60.42 225 | 0.10 240 | 0.01 244 | 0.43 241 |
|
MVS-HIRNet | | | 54.41 211 | 52.10 219 | 57.11 201 | 58.99 219 | 56.10 225 | 49.68 226 | 49.10 207 | 46.18 214 | 52.15 134 | 33.18 224 | 46.11 211 | 56.10 160 | 63.19 218 | 59.70 226 | 76.64 200 | 60.25 221 |
|
PM-MVS | | | 60.48 198 | 60.94 206 | 59.94 188 | 58.85 220 | 66.83 203 | 64.27 200 | 51.39 199 | 55.03 175 | 48.03 155 | 50.00 188 | 40.79 220 | 58.26 141 | 69.20 206 | 67.13 208 | 78.84 192 | 77.60 164 |
|
anonymousdsp | | | 65.28 159 | 67.98 145 | 62.13 175 | 58.73 221 | 73.98 179 | 67.10 183 | 50.69 203 | 48.41 207 | 47.66 159 | 54.27 141 | 52.75 173 | 61.45 126 | 76.71 155 | 80.20 80 | 87.13 85 | 89.53 47 |
|
LP | | | 53.62 214 | 53.43 215 | 53.83 211 | 58.51 222 | 62.59 219 | 57.31 216 | 46.04 219 | 47.86 208 | 42.69 186 | 36.08 219 | 36.86 224 | 46.53 196 | 64.38 215 | 64.25 214 | 71.92 216 | 62.00 219 |
|
TESTMET0.1,1 | | | 61.10 196 | 64.36 185 | 57.29 199 | 57.53 223 | 63.93 210 | 66.61 188 | 36.22 233 | 54.41 178 | 47.77 156 | 57.46 107 | 60.25 108 | 55.20 170 | 70.80 195 | 69.33 193 | 80.40 186 | 74.38 186 |
|
EU-MVSNet | | | 54.63 210 | 58.69 209 | 49.90 216 | 56.99 224 | 62.70 218 | 56.41 218 | 50.64 204 | 45.95 215 | 23.14 223 | 50.42 185 | 46.51 208 | 36.63 213 | 65.51 213 | 64.85 212 | 75.57 203 | 74.91 183 |
|
FMVSNet5 | | | 57.24 205 | 60.02 208 | 53.99 210 | 56.45 225 | 62.74 217 | 65.27 194 | 47.03 215 | 55.14 172 | 39.55 193 | 40.88 210 | 53.42 165 | 41.83 204 | 72.35 179 | 71.10 188 | 73.79 211 | 64.50 213 |
|
test2356 | | | 47.20 221 | 48.62 225 | 45.54 221 | 56.38 226 | 54.89 227 | 50.62 223 | 45.08 222 | 38.65 225 | 23.40 221 | 36.23 218 | 31.10 230 | 29.31 221 | 62.76 219 | 62.49 219 | 68.48 224 | 54.23 229 |
|
testus | | | 45.61 225 | 49.06 224 | 41.59 226 | 56.13 227 | 55.28 226 | 43.51 231 | 39.64 231 | 37.74 226 | 18.23 232 | 35.52 222 | 31.28 229 | 24.69 229 | 62.46 220 | 62.90 217 | 67.33 226 | 58.26 225 |
|
test-mter | | | 60.84 197 | 64.62 184 | 56.42 202 | 55.99 228 | 64.18 208 | 65.39 193 | 34.23 235 | 54.39 180 | 46.21 168 | 57.40 109 | 59.49 112 | 55.86 163 | 71.02 193 | 69.65 192 | 80.87 185 | 76.20 174 |
|
CHOSEN 280x420 | | | 58.70 203 | 61.88 202 | 54.98 207 | 55.45 229 | 50.55 233 | 64.92 196 | 40.36 229 | 55.21 171 | 38.13 197 | 48.31 190 | 63.76 99 | 63.03 112 | 73.73 174 | 68.58 200 | 68.00 225 | 73.04 192 |
|
PMMVS | | | 65.06 164 | 69.17 122 | 60.26 187 | 55.25 230 | 63.43 213 | 66.71 187 | 43.01 227 | 62.41 99 | 50.64 142 | 69.44 57 | 67.04 91 | 63.29 110 | 74.36 170 | 73.54 177 | 82.68 177 | 73.99 189 |
|
testmv | | | 42.58 227 | 44.36 227 | 40.49 227 | 54.63 231 | 52.76 229 | 41.21 235 | 44.37 224 | 28.83 235 | 12.87 235 | 27.16 232 | 25.03 236 | 23.01 232 | 60.83 223 | 61.13 221 | 66.88 227 | 54.81 227 |
|
test1235678 | | | 42.57 228 | 44.36 227 | 40.49 227 | 54.63 231 | 52.75 230 | 41.21 235 | 44.37 224 | 28.82 236 | 12.87 235 | 27.15 233 | 25.01 237 | 23.01 232 | 60.83 223 | 61.13 221 | 66.88 227 | 54.81 227 |
|
no-one | | | 36.35 231 | 37.59 233 | 34.91 230 | 46.13 233 | 49.89 234 | 27.99 240 | 43.56 226 | 20.91 240 | 7.03 241 | 14.64 238 | 15.50 242 | 18.92 237 | 42.95 235 | 60.20 224 | 65.84 229 | 59.03 224 |
|
Gipuma | | | 36.38 230 | 35.80 234 | 37.07 229 | 45.76 234 | 33.90 240 | 29.81 239 | 48.47 211 | 39.91 223 | 18.02 233 | 8.00 242 | 8.14 244 | 25.14 226 | 59.29 228 | 61.02 223 | 55.19 237 | 40.31 234 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
pmmvs3 | | | 47.65 218 | 49.08 223 | 45.99 220 | 44.61 235 | 54.79 228 | 50.04 224 | 31.95 238 | 33.91 231 | 29.90 210 | 30.37 225 | 33.53 227 | 46.31 197 | 63.50 216 | 63.67 216 | 73.14 214 | 63.77 215 |
|
MIMVSNet1 | | | 49.27 217 | 53.25 217 | 44.62 222 | 44.61 235 | 61.52 221 | 53.61 220 | 52.18 194 | 41.62 221 | 18.68 231 | 28.14 231 | 41.58 219 | 25.50 225 | 68.46 209 | 69.04 195 | 73.15 213 | 62.37 218 |
|
test12356 | | | 35.10 232 | 38.50 232 | 31.13 233 | 44.14 237 | 43.70 238 | 32.27 238 | 34.42 234 | 26.51 238 | 9.47 239 | 25.22 235 | 20.34 238 | 10.86 239 | 53.47 232 | 56.15 228 | 55.59 235 | 44.11 233 |
|
MDA-MVSNet-bldmvs | | | 53.37 215 | 53.01 218 | 53.79 212 | 43.67 238 | 67.95 199 | 59.69 213 | 57.92 172 | 43.69 216 | 32.41 209 | 41.47 208 | 27.89 235 | 52.38 182 | 56.97 231 | 65.99 211 | 76.68 199 | 67.13 208 |
|
E-PMN | | | 21.77 235 | 18.24 238 | 25.89 234 | 40.22 239 | 19.58 243 | 12.46 244 | 39.87 230 | 18.68 242 | 6.71 242 | 9.57 239 | 4.31 247 | 22.36 235 | 19.89 240 | 27.28 238 | 33.73 239 | 28.34 238 |
|
EMVS | | | 20.98 236 | 17.15 239 | 25.44 235 | 39.51 240 | 19.37 244 | 12.66 243 | 39.59 232 | 19.10 241 | 6.62 243 | 9.27 240 | 4.40 246 | 22.43 234 | 17.99 241 | 24.40 239 | 31.81 240 | 25.53 239 |
|
new_pmnet | | | 38.40 229 | 42.64 231 | 33.44 231 | 37.54 241 | 45.00 237 | 36.60 237 | 32.72 237 | 40.27 222 | 12.72 237 | 29.89 226 | 28.90 234 | 24.78 228 | 53.17 233 | 52.90 234 | 56.31 234 | 48.34 231 |
|
PMMVS2 | | | 25.60 234 | 29.75 235 | 20.76 237 | 28.00 242 | 30.93 241 | 23.10 241 | 29.18 239 | 23.14 239 | 1.46 245 | 18.23 237 | 16.54 240 | 5.08 240 | 40.22 236 | 41.40 236 | 37.76 238 | 37.79 236 |
|
tmp_tt | | | | | 14.50 239 | 14.68 243 | 7.17 246 | 10.46 246 | 2.21 241 | 37.73 227 | 28.71 214 | 25.26 234 | 16.98 239 | 4.37 241 | 31.49 237 | 29.77 237 | 26.56 241 | |
|
MVE | | 19.12 19 | 20.47 237 | 23.27 237 | 17.20 238 | 12.66 244 | 25.41 242 | 10.52 245 | 34.14 236 | 14.79 243 | 6.53 244 | 8.79 241 | 4.68 245 | 16.64 238 | 29.49 238 | 41.63 235 | 22.73 242 | 38.11 235 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
GG-mvs-BLEND | | | 46.86 223 | 67.51 150 | 22.75 236 | 0.05 245 | 76.21 159 | 64.69 197 | 0.04 242 | 61.90 103 | 0.09 246 | 55.57 118 | 71.32 67 | 0.08 242 | 70.54 197 | 67.19 206 | 71.58 217 | 69.86 202 |
|
testmvs | | | 0.09 238 | 0.15 240 | 0.02 240 | 0.01 246 | 0.02 247 | 0.05 248 | 0.01 243 | 0.11 244 | 0.01 247 | 0.26 244 | 0.01 248 | 0.06 244 | 0.10 242 | 0.10 240 | 0.01 244 | 0.43 241 |
|
sosnet-low-res | | | 0.00 240 | 0.00 242 | 0.00 242 | 0.00 247 | 0.00 249 | 0.00 250 | 0.00 245 | 0.00 246 | 0.00 248 | 0.00 245 | 0.00 249 | 0.00 245 | 0.00 244 | 0.00 243 | 0.00 247 | 0.00 243 |
|
sosnet | | | 0.00 240 | 0.00 242 | 0.00 242 | 0.00 247 | 0.00 249 | 0.00 250 | 0.00 245 | 0.00 246 | 0.00 248 | 0.00 245 | 0.00 249 | 0.00 245 | 0.00 244 | 0.00 243 | 0.00 247 | 0.00 243 |
|
test123 | | | 0.09 238 | 0.14 241 | 0.02 240 | 0.00 247 | 0.02 247 | 0.02 249 | 0.01 243 | 0.09 245 | 0.00 248 | 0.30 243 | 0.00 249 | 0.08 242 | 0.03 243 | 0.09 242 | 0.01 244 | 0.45 240 |
|
MTAPA | | | | | | | | | | | 83.48 1 | | 86.45 14 | | | | | |
|
MTMP | | | | | | | | | | | 82.66 3 | | 84.91 22 | | | | | |
|
Patchmatch-RL test | | | | | | | | 2.85 247 | | | | | | | | | | |
|
NP-MVS | | | | | | | | | | 80.10 42 | | | | | | | | |
|
Patchmtry | | | | | | | 65.80 206 | 65.97 191 | 52.74 191 | | 52.65 130 | | | | | | | |
|
DeepMVS_CX | | | | | | | 18.74 245 | 18.55 242 | 8.02 240 | 26.96 237 | 7.33 240 | 23.81 236 | 13.05 243 | 25.99 224 | 25.17 239 | | 22.45 243 | 36.25 237 |
|