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