DVP-MVS | | | 95.67 1 | 96.02 1 | 94.64 33 | 98.78 2 | 85.93 47 | 97.09 9 | 96.73 64 | 90.27 26 | 97.04 4 | 98.05 4 | 91.47 3 | 99.55 9 | 95.62 3 | 99.08 4 | 98.45 23 |
|
DPE-MVS | | | 95.57 2 | 95.67 2 | 95.25 6 | 98.36 21 | 87.28 11 | 95.56 66 | 97.51 4 | 89.13 51 | 97.14 3 | 97.91 6 | 91.64 2 | 99.62 1 | 94.61 8 | 99.17 2 | 98.86 5 |
|
APDe-MVS | | | 95.46 3 | 95.64 3 | 94.91 16 | 98.26 24 | 86.29 42 | 97.46 2 | 97.40 13 | 89.03 54 | 96.20 8 | 98.10 2 | 89.39 9 | 99.34 27 | 95.88 1 | 99.03 6 | 99.10 3 |
|
MSP-MVS | | | 95.42 4 | 95.56 4 | 94.98 14 | 98.49 12 | 86.52 31 | 96.91 18 | 97.47 7 | 91.73 8 | 96.10 9 | 96.69 46 | 89.90 5 | 99.30 34 | 94.70 6 | 98.04 54 | 99.13 1 |
|
CNVR-MVS | | | 95.40 5 | 95.37 5 | 95.50 4 | 98.11 32 | 88.51 4 | 95.29 76 | 96.96 44 | 92.09 3 | 95.32 14 | 97.08 31 | 89.49 8 | 99.33 31 | 95.10 5 | 98.85 12 | 98.66 10 |
|
SD-MVS | | | 94.96 9 | 95.33 6 | 93.88 54 | 97.25 60 | 86.69 24 | 96.19 35 | 97.11 35 | 90.42 25 | 96.95 6 | 97.27 19 | 89.53 7 | 96.91 219 | 94.38 10 | 98.85 12 | 98.03 56 |
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024 |
SteuartSystems-ACMMP | | | 95.20 6 | 95.32 7 | 94.85 21 | 96.99 63 | 86.33 38 | 97.33 3 | 97.30 21 | 91.38 11 | 95.39 13 | 97.46 12 | 88.98 12 | 99.40 25 | 94.12 12 | 98.89 11 | 98.82 6 |
Skip Steuart: Steuart Systems R&D Blog. |
SMA-MVS | | | 95.20 6 | 95.07 8 | 95.59 2 | 98.14 31 | 88.48 5 | 96.26 33 | 97.28 23 | 85.90 123 | 97.67 1 | 98.10 2 | 88.41 13 | 99.56 4 | 94.66 7 | 99.19 1 | 98.71 8 |
|
TSAR-MVS + MP. | | | 94.85 10 | 94.94 9 | 94.58 36 | 98.25 25 | 86.33 38 | 96.11 41 | 96.62 75 | 88.14 79 | 96.10 9 | 96.96 35 | 89.09 11 | 98.94 72 | 94.48 9 | 98.68 28 | 98.48 17 |
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition |
HPM-MVS++ | | | 95.14 8 | 94.91 10 | 95.83 1 | 98.25 25 | 89.65 1 | 95.92 50 | 96.96 44 | 91.75 7 | 94.02 27 | 96.83 39 | 88.12 15 | 99.55 9 | 93.41 20 | 98.94 9 | 98.28 35 |
|
DeepPCF-MVS | | 89.96 1 | 94.20 30 | 94.77 11 | 92.49 100 | 96.52 76 | 80.00 191 | 94.00 164 | 97.08 36 | 90.05 29 | 95.65 11 | 97.29 17 | 89.66 6 | 98.97 68 | 93.95 13 | 98.71 23 | 98.50 15 |
|
NCCC | | | 94.81 11 | 94.69 12 | 95.17 8 | 97.83 40 | 87.46 10 | 95.66 61 | 96.93 47 | 92.34 2 | 93.94 28 | 96.58 53 | 87.74 19 | 99.44 24 | 92.83 27 | 98.40 43 | 98.62 11 |
|
ACMMP_NAP | | | 94.74 12 | 94.56 13 | 95.28 5 | 98.02 37 | 87.70 6 | 95.68 59 | 97.34 15 | 88.28 74 | 95.30 15 | 97.67 8 | 85.90 39 | 99.54 13 | 93.91 14 | 98.95 8 | 98.60 12 |
|
9.14 | | | | 94.47 14 | | 97.79 41 | | 96.08 42 | 97.44 12 | 86.13 121 | 95.10 16 | 97.40 13 | 88.34 14 | 99.22 37 | 93.25 23 | 98.70 25 | |
|
HFP-MVS | | | 94.52 13 | 94.40 15 | 94.86 19 | 98.61 6 | 86.81 18 | 96.94 13 | 97.34 15 | 88.63 64 | 93.65 31 | 97.21 24 | 86.10 35 | 99.49 20 | 92.35 34 | 98.77 18 | 98.30 31 |
|
XVS | | | 94.45 15 | 94.32 16 | 94.85 21 | 98.54 9 | 86.60 29 | 96.93 15 | 97.19 28 | 90.66 23 | 92.85 46 | 97.16 29 | 85.02 50 | 99.49 20 | 91.99 43 | 98.56 39 | 98.47 19 |
|
zzz-MVS | | | 94.47 14 | 94.30 17 | 95.00 11 | 98.42 16 | 86.95 13 | 95.06 93 | 96.97 41 | 91.07 13 | 93.14 43 | 97.56 9 | 84.30 57 | 99.56 4 | 93.43 18 | 98.75 20 | 98.47 19 |
|
ACMMPR | | | 94.43 17 | 94.28 18 | 94.91 16 | 98.63 5 | 86.69 24 | 96.94 13 | 97.32 20 | 88.63 64 | 93.53 38 | 97.26 21 | 85.04 49 | 99.54 13 | 92.35 34 | 98.78 17 | 98.50 15 |
|
region2R | | | 94.43 17 | 94.27 19 | 94.92 15 | 98.65 4 | 86.67 26 | 96.92 17 | 97.23 26 | 88.60 66 | 93.58 35 | 97.27 19 | 85.22 46 | 99.54 13 | 92.21 36 | 98.74 22 | 98.56 14 |
|
MTAPA | | | 94.42 19 | 94.22 20 | 95.00 11 | 98.42 16 | 86.95 13 | 94.36 141 | 96.97 41 | 91.07 13 | 93.14 43 | 97.56 9 | 84.30 57 | 99.56 4 | 93.43 18 | 98.75 20 | 98.47 19 |
|
Regformer-2 | | | 94.33 22 | 94.22 20 | 94.68 31 | 95.54 109 | 86.75 23 | 94.57 122 | 96.70 68 | 91.84 6 | 94.41 19 | 96.56 55 | 87.19 25 | 99.13 45 | 93.50 15 | 97.65 64 | 98.16 45 |
|
CP-MVS | | | 94.34 21 | 94.21 22 | 94.74 30 | 98.39 19 | 86.64 28 | 97.60 1 | 97.24 24 | 88.53 68 | 92.73 53 | 97.23 22 | 85.20 47 | 99.32 32 | 92.15 39 | 98.83 14 | 98.25 40 |
|
MCST-MVS | | | 94.45 15 | 94.20 23 | 95.19 7 | 98.46 14 | 87.50 9 | 95.00 95 | 97.12 33 | 87.13 98 | 92.51 60 | 96.30 62 | 89.24 10 | 99.34 27 | 93.46 17 | 98.62 36 | 98.73 7 |
|
testtj | | | 94.39 20 | 94.18 24 | 95.00 11 | 98.24 27 | 86.77 22 | 96.16 36 | 97.23 26 | 87.28 96 | 94.85 18 | 97.04 32 | 86.99 28 | 99.52 17 | 91.54 55 | 98.33 46 | 98.71 8 |
|
SR-MVS | | | 94.23 26 | 94.17 25 | 94.43 42 | 98.21 29 | 85.78 54 | 96.40 29 | 96.90 49 | 88.20 77 | 94.33 21 | 97.40 13 | 84.75 54 | 99.03 55 | 93.35 21 | 97.99 55 | 98.48 17 |
|
#test# | | | 94.32 23 | 94.14 26 | 94.86 19 | 98.61 6 | 86.81 18 | 96.43 27 | 97.34 15 | 87.51 93 | 93.65 31 | 97.21 24 | 86.10 35 | 99.49 20 | 91.68 53 | 98.77 18 | 98.30 31 |
|
Regformer-1 | | | 94.22 27 | 94.13 27 | 94.51 39 | 95.54 109 | 86.36 37 | 94.57 122 | 96.44 83 | 91.69 9 | 94.32 22 | 96.56 55 | 87.05 27 | 99.03 55 | 93.35 21 | 97.65 64 | 98.15 46 |
|
MSLP-MVS++ | | | 93.72 38 | 94.08 28 | 92.65 93 | 97.31 54 | 83.43 100 | 95.79 54 | 97.33 18 | 90.03 30 | 93.58 35 | 96.96 35 | 84.87 52 | 97.76 151 | 92.19 38 | 98.66 32 | 96.76 109 |
|
MP-MVS | | | 94.25 24 | 94.07 29 | 94.77 28 | 98.47 13 | 86.31 40 | 96.71 23 | 96.98 40 | 89.04 53 | 91.98 69 | 97.19 26 | 85.43 44 | 99.56 4 | 92.06 42 | 98.79 15 | 98.44 24 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
APD-MVS | | | 94.24 25 | 94.07 29 | 94.75 29 | 98.06 35 | 86.90 16 | 95.88 51 | 96.94 46 | 85.68 129 | 95.05 17 | 97.18 27 | 87.31 24 | 99.07 49 | 91.90 50 | 98.61 37 | 98.28 35 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
MP-MVS-pluss | | | 94.21 28 | 94.00 31 | 94.85 21 | 98.17 30 | 86.65 27 | 94.82 106 | 97.17 31 | 86.26 117 | 92.83 48 | 97.87 7 | 85.57 42 | 99.56 4 | 94.37 11 | 98.92 10 | 98.34 28 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
GST-MVS | | | 94.21 28 | 93.97 32 | 94.90 18 | 98.41 18 | 86.82 17 | 96.54 26 | 97.19 28 | 88.24 75 | 93.26 39 | 96.83 39 | 85.48 43 | 99.59 3 | 91.43 59 | 98.40 43 | 98.30 31 |
|
HPM-MVS | | | 94.02 32 | 93.88 33 | 94.43 42 | 98.39 19 | 85.78 54 | 97.25 5 | 97.07 37 | 86.90 106 | 92.62 57 | 96.80 43 | 84.85 53 | 99.17 41 | 92.43 31 | 98.65 34 | 98.33 29 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
Regformer-4 | | | 93.91 35 | 93.81 34 | 94.19 49 | 95.36 113 | 85.47 58 | 94.68 114 | 96.41 86 | 91.60 10 | 93.75 30 | 96.71 44 | 85.95 38 | 99.10 48 | 93.21 24 | 96.65 81 | 98.01 58 |
|
DeepC-MVS_fast | | 89.43 2 | 94.04 31 | 93.79 35 | 94.80 27 | 97.48 49 | 86.78 20 | 95.65 63 | 96.89 50 | 89.40 43 | 92.81 49 | 96.97 34 | 85.37 45 | 99.24 36 | 90.87 69 | 98.69 26 | 98.38 27 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
mPP-MVS | | | 93.99 33 | 93.78 36 | 94.63 34 | 98.50 11 | 85.90 52 | 96.87 19 | 96.91 48 | 88.70 62 | 91.83 75 | 97.17 28 | 83.96 62 | 99.55 9 | 91.44 58 | 98.64 35 | 98.43 25 |
|
APD-MVS_3200maxsize | | | 93.78 37 | 93.77 37 | 93.80 59 | 97.92 38 | 84.19 82 | 96.30 31 | 96.87 53 | 86.96 102 | 93.92 29 | 97.47 11 | 83.88 63 | 98.96 71 | 92.71 29 | 97.87 59 | 98.26 39 |
|
PGM-MVS | | | 93.96 34 | 93.72 38 | 94.68 31 | 98.43 15 | 86.22 43 | 95.30 74 | 97.78 1 | 87.45 94 | 93.26 39 | 97.33 16 | 84.62 55 | 99.51 18 | 90.75 71 | 98.57 38 | 98.32 30 |
|
PHI-MVS | | | 93.89 36 | 93.65 39 | 94.62 35 | 96.84 66 | 86.43 34 | 96.69 24 | 97.49 5 | 85.15 142 | 93.56 37 | 96.28 63 | 85.60 41 | 99.31 33 | 92.45 30 | 98.79 15 | 98.12 49 |
|
Regformer-3 | | | 93.68 39 | 93.64 40 | 93.81 58 | 95.36 113 | 84.61 67 | 94.68 114 | 95.83 127 | 91.27 12 | 93.60 34 | 96.71 44 | 85.75 40 | 98.86 77 | 92.87 26 | 96.65 81 | 97.96 60 |
|
test_prior3 | | | 93.60 41 | 93.53 41 | 93.82 56 | 97.29 56 | 84.49 71 | 94.12 150 | 96.88 51 | 87.67 90 | 92.63 55 | 96.39 60 | 86.62 30 | 98.87 74 | 91.50 56 | 98.67 30 | 98.11 50 |
|
TSAR-MVS + GP. | | | 93.66 40 | 93.41 42 | 94.41 44 | 96.59 72 | 86.78 20 | 94.40 134 | 93.93 216 | 89.77 35 | 94.21 23 | 95.59 89 | 87.35 23 | 98.61 93 | 92.72 28 | 96.15 90 | 97.83 69 |
|
MVS_111021_HR | | | 93.45 43 | 93.31 43 | 93.84 55 | 96.99 63 | 84.84 63 | 93.24 196 | 97.24 24 | 88.76 60 | 91.60 80 | 95.85 81 | 86.07 37 | 98.66 88 | 91.91 47 | 98.16 50 | 98.03 56 |
|
DELS-MVS | | | 93.43 45 | 93.25 44 | 93.97 51 | 95.42 112 | 85.04 62 | 93.06 203 | 97.13 32 | 90.74 20 | 91.84 73 | 95.09 102 | 86.32 34 | 99.21 38 | 91.22 61 | 98.45 42 | 97.65 73 |
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 |
HPM-MVS_fast | | | 93.40 46 | 93.22 45 | 93.94 53 | 98.36 21 | 84.83 64 | 97.15 8 | 96.80 59 | 85.77 126 | 92.47 61 | 97.13 30 | 82.38 73 | 99.07 49 | 90.51 73 | 98.40 43 | 97.92 65 |
|
CANet | | | 93.54 42 | 93.20 46 | 94.55 37 | 95.65 105 | 85.73 56 | 94.94 98 | 96.69 70 | 91.89 5 | 90.69 92 | 95.88 80 | 81.99 84 | 99.54 13 | 93.14 25 | 97.95 57 | 98.39 26 |
|
train_agg | | | 93.44 44 | 93.08 47 | 94.52 38 | 97.53 45 | 86.49 32 | 94.07 157 | 96.78 60 | 81.86 209 | 92.77 50 | 96.20 67 | 87.63 21 | 99.12 46 | 92.14 40 | 98.69 26 | 97.94 61 |
|
abl_6 | | | 93.18 52 | 93.05 48 | 93.57 63 | 97.52 47 | 84.27 81 | 95.53 67 | 96.67 71 | 87.85 85 | 93.20 42 | 97.22 23 | 80.35 94 | 99.18 40 | 91.91 47 | 97.21 70 | 97.26 88 |
|
CSCG | | | 93.23 51 | 93.05 48 | 93.76 60 | 98.04 36 | 84.07 84 | 96.22 34 | 97.37 14 | 84.15 158 | 90.05 99 | 95.66 87 | 87.77 18 | 99.15 44 | 89.91 76 | 98.27 48 | 98.07 52 |
|
DeepC-MVS | | 88.79 3 | 93.31 47 | 92.99 50 | 94.26 47 | 96.07 91 | 85.83 53 | 94.89 101 | 96.99 39 | 89.02 55 | 89.56 102 | 97.37 15 | 82.51 72 | 99.38 26 | 92.20 37 | 98.30 47 | 97.57 78 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
agg_prior1 | | | 93.29 48 | 92.97 51 | 94.26 47 | 97.38 51 | 85.92 49 | 93.92 167 | 96.72 66 | 81.96 203 | 92.16 65 | 96.23 65 | 87.85 16 | 98.97 68 | 91.95 46 | 98.55 41 | 97.90 66 |
|
EI-MVSNet-Vis-set | | | 93.01 54 | 92.92 52 | 93.29 64 | 95.01 125 | 83.51 99 | 94.48 126 | 95.77 131 | 90.87 15 | 92.52 59 | 96.67 48 | 84.50 56 | 99.00 64 | 91.99 43 | 94.44 118 | 97.36 84 |
|
ACMMP | | | 93.24 50 | 92.88 53 | 94.30 46 | 98.09 34 | 85.33 60 | 96.86 20 | 97.45 10 | 88.33 72 | 90.15 98 | 97.03 33 | 81.44 87 | 99.51 18 | 90.85 70 | 95.74 93 | 98.04 55 |
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 |
canonicalmvs | | | 93.27 49 | 92.75 54 | 94.85 21 | 95.70 104 | 87.66 7 | 96.33 30 | 96.41 86 | 90.00 31 | 94.09 25 | 94.60 120 | 82.33 75 | 98.62 92 | 92.40 33 | 92.86 145 | 98.27 37 |
|
EIA-MVS | | | 92.74 57 | 92.66 55 | 92.97 78 | 95.20 121 | 84.04 86 | 95.07 90 | 96.51 81 | 90.73 21 | 92.96 45 | 91.19 232 | 84.06 60 | 98.34 112 | 91.72 52 | 96.54 84 | 96.54 117 |
|
EI-MVSNet-UG-set | | | 92.74 57 | 92.62 56 | 93.12 70 | 94.86 136 | 83.20 105 | 94.40 134 | 95.74 134 | 90.71 22 | 92.05 68 | 96.60 52 | 84.00 61 | 98.99 65 | 91.55 54 | 93.63 127 | 97.17 93 |
|
CS-MVS | | | 92.60 59 | 92.56 57 | 92.73 88 | 95.55 108 | 82.35 132 | 96.14 38 | 96.85 54 | 88.71 61 | 91.44 83 | 91.51 225 | 84.13 59 | 98.48 99 | 91.27 60 | 97.47 67 | 97.34 85 |
|
UA-Net | | | 92.83 55 | 92.54 58 | 93.68 61 | 96.10 89 | 84.71 66 | 95.66 61 | 96.39 88 | 91.92 4 | 93.22 41 | 96.49 57 | 83.16 66 | 98.87 74 | 84.47 135 | 95.47 98 | 97.45 83 |
|
alignmvs | | | 93.08 53 | 92.50 59 | 94.81 26 | 95.62 107 | 87.61 8 | 95.99 46 | 96.07 109 | 89.77 35 | 94.12 24 | 94.87 108 | 80.56 93 | 98.66 88 | 92.42 32 | 93.10 140 | 98.15 46 |
|
casdiffmvs | | | 92.51 61 | 92.43 60 | 92.74 87 | 94.41 155 | 81.98 138 | 94.54 124 | 96.23 98 | 89.57 39 | 91.96 70 | 96.17 71 | 82.58 71 | 98.01 138 | 90.95 67 | 95.45 100 | 98.23 41 |
|
CDPH-MVS | | | 92.83 55 | 92.30 61 | 94.44 40 | 97.79 41 | 86.11 45 | 94.06 159 | 96.66 72 | 80.09 230 | 92.77 50 | 96.63 50 | 86.62 30 | 99.04 54 | 87.40 102 | 98.66 32 | 98.17 44 |
|
baseline | | | 92.39 64 | 92.29 62 | 92.69 92 | 94.46 152 | 81.77 142 | 94.14 149 | 96.27 93 | 89.22 47 | 91.88 71 | 96.00 75 | 82.35 74 | 97.99 140 | 91.05 63 | 95.27 105 | 98.30 31 |
|
MVS_111021_LR | | | 92.47 62 | 92.29 62 | 92.98 77 | 95.99 94 | 84.43 78 | 93.08 201 | 96.09 107 | 88.20 77 | 91.12 89 | 95.72 86 | 81.33 89 | 97.76 151 | 91.74 51 | 97.37 69 | 96.75 110 |
|
ETV-MVS | | | 91.95 67 | 91.94 64 | 91.98 120 | 95.16 122 | 80.01 190 | 95.36 69 | 96.73 64 | 88.44 69 | 89.34 106 | 92.16 198 | 83.82 64 | 98.45 105 | 89.35 80 | 97.06 73 | 97.48 81 |
|
VNet | | | 92.24 65 | 91.91 65 | 93.24 66 | 96.59 72 | 83.43 100 | 94.84 105 | 96.44 83 | 89.19 49 | 94.08 26 | 95.90 79 | 77.85 125 | 98.17 122 | 88.90 85 | 93.38 134 | 98.13 48 |
|
CPTT-MVS | | | 91.99 66 | 91.80 66 | 92.55 97 | 98.24 27 | 81.98 138 | 96.76 22 | 96.49 82 | 81.89 208 | 90.24 96 | 96.44 59 | 78.59 115 | 98.61 93 | 89.68 77 | 97.85 60 | 97.06 98 |
|
DPM-MVS | | | 92.58 60 | 91.74 67 | 95.08 9 | 96.19 84 | 89.31 2 | 92.66 213 | 96.56 80 | 83.44 174 | 91.68 79 | 95.04 103 | 86.60 33 | 98.99 65 | 85.60 122 | 97.92 58 | 96.93 105 |
|
MG-MVS | | | 91.77 70 | 91.70 68 | 92.00 119 | 97.08 62 | 80.03 189 | 93.60 179 | 95.18 174 | 87.85 85 | 90.89 91 | 96.47 58 | 82.06 82 | 98.36 109 | 85.07 126 | 97.04 74 | 97.62 74 |
|
EPP-MVSNet | | | 91.70 73 | 91.56 69 | 92.13 116 | 95.88 97 | 80.50 177 | 97.33 3 | 95.25 170 | 86.15 119 | 89.76 101 | 95.60 88 | 83.42 65 | 98.32 115 | 87.37 104 | 93.25 137 | 97.56 79 |
|
3Dnovator+ | | 87.14 4 | 92.42 63 | 91.37 70 | 95.55 3 | 95.63 106 | 88.73 3 | 97.07 11 | 96.77 62 | 90.84 16 | 84.02 213 | 96.62 51 | 75.95 139 | 99.34 27 | 87.77 97 | 97.68 62 | 98.59 13 |
|
MVSFormer | | | 91.68 74 | 91.30 71 | 92.80 84 | 93.86 175 | 83.88 89 | 95.96 48 | 95.90 121 | 84.66 153 | 91.76 76 | 94.91 106 | 77.92 122 | 97.30 187 | 89.64 78 | 97.11 71 | 97.24 89 |
|
DP-MVS Recon | | | 91.95 67 | 91.28 72 | 93.96 52 | 98.33 23 | 85.92 49 | 94.66 117 | 96.66 72 | 82.69 192 | 90.03 100 | 95.82 82 | 82.30 76 | 99.03 55 | 84.57 134 | 96.48 87 | 96.91 106 |
|
diffmvs | | | 91.37 78 | 91.23 73 | 91.77 133 | 93.09 198 | 80.27 179 | 92.36 223 | 95.52 151 | 87.03 101 | 91.40 85 | 94.93 105 | 80.08 98 | 97.44 172 | 92.13 41 | 94.56 114 | 97.61 75 |
|
Vis-MVSNet | | | 91.75 71 | 91.23 73 | 93.29 64 | 95.32 116 | 83.78 91 | 96.14 38 | 95.98 114 | 89.89 32 | 90.45 94 | 96.58 53 | 75.09 147 | 98.31 116 | 84.75 132 | 96.90 75 | 97.78 72 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
Effi-MVS+ | | | 91.59 75 | 91.11 75 | 93.01 76 | 94.35 159 | 83.39 102 | 94.60 119 | 95.10 178 | 87.10 99 | 90.57 93 | 93.10 171 | 81.43 88 | 98.07 134 | 89.29 81 | 94.48 116 | 97.59 77 |
|
MVS_Test | | | 91.31 79 | 91.11 75 | 91.93 124 | 94.37 156 | 80.14 182 | 93.46 184 | 95.80 129 | 86.46 113 | 91.35 86 | 93.77 153 | 82.21 78 | 98.09 132 | 87.57 100 | 94.95 107 | 97.55 80 |
|
IS-MVSNet | | | 91.43 76 | 91.09 77 | 92.46 101 | 95.87 99 | 81.38 153 | 96.95 12 | 93.69 222 | 89.72 37 | 89.50 104 | 95.98 76 | 78.57 116 | 97.77 150 | 83.02 148 | 96.50 86 | 98.22 42 |
|
EPNet | | | 91.79 69 | 91.02 78 | 94.10 50 | 90.10 283 | 85.25 61 | 96.03 45 | 92.05 251 | 92.83 1 | 87.39 138 | 95.78 83 | 79.39 109 | 99.01 61 | 88.13 93 | 97.48 66 | 98.05 54 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
PS-MVSNAJ | | | 91.18 82 | 90.92 79 | 91.96 122 | 95.26 119 | 82.60 127 | 92.09 233 | 95.70 136 | 86.27 116 | 91.84 73 | 92.46 188 | 79.70 104 | 98.99 65 | 89.08 83 | 95.86 92 | 94.29 199 |
|
PVSNet_Blended_VisFu | | | 91.38 77 | 90.91 80 | 92.80 84 | 96.39 79 | 83.17 106 | 94.87 103 | 96.66 72 | 83.29 178 | 89.27 107 | 94.46 124 | 80.29 96 | 99.17 41 | 87.57 100 | 95.37 101 | 96.05 135 |
|
xiu_mvs_v2_base | | | 91.13 83 | 90.89 81 | 91.86 127 | 94.97 128 | 82.42 128 | 92.24 227 | 95.64 143 | 86.11 122 | 91.74 78 | 93.14 169 | 79.67 107 | 98.89 73 | 89.06 84 | 95.46 99 | 94.28 200 |
|
3Dnovator | | 86.66 5 | 91.73 72 | 90.82 82 | 94.44 40 | 94.59 146 | 86.37 36 | 97.18 7 | 97.02 38 | 89.20 48 | 84.31 209 | 96.66 49 | 73.74 169 | 99.17 41 | 86.74 112 | 97.96 56 | 97.79 71 |
|
PAPM_NR | | | 91.22 81 | 90.78 83 | 92.52 99 | 97.60 44 | 81.46 150 | 94.37 140 | 96.24 97 | 86.39 115 | 87.41 135 | 94.80 113 | 82.06 82 | 98.48 99 | 82.80 154 | 95.37 101 | 97.61 75 |
|
OMC-MVS | | | 91.23 80 | 90.62 84 | 93.08 72 | 96.27 82 | 84.07 84 | 93.52 181 | 95.93 117 | 86.95 103 | 89.51 103 | 96.13 73 | 78.50 117 | 98.35 111 | 85.84 120 | 92.90 144 | 96.83 108 |
|
nrg030 | | | 91.08 84 | 90.39 85 | 93.17 69 | 93.07 199 | 86.91 15 | 96.41 28 | 96.26 94 | 88.30 73 | 88.37 119 | 94.85 111 | 82.19 79 | 97.64 158 | 91.09 62 | 82.95 251 | 94.96 169 |
|
FIs | | | 90.51 96 | 90.35 86 | 90.99 161 | 93.99 171 | 80.98 162 | 95.73 56 | 97.54 3 | 89.15 50 | 86.72 148 | 94.68 116 | 81.83 86 | 97.24 195 | 85.18 125 | 88.31 203 | 94.76 179 |
|
PVSNet_Blended | | | 90.73 88 | 90.32 87 | 91.98 120 | 96.12 86 | 81.25 155 | 92.55 218 | 96.83 55 | 82.04 202 | 89.10 109 | 92.56 186 | 81.04 91 | 98.85 80 | 86.72 115 | 95.91 91 | 95.84 142 |
|
lupinMVS | | | 90.92 85 | 90.21 88 | 93.03 75 | 93.86 175 | 83.88 89 | 92.81 210 | 93.86 217 | 79.84 233 | 91.76 76 | 94.29 129 | 77.92 122 | 98.04 136 | 90.48 74 | 97.11 71 | 97.17 93 |
|
HQP_MVS | | | 90.60 95 | 90.19 89 | 91.82 130 | 94.70 142 | 82.73 121 | 95.85 52 | 96.22 99 | 90.81 17 | 86.91 144 | 94.86 109 | 74.23 158 | 98.12 124 | 88.15 91 | 89.99 172 | 94.63 181 |
|
FC-MVSNet-test | | | 90.27 99 | 90.18 90 | 90.53 172 | 93.71 181 | 79.85 195 | 95.77 55 | 97.59 2 | 89.31 45 | 86.27 157 | 94.67 117 | 81.93 85 | 97.01 212 | 84.26 137 | 88.09 207 | 94.71 180 |
|
jason | | | 90.80 86 | 90.10 91 | 92.90 81 | 93.04 201 | 83.53 98 | 93.08 201 | 94.15 211 | 80.22 227 | 91.41 84 | 94.91 106 | 76.87 128 | 97.93 145 | 90.28 75 | 96.90 75 | 97.24 89 |
jason: jason. |
API-MVS | | | 90.66 91 | 90.07 92 | 92.45 102 | 96.36 80 | 84.57 69 | 96.06 44 | 95.22 173 | 82.39 194 | 89.13 108 | 94.27 132 | 80.32 95 | 98.46 102 | 80.16 199 | 96.71 79 | 94.33 198 |
|
xiu_mvs_v1_base_debu | | | 90.64 92 | 90.05 93 | 92.40 103 | 93.97 172 | 84.46 74 | 93.32 186 | 95.46 154 | 85.17 139 | 92.25 62 | 94.03 135 | 70.59 204 | 98.57 95 | 90.97 64 | 94.67 109 | 94.18 201 |
|
xiu_mvs_v1_base | | | 90.64 92 | 90.05 93 | 92.40 103 | 93.97 172 | 84.46 74 | 93.32 186 | 95.46 154 | 85.17 139 | 92.25 62 | 94.03 135 | 70.59 204 | 98.57 95 | 90.97 64 | 94.67 109 | 94.18 201 |
|
xiu_mvs_v1_base_debi | | | 90.64 92 | 90.05 93 | 92.40 103 | 93.97 172 | 84.46 74 | 93.32 186 | 95.46 154 | 85.17 139 | 92.25 62 | 94.03 135 | 70.59 204 | 98.57 95 | 90.97 64 | 94.67 109 | 94.18 201 |
|
test_yl | | | 90.69 89 | 90.02 96 | 92.71 89 | 95.72 102 | 82.41 130 | 94.11 152 | 95.12 176 | 85.63 130 | 91.49 81 | 94.70 114 | 74.75 151 | 98.42 107 | 86.13 118 | 92.53 149 | 97.31 86 |
|
DCV-MVSNet | | | 90.69 89 | 90.02 96 | 92.71 89 | 95.72 102 | 82.41 130 | 94.11 152 | 95.12 176 | 85.63 130 | 91.49 81 | 94.70 114 | 74.75 151 | 98.42 107 | 86.13 118 | 92.53 149 | 97.31 86 |
|
VDD-MVS | | | 90.74 87 | 89.92 98 | 93.20 67 | 96.27 82 | 83.02 111 | 95.73 56 | 93.86 217 | 88.42 71 | 92.53 58 | 96.84 38 | 62.09 274 | 98.64 90 | 90.95 67 | 92.62 148 | 97.93 64 |
|
PVSNet_BlendedMVS | | | 89.98 104 | 89.70 99 | 90.82 165 | 96.12 86 | 81.25 155 | 93.92 167 | 96.83 55 | 83.49 173 | 89.10 109 | 92.26 196 | 81.04 91 | 98.85 80 | 86.72 115 | 87.86 211 | 92.35 272 |
|
PS-MVSNAJss | | | 89.97 105 | 89.62 100 | 91.02 158 | 91.90 221 | 80.85 167 | 95.26 79 | 95.98 114 | 86.26 117 | 86.21 158 | 94.29 129 | 79.70 104 | 97.65 156 | 88.87 86 | 88.10 205 | 94.57 186 |
|
OPM-MVS | | | 90.12 101 | 89.56 101 | 91.82 130 | 93.14 196 | 83.90 88 | 94.16 148 | 95.74 134 | 88.96 56 | 87.86 126 | 95.43 92 | 72.48 185 | 97.91 146 | 88.10 94 | 90.18 171 | 93.65 234 |
|
1121 | | | 90.42 97 | 89.49 102 | 93.20 67 | 97.27 58 | 84.46 74 | 92.63 214 | 95.51 152 | 71.01 308 | 91.20 88 | 96.21 66 | 82.92 68 | 99.05 51 | 80.56 192 | 98.07 53 | 96.10 131 |
|
XVG-OURS-SEG-HR | | | 89.95 106 | 89.45 103 | 91.47 141 | 94.00 170 | 81.21 158 | 91.87 236 | 96.06 111 | 85.78 125 | 88.55 115 | 95.73 85 | 74.67 154 | 97.27 191 | 88.71 87 | 89.64 181 | 95.91 138 |
|
Vis-MVSNet (Re-imp) | | | 89.59 114 | 89.44 104 | 90.03 197 | 95.74 101 | 75.85 262 | 95.61 64 | 90.80 286 | 87.66 92 | 87.83 128 | 95.40 93 | 76.79 130 | 96.46 239 | 78.37 216 | 96.73 78 | 97.80 70 |
|
CANet_DTU | | | 90.26 100 | 89.41 105 | 92.81 83 | 93.46 189 | 83.01 112 | 93.48 182 | 94.47 201 | 89.43 42 | 87.76 131 | 94.23 133 | 70.54 208 | 99.03 55 | 84.97 127 | 96.39 88 | 96.38 119 |
|
MAR-MVS | | | 90.30 98 | 89.37 106 | 93.07 74 | 96.61 71 | 84.48 73 | 95.68 59 | 95.67 138 | 82.36 196 | 87.85 127 | 92.85 176 | 76.63 134 | 98.80 84 | 80.01 200 | 96.68 80 | 95.91 138 |
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 |
mvs_anonymous | | | 89.37 125 | 89.32 107 | 89.51 219 | 93.47 188 | 74.22 270 | 91.65 244 | 94.83 193 | 82.91 187 | 85.45 177 | 93.79 151 | 81.23 90 | 96.36 245 | 86.47 117 | 94.09 121 | 97.94 61 |
|
UniMVSNet_NR-MVSNet | | | 89.92 108 | 89.29 108 | 91.81 132 | 93.39 190 | 83.72 92 | 94.43 132 | 97.12 33 | 89.80 34 | 86.46 151 | 93.32 160 | 83.16 66 | 97.23 197 | 84.92 128 | 81.02 273 | 94.49 193 |
|
HQP-MVS | | | 89.80 110 | 89.28 109 | 91.34 145 | 94.17 161 | 81.56 144 | 94.39 136 | 96.04 112 | 88.81 57 | 85.43 180 | 93.97 142 | 73.83 167 | 97.96 142 | 87.11 109 | 89.77 179 | 94.50 191 |
|
PAPR | | | 90.02 103 | 89.27 110 | 92.29 111 | 95.78 100 | 80.95 164 | 92.68 212 | 96.22 99 | 81.91 206 | 86.66 149 | 93.75 155 | 82.23 77 | 98.44 106 | 79.40 210 | 94.79 108 | 97.48 81 |
|
mvs-test1 | | | 89.45 119 | 89.14 111 | 90.38 182 | 93.33 191 | 77.63 246 | 94.95 97 | 94.36 204 | 87.70 88 | 87.10 141 | 92.81 180 | 73.45 172 | 98.03 137 | 85.57 123 | 93.04 141 | 95.48 151 |
|
LFMVS | | | 90.08 102 | 89.13 112 | 92.95 79 | 96.71 68 | 82.32 133 | 96.08 42 | 89.91 301 | 86.79 107 | 92.15 67 | 96.81 41 | 62.60 271 | 98.34 112 | 87.18 106 | 93.90 123 | 98.19 43 |
|
UniMVSNet (Re) | | | 89.80 110 | 89.07 113 | 92.01 117 | 93.60 185 | 84.52 70 | 94.78 109 | 97.47 7 | 89.26 46 | 86.44 154 | 92.32 193 | 82.10 80 | 97.39 184 | 84.81 131 | 80.84 277 | 94.12 205 |
|
AdaColmap | | | 89.89 109 | 89.07 113 | 92.37 106 | 97.41 50 | 83.03 110 | 94.42 133 | 95.92 118 | 82.81 189 | 86.34 156 | 94.65 118 | 73.89 165 | 99.02 59 | 80.69 189 | 95.51 96 | 95.05 163 |
|
VPA-MVSNet | | | 89.62 112 | 88.96 115 | 91.60 138 | 93.86 175 | 82.89 116 | 95.46 68 | 97.33 18 | 87.91 82 | 88.43 118 | 93.31 161 | 74.17 161 | 97.40 181 | 87.32 105 | 82.86 253 | 94.52 189 |
|
UGNet | | | 89.95 106 | 88.95 116 | 92.95 79 | 94.51 149 | 83.31 103 | 95.70 58 | 95.23 171 | 89.37 44 | 87.58 133 | 93.94 143 | 64.00 266 | 98.78 85 | 83.92 139 | 96.31 89 | 96.74 111 |
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 |
WTY-MVS | | | 89.60 113 | 88.92 117 | 91.67 136 | 95.47 111 | 81.15 159 | 92.38 222 | 94.78 195 | 83.11 181 | 89.06 111 | 94.32 127 | 78.67 114 | 96.61 230 | 81.57 175 | 90.89 165 | 97.24 89 |
|
LPG-MVS_test | | | 89.45 119 | 88.90 118 | 91.12 150 | 94.47 150 | 81.49 148 | 95.30 74 | 96.14 104 | 86.73 109 | 85.45 177 | 95.16 99 | 69.89 214 | 98.10 126 | 87.70 98 | 89.23 188 | 93.77 228 |
|
CLD-MVS | | | 89.47 118 | 88.90 118 | 91.18 149 | 94.22 160 | 82.07 136 | 92.13 231 | 96.09 107 | 87.90 83 | 85.37 186 | 92.45 189 | 74.38 156 | 97.56 162 | 87.15 107 | 90.43 167 | 93.93 214 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
EI-MVSNet | | | 89.10 129 | 88.86 120 | 89.80 208 | 91.84 223 | 78.30 227 | 93.70 176 | 95.01 181 | 85.73 127 | 87.15 139 | 95.28 94 | 79.87 101 | 97.21 199 | 83.81 141 | 87.36 216 | 93.88 218 |
|
XVG-OURS | | | 89.40 124 | 88.70 121 | 91.52 139 | 94.06 164 | 81.46 150 | 91.27 250 | 96.07 109 | 86.14 120 | 88.89 113 | 95.77 84 | 68.73 232 | 97.26 193 | 87.39 103 | 89.96 174 | 95.83 143 |
|
Fast-Effi-MVS+ | | | 89.41 122 | 88.64 122 | 91.71 135 | 94.74 138 | 80.81 168 | 93.54 180 | 95.10 178 | 83.11 181 | 86.82 147 | 90.67 247 | 79.74 103 | 97.75 154 | 80.51 194 | 93.55 128 | 96.57 115 |
|
test_djsdf | | | 89.03 132 | 88.64 122 | 90.21 187 | 90.74 268 | 79.28 209 | 95.96 48 | 95.90 121 | 84.66 153 | 85.33 188 | 92.94 175 | 74.02 164 | 97.30 187 | 89.64 78 | 88.53 196 | 94.05 211 |
|
CDS-MVSNet | | | 89.45 119 | 88.51 124 | 92.29 111 | 93.62 184 | 83.61 97 | 93.01 204 | 94.68 197 | 81.95 204 | 87.82 129 | 93.24 165 | 78.69 113 | 96.99 213 | 80.34 196 | 93.23 138 | 96.28 122 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
DU-MVS | | | 89.34 126 | 88.50 125 | 91.85 129 | 93.04 201 | 83.72 92 | 94.47 129 | 96.59 77 | 89.50 40 | 86.46 151 | 93.29 163 | 77.25 126 | 97.23 197 | 84.92 128 | 81.02 273 | 94.59 184 |
|
114514_t | | | 89.51 116 | 88.50 125 | 92.54 98 | 98.11 32 | 81.99 137 | 95.16 86 | 96.36 90 | 70.19 310 | 85.81 163 | 95.25 96 | 76.70 132 | 98.63 91 | 82.07 165 | 96.86 77 | 97.00 102 |
|
VDDNet | | | 89.56 115 | 88.49 127 | 92.76 86 | 95.07 124 | 82.09 135 | 96.30 31 | 93.19 228 | 81.05 222 | 91.88 71 | 96.86 37 | 61.16 284 | 98.33 114 | 88.43 90 | 92.49 151 | 97.84 68 |
|
ACMM | | 84.12 9 | 89.14 128 | 88.48 128 | 91.12 150 | 94.65 145 | 81.22 157 | 95.31 72 | 96.12 106 | 85.31 138 | 85.92 162 | 94.34 125 | 70.19 212 | 98.06 135 | 85.65 121 | 88.86 193 | 94.08 209 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
Effi-MVS+-dtu | | | 88.65 141 | 88.35 129 | 89.54 216 | 93.33 191 | 76.39 257 | 94.47 129 | 94.36 204 | 87.70 88 | 85.43 180 | 89.56 266 | 73.45 172 | 97.26 193 | 85.57 123 | 91.28 158 | 94.97 166 |
|
ab-mvs | | | 89.41 122 | 88.35 129 | 92.60 94 | 95.15 123 | 82.65 125 | 92.20 229 | 95.60 145 | 83.97 162 | 88.55 115 | 93.70 156 | 74.16 162 | 98.21 121 | 82.46 158 | 89.37 184 | 96.94 104 |
|
ACMP | | 84.23 8 | 89.01 134 | 88.35 129 | 90.99 161 | 94.73 139 | 81.27 154 | 95.07 90 | 95.89 123 | 86.48 112 | 83.67 221 | 94.30 128 | 69.33 221 | 97.99 140 | 87.10 111 | 88.55 195 | 93.72 232 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
LCM-MVSNet-Re | | | 88.30 150 | 88.32 132 | 88.27 241 | 94.71 141 | 72.41 289 | 93.15 197 | 90.98 280 | 87.77 87 | 79.25 276 | 91.96 210 | 78.35 119 | 95.75 270 | 83.04 147 | 95.62 94 | 96.65 113 |
|
MVSTER | | | 88.84 136 | 88.29 133 | 90.51 175 | 92.95 205 | 80.44 178 | 93.73 173 | 95.01 181 | 84.66 153 | 87.15 139 | 93.12 170 | 72.79 181 | 97.21 199 | 87.86 96 | 87.36 216 | 93.87 219 |
|
TAMVS | | | 89.21 127 | 88.29 133 | 91.96 122 | 93.71 181 | 82.62 126 | 93.30 190 | 94.19 209 | 82.22 198 | 87.78 130 | 93.94 143 | 78.83 111 | 96.95 216 | 77.70 224 | 92.98 143 | 96.32 120 |
|
sss | | | 88.93 135 | 88.26 135 | 90.94 164 | 94.05 165 | 80.78 169 | 91.71 241 | 95.38 164 | 81.55 215 | 88.63 114 | 93.91 147 | 75.04 148 | 95.47 282 | 82.47 157 | 91.61 156 | 96.57 115 |
|
QAPM | | | 89.51 116 | 88.15 136 | 93.59 62 | 94.92 132 | 84.58 68 | 96.82 21 | 96.70 68 | 78.43 249 | 83.41 228 | 96.19 70 | 73.18 177 | 99.30 34 | 77.11 231 | 96.54 84 | 96.89 107 |
|
BH-untuned | | | 88.60 143 | 88.13 137 | 90.01 199 | 95.24 120 | 78.50 222 | 93.29 191 | 94.15 211 | 84.75 151 | 84.46 201 | 93.40 157 | 75.76 140 | 97.40 181 | 77.59 225 | 94.52 115 | 94.12 205 |
|
PLC | | 84.53 7 | 89.06 131 | 88.03 138 | 92.15 114 | 97.27 58 | 82.69 124 | 94.29 142 | 95.44 159 | 79.71 235 | 84.01 214 | 94.18 134 | 76.68 133 | 98.75 86 | 77.28 228 | 93.41 133 | 95.02 164 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
CNLPA | | | 89.07 130 | 87.98 139 | 92.34 107 | 96.87 65 | 84.78 65 | 94.08 156 | 93.24 227 | 81.41 217 | 84.46 201 | 95.13 101 | 75.57 143 | 96.62 228 | 77.21 229 | 93.84 125 | 95.61 149 |
|
TranMVSNet+NR-MVSNet | | | 88.84 136 | 87.95 140 | 91.49 140 | 92.68 210 | 83.01 112 | 94.92 100 | 96.31 91 | 89.88 33 | 85.53 171 | 93.85 150 | 76.63 134 | 96.96 215 | 81.91 169 | 79.87 290 | 94.50 191 |
|
HY-MVS | | 83.01 12 | 89.03 132 | 87.94 141 | 92.29 111 | 94.86 136 | 82.77 117 | 92.08 234 | 94.49 200 | 81.52 216 | 86.93 143 | 92.79 182 | 78.32 120 | 98.23 118 | 79.93 201 | 90.55 166 | 95.88 140 |
|
IterMVS-LS | | | 88.36 148 | 87.91 142 | 89.70 212 | 93.80 178 | 78.29 228 | 93.73 173 | 95.08 180 | 85.73 127 | 84.75 195 | 91.90 212 | 79.88 100 | 96.92 218 | 83.83 140 | 82.51 254 | 93.89 216 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
tttt0517 | | | 88.61 142 | 87.78 143 | 91.11 153 | 94.96 129 | 77.81 240 | 95.35 70 | 89.69 305 | 85.09 144 | 88.05 124 | 94.59 121 | 66.93 244 | 98.48 99 | 83.27 145 | 92.13 154 | 97.03 100 |
|
CHOSEN 1792x2688 | | | 88.84 136 | 87.69 144 | 92.30 110 | 96.14 85 | 81.42 152 | 90.01 264 | 95.86 125 | 74.52 284 | 87.41 135 | 93.94 143 | 75.46 144 | 98.36 109 | 80.36 195 | 95.53 95 | 97.12 97 |
|
WR-MVS | | | 88.38 146 | 87.67 145 | 90.52 174 | 93.30 193 | 80.18 180 | 93.26 193 | 95.96 116 | 88.57 67 | 85.47 176 | 92.81 180 | 76.12 136 | 96.91 219 | 81.24 178 | 82.29 256 | 94.47 196 |
|
thisisatest0530 | | | 88.67 140 | 87.61 146 | 91.86 127 | 94.87 135 | 80.07 185 | 94.63 118 | 89.90 302 | 84.00 161 | 88.46 117 | 93.78 152 | 66.88 246 | 98.46 102 | 83.30 144 | 92.65 147 | 97.06 98 |
|
jajsoiax | | | 88.24 151 | 87.50 147 | 90.48 177 | 90.89 262 | 80.14 182 | 95.31 72 | 95.65 142 | 84.97 146 | 84.24 211 | 94.02 138 | 65.31 260 | 97.42 174 | 88.56 88 | 88.52 197 | 93.89 216 |
|
BH-RMVSNet | | | 88.37 147 | 87.48 148 | 91.02 158 | 95.28 117 | 79.45 201 | 92.89 209 | 93.07 230 | 85.45 135 | 86.91 144 | 94.84 112 | 70.35 209 | 97.76 151 | 73.97 257 | 94.59 113 | 95.85 141 |
|
VPNet | | | 88.20 152 | 87.47 149 | 90.39 180 | 93.56 186 | 79.46 200 | 94.04 160 | 95.54 150 | 88.67 63 | 86.96 142 | 94.58 122 | 69.33 221 | 97.15 201 | 84.05 138 | 80.53 282 | 94.56 187 |
|
NR-MVSNet | | | 88.58 144 | 87.47 149 | 91.93 124 | 93.04 201 | 84.16 83 | 94.77 110 | 96.25 96 | 89.05 52 | 80.04 269 | 93.29 163 | 79.02 110 | 97.05 210 | 81.71 174 | 80.05 287 | 94.59 184 |
|
WR-MVS_H | | | 87.80 163 | 87.37 151 | 89.10 226 | 93.23 194 | 78.12 231 | 95.61 64 | 97.30 21 | 87.90 83 | 83.72 219 | 92.01 209 | 79.65 108 | 96.01 258 | 76.36 236 | 80.54 281 | 93.16 248 |
|
1112_ss | | | 88.42 145 | 87.33 152 | 91.72 134 | 94.92 132 | 80.98 162 | 92.97 207 | 94.54 199 | 78.16 254 | 83.82 217 | 93.88 148 | 78.78 112 | 97.91 146 | 79.45 206 | 89.41 183 | 96.26 123 |
|
OpenMVS | | 83.78 11 | 88.74 139 | 87.29 153 | 93.08 72 | 92.70 209 | 85.39 59 | 96.57 25 | 96.43 85 | 78.74 246 | 80.85 255 | 96.07 74 | 69.64 218 | 99.01 61 | 78.01 222 | 96.65 81 | 94.83 176 |
|
mvs_tets | | | 88.06 158 | 87.28 154 | 90.38 182 | 90.94 258 | 79.88 193 | 95.22 81 | 95.66 140 | 85.10 143 | 84.21 212 | 93.94 143 | 63.53 268 | 97.40 181 | 88.50 89 | 88.40 201 | 93.87 219 |
|
baseline1 | | | 88.10 155 | 87.28 154 | 90.57 169 | 94.96 129 | 80.07 185 | 94.27 143 | 91.29 273 | 86.74 108 | 87.41 135 | 94.00 140 | 76.77 131 | 96.20 250 | 80.77 187 | 79.31 294 | 95.44 153 |
|
CP-MVSNet | | | 87.63 170 | 87.26 156 | 88.74 231 | 93.12 197 | 76.59 256 | 95.29 76 | 96.58 78 | 88.43 70 | 83.49 227 | 92.98 174 | 75.28 145 | 95.83 266 | 78.97 212 | 81.15 270 | 93.79 224 |
|
anonymousdsp | | | 87.84 161 | 87.09 157 | 90.12 192 | 89.13 293 | 80.54 175 | 94.67 116 | 95.55 148 | 82.05 201 | 83.82 217 | 92.12 201 | 71.47 193 | 97.15 201 | 87.15 107 | 87.80 212 | 92.67 261 |
|
v2v482 | | | 87.84 161 | 87.06 158 | 90.17 188 | 90.99 254 | 79.23 212 | 94.00 164 | 95.13 175 | 84.87 147 | 85.53 171 | 92.07 207 | 74.45 155 | 97.45 170 | 84.71 133 | 81.75 264 | 93.85 222 |
|
BH-w/o | | | 87.57 175 | 87.05 159 | 89.12 225 | 94.90 134 | 77.90 236 | 92.41 220 | 93.51 224 | 82.89 188 | 83.70 220 | 91.34 226 | 75.75 141 | 97.07 208 | 75.49 244 | 93.49 130 | 92.39 270 |
|
TAPA-MVS | | 84.62 6 | 88.16 153 | 87.01 160 | 91.62 137 | 96.64 70 | 80.65 171 | 94.39 136 | 96.21 102 | 76.38 265 | 86.19 159 | 95.44 90 | 79.75 102 | 98.08 133 | 62.75 309 | 95.29 103 | 96.13 127 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
PS-CasMVS | | | 87.32 184 | 86.88 161 | 88.63 234 | 92.99 204 | 76.33 259 | 95.33 71 | 96.61 76 | 88.22 76 | 83.30 230 | 93.07 172 | 73.03 179 | 95.79 269 | 78.36 217 | 81.00 275 | 93.75 230 |
|
V42 | | | 87.68 165 | 86.86 162 | 90.15 190 | 90.58 274 | 80.14 182 | 94.24 145 | 95.28 169 | 83.66 168 | 85.67 166 | 91.33 227 | 74.73 153 | 97.41 179 | 84.43 136 | 81.83 262 | 92.89 256 |
|
XXY-MVS | | | 87.65 167 | 86.85 163 | 90.03 197 | 92.14 217 | 80.60 174 | 93.76 172 | 95.23 171 | 82.94 185 | 84.60 197 | 94.02 138 | 74.27 157 | 95.49 281 | 81.04 180 | 83.68 244 | 94.01 213 |
|
DI_MVS_plusplus_test | | | 88.15 154 | 86.82 164 | 92.14 115 | 90.67 271 | 81.07 160 | 93.01 204 | 94.59 198 | 83.83 165 | 77.78 283 | 90.63 248 | 68.51 235 | 98.16 123 | 88.02 95 | 94.37 119 | 97.17 93 |
|
HyFIR lowres test | | | 88.09 156 | 86.81 165 | 91.93 124 | 96.00 93 | 80.63 172 | 90.01 264 | 95.79 130 | 73.42 292 | 87.68 132 | 92.10 204 | 73.86 166 | 97.96 142 | 80.75 188 | 91.70 155 | 97.19 92 |
|
F-COLMAP | | | 87.95 159 | 86.80 166 | 91.40 143 | 96.35 81 | 80.88 166 | 94.73 112 | 95.45 157 | 79.65 236 | 82.04 243 | 94.61 119 | 71.13 195 | 98.50 98 | 76.24 239 | 91.05 163 | 94.80 178 |
|
v1144 | | | 87.61 173 | 86.79 167 | 90.06 196 | 91.01 253 | 79.34 205 | 93.95 166 | 95.42 163 | 83.36 177 | 85.66 167 | 91.31 230 | 74.98 149 | 97.42 174 | 83.37 143 | 82.06 258 | 93.42 242 |
|
Fast-Effi-MVS+-dtu | | | 87.44 180 | 86.72 168 | 89.63 214 | 92.04 220 | 77.68 245 | 94.03 161 | 93.94 215 | 85.81 124 | 82.42 237 | 91.32 229 | 70.33 210 | 97.06 209 | 80.33 197 | 90.23 170 | 94.14 204 |
|
thres100view900 | | | 87.63 170 | 86.71 169 | 90.38 182 | 96.12 86 | 78.55 219 | 95.03 94 | 91.58 264 | 87.15 97 | 88.06 123 | 92.29 195 | 68.91 229 | 98.10 126 | 70.13 276 | 91.10 159 | 94.48 194 |
|
v8 | | | 87.50 179 | 86.71 169 | 89.89 202 | 91.37 240 | 79.40 202 | 94.50 125 | 95.38 164 | 84.81 149 | 83.60 224 | 91.33 227 | 76.05 137 | 97.42 174 | 82.84 152 | 80.51 284 | 92.84 258 |
|
thres600view7 | | | 87.65 167 | 86.67 171 | 90.59 168 | 96.08 90 | 78.72 215 | 94.88 102 | 91.58 264 | 87.06 100 | 88.08 122 | 92.30 194 | 68.91 229 | 98.10 126 | 70.05 279 | 91.10 159 | 94.96 169 |
|
tfpn200view9 | | | 87.58 174 | 86.64 172 | 90.41 179 | 95.99 94 | 78.64 217 | 94.58 120 | 91.98 255 | 86.94 104 | 88.09 120 | 91.77 214 | 69.18 226 | 98.10 126 | 70.13 276 | 91.10 159 | 94.48 194 |
|
thres400 | | | 87.62 172 | 86.64 172 | 90.57 169 | 95.99 94 | 78.64 217 | 94.58 120 | 91.98 255 | 86.94 104 | 88.09 120 | 91.77 214 | 69.18 226 | 98.10 126 | 70.13 276 | 91.10 159 | 94.96 169 |
|
Baseline_NR-MVSNet | | | 87.07 193 | 86.63 174 | 88.40 237 | 91.44 234 | 77.87 238 | 94.23 146 | 92.57 240 | 84.12 159 | 85.74 165 | 92.08 205 | 77.25 126 | 96.04 255 | 82.29 161 | 79.94 288 | 91.30 290 |
|
Anonymous20240529 | | | 88.09 156 | 86.59 175 | 92.58 96 | 96.53 75 | 81.92 140 | 95.99 46 | 95.84 126 | 74.11 287 | 89.06 111 | 95.21 98 | 61.44 279 | 98.81 83 | 83.67 142 | 87.47 213 | 97.01 101 |
|
1314 | | | 87.51 177 | 86.57 176 | 90.34 185 | 92.42 214 | 79.74 197 | 92.63 214 | 95.35 168 | 78.35 250 | 80.14 266 | 91.62 221 | 74.05 163 | 97.15 201 | 81.05 179 | 93.53 129 | 94.12 205 |
|
Test_1112_low_res | | | 87.65 167 | 86.51 177 | 91.08 154 | 94.94 131 | 79.28 209 | 91.77 238 | 94.30 207 | 76.04 270 | 83.51 226 | 92.37 191 | 77.86 124 | 97.73 155 | 78.69 215 | 89.13 190 | 96.22 124 |
|
v10 | | | 87.25 187 | 86.38 178 | 89.85 203 | 91.19 246 | 79.50 199 | 94.48 126 | 95.45 157 | 83.79 166 | 83.62 223 | 91.19 232 | 75.13 146 | 97.42 174 | 81.94 168 | 80.60 279 | 92.63 263 |
|
UniMVSNet_ETH3D | | | 87.53 176 | 86.37 179 | 91.00 160 | 92.44 213 | 78.96 214 | 94.74 111 | 95.61 144 | 84.07 160 | 85.36 187 | 94.52 123 | 59.78 292 | 97.34 186 | 82.93 149 | 87.88 210 | 96.71 112 |
|
v144192 | | | 87.19 191 | 86.35 180 | 89.74 209 | 90.64 272 | 78.24 229 | 93.92 167 | 95.43 161 | 81.93 205 | 85.51 173 | 91.05 240 | 74.21 160 | 97.45 170 | 82.86 151 | 81.56 266 | 93.53 237 |
|
v1192 | | | 87.25 187 | 86.33 181 | 90.00 200 | 90.76 267 | 79.04 213 | 93.80 170 | 95.48 153 | 82.57 193 | 85.48 175 | 91.18 234 | 73.38 176 | 97.42 174 | 82.30 160 | 82.06 258 | 93.53 237 |
|
v148 | | | 87.04 194 | 86.32 182 | 89.21 223 | 90.94 258 | 77.26 249 | 93.71 175 | 94.43 202 | 84.84 148 | 84.36 207 | 90.80 245 | 76.04 138 | 97.05 210 | 82.12 164 | 79.60 292 | 93.31 243 |
|
LS3D | | | 87.89 160 | 86.32 182 | 92.59 95 | 96.07 91 | 82.92 115 | 95.23 80 | 94.92 188 | 75.66 272 | 82.89 233 | 95.98 76 | 72.48 185 | 99.21 38 | 68.43 286 | 95.23 106 | 95.64 148 |
|
PEN-MVS | | | 86.80 197 | 86.27 184 | 88.40 237 | 92.32 215 | 75.71 263 | 95.18 84 | 96.38 89 | 87.97 80 | 82.82 234 | 93.15 168 | 73.39 175 | 95.92 261 | 76.15 240 | 79.03 296 | 93.59 235 |
|
thres200 | | | 87.21 190 | 86.24 185 | 90.12 192 | 95.36 113 | 78.53 220 | 93.26 193 | 92.10 248 | 86.42 114 | 88.00 125 | 91.11 238 | 69.24 225 | 98.00 139 | 69.58 280 | 91.04 164 | 93.83 223 |
|
Anonymous202405211 | | | 87.68 165 | 86.13 186 | 92.31 109 | 96.66 69 | 80.74 170 | 94.87 103 | 91.49 268 | 80.47 226 | 89.46 105 | 95.44 90 | 54.72 307 | 98.23 118 | 82.19 163 | 89.89 176 | 97.97 59 |
|
X-MVStestdata | | | 88.31 149 | 86.13 186 | 94.85 21 | 98.54 9 | 86.60 29 | 96.93 15 | 97.19 28 | 90.66 23 | 92.85 46 | 23.41 332 | 85.02 50 | 99.49 20 | 91.99 43 | 98.56 39 | 98.47 19 |
|
FMVSNet3 | | | 87.40 182 | 86.11 188 | 91.30 146 | 93.79 180 | 83.64 95 | 94.20 147 | 94.81 194 | 83.89 163 | 84.37 204 | 91.87 213 | 68.45 236 | 96.56 231 | 78.23 219 | 85.36 229 | 93.70 233 |
|
MVS | | | 87.44 180 | 86.10 189 | 91.44 142 | 92.61 211 | 83.62 96 | 92.63 214 | 95.66 140 | 67.26 314 | 81.47 247 | 92.15 199 | 77.95 121 | 98.22 120 | 79.71 203 | 95.48 97 | 92.47 267 |
|
PCF-MVS | | 84.11 10 | 87.74 164 | 86.08 190 | 92.70 91 | 94.02 166 | 84.43 78 | 89.27 274 | 95.87 124 | 73.62 291 | 84.43 203 | 94.33 126 | 78.48 118 | 98.86 77 | 70.27 272 | 94.45 117 | 94.81 177 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
v1921920 | | | 86.97 195 | 86.06 191 | 89.69 213 | 90.53 277 | 78.11 232 | 93.80 170 | 95.43 161 | 81.90 207 | 85.33 188 | 91.05 240 | 72.66 182 | 97.41 179 | 82.05 166 | 81.80 263 | 93.53 237 |
|
thisisatest0515 | | | 87.33 183 | 85.99 192 | 91.37 144 | 93.49 187 | 79.55 198 | 90.63 255 | 89.56 308 | 80.17 228 | 87.56 134 | 90.86 243 | 67.07 243 | 98.28 117 | 81.50 176 | 93.02 142 | 96.29 121 |
|
GBi-Net | | | 87.26 185 | 85.98 193 | 91.08 154 | 94.01 167 | 83.10 107 | 95.14 87 | 94.94 184 | 83.57 169 | 84.37 204 | 91.64 217 | 66.59 251 | 96.34 246 | 78.23 219 | 85.36 229 | 93.79 224 |
|
test1 | | | 87.26 185 | 85.98 193 | 91.08 154 | 94.01 167 | 83.10 107 | 95.14 87 | 94.94 184 | 83.57 169 | 84.37 204 | 91.64 217 | 66.59 251 | 96.34 246 | 78.23 219 | 85.36 229 | 93.79 224 |
|
EPNet_dtu | | | 86.49 206 | 85.94 195 | 88.14 246 | 90.24 281 | 72.82 282 | 94.11 152 | 92.20 246 | 86.66 111 | 79.42 275 | 92.36 192 | 73.52 170 | 95.81 268 | 71.26 267 | 93.66 126 | 95.80 145 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
ET-MVSNet_ETH3D | | | 87.51 177 | 85.91 196 | 92.32 108 | 93.70 183 | 83.93 87 | 92.33 224 | 90.94 282 | 84.16 157 | 72.09 309 | 92.52 187 | 69.90 213 | 95.85 265 | 89.20 82 | 88.36 202 | 97.17 93 |
|
v1240 | | | 86.78 198 | 85.85 197 | 89.56 215 | 90.45 278 | 77.79 241 | 93.61 178 | 95.37 166 | 81.65 211 | 85.43 180 | 91.15 236 | 71.50 192 | 97.43 173 | 81.47 177 | 82.05 260 | 93.47 241 |
|
FMVSNet2 | | | 87.19 191 | 85.82 198 | 91.30 146 | 94.01 167 | 83.67 94 | 94.79 108 | 94.94 184 | 83.57 169 | 83.88 215 | 92.05 208 | 66.59 251 | 96.51 234 | 77.56 226 | 85.01 233 | 93.73 231 |
|
v7n | | | 86.81 196 | 85.76 199 | 89.95 201 | 90.72 269 | 79.25 211 | 95.07 90 | 95.92 118 | 84.45 156 | 82.29 238 | 90.86 243 | 72.60 184 | 97.53 164 | 79.42 209 | 80.52 283 | 93.08 252 |
|
TR-MVS | | | 86.78 198 | 85.76 199 | 89.82 205 | 94.37 156 | 78.41 224 | 92.47 219 | 92.83 233 | 81.11 221 | 86.36 155 | 92.40 190 | 68.73 232 | 97.48 167 | 73.75 260 | 89.85 178 | 93.57 236 |
|
pm-mvs1 | | | 86.61 202 | 85.54 201 | 89.82 205 | 91.44 234 | 80.18 180 | 95.28 78 | 94.85 191 | 83.84 164 | 81.66 246 | 92.62 185 | 72.45 187 | 96.48 236 | 79.67 204 | 78.06 297 | 92.82 259 |
|
PatchMatch-RL | | | 86.77 200 | 85.54 201 | 90.47 178 | 95.88 97 | 82.71 123 | 90.54 256 | 92.31 243 | 79.82 234 | 84.32 208 | 91.57 224 | 68.77 231 | 96.39 242 | 73.16 262 | 93.48 132 | 92.32 273 |
|
DTE-MVSNet | | | 86.11 210 | 85.48 203 | 87.98 249 | 91.65 231 | 74.92 266 | 94.93 99 | 95.75 133 | 87.36 95 | 82.26 239 | 93.04 173 | 72.85 180 | 95.82 267 | 74.04 256 | 77.46 301 | 93.20 246 |
|
test-LLR | | | 85.87 215 | 85.41 204 | 87.25 265 | 90.95 256 | 71.67 292 | 89.55 268 | 89.88 303 | 83.41 175 | 84.54 199 | 87.95 286 | 67.25 240 | 95.11 287 | 81.82 171 | 93.37 135 | 94.97 166 |
|
baseline2 | | | 86.50 205 | 85.39 205 | 89.84 204 | 91.12 250 | 76.70 254 | 91.88 235 | 88.58 310 | 82.35 197 | 79.95 270 | 90.95 242 | 73.42 174 | 97.63 159 | 80.27 198 | 89.95 175 | 95.19 160 |
|
PAPM | | | 86.68 201 | 85.39 205 | 90.53 172 | 93.05 200 | 79.33 208 | 89.79 267 | 94.77 196 | 78.82 243 | 81.95 244 | 93.24 165 | 76.81 129 | 97.30 187 | 66.94 292 | 93.16 139 | 94.95 172 |
|
DP-MVS | | | 87.25 187 | 85.36 207 | 92.90 81 | 97.65 43 | 83.24 104 | 94.81 107 | 92.00 253 | 74.99 279 | 81.92 245 | 95.00 104 | 72.66 182 | 99.05 51 | 66.92 294 | 92.33 152 | 96.40 118 |
|
GA-MVS | | | 86.61 202 | 85.27 208 | 90.66 167 | 91.33 243 | 78.71 216 | 90.40 257 | 93.81 220 | 85.34 137 | 85.12 190 | 89.57 265 | 61.25 281 | 97.11 205 | 80.99 184 | 89.59 182 | 96.15 125 |
|
SCA | | | 86.32 208 | 85.18 209 | 89.73 211 | 92.15 216 | 76.60 255 | 91.12 253 | 91.69 262 | 83.53 172 | 85.50 174 | 88.81 272 | 66.79 247 | 96.48 236 | 76.65 234 | 90.35 169 | 96.12 128 |
|
Anonymous20231211 | | | 86.59 204 | 85.13 210 | 90.98 163 | 96.52 76 | 81.50 146 | 96.14 38 | 96.16 103 | 73.78 289 | 83.65 222 | 92.15 199 | 63.26 269 | 97.37 185 | 82.82 153 | 81.74 265 | 94.06 210 |
|
PatchFormer-LS_test | | | 86.02 211 | 85.13 210 | 88.70 232 | 91.52 232 | 74.12 273 | 91.19 252 | 92.09 249 | 82.71 191 | 84.30 210 | 87.24 295 | 70.87 199 | 96.98 214 | 81.04 180 | 85.17 232 | 95.00 165 |
|
D2MVS | | | 85.90 214 | 85.09 212 | 88.35 239 | 90.79 265 | 77.42 248 | 91.83 237 | 95.70 136 | 80.77 224 | 80.08 268 | 90.02 256 | 66.74 249 | 96.37 243 | 81.88 170 | 87.97 209 | 91.26 291 |
|
tpmrst | | | 85.35 224 | 84.99 213 | 86.43 279 | 90.88 263 | 67.88 313 | 88.71 282 | 91.43 270 | 80.13 229 | 86.08 161 | 88.80 274 | 73.05 178 | 96.02 257 | 82.48 156 | 83.40 250 | 95.40 155 |
|
cascas | | | 86.43 207 | 84.98 214 | 90.80 166 | 92.10 219 | 80.92 165 | 90.24 259 | 95.91 120 | 73.10 295 | 83.57 225 | 88.39 279 | 65.15 261 | 97.46 169 | 84.90 130 | 91.43 157 | 94.03 212 |
|
PMMVS | | | 85.71 219 | 84.96 215 | 87.95 250 | 88.90 296 | 77.09 250 | 88.68 283 | 90.06 297 | 72.32 301 | 86.47 150 | 90.76 246 | 72.15 188 | 94.40 293 | 81.78 173 | 93.49 130 | 92.36 271 |
|
CostFormer | | | 85.77 218 | 84.94 216 | 88.26 242 | 91.16 249 | 72.58 288 | 89.47 272 | 91.04 279 | 76.26 268 | 86.45 153 | 89.97 258 | 70.74 202 | 96.86 222 | 82.35 159 | 87.07 221 | 95.34 158 |
|
LTVRE_ROB | | 82.13 13 | 86.26 209 | 84.90 217 | 90.34 185 | 94.44 154 | 81.50 146 | 92.31 226 | 94.89 189 | 83.03 183 | 79.63 273 | 92.67 183 | 69.69 217 | 97.79 149 | 71.20 268 | 86.26 223 | 91.72 282 |
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 |
MVP-Stereo | | | 85.97 213 | 84.86 218 | 89.32 221 | 90.92 260 | 82.19 134 | 92.11 232 | 94.19 209 | 78.76 245 | 78.77 278 | 91.63 220 | 68.38 237 | 96.56 231 | 75.01 251 | 93.95 122 | 89.20 308 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
XVG-ACMP-BASELINE | | | 86.00 212 | 84.84 219 | 89.45 220 | 91.20 245 | 78.00 233 | 91.70 242 | 95.55 148 | 85.05 145 | 82.97 232 | 92.25 197 | 54.49 308 | 97.48 167 | 82.93 149 | 87.45 215 | 92.89 256 |
|
CVMVSNet | | | 84.69 238 | 84.79 220 | 84.37 295 | 91.84 223 | 64.92 320 | 93.70 176 | 91.47 269 | 66.19 316 | 86.16 160 | 95.28 94 | 67.18 242 | 93.33 305 | 80.89 186 | 90.42 168 | 94.88 174 |
|
PatchmatchNet | | | 85.85 216 | 84.70 221 | 89.29 222 | 91.76 226 | 75.54 264 | 88.49 285 | 91.30 272 | 81.63 213 | 85.05 191 | 88.70 276 | 71.71 189 | 96.24 249 | 74.61 254 | 89.05 191 | 96.08 132 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
PVSNet | | 78.82 18 | 85.55 220 | 84.65 222 | 88.23 244 | 94.72 140 | 71.93 290 | 87.12 298 | 92.75 236 | 78.80 244 | 84.95 193 | 90.53 250 | 64.43 265 | 96.71 225 | 74.74 252 | 93.86 124 | 96.06 134 |
|
OurMVSNet-221017-0 | | | 85.35 224 | 84.64 223 | 87.49 259 | 90.77 266 | 72.59 287 | 94.01 163 | 94.40 203 | 84.72 152 | 79.62 274 | 93.17 167 | 61.91 276 | 96.72 223 | 81.99 167 | 81.16 268 | 93.16 248 |
|
miper_lstm_enhance | | | 85.27 227 | 84.59 224 | 87.31 262 | 91.28 244 | 74.63 267 | 87.69 294 | 94.09 214 | 81.20 220 | 81.36 250 | 89.85 261 | 74.97 150 | 94.30 295 | 81.03 183 | 79.84 291 | 93.01 253 |
|
IterMVS-SCA-FT | | | 85.45 221 | 84.53 225 | 88.18 245 | 91.71 228 | 76.87 253 | 90.19 261 | 92.65 239 | 85.40 136 | 81.44 248 | 90.54 249 | 66.79 247 | 95.00 290 | 81.04 180 | 81.05 271 | 92.66 262 |
|
RPSCF | | | 85.07 230 | 84.27 226 | 87.48 260 | 92.91 206 | 70.62 302 | 91.69 243 | 92.46 241 | 76.20 269 | 82.67 236 | 95.22 97 | 63.94 267 | 97.29 190 | 77.51 227 | 85.80 226 | 94.53 188 |
|
MS-PatchMatch | | | 85.05 231 | 84.16 227 | 87.73 253 | 91.42 238 | 78.51 221 | 91.25 251 | 93.53 223 | 77.50 256 | 80.15 265 | 91.58 222 | 61.99 275 | 95.51 278 | 75.69 243 | 94.35 120 | 89.16 309 |
|
FMVSNet1 | | | 85.85 216 | 84.11 228 | 91.08 154 | 92.81 207 | 83.10 107 | 95.14 87 | 94.94 184 | 81.64 212 | 82.68 235 | 91.64 217 | 59.01 295 | 96.34 246 | 75.37 246 | 83.78 241 | 93.79 224 |
|
tpm | | | 84.73 236 | 84.02 229 | 86.87 276 | 90.33 279 | 68.90 310 | 89.06 278 | 89.94 300 | 80.85 223 | 85.75 164 | 89.86 260 | 68.54 234 | 95.97 259 | 77.76 223 | 84.05 240 | 95.75 146 |
|
CHOSEN 280x420 | | | 85.15 229 | 83.99 230 | 88.65 233 | 92.47 212 | 78.40 225 | 79.68 323 | 92.76 235 | 74.90 281 | 81.41 249 | 89.59 264 | 69.85 216 | 95.51 278 | 79.92 202 | 95.29 103 | 92.03 278 |
|
IterMVS | | | 84.88 234 | 83.98 231 | 87.60 255 | 91.44 234 | 76.03 261 | 90.18 262 | 92.41 242 | 83.24 180 | 81.06 254 | 90.42 251 | 66.60 250 | 94.28 296 | 79.46 205 | 80.98 276 | 92.48 266 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
pmmvs4 | | | 85.43 222 | 83.86 232 | 90.16 189 | 90.02 286 | 82.97 114 | 90.27 258 | 92.67 238 | 75.93 271 | 80.73 256 | 91.74 216 | 71.05 196 | 95.73 271 | 78.85 213 | 83.46 248 | 91.78 281 |
|
CR-MVSNet | | | 85.35 224 | 83.76 233 | 90.12 192 | 90.58 274 | 79.34 205 | 85.24 308 | 91.96 257 | 78.27 251 | 85.55 169 | 87.87 289 | 71.03 197 | 95.61 272 | 73.96 258 | 89.36 185 | 95.40 155 |
|
DWT-MVSNet_test | | | 84.95 233 | 83.68 234 | 88.77 229 | 91.43 237 | 73.75 275 | 91.74 240 | 90.98 280 | 80.66 225 | 83.84 216 | 87.36 293 | 62.44 272 | 97.11 205 | 78.84 214 | 85.81 225 | 95.46 152 |
|
ACMH | | 80.38 17 | 85.36 223 | 83.68 234 | 90.39 180 | 94.45 153 | 80.63 172 | 94.73 112 | 94.85 191 | 82.09 200 | 77.24 287 | 92.65 184 | 60.01 290 | 97.58 160 | 72.25 265 | 84.87 234 | 92.96 254 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
test-mter | | | 84.54 239 | 83.64 236 | 87.25 265 | 90.95 256 | 71.67 292 | 89.55 268 | 89.88 303 | 79.17 238 | 84.54 199 | 87.95 286 | 55.56 303 | 95.11 287 | 81.82 171 | 93.37 135 | 94.97 166 |
|
MDTV_nov1_ep13 | | | | 83.56 237 | | 91.69 230 | 69.93 306 | 87.75 293 | 91.54 266 | 78.60 247 | 84.86 194 | 88.90 271 | 69.54 219 | 96.03 256 | 70.25 273 | 88.93 192 | |
|
ACMH+ | | 81.04 14 | 85.05 231 | 83.46 238 | 89.82 205 | 94.66 144 | 79.37 203 | 94.44 131 | 94.12 213 | 82.19 199 | 78.04 281 | 92.82 179 | 58.23 297 | 97.54 163 | 73.77 259 | 82.90 252 | 92.54 264 |
|
IB-MVS | | 80.51 15 | 85.24 228 | 83.26 239 | 91.19 148 | 92.13 218 | 79.86 194 | 91.75 239 | 91.29 273 | 83.28 179 | 80.66 258 | 88.49 278 | 61.28 280 | 98.46 102 | 80.99 184 | 79.46 293 | 95.25 159 |
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 |
tfpnnormal | | | 84.72 237 | 83.23 240 | 89.20 224 | 92.79 208 | 80.05 187 | 94.48 126 | 95.81 128 | 82.38 195 | 81.08 253 | 91.21 231 | 69.01 228 | 96.95 216 | 61.69 311 | 80.59 280 | 90.58 302 |
|
MSDG | | | 84.86 235 | 83.09 241 | 90.14 191 | 93.80 178 | 80.05 187 | 89.18 277 | 93.09 229 | 78.89 241 | 78.19 279 | 91.91 211 | 65.86 259 | 97.27 191 | 68.47 285 | 88.45 199 | 93.11 250 |
|
TransMVSNet (Re) | | | 84.43 240 | 83.06 242 | 88.54 235 | 91.72 227 | 78.44 223 | 95.18 84 | 92.82 234 | 82.73 190 | 79.67 272 | 92.12 201 | 73.49 171 | 95.96 260 | 71.10 271 | 68.73 317 | 91.21 293 |
|
tpm2 | | | 84.08 242 | 82.94 243 | 87.48 260 | 91.39 239 | 71.27 294 | 89.23 276 | 90.37 291 | 71.95 303 | 84.64 196 | 89.33 267 | 67.30 239 | 96.55 233 | 75.17 248 | 87.09 220 | 94.63 181 |
|
SixPastTwentyTwo | | | 83.91 244 | 82.90 244 | 86.92 273 | 90.99 254 | 70.67 301 | 93.48 182 | 91.99 254 | 85.54 133 | 77.62 286 | 92.11 203 | 60.59 286 | 96.87 221 | 76.05 241 | 77.75 298 | 93.20 246 |
|
TESTMET0.1,1 | | | 83.74 246 | 82.85 245 | 86.42 280 | 89.96 287 | 71.21 296 | 89.55 268 | 87.88 312 | 77.41 257 | 83.37 229 | 87.31 294 | 56.71 300 | 93.65 302 | 80.62 191 | 92.85 146 | 94.40 197 |
|
pmmvs5 | | | 84.21 241 | 82.84 246 | 88.34 240 | 88.95 295 | 76.94 252 | 92.41 220 | 91.91 259 | 75.63 273 | 80.28 263 | 91.18 234 | 64.59 264 | 95.57 274 | 77.09 232 | 83.47 247 | 92.53 265 |
|
EPMVS | | | 83.90 245 | 82.70 247 | 87.51 257 | 90.23 282 | 72.67 284 | 88.62 284 | 81.96 325 | 81.37 218 | 85.01 192 | 88.34 280 | 66.31 254 | 94.45 292 | 75.30 247 | 87.12 219 | 95.43 154 |
|
tpmvs | | | 83.35 251 | 82.07 248 | 87.20 269 | 91.07 252 | 71.00 299 | 88.31 288 | 91.70 261 | 78.91 240 | 80.49 261 | 87.18 297 | 69.30 224 | 97.08 207 | 68.12 290 | 83.56 246 | 93.51 240 |
|
COLMAP_ROB | | 80.39 16 | 83.96 243 | 82.04 249 | 89.74 209 | 95.28 117 | 79.75 196 | 94.25 144 | 92.28 244 | 75.17 277 | 78.02 282 | 93.77 153 | 58.60 296 | 97.84 148 | 65.06 302 | 85.92 224 | 91.63 284 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
MVS_0304 | | | 83.46 247 | 81.92 250 | 88.10 247 | 90.63 273 | 77.49 247 | 93.26 193 | 93.75 221 | 80.04 231 | 80.44 262 | 87.24 295 | 47.94 320 | 95.55 275 | 75.79 242 | 88.16 204 | 91.26 291 |
|
test0.0.03 1 | | | 82.41 257 | 81.69 251 | 84.59 293 | 88.23 303 | 72.89 281 | 90.24 259 | 87.83 313 | 83.41 175 | 79.86 271 | 89.78 262 | 67.25 240 | 88.99 322 | 65.18 300 | 83.42 249 | 91.90 280 |
|
pmmvs6 | | | 83.42 248 | 81.60 252 | 88.87 228 | 88.01 306 | 77.87 238 | 94.96 96 | 94.24 208 | 74.67 283 | 78.80 277 | 91.09 239 | 60.17 289 | 96.49 235 | 77.06 233 | 75.40 305 | 92.23 276 |
|
AllTest | | | 83.42 248 | 81.39 253 | 89.52 217 | 95.01 125 | 77.79 241 | 93.12 198 | 90.89 284 | 77.41 257 | 76.12 292 | 93.34 158 | 54.08 310 | 97.51 165 | 68.31 287 | 84.27 238 | 93.26 244 |
|
PatchT | | | 82.68 255 | 81.27 254 | 86.89 275 | 90.09 284 | 70.94 300 | 84.06 312 | 90.15 294 | 74.91 280 | 85.63 168 | 83.57 309 | 69.37 220 | 94.87 291 | 65.19 299 | 88.50 198 | 94.84 175 |
|
USDC | | | 82.76 253 | 81.26 255 | 87.26 264 | 91.17 247 | 74.55 268 | 89.27 274 | 93.39 226 | 78.26 252 | 75.30 296 | 92.08 205 | 54.43 309 | 96.63 227 | 71.64 266 | 85.79 227 | 90.61 299 |
|
testing_2 | | | 83.40 250 | 81.02 256 | 90.56 171 | 85.06 315 | 80.51 176 | 91.37 248 | 95.57 146 | 82.92 186 | 67.06 317 | 85.54 304 | 49.47 317 | 97.24 195 | 86.74 112 | 85.44 228 | 93.93 214 |
|
EU-MVSNet | | | 81.32 270 | 80.95 257 | 82.42 302 | 88.50 299 | 63.67 321 | 93.32 186 | 91.33 271 | 64.02 318 | 80.57 260 | 92.83 178 | 61.21 283 | 92.27 312 | 76.34 237 | 80.38 285 | 91.32 289 |
|
Patchmtry | | | 82.71 254 | 80.93 258 | 88.06 248 | 90.05 285 | 76.37 258 | 84.74 310 | 91.96 257 | 72.28 302 | 81.32 251 | 87.87 289 | 71.03 197 | 95.50 280 | 68.97 282 | 80.15 286 | 92.32 273 |
|
RPMNet | | | 83.18 252 | 80.87 259 | 90.12 192 | 90.58 274 | 79.34 205 | 85.24 308 | 90.78 287 | 71.44 305 | 85.55 169 | 82.97 312 | 70.87 199 | 95.61 272 | 61.01 313 | 89.36 185 | 95.40 155 |
|
MIMVSNet | | | 82.59 256 | 80.53 260 | 88.76 230 | 91.51 233 | 78.32 226 | 86.57 301 | 90.13 295 | 79.32 237 | 80.70 257 | 88.69 277 | 52.98 312 | 93.07 309 | 66.03 297 | 88.86 193 | 94.90 173 |
|
our_test_3 | | | 81.93 260 | 80.46 261 | 86.33 281 | 88.46 300 | 73.48 277 | 88.46 286 | 91.11 275 | 76.46 263 | 76.69 289 | 88.25 282 | 66.89 245 | 94.36 294 | 68.75 283 | 79.08 295 | 91.14 295 |
|
EG-PatchMatch MVS | | | 82.37 258 | 80.34 262 | 88.46 236 | 90.27 280 | 79.35 204 | 92.80 211 | 94.33 206 | 77.14 261 | 73.26 306 | 90.18 254 | 47.47 322 | 96.72 223 | 70.25 273 | 87.32 218 | 89.30 306 |
|
tpm cat1 | | | 81.96 259 | 80.27 263 | 87.01 271 | 91.09 251 | 71.02 298 | 87.38 297 | 91.53 267 | 66.25 315 | 80.17 264 | 86.35 300 | 68.22 238 | 96.15 253 | 69.16 281 | 82.29 256 | 93.86 221 |
|
dp | | | 81.47 268 | 80.23 264 | 85.17 290 | 89.92 288 | 65.49 319 | 86.74 299 | 90.10 296 | 76.30 267 | 81.10 252 | 87.12 298 | 62.81 270 | 95.92 261 | 68.13 289 | 79.88 289 | 94.09 208 |
|
testgi | | | 80.94 275 | 80.20 265 | 83.18 299 | 87.96 307 | 66.29 316 | 91.28 249 | 90.70 289 | 83.70 167 | 78.12 280 | 92.84 177 | 51.37 314 | 90.82 319 | 63.34 306 | 82.46 255 | 92.43 268 |
|
K. test v3 | | | 81.59 265 | 80.15 266 | 85.91 285 | 89.89 289 | 69.42 309 | 92.57 217 | 87.71 314 | 85.56 132 | 73.44 305 | 89.71 263 | 55.58 302 | 95.52 277 | 77.17 230 | 69.76 313 | 92.78 260 |
|
ppachtmachnet_test | | | 81.84 261 | 80.07 267 | 87.15 270 | 88.46 300 | 74.43 269 | 89.04 279 | 92.16 247 | 75.33 275 | 77.75 284 | 88.99 269 | 66.20 255 | 95.37 283 | 65.12 301 | 77.60 299 | 91.65 283 |
|
Patchmatch-RL test | | | 81.67 263 | 79.96 268 | 86.81 277 | 85.42 313 | 71.23 295 | 82.17 319 | 87.50 316 | 78.47 248 | 77.19 288 | 82.50 313 | 70.81 201 | 93.48 303 | 82.66 155 | 72.89 309 | 95.71 147 |
|
ADS-MVSNet | | | 81.56 266 | 79.78 269 | 86.90 274 | 91.35 241 | 71.82 291 | 83.33 315 | 89.16 309 | 72.90 297 | 82.24 240 | 85.77 302 | 64.98 262 | 93.76 300 | 64.57 303 | 83.74 242 | 95.12 161 |
|
Anonymous20231206 | | | 81.03 273 | 79.77 270 | 84.82 292 | 87.85 308 | 70.26 304 | 91.42 247 | 92.08 250 | 73.67 290 | 77.75 284 | 89.25 268 | 62.43 273 | 93.08 308 | 61.50 312 | 82.00 261 | 91.12 296 |
|
ADS-MVSNet2 | | | 81.66 264 | 79.71 271 | 87.50 258 | 91.35 241 | 74.19 271 | 83.33 315 | 88.48 311 | 72.90 297 | 82.24 240 | 85.77 302 | 64.98 262 | 93.20 307 | 64.57 303 | 83.74 242 | 95.12 161 |
|
FMVSNet5 | | | 81.52 267 | 79.60 272 | 87.27 263 | 91.17 247 | 77.95 234 | 91.49 246 | 92.26 245 | 76.87 262 | 76.16 291 | 87.91 288 | 51.67 313 | 92.34 311 | 67.74 291 | 81.16 268 | 91.52 285 |
|
gg-mvs-nofinetune | | | 81.77 262 | 79.37 273 | 88.99 227 | 90.85 264 | 77.73 244 | 86.29 302 | 79.63 328 | 74.88 282 | 83.19 231 | 69.05 323 | 60.34 287 | 96.11 254 | 75.46 245 | 94.64 112 | 93.11 250 |
|
Patchmatch-test | | | 81.37 269 | 79.30 274 | 87.58 256 | 90.92 260 | 74.16 272 | 80.99 321 | 87.68 315 | 70.52 309 | 76.63 290 | 88.81 272 | 71.21 194 | 92.76 310 | 60.01 317 | 86.93 222 | 95.83 143 |
|
CMPMVS | | 59.16 21 | 80.52 276 | 79.20 275 | 84.48 294 | 83.98 317 | 67.63 315 | 89.95 266 | 93.84 219 | 64.79 317 | 66.81 318 | 91.14 237 | 57.93 298 | 95.17 285 | 76.25 238 | 88.10 205 | 90.65 298 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
test_0402 | | | 81.30 271 | 79.17 276 | 87.67 254 | 93.19 195 | 78.17 230 | 92.98 206 | 91.71 260 | 75.25 276 | 76.02 294 | 90.31 252 | 59.23 294 | 96.37 243 | 50.22 323 | 83.63 245 | 88.47 315 |
|
test20.03 | | | 79.95 279 | 79.08 277 | 82.55 301 | 85.79 312 | 67.74 314 | 91.09 254 | 91.08 276 | 81.23 219 | 74.48 301 | 89.96 259 | 61.63 277 | 90.15 320 | 60.08 315 | 76.38 303 | 89.76 304 |
|
LF4IMVS | | | 80.37 277 | 79.07 278 | 84.27 297 | 86.64 310 | 69.87 307 | 89.39 273 | 91.05 278 | 76.38 265 | 74.97 298 | 90.00 257 | 47.85 321 | 94.25 297 | 74.55 255 | 80.82 278 | 88.69 313 |
|
JIA-IIPM | | | 81.04 272 | 78.98 279 | 87.25 265 | 88.64 297 | 73.48 277 | 81.75 320 | 89.61 307 | 73.19 294 | 82.05 242 | 73.71 319 | 66.07 258 | 95.87 264 | 71.18 270 | 84.60 236 | 92.41 269 |
|
pmmvs-eth3d | | | 80.97 274 | 78.72 280 | 87.74 252 | 84.99 316 | 79.97 192 | 90.11 263 | 91.65 263 | 75.36 274 | 73.51 304 | 86.03 301 | 59.45 293 | 93.96 299 | 75.17 248 | 72.21 310 | 89.29 307 |
|
UnsupCasMVSNet_eth | | | 80.07 278 | 78.27 281 | 85.46 287 | 85.24 314 | 72.63 286 | 88.45 287 | 94.87 190 | 82.99 184 | 71.64 312 | 88.07 285 | 56.34 301 | 91.75 316 | 73.48 261 | 63.36 322 | 92.01 279 |
|
TinyColmap | | | 79.76 281 | 77.69 282 | 85.97 284 | 91.71 228 | 73.12 279 | 89.55 268 | 90.36 292 | 75.03 278 | 72.03 310 | 90.19 253 | 46.22 323 | 96.19 252 | 63.11 307 | 81.03 272 | 88.59 314 |
|
TDRefinement | | | 79.81 280 | 77.34 283 | 87.22 268 | 79.24 325 | 75.48 265 | 93.12 198 | 92.03 252 | 76.45 264 | 75.01 297 | 91.58 222 | 49.19 318 | 96.44 240 | 70.22 275 | 69.18 314 | 89.75 305 |
|
MIMVSNet1 | | | 79.38 283 | 77.28 284 | 85.69 286 | 86.35 311 | 73.67 276 | 91.61 245 | 92.75 236 | 78.11 255 | 72.64 308 | 88.12 284 | 48.16 319 | 91.97 315 | 60.32 314 | 77.49 300 | 91.43 288 |
|
YYNet1 | | | 79.22 284 | 77.20 285 | 85.28 289 | 88.20 305 | 72.66 285 | 85.87 304 | 90.05 299 | 74.33 286 | 62.70 320 | 87.61 291 | 66.09 257 | 92.03 313 | 66.94 292 | 72.97 308 | 91.15 294 |
|
MDA-MVSNet_test_wron | | | 79.21 285 | 77.19 286 | 85.29 288 | 88.22 304 | 72.77 283 | 85.87 304 | 90.06 297 | 74.34 285 | 62.62 321 | 87.56 292 | 66.14 256 | 91.99 314 | 66.90 295 | 73.01 307 | 91.10 297 |
|
OpenMVS_ROB | | 74.94 19 | 79.51 282 | 77.03 287 | 86.93 272 | 87.00 309 | 76.23 260 | 92.33 224 | 90.74 288 | 68.93 312 | 74.52 300 | 88.23 283 | 49.58 316 | 96.62 228 | 57.64 319 | 84.29 237 | 87.94 317 |
|
MDA-MVSNet-bldmvs | | | 78.85 286 | 76.31 288 | 86.46 278 | 89.76 290 | 73.88 274 | 88.79 281 | 90.42 290 | 79.16 239 | 59.18 322 | 88.33 281 | 60.20 288 | 94.04 298 | 62.00 310 | 68.96 315 | 91.48 287 |
|
DSMNet-mixed | | | 76.94 289 | 76.29 289 | 78.89 305 | 83.10 320 | 56.11 328 | 87.78 292 | 79.77 327 | 60.65 320 | 75.64 295 | 88.71 275 | 61.56 278 | 88.34 323 | 60.07 316 | 89.29 187 | 92.21 277 |
|
PM-MVS | | | 78.11 287 | 76.12 290 | 84.09 298 | 83.54 319 | 70.08 305 | 88.97 280 | 85.27 320 | 79.93 232 | 74.73 299 | 86.43 299 | 34.70 328 | 93.48 303 | 79.43 208 | 72.06 311 | 88.72 312 |
|
new-patchmatchnet | | | 76.41 290 | 75.17 291 | 80.13 304 | 82.65 322 | 59.61 323 | 87.66 295 | 91.08 276 | 78.23 253 | 69.85 313 | 83.22 310 | 54.76 306 | 91.63 318 | 64.14 305 | 64.89 320 | 89.16 309 |
|
PVSNet_0 | | 73.20 20 | 77.22 288 | 74.83 292 | 84.37 295 | 90.70 270 | 71.10 297 | 83.09 317 | 89.67 306 | 72.81 299 | 73.93 303 | 83.13 311 | 60.79 285 | 93.70 301 | 68.54 284 | 50.84 326 | 88.30 316 |
|
UnsupCasMVSNet_bld | | | 76.23 291 | 73.27 293 | 85.09 291 | 83.79 318 | 72.92 280 | 85.65 307 | 93.47 225 | 71.52 304 | 68.84 315 | 79.08 317 | 49.77 315 | 93.21 306 | 66.81 296 | 60.52 324 | 89.13 311 |
|
MVS-HIRNet | | | 73.70 293 | 72.20 294 | 78.18 307 | 91.81 225 | 56.42 327 | 82.94 318 | 82.58 323 | 55.24 322 | 68.88 314 | 66.48 324 | 55.32 305 | 95.13 286 | 58.12 318 | 88.42 200 | 83.01 320 |
|
test_normal | | | 75.12 292 | 71.44 295 | 86.17 282 | 81.33 323 | 69.54 308 | 50.52 332 | 95.44 159 | 84.80 150 | 55.21 323 | 70.88 322 | 41.07 326 | 96.66 226 | 82.25 162 | 81.48 267 | 92.30 275 |
|
new_pmnet | | | 72.15 294 | 70.13 296 | 78.20 306 | 82.95 321 | 65.68 317 | 83.91 313 | 82.40 324 | 62.94 319 | 64.47 319 | 79.82 316 | 42.85 325 | 86.26 325 | 57.41 320 | 74.44 306 | 82.65 321 |
|
pmmvs3 | | | 71.81 295 | 68.71 297 | 81.11 303 | 75.86 326 | 70.42 303 | 86.74 299 | 83.66 322 | 58.95 321 | 68.64 316 | 80.89 315 | 36.93 327 | 89.52 321 | 63.10 308 | 63.59 321 | 83.39 319 |
|
N_pmnet | | | 68.89 296 | 68.44 298 | 70.23 311 | 89.07 294 | 28.79 337 | 88.06 289 | 19.50 338 | 69.47 311 | 71.86 311 | 84.93 305 | 61.24 282 | 91.75 316 | 54.70 321 | 77.15 302 | 90.15 303 |
|
FPMVS | | | 64.63 298 | 62.55 299 | 70.88 310 | 70.80 328 | 56.71 325 | 84.42 311 | 84.42 321 | 51.78 324 | 49.57 325 | 81.61 314 | 23.49 331 | 81.48 328 | 40.61 327 | 76.25 304 | 74.46 324 |
|
LCM-MVSNet | | | 66.00 297 | 62.16 300 | 77.51 308 | 64.51 332 | 58.29 324 | 83.87 314 | 90.90 283 | 48.17 325 | 54.69 324 | 73.31 320 | 16.83 337 | 86.75 324 | 65.47 298 | 61.67 323 | 87.48 318 |
|
PMMVS2 | | | 59.60 299 | 56.40 301 | 69.21 312 | 68.83 329 | 46.58 332 | 73.02 328 | 77.48 331 | 55.07 323 | 49.21 326 | 72.95 321 | 17.43 336 | 80.04 329 | 49.32 324 | 44.33 327 | 80.99 323 |
|
Gipuma | | | 57.99 301 | 54.91 302 | 67.24 313 | 88.51 298 | 65.59 318 | 52.21 331 | 90.33 293 | 43.58 327 | 42.84 328 | 51.18 329 | 20.29 334 | 85.07 326 | 34.77 328 | 70.45 312 | 51.05 328 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
ANet_high | | | 58.88 300 | 54.22 303 | 72.86 309 | 56.50 335 | 56.67 326 | 80.75 322 | 86.00 317 | 73.09 296 | 37.39 329 | 64.63 326 | 22.17 332 | 79.49 330 | 43.51 325 | 23.96 330 | 82.43 322 |
|
PMVS | | 47.18 22 | 52.22 302 | 48.46 304 | 63.48 314 | 45.72 336 | 46.20 333 | 73.41 327 | 78.31 329 | 41.03 328 | 30.06 331 | 65.68 325 | 6.05 338 | 83.43 327 | 30.04 329 | 65.86 318 | 60.80 325 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
E-PMN | | | 43.23 304 | 42.29 305 | 46.03 317 | 65.58 331 | 37.41 334 | 73.51 326 | 64.62 332 | 33.99 329 | 28.47 333 | 47.87 330 | 19.90 335 | 67.91 331 | 22.23 331 | 24.45 329 | 32.77 329 |
|
EMVS | | | 42.07 305 | 41.12 306 | 44.92 318 | 63.45 333 | 35.56 336 | 73.65 325 | 63.48 333 | 33.05 330 | 26.88 334 | 45.45 331 | 21.27 333 | 67.14 332 | 19.80 332 | 23.02 331 | 32.06 330 |
|
tmp_tt | | | 35.64 306 | 39.24 307 | 24.84 319 | 14.87 337 | 23.90 338 | 62.71 329 | 51.51 337 | 6.58 333 | 36.66 330 | 62.08 327 | 44.37 324 | 30.34 336 | 52.40 322 | 22.00 332 | 20.27 331 |
|
MVE | | 39.65 23 | 43.39 303 | 38.59 308 | 57.77 315 | 56.52 334 | 48.77 331 | 55.38 330 | 58.64 335 | 29.33 331 | 28.96 332 | 52.65 328 | 4.68 339 | 64.62 333 | 28.11 330 | 33.07 328 | 59.93 326 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
cdsmvs_eth3d_5k | | | 22.14 307 | 29.52 309 | 0.00 323 | 0.00 340 | 0.00 341 | 0.00 334 | 95.76 132 | 0.00 336 | 0.00 338 | 94.29 129 | 75.66 142 | 0.00 339 | 0.00 336 | 0.00 336 | 0.00 335 |
|
wuyk23d | | | 21.27 308 | 20.48 310 | 23.63 320 | 68.59 330 | 36.41 335 | 49.57 333 | 6.85 339 | 9.37 332 | 7.89 335 | 4.46 337 | 4.03 340 | 31.37 335 | 17.47 333 | 16.07 333 | 3.12 332 |
|
testmvs | | | 8.92 309 | 11.52 311 | 1.12 322 | 1.06 338 | 0.46 340 | 86.02 303 | 0.65 340 | 0.62 334 | 2.74 336 | 9.52 335 | 0.31 342 | 0.45 338 | 2.38 334 | 0.39 334 | 2.46 334 |
|
test123 | | | 8.76 310 | 11.22 312 | 1.39 321 | 0.85 339 | 0.97 339 | 85.76 306 | 0.35 341 | 0.54 335 | 2.45 337 | 8.14 336 | 0.60 341 | 0.48 337 | 2.16 335 | 0.17 335 | 2.71 333 |
|
ab-mvs-re | | | 7.82 311 | 10.43 313 | 0.00 323 | 0.00 340 | 0.00 341 | 0.00 334 | 0.00 342 | 0.00 336 | 0.00 338 | 93.88 148 | 0.00 343 | 0.00 339 | 0.00 336 | 0.00 336 | 0.00 335 |
|
pcd_1.5k_mvsjas | | | 6.64 312 | 8.86 314 | 0.00 323 | 0.00 340 | 0.00 341 | 0.00 334 | 0.00 342 | 0.00 336 | 0.00 338 | 0.00 338 | 79.70 104 | 0.00 339 | 0.00 336 | 0.00 336 | 0.00 335 |
|
sosnet-low-res | | | 0.00 313 | 0.00 315 | 0.00 323 | 0.00 340 | 0.00 341 | 0.00 334 | 0.00 342 | 0.00 336 | 0.00 338 | 0.00 338 | 0.00 343 | 0.00 339 | 0.00 336 | 0.00 336 | 0.00 335 |
|
sosnet | | | 0.00 313 | 0.00 315 | 0.00 323 | 0.00 340 | 0.00 341 | 0.00 334 | 0.00 342 | 0.00 336 | 0.00 338 | 0.00 338 | 0.00 343 | 0.00 339 | 0.00 336 | 0.00 336 | 0.00 335 |
|
uncertanet | | | 0.00 313 | 0.00 315 | 0.00 323 | 0.00 340 | 0.00 341 | 0.00 334 | 0.00 342 | 0.00 336 | 0.00 338 | 0.00 338 | 0.00 343 | 0.00 339 | 0.00 336 | 0.00 336 | 0.00 335 |
|
Regformer | | | 0.00 313 | 0.00 315 | 0.00 323 | 0.00 340 | 0.00 341 | 0.00 334 | 0.00 342 | 0.00 336 | 0.00 338 | 0.00 338 | 0.00 343 | 0.00 339 | 0.00 336 | 0.00 336 | 0.00 335 |
|
uanet | | | 0.00 313 | 0.00 315 | 0.00 323 | 0.00 340 | 0.00 341 | 0.00 334 | 0.00 342 | 0.00 336 | 0.00 338 | 0.00 338 | 0.00 343 | 0.00 339 | 0.00 336 | 0.00 336 | 0.00 335 |
|
save filter2 | | | | | | | | | | | 95.61 12 | 97.28 18 | 87.84 17 | 99.34 27 | 93.50 15 | 99.00 7 | 97.94 61 |
|
save fliter | | | | | | 97.85 39 | 85.63 57 | 95.21 82 | 96.82 57 | 89.44 41 | | | | | | | |
|
test_0728_THIRD | | | | | | | | | | 90.75 19 | 97.04 4 | 98.05 4 | 92.09 1 | 99.55 9 | 95.64 2 | 99.13 3 | 99.13 1 |
|
test_0728_SECOND | | | | | 95.01 10 | 98.79 1 | 86.43 34 | 97.09 9 | 97.49 5 | | | | | 99.61 2 | 95.62 3 | 99.08 4 | 98.99 4 |
|
test0726 | | | | | | 98.78 2 | 85.93 47 | 97.19 6 | 97.47 7 | 90.27 26 | 97.64 2 | 98.13 1 | 91.47 3 | | | | |
|
GSMVS | | | | | | | | | | | | | | | | | 96.12 128 |
|
test_part2 | | | | | | 98.55 8 | 87.22 12 | | | | 96.40 7 | | | | | | |
|
test_part1 | | | | | 0.00 323 | | 0.00 341 | 0.00 334 | 97.45 10 | | | | 0.00 343 | 0.00 339 | 0.00 336 | 0.00 336 | 0.00 335 |
|
sam_mvs1 | | | | | | | | | | | | | 71.70 190 | | | | 96.12 128 |
|
sam_mvs | | | | | | | | | | | | | 70.60 203 | | | | |
|
ambc | | | | | 83.06 300 | 79.99 324 | 63.51 322 | 77.47 324 | 92.86 232 | | 74.34 302 | 84.45 306 | 28.74 329 | 95.06 289 | 73.06 263 | 68.89 316 | 90.61 299 |
|
MTGPA | | | | | | | | | 96.97 41 | | | | | | | | |
|
test_post1 | | | | | | | | 88.00 290 | | | | 9.81 334 | 69.31 223 | 95.53 276 | 76.65 234 | | |
|
test_post | | | | | | | | | | | | 10.29 333 | 70.57 207 | 95.91 263 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 83.76 308 | 71.53 191 | 96.48 236 | | | |
|
GG-mvs-BLEND | | | | | 87.94 251 | 89.73 291 | 77.91 235 | 87.80 291 | 78.23 330 | | 80.58 259 | 83.86 307 | 59.88 291 | 95.33 284 | 71.20 268 | 92.22 153 | 90.60 301 |
|
MTMP | | | | | | | | 96.16 36 | 60.64 334 | | | | | | | | |
|
gm-plane-assit | | | | | | 89.60 292 | 68.00 312 | | | 77.28 260 | | 88.99 269 | | 97.57 161 | 79.44 207 | | |
|
test9_res | | | | | | | | | | | | | | | 91.91 47 | 98.71 23 | 98.07 52 |
|
TEST9 | | | | | | 97.53 45 | 86.49 32 | 94.07 157 | 96.78 60 | 81.61 214 | 92.77 50 | 96.20 67 | 87.71 20 | 99.12 46 | | | |
|
test_8 | | | | | | 97.49 48 | 86.30 41 | 94.02 162 | 96.76 63 | 81.86 209 | 92.70 54 | 96.20 67 | 87.63 21 | 99.02 59 | | | |
|
agg_prior2 | | | | | | | | | | | | | | | 90.54 72 | 98.68 28 | 98.27 37 |
|
agg_prior | | | | | | 97.38 51 | 85.92 49 | | 96.72 66 | | 92.16 65 | | | 98.97 68 | | | |
|
TestCases | | | | | 89.52 217 | 95.01 125 | 77.79 241 | | 90.89 284 | 77.41 257 | 76.12 292 | 93.34 158 | 54.08 310 | 97.51 165 | 68.31 287 | 84.27 238 | 93.26 244 |
|
test_prior4 | | | | | | | 85.96 46 | 94.11 152 | | | | | | | | | |
|
test_prior2 | | | | | | | | 94.12 150 | | 87.67 90 | 92.63 55 | 96.39 60 | 86.62 30 | | 91.50 56 | 98.67 30 | |
|
test_prior | | | | | 93.82 56 | 97.29 56 | 84.49 71 | | 96.88 51 | | | | | 98.87 74 | | | 98.11 50 |
|
旧先验2 | | | | | | | | 93.36 185 | | 71.25 306 | 94.37 20 | | | 97.13 204 | 86.74 112 | | |
|
æ–°å‡ ä½•2 | | | | | | | | 93.11 200 | | | | | | | | | |
|
æ–°å‡ ä½•1 | | | | | 93.10 71 | 97.30 55 | 84.35 80 | | 95.56 147 | 71.09 307 | 91.26 87 | 96.24 64 | 82.87 69 | 98.86 77 | 79.19 211 | 98.10 52 | 96.07 133 |
|
旧先验1 | | | | | | 96.79 67 | 81.81 141 | | 95.67 138 | | | 96.81 41 | 86.69 29 | | | 97.66 63 | 96.97 103 |
|
æ— å…ˆéªŒ | | | | | | | | 93.28 192 | 96.26 94 | 73.95 288 | | | | 99.05 51 | 80.56 192 | | 96.59 114 |
|
原ACMM2 | | | | | | | | 92.94 208 | | | | | | | | | |
|
原ACMM1 | | | | | 92.01 117 | 97.34 53 | 81.05 161 | | 96.81 58 | 78.89 241 | 90.45 94 | 95.92 78 | 82.65 70 | 98.84 82 | 80.68 190 | 98.26 49 | 96.14 126 |
|
test222 | | | | | | 96.55 74 | 81.70 143 | 92.22 228 | 95.01 181 | 68.36 313 | 90.20 97 | 96.14 72 | 80.26 97 | | | 97.80 61 | 96.05 135 |
|
testdata2 | | | | | | | | | | | | | | 98.75 86 | 78.30 218 | | |
|
segment_acmp | | | | | | | | | | | | | 87.16 26 | | | | |
|
testdata | | | | | 90.49 176 | 96.40 78 | 77.89 237 | | 95.37 166 | 72.51 300 | 93.63 33 | 96.69 46 | 82.08 81 | 97.65 156 | 83.08 146 | 97.39 68 | 95.94 137 |
|
testdata1 | | | | | | | | 92.15 230 | | 87.94 81 | | | | | | | |
|
test12 | | | | | 94.34 45 | 97.13 61 | 86.15 44 | | 96.29 92 | | 91.04 90 | | 85.08 48 | 99.01 61 | | 98.13 51 | 97.86 67 |
|
plane_prior7 | | | | | | 94.70 142 | 82.74 120 | | | | | | | | | | |
|
plane_prior6 | | | | | | 94.52 148 | 82.75 118 | | | | | | 74.23 158 | | | | |
|
plane_prior5 | | | | | | | | | 96.22 99 | | | | | 98.12 124 | 88.15 91 | 89.99 172 | 94.63 181 |
|
plane_prior4 | | | | | | | | | | | | 94.86 109 | | | | | |
|
plane_prior3 | | | | | | | 82.75 118 | | | 90.26 28 | 86.91 144 | | | | | | |
|
plane_prior2 | | | | | | | | 95.85 52 | | 90.81 17 | | | | | | | |
|
plane_prior1 | | | | | | 94.59 146 | | | | | | | | | | | |
|
plane_prior | | | | | | | 82.73 121 | 95.21 82 | | 89.66 38 | | | | | | 89.88 177 | |
|
n2 | | | | | | | | | 0.00 342 | | | | | | | | |
|
nn | | | | | | | | | 0.00 342 | | | | | | | | |
|
door-mid | | | | | | | | | 85.49 318 | | | | | | | | |
|
lessismore_v0 | | | | | 86.04 283 | 88.46 300 | 68.78 311 | | 80.59 326 | | 73.01 307 | 90.11 255 | 55.39 304 | 96.43 241 | 75.06 250 | 65.06 319 | 92.90 255 |
|
LGP-MVS_train | | | | | 91.12 150 | 94.47 150 | 81.49 148 | | 96.14 104 | 86.73 109 | 85.45 177 | 95.16 99 | 69.89 214 | 98.10 126 | 87.70 98 | 89.23 188 | 93.77 228 |
|
test11 | | | | | | | | | 96.57 79 | | | | | | | | |
|
door | | | | | | | | | 85.33 319 | | | | | | | | |
|
HQP5-MVS | | | | | | | 81.56 144 | | | | | | | | | | |
|
HQP-NCC | | | | | | 94.17 161 | | 94.39 136 | | 88.81 57 | 85.43 180 | | | | | | |
|
ACMP_Plane | | | | | | 94.17 161 | | 94.39 136 | | 88.81 57 | 85.43 180 | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 87.11 109 | | |
|
HQP4-MVS | | | | | | | | | | | 85.43 180 | | | 97.96 142 | | | 94.51 190 |
|
HQP3-MVS | | | | | | | | | 96.04 112 | | | | | | | 89.77 179 | |
|
HQP2-MVS | | | | | | | | | | | | | 73.83 167 | | | | |
|
NP-MVS | | | | | | 94.37 156 | 82.42 128 | | | | | 93.98 141 | | | | | |
|
MDTV_nov1_ep13_2view | | | | | | | 55.91 329 | 87.62 296 | | 73.32 293 | 84.59 198 | | 70.33 210 | | 74.65 253 | | 95.50 150 |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 87.47 213 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 88.01 208 | |
|
Test By Simon | | | | | | | | | | | | | 80.02 99 | | | | |
|
ITE_SJBPF | | | | | 88.24 243 | 91.88 222 | 77.05 251 | | 92.92 231 | 85.54 133 | 80.13 267 | 93.30 162 | 57.29 299 | 96.20 250 | 72.46 264 | 84.71 235 | 91.49 286 |
|
DeepMVS_CX | | | | | 56.31 316 | 74.23 327 | 51.81 330 | | 56.67 336 | 44.85 326 | 48.54 327 | 75.16 318 | 27.87 330 | 58.74 334 | 40.92 326 | 52.22 325 | 58.39 327 |
|