DVP-MVS | | | 99.03 1 | 98.83 2 | 99.63 3 | 99.72 12 | 99.25 2 | 98.97 63 | 98.58 140 | 97.62 9 | 99.45 7 | 99.46 8 | 97.42 4 | 99.94 3 | 98.47 15 | 99.81 10 | 99.69 45 |
|
APDe-MVS | | | 99.02 2 | 98.84 1 | 99.55 5 | 99.57 31 | 98.96 9 | 99.39 5 | 98.93 37 | 97.38 22 | 99.41 9 | 99.54 1 | 96.66 11 | 99.84 51 | 98.86 1 | 99.85 3 | 99.87 1 |
|
DPE-MVS | | | 98.92 3 | 98.67 5 | 99.65 2 | 99.58 30 | 99.20 5 | 98.42 162 | 98.91 43 | 97.58 12 | 99.54 5 | 99.46 8 | 97.10 7 | 99.94 3 | 97.64 55 | 99.84 8 | 99.83 5 |
|
SteuartSystems-ACMMP | | | 98.90 4 | 98.75 3 | 99.36 20 | 99.22 87 | 98.43 30 | 99.10 43 | 98.87 55 | 97.38 22 | 99.35 12 | 99.40 12 | 97.78 2 | 99.87 43 | 97.77 46 | 99.85 3 | 99.78 12 |
Skip Steuart: Steuart Systems R&D Blog. |
TSAR-MVS + MP. | | | 98.78 5 | 98.62 6 | 99.24 38 | 99.69 23 | 98.28 44 | 99.14 36 | 98.66 126 | 96.84 49 | 99.56 3 | 99.31 31 | 96.34 17 | 99.70 109 | 98.32 23 | 99.73 41 | 99.73 35 |
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition |
CNVR-MVS | | | 98.78 5 | 98.56 8 | 99.45 13 | 99.32 62 | 98.87 12 | 98.47 154 | 98.81 75 | 97.72 5 | 98.76 47 | 99.16 57 | 97.05 8 | 99.78 90 | 98.06 31 | 99.66 54 | 99.69 45 |
|
MSP-MVS | | | 98.74 7 | 98.55 9 | 99.29 29 | 99.75 3 | 98.23 45 | 99.26 18 | 98.88 49 | 97.52 13 | 99.41 9 | 98.78 106 | 96.00 32 | 99.79 86 | 97.79 45 | 99.59 65 | 99.85 2 |
|
XVS | | | 98.70 8 | 98.49 14 | 99.34 22 | 99.70 21 | 98.35 39 | 99.29 14 | 98.88 49 | 97.40 19 | 98.46 62 | 99.20 48 | 95.90 38 | 99.89 34 | 97.85 41 | 99.74 39 | 99.78 12 |
|
Regformer-2 | | | 98.69 9 | 98.52 11 | 99.19 41 | 99.35 54 | 98.01 58 | 98.37 166 | 98.81 75 | 97.48 16 | 99.21 19 | 99.21 44 | 96.13 25 | 99.80 74 | 98.40 21 | 99.73 41 | 99.75 27 |
|
Regformer-1 | | | 98.66 10 | 98.51 12 | 99.12 53 | 99.35 54 | 97.81 66 | 98.37 166 | 98.76 94 | 97.49 15 | 99.20 20 | 99.21 44 | 96.08 27 | 99.79 86 | 98.42 19 | 99.73 41 | 99.75 27 |
|
MCST-MVS | | | 98.65 11 | 98.37 19 | 99.48 9 | 99.60 29 | 98.87 12 | 98.41 163 | 98.68 115 | 97.04 44 | 98.52 61 | 98.80 104 | 96.78 10 | 99.83 54 | 97.93 35 | 99.61 61 | 99.74 32 |
|
Regformer-4 | | | 98.64 12 | 98.53 10 | 98.99 59 | 99.43 51 | 97.37 79 | 98.40 164 | 98.79 88 | 97.46 17 | 99.09 26 | 99.31 31 | 95.86 40 | 99.80 74 | 98.64 3 | 99.76 30 | 99.79 9 |
|
SD-MVS | | | 98.64 12 | 98.68 4 | 98.53 87 | 99.33 59 | 98.36 38 | 98.90 72 | 98.85 63 | 97.28 27 | 99.72 2 | 99.39 13 | 96.63 13 | 97.60 312 | 98.17 26 | 99.85 3 | 99.64 64 |
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 |
HFP-MVS | | | 98.63 14 | 98.40 16 | 99.32 27 | 99.72 12 | 98.29 42 | 99.23 21 | 98.96 32 | 96.10 77 | 98.94 34 | 99.17 52 | 96.06 28 | 99.92 20 | 97.62 56 | 99.78 21 | 99.75 27 |
|
ACMMP_NAP | | | 98.61 15 | 98.30 30 | 99.55 5 | 99.62 28 | 98.95 10 | 98.82 91 | 98.81 75 | 95.80 85 | 99.16 22 | 99.47 7 | 95.37 54 | 99.92 20 | 97.89 39 | 99.75 36 | 99.79 9 |
|
region2R | | | 98.61 15 | 98.38 18 | 99.29 29 | 99.74 7 | 98.16 51 | 99.23 21 | 98.93 37 | 96.15 72 | 98.94 34 | 99.17 52 | 95.91 37 | 99.94 3 | 97.55 64 | 99.79 17 | 99.78 12 |
|
NCCC | | | 98.61 15 | 98.35 22 | 99.38 17 | 99.28 76 | 98.61 21 | 98.45 155 | 98.76 94 | 97.82 4 | 98.45 65 | 98.93 91 | 96.65 12 | 99.83 54 | 97.38 71 | 99.41 90 | 99.71 42 |
|
xxxxxxxxxxxxxcwj | | | 98.59 18 | 98.32 28 | 99.41 15 | 99.54 33 | 98.71 15 | 99.04 50 | 98.81 75 | 95.12 119 | 99.32 13 | 99.39 13 | 96.22 18 | 99.84 51 | 97.72 49 | 99.73 41 | 99.67 55 |
|
SF-MVS | | | 98.59 18 | 98.32 28 | 99.41 15 | 99.54 33 | 98.71 15 | 99.04 50 | 98.81 75 | 95.12 119 | 99.32 13 | 99.39 13 | 96.22 18 | 99.84 51 | 97.72 49 | 99.73 41 | 99.67 55 |
|
Regformer-3 | | | 98.59 18 | 98.50 13 | 98.86 69 | 99.43 51 | 97.05 92 | 98.40 164 | 98.68 115 | 97.43 18 | 99.06 27 | 99.31 31 | 95.80 41 | 99.77 95 | 98.62 5 | 99.76 30 | 99.78 12 |
|
ACMMPR | | | 98.59 18 | 98.36 20 | 99.29 29 | 99.74 7 | 98.15 52 | 99.23 21 | 98.95 34 | 96.10 77 | 98.93 38 | 99.19 51 | 95.70 42 | 99.94 3 | 97.62 56 | 99.79 17 | 99.78 12 |
|
SMA-MVS | | | 98.58 22 | 98.25 34 | 99.56 4 | 99.51 37 | 99.04 8 | 98.95 67 | 98.80 86 | 93.67 189 | 99.37 11 | 99.52 3 | 96.52 15 | 99.89 34 | 98.06 31 | 99.81 10 | 99.76 25 |
|
MTAPA | | | 98.58 22 | 98.29 31 | 99.46 11 | 99.76 1 | 98.64 19 | 98.90 72 | 98.74 98 | 97.27 31 | 98.02 84 | 99.39 13 | 94.81 71 | 99.96 1 | 97.91 36 | 99.79 17 | 99.77 19 |
|
HPM-MVS++ | | | 98.58 22 | 98.25 34 | 99.55 5 | 99.50 39 | 99.08 7 | 98.72 115 | 98.66 126 | 97.51 14 | 98.15 75 | 98.83 101 | 95.70 42 | 99.92 20 | 97.53 66 | 99.67 51 | 99.66 59 |
|
SR-MVS | | | 98.57 25 | 98.35 22 | 99.24 38 | 99.53 35 | 98.18 49 | 99.09 44 | 98.82 69 | 96.58 59 | 99.10 25 | 99.32 29 | 95.39 52 | 99.82 61 | 97.70 52 | 99.63 58 | 99.72 38 |
|
CP-MVS | | | 98.57 25 | 98.36 20 | 99.19 41 | 99.66 25 | 97.86 63 | 99.34 11 | 98.87 55 | 95.96 80 | 98.60 58 | 99.13 59 | 96.05 30 | 99.94 3 | 97.77 46 | 99.86 1 | 99.77 19 |
|
MSLP-MVS++ | | | 98.56 27 | 98.57 7 | 98.55 83 | 99.26 79 | 96.80 101 | 98.71 116 | 99.05 24 | 97.28 27 | 98.84 41 | 99.28 36 | 96.47 16 | 99.40 149 | 98.52 13 | 99.70 48 | 99.47 92 |
|
zzz-MVS | | | 98.55 28 | 98.25 34 | 99.46 11 | 99.76 1 | 98.64 19 | 98.55 144 | 98.74 98 | 97.27 31 | 98.02 84 | 99.39 13 | 94.81 71 | 99.96 1 | 97.91 36 | 99.79 17 | 99.77 19 |
|
DeepC-MVS_fast | | 96.70 1 | 98.55 28 | 98.34 24 | 99.18 45 | 99.25 80 | 98.04 56 | 98.50 151 | 98.78 90 | 97.72 5 | 98.92 39 | 99.28 36 | 95.27 58 | 99.82 61 | 97.55 64 | 99.77 24 | 99.69 45 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
#test# | | | 98.54 30 | 98.27 32 | 99.32 27 | 99.72 12 | 98.29 42 | 98.98 62 | 98.96 32 | 95.65 93 | 98.94 34 | 99.17 52 | 96.06 28 | 99.92 20 | 97.21 75 | 99.78 21 | 99.75 27 |
|
APD-MVS_3200maxsize | | | 98.53 31 | 98.33 27 | 99.15 50 | 99.50 39 | 97.92 62 | 99.15 35 | 98.81 75 | 96.24 68 | 99.20 20 | 99.37 21 | 95.30 57 | 99.80 74 | 97.73 48 | 99.67 51 | 99.72 38 |
|
mPP-MVS | | | 98.51 32 | 98.26 33 | 99.25 37 | 99.75 3 | 98.04 56 | 99.28 16 | 98.81 75 | 96.24 68 | 98.35 71 | 99.23 41 | 95.46 48 | 99.94 3 | 97.42 69 | 99.81 10 | 99.77 19 |
|
ZNCC-MVS | | | 98.49 33 | 98.20 40 | 99.35 21 | 99.73 11 | 98.39 31 | 99.19 31 | 98.86 60 | 95.77 86 | 98.31 74 | 99.10 64 | 95.46 48 | 99.93 14 | 97.57 63 | 99.81 10 | 99.74 32 |
|
PGM-MVS | | | 98.49 33 | 98.23 38 | 99.27 36 | 99.72 12 | 98.08 55 | 98.99 59 | 99.49 5 | 95.43 102 | 99.03 28 | 99.32 29 | 95.56 44 | 99.94 3 | 96.80 99 | 99.77 24 | 99.78 12 |
|
EI-MVSNet-Vis-set | | | 98.47 35 | 98.39 17 | 98.69 74 | 99.46 47 | 96.49 116 | 98.30 178 | 98.69 112 | 97.21 34 | 98.84 41 | 99.36 25 | 95.41 51 | 99.78 90 | 98.62 5 | 99.65 55 | 99.80 8 |
|
MVS_111021_HR | | | 98.47 35 | 98.34 24 | 98.88 68 | 99.22 87 | 97.32 80 | 97.91 223 | 99.58 3 | 97.20 35 | 98.33 72 | 99.00 80 | 95.99 33 | 99.64 120 | 98.05 33 | 99.76 30 | 99.69 45 |
|
GST-MVS | | | 98.43 37 | 98.12 43 | 99.34 22 | 99.72 12 | 98.38 32 | 99.09 44 | 98.82 69 | 95.71 89 | 98.73 50 | 99.06 73 | 95.27 58 | 99.93 14 | 97.07 79 | 99.63 58 | 99.72 38 |
|
EI-MVSNet-UG-set | | | 98.41 38 | 98.34 24 | 98.61 79 | 99.45 49 | 96.32 124 | 98.28 181 | 98.68 115 | 97.17 37 | 98.74 48 | 99.37 21 | 95.25 60 | 99.79 86 | 98.57 7 | 99.54 77 | 99.73 35 |
|
DELS-MVS | | | 98.40 39 | 98.20 40 | 98.99 59 | 99.00 102 | 97.66 68 | 97.75 239 | 98.89 46 | 97.71 7 | 98.33 72 | 98.97 82 | 94.97 68 | 99.88 42 | 98.42 19 | 99.76 30 | 99.42 101 |
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 |
TSAR-MVS + GP. | | | 98.38 40 | 98.24 37 | 98.81 70 | 99.22 87 | 97.25 86 | 98.11 205 | 98.29 196 | 97.19 36 | 98.99 33 | 99.02 75 | 96.22 18 | 99.67 116 | 98.52 13 | 98.56 128 | 99.51 83 |
|
HPM-MVS_fast | | | 98.38 40 | 98.13 42 | 99.12 53 | 99.75 3 | 97.86 63 | 99.44 4 | 98.82 69 | 94.46 151 | 98.94 34 | 99.20 48 | 95.16 63 | 99.74 101 | 97.58 60 | 99.85 3 | 99.77 19 |
|
HPM-MVS | | | 98.36 42 | 98.10 44 | 99.13 51 | 99.74 7 | 97.82 65 | 99.53 1 | 98.80 86 | 94.63 144 | 98.61 57 | 98.97 82 | 95.13 64 | 99.77 95 | 97.65 54 | 99.83 9 | 99.79 9 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
ETH3D-3000-0.1 | | | 98.35 43 | 98.00 49 | 99.38 17 | 99.47 44 | 98.68 18 | 98.67 126 | 98.84 64 | 94.66 142 | 99.11 24 | 99.25 39 | 95.46 48 | 99.81 65 | 96.80 99 | 99.73 41 | 99.63 67 |
|
APD-MVS | | | 98.35 43 | 98.00 49 | 99.42 14 | 99.51 37 | 98.72 14 | 98.80 98 | 98.82 69 | 94.52 148 | 99.23 18 | 99.25 39 | 95.54 46 | 99.80 74 | 96.52 108 | 99.77 24 | 99.74 32 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
MVS_111021_LR | | | 98.34 45 | 98.23 38 | 98.67 76 | 99.27 77 | 96.90 98 | 97.95 220 | 99.58 3 | 97.14 39 | 98.44 66 | 99.01 79 | 95.03 67 | 99.62 125 | 97.91 36 | 99.75 36 | 99.50 85 |
|
PHI-MVS | | | 98.34 45 | 98.06 45 | 99.18 45 | 99.15 94 | 98.12 54 | 99.04 50 | 99.09 20 | 93.32 202 | 98.83 43 | 99.10 64 | 96.54 14 | 99.83 54 | 97.70 52 | 99.76 30 | 99.59 74 |
|
testtj | | | 98.33 47 | 97.95 51 | 99.47 10 | 99.49 43 | 98.70 17 | 98.83 88 | 98.86 60 | 95.48 99 | 98.91 40 | 99.17 52 | 95.48 47 | 99.93 14 | 95.80 131 | 99.53 78 | 99.76 25 |
|
MP-MVS | | | 98.33 47 | 98.01 48 | 99.28 33 | 99.75 3 | 98.18 49 | 99.22 25 | 98.79 88 | 96.13 74 | 97.92 97 | 99.23 41 | 94.54 78 | 99.94 3 | 96.74 102 | 99.78 21 | 99.73 35 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
MP-MVS-pluss | | | 98.31 49 | 97.92 53 | 99.49 8 | 99.72 12 | 98.88 11 | 98.43 160 | 98.78 90 | 94.10 159 | 97.69 109 | 99.42 11 | 95.25 60 | 99.92 20 | 98.09 30 | 99.80 15 | 99.67 55 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
abl_6 | | | 98.30 50 | 98.03 47 | 99.13 51 | 99.56 32 | 97.76 67 | 99.13 39 | 98.82 69 | 96.14 73 | 99.26 16 | 99.37 21 | 93.33 96 | 99.93 14 | 96.96 84 | 99.67 51 | 99.69 45 |
|
ACMMP | | | 98.23 51 | 97.95 51 | 99.09 55 | 99.74 7 | 97.62 71 | 99.03 53 | 99.41 6 | 95.98 79 | 97.60 118 | 99.36 25 | 94.45 83 | 99.93 14 | 97.14 76 | 98.85 115 | 99.70 44 |
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 |
test_prior3 | | | 98.22 52 | 97.90 54 | 99.19 41 | 99.31 64 | 98.22 46 | 97.80 235 | 98.84 64 | 96.12 75 | 97.89 99 | 98.69 113 | 95.96 34 | 99.70 109 | 96.89 89 | 99.60 62 | 99.65 61 |
|
CANet | | | 98.05 53 | 97.76 57 | 98.90 67 | 98.73 121 | 97.27 82 | 98.35 168 | 98.78 90 | 97.37 24 | 97.72 107 | 98.96 87 | 91.53 132 | 99.92 20 | 98.79 2 | 99.65 55 | 99.51 83 |
|
train_agg | | | 97.97 54 | 97.52 68 | 99.33 26 | 99.31 64 | 98.50 26 | 97.92 221 | 98.73 102 | 92.98 214 | 97.74 105 | 98.68 115 | 96.20 21 | 99.80 74 | 96.59 105 | 99.57 68 | 99.68 51 |
|
ETH3D cwj APD-0.16 | | | 97.96 55 | 97.52 68 | 99.29 29 | 99.05 98 | 98.52 24 | 98.33 170 | 98.68 115 | 93.18 206 | 98.68 52 | 99.13 59 | 94.62 75 | 99.83 54 | 96.45 110 | 99.55 76 | 99.52 79 |
|
ETV-MVS | | | 97.96 55 | 97.81 55 | 98.40 99 | 98.42 144 | 97.27 82 | 98.73 111 | 98.55 145 | 96.84 49 | 98.38 69 | 97.44 225 | 95.39 52 | 99.35 153 | 97.62 56 | 98.89 111 | 98.58 179 |
|
UA-Net | | | 97.96 55 | 97.62 60 | 98.98 61 | 98.86 113 | 97.47 76 | 98.89 76 | 99.08 21 | 96.67 56 | 98.72 51 | 99.54 1 | 93.15 99 | 99.81 65 | 94.87 159 | 98.83 116 | 99.65 61 |
|
agg_prior1 | | | 97.95 58 | 97.51 70 | 99.28 33 | 99.30 69 | 98.38 32 | 97.81 234 | 98.72 104 | 93.16 208 | 97.57 119 | 98.66 118 | 96.14 24 | 99.81 65 | 96.63 104 | 99.56 73 | 99.66 59 |
|
CDPH-MVS | | | 97.94 59 | 97.49 71 | 99.28 33 | 99.47 44 | 98.44 28 | 97.91 223 | 98.67 123 | 92.57 230 | 98.77 46 | 98.85 98 | 95.93 36 | 99.72 103 | 95.56 141 | 99.69 49 | 99.68 51 |
|
DeepPCF-MVS | | 96.37 2 | 97.93 60 | 98.48 15 | 96.30 236 | 99.00 102 | 89.54 301 | 97.43 257 | 98.87 55 | 98.16 2 | 99.26 16 | 99.38 20 | 96.12 26 | 99.64 120 | 98.30 24 | 99.77 24 | 99.72 38 |
|
DeepC-MVS | | 95.98 3 | 97.88 61 | 97.58 63 | 98.77 71 | 99.25 80 | 96.93 96 | 98.83 88 | 98.75 97 | 96.96 47 | 96.89 142 | 99.50 4 | 90.46 153 | 99.87 43 | 97.84 43 | 99.76 30 | 99.52 79 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
DP-MVS Recon | | | 97.86 62 | 97.46 73 | 99.06 57 | 99.53 35 | 98.35 39 | 98.33 170 | 98.89 46 | 92.62 227 | 98.05 80 | 98.94 90 | 95.34 56 | 99.65 118 | 96.04 122 | 99.42 89 | 99.19 126 |
|
CSCG | | | 97.85 63 | 97.74 58 | 98.20 111 | 99.67 24 | 95.16 174 | 99.22 25 | 99.32 7 | 93.04 211 | 97.02 135 | 98.92 93 | 95.36 55 | 99.91 29 | 97.43 68 | 99.64 57 | 99.52 79 |
|
CS-MVS | | | 97.81 64 | 97.61 61 | 98.41 98 | 98.52 141 | 97.15 90 | 99.09 44 | 98.55 145 | 96.18 71 | 97.61 116 | 97.20 240 | 94.59 77 | 99.39 150 | 97.62 56 | 99.10 104 | 98.70 167 |
|
MG-MVS | | | 97.81 64 | 97.60 62 | 98.44 94 | 99.12 96 | 95.97 139 | 97.75 239 | 98.78 90 | 96.89 48 | 98.46 62 | 99.22 43 | 93.90 93 | 99.68 115 | 94.81 163 | 99.52 80 | 99.67 55 |
|
VNet | | | 97.79 66 | 97.40 77 | 98.96 63 | 98.88 111 | 97.55 73 | 98.63 131 | 98.93 37 | 96.74 53 | 99.02 29 | 98.84 100 | 90.33 156 | 99.83 54 | 98.53 9 | 96.66 180 | 99.50 85 |
|
EIA-MVS | | | 97.75 67 | 97.58 63 | 98.27 105 | 98.38 146 | 96.44 118 | 99.01 55 | 98.60 133 | 95.88 82 | 97.26 124 | 97.53 219 | 94.97 68 | 99.33 155 | 97.38 71 | 99.20 100 | 99.05 143 |
|
PS-MVSNAJ | | | 97.73 68 | 97.77 56 | 97.62 151 | 98.68 129 | 95.58 157 | 97.34 266 | 98.51 154 | 97.29 26 | 98.66 54 | 97.88 188 | 94.51 79 | 99.90 32 | 97.87 40 | 99.17 102 | 97.39 212 |
|
CPTT-MVS | | | 97.72 69 | 97.32 80 | 98.92 65 | 99.64 26 | 97.10 91 | 99.12 41 | 98.81 75 | 92.34 238 | 98.09 78 | 99.08 71 | 93.01 100 | 99.92 20 | 96.06 121 | 99.77 24 | 99.75 27 |
|
PVSNet_Blended_VisFu | | | 97.70 70 | 97.46 73 | 98.44 94 | 99.27 77 | 95.91 148 | 98.63 131 | 99.16 17 | 94.48 150 | 97.67 110 | 98.88 96 | 92.80 102 | 99.91 29 | 97.11 77 | 99.12 103 | 99.50 85 |
|
canonicalmvs | | | 97.67 71 | 97.23 83 | 98.98 61 | 98.70 126 | 98.38 32 | 99.34 11 | 98.39 176 | 96.76 52 | 97.67 110 | 97.40 228 | 92.26 110 | 99.49 140 | 98.28 25 | 96.28 196 | 99.08 141 |
|
xiu_mvs_v2_base | | | 97.66 72 | 97.70 59 | 97.56 155 | 98.61 135 | 95.46 164 | 97.44 255 | 98.46 164 | 97.15 38 | 98.65 55 | 98.15 168 | 94.33 85 | 99.80 74 | 97.84 43 | 98.66 124 | 97.41 210 |
|
baseline | | | 97.64 73 | 97.44 75 | 98.25 108 | 98.35 148 | 96.20 128 | 99.00 57 | 98.32 186 | 96.33 67 | 98.03 83 | 99.17 52 | 91.35 135 | 99.16 168 | 98.10 29 | 98.29 142 | 99.39 102 |
|
casdiffmvs | | | 97.63 74 | 97.41 76 | 98.28 104 | 98.33 153 | 96.14 131 | 98.82 91 | 98.32 186 | 96.38 65 | 97.95 92 | 99.21 44 | 91.23 139 | 99.23 162 | 98.12 28 | 98.37 137 | 99.48 90 |
|
xiu_mvs_v1_base_debu | | | 97.60 75 | 97.56 65 | 97.72 141 | 98.35 148 | 95.98 134 | 97.86 230 | 98.51 154 | 97.13 40 | 99.01 30 | 98.40 142 | 91.56 128 | 99.80 74 | 98.53 9 | 98.68 120 | 97.37 214 |
|
xiu_mvs_v1_base | | | 97.60 75 | 97.56 65 | 97.72 141 | 98.35 148 | 95.98 134 | 97.86 230 | 98.51 154 | 97.13 40 | 99.01 30 | 98.40 142 | 91.56 128 | 99.80 74 | 98.53 9 | 98.68 120 | 97.37 214 |
|
xiu_mvs_v1_base_debi | | | 97.60 75 | 97.56 65 | 97.72 141 | 98.35 148 | 95.98 134 | 97.86 230 | 98.51 154 | 97.13 40 | 99.01 30 | 98.40 142 | 91.56 128 | 99.80 74 | 98.53 9 | 98.68 120 | 97.37 214 |
|
ETH3 D test6400 | | | 97.59 78 | 97.01 92 | 99.34 22 | 99.40 53 | 98.56 22 | 98.20 189 | 98.81 75 | 91.63 260 | 98.44 66 | 98.85 98 | 93.98 92 | 99.82 61 | 94.11 186 | 99.69 49 | 99.64 64 |
|
diffmvs | | | 97.58 79 | 97.40 77 | 98.13 116 | 98.32 155 | 95.81 152 | 98.06 210 | 98.37 179 | 96.20 70 | 98.74 48 | 98.89 95 | 91.31 137 | 99.25 159 | 98.16 27 | 98.52 129 | 99.34 105 |
|
MVSFormer | | | 97.57 80 | 97.49 71 | 97.84 132 | 98.07 173 | 95.76 153 | 99.47 2 | 98.40 174 | 94.98 127 | 98.79 44 | 98.83 101 | 92.34 107 | 98.41 257 | 96.91 86 | 99.59 65 | 99.34 105 |
|
alignmvs | | | 97.56 81 | 97.07 90 | 99.01 58 | 98.66 130 | 98.37 37 | 98.83 88 | 98.06 236 | 96.74 53 | 98.00 90 | 97.65 208 | 90.80 147 | 99.48 144 | 98.37 22 | 96.56 184 | 99.19 126 |
|
DPM-MVS | | | 97.55 82 | 96.99 94 | 99.23 40 | 99.04 100 | 98.55 23 | 97.17 279 | 98.35 182 | 94.85 134 | 97.93 96 | 98.58 126 | 95.07 66 | 99.71 108 | 92.60 227 | 99.34 95 | 99.43 100 |
|
OMC-MVS | | | 97.55 82 | 97.34 79 | 98.20 111 | 99.33 59 | 95.92 146 | 98.28 181 | 98.59 135 | 95.52 98 | 97.97 91 | 99.10 64 | 93.28 98 | 99.49 140 | 95.09 156 | 98.88 112 | 99.19 126 |
|
PAPM_NR | | | 97.46 84 | 97.11 87 | 98.50 89 | 99.50 39 | 96.41 120 | 98.63 131 | 98.60 133 | 95.18 116 | 97.06 133 | 98.06 174 | 94.26 87 | 99.57 129 | 93.80 195 | 98.87 114 | 99.52 79 |
|
EPP-MVSNet | | | 97.46 84 | 97.28 81 | 97.99 125 | 98.64 132 | 95.38 166 | 99.33 13 | 98.31 188 | 93.61 192 | 97.19 126 | 99.07 72 | 94.05 89 | 99.23 162 | 96.89 89 | 98.43 136 | 99.37 104 |
|
3Dnovator | | 94.51 5 | 97.46 84 | 96.93 96 | 99.07 56 | 97.78 190 | 97.64 69 | 99.35 10 | 99.06 22 | 97.02 45 | 93.75 238 | 99.16 57 | 89.25 171 | 99.92 20 | 97.22 74 | 99.75 36 | 99.64 64 |
|
CNLPA | | | 97.45 87 | 97.03 91 | 98.73 72 | 99.05 98 | 97.44 78 | 98.07 209 | 98.53 150 | 95.32 110 | 96.80 147 | 98.53 130 | 93.32 97 | 99.72 103 | 94.31 179 | 99.31 97 | 99.02 145 |
|
lupinMVS | | | 97.44 88 | 97.22 84 | 98.12 118 | 98.07 173 | 95.76 153 | 97.68 244 | 97.76 253 | 94.50 149 | 98.79 44 | 98.61 121 | 92.34 107 | 99.30 156 | 97.58 60 | 99.59 65 | 99.31 111 |
|
3Dnovator+ | | 94.38 6 | 97.43 89 | 96.78 103 | 99.38 17 | 97.83 188 | 98.52 24 | 99.37 7 | 98.71 108 | 97.09 43 | 92.99 263 | 99.13 59 | 89.36 168 | 99.89 34 | 96.97 82 | 99.57 68 | 99.71 42 |
|
Vis-MVSNet | | | 97.42 90 | 97.11 87 | 98.34 102 | 98.66 130 | 96.23 127 | 99.22 25 | 99.00 27 | 96.63 58 | 98.04 82 | 99.21 44 | 88.05 204 | 99.35 153 | 96.01 124 | 99.21 99 | 99.45 98 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
API-MVS | | | 97.41 91 | 97.25 82 | 97.91 129 | 98.70 126 | 96.80 101 | 98.82 91 | 98.69 112 | 94.53 146 | 98.11 77 | 98.28 157 | 94.50 82 | 99.57 129 | 94.12 185 | 99.49 81 | 97.37 214 |
|
sss | | | 97.39 92 | 96.98 95 | 98.61 79 | 98.60 136 | 96.61 109 | 98.22 186 | 98.93 37 | 93.97 168 | 98.01 88 | 98.48 135 | 91.98 120 | 99.85 48 | 96.45 110 | 98.15 145 | 99.39 102 |
|
PVSNet_Blended | | | 97.38 93 | 97.12 86 | 98.14 114 | 99.25 80 | 95.35 169 | 97.28 271 | 99.26 8 | 93.13 209 | 97.94 94 | 98.21 164 | 92.74 103 | 99.81 65 | 96.88 92 | 99.40 92 | 99.27 118 |
|
1121 | | | 97.37 94 | 96.77 107 | 99.16 48 | 99.34 56 | 97.99 61 | 98.19 193 | 98.68 115 | 90.14 294 | 98.01 88 | 98.97 82 | 94.80 73 | 99.87 43 | 93.36 207 | 99.46 86 | 99.61 69 |
|
WTY-MVS | | | 97.37 94 | 96.92 97 | 98.72 73 | 98.86 113 | 96.89 100 | 98.31 176 | 98.71 108 | 95.26 112 | 97.67 110 | 98.56 129 | 92.21 113 | 99.78 90 | 95.89 126 | 96.85 175 | 99.48 90 |
|
jason | | | 97.32 96 | 97.08 89 | 98.06 122 | 97.45 219 | 95.59 156 | 97.87 229 | 97.91 248 | 94.79 135 | 98.55 60 | 98.83 101 | 91.12 140 | 99.23 162 | 97.58 60 | 99.60 62 | 99.34 105 |
jason: jason. |
MVS_Test | | | 97.28 97 | 97.00 93 | 98.13 116 | 98.33 153 | 95.97 139 | 98.74 107 | 98.07 232 | 94.27 155 | 98.44 66 | 98.07 173 | 92.48 105 | 99.26 158 | 96.43 112 | 98.19 144 | 99.16 131 |
|
EPNet | | | 97.28 97 | 96.87 99 | 98.51 88 | 94.98 317 | 96.14 131 | 98.90 72 | 97.02 299 | 98.28 1 | 95.99 176 | 99.11 62 | 91.36 134 | 99.89 34 | 96.98 81 | 99.19 101 | 99.50 85 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
test_yl | | | 97.22 99 | 96.78 103 | 98.54 85 | 98.73 121 | 96.60 110 | 98.45 155 | 98.31 188 | 94.70 136 | 98.02 84 | 98.42 140 | 90.80 147 | 99.70 109 | 96.81 97 | 96.79 177 | 99.34 105 |
|
DCV-MVSNet | | | 97.22 99 | 96.78 103 | 98.54 85 | 98.73 121 | 96.60 110 | 98.45 155 | 98.31 188 | 94.70 136 | 98.02 84 | 98.42 140 | 90.80 147 | 99.70 109 | 96.81 97 | 96.79 177 | 99.34 105 |
|
IS-MVSNet | | | 97.22 99 | 96.88 98 | 98.25 108 | 98.85 115 | 96.36 122 | 99.19 31 | 97.97 243 | 95.39 104 | 97.23 125 | 98.99 81 | 91.11 141 | 98.93 201 | 94.60 168 | 98.59 126 | 99.47 92 |
|
PLC | | 95.07 4 | 97.20 102 | 96.78 103 | 98.44 94 | 99.29 72 | 96.31 126 | 98.14 200 | 98.76 94 | 92.41 236 | 96.39 166 | 98.31 155 | 94.92 70 | 99.78 90 | 94.06 188 | 98.77 119 | 99.23 121 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
CHOSEN 280x420 | | | 97.18 103 | 97.18 85 | 97.20 167 | 98.81 117 | 93.27 247 | 95.78 322 | 99.15 18 | 95.25 113 | 96.79 148 | 98.11 171 | 92.29 109 | 99.07 184 | 98.56 8 | 99.85 3 | 99.25 120 |
|
LS3D | | | 97.16 104 | 96.66 112 | 98.68 75 | 98.53 140 | 97.19 88 | 98.93 70 | 98.90 44 | 92.83 222 | 95.99 176 | 99.37 21 | 92.12 116 | 99.87 43 | 93.67 199 | 99.57 68 | 98.97 150 |
|
AdaColmap | | | 97.15 105 | 96.70 108 | 98.48 91 | 99.16 92 | 96.69 106 | 98.01 215 | 98.89 46 | 94.44 152 | 96.83 143 | 98.68 115 | 90.69 150 | 99.76 97 | 94.36 176 | 99.29 98 | 98.98 149 |
|
Effi-MVS+ | | | 97.12 106 | 96.69 109 | 98.39 100 | 98.19 164 | 96.72 105 | 97.37 262 | 98.43 171 | 93.71 182 | 97.65 113 | 98.02 176 | 92.20 114 | 99.25 159 | 96.87 95 | 97.79 156 | 99.19 126 |
|
CHOSEN 1792x2688 | | | 97.12 106 | 96.80 100 | 98.08 120 | 99.30 69 | 94.56 206 | 98.05 211 | 99.71 1 | 93.57 193 | 97.09 129 | 98.91 94 | 88.17 199 | 99.89 34 | 96.87 95 | 99.56 73 | 99.81 7 |
|
F-COLMAP | | | 97.09 108 | 96.80 100 | 97.97 126 | 99.45 49 | 94.95 187 | 98.55 144 | 98.62 132 | 93.02 212 | 96.17 171 | 98.58 126 | 94.01 90 | 99.81 65 | 93.95 190 | 98.90 110 | 99.14 134 |
|
TAMVS | | | 97.02 109 | 96.79 102 | 97.70 144 | 98.06 175 | 95.31 171 | 98.52 146 | 98.31 188 | 93.95 169 | 97.05 134 | 98.61 121 | 93.49 95 | 98.52 239 | 95.33 147 | 97.81 155 | 99.29 116 |
|
CDS-MVSNet | | | 96.99 110 | 96.69 109 | 97.90 130 | 98.05 176 | 95.98 134 | 98.20 189 | 98.33 185 | 93.67 189 | 96.95 136 | 98.49 134 | 93.54 94 | 98.42 250 | 95.24 154 | 97.74 159 | 99.31 111 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
CANet_DTU | | | 96.96 111 | 96.55 115 | 98.21 110 | 98.17 168 | 96.07 133 | 97.98 218 | 98.21 204 | 97.24 33 | 97.13 128 | 98.93 91 | 86.88 228 | 99.91 29 | 95.00 158 | 99.37 94 | 98.66 173 |
|
114514_t | | | 96.93 112 | 96.27 124 | 98.92 65 | 99.50 39 | 97.63 70 | 98.85 84 | 98.90 44 | 84.80 327 | 97.77 102 | 99.11 62 | 92.84 101 | 99.66 117 | 94.85 160 | 99.77 24 | 99.47 92 |
|
MAR-MVS | | | 96.91 113 | 96.40 120 | 98.45 93 | 98.69 128 | 96.90 98 | 98.66 129 | 98.68 115 | 92.40 237 | 97.07 132 | 97.96 181 | 91.54 131 | 99.75 99 | 93.68 197 | 98.92 109 | 98.69 169 |
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 |
HyFIR lowres test | | | 96.90 114 | 96.49 118 | 98.14 114 | 99.33 59 | 95.56 159 | 97.38 260 | 99.65 2 | 92.34 238 | 97.61 116 | 98.20 165 | 89.29 170 | 99.10 181 | 96.97 82 | 97.60 164 | 99.77 19 |
|
Vis-MVSNet (Re-imp) | | | 96.87 115 | 96.55 115 | 97.83 133 | 98.73 121 | 95.46 164 | 99.20 29 | 98.30 194 | 94.96 129 | 96.60 154 | 98.87 97 | 90.05 159 | 98.59 234 | 93.67 199 | 98.60 125 | 99.46 96 |
|
PAPR | | | 96.84 116 | 96.24 126 | 98.65 77 | 98.72 125 | 96.92 97 | 97.36 264 | 98.57 141 | 93.33 201 | 96.67 150 | 97.57 216 | 94.30 86 | 99.56 131 | 91.05 263 | 98.59 126 | 99.47 92 |
|
HY-MVS | | 93.96 8 | 96.82 117 | 96.23 127 | 98.57 81 | 98.46 143 | 97.00 93 | 98.14 200 | 98.21 204 | 93.95 169 | 96.72 149 | 97.99 180 | 91.58 127 | 99.76 97 | 94.51 173 | 96.54 185 | 98.95 154 |
|
UGNet | | | 96.78 118 | 96.30 123 | 98.19 113 | 98.24 158 | 95.89 150 | 98.88 79 | 98.93 37 | 97.39 21 | 96.81 146 | 97.84 192 | 82.60 285 | 99.90 32 | 96.53 107 | 99.49 81 | 98.79 162 |
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 |
PVSNet_BlendedMVS | | | 96.73 119 | 96.60 113 | 97.12 173 | 99.25 80 | 95.35 169 | 98.26 184 | 99.26 8 | 94.28 154 | 97.94 94 | 97.46 222 | 92.74 103 | 99.81 65 | 96.88 92 | 93.32 241 | 96.20 302 |
|
mvs_anonymous | | | 96.70 120 | 96.53 117 | 97.18 169 | 98.19 164 | 93.78 227 | 98.31 176 | 98.19 207 | 94.01 164 | 94.47 200 | 98.27 160 | 92.08 118 | 98.46 244 | 97.39 70 | 97.91 151 | 99.31 111 |
|
1112_ss | | | 96.63 121 | 96.00 133 | 98.50 89 | 98.56 137 | 96.37 121 | 98.18 197 | 98.10 226 | 92.92 217 | 94.84 189 | 98.43 138 | 92.14 115 | 99.58 128 | 94.35 177 | 96.51 186 | 99.56 78 |
|
mvs-test1 | | | 96.60 122 | 96.68 111 | 96.37 231 | 97.89 185 | 91.81 267 | 98.56 142 | 98.10 226 | 96.57 60 | 96.52 161 | 97.94 183 | 90.81 145 | 99.45 147 | 95.72 134 | 98.01 148 | 97.86 200 |
|
PMMVS | | | 96.60 122 | 96.33 122 | 97.41 160 | 97.90 184 | 93.93 223 | 97.35 265 | 98.41 172 | 92.84 221 | 97.76 103 | 97.45 224 | 91.10 142 | 99.20 165 | 96.26 116 | 97.91 151 | 99.11 137 |
|
DP-MVS | | | 96.59 124 | 95.93 134 | 98.57 81 | 99.34 56 | 96.19 130 | 98.70 120 | 98.39 176 | 89.45 303 | 94.52 198 | 99.35 27 | 91.85 122 | 99.85 48 | 92.89 223 | 98.88 112 | 99.68 51 |
|
PatchMatch-RL | | | 96.59 124 | 96.03 132 | 98.27 105 | 99.31 64 | 96.51 115 | 97.91 223 | 99.06 22 | 93.72 181 | 96.92 140 | 98.06 174 | 88.50 194 | 99.65 118 | 91.77 251 | 99.00 107 | 98.66 173 |
|
XVG-OURS | | | 96.55 126 | 96.41 119 | 96.99 179 | 98.75 120 | 93.76 228 | 97.50 254 | 98.52 152 | 95.67 91 | 96.83 143 | 99.30 34 | 88.95 184 | 99.53 137 | 95.88 127 | 96.26 197 | 97.69 206 |
|
FIs | | | 96.51 127 | 96.12 129 | 97.67 147 | 97.13 241 | 97.54 74 | 99.36 8 | 99.22 14 | 95.89 81 | 94.03 226 | 98.35 148 | 91.98 120 | 98.44 247 | 96.40 113 | 92.76 247 | 97.01 222 |
|
XVG-OURS-SEG-HR | | | 96.51 127 | 96.34 121 | 97.02 178 | 98.77 119 | 93.76 228 | 97.79 237 | 98.50 159 | 95.45 101 | 96.94 137 | 99.09 69 | 87.87 209 | 99.55 136 | 96.76 101 | 95.83 206 | 97.74 203 |
|
PS-MVSNAJss | | | 96.43 129 | 96.26 125 | 96.92 188 | 95.84 300 | 95.08 179 | 99.16 34 | 98.50 159 | 95.87 83 | 93.84 234 | 98.34 152 | 94.51 79 | 98.61 231 | 96.88 92 | 93.45 238 | 97.06 220 |
|
FC-MVSNet-test | | | 96.42 130 | 96.05 130 | 97.53 156 | 96.95 249 | 97.27 82 | 99.36 8 | 99.23 12 | 95.83 84 | 93.93 228 | 98.37 146 | 92.00 119 | 98.32 266 | 96.02 123 | 92.72 248 | 97.00 223 |
|
ab-mvs | | | 96.42 130 | 95.71 142 | 98.55 83 | 98.63 133 | 96.75 104 | 97.88 228 | 98.74 98 | 93.84 174 | 96.54 159 | 98.18 167 | 85.34 253 | 99.75 99 | 95.93 125 | 96.35 190 | 99.15 132 |
|
PVSNet | | 91.96 18 | 96.35 132 | 96.15 128 | 96.96 183 | 99.17 91 | 92.05 264 | 96.08 315 | 98.68 115 | 93.69 185 | 97.75 104 | 97.80 198 | 88.86 185 | 99.69 114 | 94.26 181 | 99.01 106 | 99.15 132 |
|
Test_1112_low_res | | | 96.34 133 | 95.66 146 | 98.36 101 | 98.56 137 | 95.94 142 | 97.71 241 | 98.07 232 | 92.10 247 | 94.79 193 | 97.29 233 | 91.75 124 | 99.56 131 | 94.17 183 | 96.50 187 | 99.58 76 |
|
Effi-MVS+-dtu | | | 96.29 134 | 96.56 114 | 95.51 264 | 97.89 185 | 90.22 294 | 98.80 98 | 98.10 226 | 96.57 60 | 96.45 165 | 96.66 282 | 90.81 145 | 98.91 203 | 95.72 134 | 97.99 149 | 97.40 211 |
|
QAPM | | | 96.29 134 | 95.40 149 | 98.96 63 | 97.85 187 | 97.60 72 | 99.23 21 | 98.93 37 | 89.76 298 | 93.11 260 | 99.02 75 | 89.11 176 | 99.93 14 | 91.99 246 | 99.62 60 | 99.34 105 |
|
Fast-Effi-MVS+ | | | 96.28 136 | 95.70 143 | 98.03 123 | 98.29 157 | 95.97 139 | 98.58 137 | 98.25 202 | 91.74 255 | 95.29 183 | 97.23 237 | 91.03 144 | 99.15 171 | 92.90 221 | 97.96 150 | 98.97 150 |
|
nrg030 | | | 96.28 136 | 95.72 139 | 97.96 128 | 96.90 254 | 98.15 52 | 99.39 5 | 98.31 188 | 95.47 100 | 94.42 206 | 98.35 148 | 92.09 117 | 98.69 224 | 97.50 67 | 89.05 291 | 97.04 221 |
|
1314 | | | 96.25 138 | 95.73 138 | 97.79 135 | 97.13 241 | 95.55 161 | 98.19 193 | 98.59 135 | 93.47 196 | 92.03 288 | 97.82 196 | 91.33 136 | 99.49 140 | 94.62 167 | 98.44 134 | 98.32 190 |
|
HQP_MVS | | | 96.14 139 | 95.90 135 | 96.85 190 | 97.42 220 | 94.60 204 | 98.80 98 | 98.56 143 | 97.28 27 | 95.34 180 | 98.28 157 | 87.09 223 | 99.03 189 | 96.07 119 | 94.27 214 | 96.92 228 |
|
tttt0517 | | | 96.07 140 | 95.51 148 | 97.78 136 | 98.41 145 | 94.84 190 | 99.28 16 | 94.33 336 | 94.26 156 | 97.64 114 | 98.64 120 | 84.05 274 | 99.47 145 | 95.34 146 | 97.60 164 | 99.03 144 |
|
MVSTER | | | 96.06 141 | 95.72 139 | 97.08 176 | 98.23 159 | 95.93 145 | 98.73 111 | 98.27 197 | 94.86 133 | 95.07 184 | 98.09 172 | 88.21 198 | 98.54 237 | 96.59 105 | 93.46 236 | 96.79 246 |
|
thisisatest0530 | | | 96.01 142 | 95.36 154 | 97.97 126 | 98.38 146 | 95.52 162 | 98.88 79 | 94.19 338 | 94.04 161 | 97.64 114 | 98.31 155 | 83.82 281 | 99.46 146 | 95.29 150 | 97.70 161 | 98.93 155 |
|
test_djsdf | | | 96.00 143 | 95.69 144 | 96.93 186 | 95.72 302 | 95.49 163 | 99.47 2 | 98.40 174 | 94.98 127 | 94.58 196 | 97.86 189 | 89.16 174 | 98.41 257 | 96.91 86 | 94.12 222 | 96.88 237 |
|
EI-MVSNet | | | 95.96 144 | 95.83 137 | 96.36 232 | 97.93 182 | 93.70 234 | 98.12 203 | 98.27 197 | 93.70 184 | 95.07 184 | 99.02 75 | 92.23 112 | 98.54 237 | 94.68 164 | 93.46 236 | 96.84 242 |
|
BH-untuned | | | 95.95 145 | 95.72 139 | 96.65 201 | 98.55 139 | 92.26 260 | 98.23 185 | 97.79 252 | 93.73 180 | 94.62 195 | 98.01 178 | 88.97 183 | 99.00 192 | 93.04 217 | 98.51 130 | 98.68 170 |
|
MSDG | | | 95.93 146 | 95.30 160 | 97.83 133 | 98.90 109 | 95.36 167 | 96.83 303 | 98.37 179 | 91.32 271 | 94.43 205 | 98.73 112 | 90.27 157 | 99.60 126 | 90.05 277 | 98.82 117 | 98.52 180 |
|
BH-RMVSNet | | | 95.92 147 | 95.32 158 | 97.69 145 | 98.32 155 | 94.64 198 | 98.19 193 | 97.45 275 | 94.56 145 | 96.03 174 | 98.61 121 | 85.02 256 | 99.12 174 | 90.68 268 | 99.06 105 | 99.30 114 |
|
Fast-Effi-MVS+-dtu | | | 95.87 148 | 95.85 136 | 95.91 252 | 97.74 194 | 91.74 271 | 98.69 122 | 98.15 218 | 95.56 96 | 94.92 187 | 97.68 207 | 88.98 182 | 98.79 219 | 93.19 212 | 97.78 157 | 97.20 218 |
|
LFMVS | | | 95.86 149 | 94.98 174 | 98.47 92 | 98.87 112 | 96.32 124 | 98.84 87 | 96.02 318 | 93.40 199 | 98.62 56 | 99.20 48 | 74.99 325 | 99.63 123 | 97.72 49 | 97.20 170 | 99.46 96 |
|
baseline1 | | | 95.84 150 | 95.12 167 | 98.01 124 | 98.49 142 | 95.98 134 | 98.73 111 | 97.03 297 | 95.37 107 | 96.22 169 | 98.19 166 | 89.96 161 | 99.16 168 | 94.60 168 | 87.48 308 | 98.90 157 |
|
OpenMVS | | 93.04 13 | 95.83 151 | 95.00 172 | 98.32 103 | 97.18 238 | 97.32 80 | 99.21 28 | 98.97 30 | 89.96 296 | 91.14 296 | 99.05 74 | 86.64 231 | 99.92 20 | 93.38 205 | 99.47 83 | 97.73 204 |
|
VDD-MVS | | | 95.82 152 | 95.23 162 | 97.61 152 | 98.84 116 | 93.98 222 | 98.68 123 | 97.40 279 | 95.02 126 | 97.95 92 | 99.34 28 | 74.37 329 | 99.78 90 | 98.64 3 | 96.80 176 | 99.08 141 |
|
UniMVSNet (Re) | | | 95.78 153 | 95.19 164 | 97.58 153 | 96.99 248 | 97.47 76 | 98.79 102 | 99.18 16 | 95.60 94 | 93.92 229 | 97.04 257 | 91.68 125 | 98.48 241 | 95.80 131 | 87.66 307 | 96.79 246 |
|
VPA-MVSNet | | | 95.75 154 | 95.11 168 | 97.69 145 | 97.24 230 | 97.27 82 | 98.94 69 | 99.23 12 | 95.13 118 | 95.51 179 | 97.32 231 | 85.73 246 | 98.91 203 | 97.33 73 | 89.55 284 | 96.89 236 |
|
HQP-MVS | | | 95.72 155 | 95.40 149 | 96.69 199 | 97.20 234 | 94.25 217 | 98.05 211 | 98.46 164 | 96.43 62 | 94.45 201 | 97.73 201 | 86.75 229 | 98.96 196 | 95.30 148 | 94.18 218 | 96.86 241 |
|
UniMVSNet_NR-MVSNet | | | 95.71 156 | 95.15 165 | 97.40 162 | 96.84 257 | 96.97 94 | 98.74 107 | 99.24 10 | 95.16 117 | 93.88 231 | 97.72 203 | 91.68 125 | 98.31 268 | 95.81 129 | 87.25 312 | 96.92 228 |
|
PatchmatchNet | | | 95.71 156 | 95.52 147 | 96.29 237 | 97.58 204 | 90.72 287 | 96.84 302 | 97.52 268 | 94.06 160 | 97.08 130 | 96.96 266 | 89.24 172 | 98.90 206 | 92.03 245 | 98.37 137 | 99.26 119 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
OPM-MVS | | | 95.69 158 | 95.33 157 | 96.76 194 | 96.16 289 | 94.63 199 | 98.43 160 | 98.39 176 | 96.64 57 | 95.02 186 | 98.78 106 | 85.15 255 | 99.05 185 | 95.21 155 | 94.20 217 | 96.60 269 |
|
ACMM | | 93.85 9 | 95.69 158 | 95.38 153 | 96.61 206 | 97.61 201 | 93.84 226 | 98.91 71 | 98.44 168 | 95.25 113 | 94.28 212 | 98.47 136 | 86.04 244 | 99.12 174 | 95.50 143 | 93.95 227 | 96.87 239 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
tpmrst | | | 95.63 160 | 95.69 144 | 95.44 268 | 97.54 209 | 88.54 315 | 96.97 288 | 97.56 262 | 93.50 195 | 97.52 121 | 96.93 270 | 89.49 164 | 99.16 168 | 95.25 153 | 96.42 189 | 98.64 175 |
|
LPG-MVS_test | | | 95.62 161 | 95.34 155 | 96.47 223 | 97.46 215 | 93.54 237 | 98.99 59 | 98.54 148 | 94.67 140 | 94.36 208 | 98.77 108 | 85.39 250 | 99.11 178 | 95.71 136 | 94.15 220 | 96.76 249 |
|
CLD-MVS | | | 95.62 161 | 95.34 155 | 96.46 226 | 97.52 212 | 93.75 230 | 97.27 272 | 98.46 164 | 95.53 97 | 94.42 206 | 98.00 179 | 86.21 239 | 98.97 193 | 96.25 117 | 94.37 212 | 96.66 264 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
thisisatest0515 | | | 95.61 163 | 94.89 178 | 97.76 138 | 98.15 169 | 95.15 176 | 96.77 304 | 94.41 334 | 92.95 216 | 97.18 127 | 97.43 226 | 84.78 261 | 99.45 147 | 94.63 165 | 97.73 160 | 98.68 170 |
|
thres600view7 | | | 95.49 164 | 94.77 181 | 97.67 147 | 98.98 105 | 95.02 180 | 98.85 84 | 96.90 305 | 95.38 105 | 96.63 152 | 96.90 271 | 84.29 267 | 99.59 127 | 88.65 297 | 96.33 191 | 98.40 185 |
|
PatchFormer-LS_test | | | 95.47 165 | 95.27 161 | 96.08 245 | 97.59 203 | 90.66 288 | 98.10 207 | 97.34 281 | 93.98 167 | 96.08 172 | 96.15 302 | 87.65 215 | 99.12 174 | 95.27 152 | 95.24 210 | 98.44 184 |
|
SCA | | | 95.46 166 | 95.13 166 | 96.46 226 | 97.67 197 | 91.29 278 | 97.33 267 | 97.60 260 | 94.68 139 | 96.92 140 | 97.10 244 | 83.97 276 | 98.89 207 | 92.59 229 | 98.32 141 | 99.20 123 |
|
IterMVS-LS | | | 95.46 166 | 95.21 163 | 96.22 239 | 98.12 170 | 93.72 233 | 98.32 175 | 98.13 221 | 93.71 182 | 94.26 213 | 97.31 232 | 92.24 111 | 98.10 284 | 94.63 165 | 90.12 275 | 96.84 242 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
jajsoiax | | | 95.45 168 | 95.03 171 | 96.73 195 | 95.42 313 | 94.63 199 | 99.14 36 | 98.52 152 | 95.74 87 | 93.22 254 | 98.36 147 | 83.87 279 | 98.65 229 | 96.95 85 | 94.04 223 | 96.91 233 |
|
CVMVSNet | | | 95.43 169 | 96.04 131 | 93.57 305 | 97.93 182 | 83.62 331 | 98.12 203 | 98.59 135 | 95.68 90 | 96.56 155 | 99.02 75 | 87.51 216 | 97.51 316 | 93.56 203 | 97.44 166 | 99.60 72 |
|
anonymousdsp | | | 95.42 170 | 94.91 177 | 96.94 185 | 95.10 316 | 95.90 149 | 99.14 36 | 98.41 172 | 93.75 177 | 93.16 256 | 97.46 222 | 87.50 218 | 98.41 257 | 95.63 140 | 94.03 224 | 96.50 288 |
|
DU-MVS | | | 95.42 170 | 94.76 182 | 97.40 162 | 96.53 272 | 96.97 94 | 98.66 129 | 98.99 29 | 95.43 102 | 93.88 231 | 97.69 204 | 88.57 190 | 98.31 268 | 95.81 129 | 87.25 312 | 96.92 228 |
|
mvs_tets | | | 95.41 172 | 95.00 172 | 96.65 201 | 95.58 306 | 94.42 209 | 99.00 57 | 98.55 145 | 95.73 88 | 93.21 255 | 98.38 145 | 83.45 283 | 98.63 230 | 97.09 78 | 94.00 225 | 96.91 233 |
|
thres100view900 | | | 95.38 173 | 94.70 185 | 97.41 160 | 98.98 105 | 94.92 188 | 98.87 81 | 96.90 305 | 95.38 105 | 96.61 153 | 96.88 272 | 84.29 267 | 99.56 131 | 88.11 298 | 96.29 193 | 97.76 201 |
|
thres400 | | | 95.38 173 | 94.62 188 | 97.65 150 | 98.94 107 | 94.98 184 | 98.68 123 | 96.93 303 | 95.33 108 | 96.55 157 | 96.53 288 | 84.23 270 | 99.56 131 | 88.11 298 | 96.29 193 | 98.40 185 |
|
BH-w/o | | | 95.38 173 | 95.08 169 | 96.26 238 | 98.34 152 | 91.79 268 | 97.70 242 | 97.43 277 | 92.87 220 | 94.24 215 | 97.22 238 | 88.66 188 | 98.84 213 | 91.55 255 | 97.70 161 | 98.16 193 |
|
VDDNet | | | 95.36 176 | 94.53 192 | 97.86 131 | 98.10 172 | 95.13 177 | 98.85 84 | 97.75 254 | 90.46 287 | 98.36 70 | 99.39 13 | 73.27 331 | 99.64 120 | 97.98 34 | 96.58 183 | 98.81 161 |
|
TAPA-MVS | | 93.98 7 | 95.35 177 | 94.56 191 | 97.74 140 | 99.13 95 | 94.83 192 | 98.33 170 | 98.64 131 | 86.62 316 | 96.29 168 | 98.61 121 | 94.00 91 | 99.29 157 | 80.00 329 | 99.41 90 | 99.09 138 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
ACMP | | 93.49 10 | 95.34 178 | 94.98 174 | 96.43 228 | 97.67 197 | 93.48 239 | 98.73 111 | 98.44 168 | 94.94 132 | 92.53 276 | 98.53 130 | 84.50 266 | 99.14 172 | 95.48 144 | 94.00 225 | 96.66 264 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
COLMAP_ROB | | 93.27 12 | 95.33 179 | 94.87 179 | 96.71 196 | 99.29 72 | 93.24 249 | 98.58 137 | 98.11 224 | 89.92 297 | 93.57 242 | 99.10 64 | 86.37 237 | 99.79 86 | 90.78 266 | 98.10 147 | 97.09 219 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
tfpn200view9 | | | 95.32 180 | 94.62 188 | 97.43 159 | 98.94 107 | 94.98 184 | 98.68 123 | 96.93 303 | 95.33 108 | 96.55 157 | 96.53 288 | 84.23 270 | 99.56 131 | 88.11 298 | 96.29 193 | 97.76 201 |
|
Anonymous202405211 | | | 95.28 181 | 94.49 194 | 97.67 147 | 99.00 102 | 93.75 230 | 98.70 120 | 97.04 296 | 90.66 284 | 96.49 162 | 98.80 104 | 78.13 310 | 99.83 54 | 96.21 118 | 95.36 209 | 99.44 99 |
|
thres200 | | | 95.25 182 | 94.57 190 | 97.28 165 | 98.81 117 | 94.92 188 | 98.20 189 | 97.11 292 | 95.24 115 | 96.54 159 | 96.22 300 | 84.58 264 | 99.53 137 | 87.93 302 | 96.50 187 | 97.39 212 |
|
AllTest | | | 95.24 183 | 94.65 187 | 96.99 179 | 99.25 80 | 93.21 250 | 98.59 135 | 98.18 210 | 91.36 267 | 93.52 244 | 98.77 108 | 84.67 262 | 99.72 103 | 89.70 284 | 97.87 153 | 98.02 196 |
|
LCM-MVSNet-Re | | | 95.22 184 | 95.32 158 | 94.91 282 | 98.18 166 | 87.85 322 | 98.75 104 | 95.66 324 | 95.11 121 | 88.96 311 | 96.85 275 | 90.26 158 | 97.65 310 | 95.65 139 | 98.44 134 | 99.22 122 |
|
EPNet_dtu | | | 95.21 185 | 94.95 176 | 95.99 247 | 96.17 287 | 90.45 292 | 98.16 199 | 97.27 287 | 96.77 51 | 93.14 259 | 98.33 153 | 90.34 155 | 98.42 250 | 85.57 315 | 98.81 118 | 99.09 138 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
XXY-MVS | | | 95.20 186 | 94.45 199 | 97.46 157 | 96.75 262 | 96.56 113 | 98.86 83 | 98.65 130 | 93.30 204 | 93.27 253 | 98.27 160 | 84.85 260 | 98.87 210 | 94.82 162 | 91.26 264 | 96.96 225 |
|
D2MVS | | | 95.18 187 | 95.08 169 | 95.48 265 | 97.10 243 | 92.07 263 | 98.30 178 | 99.13 19 | 94.02 163 | 92.90 264 | 96.73 279 | 89.48 165 | 98.73 223 | 94.48 174 | 93.60 235 | 95.65 314 |
|
WR-MVS | | | 95.15 188 | 94.46 197 | 97.22 166 | 96.67 267 | 96.45 117 | 98.21 187 | 98.81 75 | 94.15 157 | 93.16 256 | 97.69 204 | 87.51 216 | 98.30 270 | 95.29 150 | 88.62 297 | 96.90 235 |
|
TranMVSNet+NR-MVSNet | | | 95.14 189 | 94.48 195 | 97.11 174 | 96.45 277 | 96.36 122 | 99.03 53 | 99.03 25 | 95.04 125 | 93.58 241 | 97.93 184 | 88.27 197 | 98.03 292 | 94.13 184 | 86.90 317 | 96.95 227 |
|
baseline2 | | | 95.11 190 | 94.52 193 | 96.87 189 | 96.65 268 | 93.56 236 | 98.27 183 | 94.10 340 | 93.45 197 | 92.02 289 | 97.43 226 | 87.45 220 | 99.19 166 | 93.88 192 | 97.41 168 | 97.87 199 |
|
miper_enhance_ethall | | | 95.10 191 | 94.75 183 | 96.12 244 | 97.53 211 | 93.73 232 | 96.61 310 | 98.08 230 | 92.20 246 | 93.89 230 | 96.65 284 | 92.44 106 | 98.30 270 | 94.21 182 | 91.16 265 | 96.34 296 |
|
Anonymous20240529 | | | 95.10 191 | 94.22 208 | 97.75 139 | 99.01 101 | 94.26 216 | 98.87 81 | 98.83 68 | 85.79 324 | 96.64 151 | 98.97 82 | 78.73 307 | 99.85 48 | 96.27 115 | 94.89 211 | 99.12 136 |
|
test-LLR | | | 95.10 191 | 94.87 179 | 95.80 257 | 96.77 259 | 89.70 298 | 96.91 293 | 95.21 326 | 95.11 121 | 94.83 191 | 95.72 312 | 87.71 211 | 98.97 193 | 93.06 215 | 98.50 131 | 98.72 165 |
|
WR-MVS_H | | | 95.05 194 | 94.46 197 | 96.81 192 | 96.86 256 | 95.82 151 | 99.24 20 | 99.24 10 | 93.87 173 | 92.53 276 | 96.84 276 | 90.37 154 | 98.24 276 | 93.24 210 | 87.93 304 | 96.38 295 |
|
miper_ehance_all_eth | | | 95.01 195 | 94.69 186 | 95.97 249 | 97.70 196 | 93.31 246 | 97.02 286 | 98.07 232 | 92.23 243 | 93.51 246 | 96.96 266 | 91.85 122 | 98.15 280 | 93.68 197 | 91.16 265 | 96.44 293 |
|
ADS-MVSNet | | | 95.00 196 | 94.45 199 | 96.63 204 | 98.00 177 | 91.91 266 | 96.04 316 | 97.74 255 | 90.15 292 | 96.47 163 | 96.64 285 | 87.89 207 | 98.96 196 | 90.08 275 | 97.06 171 | 99.02 145 |
|
VPNet | | | 94.99 197 | 94.19 210 | 97.40 162 | 97.16 239 | 96.57 112 | 98.71 116 | 98.97 30 | 95.67 91 | 94.84 189 | 98.24 163 | 80.36 300 | 98.67 228 | 96.46 109 | 87.32 311 | 96.96 225 |
|
EPMVS | | | 94.99 197 | 94.48 195 | 96.52 219 | 97.22 232 | 91.75 270 | 97.23 273 | 91.66 344 | 94.11 158 | 97.28 123 | 96.81 277 | 85.70 247 | 98.84 213 | 93.04 217 | 97.28 169 | 98.97 150 |
|
NR-MVSNet | | | 94.98 199 | 94.16 212 | 97.44 158 | 96.53 272 | 97.22 87 | 98.74 107 | 98.95 34 | 94.96 129 | 89.25 310 | 97.69 204 | 89.32 169 | 98.18 278 | 94.59 170 | 87.40 310 | 96.92 228 |
|
FMVSNet3 | | | 94.97 200 | 94.26 207 | 97.11 174 | 98.18 166 | 96.62 107 | 98.56 142 | 98.26 201 | 93.67 189 | 94.09 222 | 97.10 244 | 84.25 269 | 98.01 293 | 92.08 241 | 92.14 251 | 96.70 258 |
|
CostFormer | | | 94.95 201 | 94.73 184 | 95.60 263 | 97.28 228 | 89.06 308 | 97.53 253 | 96.89 307 | 89.66 300 | 96.82 145 | 96.72 280 | 86.05 242 | 98.95 200 | 95.53 142 | 96.13 202 | 98.79 162 |
|
PAPM | | | 94.95 201 | 94.00 222 | 97.78 136 | 97.04 245 | 95.65 155 | 96.03 318 | 98.25 202 | 91.23 276 | 94.19 218 | 97.80 198 | 91.27 138 | 98.86 212 | 82.61 324 | 97.61 163 | 98.84 160 |
|
CP-MVSNet | | | 94.94 203 | 94.30 206 | 96.83 191 | 96.72 264 | 95.56 159 | 99.11 42 | 98.95 34 | 93.89 171 | 92.42 281 | 97.90 186 | 87.19 222 | 98.12 283 | 94.32 178 | 88.21 301 | 96.82 245 |
|
TR-MVS | | | 94.94 203 | 94.20 209 | 97.17 170 | 97.75 191 | 94.14 219 | 97.59 250 | 97.02 299 | 92.28 242 | 95.75 178 | 97.64 210 | 83.88 278 | 98.96 196 | 89.77 281 | 96.15 201 | 98.40 185 |
|
RPSCF | | | 94.87 205 | 95.40 149 | 93.26 309 | 98.89 110 | 82.06 337 | 98.33 170 | 98.06 236 | 90.30 291 | 96.56 155 | 99.26 38 | 87.09 223 | 99.49 140 | 93.82 194 | 96.32 192 | 98.24 191 |
|
DWT-MVSNet_test | | | 94.82 206 | 94.36 204 | 96.20 240 | 97.35 225 | 90.79 285 | 98.34 169 | 96.57 317 | 92.91 218 | 95.33 182 | 96.44 292 | 82.00 287 | 99.12 174 | 94.52 172 | 95.78 207 | 98.70 167 |
|
GA-MVS | | | 94.81 207 | 94.03 218 | 97.14 171 | 97.15 240 | 93.86 225 | 96.76 305 | 97.58 261 | 94.00 165 | 94.76 194 | 97.04 257 | 80.91 294 | 98.48 241 | 91.79 250 | 96.25 198 | 99.09 138 |
|
cl_fuxian | | | 94.79 208 | 94.43 201 | 95.89 254 | 97.75 191 | 93.12 253 | 97.16 280 | 98.03 240 | 92.23 243 | 93.46 249 | 97.05 256 | 91.39 133 | 98.01 293 | 93.58 202 | 89.21 289 | 96.53 280 |
|
V42 | | | 94.78 209 | 94.14 214 | 96.70 198 | 96.33 282 | 95.22 173 | 98.97 63 | 98.09 229 | 92.32 240 | 94.31 211 | 97.06 254 | 88.39 195 | 98.55 236 | 92.90 221 | 88.87 295 | 96.34 296 |
|
CR-MVSNet | | | 94.76 210 | 94.15 213 | 96.59 209 | 97.00 246 | 93.43 240 | 94.96 326 | 97.56 262 | 92.46 231 | 96.93 138 | 96.24 296 | 88.15 200 | 97.88 305 | 87.38 304 | 96.65 181 | 98.46 182 |
|
DI_MVS_plusplus_test | | | 94.74 211 | 93.62 247 | 98.09 119 | 95.34 314 | 95.92 146 | 98.09 208 | 97.34 281 | 94.66 142 | 85.89 323 | 95.91 307 | 80.49 299 | 99.38 152 | 96.66 103 | 98.22 143 | 98.97 150 |
|
v2v482 | | | 94.69 212 | 94.03 218 | 96.65 201 | 96.17 287 | 94.79 195 | 98.67 126 | 98.08 230 | 92.72 223 | 94.00 227 | 97.16 242 | 87.69 214 | 98.45 245 | 92.91 220 | 88.87 295 | 96.72 254 |
|
pmmvs4 | | | 94.69 212 | 93.99 224 | 96.81 192 | 95.74 301 | 95.94 142 | 97.40 258 | 97.67 257 | 90.42 289 | 93.37 250 | 97.59 214 | 89.08 177 | 98.20 277 | 92.97 219 | 91.67 258 | 96.30 300 |
|
cl-mvsnet2 | | | 94.68 214 | 94.19 210 | 96.13 243 | 98.11 171 | 93.60 235 | 96.94 290 | 98.31 188 | 92.43 235 | 93.32 252 | 96.87 274 | 86.51 232 | 98.28 274 | 94.10 187 | 91.16 265 | 96.51 286 |
|
eth_miper_zixun_eth | | | 94.68 214 | 94.41 202 | 95.47 266 | 97.64 199 | 91.71 272 | 96.73 307 | 98.07 232 | 92.71 224 | 93.64 239 | 97.21 239 | 90.54 152 | 98.17 279 | 93.38 205 | 89.76 279 | 96.54 278 |
|
PCF-MVS | | 93.45 11 | 94.68 214 | 93.43 255 | 98.42 97 | 98.62 134 | 96.77 103 | 95.48 324 | 98.20 206 | 84.63 328 | 93.34 251 | 98.32 154 | 88.55 192 | 99.81 65 | 84.80 320 | 98.96 108 | 98.68 170 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
MVS | | | 94.67 217 | 93.54 251 | 98.08 120 | 96.88 255 | 96.56 113 | 98.19 193 | 98.50 159 | 78.05 336 | 92.69 271 | 98.02 176 | 91.07 143 | 99.63 123 | 90.09 274 | 98.36 139 | 98.04 195 |
|
PS-CasMVS | | | 94.67 217 | 93.99 224 | 96.71 196 | 96.68 266 | 95.26 172 | 99.13 39 | 99.03 25 | 93.68 187 | 92.33 282 | 97.95 182 | 85.35 252 | 98.10 284 | 93.59 201 | 88.16 303 | 96.79 246 |
|
cascas | | | 94.63 219 | 93.86 232 | 96.93 186 | 96.91 253 | 94.27 215 | 96.00 319 | 98.51 154 | 85.55 325 | 94.54 197 | 96.23 298 | 84.20 272 | 98.87 210 | 95.80 131 | 96.98 174 | 97.66 207 |
|
tpmvs | | | 94.60 220 | 94.36 204 | 95.33 271 | 97.46 215 | 88.60 314 | 96.88 299 | 97.68 256 | 91.29 273 | 93.80 236 | 96.42 293 | 88.58 189 | 99.24 161 | 91.06 261 | 96.04 204 | 98.17 192 |
|
LTVRE_ROB | | 92.95 15 | 94.60 220 | 93.90 229 | 96.68 200 | 97.41 223 | 94.42 209 | 98.52 146 | 98.59 135 | 91.69 258 | 91.21 295 | 98.35 148 | 84.87 259 | 99.04 188 | 91.06 261 | 93.44 239 | 96.60 269 |
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 |
v1144 | | | 94.59 222 | 93.92 227 | 96.60 208 | 96.21 284 | 94.78 196 | 98.59 135 | 98.14 220 | 91.86 254 | 94.21 217 | 97.02 259 | 87.97 205 | 98.41 257 | 91.72 252 | 89.57 282 | 96.61 268 |
|
ADS-MVSNet2 | | | 94.58 223 | 94.40 203 | 95.11 277 | 98.00 177 | 88.74 312 | 96.04 316 | 97.30 284 | 90.15 292 | 96.47 163 | 96.64 285 | 87.89 207 | 97.56 314 | 90.08 275 | 97.06 171 | 99.02 145 |
|
ACMH | | 92.88 16 | 94.55 224 | 93.95 226 | 96.34 234 | 97.63 200 | 93.26 248 | 98.81 97 | 98.49 163 | 93.43 198 | 89.74 306 | 98.53 130 | 81.91 288 | 99.08 183 | 93.69 196 | 93.30 242 | 96.70 258 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
XVG-ACMP-BASELINE | | | 94.54 225 | 94.14 214 | 95.75 260 | 96.55 271 | 91.65 273 | 98.11 205 | 98.44 168 | 94.96 129 | 94.22 216 | 97.90 186 | 79.18 306 | 99.11 178 | 94.05 189 | 93.85 229 | 96.48 290 |
|
cl-mvsnet1 | | | 94.52 226 | 94.03 218 | 95.99 247 | 97.57 208 | 93.38 244 | 97.05 284 | 97.94 246 | 91.74 255 | 92.81 266 | 97.10 244 | 89.12 175 | 98.07 288 | 92.60 227 | 90.30 273 | 96.53 280 |
|
cl-mvsnet_ | | | 94.51 227 | 94.01 221 | 96.02 246 | 97.58 204 | 93.40 243 | 97.05 284 | 97.96 245 | 91.73 257 | 92.76 268 | 97.08 250 | 89.06 178 | 98.13 282 | 92.61 226 | 90.29 274 | 96.52 283 |
|
GBi-Net | | | 94.49 228 | 93.80 235 | 96.56 214 | 98.21 161 | 95.00 181 | 98.82 91 | 98.18 210 | 92.46 231 | 94.09 222 | 97.07 251 | 81.16 291 | 97.95 297 | 92.08 241 | 92.14 251 | 96.72 254 |
|
test1 | | | 94.49 228 | 93.80 235 | 96.56 214 | 98.21 161 | 95.00 181 | 98.82 91 | 98.18 210 | 92.46 231 | 94.09 222 | 97.07 251 | 81.16 291 | 97.95 297 | 92.08 241 | 92.14 251 | 96.72 254 |
|
v8 | | | 94.47 230 | 93.77 238 | 96.57 213 | 96.36 280 | 94.83 192 | 99.05 49 | 98.19 207 | 91.92 251 | 93.16 256 | 96.97 264 | 88.82 187 | 98.48 241 | 91.69 253 | 87.79 305 | 96.39 294 |
|
FMVSNet2 | | | 94.47 230 | 93.61 248 | 97.04 177 | 98.21 161 | 96.43 119 | 98.79 102 | 98.27 197 | 92.46 231 | 93.50 247 | 97.09 248 | 81.16 291 | 98.00 295 | 91.09 259 | 91.93 255 | 96.70 258 |
|
Patchmatch-test | | | 94.42 232 | 93.68 245 | 96.63 204 | 97.60 202 | 91.76 269 | 94.83 330 | 97.49 272 | 89.45 303 | 94.14 220 | 97.10 244 | 88.99 179 | 98.83 215 | 85.37 318 | 98.13 146 | 99.29 116 |
|
PEN-MVS | | | 94.42 232 | 93.73 242 | 96.49 221 | 96.28 283 | 94.84 190 | 99.17 33 | 99.00 27 | 93.51 194 | 92.23 284 | 97.83 195 | 86.10 241 | 97.90 301 | 92.55 232 | 86.92 316 | 96.74 251 |
|
v144192 | | | 94.39 234 | 93.70 243 | 96.48 222 | 96.06 292 | 94.35 213 | 98.58 137 | 98.16 217 | 91.45 264 | 94.33 210 | 97.02 259 | 87.50 218 | 98.45 245 | 91.08 260 | 89.11 290 | 96.63 266 |
|
Baseline_NR-MVSNet | | | 94.35 235 | 93.81 234 | 95.96 250 | 96.20 285 | 94.05 221 | 98.61 134 | 96.67 315 | 91.44 265 | 93.85 233 | 97.60 213 | 88.57 190 | 98.14 281 | 94.39 175 | 86.93 315 | 95.68 313 |
|
miper_lstm_enhance | | | 94.33 236 | 94.07 217 | 95.11 277 | 97.75 191 | 90.97 282 | 97.22 274 | 98.03 240 | 91.67 259 | 92.76 268 | 96.97 264 | 90.03 160 | 97.78 308 | 92.51 234 | 89.64 281 | 96.56 275 |
|
v1192 | | | 94.32 237 | 93.58 249 | 96.53 218 | 96.10 290 | 94.45 208 | 98.50 151 | 98.17 215 | 91.54 262 | 94.19 218 | 97.06 254 | 86.95 227 | 98.43 249 | 90.14 273 | 89.57 282 | 96.70 258 |
|
ACMH+ | | 92.99 14 | 94.30 238 | 93.77 238 | 95.88 255 | 97.81 189 | 92.04 265 | 98.71 116 | 98.37 179 | 93.99 166 | 90.60 302 | 98.47 136 | 80.86 296 | 99.05 185 | 92.75 225 | 92.40 250 | 96.55 277 |
|
v148 | | | 94.29 239 | 93.76 240 | 95.91 252 | 96.10 290 | 92.93 255 | 98.58 137 | 97.97 243 | 92.59 229 | 93.47 248 | 96.95 268 | 88.53 193 | 98.32 266 | 92.56 231 | 87.06 314 | 96.49 289 |
|
v10 | | | 94.29 239 | 93.55 250 | 96.51 220 | 96.39 279 | 94.80 194 | 98.99 59 | 98.19 207 | 91.35 269 | 93.02 262 | 96.99 262 | 88.09 202 | 98.41 257 | 90.50 270 | 88.41 299 | 96.33 298 |
|
MVP-Stereo | | | 94.28 241 | 93.92 227 | 95.35 270 | 94.95 318 | 92.60 258 | 97.97 219 | 97.65 258 | 91.61 261 | 90.68 301 | 97.09 248 | 86.32 238 | 98.42 250 | 89.70 284 | 99.34 95 | 95.02 322 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
UniMVSNet_ETH3D | | | 94.24 242 | 93.33 257 | 96.97 182 | 97.19 237 | 93.38 244 | 98.74 107 | 98.57 141 | 91.21 278 | 93.81 235 | 98.58 126 | 72.85 332 | 98.77 221 | 95.05 157 | 93.93 228 | 98.77 164 |
|
OurMVSNet-221017-0 | | | 94.21 243 | 94.00 222 | 94.85 285 | 95.60 305 | 89.22 306 | 98.89 76 | 97.43 277 | 95.29 111 | 92.18 285 | 98.52 133 | 82.86 284 | 98.59 234 | 93.46 204 | 91.76 257 | 96.74 251 |
|
v1921920 | | | 94.20 244 | 93.47 254 | 96.40 230 | 95.98 295 | 94.08 220 | 98.52 146 | 98.15 218 | 91.33 270 | 94.25 214 | 97.20 240 | 86.41 236 | 98.42 250 | 90.04 278 | 89.39 287 | 96.69 263 |
|
v7n | | | 94.19 245 | 93.43 255 | 96.47 223 | 95.90 297 | 94.38 212 | 99.26 18 | 98.34 184 | 91.99 249 | 92.76 268 | 97.13 243 | 88.31 196 | 98.52 239 | 89.48 289 | 87.70 306 | 96.52 283 |
|
tpm2 | | | 94.19 245 | 93.76 240 | 95.46 267 | 97.23 231 | 89.04 309 | 97.31 269 | 96.85 310 | 87.08 315 | 96.21 170 | 96.79 278 | 83.75 282 | 98.74 222 | 92.43 237 | 96.23 199 | 98.59 177 |
|
TESTMET0.1,1 | | | 94.18 247 | 93.69 244 | 95.63 262 | 96.92 251 | 89.12 307 | 96.91 293 | 94.78 331 | 93.17 207 | 94.88 188 | 96.45 291 | 78.52 308 | 98.92 202 | 93.09 214 | 98.50 131 | 98.85 158 |
|
dp | | | 94.15 248 | 93.90 229 | 94.90 283 | 97.31 227 | 86.82 327 | 96.97 288 | 97.19 291 | 91.22 277 | 96.02 175 | 96.61 287 | 85.51 249 | 99.02 191 | 90.00 279 | 94.30 213 | 98.85 158 |
|
ET-MVSNet_ETH3D | | | 94.13 249 | 92.98 263 | 97.58 153 | 98.22 160 | 96.20 128 | 97.31 269 | 95.37 325 | 94.53 146 | 79.56 333 | 97.63 212 | 86.51 232 | 97.53 315 | 96.91 86 | 90.74 269 | 99.02 145 |
|
tpm | | | 94.13 249 | 93.80 235 | 95.12 276 | 96.50 274 | 87.91 321 | 97.44 255 | 95.89 323 | 92.62 227 | 96.37 167 | 96.30 295 | 84.13 273 | 98.30 270 | 93.24 210 | 91.66 259 | 99.14 134 |
|
IterMVS-SCA-FT | | | 94.11 251 | 93.87 231 | 94.85 285 | 97.98 181 | 90.56 291 | 97.18 277 | 98.11 224 | 93.75 177 | 92.58 274 | 97.48 221 | 83.97 276 | 97.41 317 | 92.48 236 | 91.30 262 | 96.58 271 |
|
Anonymous20231211 | | | 94.10 252 | 93.26 260 | 96.61 206 | 99.11 97 | 94.28 214 | 99.01 55 | 98.88 49 | 86.43 318 | 92.81 266 | 97.57 216 | 81.66 290 | 98.68 227 | 94.83 161 | 89.02 293 | 96.88 237 |
|
IterMVS | | | 94.09 253 | 93.85 233 | 94.80 288 | 97.99 179 | 90.35 293 | 97.18 277 | 98.12 222 | 93.68 187 | 92.46 280 | 97.34 229 | 84.05 274 | 97.41 317 | 92.51 234 | 91.33 261 | 96.62 267 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
test-mter | | | 94.08 254 | 93.51 252 | 95.80 257 | 96.77 259 | 89.70 298 | 96.91 293 | 95.21 326 | 92.89 219 | 94.83 191 | 95.72 312 | 77.69 313 | 98.97 193 | 93.06 215 | 98.50 131 | 98.72 165 |
|
test0.0.03 1 | | | 94.08 254 | 93.51 252 | 95.80 257 | 95.53 308 | 92.89 256 | 97.38 260 | 95.97 320 | 95.11 121 | 92.51 278 | 96.66 282 | 87.71 211 | 96.94 323 | 87.03 306 | 93.67 231 | 97.57 208 |
|
v1240 | | | 94.06 256 | 93.29 259 | 96.34 234 | 96.03 294 | 93.90 224 | 98.44 158 | 98.17 215 | 91.18 279 | 94.13 221 | 97.01 261 | 86.05 242 | 98.42 250 | 89.13 294 | 89.50 285 | 96.70 258 |
|
X-MVStestdata | | | 94.06 256 | 92.30 275 | 99.34 22 | 99.70 21 | 98.35 39 | 99.29 14 | 98.88 49 | 97.40 19 | 98.46 62 | 43.50 346 | 95.90 38 | 99.89 34 | 97.85 41 | 99.74 39 | 99.78 12 |
|
DTE-MVSNet | | | 93.98 258 | 93.26 260 | 96.14 242 | 96.06 292 | 94.39 211 | 99.20 29 | 98.86 60 | 93.06 210 | 91.78 290 | 97.81 197 | 85.87 245 | 97.58 313 | 90.53 269 | 86.17 321 | 96.46 292 |
|
pm-mvs1 | | | 93.94 259 | 93.06 262 | 96.59 209 | 96.49 275 | 95.16 174 | 98.95 67 | 98.03 240 | 92.32 240 | 91.08 297 | 97.84 192 | 84.54 265 | 98.41 257 | 92.16 239 | 86.13 323 | 96.19 303 |
|
MS-PatchMatch | | | 93.84 260 | 93.63 246 | 94.46 297 | 96.18 286 | 89.45 302 | 97.76 238 | 98.27 197 | 92.23 243 | 92.13 286 | 97.49 220 | 79.50 303 | 98.69 224 | 89.75 282 | 99.38 93 | 95.25 317 |
|
tfpnnormal | | | 93.66 261 | 92.70 269 | 96.55 217 | 96.94 250 | 95.94 142 | 98.97 63 | 99.19 15 | 91.04 281 | 91.38 294 | 97.34 229 | 84.94 258 | 98.61 231 | 85.45 317 | 89.02 293 | 95.11 319 |
|
EU-MVSNet | | | 93.66 261 | 94.14 214 | 92.25 314 | 95.96 296 | 83.38 332 | 98.52 146 | 98.12 222 | 94.69 138 | 92.61 273 | 98.13 170 | 87.36 221 | 96.39 332 | 91.82 249 | 90.00 277 | 96.98 224 |
|
our_test_3 | | | 93.65 263 | 93.30 258 | 94.69 290 | 95.45 311 | 89.68 300 | 96.91 293 | 97.65 258 | 91.97 250 | 91.66 292 | 96.88 272 | 89.67 163 | 97.93 300 | 88.02 301 | 91.49 260 | 96.48 290 |
|
pmmvs5 | | | 93.65 263 | 92.97 264 | 95.68 261 | 95.49 309 | 92.37 259 | 98.20 189 | 97.28 286 | 89.66 300 | 92.58 274 | 97.26 234 | 82.14 286 | 98.09 286 | 93.18 213 | 90.95 268 | 96.58 271 |
|
tpm cat1 | | | 93.36 265 | 92.80 266 | 95.07 279 | 97.58 204 | 87.97 320 | 96.76 305 | 97.86 250 | 82.17 332 | 93.53 243 | 96.04 305 | 86.13 240 | 99.13 173 | 89.24 292 | 95.87 205 | 98.10 194 |
|
JIA-IIPM | | | 93.35 266 | 92.49 272 | 95.92 251 | 96.48 276 | 90.65 289 | 95.01 325 | 96.96 301 | 85.93 322 | 96.08 172 | 87.33 336 | 87.70 213 | 98.78 220 | 91.35 258 | 95.58 208 | 98.34 188 |
|
SixPastTwentyTwo | | | 93.34 267 | 92.86 265 | 94.75 289 | 95.67 303 | 89.41 304 | 98.75 104 | 96.67 315 | 93.89 171 | 90.15 304 | 98.25 162 | 80.87 295 | 98.27 275 | 90.90 264 | 90.64 270 | 96.57 273 |
|
USDC | | | 93.33 268 | 92.71 268 | 95.21 273 | 96.83 258 | 90.83 284 | 96.91 293 | 97.50 270 | 93.84 174 | 90.72 300 | 98.14 169 | 77.69 313 | 98.82 216 | 89.51 288 | 93.21 244 | 95.97 308 |
|
IB-MVS | | 91.98 17 | 93.27 269 | 91.97 279 | 97.19 168 | 97.47 214 | 93.41 242 | 97.09 283 | 95.99 319 | 93.32 202 | 92.47 279 | 95.73 310 | 78.06 311 | 99.53 137 | 94.59 170 | 82.98 326 | 98.62 176 |
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 |
MIMVSNet | | | 93.26 270 | 92.21 276 | 96.41 229 | 97.73 195 | 93.13 252 | 95.65 323 | 97.03 297 | 91.27 275 | 94.04 225 | 96.06 304 | 75.33 323 | 97.19 320 | 86.56 308 | 96.23 199 | 98.92 156 |
|
ppachtmachnet_test | | | 93.22 271 | 92.63 270 | 94.97 281 | 95.45 311 | 90.84 283 | 96.88 299 | 97.88 249 | 90.60 285 | 92.08 287 | 97.26 234 | 88.08 203 | 97.86 307 | 85.12 319 | 90.33 272 | 96.22 301 |
|
Patchmtry | | | 93.22 271 | 92.35 274 | 95.84 256 | 96.77 259 | 93.09 254 | 94.66 331 | 97.56 262 | 87.37 314 | 92.90 264 | 96.24 296 | 88.15 200 | 97.90 301 | 87.37 305 | 90.10 276 | 96.53 280 |
|
FMVSNet1 | | | 93.19 273 | 92.07 277 | 96.56 214 | 97.54 209 | 95.00 181 | 98.82 91 | 98.18 210 | 90.38 290 | 92.27 283 | 97.07 251 | 73.68 330 | 97.95 297 | 89.36 291 | 91.30 262 | 96.72 254 |
|
LF4IMVS | | | 93.14 274 | 92.79 267 | 94.20 300 | 95.88 298 | 88.67 313 | 97.66 246 | 97.07 294 | 93.81 176 | 91.71 291 | 97.65 208 | 77.96 312 | 98.81 217 | 91.47 256 | 91.92 256 | 95.12 318 |
|
testgi | | | 93.06 275 | 92.45 273 | 94.88 284 | 96.43 278 | 89.90 295 | 98.75 104 | 97.54 267 | 95.60 94 | 91.63 293 | 97.91 185 | 74.46 328 | 97.02 322 | 86.10 311 | 93.67 231 | 97.72 205 |
|
PatchT | | | 93.06 275 | 91.97 279 | 96.35 233 | 96.69 265 | 92.67 257 | 94.48 332 | 97.08 293 | 86.62 316 | 97.08 130 | 92.23 331 | 87.94 206 | 97.90 301 | 78.89 333 | 96.69 179 | 98.49 181 |
|
MVS_0304 | | | 92.81 277 | 92.01 278 | 95.23 272 | 97.46 215 | 91.33 276 | 98.17 198 | 98.81 75 | 91.13 280 | 93.80 236 | 95.68 315 | 66.08 338 | 98.06 289 | 90.79 265 | 96.13 202 | 96.32 299 |
|
TransMVSNet (Re) | | | 92.67 278 | 91.51 283 | 96.15 241 | 96.58 270 | 94.65 197 | 98.90 72 | 96.73 311 | 90.86 283 | 89.46 309 | 97.86 189 | 85.62 248 | 98.09 286 | 86.45 309 | 81.12 331 | 95.71 312 |
|
K. test v3 | | | 92.55 279 | 91.91 281 | 94.48 295 | 95.64 304 | 89.24 305 | 99.07 47 | 94.88 330 | 94.04 161 | 86.78 319 | 97.59 214 | 77.64 316 | 97.64 311 | 92.08 241 | 89.43 286 | 96.57 273 |
|
DSMNet-mixed | | | 92.52 280 | 92.58 271 | 92.33 313 | 94.15 325 | 82.65 335 | 98.30 178 | 94.26 337 | 89.08 307 | 92.65 272 | 95.73 310 | 85.01 257 | 95.76 333 | 86.24 310 | 97.76 158 | 98.59 177 |
|
RPMNet | | | 92.52 280 | 91.17 284 | 96.59 209 | 97.00 246 | 93.43 240 | 94.96 326 | 97.26 288 | 82.27 331 | 96.93 138 | 92.12 332 | 86.98 226 | 97.88 305 | 76.32 337 | 96.65 181 | 98.46 182 |
|
TinyColmap | | | 92.31 282 | 91.53 282 | 94.65 292 | 96.92 251 | 89.75 297 | 96.92 291 | 96.68 314 | 90.45 288 | 89.62 307 | 97.85 191 | 76.06 321 | 98.81 217 | 86.74 307 | 92.51 249 | 95.41 316 |
|
gg-mvs-nofinetune | | | 92.21 283 | 90.58 289 | 97.13 172 | 96.75 262 | 95.09 178 | 95.85 320 | 89.40 347 | 85.43 326 | 94.50 199 | 81.98 339 | 80.80 297 | 98.40 263 | 92.16 239 | 98.33 140 | 97.88 198 |
|
FMVSNet5 | | | 91.81 284 | 90.92 286 | 94.49 294 | 97.21 233 | 92.09 262 | 98.00 217 | 97.55 266 | 89.31 305 | 90.86 299 | 95.61 316 | 74.48 327 | 95.32 335 | 85.57 315 | 89.70 280 | 96.07 306 |
|
pmmvs6 | | | 91.77 285 | 90.63 288 | 95.17 275 | 94.69 323 | 91.24 279 | 98.67 126 | 97.92 247 | 86.14 320 | 89.62 307 | 97.56 218 | 75.79 322 | 98.34 264 | 90.75 267 | 84.56 325 | 95.94 309 |
|
Anonymous20231206 | | | 91.66 286 | 91.10 285 | 93.33 307 | 94.02 327 | 87.35 324 | 98.58 137 | 97.26 288 | 90.48 286 | 90.16 303 | 96.31 294 | 83.83 280 | 96.53 330 | 79.36 331 | 89.90 278 | 96.12 304 |
|
Patchmatch-RL test | | | 91.49 287 | 90.85 287 | 93.41 306 | 91.37 333 | 84.40 329 | 92.81 336 | 95.93 322 | 91.87 253 | 87.25 317 | 94.87 320 | 88.99 179 | 96.53 330 | 92.54 233 | 82.00 328 | 99.30 114 |
|
test_0402 | | | 91.32 288 | 90.27 291 | 94.48 295 | 96.60 269 | 91.12 280 | 98.50 151 | 97.22 290 | 86.10 321 | 88.30 314 | 96.98 263 | 77.65 315 | 97.99 296 | 78.13 335 | 92.94 246 | 94.34 325 |
|
PVSNet_0 | | 88.72 19 | 91.28 289 | 90.03 293 | 95.00 280 | 97.99 179 | 87.29 325 | 94.84 329 | 98.50 159 | 92.06 248 | 89.86 305 | 95.19 317 | 79.81 302 | 99.39 150 | 92.27 238 | 69.79 339 | 98.33 189 |
|
EG-PatchMatch MVS | | | 91.13 290 | 90.12 292 | 94.17 302 | 94.73 322 | 89.00 310 | 98.13 202 | 97.81 251 | 89.22 306 | 85.32 327 | 96.46 290 | 67.71 335 | 98.42 250 | 87.89 303 | 93.82 230 | 95.08 320 |
|
TDRefinement | | | 91.06 291 | 89.68 295 | 95.21 273 | 85.35 341 | 91.49 274 | 98.51 150 | 97.07 294 | 91.47 263 | 88.83 312 | 97.84 192 | 77.31 317 | 99.09 182 | 92.79 224 | 77.98 334 | 95.04 321 |
|
UnsupCasMVSNet_eth | | | 90.99 292 | 89.92 294 | 94.19 301 | 94.08 326 | 89.83 296 | 97.13 282 | 98.67 123 | 93.69 185 | 85.83 325 | 96.19 301 | 75.15 324 | 96.74 324 | 89.14 293 | 79.41 333 | 96.00 307 |
|
test20.03 | | | 90.89 293 | 90.38 290 | 92.43 312 | 93.48 328 | 88.14 319 | 98.33 170 | 97.56 262 | 93.40 199 | 87.96 315 | 96.71 281 | 80.69 298 | 94.13 339 | 79.15 332 | 86.17 321 | 95.01 323 |
|
MDA-MVSNet_test_wron | | | 90.71 294 | 89.38 298 | 94.68 291 | 94.83 320 | 90.78 286 | 97.19 276 | 97.46 273 | 87.60 312 | 72.41 339 | 95.72 312 | 86.51 232 | 96.71 327 | 85.92 313 | 86.80 318 | 96.56 275 |
|
YYNet1 | | | 90.70 295 | 89.39 297 | 94.62 293 | 94.79 321 | 90.65 289 | 97.20 275 | 97.46 273 | 87.54 313 | 72.54 338 | 95.74 309 | 86.51 232 | 96.66 328 | 86.00 312 | 86.76 319 | 96.54 278 |
|
testing_2 | | | 90.61 296 | 88.50 301 | 96.95 184 | 90.08 337 | 95.57 158 | 97.69 243 | 98.06 236 | 93.02 212 | 76.55 334 | 92.48 330 | 61.18 341 | 98.44 247 | 95.45 145 | 91.98 254 | 96.84 242 |
|
pmmvs-eth3d | | | 90.36 297 | 89.05 299 | 94.32 299 | 91.10 334 | 92.12 261 | 97.63 249 | 96.95 302 | 88.86 308 | 84.91 328 | 93.13 326 | 78.32 309 | 96.74 324 | 88.70 296 | 81.81 330 | 94.09 329 |
|
new_pmnet | | | 90.06 298 | 89.00 300 | 93.22 310 | 94.18 324 | 88.32 318 | 96.42 314 | 96.89 307 | 86.19 319 | 85.67 326 | 93.62 324 | 77.18 318 | 97.10 321 | 81.61 326 | 89.29 288 | 94.23 326 |
|
MDA-MVSNet-bldmvs | | | 89.97 299 | 88.35 303 | 94.83 287 | 95.21 315 | 91.34 275 | 97.64 247 | 97.51 269 | 88.36 310 | 71.17 340 | 96.13 303 | 79.22 305 | 96.63 329 | 83.65 321 | 86.27 320 | 96.52 283 |
|
CMPMVS | | 66.06 21 | 89.70 300 | 89.67 296 | 89.78 319 | 93.19 329 | 76.56 339 | 97.00 287 | 98.35 182 | 80.97 333 | 81.57 332 | 97.75 200 | 74.75 326 | 98.61 231 | 89.85 280 | 93.63 233 | 94.17 327 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
MIMVSNet1 | | | 89.67 301 | 88.28 304 | 93.82 303 | 92.81 331 | 91.08 281 | 98.01 215 | 97.45 275 | 87.95 311 | 87.90 316 | 95.87 308 | 67.63 336 | 94.56 338 | 78.73 334 | 88.18 302 | 95.83 311 |
|
MVS-HIRNet | | | 89.46 302 | 88.40 302 | 92.64 311 | 97.58 204 | 82.15 336 | 94.16 335 | 93.05 343 | 75.73 338 | 90.90 298 | 82.52 338 | 79.42 304 | 98.33 265 | 83.53 322 | 98.68 120 | 97.43 209 |
|
OpenMVS_ROB | | 86.42 20 | 89.00 303 | 87.43 307 | 93.69 304 | 93.08 330 | 89.42 303 | 97.91 223 | 96.89 307 | 78.58 335 | 85.86 324 | 94.69 321 | 69.48 334 | 98.29 273 | 77.13 336 | 93.29 243 | 93.36 334 |
|
new-patchmatchnet | | | 88.50 304 | 87.45 306 | 91.67 317 | 90.31 336 | 85.89 328 | 97.16 280 | 97.33 283 | 89.47 302 | 83.63 330 | 92.77 327 | 76.38 319 | 95.06 337 | 82.70 323 | 77.29 335 | 94.06 330 |
|
PM-MVS | | | 87.77 305 | 86.55 308 | 91.40 318 | 91.03 335 | 83.36 333 | 96.92 291 | 95.18 328 | 91.28 274 | 86.48 322 | 93.42 325 | 53.27 342 | 96.74 324 | 89.43 290 | 81.97 329 | 94.11 328 |
|
UnsupCasMVSNet_bld | | | 87.17 306 | 85.12 309 | 93.31 308 | 91.94 332 | 88.77 311 | 94.92 328 | 98.30 194 | 84.30 329 | 82.30 331 | 90.04 333 | 63.96 340 | 97.25 319 | 85.85 314 | 74.47 338 | 93.93 332 |
|
N_pmnet | | | 87.12 307 | 87.77 305 | 85.17 324 | 95.46 310 | 61.92 346 | 97.37 262 | 70.66 353 | 85.83 323 | 88.73 313 | 96.04 305 | 85.33 254 | 97.76 309 | 80.02 328 | 90.48 271 | 95.84 310 |
|
pmmvs3 | | | 86.67 308 | 84.86 310 | 92.11 316 | 88.16 338 | 87.19 326 | 96.63 309 | 94.75 332 | 79.88 334 | 87.22 318 | 92.75 328 | 66.56 337 | 95.20 336 | 81.24 327 | 76.56 336 | 93.96 331 |
|
test_normal | | | 83.22 309 | 80.23 311 | 92.18 315 | 88.06 339 | 82.87 334 | 69.03 345 | 98.05 239 | 92.70 225 | 63.67 342 | 80.19 341 | 50.72 343 | 98.05 290 | 91.41 257 | 88.24 300 | 95.62 315 |
|
LCM-MVSNet | | | 78.70 310 | 76.24 314 | 86.08 322 | 77.26 347 | 71.99 343 | 94.34 333 | 96.72 312 | 61.62 342 | 76.53 335 | 89.33 334 | 33.91 350 | 92.78 341 | 81.85 325 | 74.60 337 | 93.46 333 |
|
Gipuma | | | 78.40 311 | 76.75 313 | 83.38 325 | 95.54 307 | 80.43 338 | 79.42 344 | 97.40 279 | 64.67 341 | 73.46 337 | 80.82 340 | 45.65 345 | 93.14 340 | 66.32 340 | 87.43 309 | 76.56 342 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
PMMVS2 | | | 77.95 312 | 75.44 315 | 85.46 323 | 82.54 342 | 74.95 341 | 94.23 334 | 93.08 342 | 72.80 339 | 74.68 336 | 87.38 335 | 36.36 349 | 91.56 342 | 73.95 338 | 63.94 340 | 89.87 336 |
|
FPMVS | | | 77.62 313 | 77.14 312 | 79.05 327 | 79.25 345 | 60.97 347 | 95.79 321 | 95.94 321 | 65.96 340 | 67.93 341 | 94.40 322 | 37.73 348 | 88.88 344 | 68.83 339 | 88.46 298 | 87.29 337 |
|
ANet_high | | | 69.08 314 | 65.37 317 | 80.22 326 | 65.99 349 | 71.96 344 | 90.91 340 | 90.09 346 | 82.62 330 | 49.93 347 | 78.39 342 | 29.36 351 | 81.75 345 | 62.49 341 | 38.52 344 | 86.95 339 |
|
tmp_tt | | | 68.90 315 | 66.97 316 | 74.68 329 | 50.78 351 | 59.95 348 | 87.13 341 | 83.47 351 | 38.80 347 | 62.21 343 | 96.23 298 | 64.70 339 | 76.91 349 | 88.91 295 | 30.49 345 | 87.19 338 |
|
PMVS | | 61.03 23 | 65.95 316 | 63.57 319 | 73.09 330 | 57.90 350 | 51.22 351 | 85.05 343 | 93.93 341 | 54.45 343 | 44.32 348 | 83.57 337 | 13.22 352 | 89.15 343 | 58.68 342 | 81.00 332 | 78.91 341 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
E-PMN | | | 64.94 317 | 64.25 318 | 67.02 331 | 82.28 343 | 59.36 349 | 91.83 339 | 85.63 349 | 52.69 344 | 60.22 344 | 77.28 343 | 41.06 347 | 80.12 347 | 46.15 344 | 41.14 342 | 61.57 344 |
|
EMVS | | | 64.07 318 | 63.26 320 | 66.53 332 | 81.73 344 | 58.81 350 | 91.85 338 | 84.75 350 | 51.93 346 | 59.09 345 | 75.13 344 | 43.32 346 | 79.09 348 | 42.03 345 | 39.47 343 | 61.69 343 |
|
MVE | | 62.14 22 | 63.28 319 | 59.38 321 | 74.99 328 | 74.33 348 | 65.47 345 | 85.55 342 | 80.50 352 | 52.02 345 | 51.10 346 | 75.00 345 | 10.91 355 | 80.50 346 | 51.60 343 | 53.40 341 | 78.99 340 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
wuyk23d | | | 30.17 320 | 30.18 323 | 30.16 333 | 78.61 346 | 43.29 352 | 66.79 346 | 14.21 354 | 17.31 348 | 14.82 351 | 11.93 351 | 11.55 354 | 41.43 350 | 37.08 346 | 19.30 346 | 5.76 347 |
|
cdsmvs_eth3d_5k | | | 23.98 321 | 31.98 322 | 0.00 336 | 0.00 354 | 0.00 355 | 0.00 347 | 98.59 135 | 0.00 351 | 0.00 352 | 98.61 121 | 90.60 151 | 0.00 353 | 0.00 349 | 0.00 349 | 0.00 348 |
|
testmvs | | | 21.48 322 | 24.95 324 | 11.09 335 | 14.89 352 | 6.47 354 | 96.56 311 | 9.87 355 | 7.55 349 | 17.93 349 | 39.02 347 | 9.43 356 | 5.90 352 | 16.56 348 | 12.72 347 | 20.91 346 |
|
test123 | | | 20.95 323 | 23.72 325 | 12.64 334 | 13.54 353 | 8.19 353 | 96.55 312 | 6.13 356 | 7.48 350 | 16.74 350 | 37.98 348 | 12.97 353 | 6.05 351 | 16.69 347 | 5.43 348 | 23.68 345 |
|
ab-mvs-re | | | 8.20 324 | 10.94 326 | 0.00 336 | 0.00 354 | 0.00 355 | 0.00 347 | 0.00 357 | 0.00 351 | 0.00 352 | 98.43 138 | 0.00 357 | 0.00 353 | 0.00 349 | 0.00 349 | 0.00 348 |
|
pcd_1.5k_mvsjas | | | 7.88 325 | 10.50 327 | 0.00 336 | 0.00 354 | 0.00 355 | 0.00 347 | 0.00 357 | 0.00 351 | 0.00 352 | 0.00 352 | 94.51 79 | 0.00 353 | 0.00 349 | 0.00 349 | 0.00 348 |
|
uanet_test | | | 0.00 326 | 0.00 328 | 0.00 336 | 0.00 354 | 0.00 355 | 0.00 347 | |