SMA-MVS | | | 89.08 5 | 89.23 5 | 88.61 2 | 94.25 23 | 73.73 7 | 92.40 18 | 93.63 19 | 74.77 99 | 92.29 2 | 95.97 2 | 74.28 27 | 97.24 5 | 88.58 7 | 96.91 1 | 94.87 7 |
|
test_0728_THIRD | | | | | | | | | | 78.38 32 | 92.12 4 | 95.78 4 | 81.46 2 | 97.40 2 | 89.42 2 | 96.57 2 | 94.67 13 |
|
HPM-MVS++ | | | 89.02 6 | 89.15 6 | 88.63 1 | 95.01 5 | 76.03 1 | 92.38 22 | 92.85 50 | 80.26 13 | 87.78 22 | 94.27 28 | 75.89 12 | 96.81 15 | 87.45 13 | 96.44 3 | 93.05 82 |
|
DVP-MVS | | | 89.60 1 | 90.35 1 | 87.33 40 | 95.27 2 | 71.25 57 | 93.49 5 | 92.73 55 | 77.33 45 | 92.12 4 | 95.78 4 | 80.98 3 | 97.40 2 | 89.08 4 | 96.41 4 | 93.33 71 |
|
test_0728_SECOND | | | | | 87.71 28 | 95.34 1 | 71.43 56 | 93.49 5 | 94.23 6 | | | | | 97.49 1 | 89.08 4 | 96.41 4 | 94.21 28 |
|
ACMMP_NAP | | | 88.05 15 | 88.08 16 | 87.94 14 | 93.70 35 | 73.05 20 | 90.86 47 | 93.59 20 | 76.27 74 | 88.14 18 | 95.09 10 | 71.06 51 | 96.67 20 | 87.67 10 | 96.37 6 | 94.09 32 |
|
DPE-MVS | | | 89.48 3 | 89.98 2 | 88.01 11 | 94.80 6 | 72.69 29 | 91.59 35 | 94.10 9 | 75.90 78 | 92.29 2 | 95.66 6 | 81.67 1 | 97.38 4 | 87.44 14 | 96.34 7 | 93.95 40 |
|
MP-MVS-pluss | | | 87.67 20 | 87.72 20 | 87.54 34 | 93.64 38 | 72.04 46 | 89.80 74 | 93.50 23 | 75.17 94 | 86.34 30 | 95.29 7 | 70.86 52 | 96.00 45 | 88.78 6 | 96.04 8 | 94.58 16 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
xxxxxxxxxxxxxcwj | | | 88.46 9 | 88.74 9 | 87.64 32 | 92.78 55 | 71.95 47 | 92.40 18 | 94.74 2 | 75.71 80 | 89.16 11 | 95.10 8 | 75.65 14 | 96.19 37 | 87.07 15 | 96.01 9 | 94.79 8 |
|
xxxxxxxxxxxx | | | 88.46 9 | 88.74 9 | 87.64 32 | 92.78 55 | 71.95 47 | 92.40 18 | 94.74 2 | 75.71 80 | 89.16 11 | 95.10 8 | 75.65 14 | 96.19 37 | 87.07 15 | 96.01 9 | 94.79 8 |
|
CNVR-MVS | | | 88.93 7 | 89.13 7 | 88.33 4 | 94.77 7 | 73.82 6 | 90.51 54 | 93.00 41 | 80.90 9 | 88.06 20 | 94.06 37 | 76.43 9 | 96.84 13 | 88.48 8 | 95.99 11 | 94.34 24 |
|
PHI-MVS | | | 86.43 43 | 86.17 46 | 87.24 41 | 90.88 84 | 70.96 62 | 92.27 26 | 94.07 11 | 72.45 136 | 85.22 40 | 91.90 73 | 69.47 67 | 96.42 30 | 83.28 47 | 95.94 12 | 94.35 23 |
|
ETH3D-3000-0.1 | | | 88.09 12 | 88.29 13 | 87.50 36 | 92.76 57 | 71.89 51 | 91.43 39 | 94.70 4 | 74.47 105 | 88.86 14 | 94.61 16 | 75.23 17 | 95.84 49 | 86.62 20 | 95.92 13 | 94.78 10 |
|
test_prior3 | | | 86.73 37 | 86.86 37 | 86.33 58 | 92.61 61 | 69.59 86 | 88.85 98 | 92.97 46 | 75.41 87 | 84.91 44 | 93.54 43 | 74.28 27 | 95.48 59 | 83.31 44 | 95.86 14 | 93.91 41 |
|
test_prior2 | | | | | | | | 88.85 98 | | 75.41 87 | 84.91 44 | 93.54 43 | 74.28 27 | | 83.31 44 | 95.86 14 | |
|
SteuartSystems-ACMMP | | | 88.72 8 | 88.86 8 | 88.32 5 | 92.14 67 | 72.96 23 | 93.73 3 | 93.67 18 | 80.19 14 | 88.10 19 | 94.80 11 | 73.76 31 | 97.11 7 | 87.51 12 | 95.82 16 | 94.90 6 |
Skip Steuart: Steuart Systems R&D Blog. |
ZNCC-MVS | | | 87.94 17 | 87.85 18 | 88.20 8 | 94.39 20 | 73.33 17 | 93.03 10 | 93.81 16 | 76.81 57 | 85.24 39 | 94.32 27 | 71.76 46 | 96.93 12 | 85.53 23 | 95.79 17 | 94.32 25 |
|
ETH3D cwj APD-0.16 | | | 87.31 30 | 87.27 26 | 87.44 38 | 91.60 74 | 72.45 38 | 90.02 68 | 94.37 5 | 71.76 146 | 87.28 25 | 94.27 28 | 75.18 18 | 96.08 41 | 85.16 24 | 95.77 18 | 93.80 51 |
|
ETH3 D test6400 | | | 87.50 23 | 87.44 24 | 87.70 29 | 93.71 34 | 71.75 52 | 90.62 52 | 94.05 12 | 70.80 161 | 87.59 24 | 93.51 45 | 77.57 7 | 96.63 23 | 83.31 44 | 95.77 18 | 94.72 12 |
|
9.14 | | | | 88.26 14 | | 92.84 54 | | 91.52 38 | 94.75 1 | 73.93 117 | 88.57 16 | 94.67 14 | 75.57 16 | 95.79 51 | 86.77 17 | 95.76 20 | |
|
DeepPCF-MVS | | 80.84 1 | 88.10 11 | 88.56 11 | 86.73 51 | 92.24 65 | 69.03 95 | 89.57 80 | 93.39 29 | 77.53 42 | 89.79 10 | 94.12 35 | 78.98 5 | 96.58 28 | 85.66 21 | 95.72 21 | 94.58 16 |
|
train_agg | | | 86.43 43 | 86.20 44 | 87.13 44 | 93.26 44 | 72.96 23 | 88.75 102 | 91.89 88 | 68.69 207 | 85.00 42 | 93.10 54 | 74.43 23 | 95.41 63 | 84.97 26 | 95.71 22 | 93.02 84 |
|
test9_res | | | | | | | | | | | | | | | 84.90 27 | 95.70 23 | 92.87 88 |
|
APDe-MVS | | | 89.15 4 | 89.63 4 | 87.73 24 | 94.49 14 | 71.69 53 | 93.83 2 | 93.96 13 | 75.70 83 | 91.06 8 | 96.03 1 | 76.84 8 | 97.03 9 | 89.09 3 | 95.65 24 | 94.47 20 |
|
agg_prior2 | | | | | | | | | | | | | | | 82.91 52 | 95.45 25 | 92.70 91 |
|
CDPH-MVS | | | 85.76 51 | 85.29 57 | 87.17 43 | 93.49 41 | 71.08 60 | 88.58 109 | 92.42 67 | 68.32 212 | 84.61 52 | 93.48 46 | 72.32 41 | 96.15 40 | 79.00 78 | 95.43 26 | 94.28 27 |
|
DeepC-MVS | | 79.81 2 | 87.08 35 | 86.88 36 | 87.69 30 | 91.16 78 | 72.32 42 | 90.31 61 | 93.94 14 | 77.12 49 | 82.82 78 | 94.23 31 | 72.13 44 | 97.09 8 | 84.83 30 | 95.37 27 | 93.65 58 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
zzz-MVS | | | 87.53 22 | 87.41 25 | 87.90 18 | 94.18 27 | 74.25 2 | 90.23 63 | 92.02 79 | 79.45 19 | 85.88 32 | 94.80 11 | 68.07 76 | 96.21 35 | 86.69 18 | 95.34 28 | 93.23 74 |
|
MTAPA | | | 87.23 31 | 87.00 32 | 87.90 18 | 94.18 27 | 74.25 2 | 86.58 169 | 92.02 79 | 79.45 19 | 85.88 32 | 94.80 11 | 68.07 76 | 96.21 35 | 86.69 18 | 95.34 28 | 93.23 74 |
|
agg_prior1 | | | 86.22 47 | 86.09 48 | 86.62 54 | 92.85 52 | 71.94 49 | 88.59 108 | 91.78 94 | 68.96 202 | 84.41 55 | 93.18 53 | 74.94 19 | 94.93 82 | 84.75 32 | 95.33 30 | 93.01 85 |
|
DeepC-MVS_fast | | 79.65 3 | 86.91 36 | 86.62 39 | 87.76 23 | 93.52 40 | 72.37 40 | 91.26 41 | 93.04 37 | 76.62 64 | 84.22 59 | 93.36 50 | 71.44 49 | 96.76 17 | 80.82 67 | 95.33 30 | 94.16 29 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
MP-MVS | | | 87.71 19 | 87.64 21 | 87.93 17 | 94.36 21 | 73.88 4 | 92.71 17 | 92.65 59 | 77.57 38 | 83.84 65 | 94.40 26 | 72.24 42 | 96.28 33 | 85.65 22 | 95.30 32 | 93.62 60 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
MCST-MVS | | | 87.37 28 | 87.25 28 | 87.73 24 | 94.53 13 | 72.46 37 | 89.82 72 | 93.82 15 | 73.07 131 | 84.86 49 | 92.89 60 | 76.22 10 | 96.33 31 | 84.89 29 | 95.13 33 | 94.40 21 |
|
GST-MVS | | | 87.42 26 | 87.26 27 | 87.89 21 | 94.12 29 | 72.97 22 | 92.39 21 | 93.43 27 | 76.89 55 | 84.68 50 | 93.99 39 | 70.67 56 | 96.82 14 | 84.18 40 | 95.01 34 | 93.90 43 |
|
APD-MVS | | | 87.44 24 | 87.52 22 | 87.19 42 | 94.24 24 | 72.39 39 | 91.86 33 | 92.83 51 | 73.01 133 | 88.58 15 | 94.52 17 | 73.36 32 | 96.49 29 | 84.26 37 | 95.01 34 | 92.70 91 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
NCCC | | | 88.06 13 | 88.01 17 | 88.24 7 | 94.41 18 | 73.62 8 | 91.22 44 | 92.83 51 | 81.50 6 | 85.79 35 | 93.47 48 | 73.02 37 | 97.00 11 | 84.90 27 | 94.94 36 | 94.10 31 |
|
ACMMPR | | | 87.44 24 | 87.23 29 | 88.08 10 | 94.64 8 | 73.59 9 | 93.04 8 | 93.20 33 | 76.78 59 | 84.66 51 | 94.52 17 | 68.81 74 | 96.65 21 | 84.53 33 | 94.90 37 | 94.00 38 |
|
HFP-MVS | | | 87.58 21 | 87.47 23 | 87.94 14 | 94.58 10 | 73.54 12 | 93.04 8 | 93.24 31 | 76.78 59 | 84.91 44 | 94.44 22 | 70.78 53 | 96.61 24 | 84.53 33 | 94.89 38 | 93.66 53 |
|
#test# | | | 87.33 29 | 87.13 31 | 87.94 14 | 94.58 10 | 73.54 12 | 92.34 24 | 93.24 31 | 75.23 91 | 84.91 44 | 94.44 22 | 70.78 53 | 96.61 24 | 83.75 43 | 94.89 38 | 93.66 53 |
|
testtj | | | 87.78 18 | 87.78 19 | 87.77 22 | 94.55 12 | 72.47 36 | 92.23 27 | 93.49 24 | 74.75 100 | 88.33 17 | 94.43 24 | 73.27 34 | 97.02 10 | 84.18 40 | 94.84 40 | 93.82 48 |
|
region2R | | | 87.42 26 | 87.20 30 | 88.09 9 | 94.63 9 | 73.55 10 | 93.03 10 | 93.12 36 | 76.73 62 | 84.45 54 | 94.52 17 | 69.09 71 | 96.70 19 | 84.37 36 | 94.83 41 | 94.03 35 |
|
原ACMM1 | | | | | 84.35 103 | 93.01 50 | 68.79 101 | | 92.44 64 | 63.96 259 | 81.09 100 | 91.57 81 | 66.06 97 | 95.45 61 | 67.19 184 | 94.82 42 | 88.81 220 |
|
HPM-MVS | | | 87.11 33 | 86.98 33 | 87.50 36 | 93.88 32 | 72.16 43 | 92.19 28 | 93.33 30 | 76.07 77 | 83.81 66 | 93.95 40 | 69.77 65 | 96.01 44 | 85.15 25 | 94.66 43 | 94.32 25 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
DPM-MVS | | | 84.93 64 | 84.29 68 | 86.84 48 | 90.20 94 | 73.04 21 | 87.12 151 | 93.04 37 | 69.80 180 | 82.85 77 | 91.22 89 | 73.06 36 | 96.02 43 | 76.72 103 | 94.63 44 | 91.46 128 |
|
TSAR-MVS + MP. | | | 88.02 16 | 88.11 15 | 87.72 26 | 93.68 37 | 72.13 44 | 91.41 40 | 92.35 69 | 74.62 103 | 88.90 13 | 93.85 41 | 75.75 13 | 96.00 45 | 87.80 9 | 94.63 44 | 95.04 3 |
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition |
PGM-MVS | | | 86.68 39 | 86.27 43 | 87.90 18 | 94.22 25 | 73.38 16 | 90.22 65 | 93.04 37 | 75.53 85 | 83.86 64 | 94.42 25 | 67.87 80 | 96.64 22 | 82.70 54 | 94.57 46 | 93.66 53 |
|
XVS | | | 87.18 32 | 86.91 35 | 88.00 12 | 94.42 16 | 73.33 17 | 92.78 13 | 92.99 43 | 79.14 21 | 83.67 68 | 94.17 32 | 67.45 83 | 96.60 26 | 83.06 49 | 94.50 47 | 94.07 33 |
|
X-MVStestdata | | | 80.37 133 | 77.83 166 | 88.00 12 | 94.42 16 | 73.33 17 | 92.78 13 | 92.99 43 | 79.14 21 | 83.67 68 | 12.47 344 | 67.45 83 | 96.60 26 | 83.06 49 | 94.50 47 | 94.07 33 |
|
test12 | | | | | 86.80 50 | 92.63 60 | 70.70 70 | | 91.79 93 | | 82.71 80 | | 71.67 47 | 96.16 39 | | 94.50 47 | 93.54 64 |
|
CP-MVS | | | 87.11 33 | 86.92 34 | 87.68 31 | 94.20 26 | 73.86 5 | 93.98 1 | 92.82 54 | 76.62 64 | 83.68 67 | 94.46 21 | 67.93 78 | 95.95 47 | 84.20 39 | 94.39 50 | 93.23 74 |
|
CSCG | | | 86.41 45 | 86.19 45 | 87.07 45 | 92.91 51 | 72.48 35 | 90.81 48 | 93.56 21 | 73.95 115 | 83.16 73 | 91.07 94 | 75.94 11 | 95.19 72 | 79.94 75 | 94.38 51 | 93.55 63 |
|
MSLP-MVS++ | | | 85.43 56 | 85.76 51 | 84.45 99 | 91.93 70 | 70.24 73 | 90.71 50 | 92.86 49 | 77.46 44 | 84.22 59 | 92.81 64 | 67.16 87 | 92.94 167 | 80.36 71 | 94.35 52 | 90.16 166 |
|
mPP-MVS | | | 86.67 40 | 86.32 42 | 87.72 26 | 94.41 18 | 73.55 10 | 92.74 15 | 92.22 72 | 76.87 56 | 82.81 79 | 94.25 30 | 66.44 92 | 96.24 34 | 82.88 53 | 94.28 53 | 93.38 68 |
|
SD-MVS | | | 88.06 13 | 88.50 12 | 86.71 52 | 92.60 63 | 72.71 27 | 91.81 34 | 93.19 34 | 77.87 33 | 90.32 9 | 94.00 38 | 74.83 20 | 93.78 130 | 87.63 11 | 94.27 54 | 93.65 58 |
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 |
MSP-MVS | | | 89.51 2 | 89.91 3 | 88.30 6 | 94.28 22 | 73.46 15 | 92.90 12 | 94.11 7 | 80.27 12 | 91.35 7 | 94.16 33 | 78.35 6 | 96.77 16 | 89.59 1 | 94.22 55 | 94.67 13 |
|
DELS-MVS | | | 85.41 57 | 85.30 56 | 85.77 67 | 88.49 149 | 67.93 124 | 85.52 199 | 93.44 26 | 78.70 28 | 83.63 70 | 89.03 140 | 74.57 21 | 95.71 54 | 80.26 73 | 94.04 56 | 93.66 53 |
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 |
EPNet | | | 83.72 72 | 82.92 79 | 86.14 63 | 84.22 230 | 69.48 89 | 91.05 46 | 85.27 236 | 81.30 7 | 76.83 159 | 91.65 77 | 66.09 96 | 95.56 57 | 76.00 108 | 93.85 57 | 93.38 68 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
3Dnovator+ | | 77.84 4 | 85.48 54 | 84.47 67 | 88.51 3 | 91.08 79 | 73.49 14 | 93.18 7 | 93.78 17 | 80.79 10 | 76.66 164 | 93.37 49 | 60.40 171 | 96.75 18 | 77.20 96 | 93.73 58 | 95.29 2 |
|
CANet | | | 86.45 42 | 86.10 47 | 87.51 35 | 90.09 96 | 70.94 64 | 89.70 78 | 92.59 61 | 81.78 4 | 81.32 95 | 91.43 86 | 70.34 58 | 97.23 6 | 84.26 37 | 93.36 59 | 94.37 22 |
|
æ–°å‡ ä½•1 | | | | | 83.42 131 | 93.13 46 | 70.71 69 | | 85.48 234 | 57.43 308 | 81.80 90 | 91.98 71 | 63.28 118 | 92.27 186 | 64.60 206 | 92.99 60 | 87.27 252 |
|
1121 | | | 80.84 116 | 79.77 122 | 84.05 114 | 93.11 48 | 70.78 68 | 84.66 212 | 85.42 235 | 57.37 309 | 81.76 93 | 92.02 70 | 63.41 116 | 94.12 113 | 67.28 181 | 92.93 61 | 87.26 253 |
|
HPM-MVS_fast | | | 85.35 58 | 84.95 62 | 86.57 56 | 93.69 36 | 70.58 71 | 92.15 30 | 91.62 98 | 73.89 118 | 82.67 81 | 94.09 36 | 62.60 130 | 95.54 58 | 80.93 65 | 92.93 61 | 93.57 62 |
|
SR-MVS | | | 86.73 37 | 86.67 38 | 86.91 47 | 94.11 30 | 72.11 45 | 92.37 23 | 92.56 62 | 74.50 104 | 86.84 28 | 94.65 15 | 67.31 85 | 95.77 52 | 84.80 31 | 92.85 63 | 92.84 89 |
|
旧先验1 | | | | | | 91.96 69 | 65.79 158 | | 86.37 226 | | | 93.08 58 | 69.31 70 | | | 92.74 64 | 88.74 223 |
|
3Dnovator | | 76.31 5 | 83.38 79 | 82.31 87 | 86.59 55 | 87.94 166 | 72.94 26 | 90.64 51 | 92.14 76 | 77.21 47 | 75.47 187 | 92.83 62 | 58.56 178 | 94.72 94 | 73.24 132 | 92.71 65 | 92.13 111 |
|
CS-MVS | | | 84.76 67 | 84.61 66 | 85.22 77 | 89.66 104 | 66.43 146 | 90.23 63 | 93.56 21 | 76.52 66 | 82.59 82 | 85.93 220 | 70.41 57 | 95.80 50 | 79.93 76 | 92.68 66 | 93.42 67 |
|
MVS_111021_HR | | | 85.14 61 | 84.75 64 | 86.32 60 | 91.65 73 | 72.70 28 | 85.98 184 | 90.33 136 | 76.11 76 | 82.08 85 | 91.61 80 | 71.36 50 | 94.17 112 | 81.02 64 | 92.58 67 | 92.08 112 |
|
APD-MVS_3200maxsize | | | 85.97 48 | 85.88 49 | 86.22 61 | 92.69 59 | 69.53 88 | 91.93 32 | 92.99 43 | 73.54 125 | 85.94 31 | 94.51 20 | 65.80 101 | 95.61 55 | 83.04 51 | 92.51 68 | 93.53 65 |
|
MAR-MVS | | | 81.84 99 | 80.70 106 | 85.27 74 | 91.32 77 | 71.53 55 | 89.82 72 | 90.92 118 | 69.77 181 | 78.50 125 | 86.21 216 | 62.36 136 | 94.52 98 | 65.36 198 | 92.05 69 | 89.77 190 |
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 |
TSAR-MVS + GP. | | | 85.71 52 | 85.33 54 | 86.84 48 | 91.34 76 | 72.50 34 | 89.07 92 | 87.28 215 | 76.41 67 | 85.80 34 | 90.22 111 | 74.15 30 | 95.37 68 | 81.82 59 | 91.88 70 | 92.65 95 |
|
IS-MVSNet | | | 83.15 81 | 82.81 80 | 84.18 109 | 89.94 100 | 63.30 208 | 91.59 35 | 88.46 192 | 79.04 25 | 79.49 112 | 92.16 68 | 65.10 106 | 94.28 103 | 67.71 176 | 91.86 71 | 94.95 5 |
|
Vis-MVSNet (Re-imp) | | | 78.36 175 | 78.45 150 | 78.07 253 | 88.64 145 | 51.78 317 | 86.70 166 | 79.63 300 | 74.14 113 | 75.11 202 | 90.83 101 | 61.29 154 | 89.75 243 | 58.10 260 | 91.60 72 | 92.69 93 |
|
MG-MVS | | | 83.41 77 | 83.45 71 | 83.28 136 | 92.74 58 | 62.28 224 | 88.17 126 | 89.50 158 | 75.22 92 | 81.49 94 | 92.74 65 | 66.75 88 | 95.11 75 | 72.85 135 | 91.58 73 | 92.45 99 |
|
CPTT-MVS | | | 83.73 71 | 83.33 73 | 84.92 87 | 93.28 43 | 70.86 67 | 92.09 31 | 90.38 132 | 68.75 206 | 79.57 111 | 92.83 62 | 60.60 167 | 93.04 165 | 80.92 66 | 91.56 74 | 90.86 142 |
|
test222 | | | | | | 91.50 75 | 68.26 118 | 84.16 227 | 83.20 265 | 54.63 320 | 79.74 109 | 91.63 79 | 58.97 176 | | | 91.42 75 | 86.77 264 |
|
ETV-MVS | | | 84.90 66 | 84.67 65 | 85.59 69 | 89.39 114 | 68.66 111 | 88.74 104 | 92.64 60 | 79.97 17 | 84.10 61 | 85.71 226 | 69.32 69 | 95.38 65 | 80.82 67 | 91.37 76 | 92.72 90 |
|
testdata | | | | | 79.97 219 | 90.90 83 | 64.21 188 | | 84.71 240 | 59.27 296 | 85.40 36 | 92.91 59 | 62.02 143 | 89.08 255 | 68.95 169 | 91.37 76 | 86.63 268 |
|
abl_6 | | | 85.23 59 | 84.95 62 | 86.07 64 | 92.23 66 | 70.48 72 | 90.80 49 | 92.08 77 | 73.51 126 | 85.26 38 | 94.16 33 | 62.75 129 | 95.92 48 | 82.46 57 | 91.30 78 | 91.81 119 |
|
API-MVS | | | 81.99 97 | 81.23 100 | 84.26 107 | 90.94 82 | 70.18 79 | 91.10 45 | 89.32 162 | 71.51 153 | 78.66 123 | 88.28 159 | 65.26 104 | 95.10 78 | 64.74 205 | 91.23 79 | 87.51 246 |
|
Vis-MVSNet | | | 83.46 76 | 82.80 81 | 85.43 71 | 90.25 93 | 68.74 105 | 90.30 62 | 90.13 142 | 76.33 73 | 80.87 103 | 92.89 60 | 61.00 160 | 94.20 109 | 72.45 139 | 90.97 80 | 93.35 70 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
OpenMVS | | 72.83 10 | 79.77 143 | 78.33 155 | 84.09 112 | 85.17 215 | 69.91 80 | 90.57 53 | 90.97 117 | 66.70 223 | 72.17 233 | 91.91 72 | 54.70 206 | 93.96 117 | 61.81 228 | 90.95 81 | 88.41 231 |
|
UA-Net | | | 85.08 63 | 84.96 61 | 85.45 70 | 92.07 68 | 68.07 122 | 89.78 75 | 90.86 121 | 82.48 2 | 84.60 53 | 93.20 52 | 69.35 68 | 95.22 71 | 71.39 146 | 90.88 82 | 93.07 81 |
|
ACMMP | | | 85.89 50 | 85.39 53 | 87.38 39 | 93.59 39 | 72.63 31 | 92.74 15 | 93.18 35 | 76.78 59 | 80.73 104 | 93.82 42 | 64.33 110 | 96.29 32 | 82.67 55 | 90.69 83 | 93.23 74 |
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 |
Regformer-1 | | | 86.41 45 | 86.33 41 | 86.64 53 | 89.33 116 | 70.93 65 | 88.43 111 | 91.39 107 | 82.14 3 | 86.65 29 | 90.09 113 | 74.39 25 | 95.01 81 | 83.97 42 | 90.63 84 | 93.97 39 |
|
Regformer-2 | | | 86.63 41 | 86.53 40 | 86.95 46 | 89.33 116 | 71.24 59 | 88.43 111 | 92.05 78 | 82.50 1 | 86.88 27 | 90.09 113 | 74.45 22 | 95.61 55 | 84.38 35 | 90.63 84 | 94.01 37 |
|
casdiffmvs | | | 85.11 62 | 85.14 58 | 85.01 82 | 87.20 189 | 65.77 159 | 87.75 136 | 92.83 51 | 77.84 34 | 84.36 58 | 92.38 66 | 72.15 43 | 93.93 123 | 81.27 63 | 90.48 86 | 95.33 1 |
|
UGNet | | | 80.83 118 | 79.59 127 | 84.54 96 | 88.04 163 | 68.09 121 | 89.42 81 | 88.16 194 | 76.95 53 | 76.22 174 | 89.46 130 | 49.30 263 | 93.94 120 | 68.48 171 | 90.31 87 | 91.60 121 |
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 |
baseline | | | 84.93 64 | 84.98 60 | 84.80 91 | 87.30 187 | 65.39 166 | 87.30 147 | 92.88 48 | 77.62 36 | 84.04 63 | 92.26 67 | 71.81 45 | 93.96 117 | 81.31 62 | 90.30 88 | 95.03 4 |
|
MVSFormer | | | 82.85 86 | 82.05 90 | 85.24 75 | 87.35 182 | 70.21 74 | 90.50 55 | 90.38 132 | 68.55 209 | 81.32 95 | 89.47 128 | 61.68 145 | 93.46 145 | 78.98 79 | 90.26 89 | 92.05 113 |
|
lupinMVS | | | 81.39 108 | 80.27 116 | 84.76 92 | 87.35 182 | 70.21 74 | 85.55 195 | 86.41 224 | 62.85 267 | 81.32 95 | 88.61 149 | 61.68 145 | 92.24 189 | 78.41 85 | 90.26 89 | 91.83 117 |
|
DP-MVS Recon | | | 83.11 83 | 82.09 89 | 86.15 62 | 94.44 15 | 70.92 66 | 88.79 100 | 92.20 73 | 70.53 168 | 79.17 115 | 91.03 97 | 64.12 112 | 96.03 42 | 68.39 173 | 90.14 91 | 91.50 125 |
|
EIA-MVS | | | 83.31 80 | 82.80 81 | 84.82 89 | 89.59 106 | 65.59 161 | 88.21 124 | 92.68 56 | 74.66 102 | 78.96 117 | 86.42 212 | 69.06 72 | 95.26 70 | 75.54 113 | 90.09 92 | 93.62 60 |
|
MVS_111021_LR | | | 82.61 89 | 82.11 88 | 84.11 110 | 88.82 138 | 71.58 54 | 85.15 202 | 86.16 229 | 74.69 101 | 80.47 106 | 91.04 95 | 62.29 137 | 90.55 233 | 80.33 72 | 90.08 93 | 90.20 165 |
|
jason | | | 81.39 108 | 80.29 115 | 84.70 93 | 86.63 199 | 69.90 81 | 85.95 185 | 86.77 220 | 63.24 261 | 81.07 101 | 89.47 128 | 61.08 159 | 92.15 191 | 78.33 86 | 90.07 94 | 92.05 113 |
jason: jason. |
LFMVS | | | 81.82 100 | 81.23 100 | 83.57 128 | 91.89 71 | 63.43 206 | 89.84 71 | 81.85 279 | 77.04 52 | 83.21 71 | 93.10 54 | 52.26 225 | 93.43 147 | 71.98 141 | 89.95 95 | 93.85 45 |
|
MVS | | | 78.19 180 | 76.99 185 | 81.78 181 | 85.66 208 | 66.99 138 | 84.66 212 | 90.47 130 | 55.08 319 | 72.02 235 | 85.27 236 | 63.83 114 | 94.11 115 | 66.10 192 | 89.80 96 | 84.24 296 |
|
CANet_DTU | | | 80.61 126 | 79.87 120 | 82.83 159 | 85.60 210 | 63.17 213 | 87.36 145 | 88.65 188 | 76.37 71 | 75.88 181 | 88.44 155 | 53.51 216 | 93.07 162 | 73.30 130 | 89.74 97 | 92.25 106 |
|
PVSNet_Blended | | | 80.98 113 | 80.34 113 | 82.90 156 | 88.85 135 | 65.40 164 | 84.43 222 | 92.00 82 | 67.62 215 | 78.11 135 | 85.05 243 | 66.02 98 | 94.27 104 | 71.52 143 | 89.50 98 | 89.01 210 |
|
PAPM_NR | | | 83.02 84 | 82.41 84 | 84.82 89 | 92.47 64 | 66.37 148 | 87.93 133 | 91.80 92 | 73.82 119 | 77.32 150 | 90.66 103 | 67.90 79 | 94.90 86 | 70.37 154 | 89.48 99 | 93.19 78 |
|
114514_t | | | 80.68 125 | 79.51 128 | 84.20 108 | 94.09 31 | 67.27 135 | 89.64 79 | 91.11 115 | 58.75 301 | 74.08 215 | 90.72 102 | 58.10 180 | 95.04 80 | 69.70 161 | 89.42 100 | 90.30 162 |
|
LCM-MVSNet-Re | | | 77.05 202 | 76.94 186 | 77.36 263 | 87.20 189 | 51.60 318 | 80.06 273 | 80.46 292 | 75.20 93 | 67.69 275 | 86.72 196 | 62.48 133 | 88.98 257 | 63.44 211 | 89.25 101 | 91.51 124 |
|
alignmvs | | | 85.48 54 | 85.32 55 | 85.96 66 | 89.51 110 | 69.47 90 | 89.74 76 | 92.47 63 | 76.17 75 | 87.73 23 | 91.46 85 | 70.32 59 | 93.78 130 | 81.51 60 | 88.95 102 | 94.63 15 |
|
VNet | | | 82.21 92 | 82.41 84 | 81.62 184 | 90.82 85 | 60.93 238 | 84.47 218 | 89.78 150 | 76.36 72 | 84.07 62 | 91.88 74 | 64.71 109 | 90.26 235 | 70.68 151 | 88.89 103 | 93.66 53 |
|
PS-MVSNAJ | | | 81.69 101 | 81.02 104 | 83.70 125 | 89.51 110 | 68.21 120 | 84.28 226 | 90.09 143 | 70.79 162 | 81.26 99 | 85.62 230 | 63.15 123 | 94.29 102 | 75.62 111 | 88.87 104 | 88.59 226 |
|
canonicalmvs | | | 85.91 49 | 85.87 50 | 86.04 65 | 89.84 102 | 69.44 93 | 90.45 59 | 93.00 41 | 76.70 63 | 88.01 21 | 91.23 88 | 73.28 33 | 93.91 124 | 81.50 61 | 88.80 105 | 94.77 11 |
|
QAPM | | | 80.88 114 | 79.50 129 | 85.03 81 | 88.01 165 | 68.97 99 | 91.59 35 | 92.00 82 | 66.63 227 | 75.15 201 | 92.16 68 | 57.70 183 | 95.45 61 | 63.52 209 | 88.76 106 | 90.66 148 |
|
VDD-MVS | | | 83.01 85 | 82.36 86 | 84.96 84 | 91.02 81 | 66.40 147 | 88.91 95 | 88.11 195 | 77.57 38 | 84.39 57 | 93.29 51 | 52.19 226 | 93.91 124 | 77.05 98 | 88.70 107 | 94.57 18 |
|
PVSNet_Blended_VisFu | | | 82.62 88 | 81.83 95 | 84.96 84 | 90.80 86 | 69.76 83 | 88.74 104 | 91.70 97 | 69.39 188 | 78.96 117 | 88.46 154 | 65.47 103 | 94.87 89 | 74.42 117 | 88.57 108 | 90.24 164 |
|
DI_MVS_plusplus_test | | | 79.89 142 | 78.58 147 | 83.85 124 | 82.89 261 | 65.32 168 | 86.12 181 | 89.55 156 | 69.64 185 | 70.55 246 | 85.82 225 | 57.24 191 | 93.81 128 | 76.85 100 | 88.55 109 | 92.41 101 |
|
xiu_mvs_v2_base | | | 81.69 101 | 81.05 103 | 83.60 126 | 89.15 127 | 68.03 123 | 84.46 220 | 90.02 144 | 70.67 165 | 81.30 98 | 86.53 210 | 63.17 122 | 94.19 110 | 75.60 112 | 88.54 110 | 88.57 227 |
|
PAPR | | | 81.66 103 | 80.89 105 | 83.99 119 | 90.27 92 | 64.00 191 | 86.76 165 | 91.77 96 | 68.84 205 | 77.13 157 | 89.50 126 | 67.63 81 | 94.88 88 | 67.55 178 | 88.52 111 | 93.09 80 |
|
MVS_Test | | | 83.15 81 | 83.06 76 | 83.41 133 | 86.86 193 | 63.21 210 | 86.11 182 | 92.00 82 | 74.31 108 | 82.87 76 | 89.44 133 | 70.03 61 | 93.21 152 | 77.39 95 | 88.50 112 | 93.81 49 |
|
AdaColmap | | | 80.58 129 | 79.42 130 | 84.06 113 | 93.09 49 | 68.91 100 | 89.36 82 | 88.97 179 | 69.27 191 | 75.70 184 | 89.69 120 | 57.20 192 | 95.77 52 | 63.06 215 | 88.41 113 | 87.50 247 |
|
VDDNet | | | 81.52 105 | 80.67 107 | 84.05 114 | 90.44 90 | 64.13 190 | 89.73 77 | 85.91 232 | 71.11 157 | 83.18 72 | 93.48 46 | 50.54 249 | 93.49 144 | 73.40 129 | 88.25 114 | 94.54 19 |
|
PCF-MVS | | 73.52 7 | 80.38 132 | 78.84 143 | 85.01 82 | 87.71 174 | 68.99 98 | 83.65 236 | 91.46 106 | 63.00 264 | 77.77 142 | 90.28 108 | 66.10 95 | 95.09 79 | 61.40 231 | 88.22 115 | 90.94 140 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
Effi-MVS+ | | | 83.62 74 | 83.08 75 | 85.24 75 | 88.38 154 | 67.45 130 | 88.89 96 | 89.15 170 | 75.50 86 | 82.27 83 | 88.28 159 | 69.61 66 | 94.45 100 | 77.81 90 | 87.84 116 | 93.84 47 |
|
gg-mvs-nofinetune | | | 69.95 274 | 67.96 276 | 75.94 275 | 83.07 253 | 54.51 307 | 77.23 298 | 70.29 329 | 63.11 262 | 70.32 250 | 62.33 331 | 43.62 294 | 88.69 263 | 53.88 280 | 87.76 117 | 84.62 294 |
|
Regformer-3 | | | 85.23 59 | 85.07 59 | 85.70 68 | 88.95 133 | 69.01 97 | 88.29 121 | 89.91 148 | 80.95 8 | 85.01 41 | 90.01 115 | 72.45 40 | 94.19 110 | 82.50 56 | 87.57 118 | 93.90 43 |
|
Regformer-4 | | | 85.68 53 | 85.45 52 | 86.35 57 | 88.95 133 | 69.67 85 | 88.29 121 | 91.29 109 | 81.73 5 | 85.36 37 | 90.01 115 | 72.62 39 | 95.35 69 | 83.28 47 | 87.57 118 | 94.03 35 |
|
xiu_mvs_v1_base_debu | | | 80.80 121 | 79.72 124 | 84.03 116 | 87.35 182 | 70.19 76 | 85.56 192 | 88.77 184 | 69.06 198 | 81.83 87 | 88.16 162 | 50.91 243 | 92.85 169 | 78.29 87 | 87.56 120 | 89.06 205 |
|
xiu_mvs_v1_base | | | 80.80 121 | 79.72 124 | 84.03 116 | 87.35 182 | 70.19 76 | 85.56 192 | 88.77 184 | 69.06 198 | 81.83 87 | 88.16 162 | 50.91 243 | 92.85 169 | 78.29 87 | 87.56 120 | 89.06 205 |
|
xiu_mvs_v1_base_debi | | | 80.80 121 | 79.72 124 | 84.03 116 | 87.35 182 | 70.19 76 | 85.56 192 | 88.77 184 | 69.06 198 | 81.83 87 | 88.16 162 | 50.91 243 | 92.85 169 | 78.29 87 | 87.56 120 | 89.06 205 |
|
CLD-MVS | | | 82.31 91 | 81.65 96 | 84.29 106 | 88.47 150 | 67.73 128 | 85.81 190 | 92.35 69 | 75.78 79 | 78.33 130 | 86.58 207 | 64.01 113 | 94.35 101 | 76.05 107 | 87.48 123 | 90.79 143 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
CDS-MVSNet | | | 79.07 160 | 77.70 172 | 83.17 143 | 87.60 177 | 68.23 119 | 84.40 224 | 86.20 228 | 67.49 217 | 76.36 171 | 86.54 209 | 61.54 148 | 90.79 229 | 61.86 227 | 87.33 124 | 90.49 156 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
diffmvs | | | 82.10 93 | 81.88 94 | 82.76 167 | 83.00 256 | 63.78 196 | 83.68 235 | 89.76 151 | 72.94 134 | 82.02 86 | 89.85 118 | 65.96 100 | 90.79 229 | 82.38 58 | 87.30 125 | 93.71 52 |
|
EPP-MVSNet | | | 83.40 78 | 83.02 77 | 84.57 95 | 90.13 95 | 64.47 184 | 92.32 25 | 90.73 123 | 74.45 107 | 79.35 114 | 91.10 92 | 69.05 73 | 95.12 74 | 72.78 136 | 87.22 126 | 94.13 30 |
|
TAMVS | | | 78.89 165 | 77.51 176 | 83.03 150 | 87.80 170 | 67.79 127 | 84.72 211 | 85.05 239 | 67.63 214 | 76.75 162 | 87.70 170 | 62.25 138 | 90.82 228 | 58.53 256 | 87.13 127 | 90.49 156 |
|
TAPA-MVS | | 73.13 9 | 79.15 157 | 77.94 162 | 82.79 164 | 89.59 106 | 62.99 217 | 88.16 127 | 91.51 102 | 65.77 236 | 77.14 156 | 91.09 93 | 60.91 161 | 93.21 152 | 50.26 295 | 87.05 128 | 92.17 110 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
PAPM | | | 77.68 194 | 76.40 199 | 81.51 187 | 87.29 188 | 61.85 229 | 83.78 234 | 89.59 155 | 64.74 248 | 71.23 242 | 88.70 145 | 62.59 131 | 93.66 137 | 52.66 285 | 87.03 129 | 89.01 210 |
|
test_yl | | | 81.17 110 | 80.47 111 | 83.24 139 | 89.13 128 | 63.62 197 | 86.21 178 | 89.95 146 | 72.43 139 | 81.78 91 | 89.61 123 | 57.50 186 | 93.58 138 | 70.75 149 | 86.90 130 | 92.52 96 |
|
DCV-MVSNet | | | 81.17 110 | 80.47 111 | 83.24 139 | 89.13 128 | 63.62 197 | 86.21 178 | 89.95 146 | 72.43 139 | 81.78 91 | 89.61 123 | 57.50 186 | 93.58 138 | 70.75 149 | 86.90 130 | 92.52 96 |
|
BH-untuned | | | 79.47 149 | 78.60 146 | 82.05 176 | 89.19 126 | 65.91 155 | 86.07 183 | 88.52 191 | 72.18 141 | 75.42 191 | 87.69 171 | 61.15 157 | 93.54 142 | 60.38 238 | 86.83 132 | 86.70 266 |
|
BH-RMVSNet | | | 79.61 145 | 78.44 151 | 83.14 144 | 89.38 115 | 65.93 154 | 84.95 207 | 87.15 216 | 73.56 124 | 78.19 133 | 89.79 119 | 56.67 195 | 93.36 148 | 59.53 245 | 86.74 133 | 90.13 168 |
|
LS3D | | | 76.95 205 | 74.82 216 | 83.37 134 | 90.45 89 | 67.36 134 | 89.15 90 | 86.94 218 | 61.87 277 | 69.52 263 | 90.61 104 | 51.71 237 | 94.53 97 | 46.38 315 | 86.71 134 | 88.21 233 |
|
Fast-Effi-MVS+ | | | 80.81 119 | 79.92 119 | 83.47 129 | 88.85 135 | 64.51 181 | 85.53 197 | 89.39 160 | 70.79 162 | 78.49 126 | 85.06 242 | 67.54 82 | 93.58 138 | 67.03 187 | 86.58 135 | 92.32 103 |
|
EPNet_dtu | | | 75.46 226 | 74.86 215 | 77.23 267 | 82.57 269 | 54.60 305 | 86.89 158 | 83.09 266 | 71.64 148 | 66.25 291 | 85.86 223 | 55.99 197 | 88.04 271 | 54.92 276 | 86.55 136 | 89.05 208 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
OPM-MVS | | | 83.50 75 | 82.95 78 | 85.14 78 | 88.79 141 | 70.95 63 | 89.13 91 | 91.52 101 | 77.55 41 | 80.96 102 | 91.75 75 | 60.71 163 | 94.50 99 | 79.67 77 | 86.51 137 | 89.97 182 |
|
OMC-MVS | | | 82.69 87 | 81.97 93 | 84.85 88 | 88.75 143 | 67.42 131 | 87.98 129 | 90.87 120 | 74.92 97 | 79.72 110 | 91.65 77 | 62.19 140 | 93.96 117 | 75.26 114 | 86.42 138 | 93.16 79 |
|
HQP_MVS | | | 83.64 73 | 83.14 74 | 85.14 78 | 90.08 97 | 68.71 107 | 91.25 42 | 92.44 64 | 79.12 23 | 78.92 119 | 91.00 98 | 60.42 169 | 95.38 65 | 78.71 81 | 86.32 139 | 91.33 129 |
|
plane_prior5 | | | | | | | | | 92.44 64 | | | | | 95.38 65 | 78.71 81 | 86.32 139 | 91.33 129 |
|
thisisatest0515 | | | 77.33 200 | 75.38 210 | 83.18 142 | 85.27 214 | 63.80 195 | 82.11 255 | 83.27 262 | 65.06 244 | 75.91 180 | 83.84 256 | 49.54 259 | 94.27 104 | 67.24 183 | 86.19 141 | 91.48 127 |
|
plane_prior | | | | | | | 68.71 107 | 90.38 60 | | 77.62 36 | | | | | | 86.16 142 | |
|
mvs_anonymous | | | 79.42 151 | 79.11 138 | 80.34 213 | 84.45 227 | 57.97 268 | 82.59 250 | 87.62 208 | 67.40 219 | 76.17 178 | 88.56 152 | 68.47 75 | 89.59 246 | 70.65 152 | 86.05 143 | 93.47 66 |
|
HQP3-MVS | | | | | | | | | 92.19 74 | | | | | | | 85.99 144 | |
|
HQP-MVS | | | 82.61 89 | 82.02 91 | 84.37 101 | 89.33 116 | 66.98 139 | 89.17 86 | 92.19 74 | 76.41 67 | 77.23 153 | 90.23 110 | 60.17 172 | 95.11 75 | 77.47 93 | 85.99 144 | 91.03 136 |
|
BH-w/o | | | 78.21 178 | 77.33 179 | 80.84 204 | 88.81 139 | 65.13 173 | 84.87 208 | 87.85 205 | 69.75 182 | 74.52 211 | 84.74 246 | 61.34 152 | 93.11 160 | 58.24 259 | 85.84 146 | 84.27 295 |
|
CHOSEN 1792x2688 | | | 77.63 195 | 75.69 203 | 83.44 130 | 89.98 99 | 68.58 113 | 78.70 288 | 87.50 211 | 56.38 314 | 75.80 183 | 86.84 192 | 58.67 177 | 91.40 213 | 61.58 230 | 85.75 147 | 90.34 161 |
|
Anonymous202405211 | | | 78.25 176 | 77.01 183 | 81.99 178 | 91.03 80 | 60.67 242 | 84.77 210 | 83.90 251 | 70.65 167 | 80.00 108 | 91.20 90 | 41.08 308 | 91.43 212 | 65.21 199 | 85.26 148 | 93.85 45 |
|
cascas | | | 76.72 208 | 74.64 217 | 82.99 152 | 85.78 207 | 65.88 156 | 82.33 253 | 89.21 168 | 60.85 283 | 72.74 225 | 81.02 286 | 47.28 273 | 93.75 134 | 67.48 179 | 85.02 149 | 89.34 200 |
|
FIs | | | 82.07 95 | 82.42 83 | 81.04 201 | 88.80 140 | 58.34 262 | 88.26 123 | 93.49 24 | 76.93 54 | 78.47 127 | 91.04 95 | 69.92 63 | 92.34 185 | 69.87 160 | 84.97 150 | 92.44 100 |
|
test-LLR | | | 72.94 252 | 72.43 241 | 74.48 288 | 81.35 288 | 58.04 266 | 78.38 289 | 77.46 310 | 66.66 224 | 69.95 258 | 79.00 303 | 48.06 269 | 79.24 313 | 66.13 190 | 84.83 151 | 86.15 274 |
|
test-mter | | | 71.41 261 | 70.39 260 | 74.48 288 | 81.35 288 | 58.04 266 | 78.38 289 | 77.46 310 | 60.32 286 | 69.95 258 | 79.00 303 | 36.08 325 | 79.24 313 | 66.13 190 | 84.83 151 | 86.15 274 |
|
EI-MVSNet-Vis-set | | | 84.19 68 | 83.81 69 | 85.31 72 | 88.18 158 | 67.85 125 | 87.66 138 | 89.73 153 | 80.05 16 | 82.95 74 | 89.59 125 | 70.74 55 | 94.82 90 | 80.66 70 | 84.72 153 | 93.28 73 |
|
thisisatest0530 | | | 79.40 152 | 77.76 170 | 84.31 105 | 87.69 176 | 65.10 174 | 87.36 145 | 84.26 247 | 70.04 175 | 77.42 147 | 88.26 161 | 49.94 255 | 94.79 92 | 70.20 155 | 84.70 154 | 93.03 83 |
|
GG-mvs-BLEND | | | | | 75.38 282 | 81.59 283 | 55.80 299 | 79.32 280 | 69.63 331 | | 67.19 280 | 73.67 323 | 43.24 295 | 88.90 262 | 50.41 292 | 84.50 155 | 81.45 317 |
|
FC-MVSNet-test | | | 81.52 105 | 82.02 91 | 80.03 218 | 88.42 153 | 55.97 298 | 87.95 131 | 93.42 28 | 77.10 50 | 77.38 148 | 90.98 100 | 69.96 62 | 91.79 202 | 68.46 172 | 84.50 155 | 92.33 102 |
|
PVSNet | | 64.34 18 | 72.08 258 | 70.87 257 | 75.69 277 | 86.21 203 | 56.44 291 | 74.37 313 | 80.73 287 | 62.06 276 | 70.17 253 | 82.23 277 | 42.86 298 | 83.31 301 | 54.77 277 | 84.45 157 | 87.32 251 |
|
MS-PatchMatch | | | 73.83 241 | 72.67 239 | 77.30 265 | 83.87 237 | 66.02 152 | 81.82 256 | 84.66 241 | 61.37 281 | 68.61 270 | 82.82 269 | 47.29 272 | 88.21 268 | 59.27 246 | 84.32 158 | 77.68 327 |
|
ET-MVSNet_ETH3D | | | 78.63 169 | 76.63 196 | 84.64 94 | 86.73 198 | 69.47 90 | 85.01 205 | 84.61 242 | 69.54 186 | 66.51 289 | 86.59 205 | 50.16 252 | 91.75 203 | 76.26 105 | 84.24 159 | 92.69 93 |
|
TESTMET0.1,1 | | | 69.89 275 | 69.00 266 | 72.55 297 | 79.27 313 | 56.85 283 | 78.38 289 | 74.71 322 | 57.64 306 | 68.09 272 | 77.19 314 | 37.75 320 | 76.70 324 | 63.92 208 | 84.09 160 | 84.10 299 |
|
EI-MVSNet-UG-set | | | 83.81 70 | 83.38 72 | 85.09 80 | 87.87 167 | 67.53 129 | 87.44 144 | 89.66 154 | 79.74 18 | 82.23 84 | 89.41 134 | 70.24 60 | 94.74 93 | 79.95 74 | 83.92 161 | 92.99 86 |
|
LPG-MVS_test | | | 82.08 94 | 81.27 99 | 84.50 97 | 89.23 124 | 68.76 103 | 90.22 65 | 91.94 86 | 75.37 89 | 76.64 165 | 91.51 82 | 54.29 209 | 94.91 84 | 78.44 83 | 83.78 162 | 89.83 187 |
|
LGP-MVS_train | | | | | 84.50 97 | 89.23 124 | 68.76 103 | | 91.94 86 | 75.37 89 | 76.64 165 | 91.51 82 | 54.29 209 | 94.91 84 | 78.44 83 | 83.78 162 | 89.83 187 |
|
thres100view900 | | | 76.50 210 | 75.55 206 | 79.33 232 | 89.52 109 | 56.99 282 | 85.83 189 | 83.23 263 | 73.94 116 | 76.32 172 | 87.12 188 | 51.89 234 | 91.95 197 | 48.33 303 | 83.75 164 | 89.07 203 |
|
tfpn200view9 | | | 76.42 213 | 75.37 211 | 79.55 231 | 89.13 128 | 57.65 274 | 85.17 200 | 83.60 254 | 73.41 127 | 76.45 167 | 86.39 213 | 52.12 227 | 91.95 197 | 48.33 303 | 83.75 164 | 89.07 203 |
|
thres400 | | | 76.50 210 | 75.37 211 | 79.86 221 | 89.13 128 | 57.65 274 | 85.17 200 | 83.60 254 | 73.41 127 | 76.45 167 | 86.39 213 | 52.12 227 | 91.95 197 | 48.33 303 | 83.75 164 | 90.00 178 |
|
thres600view7 | | | 76.50 210 | 75.44 207 | 79.68 225 | 89.40 113 | 57.16 279 | 85.53 197 | 83.23 263 | 73.79 120 | 76.26 173 | 87.09 189 | 51.89 234 | 91.89 200 | 48.05 308 | 83.72 167 | 90.00 178 |
|
thres200 | | | 75.55 225 | 74.47 221 | 78.82 241 | 87.78 173 | 57.85 271 | 83.07 247 | 83.51 257 | 72.44 138 | 75.84 182 | 84.42 248 | 52.08 229 | 91.75 203 | 47.41 310 | 83.64 168 | 86.86 262 |
|
XVG-OURS | | | 80.41 131 | 79.23 136 | 83.97 120 | 85.64 209 | 69.02 96 | 83.03 248 | 90.39 131 | 71.09 158 | 77.63 144 | 91.49 84 | 54.62 208 | 91.35 214 | 75.71 109 | 83.47 169 | 91.54 123 |
|
CNLPA | | | 78.08 182 | 76.79 190 | 81.97 179 | 90.40 91 | 71.07 61 | 87.59 140 | 84.55 243 | 66.03 234 | 72.38 231 | 89.64 122 | 57.56 185 | 86.04 286 | 59.61 244 | 83.35 170 | 88.79 221 |
|
MVP-Stereo | | | 76.12 217 | 74.46 222 | 81.13 199 | 85.37 213 | 69.79 82 | 84.42 223 | 87.95 201 | 65.03 245 | 67.46 277 | 85.33 235 | 53.28 218 | 91.73 205 | 58.01 261 | 83.27 171 | 81.85 315 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
1314 | | | 76.53 209 | 75.30 213 | 80.21 216 | 83.93 236 | 62.32 223 | 84.66 212 | 88.81 182 | 60.23 287 | 70.16 254 | 84.07 253 | 55.30 200 | 90.73 231 | 67.37 180 | 83.21 172 | 87.59 245 |
|
tttt0517 | | | 79.40 152 | 77.91 163 | 83.90 123 | 88.10 161 | 63.84 194 | 88.37 118 | 84.05 249 | 71.45 154 | 76.78 161 | 89.12 137 | 49.93 257 | 94.89 87 | 70.18 156 | 83.18 173 | 92.96 87 |
|
mvs-test1 | | | 80.88 114 | 79.40 131 | 85.29 73 | 85.13 218 | 69.75 84 | 89.28 83 | 88.10 196 | 74.99 95 | 76.44 170 | 86.72 196 | 57.27 189 | 94.26 108 | 73.53 125 | 83.18 173 | 91.87 116 |
|
HyFIR lowres test | | | 77.53 196 | 75.40 209 | 83.94 122 | 89.59 106 | 66.62 143 | 80.36 270 | 88.64 189 | 56.29 315 | 76.45 167 | 85.17 239 | 57.64 184 | 93.28 150 | 61.34 233 | 83.10 175 | 91.91 115 |
|
ACMP | | 74.13 6 | 81.51 107 | 80.57 108 | 84.36 102 | 89.42 112 | 68.69 110 | 89.97 70 | 91.50 105 | 74.46 106 | 75.04 205 | 90.41 107 | 53.82 214 | 94.54 96 | 77.56 92 | 82.91 176 | 89.86 186 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
ACMM | | 73.20 8 | 80.78 124 | 79.84 121 | 83.58 127 | 89.31 121 | 68.37 115 | 89.99 69 | 91.60 99 | 70.28 172 | 77.25 151 | 89.66 121 | 53.37 217 | 93.53 143 | 74.24 120 | 82.85 177 | 88.85 218 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
PMMVS | | | 69.34 277 | 68.67 268 | 71.35 303 | 75.67 325 | 62.03 226 | 75.17 307 | 73.46 324 | 50.00 328 | 68.68 268 | 79.05 301 | 52.07 230 | 78.13 318 | 61.16 234 | 82.77 178 | 73.90 330 |
|
PLC | | 70.83 11 | 78.05 184 | 76.37 200 | 83.08 147 | 91.88 72 | 67.80 126 | 88.19 125 | 89.46 159 | 64.33 254 | 69.87 260 | 88.38 156 | 53.66 215 | 93.58 138 | 58.86 252 | 82.73 179 | 87.86 239 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
TR-MVS | | | 77.44 197 | 76.18 201 | 81.20 196 | 88.24 157 | 63.24 209 | 84.61 216 | 86.40 225 | 67.55 216 | 77.81 140 | 86.48 211 | 54.10 211 | 93.15 157 | 57.75 263 | 82.72 180 | 87.20 254 |
|
Anonymous20240529 | | | 80.19 137 | 78.89 142 | 84.10 111 | 90.60 87 | 64.75 178 | 88.95 94 | 90.90 119 | 65.97 235 | 80.59 105 | 91.17 91 | 49.97 254 | 93.73 136 | 69.16 167 | 82.70 181 | 93.81 49 |
|
ab-mvs | | | 79.51 147 | 78.97 141 | 81.14 198 | 88.46 151 | 60.91 239 | 83.84 233 | 89.24 167 | 70.36 170 | 79.03 116 | 88.87 143 | 63.23 121 | 90.21 237 | 65.12 200 | 82.57 182 | 92.28 105 |
|
HY-MVS | | 69.67 12 | 77.95 187 | 77.15 181 | 80.36 212 | 87.57 181 | 60.21 249 | 83.37 243 | 87.78 206 | 66.11 231 | 75.37 193 | 87.06 191 | 63.27 119 | 90.48 234 | 61.38 232 | 82.43 183 | 90.40 160 |
|
PS-MVSNAJss | | | 82.07 95 | 81.31 98 | 84.34 104 | 86.51 200 | 67.27 135 | 89.27 84 | 91.51 102 | 71.75 147 | 79.37 113 | 90.22 111 | 63.15 123 | 94.27 104 | 77.69 91 | 82.36 184 | 91.49 126 |
|
UniMVSNet_ETH3D | | | 79.10 159 | 78.24 157 | 81.70 183 | 86.85 194 | 60.24 248 | 87.28 148 | 88.79 183 | 74.25 110 | 76.84 158 | 90.53 106 | 49.48 260 | 91.56 208 | 67.98 174 | 82.15 185 | 93.29 72 |
|
PVSNet_BlendedMVS | | | 80.60 127 | 80.02 117 | 82.36 173 | 88.85 135 | 65.40 164 | 86.16 180 | 92.00 82 | 69.34 190 | 78.11 135 | 86.09 219 | 66.02 98 | 94.27 104 | 71.52 143 | 82.06 186 | 87.39 248 |
|
WTY-MVS | | | 75.65 224 | 75.68 204 | 75.57 279 | 86.40 201 | 56.82 284 | 77.92 295 | 82.40 272 | 65.10 243 | 76.18 176 | 87.72 169 | 63.13 126 | 80.90 309 | 60.31 239 | 81.96 187 | 89.00 212 |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 81.95 188 | |
|
DP-MVS | | | 76.78 207 | 74.57 218 | 83.42 131 | 93.29 42 | 69.46 92 | 88.55 110 | 83.70 253 | 63.98 258 | 70.20 251 | 88.89 142 | 54.01 213 | 94.80 91 | 46.66 312 | 81.88 189 | 86.01 278 |
|
CMPMVS | | 51.72 21 | 70.19 272 | 68.16 273 | 76.28 273 | 73.15 333 | 57.55 276 | 79.47 279 | 83.92 250 | 48.02 329 | 56.48 327 | 84.81 244 | 43.13 296 | 86.42 284 | 62.67 219 | 81.81 190 | 84.89 289 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
XVG-OURS-SEG-HR | | | 80.81 119 | 79.76 123 | 83.96 121 | 85.60 210 | 68.78 102 | 83.54 241 | 90.50 129 | 70.66 166 | 76.71 163 | 91.66 76 | 60.69 164 | 91.26 216 | 76.94 99 | 81.58 191 | 91.83 117 |
|
MIMVSNet | | | 70.69 266 | 69.30 263 | 74.88 285 | 84.52 225 | 56.35 294 | 75.87 305 | 79.42 301 | 64.59 249 | 67.76 273 | 82.41 273 | 41.10 307 | 81.54 308 | 46.64 314 | 81.34 192 | 86.75 265 |
|
ACMMP++ | | | | | | | | | | | | | | | | 81.25 193 | |
|
D2MVS | | | 74.82 231 | 73.21 234 | 79.64 228 | 79.81 305 | 62.56 220 | 80.34 271 | 87.35 214 | 64.37 253 | 68.86 267 | 82.66 271 | 46.37 278 | 90.10 239 | 67.91 175 | 81.24 194 | 86.25 271 |
|
GA-MVS | | | 76.87 206 | 75.17 214 | 81.97 179 | 82.75 264 | 62.58 219 | 81.44 264 | 86.35 227 | 72.16 143 | 74.74 209 | 82.89 267 | 46.20 281 | 92.02 195 | 68.85 170 | 81.09 195 | 91.30 131 |
|
sss | | | 73.60 243 | 73.64 230 | 73.51 294 | 82.80 263 | 55.01 304 | 76.12 301 | 81.69 280 | 62.47 272 | 74.68 210 | 85.85 224 | 57.32 188 | 78.11 319 | 60.86 236 | 80.93 196 | 87.39 248 |
|
Effi-MVS+-dtu | | | 80.03 139 | 78.57 148 | 84.42 100 | 85.13 218 | 68.74 105 | 88.77 101 | 88.10 196 | 74.99 95 | 74.97 206 | 83.49 262 | 57.27 189 | 93.36 148 | 73.53 125 | 80.88 197 | 91.18 133 |
|
EG-PatchMatch MVS | | | 74.04 239 | 71.82 247 | 80.71 207 | 84.92 221 | 67.42 131 | 85.86 188 | 88.08 198 | 66.04 233 | 64.22 303 | 83.85 255 | 35.10 327 | 92.56 177 | 57.44 265 | 80.83 198 | 82.16 314 |
|
jajsoiax | | | 79.29 155 | 77.96 161 | 83.27 137 | 84.68 224 | 66.57 145 | 89.25 85 | 90.16 141 | 69.20 195 | 75.46 189 | 89.49 127 | 45.75 286 | 93.13 159 | 76.84 101 | 80.80 199 | 90.11 170 |
|
1112_ss | | | 77.40 199 | 76.43 198 | 80.32 214 | 89.11 132 | 60.41 247 | 83.65 236 | 87.72 207 | 62.13 275 | 73.05 223 | 86.72 196 | 62.58 132 | 89.97 240 | 62.11 225 | 80.80 199 | 90.59 153 |
|
mvs_tets | | | 79.13 158 | 77.77 169 | 83.22 141 | 84.70 223 | 66.37 148 | 89.17 86 | 90.19 140 | 69.38 189 | 75.40 192 | 89.46 130 | 44.17 292 | 93.15 157 | 76.78 102 | 80.70 201 | 90.14 167 |
|
PatchMatch-RL | | | 72.38 256 | 70.90 255 | 76.80 271 | 88.60 146 | 67.38 133 | 79.53 278 | 76.17 316 | 62.75 269 | 69.36 265 | 82.00 280 | 45.51 287 | 84.89 293 | 53.62 281 | 80.58 202 | 78.12 326 |
|
EI-MVSNet | | | 80.52 130 | 79.98 118 | 82.12 174 | 84.28 228 | 63.19 212 | 86.41 172 | 88.95 180 | 74.18 112 | 78.69 121 | 87.54 176 | 66.62 89 | 92.43 180 | 72.57 138 | 80.57 203 | 90.74 146 |
|
MVSTER | | | 79.01 161 | 77.88 165 | 82.38 172 | 83.07 253 | 64.80 177 | 84.08 232 | 88.95 180 | 69.01 201 | 78.69 121 | 87.17 187 | 54.70 206 | 92.43 180 | 74.69 116 | 80.57 203 | 89.89 185 |
|
testing_2 | | | 75.73 222 | 73.34 233 | 82.89 158 | 77.37 319 | 65.22 170 | 84.10 230 | 90.54 128 | 69.09 197 | 60.46 315 | 81.15 284 | 40.48 310 | 92.84 172 | 76.36 104 | 80.54 205 | 90.60 151 |
|
XVG-ACMP-BASELINE | | | 76.11 218 | 74.27 224 | 81.62 184 | 83.20 249 | 64.67 179 | 83.60 239 | 89.75 152 | 69.75 182 | 71.85 236 | 87.09 189 | 32.78 329 | 92.11 192 | 69.99 159 | 80.43 206 | 88.09 235 |
|
Fast-Effi-MVS+-dtu | | | 78.02 185 | 76.49 197 | 82.62 169 | 83.16 252 | 66.96 141 | 86.94 156 | 87.45 213 | 72.45 136 | 71.49 241 | 84.17 251 | 54.79 205 | 91.58 207 | 67.61 177 | 80.31 207 | 89.30 201 |
|
LTVRE_ROB | | 69.57 13 | 76.25 216 | 74.54 220 | 81.41 189 | 88.60 146 | 64.38 186 | 79.24 281 | 89.12 173 | 70.76 164 | 69.79 262 | 87.86 168 | 49.09 265 | 93.20 154 | 56.21 273 | 80.16 208 | 86.65 267 |
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 |
Test_1112_low_res | | | 76.40 214 | 75.44 207 | 79.27 233 | 89.28 122 | 58.09 264 | 81.69 259 | 87.07 217 | 59.53 294 | 72.48 229 | 86.67 202 | 61.30 153 | 89.33 250 | 60.81 237 | 80.15 209 | 90.41 159 |
|
test_djsdf | | | 80.30 134 | 79.32 134 | 83.27 137 | 83.98 235 | 65.37 167 | 90.50 55 | 90.38 132 | 68.55 209 | 76.19 175 | 88.70 145 | 56.44 196 | 93.46 145 | 78.98 79 | 80.14 210 | 90.97 139 |
|
CHOSEN 280x420 | | | 66.51 293 | 64.71 292 | 71.90 298 | 81.45 285 | 63.52 202 | 57.98 338 | 68.95 335 | 53.57 321 | 62.59 311 | 76.70 315 | 46.22 280 | 75.29 331 | 55.25 275 | 79.68 211 | 76.88 329 |
|
MVS_0304 | | | 72.48 254 | 70.89 256 | 77.24 266 | 82.20 275 | 59.68 251 | 84.11 229 | 83.49 258 | 67.10 220 | 66.87 284 | 80.59 290 | 35.00 328 | 87.40 276 | 59.07 250 | 79.58 212 | 84.63 293 |
|
baseline2 | | | 75.70 223 | 73.83 229 | 81.30 193 | 83.26 247 | 61.79 231 | 82.57 251 | 80.65 288 | 66.81 221 | 66.88 283 | 83.42 263 | 57.86 182 | 92.19 190 | 63.47 210 | 79.57 213 | 89.91 183 |
|
GBi-Net | | | 78.40 173 | 77.40 177 | 81.40 190 | 87.60 177 | 63.01 214 | 88.39 115 | 89.28 163 | 71.63 149 | 75.34 194 | 87.28 180 | 54.80 202 | 91.11 219 | 62.72 216 | 79.57 213 | 90.09 172 |
|
test1 | | | 78.40 173 | 77.40 177 | 81.40 190 | 87.60 177 | 63.01 214 | 88.39 115 | 89.28 163 | 71.63 149 | 75.34 194 | 87.28 180 | 54.80 202 | 91.11 219 | 62.72 216 | 79.57 213 | 90.09 172 |
|
FMVSNet3 | | | 77.88 189 | 76.85 188 | 80.97 202 | 86.84 195 | 62.36 221 | 86.52 171 | 88.77 184 | 71.13 156 | 75.34 194 | 86.66 203 | 54.07 212 | 91.10 222 | 62.72 216 | 79.57 213 | 89.45 198 |
|
FMVSNet2 | | | 78.20 179 | 77.21 180 | 81.20 196 | 87.60 177 | 62.89 218 | 87.47 143 | 89.02 175 | 71.63 149 | 75.29 198 | 87.28 180 | 54.80 202 | 91.10 222 | 62.38 220 | 79.38 217 | 89.61 194 |
|
anonymousdsp | | | 78.60 170 | 77.15 181 | 82.98 153 | 80.51 298 | 67.08 137 | 87.24 149 | 89.53 157 | 65.66 238 | 75.16 200 | 87.19 186 | 52.52 219 | 92.25 187 | 77.17 97 | 79.34 218 | 89.61 194 |
|
nrg030 | | | 83.88 69 | 83.53 70 | 84.96 84 | 86.77 197 | 69.28 94 | 90.46 58 | 92.67 57 | 74.79 98 | 82.95 74 | 91.33 87 | 72.70 38 | 93.09 161 | 80.79 69 | 79.28 219 | 92.50 98 |
|
VPA-MVSNet | | | 80.60 127 | 80.55 109 | 80.76 206 | 88.07 162 | 60.80 241 | 86.86 159 | 91.58 100 | 75.67 84 | 80.24 107 | 89.45 132 | 63.34 117 | 90.25 236 | 70.51 153 | 79.22 220 | 91.23 132 |
|
F-COLMAP | | | 76.38 215 | 74.33 223 | 82.50 170 | 89.28 122 | 66.95 142 | 88.41 114 | 89.03 174 | 64.05 256 | 66.83 285 | 88.61 149 | 46.78 276 | 92.89 168 | 57.48 264 | 78.55 221 | 87.67 242 |
|
FMVSNet1 | | | 77.44 197 | 76.12 202 | 81.40 190 | 86.81 196 | 63.01 214 | 88.39 115 | 89.28 163 | 70.49 169 | 74.39 212 | 87.28 180 | 49.06 266 | 91.11 219 | 60.91 235 | 78.52 222 | 90.09 172 |
|
MDTV_nov1_ep13 | | | | 69.97 262 | | 83.18 250 | 53.48 312 | 77.10 299 | 80.18 297 | 60.45 284 | 69.33 266 | 80.44 291 | 48.89 267 | 86.90 279 | 51.60 288 | 78.51 223 | |
|
CVMVSNet | | | 72.99 251 | 72.58 240 | 74.25 291 | 84.28 228 | 50.85 323 | 86.41 172 | 83.45 260 | 44.56 330 | 73.23 221 | 87.54 176 | 49.38 261 | 85.70 288 | 65.90 194 | 78.44 224 | 86.19 273 |
|
tpm2 | | | 73.26 247 | 71.46 249 | 78.63 243 | 83.34 245 | 56.71 287 | 80.65 268 | 80.40 293 | 56.63 313 | 73.55 217 | 82.02 279 | 51.80 236 | 91.24 217 | 56.35 272 | 78.42 225 | 87.95 236 |
|
CostFormer | | | 75.24 230 | 73.90 227 | 79.27 233 | 82.65 268 | 58.27 263 | 80.80 265 | 82.73 270 | 61.57 278 | 75.33 197 | 83.13 265 | 55.52 198 | 91.07 225 | 64.98 203 | 78.34 226 | 88.45 229 |
|
ACMH | | 67.68 16 | 75.89 220 | 73.93 226 | 81.77 182 | 88.71 144 | 66.61 144 | 88.62 107 | 89.01 176 | 69.81 179 | 66.78 286 | 86.70 201 | 41.95 305 | 91.51 211 | 55.64 274 | 78.14 227 | 87.17 255 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
CR-MVSNet | | | 73.37 244 | 71.27 252 | 79.67 226 | 81.32 290 | 65.19 171 | 75.92 303 | 80.30 294 | 59.92 290 | 72.73 226 | 81.19 282 | 52.50 220 | 86.69 280 | 59.84 242 | 77.71 228 | 87.11 258 |
|
RPMNet | | | 71.62 259 | 68.94 267 | 79.67 226 | 81.32 290 | 65.19 171 | 75.92 303 | 78.30 306 | 57.60 307 | 72.73 226 | 76.45 317 | 52.30 224 | 86.69 280 | 48.14 307 | 77.71 228 | 87.11 258 |
|
SCA | | | 74.22 237 | 72.33 243 | 79.91 220 | 84.05 234 | 62.17 225 | 79.96 275 | 79.29 302 | 66.30 230 | 72.38 231 | 80.13 294 | 51.95 232 | 88.60 264 | 59.25 247 | 77.67 230 | 88.96 214 |
|
Anonymous20231211 | | | 78.97 163 | 77.69 173 | 82.81 161 | 90.54 88 | 64.29 187 | 90.11 67 | 91.51 102 | 65.01 246 | 76.16 179 | 88.13 166 | 50.56 248 | 93.03 166 | 69.68 162 | 77.56 231 | 91.11 135 |
|
DWT-MVSNet_test | | | 73.70 242 | 71.86 246 | 79.21 235 | 82.91 260 | 58.94 257 | 82.34 252 | 82.17 274 | 65.21 241 | 71.05 245 | 78.31 305 | 44.21 291 | 90.17 238 | 63.29 214 | 77.28 232 | 88.53 228 |
|
v1144 | | | 80.03 139 | 79.03 139 | 83.01 151 | 83.78 238 | 64.51 181 | 87.11 152 | 90.57 127 | 71.96 145 | 78.08 137 | 86.20 217 | 61.41 150 | 93.94 120 | 74.93 115 | 77.23 233 | 90.60 151 |
|
WR-MVS | | | 79.49 148 | 79.22 137 | 80.27 215 | 88.79 141 | 58.35 261 | 85.06 204 | 88.61 190 | 78.56 29 | 77.65 143 | 88.34 157 | 63.81 115 | 90.66 232 | 64.98 203 | 77.22 234 | 91.80 120 |
|
v1192 | | | 79.59 146 | 78.43 152 | 83.07 148 | 83.55 242 | 64.52 180 | 86.93 157 | 90.58 126 | 70.83 160 | 77.78 141 | 85.90 221 | 59.15 175 | 93.94 120 | 73.96 122 | 77.19 235 | 90.76 144 |
|
VPNet | | | 78.69 168 | 78.66 145 | 78.76 242 | 88.31 156 | 55.72 300 | 84.45 221 | 86.63 222 | 76.79 58 | 78.26 131 | 90.55 105 | 59.30 174 | 89.70 245 | 66.63 188 | 77.05 236 | 90.88 141 |
|
v1240 | | | 78.99 162 | 77.78 168 | 82.64 168 | 83.21 248 | 63.54 201 | 86.62 168 | 90.30 138 | 69.74 184 | 77.33 149 | 85.68 227 | 57.04 193 | 93.76 133 | 73.13 133 | 76.92 237 | 90.62 149 |
|
MSDG | | | 73.36 246 | 70.99 254 | 80.49 210 | 84.51 226 | 65.80 157 | 80.71 267 | 86.13 230 | 65.70 237 | 65.46 294 | 83.74 259 | 44.60 289 | 90.91 227 | 51.13 290 | 76.89 238 | 84.74 291 |
|
IterMVS-LS | | | 80.06 138 | 79.38 132 | 82.11 175 | 85.89 205 | 63.20 211 | 86.79 162 | 89.34 161 | 74.19 111 | 75.45 190 | 86.72 196 | 66.62 89 | 92.39 182 | 72.58 137 | 76.86 239 | 90.75 145 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
v1921920 | | | 79.22 156 | 78.03 160 | 82.80 162 | 83.30 246 | 63.94 193 | 86.80 161 | 90.33 136 | 69.91 178 | 77.48 146 | 85.53 231 | 58.44 179 | 93.75 134 | 73.60 124 | 76.85 240 | 90.71 147 |
|
test_normal | | | 67.47 286 | 63.56 296 | 79.18 237 | 72.78 334 | 55.71 301 | 40.72 342 | 90.78 122 | 72.12 144 | 48.43 334 | 65.82 329 | 32.32 330 | 92.25 187 | 72.25 140 | 76.85 240 | 89.59 196 |
|
XXY-MVS | | | 75.41 228 | 75.56 205 | 74.96 284 | 83.59 241 | 57.82 272 | 80.59 269 | 83.87 252 | 66.54 228 | 74.93 207 | 88.31 158 | 63.24 120 | 80.09 312 | 62.16 223 | 76.85 240 | 86.97 260 |
|
v2v482 | | | 80.23 135 | 79.29 135 | 83.05 149 | 83.62 240 | 64.14 189 | 87.04 153 | 89.97 145 | 73.61 122 | 78.18 134 | 87.22 184 | 61.10 158 | 93.82 127 | 76.11 106 | 76.78 243 | 91.18 133 |
|
v144192 | | | 79.47 149 | 78.37 153 | 82.78 165 | 83.35 244 | 63.96 192 | 86.96 155 | 90.36 135 | 69.99 176 | 77.50 145 | 85.67 228 | 60.66 165 | 93.77 132 | 74.27 119 | 76.58 244 | 90.62 149 |
|
UniMVSNet (Re) | | | 81.60 104 | 81.11 102 | 83.09 146 | 88.38 154 | 64.41 185 | 87.60 139 | 93.02 40 | 78.42 31 | 78.56 124 | 88.16 162 | 69.78 64 | 93.26 151 | 69.58 163 | 76.49 245 | 91.60 121 |
|
UniMVSNet_NR-MVSNet | | | 81.88 98 | 81.54 97 | 82.92 155 | 88.46 151 | 63.46 204 | 87.13 150 | 92.37 68 | 80.19 14 | 78.38 128 | 89.14 136 | 71.66 48 | 93.05 163 | 70.05 157 | 76.46 246 | 92.25 106 |
|
DU-MVS | | | 81.12 112 | 80.52 110 | 82.90 156 | 87.80 170 | 63.46 204 | 87.02 154 | 91.87 90 | 79.01 26 | 78.38 128 | 89.07 138 | 65.02 107 | 93.05 163 | 70.05 157 | 76.46 246 | 92.20 108 |
|
cl-mvsnet2 | | | 78.07 183 | 77.01 183 | 81.23 195 | 82.37 274 | 61.83 230 | 83.55 240 | 87.98 200 | 68.96 202 | 75.06 204 | 83.87 254 | 61.40 151 | 91.88 201 | 73.53 125 | 76.39 248 | 89.98 181 |
|
miper_ehance_all_eth | | | 78.59 171 | 77.76 170 | 81.08 200 | 82.66 267 | 61.56 233 | 83.65 236 | 89.15 170 | 68.87 204 | 75.55 186 | 83.79 258 | 66.49 91 | 92.03 194 | 73.25 131 | 76.39 248 | 89.64 193 |
|
miper_enhance_ethall | | | 77.87 190 | 76.86 187 | 80.92 203 | 81.65 281 | 61.38 235 | 82.68 249 | 88.98 177 | 65.52 240 | 75.47 187 | 82.30 275 | 65.76 102 | 92.00 196 | 72.95 134 | 76.39 248 | 89.39 199 |
|
PatchmatchNet | | | 73.12 249 | 71.33 251 | 78.49 248 | 83.18 250 | 60.85 240 | 79.63 277 | 78.57 304 | 64.13 255 | 71.73 237 | 79.81 299 | 51.20 241 | 85.97 287 | 57.40 266 | 76.36 251 | 88.66 224 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
USDC | | | 70.33 270 | 68.37 270 | 76.21 274 | 80.60 296 | 56.23 295 | 79.19 283 | 86.49 223 | 60.89 282 | 61.29 312 | 85.47 233 | 31.78 333 | 89.47 249 | 53.37 282 | 76.21 252 | 82.94 311 |
|
OpenMVS_ROB | | 64.09 19 | 70.56 268 | 68.19 272 | 77.65 259 | 80.26 299 | 59.41 256 | 85.01 205 | 82.96 268 | 58.76 300 | 65.43 295 | 82.33 274 | 37.63 321 | 91.23 218 | 45.34 320 | 76.03 253 | 82.32 312 |
|
ACMH+ | | 68.96 14 | 76.01 219 | 74.01 225 | 82.03 177 | 88.60 146 | 65.31 169 | 88.86 97 | 87.55 209 | 70.25 173 | 67.75 274 | 87.47 178 | 41.27 306 | 93.19 155 | 58.37 257 | 75.94 254 | 87.60 244 |
|
PatchFormer-LS_test | | | 74.50 233 | 73.05 237 | 78.86 240 | 82.95 259 | 59.55 255 | 81.65 260 | 82.30 273 | 67.44 218 | 71.62 239 | 78.15 308 | 52.34 223 | 88.92 261 | 65.05 202 | 75.90 255 | 88.12 234 |
|
tpm | | | 72.37 257 | 71.71 248 | 74.35 290 | 82.19 276 | 52.00 315 | 79.22 282 | 77.29 312 | 64.56 250 | 72.95 224 | 83.68 261 | 51.35 239 | 83.26 302 | 58.33 258 | 75.80 256 | 87.81 240 |
|
Anonymous20231206 | | | 68.60 280 | 67.80 280 | 71.02 305 | 80.23 300 | 50.75 324 | 78.30 292 | 80.47 291 | 56.79 312 | 66.11 292 | 82.63 272 | 46.35 279 | 78.95 315 | 43.62 323 | 75.70 257 | 83.36 304 |
|
v7n | | | 78.97 163 | 77.58 175 | 83.14 144 | 83.45 243 | 65.51 162 | 88.32 119 | 91.21 111 | 73.69 121 | 72.41 230 | 86.32 215 | 57.93 181 | 93.81 128 | 69.18 166 | 75.65 258 | 90.11 170 |
|
NR-MVSNet | | | 80.23 135 | 79.38 132 | 82.78 165 | 87.80 170 | 63.34 207 | 86.31 175 | 91.09 116 | 79.01 26 | 72.17 233 | 89.07 138 | 67.20 86 | 92.81 173 | 66.08 193 | 75.65 258 | 92.20 108 |
|
v10 | | | 79.74 144 | 78.67 144 | 82.97 154 | 84.06 233 | 64.95 175 | 87.88 135 | 90.62 125 | 73.11 130 | 75.11 202 | 86.56 208 | 61.46 149 | 94.05 116 | 73.68 123 | 75.55 260 | 89.90 184 |
|
IB-MVS | | 68.01 15 | 75.85 221 | 73.36 232 | 83.31 135 | 84.76 222 | 66.03 151 | 83.38 242 | 85.06 238 | 70.21 174 | 69.40 264 | 81.05 285 | 45.76 285 | 94.66 95 | 65.10 201 | 75.49 261 | 89.25 202 |
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 |
cl_fuxian | | | 78.75 166 | 77.91 163 | 81.26 194 | 82.89 261 | 61.56 233 | 84.09 231 | 89.13 172 | 69.97 177 | 75.56 185 | 84.29 250 | 66.36 93 | 92.09 193 | 73.47 128 | 75.48 262 | 90.12 169 |
|
V42 | | | 79.38 154 | 78.24 157 | 82.83 159 | 81.10 292 | 65.50 163 | 85.55 195 | 89.82 149 | 71.57 152 | 78.21 132 | 86.12 218 | 60.66 165 | 93.18 156 | 75.64 110 | 75.46 263 | 89.81 189 |
|
cl-mvsnet_ | | | 77.72 192 | 76.76 191 | 80.58 208 | 82.49 271 | 60.48 245 | 83.09 245 | 87.87 203 | 69.22 193 | 74.38 213 | 85.22 238 | 62.10 141 | 91.53 209 | 71.09 147 | 75.41 264 | 89.73 192 |
|
cl-mvsnet1 | | | 77.72 192 | 76.76 191 | 80.58 208 | 82.48 272 | 60.48 245 | 83.09 245 | 87.86 204 | 69.22 193 | 74.38 213 | 85.24 237 | 62.10 141 | 91.53 209 | 71.09 147 | 75.40 265 | 89.74 191 |
|
v8 | | | 79.97 141 | 79.02 140 | 82.80 162 | 84.09 232 | 64.50 183 | 87.96 130 | 90.29 139 | 74.13 114 | 75.24 199 | 86.81 193 | 62.88 128 | 93.89 126 | 74.39 118 | 75.40 265 | 90.00 178 |
|
Baseline_NR-MVSNet | | | 78.15 181 | 78.33 155 | 77.61 260 | 85.79 206 | 56.21 296 | 86.78 163 | 85.76 233 | 73.60 123 | 77.93 139 | 87.57 174 | 65.02 107 | 88.99 256 | 67.14 185 | 75.33 267 | 87.63 243 |
|
pmmvs5 | | | 71.55 260 | 70.20 261 | 75.61 278 | 77.83 316 | 56.39 292 | 81.74 258 | 80.89 284 | 57.76 305 | 67.46 277 | 84.49 247 | 49.26 264 | 85.32 292 | 57.08 269 | 75.29 268 | 85.11 288 |
|
EPMVS | | | 69.02 279 | 68.16 273 | 71.59 299 | 79.61 309 | 49.80 328 | 77.40 297 | 66.93 336 | 62.82 268 | 70.01 255 | 79.05 301 | 45.79 284 | 77.86 321 | 56.58 271 | 75.26 269 | 87.13 257 |
|
TranMVSNet+NR-MVSNet | | | 80.84 116 | 80.31 114 | 82.42 171 | 87.85 168 | 62.33 222 | 87.74 137 | 91.33 108 | 80.55 11 | 77.99 138 | 89.86 117 | 65.23 105 | 92.62 174 | 67.05 186 | 75.24 270 | 92.30 104 |
|
tfpnnormal | | | 74.39 234 | 73.16 235 | 78.08 252 | 86.10 204 | 58.05 265 | 84.65 215 | 87.53 210 | 70.32 171 | 71.22 243 | 85.63 229 | 54.97 201 | 89.86 241 | 43.03 324 | 75.02 271 | 86.32 270 |
|
COLMAP_ROB | | 66.92 17 | 73.01 250 | 70.41 259 | 80.81 205 | 87.13 191 | 65.63 160 | 88.30 120 | 84.19 248 | 62.96 265 | 63.80 306 | 87.69 171 | 38.04 319 | 92.56 177 | 46.66 312 | 74.91 272 | 84.24 296 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
PatchT | | | 68.46 283 | 67.85 278 | 70.29 307 | 80.70 295 | 43.93 335 | 72.47 316 | 74.88 319 | 60.15 288 | 70.55 246 | 76.57 316 | 49.94 255 | 81.59 307 | 50.58 291 | 74.83 273 | 85.34 284 |
|
pmmvs4 | | | 74.03 240 | 71.91 245 | 80.39 211 | 81.96 278 | 68.32 116 | 81.45 263 | 82.14 275 | 59.32 295 | 69.87 260 | 85.13 240 | 52.40 222 | 88.13 270 | 60.21 240 | 74.74 274 | 84.73 292 |
|
ITE_SJBPF | | | | | 78.22 250 | 81.77 280 | 60.57 243 | | 83.30 261 | 69.25 192 | 67.54 276 | 87.20 185 | 36.33 324 | 87.28 278 | 54.34 278 | 74.62 275 | 86.80 263 |
|
test0.0.03 1 | | | 68.00 284 | 67.69 282 | 68.90 312 | 77.55 317 | 47.43 330 | 75.70 306 | 72.95 326 | 66.66 224 | 66.56 287 | 82.29 276 | 48.06 269 | 75.87 328 | 44.97 321 | 74.51 276 | 83.41 303 |
|
test_0402 | | | 72.79 253 | 70.44 258 | 79.84 222 | 88.13 159 | 65.99 153 | 85.93 186 | 84.29 245 | 65.57 239 | 67.40 279 | 85.49 232 | 46.92 275 | 92.61 175 | 35.88 333 | 74.38 277 | 80.94 318 |
|
CP-MVSNet | | | 78.22 177 | 78.34 154 | 77.84 255 | 87.83 169 | 54.54 306 | 87.94 132 | 91.17 113 | 77.65 35 | 73.48 218 | 88.49 153 | 62.24 139 | 88.43 266 | 62.19 222 | 74.07 278 | 90.55 154 |
|
FMVSNet5 | | | 69.50 276 | 67.96 276 | 74.15 292 | 82.97 258 | 55.35 303 | 80.01 274 | 82.12 276 | 62.56 271 | 63.02 307 | 81.53 281 | 36.92 322 | 81.92 306 | 48.42 302 | 74.06 279 | 85.17 287 |
|
MVS-HIRNet | | | 59.14 305 | 57.67 307 | 63.57 319 | 81.65 281 | 43.50 336 | 71.73 318 | 65.06 339 | 39.59 335 | 51.43 332 | 57.73 335 | 38.34 318 | 82.58 305 | 39.53 330 | 73.95 280 | 64.62 335 |
|
tpmrst | | | 72.39 255 | 72.13 244 | 73.18 296 | 80.54 297 | 49.91 326 | 79.91 276 | 79.08 303 | 63.11 262 | 71.69 238 | 79.95 296 | 55.32 199 | 82.77 304 | 65.66 197 | 73.89 281 | 86.87 261 |
|
PS-CasMVS | | | 78.01 186 | 78.09 159 | 77.77 257 | 87.71 174 | 54.39 308 | 88.02 128 | 91.22 110 | 77.50 43 | 73.26 220 | 88.64 148 | 60.73 162 | 88.41 267 | 61.88 226 | 73.88 282 | 90.53 155 |
|
v148 | | | 78.72 167 | 77.80 167 | 81.47 188 | 82.73 265 | 61.96 228 | 86.30 176 | 88.08 198 | 73.26 129 | 76.18 176 | 85.47 233 | 62.46 134 | 92.36 184 | 71.92 142 | 73.82 283 | 90.09 172 |
|
Patchmatch-test | | | 64.82 299 | 63.24 298 | 69.57 309 | 79.42 311 | 49.82 327 | 63.49 336 | 69.05 334 | 51.98 326 | 59.95 318 | 80.13 294 | 50.91 243 | 70.98 337 | 40.66 329 | 73.57 284 | 87.90 238 |
|
WR-MVS_H | | | 78.51 172 | 78.49 149 | 78.56 245 | 88.02 164 | 56.38 293 | 88.43 111 | 92.67 57 | 77.14 48 | 73.89 216 | 87.55 175 | 66.25 94 | 89.24 252 | 58.92 251 | 73.55 285 | 90.06 176 |
|
testgi | | | 66.67 292 | 66.53 288 | 67.08 317 | 75.62 326 | 41.69 338 | 75.93 302 | 76.50 315 | 66.11 231 | 65.20 299 | 86.59 205 | 35.72 326 | 74.71 332 | 43.71 322 | 73.38 286 | 84.84 290 |
|
pm-mvs1 | | | 77.25 201 | 76.68 195 | 78.93 239 | 84.22 230 | 58.62 260 | 86.41 172 | 88.36 193 | 71.37 155 | 73.31 219 | 88.01 167 | 61.22 156 | 89.15 254 | 64.24 207 | 73.01 287 | 89.03 209 |
|
eth_miper_zixun_eth | | | 77.92 188 | 76.69 194 | 81.61 186 | 83.00 256 | 61.98 227 | 83.15 244 | 89.20 169 | 69.52 187 | 74.86 208 | 84.35 249 | 61.76 144 | 92.56 177 | 71.50 145 | 72.89 288 | 90.28 163 |
|
miper_lstm_enhance | | | 74.11 238 | 73.11 236 | 77.13 268 | 80.11 301 | 59.62 252 | 72.23 317 | 86.92 219 | 66.76 222 | 70.40 249 | 82.92 266 | 56.93 194 | 82.92 303 | 69.06 168 | 72.63 289 | 88.87 217 |
|
tpmvs | | | 71.09 263 | 69.29 264 | 76.49 272 | 82.04 277 | 56.04 297 | 78.92 286 | 81.37 283 | 64.05 256 | 67.18 281 | 78.28 306 | 49.74 258 | 89.77 242 | 49.67 298 | 72.37 290 | 83.67 301 |
|
PEN-MVS | | | 77.73 191 | 77.69 173 | 77.84 255 | 87.07 192 | 53.91 310 | 87.91 134 | 91.18 112 | 77.56 40 | 73.14 222 | 88.82 144 | 61.23 155 | 89.17 253 | 59.95 241 | 72.37 290 | 90.43 158 |
|
DSMNet-mixed | | | 57.77 307 | 56.90 308 | 60.38 321 | 67.70 338 | 35.61 341 | 69.18 325 | 53.97 343 | 32.30 340 | 57.49 324 | 79.88 297 | 40.39 312 | 68.57 339 | 38.78 331 | 72.37 290 | 76.97 328 |
|
IterMVS-SCA-FT | | | 75.43 227 | 73.87 228 | 80.11 217 | 82.69 266 | 64.85 176 | 81.57 262 | 83.47 259 | 69.16 196 | 70.49 248 | 84.15 252 | 51.95 232 | 88.15 269 | 69.23 165 | 72.14 293 | 87.34 250 |
|
tpm cat1 | | | 70.57 267 | 68.31 271 | 77.35 264 | 82.41 273 | 57.95 269 | 78.08 293 | 80.22 296 | 52.04 325 | 68.54 271 | 77.66 312 | 52.00 231 | 87.84 273 | 51.77 286 | 72.07 294 | 86.25 271 |
|
RPSCF | | | 73.23 248 | 71.46 249 | 78.54 246 | 82.50 270 | 59.85 250 | 82.18 254 | 82.84 269 | 58.96 298 | 71.15 244 | 89.41 134 | 45.48 288 | 84.77 294 | 58.82 253 | 71.83 295 | 91.02 138 |
|
IterMVS | | | 74.29 235 | 72.94 238 | 78.35 249 | 81.53 284 | 63.49 203 | 81.58 261 | 82.49 271 | 68.06 213 | 69.99 257 | 83.69 260 | 51.66 238 | 85.54 289 | 65.85 195 | 71.64 296 | 86.01 278 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
AllTest | | | 70.96 264 | 68.09 275 | 79.58 229 | 85.15 216 | 63.62 197 | 84.58 217 | 79.83 298 | 62.31 273 | 60.32 316 | 86.73 194 | 32.02 331 | 88.96 259 | 50.28 293 | 71.57 297 | 86.15 274 |
|
TestCases | | | | | 79.58 229 | 85.15 216 | 63.62 197 | | 79.83 298 | 62.31 273 | 60.32 316 | 86.73 194 | 32.02 331 | 88.96 259 | 50.28 293 | 71.57 297 | 86.15 274 |
|
baseline1 | | | 76.98 204 | 76.75 193 | 77.66 258 | 88.13 159 | 55.66 302 | 85.12 203 | 81.89 277 | 73.04 132 | 76.79 160 | 88.90 141 | 62.43 135 | 87.78 274 | 63.30 213 | 71.18 299 | 89.55 197 |
|
Patchmtry | | | 70.74 265 | 69.16 265 | 75.49 281 | 80.72 294 | 54.07 309 | 74.94 312 | 80.30 294 | 58.34 302 | 70.01 255 | 81.19 282 | 52.50 220 | 86.54 282 | 53.37 282 | 71.09 300 | 85.87 281 |
|
DTE-MVSNet | | | 76.99 203 | 76.80 189 | 77.54 262 | 86.24 202 | 53.06 314 | 87.52 141 | 90.66 124 | 77.08 51 | 72.50 228 | 88.67 147 | 60.48 168 | 89.52 247 | 57.33 267 | 70.74 301 | 90.05 177 |
|
MIMVSNet1 | | | 68.58 281 | 66.78 287 | 73.98 293 | 80.07 302 | 51.82 316 | 80.77 266 | 84.37 244 | 64.40 252 | 59.75 319 | 82.16 278 | 36.47 323 | 83.63 299 | 42.73 325 | 70.33 302 | 86.48 269 |
|
pmmvs6 | | | 74.69 232 | 73.39 231 | 78.61 244 | 81.38 287 | 57.48 277 | 86.64 167 | 87.95 201 | 64.99 247 | 70.18 252 | 86.61 204 | 50.43 250 | 89.52 247 | 62.12 224 | 70.18 303 | 88.83 219 |
|
TinyColmap | | | 67.30 289 | 64.81 291 | 74.76 287 | 81.92 279 | 56.68 288 | 80.29 272 | 81.49 282 | 60.33 285 | 56.27 328 | 83.22 264 | 24.77 336 | 87.66 275 | 45.52 318 | 69.47 304 | 79.95 322 |
|
OurMVSNet-221017-0 | | | 74.26 236 | 72.42 242 | 79.80 223 | 83.76 239 | 59.59 253 | 85.92 187 | 86.64 221 | 66.39 229 | 66.96 282 | 87.58 173 | 39.46 313 | 91.60 206 | 65.76 196 | 69.27 305 | 88.22 232 |
|
JIA-IIPM | | | 66.32 295 | 62.82 302 | 76.82 270 | 77.09 321 | 61.72 232 | 65.34 333 | 75.38 317 | 58.04 304 | 64.51 301 | 62.32 332 | 42.05 304 | 86.51 283 | 51.45 289 | 69.22 306 | 82.21 313 |
|
ADS-MVSNet2 | | | 66.20 296 | 63.33 297 | 74.82 286 | 79.92 303 | 58.75 259 | 67.55 330 | 75.19 318 | 53.37 322 | 65.25 297 | 75.86 318 | 42.32 301 | 80.53 311 | 41.57 327 | 68.91 307 | 85.18 285 |
|
ADS-MVSNet | | | 64.36 300 | 62.88 301 | 68.78 314 | 79.92 303 | 47.17 331 | 67.55 330 | 71.18 327 | 53.37 322 | 65.25 297 | 75.86 318 | 42.32 301 | 73.99 335 | 41.57 327 | 68.91 307 | 85.18 285 |
|
test20.03 | | | 67.45 287 | 66.95 286 | 68.94 311 | 75.48 327 | 44.84 334 | 77.50 296 | 77.67 309 | 66.66 224 | 63.01 308 | 83.80 257 | 47.02 274 | 78.40 317 | 42.53 326 | 68.86 309 | 83.58 302 |
|
EU-MVSNet | | | 68.53 282 | 67.61 283 | 71.31 304 | 78.51 315 | 47.01 332 | 84.47 218 | 84.27 246 | 42.27 331 | 66.44 290 | 84.79 245 | 40.44 311 | 83.76 297 | 58.76 254 | 68.54 310 | 83.17 305 |
|
our_test_3 | | | 69.14 278 | 67.00 285 | 75.57 279 | 79.80 306 | 58.80 258 | 77.96 294 | 77.81 308 | 59.55 293 | 62.90 310 | 78.25 307 | 47.43 271 | 83.97 296 | 51.71 287 | 67.58 311 | 83.93 300 |
|
ppachtmachnet_test | | | 70.04 273 | 67.34 284 | 78.14 251 | 79.80 306 | 61.13 236 | 79.19 283 | 80.59 289 | 59.16 297 | 65.27 296 | 79.29 300 | 46.75 277 | 87.29 277 | 49.33 299 | 66.72 312 | 86.00 280 |
|
LF4IMVS | | | 64.02 301 | 62.19 303 | 69.50 310 | 70.90 336 | 53.29 313 | 76.13 300 | 77.18 313 | 52.65 324 | 58.59 320 | 80.98 287 | 23.55 337 | 76.52 325 | 53.06 284 | 66.66 313 | 78.68 325 |
|
Patchmatch-RL test | | | 70.24 271 | 67.78 281 | 77.61 260 | 77.43 318 | 59.57 254 | 71.16 319 | 70.33 328 | 62.94 266 | 68.65 269 | 72.77 324 | 50.62 247 | 85.49 290 | 69.58 163 | 66.58 314 | 87.77 241 |
|
dp | | | 66.80 290 | 65.43 290 | 70.90 306 | 79.74 308 | 48.82 329 | 75.12 310 | 74.77 320 | 59.61 292 | 64.08 304 | 77.23 313 | 42.89 297 | 80.72 310 | 48.86 301 | 66.58 314 | 83.16 306 |
|
FPMVS | | | 53.68 309 | 51.64 311 | 59.81 322 | 65.08 339 | 51.03 322 | 69.48 324 | 69.58 332 | 41.46 332 | 40.67 336 | 72.32 325 | 16.46 343 | 70.00 338 | 24.24 338 | 65.42 316 | 58.40 336 |
|
pmmvs-eth3d | | | 70.50 269 | 67.83 279 | 78.52 247 | 77.37 319 | 66.18 150 | 81.82 256 | 81.51 281 | 58.90 299 | 63.90 305 | 80.42 292 | 42.69 299 | 86.28 285 | 58.56 255 | 65.30 317 | 83.11 307 |
|
N_pmnet | | | 52.79 310 | 53.26 310 | 51.40 326 | 78.99 314 | 7.68 351 | 69.52 323 | 3.89 351 | 51.63 327 | 57.01 325 | 74.98 321 | 40.83 309 | 65.96 340 | 37.78 332 | 64.67 318 | 80.56 321 |
|
PM-MVS | | | 66.41 294 | 64.14 294 | 73.20 295 | 73.92 328 | 56.45 290 | 78.97 285 | 64.96 340 | 63.88 260 | 64.72 300 | 80.24 293 | 19.84 340 | 83.44 300 | 66.24 189 | 64.52 319 | 79.71 323 |
|
SixPastTwentyTwo | | | 73.37 244 | 71.26 253 | 79.70 224 | 85.08 220 | 57.89 270 | 85.57 191 | 83.56 256 | 71.03 159 | 65.66 293 | 85.88 222 | 42.10 303 | 92.57 176 | 59.11 249 | 63.34 320 | 88.65 225 |
|
TransMVSNet (Re) | | | 75.39 229 | 74.56 219 | 77.86 254 | 85.50 212 | 57.10 281 | 86.78 163 | 86.09 231 | 72.17 142 | 71.53 240 | 87.34 179 | 63.01 127 | 89.31 251 | 56.84 270 | 61.83 321 | 87.17 255 |
|
MDA-MVSNet_test_wron | | | 65.03 297 | 62.92 299 | 71.37 301 | 75.93 323 | 56.73 285 | 69.09 328 | 74.73 321 | 57.28 310 | 54.03 330 | 77.89 309 | 45.88 282 | 74.39 334 | 49.89 297 | 61.55 322 | 82.99 310 |
|
YYNet1 | | | 65.03 297 | 62.91 300 | 71.38 300 | 75.85 324 | 56.60 289 | 69.12 327 | 74.66 323 | 57.28 310 | 54.12 329 | 77.87 310 | 45.85 283 | 74.48 333 | 49.95 296 | 61.52 323 | 83.05 308 |
|
ambc | | | | | 75.24 283 | 73.16 332 | 50.51 325 | 63.05 337 | 87.47 212 | | 64.28 302 | 77.81 311 | 17.80 341 | 89.73 244 | 57.88 262 | 60.64 324 | 85.49 282 |
|
TDRefinement | | | 67.49 285 | 64.34 293 | 76.92 269 | 73.47 331 | 61.07 237 | 84.86 209 | 82.98 267 | 59.77 291 | 58.30 322 | 85.13 240 | 26.06 335 | 87.89 272 | 47.92 309 | 60.59 325 | 81.81 316 |
|
Gipuma | | | 45.18 313 | 41.86 315 | 55.16 324 | 77.03 322 | 51.52 319 | 32.50 345 | 80.52 290 | 32.46 339 | 27.12 340 | 35.02 340 | 9.52 347 | 75.50 329 | 22.31 339 | 60.21 326 | 38.45 339 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
new-patchmatchnet | | | 61.73 303 | 61.73 304 | 61.70 320 | 72.74 335 | 24.50 348 | 69.16 326 | 78.03 307 | 61.40 279 | 56.72 326 | 75.53 320 | 38.42 317 | 76.48 326 | 45.95 317 | 57.67 327 | 84.13 298 |
|
MDA-MVSNet-bldmvs | | | 66.68 291 | 63.66 295 | 75.75 276 | 79.28 312 | 60.56 244 | 73.92 314 | 78.35 305 | 64.43 251 | 50.13 333 | 79.87 298 | 44.02 293 | 83.67 298 | 46.10 316 | 56.86 328 | 83.03 309 |
|
new_pmnet | | | 50.91 311 | 50.29 312 | 52.78 325 | 68.58 337 | 34.94 343 | 63.71 335 | 56.63 342 | 39.73 |