HSP-MVS | | | 80.69 1 | 81.20 1 | 79.14 8 | 86.21 16 | 62.73 12 | 86.09 8 | 85.03 12 | 65.51 14 | 83.81 1 | 90.51 11 | 63.71 1 | 89.23 5 | 81.51 1 | 88.44 11 | 85.45 77 |
|
APDe-MVS | | | 80.16 2 | 80.59 2 | 78.86 18 | 86.64 9 | 60.02 39 | 88.12 1 | 86.42 4 | 62.94 39 | 82.40 2 | 92.12 1 | 59.64 6 | 89.76 3 | 78.70 5 | 88.32 15 | 86.79 37 |
|
HPM-MVS++ | | | 79.88 3 | 80.14 3 | 79.10 11 | 88.17 1 | 64.80 1 | 86.59 4 | 83.70 37 | 65.37 15 | 78.78 7 | 90.64 7 | 58.63 11 | 87.24 30 | 79.00 4 | 90.37 2 | 85.26 90 |
|
CNVR-MVS | | | 79.84 4 | 79.97 4 | 79.45 4 | 87.90 2 | 62.17 20 | 84.37 20 | 85.03 12 | 66.96 5 | 77.58 10 | 90.06 20 | 59.47 8 | 89.13 7 | 78.67 6 | 89.73 3 | 87.03 32 |
|
SteuartSystems-ACMMP | | | 79.48 5 | 79.31 5 | 79.98 1 | 83.01 55 | 62.18 19 | 87.60 2 | 85.83 6 | 66.69 10 | 78.03 9 | 90.98 4 | 54.26 31 | 90.06 1 | 78.42 7 | 89.02 7 | 87.69 13 |
Skip Steuart: Steuart Systems R&D Blog. |
DeepPCF-MVS | | 69.58 1 | 79.03 6 | 79.00 6 | 79.13 9 | 84.92 41 | 60.32 37 | 83.03 37 | 85.33 9 | 62.86 42 | 80.17 3 | 90.03 21 | 61.76 2 | 88.95 9 | 74.21 19 | 88.67 10 | 88.12 7 |
|
ACMMP_Plus | | | 78.77 7 | 78.78 7 | 78.74 19 | 85.44 29 | 61.04 31 | 83.84 28 | 85.16 10 | 62.88 41 | 78.10 8 | 91.26 3 | 52.51 47 | 88.39 12 | 79.34 3 | 90.52 1 | 86.78 38 |
|
NCCC | | | 78.58 8 | 78.31 9 | 79.39 5 | 87.51 3 | 62.61 16 | 85.20 17 | 84.42 19 | 66.73 9 | 74.67 28 | 89.38 30 | 55.30 23 | 89.18 6 | 74.19 20 | 87.34 27 | 86.38 40 |
|
DeepC-MVS | | 69.38 2 | 78.56 9 | 78.14 12 | 79.83 2 | 83.60 49 | 61.62 24 | 84.17 24 | 86.85 2 | 63.23 34 | 73.84 37 | 90.25 18 | 57.68 12 | 89.96 2 | 74.62 18 | 89.03 6 | 87.89 9 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
TSAR-MVS + MP. | | | 78.44 10 | 78.28 10 | 78.90 16 | 84.96 37 | 61.41 27 | 84.03 26 | 83.82 35 | 59.34 111 | 79.37 5 | 89.76 26 | 59.84 4 | 87.62 27 | 76.69 11 | 86.74 35 | 87.68 14 |
|
MP-MVS-pluss | | | 78.35 11 | 78.46 8 | 78.03 28 | 84.96 37 | 59.52 44 | 82.93 39 | 85.39 8 | 62.15 52 | 76.41 14 | 91.51 2 | 52.47 49 | 86.78 42 | 80.66 2 | 89.64 5 | 87.80 11 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
MP-MVS | | | 78.35 11 | 78.26 11 | 78.64 20 | 86.54 11 | 63.47 5 | 86.02 9 | 83.55 40 | 63.89 30 | 73.60 41 | 90.60 8 | 54.85 27 | 86.72 43 | 77.20 10 | 88.06 20 | 85.74 65 |
|
APD-MVS | | | 78.02 13 | 78.04 13 | 77.98 29 | 86.44 13 | 60.81 34 | 85.52 15 | 84.36 20 | 60.61 72 | 79.05 6 | 90.30 16 | 55.54 22 | 88.32 15 | 73.48 27 | 87.03 30 | 84.83 101 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
HFP-MVS | | | 78.01 14 | 77.65 14 | 79.10 11 | 86.71 6 | 62.81 10 | 86.29 5 | 84.32 21 | 62.82 43 | 73.96 32 | 90.50 12 | 53.20 44 | 88.35 13 | 74.02 21 | 87.05 28 | 86.13 51 |
|
#test# | | | 77.83 15 | 77.41 17 | 79.10 11 | 86.71 6 | 62.81 10 | 85.69 14 | 84.32 21 | 61.61 61 | 73.96 32 | 90.50 12 | 53.20 44 | 88.35 13 | 73.68 24 | 87.05 28 | 86.13 51 |
|
ACMMPR | | | 77.71 16 | 77.23 19 | 79.16 6 | 86.75 5 | 62.93 9 | 86.29 5 | 84.24 23 | 62.82 43 | 73.55 42 | 90.56 10 | 49.80 71 | 88.24 16 | 74.02 21 | 87.03 30 | 86.32 47 |
|
SD-MVS | | | 77.70 17 | 77.62 15 | 77.93 31 | 84.47 44 | 61.88 23 | 84.55 19 | 83.87 33 | 60.37 77 | 79.89 4 | 89.38 30 | 54.97 25 | 85.58 69 | 76.12 12 | 84.94 44 | 86.33 46 |
|
region2R | | | 77.67 18 | 77.18 20 | 79.15 7 | 86.76 4 | 62.95 8 | 86.29 5 | 84.16 25 | 62.81 45 | 73.30 44 | 90.58 9 | 49.90 69 | 88.21 17 | 73.78 23 | 87.03 30 | 86.29 49 |
|
MPTG | | | 77.61 19 | 77.36 18 | 78.35 23 | 86.08 20 | 63.57 2 | 83.37 33 | 80.97 95 | 65.13 17 | 75.77 16 | 90.88 5 | 48.63 99 | 86.66 44 | 77.23 8 | 88.17 17 | 84.81 102 |
|
MCST-MVS | | | 77.48 20 | 77.45 16 | 77.54 33 | 86.67 8 | 58.36 58 | 83.22 35 | 86.93 1 | 56.91 137 | 74.91 24 | 88.19 44 | 59.15 9 | 87.68 26 | 73.67 25 | 87.45 26 | 86.57 39 |
|
HPM-MVS | | | 77.28 21 | 76.85 22 | 78.54 21 | 85.00 36 | 60.81 34 | 82.91 40 | 85.08 11 | 62.57 46 | 73.09 47 | 89.97 23 | 50.90 65 | 87.48 28 | 75.30 13 | 86.85 33 | 87.33 27 |
|
DeepC-MVS_fast | | 68.24 3 | 77.25 22 | 76.63 25 | 79.12 10 | 86.15 18 | 60.86 33 | 84.71 18 | 84.85 16 | 61.98 58 | 73.06 48 | 88.88 38 | 53.72 38 | 89.06 8 | 68.27 46 | 88.04 21 | 87.42 23 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
XVS | | | 77.17 23 | 76.56 26 | 79.00 14 | 86.32 14 | 62.62 14 | 85.83 10 | 83.92 29 | 64.55 21 | 72.17 57 | 90.01 22 | 47.95 108 | 88.01 21 | 71.55 35 | 86.74 35 | 86.37 43 |
|
CP-MVS | | | 77.12 24 | 76.68 24 | 78.43 22 | 86.05 22 | 63.18 7 | 87.55 3 | 83.45 43 | 62.44 49 | 72.68 52 | 90.50 12 | 48.18 106 | 87.34 29 | 73.59 26 | 85.71 41 | 84.76 106 |
|
CSCG | | | 76.92 25 | 76.75 23 | 77.41 35 | 83.96 48 | 59.60 43 | 82.95 38 | 86.50 3 | 60.78 70 | 75.27 19 | 84.83 90 | 60.76 3 | 86.56 49 | 67.86 50 | 87.87 25 | 86.06 54 |
|
MTAPA | | | 76.90 26 | 76.42 27 | 78.35 23 | 86.08 20 | 63.57 2 | 74.92 184 | 80.97 95 | 65.13 17 | 75.77 16 | 90.88 5 | 48.63 99 | 86.66 44 | 77.23 8 | 88.17 17 | 84.81 102 |
|
test_prior3 | | | 76.89 27 | 76.96 21 | 76.69 43 | 84.20 46 | 57.27 70 | 81.75 58 | 84.88 14 | 60.37 77 | 75.01 20 | 89.06 33 | 56.22 17 | 86.43 53 | 72.19 31 | 88.96 8 | 86.38 40 |
|
PGM-MVS | | | 76.77 28 | 76.06 29 | 78.88 17 | 86.14 19 | 62.73 12 | 82.55 47 | 83.74 36 | 61.71 59 | 72.45 56 | 90.34 15 | 48.48 103 | 88.13 18 | 72.32 30 | 86.85 33 | 85.78 60 |
|
mPP-MVS | | | 76.54 29 | 75.93 31 | 78.34 25 | 86.47 12 | 63.50 4 | 85.74 13 | 82.28 63 | 62.90 40 | 71.77 60 | 90.26 17 | 46.61 126 | 86.55 50 | 71.71 34 | 85.66 42 | 84.97 98 |
|
CDPH-MVS | | | 76.31 30 | 75.67 33 | 78.22 26 | 85.35 32 | 59.14 49 | 81.31 68 | 84.02 26 | 56.32 147 | 74.05 31 | 88.98 36 | 53.34 42 | 87.92 23 | 69.23 43 | 88.42 12 | 87.59 16 |
|
train_agg | | | 76.27 31 | 76.15 28 | 76.64 46 | 85.58 27 | 61.59 25 | 81.62 62 | 81.26 86 | 55.86 154 | 74.93 22 | 88.81 39 | 53.70 39 | 84.68 94 | 75.24 15 | 88.33 13 | 83.65 147 |
|
agg_prior3 | | | 76.13 32 | 75.89 32 | 76.85 41 | 85.76 23 | 62.02 21 | 81.65 60 | 81.01 94 | 55.51 162 | 73.73 38 | 88.60 43 | 53.23 43 | 84.90 87 | 75.24 15 | 88.33 13 | 83.65 147 |
|
ACMMP | | | 76.02 33 | 75.33 35 | 78.07 27 | 85.20 33 | 61.91 22 | 85.49 16 | 84.44 18 | 63.04 37 | 69.80 81 | 89.74 27 | 45.43 137 | 87.16 34 | 72.01 33 | 82.87 58 | 85.14 92 |
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 |
MVS_0305 | | | 75.96 34 | 75.30 36 | 77.97 30 | 81.13 77 | 57.59 66 | 82.11 54 | 83.22 51 | 67.54 4 | 69.39 92 | 85.95 75 | 51.47 57 | 88.72 10 | 72.57 29 | 84.29 48 | 87.59 16 |
|
agg_prior1 | | | 75.94 35 | 76.01 30 | 75.72 56 | 85.04 34 | 59.96 40 | 81.44 66 | 81.04 92 | 56.14 152 | 74.68 26 | 88.90 37 | 53.91 35 | 84.04 106 | 75.01 17 | 87.92 24 | 83.16 158 |
|
PHI-MVS | | | 75.87 36 | 75.36 34 | 77.41 35 | 80.62 84 | 55.91 94 | 84.28 21 | 85.78 7 | 56.08 153 | 73.41 43 | 86.58 64 | 50.94 64 | 88.54 11 | 70.79 39 | 89.71 4 | 87.79 12 |
|
3Dnovator+ | | 66.72 4 | 75.84 37 | 74.57 40 | 79.66 3 | 82.40 59 | 59.92 42 | 85.83 10 | 86.32 5 | 66.92 8 | 67.80 121 | 89.24 32 | 42.03 164 | 89.38 4 | 64.07 85 | 86.50 38 | 89.69 1 |
|
Regformer-2 | | | 75.63 38 | 74.99 37 | 77.54 33 | 80.43 86 | 58.32 59 | 79.50 91 | 82.92 56 | 67.84 1 | 75.94 15 | 80.75 172 | 55.73 20 | 86.80 40 | 71.44 37 | 80.38 78 | 87.50 19 |
|
Regformer-1 | | | 75.47 39 | 74.93 39 | 77.09 39 | 80.43 86 | 57.70 65 | 79.50 91 | 82.13 64 | 67.84 1 | 75.73 18 | 80.75 172 | 56.50 14 | 86.07 57 | 71.07 38 | 80.38 78 | 87.50 19 |
|
APD-MVS_3200maxsize | | | 74.96 40 | 74.39 42 | 76.67 45 | 82.20 60 | 58.24 60 | 83.67 29 | 83.29 49 | 58.41 123 | 73.71 39 | 90.14 19 | 45.62 132 | 85.99 61 | 69.64 41 | 82.85 59 | 85.78 60 |
|
TSAR-MVS + GP. | | | 74.90 41 | 74.15 44 | 77.17 38 | 82.00 62 | 58.77 54 | 81.80 57 | 78.57 151 | 58.58 119 | 74.32 30 | 84.51 99 | 55.94 19 | 87.22 31 | 67.11 56 | 84.48 47 | 85.52 72 |
|
DELS-MVS | | | 74.76 42 | 74.46 41 | 75.65 59 | 77.84 136 | 52.25 138 | 75.59 168 | 84.17 24 | 63.76 31 | 73.15 46 | 82.79 120 | 59.58 7 | 86.80 40 | 67.24 55 | 86.04 40 | 87.89 9 |
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 |
OPM-MVS | | | 74.73 43 | 74.25 43 | 76.19 49 | 80.81 80 | 59.01 51 | 82.60 46 | 83.64 38 | 63.74 32 | 72.52 54 | 87.49 49 | 47.18 118 | 85.88 65 | 69.47 42 | 80.78 70 | 83.66 146 |
|
canonicalmvs | | | 74.67 44 | 74.98 38 | 73.71 94 | 78.94 108 | 50.56 163 | 80.23 78 | 83.87 33 | 60.30 81 | 77.15 11 | 86.56 65 | 59.65 5 | 82.00 158 | 66.01 63 | 82.12 62 | 88.58 4 |
|
abl_6 | | | 74.34 45 | 73.50 48 | 76.86 40 | 82.43 58 | 60.16 38 | 83.48 32 | 81.86 70 | 58.81 117 | 73.95 34 | 89.86 24 | 41.87 167 | 86.62 46 | 67.98 49 | 81.23 69 | 83.80 139 |
|
HQP_MVS | | | 74.31 46 | 73.73 47 | 76.06 50 | 81.41 70 | 56.31 83 | 84.22 22 | 84.01 27 | 64.52 23 | 69.27 94 | 86.10 71 | 45.26 141 | 87.21 32 | 68.16 47 | 80.58 74 | 84.65 107 |
|
HPM-MVS_fast | | | 74.30 47 | 73.46 51 | 76.80 42 | 84.45 45 | 59.04 50 | 83.65 30 | 81.05 91 | 60.15 83 | 70.43 67 | 89.84 25 | 41.09 181 | 85.59 68 | 67.61 53 | 82.90 57 | 85.77 62 |
|
Regformer-4 | | | 74.25 48 | 73.48 49 | 76.57 47 | 79.75 95 | 56.54 82 | 78.54 101 | 81.49 78 | 66.93 7 | 73.90 35 | 80.30 182 | 53.84 37 | 85.98 62 | 69.76 40 | 76.84 122 | 87.17 29 |
|
MVS_111021_HR | | | 74.02 49 | 73.46 51 | 75.69 58 | 83.01 55 | 60.63 36 | 77.29 136 | 78.40 160 | 61.18 66 | 70.58 66 | 85.97 74 | 54.18 33 | 84.00 110 | 67.52 54 | 82.98 56 | 82.45 170 |
|
MG-MVS | | | 73.96 50 | 73.89 46 | 74.16 81 | 85.65 25 | 49.69 191 | 81.59 64 | 81.29 85 | 61.45 62 | 71.05 64 | 88.11 45 | 51.77 54 | 87.73 25 | 61.05 120 | 83.09 53 | 85.05 95 |
|
Regformer-3 | | | 73.89 51 | 73.28 53 | 75.71 57 | 79.75 95 | 55.48 102 | 78.54 101 | 79.93 116 | 66.58 11 | 73.62 40 | 80.30 182 | 54.87 26 | 84.54 97 | 69.09 44 | 76.84 122 | 87.10 31 |
|
alignmvs | | | 73.86 52 | 73.99 45 | 73.45 106 | 78.20 125 | 50.50 165 | 78.57 99 | 82.43 62 | 59.40 109 | 76.57 12 | 86.71 58 | 56.42 16 | 81.23 171 | 65.84 65 | 81.79 64 | 88.62 2 |
|
MSLP-MVS++ | | | 73.77 53 | 73.47 50 | 74.66 73 | 83.02 54 | 59.29 48 | 82.30 53 | 81.88 69 | 59.34 111 | 71.59 62 | 86.83 55 | 45.94 130 | 83.65 116 | 65.09 70 | 85.22 43 | 81.06 191 |
|
HQP-MVS | | | 73.45 54 | 72.80 56 | 75.40 63 | 80.66 81 | 54.94 105 | 82.31 50 | 83.90 31 | 62.10 53 | 67.85 116 | 85.54 84 | 45.46 135 | 86.93 38 | 67.04 57 | 80.35 80 | 84.32 115 |
|
CLD-MVS | | | 73.33 55 | 72.68 57 | 75.29 66 | 78.82 110 | 53.33 122 | 78.23 107 | 84.79 17 | 61.30 65 | 70.41 68 | 81.04 163 | 52.41 50 | 87.12 35 | 64.61 75 | 82.49 61 | 85.41 84 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
Effi-MVS+ | | | 73.31 56 | 72.54 58 | 75.62 60 | 77.87 135 | 53.64 116 | 79.62 89 | 79.61 121 | 61.63 60 | 72.02 59 | 82.61 125 | 56.44 15 | 85.97 63 | 63.99 88 | 79.07 100 | 87.25 28 |
|
UA-Net | | | 73.13 57 | 72.93 55 | 73.76 90 | 83.58 50 | 51.66 144 | 78.75 95 | 77.66 169 | 67.75 3 | 72.61 53 | 89.42 28 | 49.82 70 | 83.29 123 | 53.61 160 | 83.14 52 | 86.32 47 |
|
EPNet | | | 73.09 58 | 72.16 60 | 75.90 52 | 75.95 166 | 56.28 85 | 83.05 36 | 72.39 218 | 66.53 12 | 65.27 150 | 87.00 54 | 50.40 67 | 85.47 73 | 62.48 102 | 86.32 39 | 85.94 56 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
nrg030 | | | 72.96 59 | 73.01 54 | 72.84 127 | 75.41 174 | 50.24 172 | 80.02 81 | 82.89 59 | 58.36 125 | 74.44 29 | 86.73 56 | 58.90 10 | 80.83 178 | 65.84 65 | 74.46 137 | 87.44 22 |
|
CPTT-MVS | | | 72.78 60 | 72.08 62 | 74.87 70 | 84.88 42 | 61.41 27 | 84.15 25 | 77.86 165 | 55.27 164 | 67.51 125 | 88.08 47 | 41.93 166 | 81.85 160 | 69.04 45 | 80.01 84 | 81.35 185 |
|
LPG-MVS_test | | | 72.74 61 | 71.74 64 | 75.76 54 | 80.22 89 | 57.51 68 | 82.55 47 | 83.40 45 | 61.32 63 | 66.67 133 | 87.33 52 | 39.15 194 | 86.59 47 | 67.70 51 | 77.30 118 | 83.19 155 |
|
PAPM_NR | | | 72.63 62 | 71.80 63 | 75.13 67 | 81.72 65 | 53.42 121 | 79.91 83 | 83.28 50 | 59.14 113 | 66.31 139 | 85.90 76 | 51.86 53 | 86.06 58 | 57.45 136 | 80.62 72 | 85.91 57 |
|
VDD-MVS | | | 72.50 63 | 72.09 61 | 73.75 92 | 81.58 66 | 49.69 191 | 77.76 123 | 77.63 170 | 63.21 35 | 73.21 45 | 89.02 35 | 42.14 163 | 83.32 122 | 61.72 117 | 82.50 60 | 88.25 6 |
|
3Dnovator | | 64.47 5 | 72.49 64 | 71.39 69 | 75.79 53 | 77.70 138 | 58.99 52 | 80.66 75 | 83.15 53 | 62.24 51 | 65.46 147 | 86.59 63 | 42.38 162 | 85.52 71 | 59.59 129 | 84.72 45 | 82.85 164 |
|
MVS_Test | | | 72.45 65 | 72.46 59 | 72.42 144 | 74.88 178 | 48.50 203 | 76.28 155 | 83.14 54 | 59.40 109 | 72.46 55 | 84.68 92 | 55.66 21 | 81.12 172 | 65.98 64 | 79.66 89 | 87.63 15 |
|
EI-MVSNet-Vis-set | | | 72.42 66 | 71.59 65 | 74.91 68 | 78.47 120 | 54.02 112 | 77.05 141 | 79.33 138 | 65.03 19 | 71.68 61 | 79.35 197 | 52.75 46 | 84.89 88 | 66.46 60 | 74.23 140 | 85.83 59 |
|
ACMP | | 63.53 6 | 72.30 67 | 71.20 74 | 75.59 62 | 80.28 88 | 57.54 67 | 82.74 43 | 82.84 60 | 60.58 73 | 65.24 152 | 86.18 69 | 39.25 193 | 86.03 60 | 66.95 59 | 76.79 124 | 83.22 153 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
PS-MVSNAJss | | | 72.24 68 | 71.21 73 | 75.31 65 | 78.50 118 | 55.93 93 | 81.63 61 | 82.12 65 | 56.24 150 | 70.02 75 | 85.68 81 | 47.05 119 | 84.34 101 | 65.27 69 | 74.41 139 | 85.67 67 |
|
Vis-MVSNet | | | 72.18 69 | 71.37 70 | 74.61 76 | 81.29 72 | 55.41 103 | 80.90 71 | 78.28 162 | 60.73 71 | 69.23 97 | 88.09 46 | 44.36 150 | 82.65 148 | 57.68 134 | 81.75 66 | 85.77 62 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
API-MVS | | | 72.17 70 | 71.41 68 | 74.45 79 | 81.95 63 | 57.22 72 | 84.03 26 | 80.38 112 | 59.89 91 | 68.40 106 | 82.33 131 | 49.64 72 | 87.83 24 | 51.87 169 | 84.16 50 | 78.30 217 |
|
EPP-MVSNet | | | 72.16 71 | 71.31 72 | 74.71 71 | 78.68 115 | 49.70 189 | 82.10 55 | 81.65 74 | 60.40 76 | 65.94 142 | 85.84 77 | 51.74 55 | 86.37 55 | 55.93 143 | 79.55 92 | 88.07 8 |
|
DP-MVS Recon | | | 72.15 72 | 70.73 79 | 76.40 48 | 86.57 10 | 57.99 62 | 81.15 70 | 82.96 55 | 57.03 135 | 66.78 132 | 85.56 82 | 44.50 147 | 88.11 19 | 51.77 171 | 80.23 83 | 83.10 159 |
|
EI-MVSNet-UG-set | | | 71.92 73 | 71.06 75 | 74.52 78 | 77.98 133 | 53.56 118 | 76.62 148 | 79.16 140 | 64.40 25 | 71.18 63 | 78.95 202 | 52.19 52 | 84.66 96 | 65.47 68 | 73.57 149 | 85.32 87 |
|
VDDNet | | | 71.81 74 | 71.33 71 | 73.26 118 | 82.80 57 | 47.60 214 | 78.74 96 | 75.27 194 | 59.59 104 | 72.94 49 | 89.40 29 | 41.51 176 | 83.91 111 | 58.75 131 | 82.99 55 | 88.26 5 |
|
LFMVS | | | 71.78 75 | 71.59 65 | 72.32 147 | 83.40 51 | 46.38 222 | 79.75 86 | 71.08 222 | 64.18 27 | 72.80 51 | 88.64 42 | 42.58 161 | 83.72 114 | 57.41 137 | 84.49 46 | 86.86 35 |
|
PAPR | | | 71.72 76 | 70.82 78 | 74.41 80 | 81.20 75 | 51.17 148 | 79.55 90 | 83.33 48 | 55.81 157 | 66.93 131 | 84.61 95 | 50.95 63 | 86.06 58 | 55.79 146 | 79.20 98 | 86.00 55 |
|
IS-MVSNet | | | 71.57 77 | 71.00 76 | 73.27 117 | 78.86 109 | 45.63 225 | 80.22 79 | 78.69 149 | 64.14 28 | 66.46 135 | 87.36 51 | 49.30 76 | 85.60 67 | 50.26 180 | 83.71 51 | 88.59 3 |
|
MAR-MVS | | | 71.51 78 | 70.15 86 | 75.60 61 | 81.84 64 | 59.39 46 | 81.38 67 | 82.90 58 | 54.90 170 | 68.08 114 | 78.70 203 | 47.73 110 | 85.51 72 | 51.68 173 | 84.17 49 | 81.88 178 |
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 |
MVSFormer | | | 71.50 79 | 70.38 83 | 74.88 69 | 78.76 112 | 57.15 77 | 82.79 41 | 78.48 155 | 51.26 206 | 69.49 88 | 83.22 116 | 43.99 153 | 83.24 124 | 66.06 61 | 79.37 93 | 84.23 122 |
|
PVSNet_Blended_VisFu | | | 71.45 80 | 70.39 82 | 74.65 74 | 82.01 61 | 58.82 53 | 79.93 82 | 80.35 114 | 55.09 166 | 65.82 146 | 82.16 137 | 49.17 91 | 82.64 149 | 60.34 124 | 78.62 106 | 82.50 169 |
|
OMC-MVS | | | 71.40 81 | 70.60 80 | 73.78 88 | 76.60 158 | 53.15 124 | 79.74 87 | 79.78 117 | 58.37 124 | 68.75 101 | 86.45 67 | 45.43 137 | 80.60 183 | 62.58 100 | 77.73 111 | 87.58 18 |
|
UniMVSNet_NR-MVSNet | | | 71.11 82 | 71.00 76 | 71.44 159 | 79.20 102 | 44.13 236 | 76.02 164 | 82.60 61 | 66.48 13 | 68.20 109 | 84.60 96 | 56.82 13 | 82.82 138 | 54.62 152 | 70.43 181 | 87.36 26 |
|
PCF-MVS | | 61.88 8 | 70.95 83 | 69.49 99 | 75.35 64 | 77.63 141 | 55.71 96 | 76.04 163 | 81.81 72 | 50.30 212 | 69.66 82 | 85.40 87 | 52.51 47 | 84.89 88 | 51.82 170 | 80.24 82 | 85.45 77 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
114514_t | | | 70.83 84 | 69.56 94 | 74.64 75 | 86.21 16 | 54.63 109 | 82.34 49 | 81.81 72 | 48.22 229 | 63.01 174 | 85.83 78 | 40.92 184 | 87.10 36 | 57.91 133 | 79.79 88 | 82.18 173 |
|
FIs | | | 70.82 85 | 71.43 67 | 68.98 196 | 78.33 122 | 38.14 272 | 76.96 143 | 83.59 39 | 61.02 67 | 67.33 127 | 86.73 56 | 55.07 24 | 81.64 163 | 54.61 154 | 79.22 97 | 87.14 30 |
|
ACMM | | 61.98 7 | 70.80 86 | 69.73 89 | 74.02 82 | 80.59 85 | 58.59 56 | 82.68 44 | 82.02 68 | 55.46 163 | 67.18 128 | 84.39 101 | 38.51 199 | 83.17 126 | 60.65 121 | 76.10 128 | 80.30 200 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
v1neww | | | 70.66 87 | 69.70 90 | 73.53 101 | 73.15 206 | 50.22 173 | 78.11 110 | 80.68 100 | 59.65 99 | 69.83 78 | 81.67 144 | 49.29 78 | 84.96 83 | 64.55 76 | 70.38 184 | 85.42 80 |
|
v7new | | | 70.66 87 | 69.70 90 | 73.53 101 | 73.15 206 | 50.22 173 | 78.11 110 | 80.68 100 | 59.65 99 | 69.83 78 | 81.67 144 | 49.29 78 | 84.96 83 | 64.55 76 | 70.38 184 | 85.42 80 |
|
v6 | | | 70.66 87 | 69.70 90 | 73.53 101 | 73.14 209 | 50.21 176 | 78.11 110 | 80.67 102 | 59.65 99 | 69.82 80 | 81.65 146 | 49.29 78 | 84.96 83 | 64.55 76 | 70.39 183 | 85.42 80 |
|
UniMVSNet (Re) | | | 70.63 90 | 70.20 84 | 71.89 151 | 78.55 117 | 45.29 226 | 75.94 165 | 82.92 56 | 63.68 33 | 68.16 111 | 83.59 111 | 53.89 36 | 83.49 119 | 53.97 156 | 71.12 174 | 86.89 34 |
|
v7 | | | 70.57 91 | 69.48 100 | 73.85 85 | 73.50 194 | 50.92 151 | 78.27 105 | 81.43 79 | 58.93 114 | 69.61 83 | 81.49 152 | 47.56 113 | 85.43 75 | 63.94 89 | 70.62 178 | 85.21 91 |
|
xiu_mvs_v2_base | | | 70.52 92 | 69.75 88 | 72.84 127 | 81.21 74 | 55.63 99 | 75.11 179 | 78.92 144 | 54.92 169 | 69.96 76 | 79.68 193 | 47.00 123 | 82.09 157 | 61.60 119 | 79.37 93 | 80.81 195 |
|
PS-MVSNAJ | | | 70.51 93 | 69.70 90 | 72.93 123 | 81.52 67 | 55.79 95 | 74.92 184 | 79.00 143 | 55.04 168 | 69.88 77 | 78.66 204 | 47.05 119 | 82.19 155 | 61.61 118 | 79.58 90 | 80.83 194 |
|
v1141 | | | 70.50 94 | 69.53 95 | 73.41 110 | 72.92 216 | 50.00 183 | 77.69 124 | 80.60 104 | 59.50 106 | 69.60 84 | 81.43 153 | 49.24 88 | 84.77 91 | 64.48 80 | 70.30 190 | 85.46 76 |
|
divwei89l23v2f112 | | | 70.50 94 | 69.53 95 | 73.41 110 | 72.91 217 | 50.00 183 | 77.69 124 | 80.59 105 | 59.50 106 | 69.60 84 | 81.43 153 | 49.26 83 | 84.77 91 | 64.48 80 | 70.31 189 | 85.47 74 |
|
v2v482 | | | 70.50 94 | 69.45 102 | 73.66 96 | 72.62 223 | 50.03 182 | 77.58 128 | 80.51 109 | 59.90 89 | 69.52 87 | 82.14 138 | 47.53 114 | 84.88 90 | 65.07 71 | 70.17 193 | 86.09 53 |
|
v1 | | | 70.50 94 | 69.53 95 | 73.42 109 | 72.91 217 | 50.00 183 | 77.69 124 | 80.59 105 | 59.50 106 | 69.59 86 | 81.42 155 | 49.26 83 | 84.77 91 | 64.49 79 | 70.30 190 | 85.47 74 |
|
mvs-test1 | | | 70.44 98 | 68.19 118 | 77.18 37 | 76.10 163 | 63.22 6 | 80.59 76 | 76.06 187 | 59.83 93 | 66.32 138 | 79.87 188 | 41.56 173 | 85.53 70 | 60.60 122 | 72.77 156 | 82.80 165 |
|
v1144 | | | 70.42 99 | 69.31 103 | 73.76 90 | 73.22 202 | 50.64 158 | 77.83 121 | 81.43 79 | 58.58 119 | 69.40 91 | 81.16 160 | 47.53 114 | 85.29 78 | 64.01 87 | 70.64 177 | 85.34 86 |
|
TranMVSNet+NR-MVSNet | | | 70.36 100 | 70.10 87 | 71.17 168 | 78.64 116 | 42.97 247 | 76.53 150 | 81.16 90 | 66.95 6 | 68.53 105 | 85.42 86 | 51.61 56 | 83.07 129 | 52.32 167 | 69.70 203 | 87.46 21 |
|
v8 | | | 70.33 101 | 69.28 104 | 73.49 104 | 73.15 206 | 50.22 173 | 78.62 98 | 80.78 99 | 60.79 69 | 66.45 136 | 82.11 139 | 49.35 74 | 84.98 81 | 63.58 94 | 68.71 211 | 85.28 88 |
|
Fast-Effi-MVS+ | | | 70.28 102 | 69.12 105 | 73.73 93 | 78.50 118 | 51.50 147 | 75.01 181 | 79.46 134 | 56.16 151 | 68.59 102 | 79.55 194 | 53.97 34 | 84.05 105 | 53.34 162 | 77.53 113 | 85.65 69 |
|
X-MVStestdata | | | 70.21 103 | 67.28 138 | 79.00 14 | 86.32 14 | 62.62 14 | 85.83 10 | 83.92 29 | 64.55 21 | 72.17 57 | 6.49 329 | 47.95 108 | 88.01 21 | 71.55 35 | 86.74 35 | 86.37 43 |
|
v10 | | | 70.21 103 | 69.02 106 | 73.81 87 | 73.51 193 | 50.92 151 | 78.74 96 | 81.39 81 | 60.05 85 | 66.39 137 | 81.83 141 | 47.58 112 | 85.41 76 | 62.80 99 | 68.86 210 | 85.09 94 |
|
QAPM | | | 70.05 105 | 68.81 108 | 73.78 88 | 76.54 160 | 53.43 120 | 83.23 34 | 83.48 41 | 52.89 187 | 65.90 143 | 86.29 68 | 41.55 175 | 86.49 52 | 51.01 175 | 78.40 108 | 81.42 183 |
|
DU-MVS | | | 70.01 106 | 69.53 95 | 71.44 159 | 78.05 131 | 44.13 236 | 75.01 181 | 81.51 77 | 64.37 26 | 68.20 109 | 84.52 97 | 49.12 94 | 82.82 138 | 54.62 152 | 70.43 181 | 87.37 24 |
|
AdaColmap | | | 69.99 107 | 68.66 111 | 73.97 84 | 84.94 39 | 57.83 63 | 82.63 45 | 78.71 148 | 56.28 149 | 64.34 164 | 84.14 103 | 41.57 172 | 87.06 37 | 46.45 205 | 78.88 101 | 77.02 231 |
|
v1192 | | | 69.97 108 | 68.68 110 | 73.85 85 | 73.19 205 | 50.94 149 | 77.68 127 | 81.36 82 | 57.51 131 | 68.95 100 | 80.85 168 | 45.28 140 | 85.33 77 | 62.97 98 | 70.37 186 | 85.27 89 |
|
FC-MVSNet-test | | | 69.80 109 | 70.58 81 | 67.46 209 | 77.61 146 | 34.73 292 | 76.05 162 | 83.19 52 | 60.84 68 | 65.88 144 | 86.46 66 | 54.52 30 | 80.76 182 | 52.52 166 | 78.12 109 | 86.91 33 |
|
v144192 | | | 69.71 110 | 68.51 112 | 73.33 114 | 73.10 211 | 50.13 180 | 77.54 130 | 80.64 103 | 56.65 140 | 68.57 104 | 80.55 175 | 46.87 124 | 84.96 83 | 62.98 97 | 69.66 204 | 84.89 100 |
|
VNet | | | 69.68 111 | 70.19 85 | 68.16 204 | 79.73 98 | 41.63 256 | 70.53 231 | 77.38 175 | 60.37 77 | 70.69 65 | 86.63 61 | 51.08 61 | 77.09 223 | 53.61 160 | 81.69 68 | 85.75 64 |
|
jason | | | 69.65 112 | 68.39 116 | 73.43 108 | 78.27 124 | 56.88 79 | 77.12 139 | 73.71 212 | 46.53 243 | 69.34 93 | 83.22 116 | 43.37 157 | 79.18 199 | 64.77 72 | 79.20 98 | 84.23 122 |
jason: jason. |
Effi-MVS+-dtu | | | 69.64 113 | 67.53 129 | 75.95 51 | 76.10 163 | 62.29 18 | 80.20 80 | 76.06 187 | 59.83 93 | 65.26 151 | 77.09 231 | 41.56 173 | 84.02 109 | 60.60 122 | 71.09 175 | 81.53 181 |
|
lupinMVS | | | 69.57 114 | 68.28 117 | 73.44 107 | 78.76 112 | 57.15 77 | 76.57 149 | 73.29 214 | 46.19 247 | 69.49 88 | 82.18 134 | 43.99 153 | 79.23 198 | 64.66 73 | 79.37 93 | 83.93 131 |
|
NR-MVSNet | | | 69.54 115 | 68.85 107 | 71.59 158 | 78.05 131 | 43.81 240 | 74.20 193 | 80.86 98 | 65.18 16 | 62.76 176 | 84.52 97 | 52.35 51 | 83.59 117 | 50.96 176 | 70.78 176 | 87.37 24 |
|
MVS_111021_LR | | | 69.50 116 | 68.78 109 | 71.65 156 | 78.38 121 | 59.33 47 | 74.82 186 | 70.11 228 | 58.08 127 | 67.83 120 | 84.68 92 | 41.96 165 | 76.34 231 | 65.62 67 | 77.54 112 | 79.30 212 |
|
v1921920 | | | 69.47 117 | 68.17 119 | 73.36 113 | 73.06 212 | 50.10 181 | 77.39 132 | 80.56 107 | 56.58 144 | 68.59 102 | 80.37 178 | 44.72 143 | 84.98 81 | 62.47 103 | 69.82 199 | 85.00 96 |
|
test_djsdf | | | 69.45 118 | 67.74 124 | 74.58 77 | 74.57 183 | 54.92 107 | 82.79 41 | 78.48 155 | 51.26 206 | 65.41 148 | 83.49 114 | 38.37 201 | 83.24 124 | 66.06 61 | 69.25 207 | 85.56 70 |
|
DI_MVS_plusplus_test | | | 69.35 119 | 68.03 121 | 73.30 116 | 71.11 244 | 50.14 179 | 75.49 170 | 79.16 140 | 54.57 174 | 62.45 184 | 80.76 171 | 44.67 145 | 84.20 102 | 64.23 83 | 79.81 87 | 85.54 71 |
|
EI-MVSNet | | | 69.27 120 | 68.44 115 | 71.73 155 | 74.47 184 | 49.39 195 | 75.20 177 | 78.45 157 | 59.60 102 | 69.16 98 | 76.51 240 | 51.29 58 | 82.50 150 | 59.86 128 | 71.45 172 | 83.30 151 |
|
test_normal | | | 69.26 121 | 67.90 123 | 73.32 115 | 70.84 247 | 50.38 168 | 75.30 173 | 79.17 139 | 54.23 178 | 62.00 188 | 80.61 174 | 44.69 144 | 83.89 112 | 64.33 82 | 79.95 86 | 85.69 66 |
|
v1240 | | | 69.24 122 | 67.91 122 | 73.25 119 | 73.02 214 | 49.82 186 | 77.21 137 | 80.54 108 | 56.43 146 | 68.34 108 | 80.51 176 | 43.33 158 | 84.99 79 | 62.03 113 | 69.77 202 | 84.95 99 |
|
IterMVS-LS | | | 69.22 123 | 68.48 113 | 71.43 161 | 74.44 186 | 49.40 194 | 76.23 157 | 77.55 171 | 59.60 102 | 65.85 145 | 81.59 150 | 51.28 59 | 81.58 166 | 59.87 127 | 69.90 198 | 83.30 151 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
VPA-MVSNet | | | 69.02 124 | 69.47 101 | 67.69 208 | 77.42 149 | 41.00 260 | 74.04 194 | 79.68 119 | 60.06 84 | 69.26 96 | 84.81 91 | 51.06 62 | 77.58 218 | 54.44 155 | 74.43 138 | 84.48 113 |
|
v7n | | | 69.01 125 | 67.36 135 | 73.98 83 | 72.51 225 | 52.65 130 | 78.54 101 | 81.30 84 | 60.26 82 | 62.67 178 | 81.62 147 | 43.61 155 | 84.49 98 | 57.01 139 | 68.70 212 | 84.79 104 |
|
OpenMVS | | 61.03 9 | 68.85 126 | 67.56 127 | 72.70 131 | 74.26 189 | 53.99 113 | 81.21 69 | 81.34 83 | 52.70 188 | 62.75 177 | 85.55 83 | 38.86 197 | 84.14 104 | 48.41 195 | 83.01 54 | 79.97 204 |
|
XVG-OURS-SEG-HR | | | 68.81 127 | 67.47 131 | 72.82 129 | 74.40 187 | 56.87 80 | 70.59 230 | 79.04 142 | 54.77 171 | 66.99 130 | 86.01 73 | 39.57 191 | 78.21 211 | 62.54 101 | 73.33 153 | 83.37 150 |
|
BH-RMVSNet | | | 68.81 127 | 67.42 132 | 72.97 122 | 80.11 92 | 52.53 133 | 74.26 192 | 76.29 183 | 58.48 122 | 68.38 107 | 84.20 102 | 42.59 160 | 83.83 113 | 46.53 204 | 75.91 129 | 82.56 166 |
|
UGNet | | | 68.81 127 | 67.39 133 | 73.06 121 | 78.33 122 | 54.47 110 | 79.77 85 | 75.40 193 | 60.45 75 | 63.22 171 | 84.40 100 | 32.71 249 | 80.91 177 | 51.71 172 | 80.56 76 | 83.81 136 |
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 |
XVG-OURS | | | 68.76 130 | 67.37 134 | 72.90 124 | 74.32 188 | 57.22 72 | 70.09 237 | 78.81 146 | 55.24 165 | 67.79 122 | 85.81 80 | 36.54 217 | 78.28 210 | 62.04 112 | 75.74 130 | 83.19 155 |
|
V42 | | | 68.65 131 | 67.35 136 | 72.56 135 | 68.93 263 | 50.18 177 | 72.90 209 | 79.47 133 | 56.92 136 | 69.45 90 | 80.26 184 | 46.29 128 | 82.99 130 | 64.07 85 | 67.82 217 | 84.53 111 |
|
PVSNet_Blended | | | 68.59 132 | 67.72 125 | 71.19 167 | 77.03 154 | 50.57 161 | 72.51 214 | 81.52 75 | 51.91 196 | 64.22 168 | 77.77 218 | 49.13 92 | 82.87 136 | 55.82 144 | 79.58 90 | 80.14 203 |
|
xiu_mvs_v1_base_debu | | | 68.58 133 | 67.28 138 | 72.48 138 | 78.19 126 | 57.19 74 | 75.28 174 | 75.09 198 | 51.61 198 | 70.04 72 | 81.41 156 | 32.79 245 | 79.02 203 | 63.81 90 | 77.31 115 | 81.22 187 |
|
xiu_mvs_v1_base | | | 68.58 133 | 67.28 138 | 72.48 138 | 78.19 126 | 57.19 74 | 75.28 174 | 75.09 198 | 51.61 198 | 70.04 72 | 81.41 156 | 32.79 245 | 79.02 203 | 63.81 90 | 77.31 115 | 81.22 187 |
|
xiu_mvs_v1_base_debi | | | 68.58 133 | 67.28 138 | 72.48 138 | 78.19 126 | 57.19 74 | 75.28 174 | 75.09 198 | 51.61 198 | 70.04 72 | 81.41 156 | 32.79 245 | 79.02 203 | 63.81 90 | 77.31 115 | 81.22 187 |
|
PVSNet_BlendedMVS | | | 68.56 136 | 67.72 125 | 71.07 171 | 77.03 154 | 50.57 161 | 74.50 190 | 81.52 75 | 53.66 182 | 64.22 168 | 79.72 192 | 49.13 92 | 82.87 136 | 55.82 144 | 73.92 144 | 79.77 207 |
|
1121 | | | 68.53 137 | 67.16 143 | 72.63 132 | 85.64 26 | 61.14 29 | 73.95 195 | 66.46 249 | 44.61 261 | 70.28 70 | 86.68 59 | 41.42 177 | 80.78 180 | 53.62 158 | 81.79 64 | 75.97 238 |
|
WR-MVS | | | 68.47 138 | 68.47 114 | 68.44 203 | 80.20 91 | 39.84 262 | 73.75 200 | 76.07 186 | 64.68 20 | 68.11 113 | 83.63 110 | 50.39 68 | 79.14 201 | 49.78 182 | 69.66 204 | 86.34 45 |
|
v16 | | | 68.38 139 | 67.01 144 | 72.47 142 | 73.22 202 | 50.29 170 | 78.10 113 | 79.59 126 | 59.71 97 | 61.72 193 | 77.60 223 | 49.28 81 | 82.89 134 | 62.36 105 | 61.54 259 | 84.23 122 |
|
v17 | | | 68.37 140 | 67.00 145 | 72.48 138 | 73.22 202 | 50.31 169 | 78.10 113 | 79.58 128 | 59.71 97 | 61.67 194 | 77.60 223 | 49.31 75 | 82.89 134 | 62.37 104 | 61.48 262 | 84.23 122 |
|
v18 | | | 68.33 141 | 66.96 146 | 72.42 144 | 73.13 210 | 50.16 178 | 77.97 118 | 79.57 130 | 59.57 105 | 61.80 191 | 77.50 228 | 49.30 76 | 82.90 133 | 62.31 106 | 61.50 260 | 84.20 128 |
|
v13 | | | 68.29 142 | 66.84 148 | 72.63 132 | 73.50 194 | 50.83 154 | 78.25 106 | 79.58 128 | 60.05 85 | 60.76 206 | 77.68 220 | 49.11 97 | 82.77 140 | 62.17 109 | 60.45 273 | 84.30 117 |
|
v12 | | | 68.28 143 | 66.83 150 | 72.60 134 | 73.43 196 | 50.74 156 | 78.18 108 | 79.59 126 | 60.01 87 | 60.89 205 | 77.66 221 | 49.12 94 | 82.77 140 | 62.18 107 | 60.46 272 | 84.29 118 |
|
V9 | | | 68.27 144 | 66.84 148 | 72.56 135 | 73.39 199 | 50.63 159 | 78.10 113 | 79.60 123 | 59.94 88 | 61.05 203 | 77.62 222 | 49.18 90 | 82.77 140 | 62.17 109 | 60.48 271 | 84.27 119 |
|
BH-untuned | | | 68.27 144 | 67.29 137 | 71.21 166 | 79.74 97 | 53.22 123 | 76.06 161 | 77.46 174 | 57.19 133 | 66.10 140 | 81.61 148 | 45.37 139 | 83.50 118 | 45.42 220 | 76.68 126 | 76.91 234 |
|
jajsoiax | | | 68.25 146 | 66.45 159 | 73.66 96 | 75.62 170 | 55.49 101 | 80.82 72 | 78.51 154 | 52.33 192 | 64.33 165 | 84.11 104 | 28.28 273 | 81.81 162 | 63.48 95 | 70.62 178 | 83.67 145 |
|
V14 | | | 68.25 146 | 66.82 151 | 72.52 137 | 73.33 200 | 50.53 164 | 78.02 116 | 79.60 123 | 59.83 93 | 61.16 201 | 77.57 225 | 49.19 89 | 82.77 140 | 62.18 107 | 60.50 270 | 84.26 120 |
|
v148 | | | 68.24 148 | 67.19 142 | 71.40 162 | 70.43 250 | 47.77 212 | 75.76 167 | 77.03 179 | 58.91 115 | 67.36 126 | 80.10 186 | 48.60 102 | 81.89 159 | 60.01 126 | 66.52 225 | 84.53 111 |
|
v15 | | | 68.22 149 | 66.81 152 | 72.47 142 | 73.25 201 | 50.40 167 | 77.92 120 | 79.60 123 | 59.77 96 | 61.28 199 | 77.52 227 | 49.25 85 | 82.77 140 | 62.16 111 | 60.51 269 | 84.24 121 |
|
mvs_tets | | | 68.18 150 | 66.36 163 | 73.63 99 | 75.61 171 | 55.35 104 | 80.77 73 | 78.56 152 | 52.48 191 | 64.27 167 | 84.10 105 | 27.45 279 | 81.84 161 | 63.45 96 | 70.56 180 | 83.69 142 |
|
v11 | | | 68.15 151 | 66.73 153 | 72.42 144 | 73.43 196 | 50.28 171 | 77.94 119 | 79.65 120 | 59.88 92 | 61.11 202 | 77.55 226 | 48.25 105 | 82.75 145 | 61.88 116 | 60.85 266 | 84.23 122 |
|
mvs_anonymous | | | 68.03 152 | 67.51 130 | 69.59 190 | 72.08 231 | 44.57 233 | 71.99 220 | 75.23 195 | 51.67 197 | 67.06 129 | 82.57 126 | 54.68 28 | 77.94 214 | 56.56 140 | 75.71 131 | 86.26 50 |
|
PAPM | | | 67.92 153 | 66.69 155 | 71.63 157 | 78.09 129 | 49.02 198 | 77.09 140 | 81.24 88 | 51.04 208 | 60.91 204 | 83.98 107 | 47.71 111 | 84.99 79 | 40.81 250 | 79.32 96 | 80.90 193 |
|
Test4 | | | 67.77 154 | 65.97 168 | 73.19 120 | 68.64 264 | 50.58 160 | 74.80 187 | 80.48 110 | 54.13 179 | 59.11 224 | 79.07 201 | 33.89 235 | 83.12 128 | 63.61 93 | 79.98 85 | 85.87 58 |
|
MVS_test0326 | | | 67.73 155 | 66.55 156 | 71.28 165 | 75.71 168 | 47.88 209 | 77.14 138 | 78.26 163 | 56.89 138 | 60.74 207 | 79.74 191 | 35.74 221 | 82.26 154 | 57.39 138 | 77.35 114 | 83.71 141 |
|
MVS_dtu | | | 67.73 155 | 66.31 166 | 72.00 150 | 77.04 153 | 48.70 201 | 76.70 147 | 74.99 202 | 57.07 134 | 61.42 198 | 81.29 159 | 34.24 230 | 82.66 147 | 57.56 135 | 76.47 127 | 84.65 107 |
|
diffmvs | | | 67.72 157 | 66.73 153 | 70.70 177 | 69.74 260 | 47.69 213 | 73.33 204 | 74.74 203 | 53.30 184 | 64.51 163 | 81.80 142 | 49.25 85 | 79.02 203 | 59.15 130 | 74.75 135 | 85.39 85 |
|
VPNet | | | 67.52 158 | 68.11 120 | 65.74 228 | 79.18 103 | 36.80 280 | 72.17 218 | 72.83 216 | 62.04 56 | 67.79 122 | 85.83 78 | 48.88 98 | 76.60 228 | 51.30 174 | 72.97 155 | 83.81 136 |
|
Fast-Effi-MVS+-dtu | | | 67.37 159 | 65.33 176 | 73.48 105 | 72.94 215 | 57.78 64 | 77.47 131 | 76.88 180 | 57.60 130 | 61.97 189 | 76.85 234 | 39.31 192 | 80.49 184 | 54.72 151 | 70.28 192 | 82.17 174 |
|
MVS | | | 67.37 159 | 66.33 164 | 70.51 179 | 75.46 173 | 50.94 149 | 73.95 195 | 81.85 71 | 41.57 283 | 62.54 180 | 78.57 208 | 47.98 107 | 85.47 73 | 52.97 164 | 82.05 63 | 75.14 248 |
|
v748 | | | 67.26 161 | 65.67 171 | 72.02 149 | 69.90 258 | 49.77 188 | 76.24 156 | 79.57 130 | 58.58 119 | 60.49 210 | 80.38 177 | 44.47 149 | 82.17 156 | 56.16 142 | 65.26 233 | 84.12 130 |
|
GBi-Net | | | 67.21 162 | 66.55 156 | 69.19 193 | 77.63 141 | 43.33 243 | 77.31 133 | 77.83 166 | 56.62 141 | 65.04 155 | 82.70 121 | 41.85 168 | 80.33 186 | 47.18 199 | 72.76 157 | 83.92 132 |
|
test1 | | | 67.21 162 | 66.55 156 | 69.19 193 | 77.63 141 | 43.33 243 | 77.31 133 | 77.83 166 | 56.62 141 | 65.04 155 | 82.70 121 | 41.85 168 | 80.33 186 | 47.18 199 | 72.76 157 | 83.92 132 |
|
MVSTER | | | 67.16 164 | 65.58 173 | 71.88 152 | 70.37 252 | 49.70 189 | 70.25 236 | 78.45 157 | 51.52 201 | 69.16 98 | 80.37 178 | 38.45 200 | 82.50 150 | 60.19 125 | 71.46 171 | 83.44 149 |
|
v52 | | | 67.09 165 | 65.16 179 | 72.87 125 | 66.77 278 | 51.60 145 | 73.69 201 | 79.45 135 | 57.88 128 | 62.46 183 | 78.57 208 | 40.95 183 | 83.34 120 | 61.99 114 | 64.70 238 | 83.68 143 |
|
V4 | | | 67.09 165 | 65.16 179 | 72.87 125 | 66.76 279 | 51.60 145 | 73.69 201 | 79.45 135 | 57.88 128 | 62.45 184 | 78.58 207 | 40.96 182 | 83.34 120 | 61.99 114 | 64.71 236 | 83.68 143 |
|
Baseline_NR-MVSNet | | | 67.05 167 | 67.56 127 | 65.50 230 | 75.65 169 | 37.70 276 | 75.42 171 | 74.65 205 | 59.90 89 | 68.14 112 | 83.15 119 | 49.12 94 | 77.20 221 | 52.23 168 | 69.78 200 | 81.60 180 |
|
WR-MVS_H | | | 67.02 168 | 66.92 147 | 67.33 212 | 77.95 134 | 37.75 275 | 77.57 129 | 82.11 66 | 62.03 57 | 62.65 179 | 82.48 128 | 50.57 66 | 79.46 194 | 42.91 238 | 64.01 242 | 84.79 104 |
|
anonymousdsp | | | 67.00 169 | 64.82 183 | 73.57 100 | 70.09 254 | 56.13 88 | 76.35 153 | 77.35 176 | 48.43 227 | 64.99 158 | 80.84 169 | 33.01 242 | 80.34 185 | 64.66 73 | 67.64 220 | 84.23 122 |
|
FMVSNet2 | | | 66.93 170 | 66.31 166 | 68.79 199 | 77.63 141 | 42.98 246 | 76.11 159 | 77.47 172 | 56.62 141 | 65.22 154 | 82.17 136 | 41.85 168 | 80.18 189 | 47.05 202 | 72.72 160 | 83.20 154 |
|
BH-w/o | | | 66.85 171 | 65.83 170 | 69.90 187 | 79.29 100 | 52.46 135 | 74.66 188 | 76.65 181 | 54.51 175 | 64.85 159 | 78.12 210 | 45.59 134 | 82.95 132 | 43.26 234 | 75.54 132 | 74.27 260 |
|
CDS-MVSNet | | | 66.80 172 | 65.37 174 | 71.10 170 | 78.98 107 | 53.13 126 | 73.27 205 | 71.07 223 | 52.15 194 | 64.72 160 | 80.23 185 | 43.56 156 | 77.10 222 | 45.48 218 | 78.88 101 | 83.05 160 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
TAMVS | | | 66.78 173 | 65.27 177 | 71.33 164 | 79.16 105 | 53.67 115 | 73.84 199 | 69.59 234 | 52.32 193 | 65.28 149 | 81.72 143 | 44.49 148 | 77.40 220 | 42.32 241 | 78.66 105 | 82.92 161 |
|
FMVSNet1 | | | 66.70 174 | 65.87 169 | 69.19 193 | 77.49 148 | 43.33 243 | 77.31 133 | 77.83 166 | 56.45 145 | 64.60 162 | 82.70 121 | 38.08 206 | 80.33 186 | 46.08 209 | 72.31 164 | 83.92 132 |
|
ab-mvs | | | 66.65 175 | 66.42 161 | 67.37 210 | 76.17 162 | 41.73 254 | 70.41 234 | 76.14 185 | 53.99 180 | 65.98 141 | 83.51 113 | 49.48 73 | 76.24 232 | 48.60 193 | 73.46 151 | 84.14 129 |
|
PEN-MVS | | | 66.60 176 | 66.45 159 | 67.04 213 | 77.11 151 | 36.56 282 | 77.03 142 | 80.42 111 | 62.95 38 | 62.51 182 | 84.03 106 | 46.69 125 | 79.07 202 | 44.22 224 | 63.08 250 | 85.51 73 |
|
TAPA-MVS | | 59.36 10 | 66.60 176 | 65.20 178 | 70.81 173 | 76.63 157 | 48.75 200 | 76.52 151 | 80.04 115 | 50.64 210 | 65.24 152 | 84.93 89 | 39.15 194 | 78.54 207 | 36.77 267 | 76.88 121 | 85.14 92 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
TR-MVS | | | 66.59 178 | 65.07 181 | 71.17 168 | 79.18 103 | 49.63 193 | 73.48 203 | 75.20 196 | 52.95 186 | 67.90 115 | 80.33 181 | 39.81 188 | 83.68 115 | 43.20 235 | 73.56 150 | 80.20 201 |
|
CP-MVSNet | | | 66.49 179 | 66.41 162 | 66.72 215 | 77.67 140 | 36.33 284 | 76.83 146 | 79.52 132 | 62.45 48 | 62.54 180 | 83.47 115 | 46.32 127 | 78.37 208 | 45.47 219 | 63.43 247 | 85.45 77 |
|
PS-CasMVS | | | 66.42 180 | 66.32 165 | 66.70 216 | 77.60 147 | 36.30 286 | 76.94 144 | 79.61 121 | 62.36 50 | 62.43 186 | 83.66 109 | 45.69 131 | 78.37 208 | 45.35 221 | 63.26 248 | 85.42 80 |
|
FMVSNet3 | | | 66.32 181 | 65.61 172 | 68.46 202 | 76.48 161 | 42.34 250 | 74.98 183 | 77.15 178 | 55.83 156 | 65.04 155 | 81.16 160 | 39.91 186 | 80.14 190 | 47.18 199 | 72.76 157 | 82.90 163 |
|
ACMH+ | | 57.40 11 | 66.12 182 | 64.06 185 | 72.30 148 | 77.79 137 | 52.83 128 | 80.39 77 | 78.03 164 | 57.30 132 | 57.47 238 | 82.55 127 | 27.68 277 | 84.17 103 | 45.54 216 | 69.78 200 | 79.90 205 |
|
testing_2 | | | 66.02 183 | 63.77 190 | 72.76 130 | 66.03 284 | 50.48 166 | 72.93 208 | 80.36 113 | 54.41 176 | 54.25 263 | 76.76 236 | 30.89 258 | 83.16 127 | 64.19 84 | 74.08 142 | 84.65 107 |
|
cascas | | | 65.98 184 | 63.42 194 | 73.64 98 | 77.26 150 | 52.58 132 | 72.26 217 | 77.21 177 | 48.56 224 | 61.21 200 | 74.60 256 | 32.57 253 | 85.82 66 | 50.38 179 | 76.75 125 | 82.52 168 |
|
DP-MVS | | | 65.68 185 | 63.66 192 | 71.75 154 | 84.93 40 | 56.87 80 | 80.74 74 | 73.16 215 | 53.06 185 | 59.09 225 | 82.35 130 | 36.79 215 | 85.94 64 | 32.82 284 | 69.96 197 | 72.45 273 |
|
HyFIR lowres test | | | 65.67 186 | 63.01 198 | 73.67 95 | 79.97 94 | 55.65 98 | 69.07 243 | 75.52 191 | 42.68 277 | 63.53 170 | 77.95 212 | 40.43 185 | 81.64 163 | 46.01 210 | 71.91 167 | 83.73 140 |
|
DTE-MVSNet | | | 65.58 187 | 65.34 175 | 66.31 218 | 76.06 165 | 34.79 290 | 76.43 152 | 79.38 137 | 62.55 47 | 61.66 195 | 83.83 108 | 45.60 133 | 79.15 200 | 41.64 248 | 60.88 265 | 85.00 96 |
|
GA-MVS | | | 65.53 188 | 63.70 191 | 71.02 172 | 70.87 246 | 48.10 206 | 70.48 232 | 74.40 207 | 56.69 139 | 64.70 161 | 76.77 235 | 33.66 237 | 81.10 173 | 55.42 149 | 70.32 188 | 83.87 135 |
|
CNLPA | | | 65.43 189 | 64.02 186 | 69.68 188 | 78.73 114 | 58.07 61 | 77.82 122 | 70.71 225 | 51.49 202 | 61.57 197 | 83.58 112 | 38.23 204 | 70.82 249 | 43.90 228 | 70.10 195 | 80.16 202 |
|
MVP-Stereo | | | 65.41 190 | 63.80 189 | 70.22 181 | 77.62 145 | 55.53 100 | 76.30 154 | 78.53 153 | 50.59 211 | 56.47 244 | 78.65 205 | 39.84 187 | 82.68 146 | 44.10 227 | 72.12 166 | 72.44 274 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
IB-MVS | | 56.42 12 | 65.40 191 | 62.73 202 | 73.40 112 | 74.89 177 | 52.78 129 | 73.09 207 | 75.13 197 | 55.69 159 | 58.48 233 | 73.73 261 | 32.86 244 | 86.32 56 | 50.63 177 | 70.11 194 | 81.10 190 |
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 |
pm-mvs1 | | | 65.24 192 | 64.97 182 | 66.04 223 | 72.38 226 | 39.40 265 | 72.62 212 | 75.63 190 | 55.53 161 | 62.35 187 | 83.18 118 | 47.45 116 | 76.47 229 | 49.06 190 | 66.54 224 | 82.24 172 |
|
ACMH | | 55.70 15 | 65.20 193 | 63.57 193 | 70.07 184 | 78.07 130 | 52.01 143 | 79.48 93 | 79.69 118 | 55.75 158 | 56.59 243 | 80.98 164 | 27.12 281 | 80.94 175 | 42.90 239 | 71.58 170 | 77.25 230 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
PLC | | 56.13 14 | 65.09 194 | 63.21 196 | 70.72 176 | 81.04 78 | 54.87 108 | 78.57 99 | 77.47 172 | 48.51 225 | 55.71 247 | 81.89 140 | 33.71 236 | 79.71 191 | 41.66 246 | 70.37 186 | 77.58 223 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
CHOSEN 1792x2688 | | | 65.08 195 | 62.84 200 | 71.82 153 | 81.49 69 | 56.26 86 | 66.32 252 | 74.20 209 | 40.53 289 | 63.16 173 | 78.65 205 | 41.30 178 | 77.80 216 | 45.80 212 | 74.09 141 | 81.40 184 |
|
TransMVSNet (Re) | | | 64.72 196 | 64.33 184 | 65.87 227 | 75.22 175 | 38.56 269 | 74.66 188 | 75.08 201 | 58.90 116 | 61.79 192 | 82.63 124 | 51.18 60 | 78.07 213 | 43.63 231 | 55.87 284 | 80.99 192 |
|
EG-PatchMatch MVS | | | 64.71 197 | 62.87 199 | 70.22 181 | 77.68 139 | 53.48 119 | 77.99 117 | 78.82 145 | 53.37 183 | 56.03 246 | 77.41 230 | 24.75 295 | 84.04 106 | 46.37 206 | 73.42 152 | 73.14 266 |
|
LS3D | | | 64.71 197 | 62.50 204 | 71.34 163 | 79.72 99 | 55.71 96 | 79.82 84 | 74.72 204 | 48.50 226 | 56.62 242 | 84.62 94 | 33.59 238 | 82.34 153 | 29.65 296 | 75.23 133 | 75.97 238 |
|
1314 | | | 64.61 199 | 63.21 196 | 68.80 198 | 71.87 236 | 47.46 215 | 73.95 195 | 78.39 161 | 42.88 276 | 59.97 212 | 76.60 239 | 38.11 205 | 79.39 196 | 54.84 150 | 72.32 163 | 79.55 208 |
|
HY-MVS | | 56.14 13 | 64.55 200 | 63.89 187 | 66.55 217 | 74.73 180 | 41.02 258 | 69.96 238 | 74.43 206 | 49.29 218 | 61.66 195 | 80.92 165 | 47.43 117 | 76.68 227 | 44.91 223 | 71.69 169 | 81.94 176 |
|
XVG-ACMP-BASELINE | | | 64.36 201 | 62.23 205 | 70.74 175 | 72.35 227 | 52.45 136 | 70.80 229 | 78.45 157 | 53.84 181 | 59.87 214 | 81.10 162 | 16.24 308 | 79.32 197 | 55.64 148 | 71.76 168 | 80.47 197 |
|
CostFormer | | | 64.04 202 | 62.51 203 | 68.61 201 | 71.88 235 | 45.77 224 | 71.30 223 | 70.60 226 | 47.55 236 | 64.31 166 | 76.61 238 | 41.63 171 | 79.62 193 | 49.74 184 | 69.00 208 | 80.42 198 |
|
1112_ss | | | 64.00 203 | 63.36 195 | 65.93 226 | 79.28 101 | 42.58 249 | 71.35 222 | 72.36 219 | 46.41 245 | 60.55 209 | 77.89 215 | 46.27 129 | 73.28 241 | 46.18 207 | 69.97 196 | 81.92 177 |
|
pmmvs6 | | | 63.69 204 | 62.82 201 | 66.27 220 | 70.63 248 | 39.27 266 | 73.13 206 | 75.47 192 | 52.69 189 | 59.75 217 | 82.30 132 | 39.71 189 | 77.03 224 | 47.40 198 | 64.35 241 | 82.53 167 |
|
Vis-MVSNet (Re-imp) | | | 63.69 204 | 63.88 188 | 63.14 243 | 74.75 179 | 31.04 306 | 71.16 225 | 63.64 263 | 56.32 147 | 59.80 216 | 84.99 88 | 44.51 146 | 75.46 234 | 39.12 257 | 80.62 72 | 82.92 161 |
|
test_0402 | | | 63.25 206 | 61.01 213 | 69.96 185 | 80.00 93 | 54.37 111 | 76.86 145 | 72.02 220 | 54.58 173 | 58.71 228 | 80.79 170 | 35.00 223 | 84.36 100 | 26.41 305 | 64.71 236 | 71.15 285 |
|
LTVRE_ROB | | 55.42 16 | 63.15 207 | 61.23 212 | 68.92 197 | 76.57 159 | 47.80 210 | 59.92 281 | 76.39 182 | 54.35 177 | 58.67 229 | 82.46 129 | 29.44 268 | 81.49 167 | 42.12 242 | 71.14 173 | 77.46 224 |
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 |
F-COLMAP | | | 63.05 208 | 60.87 214 | 69.58 191 | 76.99 156 | 53.63 117 | 78.12 109 | 76.16 184 | 47.97 233 | 52.41 273 | 81.61 148 | 27.87 275 | 78.11 212 | 40.07 253 | 66.66 223 | 77.00 232 |
|
IterMVS | | | 62.79 209 | 61.27 211 | 67.35 211 | 69.37 261 | 52.04 142 | 71.17 224 | 68.24 241 | 52.63 190 | 59.82 215 | 76.91 233 | 37.32 211 | 72.36 244 | 52.80 165 | 63.19 249 | 77.66 222 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
tpmp4_e23 | | | 62.71 210 | 60.13 217 | 70.45 180 | 73.40 198 | 48.39 204 | 72.82 210 | 69.49 236 | 44.88 257 | 59.91 213 | 74.99 252 | 37.79 208 | 81.47 168 | 40.22 252 | 67.71 219 | 81.48 182 |
|
MS-PatchMatch | | | 62.42 211 | 61.46 209 | 65.31 232 | 75.21 176 | 52.10 139 | 72.05 219 | 74.05 210 | 46.41 245 | 57.42 239 | 74.36 257 | 34.35 229 | 77.57 219 | 45.62 215 | 73.67 146 | 66.26 296 |
|
Test_1112_low_res | | | 62.32 212 | 61.77 207 | 64.00 239 | 79.08 106 | 39.53 264 | 68.17 245 | 70.17 227 | 43.25 272 | 59.03 226 | 79.90 187 | 44.08 151 | 71.24 248 | 43.79 230 | 68.42 213 | 81.25 186 |
|
PatchFormer-LS_test | | | 62.20 213 | 60.59 215 | 67.04 213 | 72.18 230 | 46.82 220 | 70.36 235 | 68.62 239 | 51.92 195 | 59.19 223 | 70.23 279 | 36.86 214 | 75.07 236 | 50.23 181 | 65.68 230 | 79.23 213 |
|
tpm2 | | | 62.07 214 | 60.10 218 | 67.99 205 | 72.79 220 | 43.86 239 | 71.05 226 | 66.85 247 | 43.14 274 | 62.77 175 | 75.39 250 | 38.32 202 | 80.80 179 | 41.69 245 | 68.88 209 | 79.32 211 |
|
DWT-MVSNet_test | | | 61.90 215 | 59.93 219 | 67.83 206 | 71.98 234 | 46.09 223 | 71.03 227 | 69.71 230 | 50.09 213 | 58.51 232 | 70.62 276 | 30.21 263 | 77.63 217 | 49.28 188 | 67.91 215 | 79.78 206 |
|
EPNet_dtu | | | 61.90 215 | 61.97 206 | 61.68 251 | 72.89 219 | 39.78 263 | 75.85 166 | 65.62 252 | 55.09 166 | 54.56 259 | 79.36 196 | 37.59 209 | 67.02 264 | 39.80 256 | 76.95 120 | 78.25 218 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
LCM-MVSNet-Re | | | 61.88 217 | 61.35 210 | 63.46 240 | 74.58 182 | 31.48 305 | 61.42 275 | 58.14 286 | 58.71 118 | 53.02 272 | 79.55 194 | 43.07 159 | 76.80 225 | 45.69 213 | 77.96 110 | 82.11 175 |
|
MSDG | | | 61.81 218 | 59.23 221 | 69.55 192 | 72.64 222 | 52.63 131 | 70.45 233 | 75.81 189 | 51.38 204 | 53.70 266 | 76.11 243 | 29.52 266 | 81.08 174 | 37.70 262 | 65.79 229 | 74.93 253 |
|
SixPastTwentyTwo | | | 61.65 219 | 58.80 224 | 70.20 183 | 75.80 167 | 47.22 217 | 75.59 168 | 69.68 232 | 54.61 172 | 54.11 264 | 79.26 198 | 27.07 282 | 82.96 131 | 43.27 233 | 49.79 302 | 80.41 199 |
|
pmmvs4 | | | 61.48 220 | 59.39 220 | 67.76 207 | 71.57 238 | 53.86 114 | 71.42 221 | 65.34 253 | 44.20 265 | 59.46 218 | 77.92 214 | 35.90 218 | 74.71 238 | 43.87 229 | 64.87 235 | 74.71 257 |
|
OurMVSNet-221017-0 | | | 61.37 221 | 58.63 226 | 69.61 189 | 72.05 232 | 48.06 207 | 73.93 198 | 72.51 217 | 47.23 239 | 54.74 256 | 80.92 165 | 21.49 302 | 81.24 170 | 48.57 194 | 56.22 283 | 79.53 209 |
|
COLMAP_ROB | | 52.97 17 | 61.27 222 | 58.81 223 | 68.64 200 | 74.63 181 | 52.51 134 | 78.42 104 | 73.30 213 | 49.92 216 | 50.96 278 | 81.51 151 | 23.06 298 | 79.40 195 | 31.63 289 | 65.85 227 | 74.01 263 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
XXY-MVS | | | 60.68 223 | 61.67 208 | 57.70 269 | 70.43 250 | 38.45 270 | 64.19 265 | 66.47 248 | 48.05 232 | 63.22 171 | 80.86 167 | 49.28 81 | 60.47 286 | 45.25 222 | 67.28 222 | 74.19 261 |
|
K. test v3 | | | 60.47 224 | 57.11 234 | 70.56 178 | 73.74 192 | 48.22 205 | 75.10 180 | 62.55 271 | 58.27 126 | 53.62 268 | 76.31 242 | 27.81 276 | 81.59 165 | 47.42 197 | 39.18 314 | 81.88 178 |
|
OpenMVS_ROB | | 52.78 18 | 60.03 225 | 58.14 229 | 65.69 229 | 70.47 249 | 44.82 228 | 75.33 172 | 70.86 224 | 45.04 256 | 56.06 245 | 76.00 244 | 26.89 284 | 79.65 192 | 35.36 276 | 67.29 221 | 72.60 270 |
|
CR-MVSNet | | | 59.91 226 | 57.90 232 | 65.96 224 | 69.96 256 | 52.07 140 | 65.31 259 | 63.15 267 | 42.48 278 | 59.36 219 | 74.84 253 | 35.83 219 | 70.75 250 | 45.50 217 | 64.65 239 | 75.06 249 |
|
PatchmatchNet | | | 59.84 227 | 58.24 227 | 64.65 236 | 73.05 213 | 46.70 221 | 69.42 241 | 62.18 273 | 47.55 236 | 58.88 227 | 71.96 269 | 34.49 227 | 69.16 256 | 42.99 237 | 63.60 245 | 78.07 220 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
Patchmatch-test1 | | | 59.75 228 | 58.00 231 | 64.98 234 | 74.14 191 | 48.06 207 | 63.35 267 | 63.23 266 | 49.13 220 | 59.33 222 | 71.46 271 | 37.45 210 | 69.59 254 | 41.39 249 | 62.57 253 | 77.30 226 |
|
WTY-MVS | | | 59.75 228 | 60.39 216 | 57.85 267 | 72.32 228 | 37.83 274 | 61.05 279 | 64.18 261 | 45.95 252 | 61.91 190 | 79.11 200 | 47.01 122 | 60.88 285 | 42.50 240 | 69.49 206 | 74.83 254 |
|
CVMVSNet | | | 59.63 230 | 59.14 222 | 61.08 256 | 74.47 184 | 38.84 267 | 75.20 177 | 68.74 238 | 31.15 311 | 58.24 234 | 76.51 240 | 32.39 254 | 68.58 259 | 49.77 183 | 65.84 228 | 75.81 242 |
|
tpm cat1 | | | 59.25 231 | 56.95 237 | 66.15 221 | 72.19 229 | 46.96 218 | 68.09 246 | 65.76 251 | 40.03 292 | 57.81 236 | 70.56 277 | 38.32 202 | 74.51 239 | 38.26 260 | 61.50 260 | 77.00 232 |
|
pmmvs-eth3d | | | 58.81 232 | 56.31 242 | 66.30 219 | 67.61 272 | 52.42 137 | 72.30 216 | 64.76 257 | 43.55 270 | 54.94 255 | 74.19 259 | 28.95 270 | 72.60 243 | 43.31 232 | 57.21 281 | 73.88 264 |
|
RPMNet | | | 58.70 233 | 56.29 243 | 65.96 224 | 69.96 256 | 52.07 140 | 65.31 259 | 62.15 274 | 43.20 273 | 59.36 219 | 70.15 281 | 35.37 222 | 70.75 250 | 36.42 273 | 64.65 239 | 75.06 249 |
|
tpmvs | | | 58.47 234 | 56.95 237 | 63.03 245 | 70.20 253 | 41.21 257 | 67.90 247 | 67.23 245 | 49.62 217 | 54.73 257 | 70.84 275 | 34.14 231 | 76.24 232 | 36.64 270 | 61.29 263 | 71.64 281 |
|
PVSNet | | 50.76 19 | 58.40 235 | 57.39 233 | 61.42 253 | 75.53 172 | 44.04 238 | 61.43 274 | 63.45 264 | 47.04 241 | 56.91 240 | 73.61 262 | 27.00 283 | 64.76 273 | 39.12 257 | 72.40 161 | 75.47 246 |
|
tpmrst | | | 58.24 236 | 58.70 225 | 56.84 271 | 66.97 275 | 34.32 294 | 69.57 240 | 61.14 277 | 47.17 240 | 58.58 231 | 71.60 270 | 41.28 180 | 60.41 287 | 49.20 189 | 62.84 251 | 75.78 243 |
|
Patchmatch-RL test | | | 58.16 237 | 55.49 247 | 66.15 221 | 67.92 271 | 48.89 199 | 60.66 280 | 51.07 311 | 47.86 234 | 59.36 219 | 62.71 304 | 34.02 233 | 72.27 245 | 56.41 141 | 59.40 276 | 77.30 226 |
|
test-LLR | | | 58.15 238 | 58.13 230 | 58.22 264 | 68.57 265 | 44.80 229 | 65.46 256 | 57.92 287 | 50.08 214 | 55.44 250 | 69.82 282 | 32.62 250 | 57.44 296 | 49.66 186 | 73.62 147 | 72.41 275 |
|
gg-mvs-nofinetune | | | 57.86 239 | 56.43 241 | 62.18 249 | 72.62 223 | 35.35 289 | 66.57 249 | 56.33 295 | 50.65 209 | 57.64 237 | 57.10 311 | 30.65 259 | 76.36 230 | 37.38 264 | 78.88 101 | 74.82 255 |
|
CMPMVS | | 42.80 21 | 57.81 240 | 55.97 244 | 63.32 241 | 60.98 304 | 47.38 216 | 64.66 263 | 69.50 235 | 32.06 310 | 46.83 291 | 77.80 217 | 29.50 267 | 71.36 247 | 48.68 192 | 73.75 145 | 71.21 284 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
MIMVSNet | | | 57.35 241 | 57.07 235 | 58.22 264 | 74.21 190 | 37.18 277 | 62.46 270 | 60.88 278 | 48.88 222 | 55.29 253 | 75.99 246 | 31.68 256 | 62.04 282 | 31.87 287 | 72.35 162 | 75.43 247 |
|
tpm | | | 57.34 242 | 58.16 228 | 54.86 279 | 71.80 237 | 34.77 291 | 67.47 248 | 56.04 298 | 48.20 230 | 60.10 211 | 76.92 232 | 37.17 212 | 53.41 313 | 40.76 251 | 65.01 234 | 76.40 237 |
|
Patchmtry | | | 57.16 243 | 56.47 240 | 59.23 258 | 69.17 262 | 34.58 293 | 62.98 268 | 63.15 267 | 44.53 262 | 56.83 241 | 74.84 253 | 35.83 219 | 68.71 258 | 40.03 254 | 60.91 264 | 74.39 259 |
|
AllTest | | | 57.08 244 | 54.65 251 | 64.39 237 | 71.44 239 | 49.03 196 | 69.92 239 | 67.30 243 | 45.97 250 | 47.16 289 | 79.77 189 | 17.47 305 | 67.56 261 | 33.65 281 | 59.16 277 | 76.57 235 |
|
pmmvs5 | | | 56.47 245 | 55.68 246 | 58.86 261 | 61.41 301 | 36.71 281 | 66.37 251 | 62.75 270 | 40.38 290 | 53.70 266 | 76.62 237 | 34.56 225 | 67.05 263 | 40.02 255 | 65.27 232 | 72.83 268 |
|
test-mter | | | 56.42 246 | 55.82 245 | 58.22 264 | 68.57 265 | 44.80 229 | 65.46 256 | 57.92 287 | 39.94 293 | 55.44 250 | 69.82 282 | 21.92 301 | 57.44 296 | 49.66 186 | 73.62 147 | 72.41 275 |
|
USDC | | | 56.35 247 | 54.24 255 | 62.69 247 | 64.74 289 | 40.31 261 | 65.05 261 | 73.83 211 | 43.93 268 | 47.58 287 | 77.71 219 | 15.36 310 | 75.05 237 | 38.19 261 | 61.81 257 | 72.70 269 |
|
PatchMatch-RL | | | 56.25 248 | 54.55 252 | 61.32 255 | 77.06 152 | 56.07 90 | 65.57 255 | 54.10 307 | 44.13 267 | 53.49 271 | 71.27 274 | 25.20 292 | 66.78 265 | 36.52 272 | 63.66 244 | 61.12 305 |
|
sss | | | 56.17 249 | 56.57 239 | 54.96 278 | 66.93 276 | 36.32 285 | 57.94 287 | 61.69 276 | 41.67 281 | 58.64 230 | 75.32 251 | 38.72 198 | 56.25 305 | 42.04 243 | 66.19 226 | 72.31 278 |
|
Anonymous20231211 | | | 55.92 250 | 53.63 259 | 62.77 246 | 68.22 270 | 35.56 288 | 74.48 191 | 69.89 229 | 46.42 244 | 49.07 285 | 73.45 263 | 21.13 303 | 76.77 226 | 28.74 298 | 51.30 298 | 75.97 238 |
|
FMVSNet5 | | | 55.86 251 | 54.93 249 | 58.66 263 | 71.05 245 | 36.35 283 | 64.18 266 | 62.48 272 | 46.76 242 | 50.66 281 | 74.73 255 | 25.80 289 | 64.04 275 | 33.11 283 | 65.57 231 | 75.59 245 |
|
RPSCF | | | 55.80 252 | 54.22 256 | 60.53 257 | 65.13 288 | 42.91 248 | 64.30 264 | 57.62 289 | 36.84 302 | 58.05 235 | 82.28 133 | 28.01 274 | 56.24 306 | 37.14 265 | 58.61 279 | 82.44 171 |
|
EU-MVSNet | | | 55.61 253 | 54.41 253 | 59.19 259 | 65.41 287 | 33.42 298 | 72.44 215 | 71.91 221 | 28.81 313 | 51.27 276 | 73.87 260 | 24.76 294 | 69.08 257 | 43.04 236 | 58.20 280 | 75.06 249 |
|
TESTMET0.1,1 | | | 55.28 254 | 54.90 250 | 56.42 272 | 66.56 280 | 43.67 241 | 65.46 256 | 56.27 296 | 39.18 295 | 53.83 265 | 67.44 289 | 24.21 296 | 55.46 310 | 48.04 196 | 73.11 154 | 70.13 288 |
|
MIMVSNet1 | | | 55.17 255 | 54.31 254 | 57.77 268 | 70.03 255 | 32.01 303 | 65.68 254 | 64.81 256 | 49.19 219 | 46.75 292 | 76.00 244 | 25.53 291 | 64.04 275 | 28.65 299 | 62.13 256 | 77.26 229 |
|
Anonymous20231206 | | | 55.10 256 | 55.30 248 | 54.48 281 | 69.81 259 | 33.94 296 | 62.91 269 | 62.13 275 | 41.08 284 | 55.18 254 | 75.65 248 | 32.75 248 | 56.59 302 | 30.32 294 | 67.86 216 | 72.91 267 |
|
TinyColmap | | | 54.14 257 | 51.72 265 | 61.40 254 | 66.84 277 | 41.97 251 | 66.52 250 | 68.51 240 | 44.81 258 | 42.69 306 | 75.77 247 | 11.66 318 | 72.94 242 | 31.96 286 | 56.77 282 | 69.27 292 |
|
EPMVS | | | 53.96 258 | 53.69 258 | 54.79 280 | 66.12 283 | 31.96 304 | 62.34 272 | 49.05 314 | 44.42 264 | 55.54 248 | 71.33 273 | 30.22 262 | 56.70 300 | 41.65 247 | 62.54 254 | 75.71 244 |
|
PMMVS | | | 53.96 258 | 53.26 262 | 56.04 273 | 62.60 297 | 50.92 151 | 61.17 278 | 56.09 297 | 32.81 308 | 53.51 270 | 66.84 291 | 34.04 232 | 59.93 289 | 44.14 226 | 68.18 214 | 57.27 311 |
|
test20.03 | | | 53.87 260 | 54.02 257 | 53.41 285 | 61.47 300 | 28.11 312 | 61.30 276 | 59.21 282 | 51.34 205 | 52.09 274 | 77.43 229 | 33.29 241 | 58.55 293 | 29.76 295 | 60.27 274 | 73.58 265 |
|
MDA-MVSNet-bldmvs | | | 53.87 260 | 50.81 267 | 63.05 244 | 66.25 281 | 48.58 202 | 56.93 290 | 63.82 262 | 48.09 231 | 41.22 307 | 70.48 278 | 30.34 261 | 68.00 260 | 34.24 279 | 45.92 309 | 72.57 271 |
|
TDRefinement | | | 53.44 262 | 50.72 268 | 61.60 252 | 64.31 292 | 46.96 218 | 70.89 228 | 65.27 255 | 41.78 279 | 44.61 298 | 77.98 211 | 11.52 319 | 66.36 267 | 28.57 300 | 51.59 296 | 71.49 282 |
|
test0.0.03 1 | | | 53.32 263 | 53.59 260 | 52.50 289 | 62.81 296 | 29.45 309 | 59.51 282 | 54.11 306 | 50.08 214 | 54.40 261 | 74.31 258 | 32.62 250 | 55.92 307 | 30.50 293 | 63.95 243 | 72.15 280 |
|
PatchT | | | 53.17 264 | 53.44 261 | 52.33 290 | 68.29 269 | 25.34 320 | 58.21 286 | 54.41 304 | 44.46 263 | 54.56 259 | 69.05 284 | 33.32 240 | 60.94 284 | 36.93 266 | 61.76 258 | 70.73 287 |
|
UnsupCasMVSNet_eth | | | 53.16 265 | 52.47 263 | 55.23 276 | 59.45 311 | 33.39 299 | 59.43 283 | 69.13 237 | 45.98 249 | 50.35 283 | 72.32 267 | 29.30 269 | 58.26 294 | 42.02 244 | 44.30 311 | 74.05 262 |
|
PM-MVS | | | 52.33 266 | 50.19 269 | 58.75 262 | 62.10 298 | 45.14 227 | 65.75 253 | 40.38 324 | 43.60 269 | 53.52 269 | 72.65 265 | 9.16 324 | 65.87 271 | 50.41 178 | 54.18 290 | 65.24 298 |
|
testgi | | | 51.90 267 | 52.37 264 | 50.51 294 | 60.39 307 | 23.55 323 | 58.42 285 | 58.15 285 | 49.03 221 | 51.83 275 | 79.21 199 | 22.39 299 | 55.59 308 | 29.24 297 | 62.64 252 | 72.40 277 |
|
dp | | | 51.89 268 | 51.60 266 | 52.77 288 | 68.44 268 | 32.45 301 | 62.36 271 | 54.57 303 | 44.16 266 | 49.31 284 | 67.91 286 | 28.87 272 | 56.61 301 | 33.89 280 | 54.89 287 | 69.24 293 |
|
JIA-IIPM | | | 51.56 269 | 47.68 277 | 63.21 242 | 64.61 290 | 50.73 157 | 47.71 310 | 58.77 284 | 42.90 275 | 48.46 286 | 51.72 315 | 24.97 293 | 70.24 253 | 36.06 275 | 53.89 291 | 68.64 294 |
|
ADS-MVSNet2 | | | 51.33 270 | 48.76 273 | 59.07 260 | 66.02 285 | 44.60 232 | 50.90 305 | 59.76 281 | 36.90 300 | 50.74 279 | 66.18 295 | 26.38 285 | 63.11 277 | 27.17 301 | 54.76 288 | 69.50 290 |
|
YYNet1 | | | 50.73 271 | 48.96 270 | 56.03 274 | 61.10 303 | 41.78 253 | 51.94 303 | 56.44 294 | 40.94 286 | 44.84 296 | 67.80 288 | 30.08 264 | 55.08 311 | 36.77 267 | 50.71 299 | 71.22 283 |
|
MDA-MVSNet_test_wron | | | 50.71 272 | 48.95 271 | 56.00 275 | 61.17 302 | 41.84 252 | 51.90 304 | 56.45 293 | 40.96 285 | 44.79 297 | 67.84 287 | 30.04 265 | 55.07 312 | 36.71 269 | 50.69 300 | 71.11 286 |
|
UnsupCasMVSNet_bld | | | 50.07 273 | 48.87 272 | 53.66 283 | 60.97 305 | 33.67 297 | 57.62 288 | 64.56 259 | 39.47 294 | 47.38 288 | 64.02 300 | 27.47 278 | 59.32 290 | 34.69 278 | 43.68 312 | 67.98 295 |
|
Patchmatch-test | | | 49.08 274 | 48.28 274 | 51.50 292 | 64.40 291 | 30.85 307 | 45.68 313 | 48.46 317 | 35.60 304 | 46.10 295 | 72.10 268 | 34.47 228 | 46.37 319 | 27.08 303 | 60.65 268 | 77.27 228 |
|
LP | | | 48.51 275 | 45.51 280 | 57.52 270 | 62.86 295 | 44.53 235 | 52.38 302 | 59.84 280 | 38.11 297 | 42.81 305 | 61.02 305 | 23.23 297 | 63.02 278 | 24.10 308 | 45.24 310 | 65.02 299 |
|
ADS-MVSNet | | | 48.48 276 | 47.77 275 | 50.63 293 | 66.02 285 | 29.92 308 | 50.90 305 | 50.87 313 | 36.90 300 | 50.74 279 | 66.18 295 | 26.38 285 | 52.47 315 | 27.17 301 | 54.76 288 | 69.50 290 |
|
CHOSEN 280x420 | | | 47.83 277 | 46.36 278 | 52.24 291 | 67.37 274 | 49.78 187 | 38.91 321 | 43.11 323 | 35.00 305 | 43.27 304 | 63.30 303 | 28.95 270 | 49.19 318 | 36.53 271 | 60.80 267 | 57.76 310 |
|
new-patchmatchnet | | | 47.56 278 | 47.73 276 | 47.06 299 | 58.81 312 | 9.37 331 | 48.78 309 | 59.21 282 | 43.28 271 | 44.22 299 | 68.66 285 | 25.67 290 | 57.20 299 | 31.57 291 | 49.35 305 | 74.62 258 |
|
PVSNet_0 | | 43.31 20 | 47.46 279 | 45.64 279 | 52.92 287 | 67.60 273 | 44.65 231 | 54.06 296 | 54.64 302 | 41.59 282 | 46.15 293 | 58.75 310 | 30.99 257 | 58.66 292 | 32.18 285 | 24.81 320 | 55.46 312 |
|
test1235678 | | | 45.66 280 | 44.46 285 | 49.26 295 | 59.88 309 | 28.68 311 | 56.36 292 | 55.54 301 | 39.12 296 | 40.89 309 | 63.40 302 | 14.41 312 | 57.32 298 | 21.05 314 | 49.47 304 | 61.78 303 |
|
test2356 | | | 45.61 281 | 44.66 283 | 48.47 298 | 60.15 308 | 28.08 313 | 52.44 301 | 52.83 310 | 38.01 298 | 46.13 294 | 60.98 306 | 15.08 311 | 55.54 309 | 20.43 317 | 55.85 285 | 61.78 303 |
|
MVS-HIRNet | | | 45.52 282 | 44.48 284 | 48.65 297 | 68.49 267 | 34.05 295 | 59.41 284 | 44.50 322 | 27.03 315 | 37.96 313 | 50.47 318 | 26.16 288 | 64.10 274 | 26.74 304 | 59.52 275 | 47.82 315 |
|
pmmvs3 | | | 44.92 283 | 41.95 288 | 53.86 282 | 52.58 318 | 43.55 242 | 62.11 273 | 46.90 321 | 26.05 317 | 40.63 310 | 60.19 308 | 11.08 321 | 57.91 295 | 31.83 288 | 46.15 308 | 60.11 307 |
|
testus | | | 44.59 284 | 43.87 286 | 46.76 300 | 59.85 310 | 24.65 321 | 53.86 297 | 55.82 299 | 36.26 303 | 43.97 302 | 63.42 301 | 8.39 325 | 53.14 314 | 20.70 316 | 52.52 294 | 62.51 301 |
|
1111 | | | 44.40 285 | 45.00 282 | 42.61 306 | 57.55 314 | 17.33 328 | 53.82 299 | 57.05 291 | 40.78 287 | 44.11 300 | 66.57 292 | 13.37 313 | 45.77 320 | 22.15 310 | 49.58 303 | 64.73 300 |
|
testpf | | | 44.11 286 | 45.40 281 | 40.26 308 | 60.52 306 | 27.34 314 | 33.26 323 | 54.33 305 | 45.87 253 | 41.08 308 | 60.26 307 | 16.46 307 | 59.14 291 | 46.09 208 | 50.68 301 | 34.31 321 |
|
LF4IMVS | | | 42.95 287 | 42.26 287 | 45.04 302 | 48.30 322 | 32.50 300 | 54.80 294 | 48.49 316 | 28.03 314 | 40.51 311 | 70.16 280 | 9.24 323 | 43.89 323 | 31.63 289 | 49.18 306 | 58.72 308 |
|
testmv | | | 42.25 288 | 40.11 291 | 48.66 296 | 53.23 316 | 27.02 315 | 56.62 291 | 55.74 300 | 37.25 299 | 33.10 315 | 59.52 309 | 7.78 326 | 56.58 303 | 19.61 318 | 38.13 316 | 62.40 302 |
|
FPMVS | | | 42.18 289 | 41.11 289 | 45.39 301 | 58.03 313 | 41.01 259 | 49.50 307 | 53.81 308 | 30.07 312 | 33.71 314 | 64.03 298 | 11.69 317 | 52.08 316 | 14.01 323 | 55.11 286 | 43.09 318 |
|
ANet_high | | | 41.38 290 | 37.47 294 | 53.11 286 | 39.73 328 | 24.45 322 | 56.94 289 | 69.69 231 | 47.65 235 | 26.04 319 | 52.32 314 | 12.44 315 | 62.38 281 | 21.80 313 | 10.61 328 | 72.49 272 |
|
no-one | | | 40.85 291 | 36.09 295 | 55.14 277 | 48.55 321 | 38.72 268 | 42.15 319 | 62.92 269 | 34.60 307 | 23.55 320 | 49.74 319 | 12.21 316 | 66.16 269 | 26.27 306 | 24.84 319 | 60.54 306 |
|
LCM-MVSNet | | | 40.30 292 | 35.88 297 | 53.57 284 | 42.24 325 | 29.15 310 | 45.21 315 | 60.53 279 | 22.23 321 | 28.02 318 | 50.98 317 | 3.72 332 | 61.78 283 | 31.22 292 | 38.76 315 | 69.78 289 |
|
N_pmnet | | | 39.35 293 | 40.28 290 | 36.54 310 | 63.76 293 | 1.62 335 | 49.37 308 | 0.76 336 | 34.62 306 | 43.61 303 | 66.38 294 | 26.25 287 | 42.57 325 | 26.02 307 | 51.77 295 | 65.44 297 |
|
DSMNet-mixed | | | 39.30 294 | 38.72 293 | 41.03 307 | 51.22 319 | 19.66 325 | 45.53 314 | 31.35 329 | 15.83 325 | 39.80 312 | 67.42 290 | 22.19 300 | 45.13 322 | 22.43 309 | 52.69 293 | 58.31 309 |
|
PMVS | | 28.69 22 | 36.22 295 | 33.29 299 | 45.02 303 | 36.82 330 | 35.98 287 | 54.68 295 | 48.74 315 | 26.31 316 | 21.02 321 | 51.61 316 | 2.88 334 | 60.10 288 | 9.99 327 | 47.58 307 | 38.99 320 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
test12356 | | | 36.16 296 | 35.94 296 | 36.83 309 | 50.82 320 | 8.52 332 | 44.84 316 | 53.49 309 | 32.72 309 | 30.11 317 | 55.08 312 | 7.11 328 | 49.47 317 | 16.60 320 | 32.68 318 | 52.50 313 |
|
.test1245 | | | 34.88 297 | 39.49 292 | 21.04 318 | 57.55 314 | 17.33 328 | 53.82 299 | 57.05 291 | 40.78 287 | 44.11 300 | 66.57 292 | 13.37 313 | 45.77 320 | 22.15 310 | 0.00 331 | 0.03 330 |
|
Gipuma | | | 34.77 298 | 31.91 300 | 43.33 305 | 62.05 299 | 37.87 273 | 20.39 326 | 67.03 246 | 23.23 319 | 18.41 323 | 25.84 324 | 4.24 330 | 62.73 279 | 14.71 322 | 51.32 297 | 29.38 323 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
new_pmnet | | | 34.13 299 | 34.29 298 | 33.64 311 | 52.63 317 | 18.23 327 | 44.43 317 | 33.90 327 | 22.81 320 | 30.89 316 | 53.18 313 | 10.48 322 | 35.72 329 | 20.77 315 | 39.51 313 | 46.98 316 |
|
pcd1.5k->3k | | | 30.06 300 | 30.56 301 | 28.55 315 | 78.81 111 | 0.00 337 | 0.00 329 | 82.07 67 | 0.00 333 | 0.00 334 | 0.00 335 | 39.61 190 | 0.00 334 | 0.00 333 | 74.56 136 | 85.66 68 |
|
wuykxyi23d | | | 28.12 301 | 22.54 306 | 44.87 304 | 34.97 331 | 32.11 302 | 37.96 322 | 47.31 319 | 13.32 326 | 9.29 330 | 23.72 326 | 0.45 337 | 56.58 303 | 21.85 312 | 13.98 324 | 45.93 317 |
|
PNet_i23d | | | 27.88 302 | 25.99 302 | 33.55 312 | 47.54 323 | 25.89 317 | 47.24 312 | 32.91 328 | 21.44 322 | 15.90 324 | 38.09 321 | 0.85 336 | 42.76 324 | 16.90 319 | 13.03 326 | 32.00 322 |
|
PMMVS2 | | | 27.40 303 | 25.91 303 | 31.87 314 | 39.46 329 | 6.57 333 | 31.17 324 | 28.52 330 | 23.96 318 | 20.45 322 | 48.94 320 | 4.20 331 | 37.94 328 | 16.51 321 | 19.97 321 | 51.09 314 |
|
E-PMN | | | 23.77 304 | 22.73 305 | 26.90 316 | 42.02 326 | 20.67 324 | 42.66 318 | 35.70 325 | 17.43 323 | 10.28 328 | 25.05 325 | 6.42 329 | 42.39 326 | 10.28 326 | 14.71 323 | 17.63 324 |
|
EMVS | | | 22.97 305 | 21.84 307 | 26.36 317 | 40.20 327 | 19.53 326 | 41.95 320 | 34.64 326 | 17.09 324 | 9.73 329 | 22.83 327 | 7.29 327 | 42.22 327 | 9.18 328 | 13.66 325 | 17.32 325 |
|
MVE | | 17.77 23 | 21.41 306 | 17.77 308 | 32.34 313 | 34.34 332 | 25.44 319 | 16.11 327 | 24.11 331 | 11.19 327 | 13.22 326 | 31.92 322 | 1.58 335 | 30.95 330 | 10.47 325 | 17.03 322 | 40.62 319 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
cdsmvs_eth3d_5k | | | 17.50 307 | 23.34 304 | 0.00 324 | 0.00 337 | 0.00 337 | 0.00 329 | 78.63 150 | 0.00 333 | 0.00 334 | 82.18 134 | 49.25 85 | 0.00 334 | 0.00 333 | 0.00 331 | 0.00 332 |
|
wuyk23d | | | 13.32 308 | 12.52 309 | 15.71 319 | 47.54 323 | 26.27 316 | 31.06 325 | 1.98 335 | 4.93 329 | 5.18 331 | 1.94 332 | 0.45 337 | 18.54 331 | 6.81 330 | 12.83 327 | 2.33 328 |
|
tmp_tt | | | 9.43 309 | 11.14 310 | 4.30 321 | 2.38 334 | 4.40 334 | 13.62 328 | 16.08 333 | 0.39 330 | 15.89 325 | 13.06 328 | 15.80 309 | 5.54 333 | 12.63 324 | 10.46 329 | 2.95 327 |
|
ab-mvs-re | | | 6.49 310 | 8.65 311 | 0.00 324 | 0.00 337 | 0.00 337 | 0.00 329 | 0.00 337 | 0.00 333 | 0.00 334 | 77.89 215 | 0.00 340 | 0.00 334 | 0.00 333 | 0.00 331 | 0.00 332 |
|
test123 | | | 4.73 311 | 6.30 312 | 0.02 322 | 0.01 335 | 0.01 336 | 56.36 292 | 0.00 337 | 0.01 331 | 0.04 332 | 0.21 334 | 0.01 339 | 0.00 334 | 0.03 332 | 0.00 331 | 0.04 329 |
|
testmvs | | | 4.52 312 | 6.03 313 | 0.01 323 | 0.01 335 | 0.00 337 | 53.86 297 | 0.00 337 | 0.01 331 | 0.04 332 | 0.27 333 | 0.00 340 | 0.00 334 | 0.04 331 | 0.00 331 | 0.03 330 |
|
pcd_1.5k_mvsjas | | | 3.92 313 | 5.23 314 | 0.00 324 | 0.00 337 | 0.00 337 | 0.00 329 | 0.00 337 | 0.00 333 | 0.00 334 | 0.00 335 | 47.05 119 | 0.00 334 | 0.00 333 | 0.00 331 | 0.00 332 |
|
sosnet-low-res | | | 0.00 314 | 0.00 315 | 0.00 324 | 0.00 337 | 0.00 337 | 0.00 329 | 0.00 337 | 0.00 333 | 0.00 334 | 0.00 335 | 0.00 340 | 0.00 334 | 0.00 333 | 0.00 331 | 0.00 332 |
|
sosnet | | | 0.00 314 | 0.00 315 | 0.00 324 | 0.00 337 | 0.00 337 | 0.00 329 | 0.00 337 | 0.00 333 | 0.00 334 | 0.00 335 | 0.00 340 | 0.00 334 | 0.00 333 | 0.00 331 | 0.00 332 |
|
uncertanet | | | 0.00 314 | 0.00 315 | 0.00 324 | 0.00 337 | 0.00 337 | 0.00 329 | 0.00 337 | 0.00 333 | 0.00 334 | 0.00 335 | 0.00 340 | 0.00 334 | 0.00 333 | 0.00 331 | 0.00 332 |
|
Regformer | | | 0.00 314 | 0.00 315 | 0.00 324 | 0.00 337 | 0.00 337 | 0.00 329 | 0.00 337 | 0.00 333 | 0.00 334 | 0.00 335 | 0.00 340 | 0.00 334 | 0.00 333 | 0.00 331 | 0.00 332 |
|
uanet | | | 0.00 314 | 0.00 315 | 0.00 324 | 0.00 337 | 0.00 337 | 0.00 329 | 0.00 337 | 0.00 333 | 0.00 334 | 0.00 335 | 0.00 340 | 0.00 334 | 0.00 333 | 0.00 331 | 0.00 332 |
|
sam_mvs1 | | | | | | | | | | | | | 34.74 224 | | | | |
|
sam_mvs | | | | | | | | | | | | | 33.43 239 | | | | |
|
semantic-postprocess | | | | | 65.40 231 | 71.99 233 | 50.80 155 | | 69.63 233 | 45.71 254 | 60.61 208 | 77.93 213 | 36.56 216 | 65.99 270 | 55.67 147 | 63.50 246 | 79.42 210 |
|
ambc | | | | | 65.13 233 | 63.72 294 | 37.07 278 | 47.66 311 | 78.78 147 | | 54.37 262 | 71.42 272 | 11.24 320 | 80.94 175 | 45.64 214 | 53.85 292 | 77.38 225 |
|
MTGPA | | | | | | | | | 80.97 95 | | | | | | | | |
|
test_post1 | | | | | | | | 68.67 244 | | | | 3.64 330 | 32.39 254 | 69.49 255 | 44.17 225 | | |
|
test_post | | | | | | | | | | | | 3.55 331 | 33.90 234 | 66.52 266 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 64.03 298 | 34.50 226 | 74.27 240 | | | |
|
GG-mvs-BLEND | | | | | 62.34 248 | 71.36 243 | 37.04 279 | 69.20 242 | 57.33 290 | | 54.73 257 | 65.48 297 | 30.37 260 | 77.82 215 | 34.82 277 | 74.93 134 | 72.17 279 |
|
MTMP | | | | | | | | | 17.08 332 | | | | | | | | |
|
gm-plane-assit | | | | | | 71.40 242 | 41.72 255 | | | 48.85 223 | | 73.31 264 | | 82.48 152 | 48.90 191 | | |
|
test9_res | | | | | | | | | | | | | | | 75.28 14 | 88.31 16 | 83.81 136 |
|
TEST9 | | | | | | 85.58 27 | 61.59 25 | 81.62 62 | 81.26 86 | 55.65 160 | 74.93 22 | 88.81 39 | 53.70 39 | 84.68 94 | | | |
|
test_8 | | | | | | 85.40 30 | 60.96 32 | 81.54 65 | 81.18 89 | 55.86 154 | 74.81 25 | 88.80 41 | 53.70 39 | 84.45 99 | | | |
|
agg_prior2 | | | | | | | | | | | | | | | 73.09 28 | 87.93 23 | 84.33 114 |
|
agg_prior | | | | | | 85.04 34 | 59.96 40 | | 81.04 92 | | 74.68 26 | | | 84.04 106 | | | |
|
TestCases | | | | | 64.39 237 | 71.44 239 | 49.03 196 | | 67.30 243 | 45.97 250 | 47.16 289 | 79.77 189 | 17.47 305 | 67.56 261 | 33.65 281 | 59.16 277 | 76.57 235 |
|
test_prior4 | | | | | | | 62.51 17 | 82.08 56 | | | | | | | | | |
|
test_prior2 | | | | | | | | 81.75 58 | | 60.37 77 | 75.01 20 | 89.06 33 | 56.22 17 | | 72.19 31 | 88.96 8 | |
|
test_prior | | | | | 76.69 43 | 84.20 46 | 57.27 70 | | 84.88 14 | | | | | 86.43 53 | | | 86.38 40 |
|
旧先验2 | | | | | | | | 76.08 160 | | 45.32 255 | 76.55 13 | | | 65.56 272 | 58.75 131 | | |
|
æ–°å‡ ä½•2 | | | | | | | | 76.12 158 | | | | | | | | | |
|
æ–°å‡ ä½•1 | | | | | 70.76 174 | 85.66 24 | 61.13 30 | | 66.43 250 | 44.68 260 | 70.29 69 | 86.64 60 | 41.29 179 | 75.23 235 | 49.72 185 | 81.75 66 | 75.93 241 |
|
旧先验1 | | | | | | 83.04 53 | 53.15 124 | | 67.52 242 | | | 87.85 48 | 44.08 151 | | | 80.76 71 | 78.03 221 |
|
æ— å…ˆéªŒ | | | | | | | | 79.66 88 | 74.30 208 | 48.40 228 | | | | 80.78 180 | 53.62 158 | | 79.03 215 |
|
原ACMM2 | | | | | | | | 79.02 94 | | | | | | | | | |
|
原ACMM1 | | | | | 74.69 72 | 85.39 31 | 59.40 45 | | 83.42 44 | 51.47 203 | 70.27 71 | 86.61 62 | 48.61 101 | 86.51 51 | 53.85 157 | 87.96 22 | 78.16 219 |
|
test222 | | | | | | 83.14 52 | 58.68 55 | 72.57 213 | 63.45 264 | 41.78 279 | 67.56 124 | 86.12 70 | 37.13 213 | | | 78.73 104 | 74.98 252 |
|
testdata2 | | | | | | | | | | | | | | 72.18 246 | 46.95 203 | | |
|
segment_acmp | | | | | | | | | | | | | 54.23 32 | | | | |
|
testdata | | | | | 64.66 235 | 81.52 67 | 52.93 127 | | 65.29 254 | 46.09 248 | 73.88 36 | 87.46 50 | 38.08 206 | 66.26 268 | 53.31 163 | 78.48 107 | 74.78 256 |
|
testdata1 | | | | | | | | 72.65 211 | | 60.50 74 | | | | | | | |
|
test12 | | | | | 77.76 32 | 84.52 43 | 58.41 57 | | 83.36 47 | | 72.93 50 | | 54.61 29 | 88.05 20 | | 88.12 19 | 86.81 36 |
|
plane_prior7 | | | | | | 81.41 70 | 55.96 92 | | | | | | | | | | |
|
plane_prior6 | | | | | | 81.20 75 | 56.24 87 | | | | | | 45.26 141 | | | | |
|
plane_prior5 | | | | | | | | | 84.01 27 | | | | | 87.21 32 | 68.16 47 | 80.58 74 | 84.65 107 |
|
plane_prior4 | | | | | | | | | | | | 86.10 71 | | | | | |
|
plane_prior3 | | | | | | | 56.09 89 | | | 63.92 29 | 69.27 94 | | | | | | |
|
plane_prior2 | | | | | | | | 84.22 22 | | 64.52 23 | | | | | | | |
|
plane_prior1 | | | | | | 81.27 73 | | | | | | | | | | | |
|
plane_prior | | | | | | | 56.31 83 | 83.58 31 | | 63.19 36 | | | | | | 80.48 77 | |
|
n2 | | | | | | | | | 0.00 337 | | | | | | | | |
|
nn | | | | | | | | | 0.00 337 | | | | | | | | |
|
door-mid | | | | | | | | | 47.19 320 | | | | | | | | |
|
lessismore_v0 | | | | | 69.91 186 | 71.42 241 | 47.80 210 | | 50.90 312 | | 50.39 282 | 75.56 249 | 27.43 280 | 81.33 169 | 45.91 211 | 34.10 317 | 80.59 196 |
|
LGP-MVS_train | | | | | 75.76 54 | 80.22 89 | 57.51 68 | | 83.40 45 | 61.32 63 | 66.67 133 | 87.33 52 | 39.15 194 | 86.59 47 | 67.70 51 | 77.30 118 | 83.19 155 |
|
test11 | | | | | | | | | 83.47 42 | | | | | | | | |
|
door | | | | | | | | | 47.60 318 | | | | | | | | |
|
HQP5-MVS | | | | | | | 54.94 105 | | | | | | | | | | |
|
HQP-NCC | | | | | | 80.66 81 | | 82.31 50 | | 62.10 53 | 67.85 116 | | | | | | |
|
ACMP_Plane | | | | | | 80.66 81 | | 82.31 50 | | 62.10 53 | 67.85 116 | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 67.04 57 | | |
|
HQP4-MVS | | | | | | | | | | | 67.85 116 | | | 86.93 38 | | | 84.32 115 |
|
HQP3-MVS | | | | | | | | | 83.90 31 | | | | | | | 80.35 80 | |
|
HQP2-MVS | | | | | | | | | | | | | 45.46 135 | | | | |
|
NP-MVS | | | | | | 80.98 79 | 56.05 91 | | | | | 85.54 84 | | | | | |
|
MDTV_nov1_ep13_2view | | | | | | | 25.89 317 | 61.22 277 | | 40.10 291 | 51.10 277 | | 32.97 243 | | 38.49 259 | | 78.61 216 |
|
MDTV_nov1_ep13 | | | | 57.00 236 | | 72.73 221 | 38.26 271 | 65.02 262 | 64.73 258 | 44.74 259 | 55.46 249 | 72.48 266 | 32.61 252 | 70.47 252 | 37.47 263 | 67.75 218 | |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 74.07 143 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 72.16 165 | |
|
Test By Simon | | | | | | | | | | | | | 48.33 104 | | | | |
|
ITE_SJBPF | | | | | 62.09 250 | 66.16 282 | 44.55 234 | | 64.32 260 | 47.36 238 | 55.31 252 | 80.34 180 | 19.27 304 | 62.68 280 | 36.29 274 | 62.39 255 | 79.04 214 |
|
DeepMVS_CX | | | | | 12.03 320 | 17.97 333 | 10.91 330 | | 10.60 334 | 7.46 328 | 11.07 327 | 28.36 323 | 3.28 333 | 11.29 332 | 8.01 329 | 9.74 330 | 13.89 326 |
|