ESAPD | | | 89.48 1 | 89.98 1 | 88.01 10 | 94.80 5 | 72.69 29 | 91.59 29 | 94.10 2 | 75.90 72 | 92.29 2 | 95.66 4 | 81.67 1 | 97.38 1 | 87.44 11 | 96.34 4 | 93.95 32 |
|
HSP-MVS | | | 89.28 2 | 89.76 2 | 87.85 21 | 94.28 19 | 73.46 15 | 92.90 9 | 92.73 44 | 80.27 13 | 91.35 6 | 94.16 25 | 78.35 4 | 96.77 12 | 89.59 1 | 94.22 46 | 93.33 60 |
|
APDe-MVS | | | 89.15 3 | 89.63 3 | 87.73 23 | 94.49 12 | 71.69 47 | 93.83 2 | 93.96 6 | 75.70 75 | 91.06 7 | 96.03 1 | 76.84 5 | 97.03 7 | 89.09 2 | 95.65 15 | 94.47 13 |
|
SMA-MVS | | | 89.08 4 | 89.23 4 | 88.61 2 | 94.25 20 | 73.73 7 | 92.40 15 | 93.63 11 | 74.77 94 | 92.29 2 | 95.97 2 | 74.28 19 | 97.24 3 | 88.58 4 | 96.91 1 | 94.87 5 |
|
HPM-MVS++ | | | 89.02 5 | 89.15 5 | 88.63 1 | 95.01 4 | 76.03 1 | 92.38 17 | 92.85 39 | 80.26 14 | 87.78 16 | 94.27 21 | 75.89 9 | 96.81 11 | 87.45 10 | 96.44 2 | 93.05 70 |
|
CNVR-MVS | | | 88.93 6 | 89.13 6 | 88.33 4 | 94.77 6 | 73.82 6 | 90.51 45 | 93.00 31 | 80.90 10 | 88.06 14 | 94.06 29 | 76.43 6 | 96.84 9 | 88.48 5 | 95.99 6 | 94.34 17 |
|
SteuartSystems-ACMMP | | | 88.72 7 | 88.86 7 | 88.32 5 | 92.14 59 | 72.96 22 | 93.73 3 | 93.67 10 | 80.19 15 | 88.10 13 | 94.80 8 | 73.76 23 | 97.11 5 | 87.51 9 | 95.82 10 | 94.90 4 |
Skip Steuart: Steuart Systems R&D Blog. |
DeepPCF-MVS | | 80.84 1 | 88.10 8 | 88.56 8 | 86.73 44 | 92.24 57 | 69.03 87 | 89.57 70 | 93.39 19 | 77.53 39 | 89.79 9 | 94.12 27 | 78.98 3 | 96.58 23 | 85.66 14 | 95.72 11 | 94.58 8 |
|
SD-MVS | | | 88.06 9 | 88.50 9 | 86.71 45 | 92.60 55 | 72.71 27 | 91.81 28 | 93.19 24 | 77.87 32 | 90.32 8 | 94.00 30 | 74.83 12 | 93.78 122 | 87.63 8 | 94.27 44 | 93.65 49 |
|
NCCC | | | 88.06 9 | 88.01 12 | 88.24 6 | 94.41 16 | 73.62 8 | 91.22 36 | 92.83 40 | 81.50 7 | 85.79 27 | 93.47 39 | 73.02 28 | 97.00 8 | 84.90 19 | 94.94 27 | 94.10 23 |
|
ACMMP_NAP | | | 88.05 11 | 88.08 11 | 87.94 13 | 93.70 30 | 73.05 19 | 90.86 39 | 93.59 12 | 76.27 68 | 88.14 12 | 95.09 7 | 71.06 41 | 96.67 16 | 87.67 7 | 96.37 3 | 94.09 24 |
|
TSAR-MVS + MP. | | | 88.02 12 | 88.11 10 | 87.72 25 | 93.68 32 | 72.13 42 | 91.41 32 | 92.35 58 | 74.62 96 | 88.90 10 | 93.85 33 | 75.75 10 | 96.00 37 | 87.80 6 | 94.63 34 | 95.04 2 |
|
MP-MVS | | | 87.71 13 | 87.64 14 | 87.93 16 | 94.36 18 | 73.88 4 | 92.71 14 | 92.65 47 | 77.57 35 | 83.84 55 | 94.40 20 | 72.24 35 | 96.28 28 | 85.65 15 | 95.30 23 | 93.62 51 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
MP-MVS-pluss | | | 87.67 14 | 87.72 13 | 87.54 29 | 93.64 33 | 72.04 44 | 89.80 62 | 93.50 14 | 75.17 88 | 86.34 22 | 95.29 5 | 70.86 42 | 96.00 37 | 88.78 3 | 96.04 5 | 94.58 8 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
HFP-MVS | | | 87.58 15 | 87.47 16 | 87.94 13 | 94.58 9 | 73.54 12 | 93.04 6 | 93.24 21 | 76.78 54 | 84.91 36 | 94.44 17 | 70.78 43 | 96.61 19 | 84.53 26 | 94.89 29 | 93.66 44 |
|
zzz-MVS | | | 87.53 16 | 87.41 17 | 87.90 17 | 94.18 24 | 74.25 2 | 90.23 53 | 92.02 68 | 79.45 19 | 85.88 24 | 94.80 8 | 68.07 64 | 96.21 30 | 86.69 12 | 95.34 19 | 93.23 62 |
|
ACMMPR | | | 87.44 17 | 87.23 20 | 88.08 8 | 94.64 7 | 73.59 9 | 93.04 6 | 93.20 23 | 76.78 54 | 84.66 43 | 94.52 12 | 68.81 62 | 96.65 17 | 84.53 26 | 94.90 28 | 94.00 30 |
|
APD-MVS | | | 87.44 17 | 87.52 15 | 87.19 34 | 94.24 21 | 72.39 37 | 91.86 27 | 92.83 40 | 73.01 134 | 88.58 11 | 94.52 12 | 73.36 24 | 96.49 24 | 84.26 30 | 95.01 25 | 92.70 80 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
GST-MVS | | | 87.42 19 | 87.26 18 | 87.89 20 | 94.12 26 | 72.97 21 | 92.39 16 | 93.43 17 | 76.89 51 | 84.68 42 | 93.99 31 | 70.67 46 | 96.82 10 | 84.18 33 | 95.01 25 | 93.90 35 |
|
region2R | | | 87.42 19 | 87.20 21 | 88.09 7 | 94.63 8 | 73.55 10 | 93.03 8 | 93.12 26 | 76.73 57 | 84.45 46 | 94.52 12 | 69.09 60 | 96.70 15 | 84.37 29 | 94.83 31 | 94.03 27 |
|
MCST-MVS | | | 87.37 21 | 87.25 19 | 87.73 23 | 94.53 11 | 72.46 36 | 89.82 60 | 93.82 8 | 73.07 133 | 84.86 41 | 92.89 51 | 76.22 7 | 96.33 26 | 84.89 21 | 95.13 24 | 94.40 14 |
|
#test# | | | 87.33 22 | 87.13 22 | 87.94 13 | 94.58 9 | 73.54 12 | 92.34 19 | 93.24 21 | 75.23 85 | 84.91 36 | 94.44 17 | 70.78 43 | 96.61 19 | 83.75 35 | 94.89 29 | 93.66 44 |
|
MTAPA | | | 87.23 23 | 87.00 23 | 87.90 17 | 94.18 24 | 74.25 2 | 86.58 173 | 92.02 68 | 79.45 19 | 85.88 24 | 94.80 8 | 68.07 64 | 96.21 30 | 86.69 12 | 95.34 19 | 93.23 62 |
|
XVS | | | 87.18 24 | 86.91 26 | 88.00 11 | 94.42 14 | 73.33 17 | 92.78 10 | 92.99 33 | 79.14 21 | 83.67 58 | 94.17 24 | 67.45 71 | 96.60 21 | 83.06 40 | 94.50 37 | 94.07 25 |
|
HPM-MVS | | | 87.11 25 | 86.98 24 | 87.50 31 | 93.88 29 | 72.16 41 | 92.19 22 | 93.33 20 | 76.07 71 | 83.81 56 | 93.95 32 | 69.77 54 | 96.01 36 | 85.15 16 | 94.66 33 | 94.32 19 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
CP-MVS | | | 87.11 25 | 86.92 25 | 87.68 28 | 94.20 23 | 73.86 5 | 93.98 1 | 92.82 42 | 76.62 59 | 83.68 57 | 94.46 16 | 67.93 66 | 95.95 39 | 84.20 32 | 94.39 40 | 93.23 62 |
|
DeepC-MVS | | 79.81 2 | 87.08 27 | 86.88 27 | 87.69 27 | 91.16 69 | 72.32 40 | 90.31 51 | 93.94 7 | 77.12 45 | 82.82 68 | 94.23 23 | 72.13 36 | 97.09 6 | 84.83 22 | 95.37 18 | 93.65 49 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
DeepC-MVS_fast | | 79.65 3 | 86.91 28 | 86.62 30 | 87.76 22 | 93.52 35 | 72.37 39 | 91.26 33 | 93.04 27 | 76.62 59 | 84.22 51 | 93.36 41 | 71.44 39 | 96.76 13 | 80.82 58 | 95.33 21 | 94.16 21 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
SR-MVS | | | 86.73 29 | 86.67 29 | 86.91 40 | 94.11 27 | 72.11 43 | 92.37 18 | 92.56 49 | 74.50 97 | 86.84 20 | 94.65 11 | 67.31 74 | 95.77 41 | 84.80 23 | 92.85 54 | 92.84 78 |
|
test_prior3 | | | 86.73 29 | 86.86 28 | 86.33 51 | 92.61 53 | 69.59 79 | 88.85 89 | 92.97 36 | 75.41 81 | 84.91 36 | 93.54 35 | 74.28 19 | 95.48 48 | 83.31 36 | 95.86 8 | 93.91 33 |
|
PGM-MVS | | | 86.68 31 | 86.27 34 | 87.90 17 | 94.22 22 | 73.38 16 | 90.22 54 | 93.04 27 | 75.53 79 | 83.86 54 | 94.42 19 | 67.87 68 | 96.64 18 | 82.70 45 | 94.57 36 | 93.66 44 |
|
mPP-MVS | | | 86.67 32 | 86.32 33 | 87.72 25 | 94.41 16 | 73.55 10 | 92.74 12 | 92.22 61 | 76.87 52 | 82.81 70 | 94.25 22 | 66.44 80 | 96.24 29 | 82.88 44 | 94.28 43 | 93.38 57 |
|
Regformer-2 | | | 86.63 33 | 86.53 31 | 86.95 39 | 89.33 112 | 71.24 51 | 88.43 102 | 92.05 67 | 82.50 1 | 86.88 19 | 90.09 104 | 74.45 14 | 95.61 44 | 84.38 28 | 90.63 73 | 94.01 29 |
|
CANet | | | 86.45 34 | 86.10 38 | 87.51 30 | 90.09 88 | 70.94 56 | 89.70 66 | 92.59 48 | 81.78 4 | 81.32 86 | 91.43 77 | 70.34 47 | 97.23 4 | 84.26 30 | 93.36 50 | 94.37 15 |
|
train_agg | | | 86.43 35 | 86.20 35 | 87.13 36 | 93.26 40 | 72.96 22 | 88.75 94 | 91.89 77 | 68.69 211 | 85.00 34 | 93.10 45 | 74.43 15 | 95.41 53 | 84.97 17 | 95.71 12 | 93.02 72 |
|
PHI-MVS | | | 86.43 35 | 86.17 37 | 87.24 33 | 90.88 75 | 70.96 54 | 92.27 21 | 94.07 5 | 72.45 145 | 85.22 32 | 91.90 64 | 69.47 57 | 96.42 25 | 83.28 38 | 95.94 7 | 94.35 16 |
|
Regformer-1 | | | 86.41 37 | 86.33 32 | 86.64 46 | 89.33 112 | 70.93 57 | 88.43 102 | 91.39 98 | 82.14 3 | 86.65 21 | 90.09 104 | 74.39 17 | 95.01 69 | 83.97 34 | 90.63 73 | 93.97 31 |
|
CSCG | | | 86.41 37 | 86.19 36 | 87.07 38 | 92.91 47 | 72.48 35 | 90.81 40 | 93.56 13 | 73.95 105 | 83.16 63 | 91.07 85 | 75.94 8 | 95.19 60 | 79.94 65 | 94.38 41 | 93.55 53 |
|
MVS_0304 | | | 86.37 39 | 85.81 43 | 88.02 9 | 90.13 86 | 72.39 37 | 89.66 68 | 92.75 43 | 81.64 6 | 82.66 73 | 92.04 60 | 64.44 96 | 97.35 2 | 84.76 24 | 94.25 45 | 94.33 18 |
|
agg_prior1 | | | 86.22 40 | 86.09 39 | 86.62 47 | 92.85 48 | 71.94 45 | 88.59 99 | 91.78 83 | 68.96 208 | 84.41 47 | 93.18 44 | 74.94 11 | 94.93 70 | 84.75 25 | 95.33 21 | 93.01 74 |
|
agg_prior3 | | | 86.16 41 | 85.85 42 | 87.10 37 | 93.31 37 | 72.86 26 | 88.77 92 | 91.68 87 | 68.29 223 | 84.26 50 | 92.83 53 | 72.83 30 | 95.42 52 | 84.97 17 | 95.71 12 | 93.02 72 |
|
APD-MVS_3200maxsize | | | 85.97 42 | 85.88 40 | 86.22 54 | 92.69 51 | 69.53 81 | 91.93 26 | 92.99 33 | 73.54 120 | 85.94 23 | 94.51 15 | 65.80 87 | 95.61 44 | 83.04 42 | 92.51 58 | 93.53 55 |
|
canonicalmvs | | | 85.91 43 | 85.87 41 | 86.04 59 | 89.84 94 | 69.44 85 | 90.45 49 | 93.00 31 | 76.70 58 | 88.01 15 | 91.23 79 | 73.28 25 | 93.91 113 | 81.50 53 | 88.80 93 | 94.77 6 |
|
ACMMP | | | 85.89 44 | 85.39 46 | 87.38 32 | 93.59 34 | 72.63 31 | 92.74 12 | 93.18 25 | 76.78 54 | 80.73 95 | 93.82 34 | 64.33 97 | 96.29 27 | 82.67 46 | 90.69 72 | 93.23 62 |
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 |
CDPH-MVS | | | 85.76 45 | 85.29 50 | 87.17 35 | 93.49 36 | 71.08 52 | 88.58 100 | 92.42 55 | 68.32 222 | 84.61 44 | 93.48 37 | 72.32 34 | 96.15 33 | 79.00 68 | 95.43 17 | 94.28 20 |
|
TSAR-MVS + GP. | | | 85.71 46 | 85.33 47 | 86.84 41 | 91.34 67 | 72.50 34 | 89.07 83 | 87.28 217 | 76.41 61 | 85.80 26 | 90.22 102 | 74.15 22 | 95.37 57 | 81.82 49 | 91.88 60 | 92.65 83 |
|
Regformer-4 | | | 85.68 47 | 85.45 45 | 86.35 50 | 88.95 129 | 69.67 77 | 88.29 112 | 91.29 100 | 81.73 5 | 85.36 30 | 90.01 107 | 72.62 32 | 95.35 58 | 83.28 38 | 87.57 108 | 94.03 27 |
|
alignmvs | | | 85.48 48 | 85.32 48 | 85.96 61 | 89.51 106 | 69.47 83 | 89.74 64 | 92.47 51 | 76.17 69 | 87.73 17 | 91.46 76 | 70.32 48 | 93.78 122 | 81.51 52 | 88.95 89 | 94.63 7 |
|
3Dnovator+ | | 77.84 4 | 85.48 48 | 84.47 56 | 88.51 3 | 91.08 70 | 73.49 14 | 93.18 5 | 93.78 9 | 80.79 11 | 76.66 160 | 93.37 40 | 60.40 169 | 96.75 14 | 77.20 86 | 93.73 49 | 95.29 1 |
|
MSLP-MVS++ | | | 85.43 50 | 85.76 44 | 84.45 88 | 91.93 62 | 70.24 65 | 90.71 42 | 92.86 38 | 77.46 41 | 84.22 51 | 92.81 56 | 67.16 76 | 92.94 166 | 80.36 61 | 94.35 42 | 90.16 163 |
|
DELS-MVS | | | 85.41 51 | 85.30 49 | 85.77 62 | 88.49 145 | 67.93 116 | 85.52 212 | 93.44 16 | 78.70 28 | 83.63 60 | 89.03 132 | 74.57 13 | 95.71 43 | 80.26 63 | 94.04 47 | 93.66 44 |
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023 |
HPM-MVS_fast | | | 85.35 52 | 84.95 53 | 86.57 49 | 93.69 31 | 70.58 63 | 92.15 24 | 91.62 88 | 73.89 109 | 82.67 72 | 94.09 28 | 62.60 129 | 95.54 47 | 80.93 56 | 92.93 52 | 93.57 52 |
|
Regformer-3 | | | 85.23 53 | 85.07 51 | 85.70 63 | 88.95 129 | 69.01 89 | 88.29 112 | 89.91 148 | 80.95 9 | 85.01 33 | 90.01 107 | 72.45 33 | 94.19 100 | 82.50 47 | 87.57 108 | 93.90 35 |
|
abl_6 | | | 85.23 53 | 84.95 53 | 86.07 57 | 92.23 58 | 70.48 64 | 90.80 41 | 92.08 66 | 73.51 121 | 85.26 31 | 94.16 25 | 62.75 122 | 95.92 40 | 82.46 48 | 91.30 67 | 91.81 109 |
|
MVS_111021_HR | | | 85.14 55 | 84.75 55 | 86.32 53 | 91.65 65 | 72.70 28 | 85.98 190 | 90.33 128 | 76.11 70 | 82.08 76 | 91.61 71 | 71.36 40 | 94.17 102 | 81.02 54 | 92.58 57 | 92.08 102 |
|
UA-Net | | | 85.08 56 | 84.96 52 | 85.45 64 | 92.07 60 | 68.07 114 | 89.78 63 | 90.86 111 | 82.48 2 | 84.60 45 | 93.20 43 | 69.35 58 | 95.22 59 | 71.39 148 | 90.88 71 | 93.07 69 |
|
DPM-MVS | | | 84.93 57 | 84.29 58 | 86.84 41 | 90.20 85 | 73.04 20 | 87.12 154 | 93.04 27 | 69.80 188 | 82.85 67 | 91.22 80 | 73.06 27 | 96.02 35 | 76.72 94 | 94.63 34 | 91.46 119 |
|
casdiffmvs1 | | | 84.76 58 | 84.33 57 | 86.04 59 | 89.40 109 | 68.78 94 | 89.67 67 | 92.54 50 | 66.43 240 | 85.41 28 | 90.75 93 | 72.88 29 | 94.76 82 | 81.64 51 | 90.24 78 | 94.57 10 |
|
EI-MVSNet-Vis-set | | | 84.19 59 | 83.81 59 | 85.31 66 | 88.18 155 | 67.85 117 | 87.66 127 | 89.73 152 | 80.05 17 | 82.95 64 | 89.59 117 | 70.74 45 | 94.82 79 | 80.66 60 | 84.72 144 | 93.28 61 |
|
casdiffmvs | | | 83.96 60 | 83.25 64 | 86.07 57 | 88.48 146 | 69.60 78 | 89.26 75 | 92.40 56 | 68.07 224 | 82.82 68 | 90.03 106 | 69.77 54 | 94.86 78 | 81.79 50 | 86.64 125 | 93.75 42 |
|
nrg030 | | | 83.88 61 | 83.53 60 | 84.96 76 | 86.77 199 | 69.28 86 | 90.46 48 | 92.67 45 | 74.79 93 | 82.95 64 | 91.33 78 | 72.70 31 | 93.09 159 | 80.79 59 | 79.28 220 | 92.50 88 |
|
EI-MVSNet-UG-set | | | 83.81 62 | 83.38 62 | 85.09 73 | 87.87 163 | 67.53 121 | 87.44 138 | 89.66 153 | 79.74 18 | 82.23 75 | 89.41 126 | 70.24 49 | 94.74 83 | 79.95 64 | 83.92 151 | 92.99 75 |
|
CPTT-MVS | | | 83.73 63 | 83.33 63 | 84.92 79 | 93.28 39 | 70.86 59 | 92.09 25 | 90.38 123 | 68.75 210 | 79.57 102 | 92.83 53 | 60.60 165 | 93.04 163 | 80.92 57 | 91.56 64 | 90.86 133 |
|
EPNet | | | 83.72 64 | 82.92 70 | 86.14 56 | 84.22 232 | 69.48 82 | 91.05 38 | 85.27 237 | 81.30 8 | 76.83 156 | 91.65 68 | 66.09 83 | 95.56 46 | 76.00 98 | 93.85 48 | 93.38 57 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
HQP_MVS | | | 83.64 65 | 83.14 65 | 85.14 71 | 90.08 89 | 68.71 100 | 91.25 34 | 92.44 52 | 79.12 23 | 78.92 109 | 91.00 89 | 60.42 167 | 95.38 55 | 78.71 71 | 86.32 130 | 91.33 120 |
|
Effi-MVS+ | | | 83.62 66 | 83.08 66 | 85.24 69 | 88.38 151 | 67.45 122 | 88.89 87 | 89.15 170 | 75.50 80 | 82.27 74 | 88.28 151 | 69.61 56 | 94.45 90 | 77.81 80 | 87.84 106 | 93.84 39 |
|
OPM-MVS | | | 83.50 67 | 82.95 69 | 85.14 71 | 88.79 137 | 70.95 55 | 89.13 82 | 91.52 92 | 77.55 38 | 80.96 93 | 91.75 66 | 60.71 161 | 94.50 89 | 79.67 66 | 86.51 128 | 89.97 181 |
|
Vis-MVSNet | | | 83.46 68 | 82.80 72 | 85.43 65 | 90.25 84 | 68.74 98 | 90.30 52 | 90.13 137 | 76.33 67 | 80.87 94 | 92.89 51 | 61.00 158 | 94.20 99 | 72.45 137 | 90.97 69 | 93.35 59 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
MG-MVS | | | 83.41 69 | 83.45 61 | 83.28 127 | 92.74 50 | 62.28 233 | 88.17 116 | 89.50 157 | 75.22 86 | 81.49 84 | 92.74 57 | 66.75 77 | 95.11 63 | 72.85 130 | 91.58 63 | 92.45 89 |
|
EPP-MVSNet | | | 83.40 70 | 83.02 68 | 84.57 84 | 90.13 86 | 64.47 188 | 92.32 20 | 90.73 112 | 74.45 99 | 79.35 105 | 91.10 83 | 69.05 61 | 95.12 62 | 72.78 131 | 87.22 115 | 94.13 22 |
|
3Dnovator | | 76.31 5 | 83.38 71 | 82.31 78 | 86.59 48 | 87.94 162 | 72.94 25 | 90.64 43 | 92.14 65 | 77.21 43 | 75.47 187 | 92.83 53 | 58.56 176 | 94.72 84 | 73.24 128 | 92.71 56 | 92.13 101 |
|
MVS_Test | | | 83.15 72 | 83.06 67 | 83.41 124 | 86.86 196 | 63.21 219 | 86.11 188 | 92.00 71 | 74.31 100 | 82.87 66 | 89.44 125 | 70.03 50 | 93.21 150 | 77.39 85 | 88.50 102 | 93.81 40 |
|
IS-MVSNet | | | 83.15 72 | 82.81 71 | 84.18 98 | 89.94 92 | 63.30 216 | 91.59 29 | 88.46 198 | 79.04 25 | 79.49 103 | 92.16 58 | 65.10 92 | 94.28 93 | 67.71 174 | 91.86 61 | 94.95 3 |
|
DP-MVS Recon | | | 83.11 74 | 82.09 80 | 86.15 55 | 94.44 13 | 70.92 58 | 88.79 91 | 92.20 62 | 70.53 177 | 79.17 106 | 91.03 88 | 64.12 99 | 96.03 34 | 68.39 173 | 90.14 79 | 91.50 115 |
|
PAPM_NR | | | 83.02 75 | 82.41 75 | 84.82 81 | 92.47 56 | 66.37 139 | 87.93 123 | 91.80 81 | 73.82 114 | 77.32 148 | 90.66 95 | 67.90 67 | 94.90 74 | 70.37 154 | 89.48 86 | 93.19 66 |
|
VDD-MVS | | | 83.01 76 | 82.36 77 | 84.96 76 | 91.02 72 | 66.40 138 | 88.91 86 | 88.11 201 | 77.57 35 | 84.39 49 | 93.29 42 | 52.19 227 | 93.91 113 | 77.05 89 | 88.70 95 | 94.57 10 |
|
MVSFormer | | | 82.85 77 | 82.05 81 | 85.24 69 | 87.35 186 | 70.21 66 | 90.50 46 | 90.38 123 | 68.55 213 | 81.32 86 | 89.47 120 | 61.68 143 | 93.46 141 | 78.98 69 | 90.26 76 | 92.05 103 |
|
OMC-MVS | | | 82.69 78 | 81.97 84 | 84.85 80 | 88.75 139 | 67.42 123 | 87.98 119 | 90.87 110 | 74.92 92 | 79.72 101 | 91.65 68 | 62.19 140 | 93.96 108 | 75.26 110 | 86.42 129 | 93.16 67 |
|
diffmvs1 | | | 82.63 79 | 82.51 73 | 82.96 150 | 83.87 251 | 63.47 211 | 85.19 214 | 89.42 160 | 75.58 78 | 81.38 85 | 89.89 109 | 67.42 73 | 91.69 208 | 81.01 55 | 88.88 91 | 93.71 43 |
|
PVSNet_Blended_VisFu | | | 82.62 80 | 81.83 85 | 84.96 76 | 90.80 77 | 69.76 75 | 88.74 96 | 91.70 86 | 69.39 194 | 78.96 108 | 88.46 146 | 65.47 89 | 94.87 77 | 74.42 114 | 88.57 98 | 90.24 161 |
|
MVS_111021_LR | | | 82.61 81 | 82.11 79 | 84.11 99 | 88.82 134 | 71.58 48 | 85.15 217 | 86.16 230 | 74.69 95 | 80.47 97 | 91.04 86 | 62.29 137 | 90.55 239 | 80.33 62 | 90.08 80 | 90.20 162 |
|
HQP-MVS | | | 82.61 81 | 82.02 82 | 84.37 90 | 89.33 112 | 66.98 131 | 89.17 77 | 92.19 63 | 76.41 61 | 77.23 151 | 90.23 101 | 60.17 170 | 95.11 63 | 77.47 83 | 85.99 135 | 91.03 127 |
|
CLD-MVS | | | 82.31 83 | 81.65 86 | 84.29 95 | 88.47 147 | 67.73 120 | 85.81 199 | 92.35 58 | 75.78 73 | 78.33 123 | 86.58 207 | 64.01 100 | 94.35 91 | 76.05 97 | 87.48 113 | 90.79 134 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
VNet | | | 82.21 84 | 82.41 75 | 81.62 193 | 90.82 76 | 60.93 241 | 84.47 232 | 89.78 150 | 76.36 66 | 84.07 53 | 91.88 65 | 64.71 95 | 90.26 241 | 70.68 151 | 88.89 90 | 93.66 44 |
|
LPG-MVS_test | | | 82.08 85 | 81.27 89 | 84.50 86 | 89.23 120 | 68.76 96 | 90.22 54 | 91.94 75 | 75.37 83 | 76.64 161 | 91.51 73 | 54.29 210 | 94.91 72 | 78.44 73 | 83.78 152 | 89.83 185 |
|
FIs | | | 82.07 86 | 82.42 74 | 81.04 206 | 88.80 136 | 58.34 262 | 88.26 114 | 93.49 15 | 76.93 50 | 78.47 117 | 91.04 86 | 69.92 52 | 92.34 184 | 69.87 160 | 84.97 141 | 92.44 90 |
|
PS-MVSNAJss | | | 82.07 86 | 81.31 88 | 84.34 93 | 86.51 201 | 67.27 127 | 89.27 74 | 91.51 93 | 71.75 157 | 79.37 104 | 90.22 102 | 63.15 110 | 94.27 94 | 77.69 81 | 82.36 181 | 91.49 116 |
|
API-MVS | | | 81.99 88 | 81.23 90 | 84.26 96 | 90.94 73 | 70.18 71 | 91.10 37 | 89.32 163 | 71.51 163 | 78.66 113 | 88.28 151 | 65.26 90 | 95.10 66 | 64.74 203 | 91.23 68 | 87.51 254 |
|
UniMVSNet_NR-MVSNet | | | 81.88 89 | 81.54 87 | 82.92 151 | 88.46 148 | 63.46 212 | 87.13 152 | 92.37 57 | 80.19 15 | 78.38 121 | 89.14 128 | 71.66 38 | 93.05 161 | 70.05 157 | 76.46 254 | 92.25 96 |
|
MAR-MVS | | | 81.84 90 | 80.70 97 | 85.27 68 | 91.32 68 | 71.53 49 | 89.82 60 | 90.92 108 | 69.77 189 | 78.50 115 | 86.21 220 | 62.36 136 | 94.52 88 | 65.36 196 | 92.05 59 | 89.77 192 |
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 |
LFMVS | | | 81.82 91 | 81.23 90 | 83.57 119 | 91.89 63 | 63.43 214 | 89.84 59 | 81.85 284 | 77.04 48 | 83.21 61 | 93.10 45 | 52.26 226 | 93.43 145 | 71.98 143 | 89.95 82 | 93.85 37 |
|
xiu_mvs_v2_base | | | 81.69 92 | 81.05 94 | 83.60 117 | 89.15 123 | 68.03 115 | 84.46 234 | 90.02 142 | 70.67 174 | 81.30 89 | 86.53 210 | 63.17 109 | 94.19 100 | 75.60 106 | 88.54 100 | 88.57 233 |
|
PS-MVSNAJ | | | 81.69 92 | 81.02 95 | 83.70 115 | 89.51 106 | 68.21 112 | 84.28 241 | 90.09 138 | 70.79 171 | 81.26 90 | 85.62 239 | 63.15 110 | 94.29 92 | 75.62 105 | 88.87 92 | 88.59 231 |
|
PAPR | | | 81.66 94 | 80.89 96 | 83.99 108 | 90.27 83 | 64.00 199 | 86.76 169 | 91.77 85 | 68.84 209 | 77.13 155 | 89.50 118 | 67.63 69 | 94.88 76 | 67.55 176 | 88.52 101 | 93.09 68 |
|
UniMVSNet (Re) | | | 81.60 95 | 81.11 93 | 83.09 138 | 88.38 151 | 64.41 190 | 87.60 128 | 93.02 30 | 78.42 31 | 78.56 114 | 88.16 154 | 69.78 53 | 93.26 149 | 69.58 163 | 76.49 253 | 91.60 111 |
|
FC-MVSNet-test | | | 81.52 96 | 82.02 82 | 80.03 221 | 88.42 150 | 55.97 300 | 87.95 121 | 93.42 18 | 77.10 46 | 77.38 146 | 90.98 91 | 69.96 51 | 91.79 198 | 68.46 172 | 84.50 146 | 92.33 92 |
|
VDDNet | | | 81.52 96 | 80.67 98 | 84.05 103 | 90.44 81 | 64.13 195 | 89.73 65 | 85.91 233 | 71.11 167 | 83.18 62 | 93.48 37 | 50.54 263 | 93.49 140 | 73.40 126 | 88.25 104 | 94.54 12 |
|
ACMP | | 74.13 6 | 81.51 98 | 80.57 99 | 84.36 91 | 89.42 108 | 68.69 103 | 89.97 58 | 91.50 96 | 74.46 98 | 75.04 203 | 90.41 98 | 53.82 215 | 94.54 86 | 77.56 82 | 82.91 172 | 89.86 184 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
diffmvs | | | 81.48 99 | 81.21 92 | 82.31 174 | 83.28 266 | 62.72 228 | 85.09 218 | 88.63 195 | 74.99 89 | 78.31 124 | 88.81 136 | 65.80 87 | 91.36 220 | 79.03 67 | 86.95 119 | 92.84 78 |
|
jason | | | 81.39 100 | 80.29 106 | 84.70 83 | 86.63 200 | 69.90 73 | 85.95 191 | 86.77 221 | 63.24 271 | 81.07 92 | 89.47 120 | 61.08 157 | 92.15 188 | 78.33 76 | 90.07 81 | 92.05 103 |
jason: jason. |
lupinMVS | | | 81.39 100 | 80.27 107 | 84.76 82 | 87.35 186 | 70.21 66 | 85.55 208 | 86.41 225 | 62.85 277 | 81.32 86 | 88.61 141 | 61.68 143 | 92.24 187 | 78.41 75 | 90.26 76 | 91.83 107 |
|
test_yl | | | 81.17 102 | 80.47 102 | 83.24 130 | 89.13 124 | 63.62 204 | 86.21 184 | 89.95 144 | 72.43 148 | 81.78 81 | 89.61 115 | 57.50 184 | 93.58 134 | 70.75 149 | 86.90 120 | 92.52 86 |
|
DCV-MVSNet | | | 81.17 102 | 80.47 102 | 83.24 130 | 89.13 124 | 63.62 204 | 86.21 184 | 89.95 144 | 72.43 148 | 81.78 81 | 89.61 115 | 57.50 184 | 93.58 134 | 70.75 149 | 86.90 120 | 92.52 86 |
|
DU-MVS | | | 81.12 104 | 80.52 101 | 82.90 152 | 87.80 173 | 63.46 212 | 87.02 158 | 91.87 79 | 79.01 26 | 78.38 121 | 89.07 130 | 65.02 93 | 93.05 161 | 70.05 157 | 76.46 254 | 92.20 98 |
|
PVSNet_Blended | | | 80.98 105 | 80.34 104 | 82.90 152 | 88.85 131 | 65.40 155 | 84.43 236 | 92.00 71 | 67.62 228 | 78.11 133 | 85.05 251 | 66.02 85 | 94.27 94 | 71.52 146 | 89.50 85 | 89.01 210 |
|
mvs-test1 | | | 80.88 106 | 79.40 125 | 85.29 67 | 85.13 219 | 69.75 76 | 89.28 73 | 88.10 202 | 74.99 89 | 76.44 166 | 86.72 194 | 57.27 187 | 94.26 98 | 73.53 124 | 83.18 169 | 91.87 106 |
|
QAPM | | | 80.88 106 | 79.50 123 | 85.03 74 | 88.01 161 | 68.97 91 | 91.59 29 | 92.00 71 | 66.63 238 | 75.15 200 | 92.16 58 | 57.70 181 | 95.45 50 | 63.52 207 | 88.76 94 | 90.66 142 |
|
1121 | | | 80.84 108 | 79.77 113 | 84.05 103 | 93.11 44 | 70.78 60 | 84.66 226 | 85.42 236 | 57.37 321 | 81.76 83 | 92.02 61 | 63.41 103 | 94.12 103 | 67.28 179 | 92.93 52 | 87.26 261 |
|
TranMVSNet+NR-MVSNet | | | 80.84 108 | 80.31 105 | 82.42 171 | 87.85 164 | 62.33 231 | 87.74 126 | 91.33 99 | 80.55 12 | 77.99 136 | 89.86 110 | 65.23 91 | 92.62 174 | 67.05 184 | 75.24 272 | 92.30 94 |
|
UGNet | | | 80.83 110 | 79.59 118 | 84.54 85 | 88.04 159 | 68.09 113 | 89.42 71 | 88.16 200 | 76.95 49 | 76.22 172 | 89.46 122 | 49.30 277 | 93.94 110 | 68.48 171 | 90.31 75 | 91.60 111 |
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022 |
Fast-Effi-MVS+ | | | 80.81 111 | 79.92 110 | 83.47 120 | 88.85 131 | 64.51 182 | 85.53 210 | 89.39 161 | 70.79 171 | 78.49 116 | 85.06 250 | 67.54 70 | 93.58 134 | 67.03 185 | 86.58 126 | 92.32 93 |
|
XVG-OURS-SEG-HR | | | 80.81 111 | 79.76 114 | 83.96 110 | 85.60 211 | 68.78 94 | 83.54 252 | 90.50 120 | 70.66 175 | 76.71 159 | 91.66 67 | 60.69 162 | 91.26 223 | 76.94 90 | 81.58 188 | 91.83 107 |
|
xiu_mvs_v1_base_debu | | | 80.80 113 | 79.72 115 | 84.03 105 | 87.35 186 | 70.19 68 | 85.56 205 | 88.77 189 | 69.06 203 | 81.83 77 | 88.16 154 | 50.91 251 | 92.85 168 | 78.29 77 | 87.56 110 | 89.06 203 |
|
xiu_mvs_v1_base | | | 80.80 113 | 79.72 115 | 84.03 105 | 87.35 186 | 70.19 68 | 85.56 205 | 88.77 189 | 69.06 203 | 81.83 77 | 88.16 154 | 50.91 251 | 92.85 168 | 78.29 77 | 87.56 110 | 89.06 203 |
|
xiu_mvs_v1_base_debi | | | 80.80 113 | 79.72 115 | 84.03 105 | 87.35 186 | 70.19 68 | 85.56 205 | 88.77 189 | 69.06 203 | 81.83 77 | 88.16 154 | 50.91 251 | 92.85 168 | 78.29 77 | 87.56 110 | 89.06 203 |
|
ACMM | | 73.20 8 | 80.78 116 | 79.84 112 | 83.58 118 | 89.31 117 | 68.37 107 | 89.99 57 | 91.60 89 | 70.28 181 | 77.25 149 | 89.66 113 | 53.37 218 | 93.53 139 | 74.24 117 | 82.85 173 | 88.85 217 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
114514_t | | | 80.68 117 | 79.51 122 | 84.20 97 | 94.09 28 | 67.27 127 | 89.64 69 | 91.11 105 | 58.75 311 | 74.08 210 | 90.72 94 | 58.10 179 | 95.04 68 | 69.70 161 | 89.42 87 | 90.30 160 |
|
CANet_DTU | | | 80.61 118 | 79.87 111 | 82.83 158 | 85.60 211 | 63.17 222 | 87.36 139 | 88.65 193 | 76.37 65 | 75.88 179 | 88.44 147 | 53.51 217 | 93.07 160 | 73.30 127 | 89.74 84 | 92.25 96 |
|
VPA-MVSNet | | | 80.60 119 | 80.55 100 | 80.76 210 | 88.07 158 | 60.80 244 | 86.86 163 | 91.58 90 | 75.67 76 | 80.24 98 | 89.45 124 | 63.34 104 | 90.25 242 | 70.51 153 | 79.22 221 | 91.23 123 |
|
PVSNet_BlendedMVS | | | 80.60 119 | 80.02 108 | 82.36 173 | 88.85 131 | 65.40 155 | 86.16 186 | 92.00 71 | 69.34 197 | 78.11 133 | 86.09 223 | 66.02 85 | 94.27 94 | 71.52 146 | 82.06 182 | 87.39 256 |
|
AdaColmap | | | 80.58 121 | 79.42 124 | 84.06 102 | 93.09 45 | 68.91 92 | 89.36 72 | 88.97 180 | 69.27 198 | 75.70 186 | 89.69 112 | 57.20 190 | 95.77 41 | 63.06 211 | 88.41 103 | 87.50 255 |
|
EI-MVSNet | | | 80.52 122 | 79.98 109 | 82.12 175 | 84.28 229 | 63.19 221 | 86.41 178 | 88.95 182 | 74.18 102 | 78.69 111 | 87.54 172 | 66.62 78 | 92.43 179 | 72.57 136 | 80.57 200 | 90.74 137 |
|
XVG-OURS | | | 80.41 123 | 79.23 134 | 83.97 109 | 85.64 210 | 69.02 88 | 83.03 262 | 90.39 122 | 71.09 168 | 77.63 142 | 91.49 75 | 54.62 209 | 91.35 221 | 75.71 103 | 83.47 161 | 91.54 113 |
|
v1neww | | | 80.40 124 | 79.54 119 | 82.98 145 | 84.10 240 | 64.51 182 | 87.57 130 | 90.22 132 | 73.25 126 | 78.47 117 | 86.65 202 | 62.83 118 | 93.86 116 | 75.72 101 | 77.02 240 | 90.58 148 |
|
v7new | | | 80.40 124 | 79.54 119 | 82.98 145 | 84.10 240 | 64.51 182 | 87.57 130 | 90.22 132 | 73.25 126 | 78.47 117 | 86.65 202 | 62.83 118 | 93.86 116 | 75.72 101 | 77.02 240 | 90.58 148 |
|
v6 | | | 80.40 124 | 79.54 119 | 82.98 145 | 84.09 242 | 64.50 186 | 87.57 130 | 90.22 132 | 73.25 126 | 78.47 117 | 86.63 204 | 62.84 117 | 93.86 116 | 75.73 100 | 77.02 240 | 90.58 148 |
|
PCF-MVS | | 73.52 7 | 80.38 127 | 78.84 141 | 85.01 75 | 87.71 178 | 68.99 90 | 83.65 249 | 91.46 97 | 63.00 274 | 77.77 140 | 90.28 99 | 66.10 82 | 95.09 67 | 61.40 227 | 88.22 105 | 90.94 131 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
X-MVStestdata | | | 80.37 128 | 77.83 163 | 88.00 11 | 94.42 14 | 73.33 17 | 92.78 10 | 92.99 33 | 79.14 21 | 83.67 58 | 12.47 368 | 67.45 71 | 96.60 21 | 83.06 40 | 94.50 37 | 94.07 25 |
|
test_djsdf | | | 80.30 129 | 79.32 128 | 83.27 128 | 83.98 249 | 65.37 158 | 90.50 46 | 90.38 123 | 68.55 213 | 76.19 173 | 88.70 137 | 56.44 194 | 93.46 141 | 78.98 69 | 80.14 207 | 90.97 130 |
|
v7 | | | 80.24 130 | 79.26 133 | 83.15 135 | 84.07 246 | 64.94 170 | 87.56 133 | 90.67 113 | 72.26 152 | 78.28 125 | 86.51 211 | 61.45 148 | 94.03 107 | 75.14 111 | 77.41 234 | 90.49 153 |
|
v2v482 | | | 80.23 131 | 79.29 132 | 83.05 141 | 83.62 258 | 64.14 194 | 87.04 157 | 89.97 143 | 73.61 117 | 78.18 132 | 87.22 180 | 61.10 156 | 93.82 119 | 76.11 96 | 76.78 251 | 91.18 124 |
|
NR-MVSNet | | | 80.23 131 | 79.38 126 | 82.78 164 | 87.80 173 | 63.34 215 | 86.31 181 | 91.09 106 | 79.01 26 | 72.17 235 | 89.07 130 | 67.20 75 | 92.81 172 | 66.08 191 | 75.65 263 | 92.20 98 |
|
Anonymous20240529 | | | 80.19 133 | 78.89 140 | 84.10 100 | 90.60 78 | 64.75 175 | 88.95 85 | 90.90 109 | 65.97 247 | 80.59 96 | 91.17 82 | 49.97 268 | 93.73 131 | 69.16 167 | 82.70 177 | 93.81 40 |
|
v1141 | | | 80.19 133 | 79.31 129 | 82.85 155 | 83.84 253 | 64.12 196 | 87.14 149 | 90.08 139 | 73.13 129 | 78.27 126 | 86.39 213 | 62.67 127 | 93.75 126 | 75.40 108 | 76.83 248 | 90.68 139 |
|
divwei89l23v2f112 | | | 80.19 133 | 79.31 129 | 82.85 155 | 83.84 253 | 64.11 198 | 87.13 152 | 90.08 139 | 73.13 129 | 78.27 126 | 86.39 213 | 62.69 125 | 93.75 126 | 75.40 108 | 76.82 249 | 90.68 139 |
|
v1 | | | 80.19 133 | 79.31 129 | 82.85 155 | 83.83 255 | 64.12 196 | 87.14 149 | 90.07 141 | 73.13 129 | 78.27 126 | 86.38 217 | 62.72 124 | 93.75 126 | 75.41 107 | 76.82 249 | 90.68 139 |
|
IterMVS-LS | | | 80.06 137 | 79.38 126 | 82.11 176 | 85.89 206 | 63.20 220 | 86.79 166 | 89.34 162 | 74.19 101 | 75.45 189 | 86.72 194 | 66.62 78 | 92.39 181 | 72.58 135 | 76.86 245 | 90.75 136 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
Effi-MVS+-dtu | | | 80.03 138 | 78.57 146 | 84.42 89 | 85.13 219 | 68.74 98 | 88.77 92 | 88.10 202 | 74.99 89 | 74.97 204 | 83.49 270 | 57.27 187 | 93.36 146 | 73.53 124 | 80.88 194 | 91.18 124 |
|
v1144 | | | 80.03 138 | 79.03 137 | 83.01 143 | 83.78 256 | 64.51 182 | 87.11 155 | 90.57 118 | 71.96 156 | 78.08 135 | 86.20 221 | 61.41 149 | 93.94 110 | 74.93 112 | 77.23 236 | 90.60 145 |
|
v8 | | | 79.97 140 | 79.02 138 | 82.80 161 | 84.09 242 | 64.50 186 | 87.96 120 | 90.29 131 | 74.13 104 | 75.24 198 | 86.81 191 | 62.88 115 | 93.89 115 | 74.39 115 | 75.40 268 | 90.00 174 |
|
DI_MVS_plusplus_test | | | 79.89 141 | 78.58 145 | 83.85 114 | 82.89 278 | 65.32 159 | 86.12 187 | 89.55 155 | 69.64 193 | 70.55 252 | 85.82 234 | 57.24 189 | 93.81 120 | 76.85 91 | 88.55 99 | 92.41 91 |
|
test_normal | | | 79.81 142 | 78.45 148 | 83.89 113 | 82.70 282 | 65.40 155 | 85.82 198 | 89.48 158 | 69.39 194 | 70.12 261 | 85.66 237 | 57.15 191 | 93.71 132 | 77.08 88 | 88.62 97 | 92.56 85 |
|
OpenMVS | | 72.83 10 | 79.77 143 | 78.33 154 | 84.09 101 | 85.17 216 | 69.91 72 | 90.57 44 | 90.97 107 | 66.70 234 | 72.17 235 | 91.91 63 | 54.70 207 | 93.96 108 | 61.81 224 | 90.95 70 | 88.41 239 |
|
v10 | | | 79.74 144 | 78.67 142 | 82.97 149 | 84.06 247 | 64.95 169 | 87.88 125 | 90.62 116 | 73.11 132 | 75.11 201 | 86.56 208 | 61.46 147 | 94.05 106 | 73.68 120 | 75.55 265 | 89.90 182 |
|
BH-RMVSNet | | | 79.61 145 | 78.44 150 | 83.14 136 | 89.38 111 | 65.93 145 | 84.95 221 | 87.15 218 | 73.56 119 | 78.19 131 | 89.79 111 | 56.67 193 | 93.36 146 | 59.53 242 | 86.74 123 | 90.13 165 |
|
v1192 | | | 79.59 146 | 78.43 151 | 83.07 140 | 83.55 260 | 64.52 180 | 86.93 161 | 90.58 117 | 70.83 170 | 77.78 139 | 85.90 230 | 59.15 173 | 93.94 110 | 73.96 119 | 77.19 238 | 90.76 135 |
|
ab-mvs | | | 79.51 147 | 78.97 139 | 81.14 204 | 88.46 148 | 60.91 242 | 83.84 247 | 89.24 168 | 70.36 179 | 79.03 107 | 88.87 134 | 63.23 108 | 90.21 243 | 65.12 198 | 82.57 179 | 92.28 95 |
|
WR-MVS | | | 79.49 148 | 79.22 135 | 80.27 218 | 88.79 137 | 58.35 261 | 85.06 219 | 88.61 196 | 78.56 29 | 77.65 141 | 88.34 149 | 63.81 102 | 90.66 238 | 64.98 201 | 77.22 237 | 91.80 110 |
|
v144192 | | | 79.47 149 | 78.37 152 | 82.78 164 | 83.35 263 | 63.96 200 | 86.96 159 | 90.36 126 | 69.99 185 | 77.50 143 | 85.67 236 | 60.66 163 | 93.77 124 | 74.27 116 | 76.58 252 | 90.62 143 |
|
BH-untuned | | | 79.47 149 | 78.60 144 | 82.05 177 | 89.19 122 | 65.91 146 | 86.07 189 | 88.52 197 | 72.18 153 | 75.42 190 | 87.69 167 | 61.15 155 | 93.54 138 | 60.38 234 | 86.83 122 | 86.70 274 |
|
mvs_anonymous | | | 79.42 151 | 79.11 136 | 80.34 215 | 84.45 228 | 57.97 268 | 82.59 263 | 87.62 211 | 67.40 233 | 76.17 176 | 88.56 144 | 68.47 63 | 89.59 251 | 70.65 152 | 86.05 134 | 93.47 56 |
|
thisisatest0530 | | | 79.40 152 | 77.76 167 | 84.31 94 | 87.69 180 | 65.10 166 | 87.36 139 | 84.26 247 | 70.04 184 | 77.42 145 | 88.26 153 | 49.94 270 | 94.79 81 | 70.20 155 | 84.70 145 | 93.03 71 |
|
tttt0517 | | | 79.40 152 | 77.91 161 | 83.90 112 | 88.10 157 | 63.84 202 | 88.37 109 | 84.05 249 | 71.45 164 | 76.78 157 | 89.12 129 | 49.93 272 | 94.89 75 | 70.18 156 | 83.18 169 | 92.96 76 |
|
V42 | | | 79.38 154 | 78.24 156 | 82.83 158 | 81.10 304 | 65.50 154 | 85.55 208 | 89.82 149 | 71.57 162 | 78.21 130 | 86.12 222 | 60.66 163 | 93.18 154 | 75.64 104 | 75.46 267 | 89.81 187 |
|
jajsoiax | | | 79.29 155 | 77.96 159 | 83.27 128 | 84.68 225 | 66.57 137 | 89.25 76 | 90.16 136 | 69.20 200 | 75.46 188 | 89.49 119 | 45.75 299 | 93.13 157 | 76.84 92 | 80.80 196 | 90.11 166 |
|
v1921920 | | | 79.22 156 | 78.03 158 | 82.80 161 | 83.30 265 | 63.94 201 | 86.80 165 | 90.33 128 | 69.91 186 | 77.48 144 | 85.53 241 | 58.44 177 | 93.75 126 | 73.60 123 | 76.85 246 | 90.71 138 |
|
TAPA-MVS | | 73.13 9 | 79.15 157 | 77.94 160 | 82.79 163 | 89.59 101 | 62.99 226 | 88.16 117 | 91.51 93 | 65.77 248 | 77.14 154 | 91.09 84 | 60.91 159 | 93.21 150 | 50.26 292 | 87.05 117 | 92.17 100 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
mvs_tets | | | 79.13 158 | 77.77 166 | 83.22 132 | 84.70 224 | 66.37 139 | 89.17 77 | 90.19 135 | 69.38 196 | 75.40 191 | 89.46 122 | 44.17 305 | 93.15 155 | 76.78 93 | 80.70 198 | 90.14 164 |
|
CDS-MVSNet | | | 79.07 159 | 77.70 168 | 83.17 134 | 87.60 181 | 68.23 111 | 84.40 238 | 86.20 229 | 67.49 231 | 76.36 167 | 86.54 209 | 61.54 146 | 90.79 236 | 61.86 223 | 87.33 114 | 90.49 153 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
MVSTER | | | 79.01 160 | 77.88 162 | 82.38 172 | 83.07 272 | 64.80 173 | 84.08 245 | 88.95 182 | 69.01 207 | 78.69 111 | 87.17 183 | 54.70 207 | 92.43 179 | 74.69 113 | 80.57 200 | 89.89 183 |
|
v1240 | | | 78.99 161 | 77.78 165 | 82.64 168 | 83.21 267 | 63.54 208 | 86.62 172 | 90.30 130 | 69.74 192 | 77.33 147 | 85.68 235 | 57.04 192 | 93.76 125 | 73.13 129 | 76.92 243 | 90.62 143 |
|
Anonymous20231211 | | | 78.97 162 | 77.69 169 | 82.81 160 | 90.54 79 | 64.29 192 | 90.11 56 | 91.51 93 | 65.01 257 | 76.16 177 | 88.13 158 | 50.56 262 | 93.03 164 | 69.68 162 | 77.56 232 | 91.11 126 |
|
v7n | | | 78.97 162 | 77.58 171 | 83.14 136 | 83.45 262 | 65.51 153 | 88.32 110 | 91.21 102 | 73.69 116 | 72.41 232 | 86.32 218 | 57.93 180 | 93.81 120 | 69.18 166 | 75.65 263 | 90.11 166 |
|
TAMVS | | | 78.89 164 | 77.51 172 | 83.03 142 | 87.80 173 | 67.79 119 | 84.72 225 | 85.05 240 | 67.63 227 | 76.75 158 | 87.70 166 | 62.25 138 | 90.82 235 | 58.53 252 | 87.13 116 | 90.49 153 |
|
v148 | | | 78.72 165 | 77.80 164 | 81.47 197 | 82.73 281 | 61.96 236 | 86.30 182 | 88.08 204 | 73.26 125 | 76.18 174 | 85.47 243 | 62.46 135 | 92.36 183 | 71.92 145 | 73.82 285 | 90.09 168 |
|
VPNet | | | 78.69 166 | 78.66 143 | 78.76 249 | 88.31 153 | 55.72 306 | 84.45 235 | 86.63 223 | 76.79 53 | 78.26 129 | 90.55 97 | 59.30 172 | 89.70 250 | 66.63 186 | 77.05 239 | 90.88 132 |
|
anonymousdsp | | | 78.60 167 | 77.15 177 | 82.98 145 | 80.51 310 | 67.08 129 | 87.24 147 | 89.53 156 | 65.66 250 | 75.16 199 | 87.19 182 | 52.52 220 | 92.25 186 | 77.17 87 | 79.34 219 | 89.61 195 |
|
WR-MVS_H | | | 78.51 168 | 78.49 147 | 78.56 252 | 88.02 160 | 56.38 295 | 88.43 102 | 92.67 45 | 77.14 44 | 73.89 211 | 87.55 171 | 66.25 81 | 89.24 258 | 58.92 247 | 73.55 287 | 90.06 172 |
|
GBi-Net | | | 78.40 169 | 77.40 173 | 81.40 199 | 87.60 181 | 63.01 223 | 88.39 106 | 89.28 164 | 71.63 159 | 75.34 193 | 87.28 176 | 54.80 203 | 91.11 226 | 62.72 212 | 79.57 215 | 90.09 168 |
|
test1 | | | 78.40 169 | 77.40 173 | 81.40 199 | 87.60 181 | 63.01 223 | 88.39 106 | 89.28 164 | 71.63 159 | 75.34 193 | 87.28 176 | 54.80 203 | 91.11 226 | 62.72 212 | 79.57 215 | 90.09 168 |
|
Vis-MVSNet (Re-imp) | | | 78.36 171 | 78.45 148 | 78.07 260 | 88.64 141 | 51.78 329 | 86.70 170 | 79.63 305 | 74.14 103 | 75.11 201 | 90.83 92 | 61.29 152 | 89.75 248 | 58.10 256 | 91.60 62 | 92.69 82 |
|
Anonymous202405211 | | | 78.25 172 | 77.01 179 | 81.99 179 | 91.03 71 | 60.67 245 | 84.77 224 | 83.90 251 | 70.65 176 | 80.00 99 | 91.20 81 | 41.08 322 | 91.43 218 | 65.21 197 | 85.26 139 | 93.85 37 |
|
CP-MVSNet | | | 78.22 173 | 78.34 153 | 77.84 262 | 87.83 171 | 54.54 311 | 87.94 122 | 91.17 104 | 77.65 33 | 73.48 213 | 88.49 145 | 62.24 139 | 88.43 279 | 62.19 218 | 74.07 280 | 90.55 151 |
|
BH-w/o | | | 78.21 174 | 77.33 175 | 80.84 208 | 88.81 135 | 65.13 165 | 84.87 222 | 87.85 208 | 69.75 190 | 74.52 208 | 84.74 257 | 61.34 150 | 93.11 158 | 58.24 255 | 85.84 137 | 84.27 307 |
|
FMVSNet2 | | | 78.20 175 | 77.21 176 | 81.20 202 | 87.60 181 | 62.89 227 | 87.47 137 | 89.02 173 | 71.63 159 | 75.29 197 | 87.28 176 | 54.80 203 | 91.10 229 | 62.38 216 | 79.38 218 | 89.61 195 |
|
MVS | | | 78.19 176 | 76.99 180 | 81.78 183 | 85.66 209 | 66.99 130 | 84.66 226 | 90.47 121 | 55.08 331 | 72.02 240 | 85.27 246 | 63.83 101 | 94.11 105 | 66.10 190 | 89.80 83 | 84.24 308 |
|
Baseline_NR-MVSNet | | | 78.15 177 | 78.33 154 | 77.61 266 | 85.79 207 | 56.21 298 | 86.78 167 | 85.76 234 | 73.60 118 | 77.93 137 | 87.57 170 | 65.02 93 | 88.99 269 | 67.14 183 | 75.33 269 | 87.63 251 |
|
CNLPA | | | 78.08 178 | 76.79 184 | 81.97 180 | 90.40 82 | 71.07 53 | 87.59 129 | 84.55 243 | 66.03 246 | 72.38 233 | 89.64 114 | 57.56 183 | 86.04 297 | 59.61 240 | 83.35 166 | 88.79 220 |
|
PLC | | 70.83 11 | 78.05 179 | 76.37 190 | 83.08 139 | 91.88 64 | 67.80 118 | 88.19 115 | 89.46 159 | 64.33 264 | 69.87 267 | 88.38 148 | 53.66 216 | 93.58 134 | 58.86 248 | 82.73 175 | 87.86 247 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
Fast-Effi-MVS+-dtu | | | 78.02 180 | 76.49 187 | 82.62 169 | 83.16 271 | 66.96 133 | 86.94 160 | 87.45 216 | 72.45 145 | 71.49 246 | 84.17 260 | 54.79 206 | 91.58 216 | 67.61 175 | 80.31 204 | 89.30 199 |
|
PS-CasMVS | | | 78.01 181 | 78.09 157 | 77.77 264 | 87.71 178 | 54.39 313 | 88.02 118 | 91.22 101 | 77.50 40 | 73.26 215 | 88.64 140 | 60.73 160 | 88.41 280 | 61.88 222 | 73.88 284 | 90.53 152 |
|
v748 | | | 77.97 182 | 76.65 186 | 81.92 182 | 82.29 288 | 63.28 217 | 87.53 134 | 90.35 127 | 73.50 122 | 70.76 251 | 85.55 240 | 58.28 178 | 92.81 172 | 68.81 170 | 72.76 292 | 89.67 194 |
|
V4 | | | 77.95 183 | 76.37 190 | 82.67 166 | 79.40 323 | 65.52 151 | 86.43 176 | 89.94 146 | 72.28 150 | 72.14 238 | 84.95 252 | 55.72 197 | 93.44 143 | 73.64 121 | 72.86 290 | 89.05 207 |
|
HY-MVS | | 69.67 12 | 77.95 183 | 77.15 177 | 80.36 214 | 87.57 185 | 60.21 249 | 83.37 260 | 87.78 209 | 66.11 243 | 75.37 192 | 87.06 189 | 63.27 106 | 90.48 240 | 61.38 228 | 82.43 180 | 90.40 158 |
|
v52 | | | 77.94 185 | 76.37 190 | 82.67 166 | 79.39 324 | 65.52 151 | 86.43 176 | 89.94 146 | 72.28 150 | 72.15 237 | 84.94 253 | 55.70 198 | 93.44 143 | 73.64 121 | 72.84 291 | 89.06 203 |
|
FMVSNet3 | | | 77.88 186 | 76.85 182 | 80.97 207 | 86.84 197 | 62.36 230 | 86.52 175 | 88.77 189 | 71.13 166 | 75.34 193 | 86.66 201 | 54.07 213 | 91.10 229 | 62.72 212 | 79.57 215 | 89.45 197 |
|
Test4 | | | 77.83 187 | 75.90 204 | 83.62 116 | 80.24 312 | 65.25 161 | 85.27 213 | 90.67 113 | 69.03 206 | 66.48 302 | 83.75 266 | 43.07 310 | 93.00 165 | 75.93 99 | 88.66 96 | 92.62 84 |
|
PEN-MVS | | | 77.73 188 | 77.69 169 | 77.84 262 | 87.07 194 | 53.91 315 | 87.91 124 | 91.18 103 | 77.56 37 | 73.14 217 | 88.82 135 | 61.23 153 | 89.17 265 | 59.95 237 | 72.37 293 | 90.43 156 |
|
v16 | | | 77.69 189 | 76.36 193 | 81.68 190 | 84.15 237 | 64.63 179 | 87.33 142 | 88.99 177 | 72.69 143 | 69.31 275 | 82.08 283 | 62.80 121 | 91.79 198 | 72.70 133 | 67.23 316 | 88.63 225 |
|
v17 | | | 77.68 190 | 76.35 194 | 81.69 189 | 84.15 237 | 64.65 177 | 87.33 142 | 88.99 177 | 72.70 142 | 69.25 276 | 82.07 284 | 62.82 120 | 91.79 198 | 72.69 134 | 67.15 318 | 88.63 225 |
|
PAPM | | | 77.68 190 | 76.40 189 | 81.51 196 | 87.29 191 | 61.85 237 | 83.78 248 | 89.59 154 | 64.74 259 | 71.23 247 | 88.70 137 | 62.59 130 | 93.66 133 | 52.66 282 | 87.03 118 | 89.01 210 |
|
v18 | | | 77.67 192 | 76.35 194 | 81.64 192 | 84.09 242 | 64.47 188 | 87.27 145 | 89.01 175 | 72.59 144 | 69.39 272 | 82.04 285 | 62.85 116 | 91.80 197 | 72.72 132 | 67.20 317 | 88.63 225 |
|
CHOSEN 1792x2688 | | | 77.63 193 | 75.69 205 | 83.44 121 | 89.98 91 | 68.58 105 | 78.70 300 | 87.50 214 | 56.38 326 | 75.80 181 | 86.84 190 | 58.67 175 | 91.40 219 | 61.58 226 | 85.75 138 | 90.34 159 |
|
HyFIR lowres test | | | 77.53 194 | 75.40 213 | 83.94 111 | 89.59 101 | 66.62 135 | 80.36 283 | 88.64 194 | 56.29 327 | 76.45 163 | 85.17 247 | 57.64 182 | 93.28 148 | 61.34 229 | 83.10 171 | 91.91 105 |
|
V14 | | | 77.52 195 | 76.12 197 | 81.70 188 | 84.15 237 | 64.77 174 | 87.21 148 | 88.95 182 | 72.80 139 | 68.79 278 | 81.94 291 | 62.69 125 | 91.72 204 | 72.31 139 | 66.27 325 | 88.60 229 |
|
V9 | | | 77.52 195 | 76.11 200 | 81.73 187 | 84.19 236 | 64.89 171 | 87.26 146 | 88.94 185 | 72.87 138 | 68.65 281 | 81.96 290 | 62.65 128 | 91.72 204 | 72.27 140 | 66.24 326 | 88.60 229 |
|
v15 | | | 77.51 197 | 76.12 197 | 81.66 191 | 84.09 242 | 64.65 177 | 87.14 149 | 88.96 181 | 72.76 140 | 68.90 277 | 81.91 292 | 62.74 123 | 91.73 202 | 72.32 138 | 66.29 324 | 88.61 228 |
|
v12 | | | 77.51 197 | 76.09 201 | 81.76 186 | 84.22 232 | 64.99 168 | 87.30 144 | 88.93 186 | 72.92 135 | 68.48 285 | 81.97 288 | 62.54 132 | 91.70 207 | 72.24 141 | 66.21 328 | 88.58 232 |
|
v13 | | | 77.50 199 | 76.07 202 | 81.77 184 | 84.23 231 | 65.07 167 | 87.34 141 | 88.91 187 | 72.92 135 | 68.35 286 | 81.97 288 | 62.53 133 | 91.69 208 | 72.20 142 | 66.22 327 | 88.56 234 |
|
v11 | | | 77.45 200 | 76.06 203 | 81.59 195 | 84.22 232 | 64.52 180 | 87.11 155 | 89.02 173 | 72.76 140 | 68.76 279 | 81.90 293 | 62.09 141 | 91.71 206 | 71.98 143 | 66.73 319 | 88.56 234 |
|
FMVSNet1 | | | 77.44 201 | 76.12 197 | 81.40 199 | 86.81 198 | 63.01 223 | 88.39 106 | 89.28 164 | 70.49 178 | 74.39 209 | 87.28 176 | 49.06 280 | 91.11 226 | 60.91 231 | 78.52 223 | 90.09 168 |
|
TR-MVS | | | 77.44 201 | 76.18 196 | 81.20 202 | 88.24 154 | 63.24 218 | 84.61 230 | 86.40 226 | 67.55 230 | 77.81 138 | 86.48 212 | 54.10 212 | 93.15 155 | 57.75 259 | 82.72 176 | 87.20 262 |
|
1112_ss | | | 77.40 203 | 76.43 188 | 80.32 216 | 89.11 128 | 60.41 248 | 83.65 249 | 87.72 210 | 62.13 285 | 73.05 218 | 86.72 194 | 62.58 131 | 89.97 245 | 62.11 221 | 80.80 196 | 90.59 147 |
|
thisisatest0515 | | | 77.33 204 | 75.38 214 | 83.18 133 | 85.27 215 | 63.80 203 | 82.11 267 | 83.27 261 | 65.06 255 | 75.91 178 | 83.84 264 | 49.54 274 | 94.27 94 | 67.24 181 | 86.19 132 | 91.48 118 |
|
pm-mvs1 | | | 77.25 205 | 76.68 185 | 78.93 246 | 84.22 232 | 58.62 259 | 86.41 178 | 88.36 199 | 71.37 165 | 73.31 214 | 88.01 159 | 61.22 154 | 89.15 266 | 64.24 205 | 73.01 289 | 89.03 209 |
|
LCM-MVSNet-Re | | | 77.05 206 | 76.94 181 | 77.36 271 | 87.20 192 | 51.60 330 | 80.06 285 | 80.46 296 | 75.20 87 | 67.69 290 | 86.72 194 | 62.48 134 | 88.98 270 | 63.44 208 | 89.25 88 | 91.51 114 |
|
DTE-MVSNet | | | 76.99 207 | 76.80 183 | 77.54 268 | 86.24 203 | 53.06 326 | 87.52 135 | 90.66 115 | 77.08 47 | 72.50 223 | 88.67 139 | 60.48 166 | 89.52 252 | 57.33 263 | 70.74 304 | 90.05 173 |
|
LS3D | | | 76.95 208 | 74.82 224 | 83.37 125 | 90.45 80 | 67.36 126 | 89.15 81 | 86.94 220 | 61.87 287 | 69.52 270 | 90.61 96 | 51.71 244 | 94.53 87 | 46.38 318 | 86.71 124 | 88.21 241 |
|
GA-MVS | | | 76.87 209 | 75.17 222 | 81.97 180 | 82.75 280 | 62.58 229 | 81.44 277 | 86.35 228 | 72.16 155 | 74.74 206 | 82.89 273 | 46.20 294 | 92.02 191 | 68.85 169 | 81.09 192 | 91.30 122 |
|
DP-MVS | | | 76.78 210 | 74.57 226 | 83.42 122 | 93.29 38 | 69.46 84 | 88.55 101 | 83.70 253 | 63.98 268 | 70.20 257 | 88.89 133 | 54.01 214 | 94.80 80 | 46.66 315 | 81.88 185 | 86.01 290 |
|
cascas | | | 76.72 211 | 74.64 225 | 82.99 144 | 85.78 208 | 65.88 147 | 82.33 265 | 89.21 169 | 60.85 293 | 72.74 220 | 81.02 300 | 47.28 287 | 93.75 126 | 67.48 177 | 85.02 140 | 89.34 198 |
|
conf200view11 | | | 76.55 212 | 75.55 208 | 79.57 234 | 89.52 103 | 56.99 282 | 85.83 195 | 83.23 262 | 73.94 106 | 76.32 168 | 87.12 184 | 51.89 235 | 91.95 192 | 48.33 300 | 83.75 154 | 89.78 188 |
|
tfpn111 | | | 76.54 213 | 75.51 210 | 79.61 231 | 89.52 103 | 56.99 282 | 85.83 195 | 83.23 262 | 73.94 106 | 76.32 168 | 87.12 184 | 51.89 235 | 92.06 190 | 48.04 307 | 83.73 158 | 89.78 188 |
|
1314 | | | 76.53 214 | 75.30 217 | 80.21 219 | 83.93 250 | 62.32 232 | 84.66 226 | 88.81 188 | 60.23 297 | 70.16 260 | 84.07 262 | 55.30 201 | 90.73 237 | 67.37 178 | 83.21 168 | 87.59 253 |
|
thres100view900 | | | 76.50 215 | 75.55 208 | 79.33 236 | 89.52 103 | 56.99 282 | 85.83 195 | 83.23 262 | 73.94 106 | 76.32 168 | 87.12 184 | 51.89 235 | 91.95 192 | 48.33 300 | 83.75 154 | 89.07 201 |
|
thres600view7 | | | 76.50 215 | 75.44 211 | 79.68 228 | 89.40 109 | 57.16 279 | 85.53 210 | 83.23 262 | 73.79 115 | 76.26 171 | 87.09 187 | 51.89 235 | 91.89 196 | 48.05 306 | 83.72 159 | 90.00 174 |
|
thres400 | | | 76.50 215 | 75.37 215 | 79.86 224 | 89.13 124 | 57.65 274 | 85.17 215 | 83.60 254 | 73.41 123 | 76.45 163 | 86.39 213 | 52.12 228 | 91.95 192 | 48.33 300 | 83.75 154 | 90.00 174 |
|
tfpn200view9 | | | 76.42 218 | 75.37 215 | 79.55 235 | 89.13 124 | 57.65 274 | 85.17 215 | 83.60 254 | 73.41 123 | 76.45 163 | 86.39 213 | 52.12 228 | 91.95 192 | 48.33 300 | 83.75 154 | 89.07 201 |
|
Test_1112_low_res | | | 76.40 219 | 75.44 211 | 79.27 237 | 89.28 118 | 58.09 264 | 81.69 272 | 87.07 219 | 59.53 304 | 72.48 225 | 86.67 200 | 61.30 151 | 89.33 256 | 60.81 233 | 80.15 206 | 90.41 157 |
|
F-COLMAP | | | 76.38 220 | 74.33 231 | 82.50 170 | 89.28 118 | 66.95 134 | 88.41 105 | 89.03 172 | 64.05 266 | 66.83 298 | 88.61 141 | 46.78 290 | 92.89 167 | 57.48 260 | 78.55 222 | 87.67 250 |
|
LTVRE_ROB | | 69.57 13 | 76.25 221 | 74.54 228 | 81.41 198 | 88.60 142 | 64.38 191 | 79.24 293 | 89.12 171 | 70.76 173 | 69.79 269 | 87.86 160 | 49.09 279 | 93.20 152 | 56.21 269 | 80.16 205 | 86.65 275 |
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 |
view600 | | | 76.20 222 | 75.21 218 | 79.16 241 | 89.64 96 | 55.82 301 | 85.74 200 | 82.06 279 | 73.88 110 | 75.74 182 | 87.85 161 | 51.84 239 | 91.66 210 | 46.75 311 | 83.42 162 | 90.00 174 |
|
view800 | | | 76.20 222 | 75.21 218 | 79.16 241 | 89.64 96 | 55.82 301 | 85.74 200 | 82.06 279 | 73.88 110 | 75.74 182 | 87.85 161 | 51.84 239 | 91.66 210 | 46.75 311 | 83.42 162 | 90.00 174 |
|
conf0.05thres1000 | | | 76.20 222 | 75.21 218 | 79.16 241 | 89.64 96 | 55.82 301 | 85.74 200 | 82.06 279 | 73.88 110 | 75.74 182 | 87.85 161 | 51.84 239 | 91.66 210 | 46.75 311 | 83.42 162 | 90.00 174 |
|
tfpn | | | 76.20 222 | 75.21 218 | 79.16 241 | 89.64 96 | 55.82 301 | 85.74 200 | 82.06 279 | 73.88 110 | 75.74 182 | 87.85 161 | 51.84 239 | 91.66 210 | 46.75 311 | 83.42 162 | 90.00 174 |
|
MVP-Stereo | | | 76.12 226 | 74.46 230 | 81.13 205 | 85.37 214 | 69.79 74 | 84.42 237 | 87.95 206 | 65.03 256 | 67.46 292 | 85.33 245 | 53.28 219 | 91.73 202 | 58.01 257 | 83.27 167 | 81.85 327 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
XVG-ACMP-BASELINE | | | 76.11 227 | 74.27 232 | 81.62 193 | 83.20 268 | 64.67 176 | 83.60 251 | 89.75 151 | 69.75 190 | 71.85 241 | 87.09 187 | 32.78 343 | 92.11 189 | 69.99 159 | 80.43 203 | 88.09 243 |
|
ACMH+ | | 68.96 14 | 76.01 228 | 74.01 233 | 82.03 178 | 88.60 142 | 65.31 160 | 88.86 88 | 87.55 212 | 70.25 182 | 67.75 289 | 87.47 174 | 41.27 320 | 93.19 153 | 58.37 253 | 75.94 259 | 87.60 252 |
|
ACMH | | 67.68 16 | 75.89 229 | 73.93 234 | 81.77 184 | 88.71 140 | 66.61 136 | 88.62 98 | 89.01 175 | 69.81 187 | 66.78 299 | 86.70 199 | 41.95 319 | 91.51 217 | 55.64 270 | 78.14 228 | 87.17 263 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
IB-MVS | | 68.01 15 | 75.85 230 | 73.36 239 | 83.31 126 | 84.76 223 | 66.03 142 | 83.38 253 | 85.06 239 | 70.21 183 | 69.40 271 | 81.05 299 | 45.76 298 | 94.66 85 | 65.10 199 | 75.49 266 | 89.25 200 |
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 |
testing_2 | | | 75.73 231 | 73.34 240 | 82.89 154 | 77.37 332 | 65.22 162 | 84.10 244 | 90.54 119 | 69.09 202 | 60.46 328 | 81.15 298 | 40.48 324 | 92.84 171 | 76.36 95 | 80.54 202 | 90.60 145 |
|
WTY-MVS | | | 75.65 232 | 75.68 206 | 75.57 291 | 86.40 202 | 56.82 286 | 77.92 307 | 82.40 273 | 65.10 254 | 76.18 174 | 87.72 165 | 63.13 113 | 80.90 319 | 60.31 235 | 81.96 183 | 89.00 212 |
|
thres200 | | | 75.55 233 | 74.47 229 | 78.82 248 | 87.78 176 | 57.85 271 | 83.07 261 | 83.51 257 | 72.44 147 | 75.84 180 | 84.42 259 | 52.08 230 | 91.75 201 | 47.41 309 | 83.64 160 | 86.86 270 |
|
EPNet_dtu | | | 75.46 234 | 74.86 223 | 77.23 274 | 82.57 285 | 54.60 310 | 86.89 162 | 83.09 267 | 71.64 158 | 66.25 304 | 85.86 232 | 55.99 196 | 88.04 284 | 54.92 272 | 86.55 127 | 89.05 207 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
IterMVS-SCA-FT | | | 75.43 235 | 73.87 236 | 80.11 220 | 82.69 283 | 64.85 172 | 81.57 275 | 83.47 258 | 69.16 201 | 70.49 255 | 84.15 261 | 51.95 233 | 88.15 282 | 69.23 165 | 72.14 296 | 87.34 258 |
|
XXY-MVS | | | 75.41 236 | 75.56 207 | 74.96 296 | 83.59 259 | 57.82 272 | 80.59 282 | 83.87 252 | 66.54 239 | 74.93 205 | 88.31 150 | 63.24 107 | 80.09 323 | 62.16 219 | 76.85 246 | 86.97 268 |
|
TransMVSNet (Re) | | | 75.39 237 | 74.56 227 | 77.86 261 | 85.50 213 | 57.10 281 | 86.78 167 | 86.09 232 | 72.17 154 | 71.53 245 | 87.34 175 | 63.01 114 | 89.31 257 | 56.84 266 | 61.83 336 | 87.17 263 |
|
CostFormer | | | 75.24 238 | 73.90 235 | 79.27 237 | 82.65 284 | 58.27 263 | 80.80 278 | 82.73 271 | 61.57 288 | 75.33 196 | 83.13 272 | 55.52 199 | 91.07 232 | 64.98 201 | 78.34 227 | 88.45 237 |
|
pmmvs6 | | | 74.69 239 | 73.39 238 | 78.61 251 | 81.38 299 | 57.48 277 | 86.64 171 | 87.95 206 | 64.99 258 | 70.18 258 | 86.61 205 | 50.43 264 | 89.52 252 | 62.12 220 | 70.18 306 | 88.83 218 |
|
PatchFormer-LS_test | | | 74.50 240 | 73.05 242 | 78.86 247 | 82.95 276 | 59.55 254 | 81.65 273 | 82.30 275 | 67.44 232 | 71.62 244 | 78.15 321 | 52.34 224 | 88.92 274 | 65.05 200 | 75.90 260 | 88.12 242 |
|
tfpnnormal | | | 74.39 241 | 73.16 241 | 78.08 259 | 86.10 205 | 58.05 265 | 84.65 229 | 87.53 213 | 70.32 180 | 71.22 248 | 85.63 238 | 54.97 202 | 89.86 246 | 43.03 335 | 75.02 273 | 86.32 282 |
|
IterMVS | | | 74.29 242 | 72.94 243 | 78.35 256 | 81.53 296 | 63.49 210 | 81.58 274 | 82.49 272 | 68.06 225 | 69.99 264 | 83.69 268 | 51.66 245 | 85.54 300 | 65.85 193 | 71.64 299 | 86.01 290 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
OurMVSNet-221017-0 | | | 74.26 243 | 72.42 249 | 79.80 226 | 83.76 257 | 59.59 251 | 85.92 193 | 86.64 222 | 66.39 241 | 66.96 297 | 87.58 169 | 39.46 327 | 91.60 215 | 65.76 194 | 69.27 308 | 88.22 240 |
|
SCA | | | 74.22 244 | 72.33 256 | 79.91 223 | 84.05 248 | 62.17 234 | 79.96 287 | 79.29 307 | 66.30 242 | 72.38 233 | 80.13 307 | 51.95 233 | 88.60 277 | 59.25 244 | 77.67 231 | 88.96 214 |
|
EG-PatchMatch MVS | | | 74.04 245 | 71.82 260 | 80.71 211 | 84.92 222 | 67.42 123 | 85.86 194 | 88.08 204 | 66.04 245 | 64.22 316 | 83.85 263 | 35.10 342 | 92.56 177 | 57.44 261 | 80.83 195 | 82.16 326 |
|
pmmvs4 | | | 74.03 246 | 71.91 258 | 80.39 213 | 81.96 291 | 68.32 108 | 81.45 276 | 82.14 277 | 59.32 305 | 69.87 267 | 85.13 248 | 52.40 223 | 88.13 283 | 60.21 236 | 74.74 276 | 84.73 305 |
|
MS-PatchMatch | | | 73.83 247 | 72.67 245 | 77.30 273 | 83.87 251 | 66.02 143 | 81.82 269 | 84.66 242 | 61.37 291 | 68.61 283 | 82.82 275 | 47.29 286 | 88.21 281 | 59.27 243 | 84.32 149 | 77.68 340 |
|
tfpn_ndepth | | | 73.70 248 | 72.75 244 | 76.52 278 | 87.78 176 | 54.92 309 | 84.32 240 | 80.28 300 | 67.57 229 | 72.50 223 | 84.82 254 | 50.12 266 | 89.44 255 | 45.73 321 | 81.66 187 | 85.20 297 |
|
DWT-MVSNet_test | | | 73.70 248 | 71.86 259 | 79.21 239 | 82.91 277 | 58.94 256 | 82.34 264 | 82.17 276 | 65.21 252 | 71.05 250 | 78.31 318 | 44.21 304 | 90.17 244 | 63.29 210 | 77.28 235 | 88.53 236 |
|
conf0.01 | | | 73.67 250 | 72.42 249 | 77.42 269 | 87.85 164 | 53.28 320 | 83.38 253 | 79.08 308 | 68.40 216 | 72.45 226 | 86.08 224 | 50.60 256 | 89.19 259 | 44.25 326 | 79.66 209 | 89.78 188 |
|
conf0.002 | | | 73.67 250 | 72.42 249 | 77.42 269 | 87.85 164 | 53.28 320 | 83.38 253 | 79.08 308 | 68.40 216 | 72.45 226 | 86.08 224 | 50.60 256 | 89.19 259 | 44.25 326 | 79.66 209 | 89.78 188 |
|
sss | | | 73.60 252 | 73.64 237 | 73.51 306 | 82.80 279 | 55.01 308 | 76.12 313 | 81.69 285 | 62.47 282 | 74.68 207 | 85.85 233 | 57.32 186 | 78.11 331 | 60.86 232 | 80.93 193 | 87.39 256 |
|
tpmp4_e23 | | | 73.45 253 | 71.17 267 | 80.31 217 | 83.55 260 | 59.56 253 | 81.88 268 | 82.33 274 | 57.94 316 | 70.51 254 | 81.62 294 | 51.19 249 | 91.63 214 | 53.96 276 | 77.51 233 | 89.75 193 |
|
tfpn1000 | | | 73.44 254 | 72.49 247 | 76.29 284 | 87.81 172 | 53.69 317 | 84.05 246 | 78.81 315 | 67.99 226 | 72.09 239 | 86.27 219 | 49.95 269 | 89.04 268 | 44.09 332 | 81.38 189 | 86.15 285 |
|
thresconf0.02 | | | 73.39 255 | 72.42 249 | 76.31 280 | 87.85 164 | 53.28 320 | 83.38 253 | 79.08 308 | 68.40 216 | 72.45 226 | 86.08 224 | 50.60 256 | 89.19 259 | 44.25 326 | 79.66 209 | 86.48 277 |
|
tfpn_n400 | | | 73.39 255 | 72.42 249 | 76.31 280 | 87.85 164 | 53.28 320 | 83.38 253 | 79.08 308 | 68.40 216 | 72.45 226 | 86.08 224 | 50.60 256 | 89.19 259 | 44.25 326 | 79.66 209 | 86.48 277 |
|
tfpnconf | | | 73.39 255 | 72.42 249 | 76.31 280 | 87.85 164 | 53.28 320 | 83.38 253 | 79.08 308 | 68.40 216 | 72.45 226 | 86.08 224 | 50.60 256 | 89.19 259 | 44.25 326 | 79.66 209 | 86.48 277 |
|
tfpnview11 | | | 73.39 255 | 72.42 249 | 76.31 280 | 87.85 164 | 53.28 320 | 83.38 253 | 79.08 308 | 68.40 216 | 72.45 226 | 86.08 224 | 50.60 256 | 89.19 259 | 44.25 326 | 79.66 209 | 86.48 277 |
|
SixPastTwentyTwo | | | 73.37 259 | 71.26 266 | 79.70 227 | 85.08 221 | 57.89 270 | 85.57 204 | 83.56 256 | 71.03 169 | 65.66 306 | 85.88 231 | 42.10 317 | 92.57 176 | 59.11 246 | 63.34 333 | 88.65 224 |
|
CR-MVSNet | | | 73.37 259 | 71.27 265 | 79.67 229 | 81.32 302 | 65.19 163 | 75.92 315 | 80.30 298 | 59.92 300 | 72.73 221 | 81.19 296 | 52.50 221 | 86.69 291 | 59.84 238 | 77.71 229 | 87.11 266 |
|
MSDG | | | 73.36 261 | 70.99 268 | 80.49 212 | 84.51 227 | 65.80 148 | 80.71 280 | 86.13 231 | 65.70 249 | 65.46 307 | 83.74 267 | 44.60 302 | 90.91 234 | 51.13 287 | 76.89 244 | 84.74 304 |
|
tpm2 | | | 73.26 262 | 71.46 262 | 78.63 250 | 83.34 264 | 56.71 289 | 80.65 281 | 80.40 297 | 56.63 325 | 73.55 212 | 82.02 286 | 51.80 243 | 91.24 224 | 56.35 268 | 78.42 226 | 87.95 244 |
|
RPSCF | | | 73.23 263 | 71.46 262 | 78.54 253 | 82.50 286 | 59.85 250 | 82.18 266 | 82.84 270 | 58.96 308 | 71.15 249 | 89.41 126 | 45.48 301 | 84.77 305 | 58.82 249 | 71.83 298 | 91.02 129 |
|
PatchmatchNet | | | 73.12 264 | 71.33 264 | 78.49 255 | 83.18 269 | 60.85 243 | 79.63 289 | 78.57 316 | 64.13 265 | 71.73 242 | 79.81 312 | 51.20 248 | 85.97 298 | 57.40 262 | 76.36 256 | 88.66 223 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
COLMAP_ROB | | 66.92 17 | 73.01 265 | 70.41 272 | 80.81 209 | 87.13 193 | 65.63 150 | 88.30 111 | 84.19 248 | 62.96 275 | 63.80 319 | 87.69 167 | 38.04 333 | 92.56 177 | 46.66 315 | 74.91 274 | 84.24 308 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
CVMVSNet | | | 72.99 266 | 72.58 246 | 74.25 303 | 84.28 229 | 50.85 335 | 86.41 178 | 83.45 259 | 44.56 351 | 73.23 216 | 87.54 172 | 49.38 275 | 85.70 299 | 65.90 192 | 78.44 225 | 86.19 284 |
|
test-LLR | | | 72.94 267 | 72.43 248 | 74.48 300 | 81.35 300 | 58.04 266 | 78.38 301 | 77.46 322 | 66.66 235 | 69.95 265 | 79.00 316 | 48.06 283 | 79.24 325 | 66.13 188 | 84.83 142 | 86.15 285 |
|
test_0402 | | | 72.79 268 | 70.44 271 | 79.84 225 | 88.13 156 | 65.99 144 | 85.93 192 | 84.29 245 | 65.57 251 | 67.40 294 | 85.49 242 | 46.92 289 | 92.61 175 | 35.88 345 | 74.38 279 | 80.94 330 |
|
tpmrst | | | 72.39 269 | 72.13 257 | 73.18 308 | 80.54 309 | 49.91 339 | 79.91 288 | 79.08 308 | 63.11 272 | 71.69 243 | 79.95 309 | 55.32 200 | 82.77 314 | 65.66 195 | 73.89 283 | 86.87 269 |
|
PatchMatch-RL | | | 72.38 270 | 70.90 269 | 76.80 277 | 88.60 142 | 67.38 125 | 79.53 290 | 76.17 328 | 62.75 279 | 69.36 273 | 82.00 287 | 45.51 300 | 84.89 304 | 53.62 278 | 80.58 199 | 78.12 338 |
|
tpm | | | 72.37 271 | 71.71 261 | 74.35 302 | 82.19 289 | 52.00 327 | 79.22 294 | 77.29 324 | 64.56 261 | 72.95 219 | 83.68 269 | 51.35 246 | 83.26 313 | 58.33 254 | 75.80 261 | 87.81 248 |
|
PVSNet | | 64.34 18 | 72.08 272 | 70.87 270 | 75.69 289 | 86.21 204 | 56.44 293 | 74.37 325 | 80.73 292 | 62.06 286 | 70.17 259 | 82.23 281 | 42.86 312 | 83.31 312 | 54.77 273 | 84.45 148 | 87.32 259 |
|
RPMNet | | | 71.62 273 | 68.94 280 | 79.67 229 | 81.32 302 | 65.19 163 | 75.92 315 | 78.30 318 | 57.60 319 | 72.73 221 | 76.45 330 | 52.30 225 | 86.69 291 | 48.14 305 | 77.71 229 | 87.11 266 |
|
pmmvs5 | | | 71.55 274 | 70.20 274 | 75.61 290 | 77.83 329 | 56.39 294 | 81.74 271 | 80.89 289 | 57.76 317 | 67.46 292 | 84.49 258 | 49.26 278 | 85.32 303 | 57.08 265 | 75.29 270 | 85.11 301 |
|
test-mter | | | 71.41 275 | 70.39 273 | 74.48 300 | 81.35 300 | 58.04 266 | 78.38 301 | 77.46 322 | 60.32 296 | 69.95 265 | 79.00 316 | 36.08 340 | 79.24 325 | 66.13 188 | 84.83 142 | 86.15 285 |
|
K. test v3 | | | 71.19 276 | 68.51 282 | 79.21 239 | 83.04 274 | 57.78 273 | 84.35 239 | 76.91 326 | 72.90 137 | 62.99 322 | 82.86 274 | 39.27 328 | 91.09 231 | 61.65 225 | 52.66 350 | 88.75 221 |
|
tpmvs | | | 71.09 277 | 69.29 277 | 76.49 279 | 82.04 290 | 56.04 299 | 78.92 298 | 81.37 288 | 64.05 266 | 67.18 296 | 78.28 319 | 49.74 273 | 89.77 247 | 49.67 295 | 72.37 293 | 83.67 313 |
|
AllTest | | | 70.96 278 | 68.09 288 | 79.58 232 | 85.15 217 | 63.62 204 | 84.58 231 | 79.83 303 | 62.31 283 | 60.32 329 | 86.73 192 | 32.02 344 | 88.96 272 | 50.28 290 | 71.57 300 | 86.15 285 |
|
Patchmtry | | | 70.74 279 | 69.16 278 | 75.49 293 | 80.72 306 | 54.07 314 | 74.94 324 | 80.30 298 | 58.34 312 | 70.01 262 | 81.19 296 | 52.50 221 | 86.54 293 | 53.37 279 | 71.09 302 | 85.87 293 |
|
MIMVSNet | | | 70.69 280 | 69.30 276 | 74.88 297 | 84.52 226 | 56.35 296 | 75.87 317 | 79.42 306 | 64.59 260 | 67.76 288 | 82.41 278 | 41.10 321 | 81.54 318 | 46.64 317 | 81.34 190 | 86.75 273 |
|
tpm cat1 | | | 70.57 281 | 68.31 284 | 77.35 272 | 82.41 287 | 57.95 269 | 78.08 305 | 80.22 301 | 52.04 342 | 68.54 284 | 77.66 325 | 52.00 232 | 87.84 286 | 51.77 283 | 72.07 297 | 86.25 283 |
|
OpenMVS_ROB | | 64.09 19 | 70.56 282 | 68.19 285 | 77.65 265 | 80.26 311 | 59.41 255 | 85.01 220 | 82.96 269 | 58.76 310 | 65.43 308 | 82.33 279 | 37.63 336 | 91.23 225 | 45.34 324 | 76.03 258 | 82.32 324 |
|
pmmvs-eth3d | | | 70.50 283 | 67.83 292 | 78.52 254 | 77.37 332 | 66.18 141 | 81.82 269 | 81.51 286 | 58.90 309 | 63.90 318 | 80.42 305 | 42.69 313 | 86.28 296 | 58.56 251 | 65.30 330 | 83.11 319 |
|
USDC | | | 70.33 284 | 68.37 283 | 76.21 286 | 80.60 308 | 56.23 297 | 79.19 295 | 86.49 224 | 60.89 292 | 61.29 325 | 85.47 243 | 31.78 346 | 89.47 254 | 53.37 279 | 76.21 257 | 82.94 323 |
|
Patchmatch-RL test | | | 70.24 285 | 67.78 294 | 77.61 266 | 77.43 331 | 59.57 252 | 71.16 330 | 70.33 349 | 62.94 276 | 68.65 281 | 72.77 340 | 50.62 255 | 85.49 301 | 69.58 163 | 66.58 322 | 87.77 249 |
|
CMPMVS | | 51.72 21 | 70.19 286 | 68.16 286 | 76.28 285 | 73.15 347 | 57.55 276 | 79.47 291 | 83.92 250 | 48.02 349 | 56.48 342 | 84.81 255 | 43.13 309 | 86.42 295 | 62.67 215 | 81.81 186 | 84.89 302 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
ppachtmachnet_test | | | 70.04 287 | 67.34 297 | 78.14 258 | 79.80 317 | 61.13 239 | 79.19 295 | 80.59 293 | 59.16 307 | 65.27 309 | 79.29 313 | 46.75 291 | 87.29 288 | 49.33 296 | 66.72 320 | 86.00 292 |
|
gg-mvs-nofinetune | | | 69.95 288 | 67.96 289 | 75.94 287 | 83.07 272 | 54.51 312 | 77.23 310 | 70.29 350 | 63.11 272 | 70.32 256 | 62.33 352 | 43.62 307 | 88.69 276 | 53.88 277 | 87.76 107 | 84.62 306 |
|
TESTMET0.1,1 | | | 69.89 289 | 69.00 279 | 72.55 309 | 79.27 326 | 56.85 285 | 78.38 301 | 74.71 338 | 57.64 318 | 68.09 287 | 77.19 327 | 37.75 334 | 76.70 336 | 63.92 206 | 84.09 150 | 84.10 311 |
|
FMVSNet5 | | | 69.50 290 | 67.96 289 | 74.15 304 | 82.97 275 | 55.35 307 | 80.01 286 | 82.12 278 | 62.56 281 | 63.02 320 | 81.53 295 | 36.92 337 | 81.92 316 | 48.42 299 | 74.06 281 | 85.17 300 |
|
PMMVS | | | 69.34 291 | 68.67 281 | 71.35 316 | 75.67 338 | 62.03 235 | 75.17 319 | 73.46 343 | 50.00 347 | 68.68 280 | 79.05 314 | 52.07 231 | 78.13 330 | 61.16 230 | 82.77 174 | 73.90 348 |
|
our_test_3 | | | 69.14 292 | 67.00 298 | 75.57 291 | 79.80 317 | 58.80 257 | 77.96 306 | 77.81 320 | 59.55 303 | 62.90 323 | 78.25 320 | 47.43 285 | 83.97 307 | 51.71 284 | 67.58 315 | 83.93 312 |
|
EPMVS | | | 69.02 293 | 68.16 286 | 71.59 312 | 79.61 320 | 49.80 341 | 77.40 309 | 66.93 359 | 62.82 278 | 70.01 262 | 79.05 314 | 45.79 297 | 77.86 333 | 56.58 267 | 75.26 271 | 87.13 265 |
|
Anonymous20231206 | | | 68.60 294 | 67.80 293 | 71.02 318 | 80.23 313 | 50.75 336 | 78.30 304 | 80.47 295 | 56.79 324 | 66.11 305 | 82.63 277 | 46.35 292 | 78.95 327 | 43.62 334 | 75.70 262 | 83.36 316 |
|
MIMVSNet1 | | | 68.58 295 | 66.78 300 | 73.98 305 | 80.07 314 | 51.82 328 | 80.77 279 | 84.37 244 | 64.40 263 | 59.75 332 | 82.16 282 | 36.47 338 | 83.63 310 | 42.73 336 | 70.33 305 | 86.48 277 |
|
EU-MVSNet | | | 68.53 296 | 67.61 296 | 71.31 317 | 78.51 328 | 47.01 345 | 84.47 232 | 84.27 246 | 42.27 352 | 66.44 303 | 84.79 256 | 40.44 325 | 83.76 308 | 58.76 250 | 68.54 314 | 83.17 317 |
|
PatchT | | | 68.46 297 | 67.85 291 | 70.29 320 | 80.70 307 | 43.93 349 | 72.47 328 | 74.88 334 | 60.15 298 | 70.55 252 | 76.57 329 | 49.94 270 | 81.59 317 | 50.58 288 | 74.83 275 | 85.34 296 |
|
test0.0.03 1 | | | 68.00 298 | 67.69 295 | 68.90 325 | 77.55 330 | 47.43 343 | 75.70 318 | 72.95 345 | 66.66 235 | 66.56 300 | 82.29 280 | 48.06 283 | 75.87 340 | 44.97 325 | 74.51 278 | 83.41 315 |
|
TDRefinement | | | 67.49 299 | 64.34 306 | 76.92 275 | 73.47 345 | 61.07 240 | 84.86 223 | 82.98 268 | 59.77 301 | 58.30 335 | 85.13 248 | 26.06 350 | 87.89 285 | 47.92 308 | 60.59 341 | 81.81 328 |
|
test20.03 | | | 67.45 300 | 66.95 299 | 68.94 324 | 75.48 341 | 44.84 347 | 77.50 308 | 77.67 321 | 66.66 235 | 63.01 321 | 83.80 265 | 47.02 288 | 78.40 329 | 42.53 337 | 68.86 312 | 83.58 314 |
|
UnsupCasMVSNet_eth | | | 67.33 301 | 65.99 302 | 71.37 314 | 73.48 344 | 51.47 332 | 75.16 320 | 85.19 238 | 65.20 253 | 60.78 327 | 80.93 303 | 42.35 314 | 77.20 335 | 57.12 264 | 53.69 349 | 85.44 295 |
|
TinyColmap | | | 67.30 302 | 64.81 304 | 74.76 299 | 81.92 292 | 56.68 290 | 80.29 284 | 81.49 287 | 60.33 295 | 56.27 343 | 83.22 271 | 24.77 352 | 87.66 287 | 45.52 322 | 69.47 307 | 79.95 334 |
|
dp | | | 66.80 303 | 65.43 303 | 70.90 319 | 79.74 319 | 48.82 342 | 75.12 322 | 74.77 336 | 59.61 302 | 64.08 317 | 77.23 326 | 42.89 311 | 80.72 320 | 48.86 298 | 66.58 322 | 83.16 318 |
|
MDA-MVSNet-bldmvs | | | 66.68 304 | 63.66 308 | 75.75 288 | 79.28 325 | 60.56 247 | 73.92 326 | 78.35 317 | 64.43 262 | 50.13 353 | 79.87 311 | 44.02 306 | 83.67 309 | 46.10 319 | 56.86 344 | 83.03 321 |
|
testgi | | | 66.67 305 | 66.53 301 | 67.08 330 | 75.62 339 | 41.69 354 | 75.93 314 | 76.50 327 | 66.11 243 | 65.20 312 | 86.59 206 | 35.72 341 | 74.71 344 | 43.71 333 | 73.38 288 | 84.84 303 |
|
CHOSEN 280x420 | | | 66.51 306 | 64.71 305 | 71.90 311 | 81.45 297 | 63.52 209 | 57.98 359 | 68.95 357 | 53.57 337 | 62.59 324 | 76.70 328 | 46.22 293 | 75.29 343 | 55.25 271 | 79.68 208 | 76.88 346 |
|
PM-MVS | | | 66.41 307 | 64.14 307 | 73.20 307 | 73.92 342 | 56.45 292 | 78.97 297 | 64.96 363 | 63.88 270 | 64.72 313 | 80.24 306 | 19.84 358 | 83.44 311 | 66.24 187 | 64.52 332 | 79.71 335 |
|
JIA-IIPM | | | 66.32 308 | 62.82 314 | 76.82 276 | 77.09 334 | 61.72 238 | 65.34 351 | 75.38 330 | 58.04 315 | 64.51 314 | 62.32 353 | 42.05 318 | 86.51 294 | 51.45 286 | 69.22 309 | 82.21 325 |
|
ADS-MVSNet2 | | | 66.20 309 | 63.33 309 | 74.82 298 | 79.92 315 | 58.75 258 | 67.55 347 | 75.19 332 | 53.37 338 | 65.25 310 | 75.86 331 | 42.32 315 | 80.53 321 | 41.57 338 | 68.91 310 | 85.18 298 |
|
YYNet1 | | | 65.03 310 | 62.91 312 | 71.38 313 | 75.85 337 | 56.60 291 | 69.12 341 | 74.66 340 | 57.28 322 | 54.12 345 | 77.87 323 | 45.85 296 | 74.48 345 | 49.95 293 | 61.52 338 | 83.05 320 |
|
MDA-MVSNet_test_wron | | | 65.03 310 | 62.92 311 | 71.37 314 | 75.93 336 | 56.73 287 | 69.09 342 | 74.73 337 | 57.28 322 | 54.03 346 | 77.89 322 | 45.88 295 | 74.39 346 | 49.89 294 | 61.55 337 | 82.99 322 |
|
Patchmatch-test | | | 64.82 312 | 63.24 310 | 69.57 322 | 79.42 322 | 49.82 340 | 63.49 354 | 69.05 356 | 51.98 343 | 59.95 331 | 80.13 307 | 50.91 251 | 70.98 354 | 40.66 340 | 73.57 286 | 87.90 246 |
|
ADS-MVSNet | | | 64.36 313 | 62.88 313 | 68.78 327 | 79.92 315 | 47.17 344 | 67.55 347 | 71.18 348 | 53.37 338 | 65.25 310 | 75.86 331 | 42.32 315 | 73.99 348 | 41.57 338 | 68.91 310 | 85.18 298 |
|
LF4IMVS | | | 64.02 314 | 62.19 315 | 69.50 323 | 70.90 352 | 53.29 319 | 76.13 312 | 77.18 325 | 52.65 341 | 58.59 333 | 80.98 301 | 23.55 353 | 76.52 337 | 53.06 281 | 66.66 321 | 78.68 337 |
|
UnsupCasMVSNet_bld | | | 63.70 315 | 61.53 317 | 70.21 321 | 73.69 343 | 51.39 333 | 72.82 327 | 81.89 283 | 55.63 329 | 57.81 336 | 71.80 342 | 38.67 330 | 78.61 328 | 49.26 297 | 52.21 351 | 80.63 331 |
|
new-patchmatchnet | | | 61.73 316 | 61.73 316 | 61.70 338 | 72.74 348 | 24.50 370 | 69.16 340 | 78.03 319 | 61.40 289 | 56.72 341 | 75.53 333 | 38.42 331 | 76.48 338 | 45.95 320 | 57.67 343 | 84.13 310 |
|
PVSNet_0 | | 57.27 20 | 61.67 317 | 59.27 318 | 68.85 326 | 79.61 320 | 57.44 278 | 68.01 345 | 73.44 344 | 55.93 328 | 58.54 334 | 70.41 345 | 44.58 303 | 77.55 334 | 47.01 310 | 35.91 356 | 71.55 350 |
|
LP | | | 61.36 318 | 57.78 321 | 72.09 310 | 75.54 340 | 58.53 260 | 67.16 349 | 75.22 331 | 51.90 344 | 54.13 344 | 69.97 346 | 37.73 335 | 80.45 322 | 32.74 349 | 55.63 346 | 77.29 342 |
|
test2356 | | | 59.50 319 | 58.08 319 | 63.74 334 | 71.23 351 | 41.88 352 | 67.59 346 | 72.42 347 | 53.72 336 | 57.65 337 | 70.74 344 | 26.31 349 | 72.40 351 | 32.03 352 | 71.06 303 | 76.93 344 |
|
MVS-HIRNet | | | 59.14 320 | 57.67 322 | 63.57 335 | 81.65 294 | 43.50 350 | 71.73 329 | 65.06 362 | 39.59 356 | 51.43 351 | 57.73 356 | 38.34 332 | 82.58 315 | 39.53 341 | 73.95 282 | 64.62 355 |
|
testus | | | 59.00 321 | 57.91 320 | 62.25 337 | 72.25 349 | 39.09 357 | 69.74 335 | 75.02 333 | 53.04 340 | 57.21 339 | 73.72 338 | 18.76 360 | 70.33 355 | 32.86 348 | 68.57 313 | 77.35 341 |
|
test1235678 | | | 58.74 322 | 56.89 325 | 64.30 332 | 69.70 353 | 41.87 353 | 71.05 331 | 74.87 335 | 54.06 333 | 50.63 352 | 71.53 343 | 25.30 351 | 74.10 347 | 31.80 353 | 63.10 334 | 76.93 344 |
|
pmmvs3 | | | 57.79 323 | 54.26 327 | 68.37 328 | 64.02 359 | 56.72 288 | 75.12 322 | 65.17 361 | 40.20 354 | 52.93 349 | 69.86 347 | 20.36 357 | 75.48 342 | 45.45 323 | 55.25 348 | 72.90 349 |
|
DSMNet-mixed | | | 57.77 324 | 56.90 324 | 60.38 339 | 67.70 357 | 35.61 360 | 69.18 339 | 53.97 366 | 32.30 362 | 57.49 338 | 79.88 310 | 40.39 326 | 68.57 358 | 38.78 342 | 72.37 293 | 76.97 343 |
|
1111 | | | 57.11 325 | 56.82 326 | 57.97 342 | 69.10 354 | 28.28 365 | 68.90 343 | 74.54 341 | 54.01 334 | 53.71 347 | 74.51 335 | 23.09 354 | 67.90 359 | 32.28 350 | 61.26 339 | 77.73 339 |
|
testpf | | | 56.51 326 | 57.58 323 | 53.30 345 | 71.99 350 | 41.19 355 | 46.89 364 | 69.32 355 | 58.06 314 | 52.87 350 | 69.45 348 | 27.99 348 | 72.73 350 | 59.59 241 | 62.07 335 | 45.98 360 |
|
LCM-MVSNet | | | 54.25 327 | 49.68 334 | 67.97 329 | 53.73 366 | 45.28 346 | 66.85 350 | 80.78 291 | 35.96 358 | 39.45 358 | 62.23 354 | 8.70 370 | 78.06 332 | 48.24 304 | 51.20 352 | 80.57 332 |
|
testmv | | | 53.85 328 | 51.03 330 | 62.31 336 | 61.46 361 | 38.88 358 | 70.95 334 | 74.69 339 | 51.11 346 | 41.26 355 | 66.85 349 | 14.28 364 | 72.13 352 | 29.19 355 | 49.51 353 | 75.93 347 |
|
FPMVS | | | 53.68 329 | 51.64 329 | 59.81 340 | 65.08 358 | 51.03 334 | 69.48 338 | 69.58 353 | 41.46 353 | 40.67 356 | 72.32 341 | 16.46 363 | 70.00 356 | 24.24 360 | 65.42 329 | 58.40 357 |
|
N_pmnet | | | 52.79 330 | 53.26 328 | 51.40 348 | 78.99 327 | 7.68 374 | 69.52 337 | 3.89 375 | 51.63 345 | 57.01 340 | 74.98 334 | 40.83 323 | 65.96 361 | 37.78 343 | 64.67 331 | 80.56 333 |
|
no-one | | | 51.08 331 | 45.79 337 | 66.95 331 | 57.92 364 | 50.49 338 | 59.63 358 | 76.04 329 | 48.04 348 | 31.85 359 | 56.10 359 | 19.12 359 | 80.08 324 | 36.89 344 | 26.52 358 | 70.29 351 |
|
new_pmnet | | | 50.91 332 | 50.29 331 | 52.78 346 | 68.58 356 | 34.94 363 | 63.71 353 | 56.63 365 | 39.73 355 | 44.95 354 | 65.47 351 | 21.93 356 | 58.48 363 | 34.98 346 | 56.62 345 | 64.92 354 |
|
ANet_high | | | 50.57 333 | 46.10 336 | 63.99 333 | 48.67 369 | 39.13 356 | 70.99 333 | 80.85 290 | 61.39 290 | 31.18 361 | 57.70 357 | 17.02 362 | 73.65 349 | 31.22 354 | 15.89 365 | 79.18 336 |
|
test12356 | | | 49.28 334 | 48.51 335 | 51.59 347 | 62.06 360 | 19.11 371 | 60.40 356 | 72.45 346 | 47.60 350 | 40.64 357 | 65.68 350 | 13.84 365 | 68.72 357 | 27.29 357 | 46.67 355 | 66.94 353 |
|
.test1245 | | | 45.55 335 | 50.02 333 | 32.14 354 | 69.10 354 | 28.28 365 | 68.90 343 | 74.54 341 | 54.01 334 | 53.71 347 | 74.51 335 | 23.09 354 | 67.90 359 | 32.28 350 | 0.02 368 | 0.25 369 |
|
Gipuma | | | 45.18 336 | 41.86 338 | 55.16 344 | 77.03 335 | 51.52 331 | 32.50 367 | 80.52 294 | 32.46 360 | 27.12 362 | 35.02 363 | 9.52 369 | 75.50 341 | 22.31 361 | 60.21 342 | 38.45 362 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
PMVS | | 37.38 22 | 44.16 337 | 40.28 339 | 55.82 343 | 40.82 372 | 42.54 351 | 65.12 352 | 63.99 364 | 34.43 359 | 24.48 363 | 57.12 358 | 3.92 372 | 76.17 339 | 17.10 363 | 55.52 347 | 48.75 358 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
PMMVS2 | | | 40.82 338 | 38.86 340 | 46.69 350 | 53.84 365 | 16.45 372 | 48.61 363 | 49.92 368 | 37.49 357 | 31.67 360 | 60.97 355 | 8.14 371 | 56.42 364 | 28.42 356 | 30.72 357 | 67.19 352 |
|
wuykxyi23d | | | 39.76 339 | 33.18 343 | 59.51 341 | 46.98 370 | 44.01 348 | 57.70 360 | 67.74 358 | 24.13 364 | 13.98 370 | 34.33 364 | 1.27 375 | 71.33 353 | 34.23 347 | 18.23 361 | 63.18 356 |
|
PNet_i23d | | | 38.26 340 | 35.42 341 | 46.79 349 | 58.74 362 | 35.48 361 | 59.65 357 | 51.25 367 | 32.45 361 | 23.44 366 | 47.53 361 | 2.04 374 | 58.96 362 | 25.60 359 | 18.09 363 | 45.92 361 |
|
v1.0 | | | 37.66 341 | 50.21 332 | 0.00 361 | 95.06 2 | 0.00 377 | 0.00 369 | 94.09 3 | 75.63 77 | 91.80 4 | 95.29 5 | 0.00 379 | 0.00 373 | 0.00 370 | 0.00 371 | 0.00 371 |
|
pcd1.5k->3k | | | 34.07 342 | 35.26 342 | 30.50 355 | 86.92 195 | 0.00 377 | 0.00 369 | 91.58 90 | 0.00 372 | 0.00 374 | 0.00 374 | 56.23 195 | 0.00 373 | 0.00 370 | 82.60 178 | 91.49 116 |
|
E-PMN | | | 31.77 343 | 30.64 344 | 35.15 352 | 52.87 367 | 27.67 367 | 57.09 361 | 47.86 369 | 24.64 363 | 16.40 368 | 33.05 365 | 11.23 367 | 54.90 365 | 14.46 365 | 18.15 362 | 22.87 364 |
|
EMVS | | | 30.81 344 | 29.65 345 | 34.27 353 | 50.96 368 | 25.95 369 | 56.58 362 | 46.80 370 | 24.01 365 | 15.53 369 | 30.68 366 | 12.47 366 | 54.43 366 | 12.81 366 | 17.05 364 | 22.43 365 |
|
MVE | | 26.22 23 | 30.37 345 | 25.89 347 | 43.81 351 | 44.55 371 | 35.46 362 | 28.87 368 | 39.07 371 | 18.20 366 | 18.58 367 | 40.18 362 | 2.68 373 | 47.37 367 | 17.07 364 | 23.78 360 | 48.60 359 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
cdsmvs_eth3d_5k | | | 19.96 346 | 26.61 346 | 0.00 361 | 0.00 377 | 0.00 377 | 0.00 369 | 89.26 167 | 0.00 372 | 0.00 374 | 88.61 141 | 61.62 145 | 0.00 373 | 0.00 370 | 0.00 371 | 0.00 371 |
|
tmp_tt | | | 18.61 347 | 21.40 348 | 10.23 358 | 4.82 374 | 10.11 373 | 34.70 366 | 30.74 373 | 1.48 369 | 23.91 365 | 26.07 367 | 28.42 347 | 13.41 370 | 27.12 358 | 15.35 366 | 7.17 366 |
|
wuyk23d | | | 16.82 348 | 15.94 349 | 19.46 357 | 58.74 362 | 31.45 364 | 39.22 365 | 3.74 376 | 6.84 368 | 6.04 371 | 2.70 371 | 1.27 375 | 24.29 369 | 10.54 367 | 14.40 367 | 2.63 367 |
|
ab-mvs-re | | | 7.23 349 | 9.64 350 | 0.00 361 | 0.00 377 | 0.00 377 | 0.00 369 | 0.00 379 | 0.00 372 | 0.00 374 | 86.72 194 | 0.00 379 | 0.00 373 | 0.00 370 | 0.00 371 | 0.00 371 |
|
test123 | | | 6.12 350 | 8.11 351 | 0.14 359 | 0.06 376 | 0.09 375 | 71.05 331 | 0.03 378 | 0.04 371 | 0.25 373 | 1.30 373 | 0.05 377 | 0.03 372 | 0.21 369 | 0.01 370 | 0.29 368 |
|
testmvs | | | 6.04 351 | 8.02 352 | 0.10 360 | 0.08 375 | 0.03 376 | 69.74 335 | 0.04 377 | 0.05 370 | 0.31 372 | 1.68 372 | 0.02 378 | 0.04 371 | 0.24 368 | 0.02 368 | 0.25 369 |
|
pcd_1.5k_mvsjas | | | 5.26 352 | 7.02 353 | 0.00 361 | 0.00 377 | 0.00 377 | 0.00 369 | 0.00 379 | 0.00 372 | 0.00 374 | 0.00 374 | 63.15 110 | 0.00 373 | 0.00 370 | 0.00 371 | 0.00 371 |
|
sosnet-low-res | | | 0.00 353 | 0.00 354 | 0.00 361 | 0.00 377 | 0.00 377 | 0.00 369 | 0.00 379 | 0.00 372 | 0.00 374 | 0.00 374 | 0.00 379 | 0.00 373 | 0.00 370 | 0.00 371 | 0.00 371 |
|
sosnet | | | 0.00 353 | 0.00 354 | 0.00 361 | 0.00 377 | 0.00 377 | 0.00 369 | 0.00 379 | 0.00 372 | 0.00 374 | 0.00 374 | 0.00 379 | 0.00 373 | 0.00 370 | 0.00 371 | 0.00 371 |
|
uncertanet | | | 0.00 353 | 0.00 354 | 0.00 361 | 0.00 377 | 0.00 377 | 0.00 369 | 0.00 379 | 0.00 372 | 0.00 374 | 0.00 374 | 0.00 379 | 0.00 373 | 0.00 370 | 0.00 371 | 0.00 371 |
|
Regformer | | | 0.00 353 | 0.00 354 | 0.00 361 | 0.00 377 | 0.00 377 | 0.00 369 | 0.00 379 | 0.00 372 | 0.00 374 | 0.00 374 | 0.00 379 | 0.00 373 | 0.00 370 | 0.00 371 | 0.00 371 |
|
uanet | | | 0.00 353 | 0.00 354 | 0.00 361 | 0.00 377 | 0.00 377 | 0.00 369 | 0.00 379 | 0.00 372 | 0.00 374 | 0.00 374 | 0.00 379 | 0.00 373 | 0.00 370 | 0.00 371 | 0.00 371 |
|
test0726 | | | | | | 95.27 1 | 71.25 50 | 93.60 4 | 94.11 1 | 77.33 42 | 92.81 1 | 95.79 3 | 80.98 2 | | | | |
|
GSMVS | | | | | | | | | | | | | | | | | 88.96 214 |
|
test_part2 | | | | | | 95.06 2 | 72.65 30 | | | | 91.80 4 | | | | | | |
|
test_part1 | | | | | 0.00 361 | | 0.00 377 | 0.00 369 | 94.09 3 | | | | 0.00 379 | 0.00 373 | 0.00 370 | 0.00 371 | 0.00 371 |
|
sam_mvs1 | | | | | | | | | | | | | 51.32 247 | | | | 88.96 214 |
|
sam_mvs | | | | | | | | | | | | | 50.01 267 | | | | |
|
ambc | | | | | 75.24 295 | 73.16 346 | 50.51 337 | 63.05 355 | 87.47 215 | | 64.28 315 | 77.81 324 | 17.80 361 | 89.73 249 | 57.88 258 | 60.64 340 | 85.49 294 |
|
MTGPA | | | | | | | | | 92.02 68 | | | | | | | | |
|
test_post1 | | | | | | | | 78.90 299 | | | | 5.43 370 | 48.81 282 | 85.44 302 | 59.25 244 | | |
|
test_post | | | | | | | | | | | | 5.46 369 | 50.36 265 | 84.24 306 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 74.00 337 | 51.12 250 | 88.60 277 | | | |
|
GG-mvs-BLEND | | | | | 75.38 294 | 81.59 295 | 55.80 305 | 79.32 292 | 69.63 352 | | 67.19 295 | 73.67 339 | 43.24 308 | 88.90 275 | 50.41 289 | 84.50 146 | 81.45 329 |
|
MTMP | | | | | | | | 92.18 23 | 32.83 372 | | | | | | | | |
|
gm-plane-assit | | | | | | 81.40 298 | 53.83 316 | | | 62.72 280 | | 80.94 302 | | 92.39 181 | 63.40 209 | | |
|
test9_res | | | | | | | | | | | | | | | 84.90 19 | 95.70 14 | 92.87 77 |
|
TEST9 | | | | | | 93.26 40 | 72.96 22 | 88.75 94 | 91.89 77 | 68.44 215 | 85.00 34 | 93.10 45 | 74.36 18 | 95.41 53 | | | |
|
test_8 | | | | | | 93.13 42 | 72.57 33 | 88.68 97 | 91.84 80 | 68.69 211 | 84.87 40 | 93.10 45 | 74.43 15 | 95.16 61 | | | |
|
agg_prior2 | | | | | | | | | | | | | | | 82.91 43 | 95.45 16 | 92.70 80 |
|
agg_prior | | | | | | 92.85 48 | 71.94 45 | | 91.78 83 | | 84.41 47 | | | 94.93 70 | | | |
|
TestCases | | | | | 79.58 232 | 85.15 217 | 63.62 204 | | 79.83 303 | 62.31 283 | 60.32 329 | 86.73 192 | 32.02 344 | 88.96 272 | 50.28 290 | 71.57 300 | 86.15 285 |
|
test_prior4 | | | | | | | 72.60 32 | 89.01 84 | | | | | | | | | |
|
test_prior2 | | | | | | | | 88.85 89 | | 75.41 81 | 84.91 36 | 93.54 35 | 74.28 19 | | 83.31 36 | 95.86 8 | |
|
test_prior | | | | | 86.33 51 | 92.61 53 | 69.59 79 | | 92.97 36 | | | | | 95.48 48 | | | 93.91 33 |
|
旧先验2 | | | | | | | | 86.56 174 | | 58.10 313 | 87.04 18 | | | 88.98 270 | 74.07 118 | | |
|
æ–°å‡ ä½•2 | | | | | | | | 86.29 183 | | | | | | | | | |
|
æ–°å‡ ä½•1 | | | | | 83.42 122 | 93.13 42 | 70.71 61 | | 85.48 235 | 57.43 320 | 81.80 80 | 91.98 62 | 63.28 105 | 92.27 185 | 64.60 204 | 92.99 51 | 87.27 260 |
|
旧先验1 | | | | | | 91.96 61 | 65.79 149 | | 86.37 227 | | | 93.08 49 | 69.31 59 | | | 92.74 55 | 88.74 222 |
|
æ— å…ˆéªŒ | | | | | | | | 87.48 136 | 88.98 179 | 60.00 299 | | | | 94.12 103 | 67.28 179 | | 88.97 213 |
|
原ACMM2 | | | | | | | | 86.86 163 | | | | | | | | | |
|
原ACMM1 | | | | | 84.35 92 | 93.01 46 | 68.79 93 | | 92.44 52 | 63.96 269 | 81.09 91 | 91.57 72 | 66.06 84 | 95.45 50 | 67.19 182 | 94.82 32 | 88.81 219 |
|
test222 | | | | | | 91.50 66 | 68.26 110 | 84.16 242 | 83.20 266 | 54.63 332 | 79.74 100 | 91.63 70 | 58.97 174 | | | 91.42 65 | 86.77 272 |
|
testdata2 | | | | | | | | | | | | | | 91.01 233 | 62.37 217 | | |
|
segment_acmp | | | | | | | | | | | | | 73.08 26 | | | | |
|
testdata | | | | | 79.97 222 | 90.90 74 | 64.21 193 | | 84.71 241 | 59.27 306 | 85.40 29 | 92.91 50 | 62.02 142 | 89.08 267 | 68.95 168 | 91.37 66 | 86.63 276 |
|
testdata1 | | | | | | | | 84.14 243 | | 75.71 74 | | | | | | | |
|
test12 | | | | | 86.80 43 | 92.63 52 | 70.70 62 | | 91.79 82 | | 82.71 71 | | 71.67 37 | 96.16 32 | | 94.50 37 | 93.54 54 |
|
plane_prior7 | | | | | | 90.08 89 | 68.51 106 | | | | | | | | | | |
|
plane_prior6 | | | | | | 89.84 94 | 68.70 102 | | | | | | 60.42 167 | | | | |
|
plane_prior5 | | | | | | | | | 92.44 52 | | | | | 95.38 55 | 78.71 71 | 86.32 130 | 91.33 120 |
|
plane_prior4 | | | | | | | | | | | | 91.00 89 | | | | | |
|
plane_prior3 | | | | | | | 68.60 104 | | | 78.44 30 | 78.92 109 | | | | | | |
|
plane_prior2 | | | | | | | | 91.25 34 | | 79.12 23 | | | | | | | |
|
plane_prior1 | | | | | | 89.90 93 | | | | | | | | | | | |
|
plane_prior | | | | | | | 68.71 100 | 90.38 50 | | 77.62 34 | | | | | | 86.16 133 | |
|
n2 | | | | | | | | | 0.00 379 | | | | | | | | |
|
nn | | | | | | | | | 0.00 379 | | | | | | | | |
|
door-mid | | | | | | | | | 69.98 351 | | | | | | | | |
|
lessismore_v0 | | | | | 78.97 245 | 81.01 305 | 57.15 280 | | 65.99 360 | | 61.16 326 | 82.82 275 | 39.12 329 | 91.34 222 | 59.67 239 | 46.92 354 | 88.43 238 |
|
LGP-MVS_train | | | | | 84.50 86 | 89.23 120 | 68.76 96 | | 91.94 75 | 75.37 83 | 76.64 161 | 91.51 73 | 54.29 210 | 94.91 72 | 78.44 73 | 83.78 152 | 89.83 185 |
|
test11 | | | | | | | | | 92.23 60 | | | | | | | | |
|
door | | | | | | | | | 69.44 354 | | | | | | | | |
|
HQP5-MVS | | | | | | | 66.98 131 | | | | | | | | | | |
|
HQP-NCC | | | | | | 89.33 112 | | 89.17 77 | | 76.41 61 | 77.23 151 | | | | | | |
|
ACMP_Plane | | | | | | 89.33 112 | | 89.17 77 | | 76.41 61 | 77.23 151 | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 77.47 83 | | |
|
HQP4-MVS | | | | | | | | | | | 77.24 150 | | | 95.11 63 | | | 91.03 127 |
|
HQP3-MVS | | | | | | | | | 92.19 63 | | | | | | | 85.99 135 | |
|
HQP2-MVS | | | | | | | | | | | | | 60.17 170 | | | | |
|
NP-MVS | | | | | | 89.62 100 | 68.32 108 | | | | | 90.24 100 | | | | | |
|
MDTV_nov1_ep13_2view | | | | | | | 37.79 359 | 75.16 320 | | 55.10 330 | 66.53 301 | | 49.34 276 | | 53.98 275 | | 87.94 245 |
|
MDTV_nov1_ep13 | | | | 69.97 275 | | 83.18 269 | 53.48 318 | 77.10 311 | 80.18 302 | 60.45 294 | 69.33 274 | 80.44 304 | 48.89 281 | 86.90 290 | 51.60 285 | 78.51 224 | |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 81.95 184 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 81.25 191 | |
|
Test By Simon | | | | | | | | | | | | | 64.33 97 | | | | |
|
ITE_SJBPF | | | | | 78.22 257 | 81.77 293 | 60.57 246 | | 83.30 260 | 69.25 199 | 67.54 291 | 87.20 181 | 36.33 339 | 87.28 289 | 54.34 274 | 74.62 277 | 86.80 271 |
|
DeepMVS_CX | | | | | 27.40 356 | 40.17 373 | 26.90 368 | | 24.59 374 | 17.44 367 | 23.95 364 | 48.61 360 | 9.77 368 | 26.48 368 | 18.06 362 | 24.47 359 | 28.83 363 |
|