test_part1 | | | | | | | | | 94.09 1 | | | | 81.79 1 | | | 96.38 3 | 93.74 40 |
|
ESAPD | | | 89.40 1 | 89.87 1 | 87.98 12 | 95.06 1 | 72.65 27 | 92.22 19 | 94.09 1 | 75.63 74 | 91.80 2 | 95.29 3 | 81.79 1 | 97.56 1 | 86.60 13 | 96.38 3 | 93.74 40 |
|
DeepPCF-MVS | | 80.84 1 | 88.10 8 | 88.56 8 | 86.73 41 | 92.24 53 | 69.03 83 | 89.57 67 | 93.39 16 | 77.53 39 | 89.79 7 | 94.12 25 | 78.98 3 | 96.58 23 | 85.66 15 | 95.72 12 | 94.58 7 |
|
HSP-MVS | | | 89.28 2 | 89.76 2 | 87.85 20 | 94.28 17 | 73.46 15 | 92.90 8 | 92.73 40 | 80.27 13 | 91.35 4 | 94.16 23 | 78.35 4 | 96.77 12 | 89.59 1 | 94.22 45 | 93.33 58 |
|
APDe-MVS | | | 89.15 3 | 89.63 3 | 87.73 22 | 94.49 10 | 71.69 44 | 93.83 2 | 93.96 4 | 75.70 72 | 91.06 5 | 96.03 1 | 76.84 5 | 97.03 8 | 89.09 2 | 95.65 16 | 94.47 11 |
|
CNVR-MVS | | | 88.93 6 | 89.13 6 | 88.33 4 | 94.77 4 | 73.82 6 | 90.51 43 | 93.00 27 | 80.90 10 | 88.06 12 | 94.06 27 | 76.43 6 | 96.84 10 | 88.48 4 | 95.99 7 | 94.34 16 |
|
MCST-MVS | | | 87.37 20 | 87.25 18 | 87.73 22 | 94.53 9 | 72.46 34 | 89.82 58 | 93.82 6 | 73.07 129 | 84.86 37 | 92.89 48 | 76.22 7 | 96.33 26 | 84.89 22 | 95.13 25 | 94.40 13 |
|
CSCG | | | 86.41 35 | 86.19 34 | 87.07 37 | 92.91 43 | 72.48 33 | 90.81 38 | 93.56 11 | 73.95 101 | 83.16 58 | 91.07 81 | 75.94 8 | 95.19 58 | 79.94 62 | 94.38 40 | 93.55 51 |
|
HPM-MVS++ | | | 89.02 4 | 89.15 5 | 88.63 1 | 95.01 3 | 76.03 1 | 92.38 15 | 92.85 35 | 80.26 14 | 87.78 14 | 94.27 19 | 75.89 9 | 96.81 11 | 87.45 10 | 96.44 2 | 93.05 68 |
|
TSAR-MVS + MP. | | | 88.02 12 | 88.11 10 | 87.72 24 | 93.68 28 | 72.13 40 | 91.41 30 | 92.35 52 | 74.62 93 | 88.90 8 | 93.85 30 | 75.75 10 | 96.00 36 | 87.80 6 | 94.63 34 | 95.04 2 |
|
agg_prior1 | | | 86.22 38 | 86.09 37 | 86.62 44 | 92.85 44 | 71.94 42 | 88.59 96 | 91.78 77 | 68.96 201 | 84.41 42 | 93.18 41 | 74.94 11 | 94.93 68 | 84.75 25 | 95.33 22 | 93.01 71 |
|
SD-MVS | | | 88.06 9 | 88.50 9 | 86.71 42 | 92.60 51 | 72.71 25 | 91.81 27 | 93.19 21 | 77.87 32 | 90.32 6 | 94.00 28 | 74.83 12 | 93.78 116 | 87.63 8 | 94.27 43 | 93.65 47 |
|
DELS-MVS | | | 85.41 49 | 85.30 47 | 85.77 58 | 88.49 138 | 67.93 111 | 85.52 206 | 93.44 14 | 78.70 28 | 83.63 55 | 89.03 124 | 74.57 13 | 95.71 41 | 80.26 60 | 94.04 46 | 93.66 42 |
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 |
Regformer-2 | | | 86.63 31 | 86.53 29 | 86.95 38 | 89.33 106 | 71.24 47 | 88.43 99 | 92.05 61 | 82.50 1 | 86.88 17 | 90.09 99 | 74.45 14 | 95.61 42 | 84.38 28 | 90.63 71 | 94.01 28 |
|
train_agg | | | 86.43 33 | 86.20 33 | 87.13 35 | 93.26 36 | 72.96 20 | 88.75 91 | 91.89 71 | 68.69 204 | 85.00 30 | 93.10 42 | 74.43 15 | 95.41 51 | 84.97 18 | 95.71 13 | 93.02 69 |
|
test_8 | | | | | | 93.13 38 | 72.57 31 | 88.68 94 | 91.84 74 | 68.69 204 | 84.87 36 | 93.10 42 | 74.43 15 | 95.16 59 | | | |
|
Regformer-1 | | | 86.41 35 | 86.33 30 | 86.64 43 | 89.33 106 | 70.93 53 | 88.43 99 | 91.39 92 | 82.14 3 | 86.65 18 | 90.09 99 | 74.39 17 | 95.01 67 | 83.97 33 | 90.63 71 | 93.97 30 |
|
TEST9 | | | | | | 93.26 36 | 72.96 20 | 88.75 91 | 91.89 71 | 68.44 208 | 85.00 30 | 93.10 42 | 74.36 18 | 95.41 51 | | | |
|
SMA-MVS | | | 89.01 5 | 89.19 4 | 88.46 3 | 94.19 21 | 73.73 7 | 92.40 14 | 93.63 9 | 74.77 91 | 92.29 1 | 95.82 2 | 74.28 19 | 97.22 5 | 88.44 5 | 96.91 1 | 94.44 12 |
|
test_prior3 | | | 86.73 28 | 86.86 27 | 86.33 48 | 92.61 49 | 69.59 75 | 88.85 86 | 92.97 32 | 75.41 78 | 84.91 32 | 93.54 32 | 74.28 19 | 95.48 46 | 83.31 35 | 95.86 9 | 93.91 31 |
|
test_prior2 | | | | | | | | 88.85 86 | | 75.41 78 | 84.91 32 | 93.54 32 | 74.28 19 | | 83.31 35 | 95.86 9 | |
|
TSAR-MVS + GP. | | | 85.71 44 | 85.33 45 | 86.84 39 | 91.34 63 | 72.50 32 | 89.07 80 | 87.28 210 | 76.41 59 | 85.80 23 | 90.22 97 | 74.15 22 | 95.37 55 | 81.82 48 | 91.88 58 | 92.65 78 |
|
SteuartSystems-ACMMP | | | 88.72 7 | 88.86 7 | 88.32 5 | 92.14 55 | 72.96 20 | 93.73 3 | 93.67 8 | 80.19 15 | 88.10 11 | 94.80 7 | 73.76 23 | 97.11 6 | 87.51 9 | 95.82 11 | 94.90 4 |
Skip Steuart: Steuart Systems R&D Blog. |
APD-MVS | | | 87.44 17 | 87.52 15 | 87.19 33 | 94.24 18 | 72.39 35 | 91.86 26 | 92.83 36 | 73.01 130 | 88.58 9 | 94.52 10 | 73.36 24 | 96.49 24 | 84.26 30 | 95.01 26 | 92.70 75 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
canonicalmvs | | | 85.91 41 | 85.87 39 | 86.04 56 | 89.84 89 | 69.44 81 | 90.45 47 | 93.00 27 | 76.70 56 | 88.01 13 | 91.23 76 | 73.28 25 | 93.91 107 | 81.50 51 | 88.80 89 | 94.77 5 |
|
segment_acmp | | | | | | | | | | | | | 73.08 26 | | | | |
|
NCCC | | | 88.06 9 | 88.01 12 | 88.24 6 | 94.41 14 | 73.62 8 | 91.22 34 | 92.83 36 | 81.50 7 | 85.79 24 | 93.47 36 | 73.02 27 | 97.00 9 | 84.90 20 | 94.94 27 | 94.10 22 |
|
agg_prior3 | | | 86.16 39 | 85.85 40 | 87.10 36 | 93.31 33 | 72.86 24 | 88.77 89 | 91.68 81 | 68.29 216 | 84.26 45 | 92.83 50 | 72.83 28 | 95.42 50 | 84.97 18 | 95.71 13 | 93.02 69 |
|
nrg030 | | | 83.88 57 | 83.53 56 | 84.96 72 | 86.77 190 | 69.28 82 | 90.46 46 | 92.67 41 | 74.79 90 | 82.95 59 | 91.33 75 | 72.70 29 | 93.09 152 | 80.79 56 | 79.28 212 | 92.50 82 |
|
Regformer-4 | | | 85.68 45 | 85.45 43 | 86.35 47 | 88.95 122 | 69.67 73 | 88.29 108 | 91.29 94 | 81.73 5 | 85.36 26 | 90.01 102 | 72.62 30 | 95.35 56 | 83.28 37 | 87.57 104 | 94.03 26 |
|
Regformer-3 | | | 85.23 51 | 85.07 49 | 85.70 59 | 88.95 122 | 69.01 85 | 88.29 108 | 89.91 142 | 80.95 9 | 85.01 29 | 90.01 102 | 72.45 31 | 94.19 94 | 82.50 46 | 87.57 104 | 93.90 33 |
|
CDPH-MVS | | | 85.76 43 | 85.29 48 | 87.17 34 | 93.49 32 | 71.08 48 | 88.58 97 | 92.42 49 | 68.32 215 | 84.61 39 | 93.48 34 | 72.32 32 | 96.15 33 | 79.00 65 | 95.43 18 | 94.28 19 |
|
MP-MVS | | | 87.71 13 | 87.64 14 | 87.93 16 | 94.36 16 | 73.88 4 | 92.71 13 | 92.65 43 | 77.57 35 | 83.84 50 | 94.40 18 | 72.24 33 | 96.28 28 | 85.65 16 | 95.30 24 | 93.62 49 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
DeepC-MVS | | 79.81 2 | 87.08 26 | 86.88 26 | 87.69 26 | 91.16 65 | 72.32 38 | 90.31 49 | 93.94 5 | 77.12 44 | 82.82 62 | 94.23 21 | 72.13 34 | 97.09 7 | 84.83 23 | 95.37 19 | 93.65 47 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
test12 | | | | | 86.80 40 | 92.63 48 | 70.70 58 | | 91.79 76 | | 82.71 65 | | 71.67 35 | 96.16 32 | | 94.50 36 | 93.54 52 |
|
UniMVSNet_NR-MVSNet | | | 81.88 84 | 81.54 82 | 82.92 142 | 88.46 141 | 63.46 202 | 87.13 148 | 92.37 51 | 80.19 15 | 78.38 113 | 89.14 121 | 71.66 36 | 93.05 154 | 70.05 150 | 76.46 247 | 92.25 90 |
|
DeepC-MVS_fast | | 79.65 3 | 86.91 27 | 86.62 28 | 87.76 21 | 93.52 31 | 72.37 37 | 91.26 31 | 93.04 24 | 76.62 57 | 84.22 46 | 93.36 38 | 71.44 37 | 96.76 13 | 80.82 55 | 95.33 22 | 94.16 20 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
MVS_111021_HR | | | 85.14 53 | 84.75 53 | 86.32 50 | 91.65 61 | 72.70 26 | 85.98 184 | 90.33 123 | 76.11 68 | 82.08 70 | 91.61 68 | 71.36 38 | 94.17 96 | 81.02 52 | 92.58 55 | 92.08 96 |
|
ACMMP_Plus | | | 88.05 11 | 88.08 11 | 87.94 13 | 93.70 26 | 73.05 19 | 90.86 37 | 93.59 10 | 76.27 66 | 88.14 10 | 95.09 6 | 71.06 39 | 96.67 16 | 87.67 7 | 96.37 5 | 94.09 23 |
|
MP-MVS-pluss | | | 87.67 14 | 87.72 13 | 87.54 28 | 93.64 29 | 72.04 41 | 89.80 60 | 93.50 12 | 75.17 85 | 86.34 19 | 95.29 3 | 70.86 40 | 96.00 36 | 88.78 3 | 96.04 6 | 94.58 7 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
HFP-MVS | | | 87.58 15 | 87.47 16 | 87.94 13 | 94.58 7 | 73.54 12 | 93.04 5 | 93.24 18 | 76.78 52 | 84.91 32 | 94.44 15 | 70.78 41 | 96.61 19 | 84.53 26 | 94.89 29 | 93.66 42 |
|
#test# | | | 87.33 21 | 87.13 21 | 87.94 13 | 94.58 7 | 73.54 12 | 92.34 16 | 93.24 18 | 75.23 82 | 84.91 32 | 94.44 15 | 70.78 41 | 96.61 19 | 83.75 34 | 94.89 29 | 93.66 42 |
|
EI-MVSNet-Vis-set | | | 84.19 55 | 83.81 55 | 85.31 62 | 88.18 148 | 67.85 112 | 87.66 123 | 89.73 146 | 80.05 17 | 82.95 59 | 89.59 110 | 70.74 43 | 94.82 76 | 80.66 57 | 84.72 138 | 93.28 59 |
|
CANet | | | 86.45 32 | 86.10 36 | 87.51 29 | 90.09 83 | 70.94 52 | 89.70 64 | 92.59 44 | 81.78 4 | 81.32 78 | 91.43 74 | 70.34 44 | 97.23 4 | 84.26 30 | 93.36 49 | 94.37 14 |
|
alignmvs | | | 85.48 46 | 85.32 46 | 85.96 57 | 89.51 101 | 69.47 79 | 89.74 62 | 92.47 45 | 76.17 67 | 87.73 15 | 91.46 73 | 70.32 45 | 93.78 116 | 81.51 50 | 88.95 86 | 94.63 6 |
|
EI-MVSNet-UG-set | | | 83.81 58 | 83.38 58 | 85.09 69 | 87.87 155 | 67.53 116 | 87.44 135 | 89.66 147 | 79.74 18 | 82.23 69 | 89.41 119 | 70.24 46 | 94.74 78 | 79.95 61 | 83.92 144 | 92.99 72 |
|
MVS_Test | | | 83.15 68 | 83.06 63 | 83.41 118 | 86.86 187 | 63.21 209 | 86.11 182 | 92.00 65 | 74.31 96 | 82.87 61 | 89.44 118 | 70.03 47 | 93.21 143 | 77.39 82 | 88.50 98 | 93.81 37 |
|
FC-MVSNet-test | | | 81.52 91 | 82.02 77 | 80.03 212 | 88.42 143 | 55.97 291 | 87.95 117 | 93.42 15 | 77.10 45 | 77.38 137 | 90.98 87 | 69.96 48 | 91.79 191 | 68.46 165 | 84.50 139 | 92.33 86 |
|
FIs | | | 82.07 81 | 82.42 69 | 81.04 197 | 88.80 129 | 58.34 252 | 88.26 110 | 93.49 13 | 76.93 49 | 78.47 109 | 91.04 82 | 69.92 49 | 92.34 177 | 69.87 153 | 84.97 135 | 92.44 84 |
|
UniMVSNet (Re) | | | 81.60 90 | 81.11 88 | 83.09 130 | 88.38 144 | 64.41 184 | 87.60 124 | 93.02 26 | 78.42 31 | 78.56 106 | 88.16 145 | 69.78 50 | 93.26 142 | 69.58 156 | 76.49 246 | 91.60 105 |
|
casdiffmvs | | | 83.96 56 | 83.25 60 | 86.07 54 | 88.48 139 | 69.60 74 | 89.26 72 | 92.40 50 | 68.07 217 | 82.82 62 | 90.03 101 | 69.77 51 | 94.86 75 | 81.79 49 | 86.64 120 | 93.75 39 |
|
HPM-MVS | | | 87.11 24 | 86.98 23 | 87.50 30 | 93.88 25 | 72.16 39 | 92.19 21 | 93.33 17 | 76.07 69 | 83.81 51 | 93.95 29 | 69.77 51 | 96.01 35 | 85.15 17 | 94.66 33 | 94.32 18 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
Effi-MVS+ | | | 83.62 62 | 83.08 62 | 85.24 65 | 88.38 144 | 67.45 117 | 88.89 84 | 89.15 163 | 75.50 77 | 82.27 68 | 88.28 143 | 69.61 53 | 94.45 85 | 77.81 77 | 87.84 102 | 93.84 36 |
|
PHI-MVS | | | 86.43 33 | 86.17 35 | 87.24 32 | 90.88 71 | 70.96 50 | 92.27 18 | 94.07 3 | 72.45 141 | 85.22 28 | 91.90 61 | 69.47 54 | 96.42 25 | 83.28 37 | 95.94 8 | 94.35 15 |
|
UA-Net | | | 85.08 54 | 84.96 50 | 85.45 60 | 92.07 56 | 68.07 109 | 89.78 61 | 90.86 106 | 82.48 2 | 84.60 40 | 93.20 40 | 69.35 55 | 95.22 57 | 71.39 144 | 90.88 69 | 93.07 67 |
|
旧先验1 | | | | | | 91.96 57 | 65.79 144 | | 86.37 220 | | | 93.08 46 | 69.31 56 | | | 92.74 53 | 88.74 214 |
|
region2R | | | 87.42 19 | 87.20 20 | 88.09 7 | 94.63 6 | 73.55 10 | 93.03 7 | 93.12 23 | 76.73 55 | 84.45 41 | 94.52 10 | 69.09 57 | 96.70 15 | 84.37 29 | 94.83 31 | 94.03 26 |
|
EPP-MVSNet | | | 83.40 66 | 83.02 64 | 84.57 80 | 90.13 81 | 64.47 182 | 92.32 17 | 90.73 107 | 74.45 95 | 79.35 97 | 91.10 79 | 69.05 58 | 95.12 60 | 72.78 127 | 87.22 111 | 94.13 21 |
|
ACMMPR | | | 87.44 17 | 87.23 19 | 88.08 8 | 94.64 5 | 73.59 9 | 93.04 5 | 93.20 20 | 76.78 52 | 84.66 38 | 94.52 10 | 68.81 59 | 96.65 17 | 84.53 26 | 94.90 28 | 94.00 29 |
|
mvs_anonymous | | | 79.42 145 | 79.11 130 | 80.34 206 | 84.45 219 | 57.97 258 | 82.59 256 | 87.62 204 | 67.40 226 | 76.17 166 | 88.56 136 | 68.47 60 | 89.59 244 | 70.65 147 | 86.05 128 | 93.47 54 |
|
zzz-MVS | | | 87.53 16 | 87.41 17 | 87.90 17 | 94.18 22 | 74.25 2 | 90.23 51 | 92.02 62 | 79.45 19 | 85.88 21 | 94.80 7 | 68.07 61 | 96.21 30 | 86.69 11 | 95.34 20 | 93.23 60 |
|
MTAPA | | | 87.23 22 | 87.00 22 | 87.90 17 | 94.18 22 | 74.25 2 | 86.58 168 | 92.02 62 | 79.45 19 | 85.88 21 | 94.80 7 | 68.07 61 | 96.21 30 | 86.69 11 | 95.34 20 | 93.23 60 |
|
CP-MVS | | | 87.11 24 | 86.92 24 | 87.68 27 | 94.20 20 | 73.86 5 | 93.98 1 | 92.82 38 | 76.62 57 | 83.68 52 | 94.46 14 | 67.93 63 | 95.95 38 | 84.20 32 | 94.39 39 | 93.23 60 |
|
PAPM_NR | | | 83.02 71 | 82.41 70 | 84.82 77 | 92.47 52 | 66.37 134 | 87.93 119 | 91.80 75 | 73.82 110 | 77.32 139 | 90.66 90 | 67.90 64 | 94.90 72 | 70.37 149 | 89.48 83 | 93.19 64 |
|
PGM-MVS | | | 86.68 29 | 86.27 32 | 87.90 17 | 94.22 19 | 73.38 16 | 90.22 52 | 93.04 24 | 75.53 76 | 83.86 49 | 94.42 17 | 67.87 65 | 96.64 18 | 82.70 44 | 94.57 35 | 93.66 42 |
|
PAPR | | | 81.66 89 | 80.89 91 | 83.99 103 | 90.27 79 | 64.00 193 | 86.76 164 | 91.77 79 | 68.84 202 | 77.13 146 | 89.50 111 | 67.63 66 | 94.88 73 | 67.55 169 | 88.52 97 | 93.09 66 |
|
Fast-Effi-MVS+ | | | 80.81 105 | 79.92 104 | 83.47 114 | 88.85 124 | 64.51 176 | 85.53 204 | 89.39 154 | 70.79 166 | 78.49 108 | 85.06 242 | 67.54 67 | 93.58 128 | 67.03 177 | 86.58 121 | 92.32 87 |
|
XVS | | | 87.18 23 | 86.91 25 | 88.00 10 | 94.42 12 | 73.33 17 | 92.78 9 | 92.99 29 | 79.14 21 | 83.67 53 | 94.17 22 | 67.45 68 | 96.60 21 | 83.06 39 | 94.50 36 | 94.07 24 |
|
X-MVStestdata | | | 80.37 122 | 77.83 156 | 88.00 10 | 94.42 12 | 73.33 17 | 92.78 9 | 92.99 29 | 79.14 21 | 83.67 53 | 12.47 359 | 67.45 68 | 96.60 21 | 83.06 39 | 94.50 36 | 94.07 24 |
|
NR-MVSNet | | | 80.23 125 | 79.38 120 | 82.78 155 | 87.80 165 | 63.34 205 | 86.31 176 | 91.09 100 | 79.01 26 | 72.17 224 | 89.07 122 | 67.20 70 | 92.81 165 | 66.08 183 | 75.65 256 | 92.20 92 |
|
MSLP-MVS++ | | | 85.43 48 | 85.76 42 | 84.45 84 | 91.93 58 | 70.24 61 | 90.71 40 | 92.86 34 | 77.46 41 | 84.22 46 | 92.81 53 | 67.16 71 | 92.94 159 | 80.36 58 | 94.35 41 | 90.16 155 |
|
MG-MVS | | | 83.41 65 | 83.45 57 | 83.28 121 | 92.74 46 | 62.28 223 | 88.17 112 | 89.50 151 | 75.22 83 | 81.49 77 | 92.74 54 | 66.75 72 | 95.11 61 | 72.85 126 | 91.58 61 | 92.45 83 |
|
EI-MVSNet | | | 80.52 116 | 79.98 103 | 82.12 166 | 84.28 220 | 63.19 211 | 86.41 173 | 88.95 175 | 74.18 98 | 78.69 103 | 87.54 164 | 66.62 73 | 92.43 172 | 72.57 132 | 80.57 192 | 90.74 129 |
|
IterMVS-LS | | | 80.06 131 | 79.38 120 | 82.11 167 | 85.89 198 | 63.20 210 | 86.79 161 | 89.34 155 | 74.19 97 | 75.45 178 | 86.72 186 | 66.62 73 | 92.39 174 | 72.58 131 | 76.86 238 | 90.75 128 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
mPP-MVS | | | 86.67 30 | 86.32 31 | 87.72 24 | 94.41 14 | 73.55 10 | 92.74 11 | 92.22 55 | 76.87 50 | 82.81 64 | 94.25 20 | 66.44 75 | 96.24 29 | 82.88 43 | 94.28 42 | 93.38 55 |
|
WR-MVS_H | | | 78.51 160 | 78.49 141 | 78.56 243 | 88.02 152 | 56.38 285 | 88.43 99 | 92.67 41 | 77.14 43 | 73.89 200 | 87.55 163 | 66.25 76 | 89.24 251 | 58.92 238 | 73.55 280 | 90.06 164 |
|
PCF-MVS | | 73.52 7 | 80.38 121 | 78.84 135 | 85.01 71 | 87.71 170 | 68.99 86 | 83.65 242 | 91.46 91 | 63.00 265 | 77.77 132 | 90.28 94 | 66.10 77 | 95.09 65 | 61.40 219 | 88.22 101 | 90.94 123 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
EPNet | | | 83.72 60 | 82.92 66 | 86.14 53 | 84.22 223 | 69.48 78 | 91.05 36 | 85.27 230 | 81.30 8 | 76.83 147 | 91.65 65 | 66.09 78 | 95.56 44 | 76.00 94 | 93.85 47 | 93.38 55 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
原ACMM1 | | | | | 84.35 88 | 93.01 42 | 68.79 89 | | 92.44 46 | 63.96 260 | 81.09 83 | 91.57 69 | 66.06 79 | 95.45 48 | 67.19 174 | 94.82 32 | 88.81 211 |
|
PVSNet_BlendedMVS | | | 80.60 113 | 80.02 102 | 82.36 164 | 88.85 124 | 65.40 150 | 86.16 180 | 92.00 65 | 69.34 190 | 78.11 125 | 86.09 215 | 66.02 80 | 94.27 89 | 71.52 142 | 82.06 174 | 87.39 249 |
|
PVSNet_Blended | | | 80.98 99 | 80.34 98 | 82.90 143 | 88.85 124 | 65.40 150 | 84.43 229 | 92.00 65 | 67.62 221 | 78.11 125 | 85.05 243 | 66.02 80 | 94.27 89 | 71.52 142 | 89.50 82 | 89.01 203 |
|
APD-MVS_3200maxsize | | | 85.97 40 | 85.88 38 | 86.22 51 | 92.69 47 | 69.53 77 | 91.93 25 | 92.99 29 | 73.54 116 | 85.94 20 | 94.51 13 | 65.80 82 | 95.61 42 | 83.04 41 | 92.51 56 | 93.53 53 |
|
diffmvs | | | 81.48 94 | 81.21 87 | 82.31 165 | 83.28 256 | 62.72 218 | 85.09 211 | 88.63 188 | 74.99 86 | 78.31 116 | 88.81 128 | 65.80 82 | 91.36 212 | 79.03 64 | 86.95 115 | 92.84 74 |
|
PVSNet_Blended_VisFu | | | 82.62 75 | 81.83 80 | 84.96 72 | 90.80 73 | 69.76 71 | 88.74 93 | 91.70 80 | 69.39 187 | 78.96 100 | 88.46 138 | 65.47 84 | 94.87 74 | 74.42 110 | 88.57 94 | 90.24 153 |
|
API-MVS | | | 81.99 83 | 81.23 85 | 84.26 91 | 90.94 69 | 70.18 67 | 91.10 35 | 89.32 156 | 71.51 159 | 78.66 105 | 88.28 143 | 65.26 85 | 95.10 64 | 64.74 195 | 91.23 66 | 87.51 247 |
|
TranMVSNet+NR-MVSNet | | | 80.84 102 | 80.31 99 | 82.42 162 | 87.85 156 | 62.33 221 | 87.74 122 | 91.33 93 | 80.55 12 | 77.99 128 | 89.86 104 | 65.23 86 | 92.62 167 | 67.05 176 | 75.24 265 | 92.30 88 |
|
IS-MVSNet | | | 83.15 68 | 82.81 67 | 84.18 93 | 89.94 87 | 63.30 206 | 91.59 28 | 88.46 191 | 79.04 25 | 79.49 95 | 92.16 55 | 65.10 87 | 94.28 88 | 67.71 167 | 91.86 59 | 94.95 3 |
|
DU-MVS | | | 81.12 98 | 80.52 96 | 82.90 143 | 87.80 165 | 63.46 202 | 87.02 153 | 91.87 73 | 79.01 26 | 78.38 113 | 89.07 122 | 65.02 88 | 93.05 154 | 70.05 150 | 76.46 247 | 92.20 92 |
|
Baseline_NR-MVSNet | | | 78.15 169 | 78.33 148 | 77.61 258 | 85.79 199 | 56.21 289 | 86.78 162 | 85.76 227 | 73.60 114 | 77.93 129 | 87.57 162 | 65.02 88 | 88.99 262 | 67.14 175 | 75.33 262 | 87.63 244 |
|
VNet | | | 82.21 79 | 82.41 70 | 81.62 184 | 90.82 72 | 60.93 231 | 84.47 225 | 89.78 144 | 76.36 64 | 84.07 48 | 91.88 62 | 64.71 90 | 90.26 234 | 70.68 146 | 88.89 87 | 93.66 42 |
|
MVS_0304 | | | 86.37 37 | 85.81 41 | 88.02 9 | 90.13 81 | 72.39 35 | 89.66 65 | 92.75 39 | 81.64 6 | 82.66 67 | 92.04 57 | 64.44 91 | 97.35 3 | 84.76 24 | 94.25 44 | 94.33 17 |
|
Test By Simon | | | | | | | | | | | | | 64.33 92 | | | | |
|
ACMMP | | | 85.89 42 | 85.39 44 | 87.38 31 | 93.59 30 | 72.63 29 | 92.74 11 | 93.18 22 | 76.78 52 | 80.73 87 | 93.82 31 | 64.33 92 | 96.29 27 | 82.67 45 | 90.69 70 | 93.23 60 |
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 |
DP-MVS Recon | | | 83.11 70 | 82.09 75 | 86.15 52 | 94.44 11 | 70.92 54 | 88.79 88 | 92.20 56 | 70.53 172 | 79.17 98 | 91.03 84 | 64.12 94 | 96.03 34 | 68.39 166 | 90.14 76 | 91.50 109 |
|
CLD-MVS | | | 82.31 78 | 81.65 81 | 84.29 90 | 88.47 140 | 67.73 115 | 85.81 193 | 92.35 52 | 75.78 70 | 78.33 115 | 86.58 199 | 64.01 95 | 94.35 86 | 76.05 93 | 87.48 109 | 90.79 126 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
MVS | | | 78.19 168 | 76.99 172 | 81.78 174 | 85.66 201 | 66.99 125 | 84.66 219 | 90.47 116 | 55.08 322 | 72.02 229 | 85.27 238 | 63.83 96 | 94.11 99 | 66.10 182 | 89.80 80 | 84.24 301 |
|
WR-MVS | | | 79.49 142 | 79.22 129 | 80.27 209 | 88.79 130 | 58.35 251 | 85.06 212 | 88.61 189 | 78.56 29 | 77.65 133 | 88.34 141 | 63.81 97 | 90.66 231 | 64.98 193 | 77.22 229 | 91.80 104 |
|
1121 | | | 80.84 102 | 79.77 107 | 84.05 98 | 93.11 40 | 70.78 56 | 84.66 219 | 85.42 229 | 57.37 312 | 81.76 76 | 92.02 58 | 63.41 98 | 94.12 97 | 67.28 172 | 92.93 51 | 87.26 254 |
|
VPA-MVSNet | | | 80.60 113 | 80.55 95 | 80.76 201 | 88.07 150 | 60.80 234 | 86.86 158 | 91.58 84 | 75.67 73 | 80.24 90 | 89.45 117 | 63.34 99 | 90.25 235 | 70.51 148 | 79.22 213 | 91.23 115 |
|
新几何1 | | | | | 83.42 116 | 93.13 38 | 70.71 57 | | 85.48 228 | 57.43 311 | 81.80 74 | 91.98 59 | 63.28 100 | 92.27 178 | 64.60 196 | 92.99 50 | 87.27 253 |
|
HY-MVS | | 69.67 12 | 77.95 175 | 77.15 169 | 80.36 205 | 87.57 176 | 60.21 239 | 83.37 253 | 87.78 202 | 66.11 235 | 75.37 181 | 87.06 181 | 63.27 101 | 90.48 233 | 61.38 220 | 82.43 172 | 90.40 150 |
|
XXY-MVS | | | 75.41 227 | 75.56 200 | 74.96 288 | 83.59 249 | 57.82 262 | 80.59 273 | 83.87 243 | 66.54 232 | 74.93 194 | 88.31 142 | 63.24 102 | 80.09 316 | 62.16 211 | 76.85 239 | 86.97 261 |
|
ab-mvs | | | 79.51 141 | 78.97 133 | 81.14 195 | 88.46 141 | 60.91 232 | 83.84 240 | 89.24 161 | 70.36 174 | 79.03 99 | 88.87 126 | 63.23 103 | 90.21 236 | 65.12 190 | 82.57 171 | 92.28 89 |
|
xiu_mvs_v2_base | | | 81.69 87 | 81.05 89 | 83.60 111 | 89.15 117 | 68.03 110 | 84.46 227 | 90.02 137 | 70.67 169 | 81.30 81 | 86.53 202 | 63.17 104 | 94.19 94 | 75.60 102 | 88.54 96 | 88.57 225 |
|
pcd_1.5k_mvsjas | | | 5.26 342 | 7.02 343 | 0.00 353 | 0.00 367 | 0.00 368 | 0.00 360 | 0.00 369 | 0.00 363 | 0.00 364 | 0.00 365 | 63.15 105 | 0.00 366 | 0.00 363 | 0.00 364 | 0.00 364 |
|
PS-MVSNAJss | | | 82.07 81 | 81.31 83 | 84.34 89 | 86.51 193 | 67.27 122 | 89.27 71 | 91.51 87 | 71.75 153 | 79.37 96 | 90.22 97 | 63.15 105 | 94.27 89 | 77.69 78 | 82.36 173 | 91.49 110 |
|
PS-MVSNAJ | | | 81.69 87 | 81.02 90 | 83.70 109 | 89.51 101 | 68.21 107 | 84.28 234 | 90.09 133 | 70.79 166 | 81.26 82 | 85.62 231 | 63.15 105 | 94.29 87 | 75.62 101 | 88.87 88 | 88.59 223 |
|
WTY-MVS | | | 75.65 224 | 75.68 199 | 75.57 283 | 86.40 194 | 56.82 276 | 77.92 298 | 82.40 263 | 65.10 246 | 76.18 164 | 87.72 157 | 63.13 108 | 80.90 312 | 60.31 227 | 81.96 175 | 89.00 205 |
|
TransMVSNet (Re) | | | 75.39 228 | 74.56 219 | 77.86 253 | 85.50 205 | 57.10 271 | 86.78 162 | 86.09 225 | 72.17 150 | 71.53 234 | 87.34 167 | 63.01 109 | 89.31 250 | 56.84 258 | 61.83 329 | 87.17 256 |
|
v8 | | | 79.97 134 | 79.02 132 | 82.80 152 | 84.09 233 | 64.50 180 | 87.96 116 | 90.29 126 | 74.13 100 | 75.24 187 | 86.81 183 | 62.88 110 | 93.89 109 | 74.39 111 | 75.40 261 | 90.00 166 |
|
v18 | | | 77.67 184 | 76.35 186 | 81.64 183 | 84.09 233 | 64.47 182 | 87.27 141 | 89.01 168 | 72.59 140 | 69.39 262 | 82.04 276 | 62.85 111 | 91.80 190 | 72.72 128 | 67.20 310 | 88.63 217 |
|
v6 | | | 80.40 118 | 79.54 113 | 82.98 137 | 84.09 233 | 64.50 180 | 87.57 126 | 90.22 127 | 73.25 122 | 78.47 109 | 86.63 196 | 62.84 112 | 93.86 110 | 75.73 96 | 77.02 232 | 90.58 140 |
|
v1neww | | | 80.40 118 | 79.54 113 | 82.98 137 | 84.10 231 | 64.51 176 | 87.57 126 | 90.22 127 | 73.25 122 | 78.47 109 | 86.65 194 | 62.83 113 | 93.86 110 | 75.72 97 | 77.02 232 | 90.58 140 |
|
v7new | | | 80.40 118 | 79.54 113 | 82.98 137 | 84.10 231 | 64.51 176 | 87.57 126 | 90.22 127 | 73.25 122 | 78.47 109 | 86.65 194 | 62.83 113 | 93.86 110 | 75.72 97 | 77.02 232 | 90.58 140 |
|
v17 | | | 77.68 182 | 76.35 186 | 81.69 180 | 84.15 228 | 64.65 171 | 87.33 138 | 88.99 170 | 72.70 138 | 69.25 266 | 82.07 275 | 62.82 115 | 91.79 191 | 72.69 130 | 67.15 311 | 88.63 217 |
|
v16 | | | 77.69 181 | 76.36 185 | 81.68 181 | 84.15 228 | 64.63 173 | 87.33 138 | 88.99 170 | 72.69 139 | 69.31 265 | 82.08 274 | 62.80 116 | 91.79 191 | 72.70 129 | 67.23 309 | 88.63 217 |
|
abl_6 | | | 85.23 51 | 84.95 51 | 86.07 54 | 92.23 54 | 70.48 60 | 90.80 39 | 92.08 60 | 73.51 117 | 85.26 27 | 94.16 23 | 62.75 117 | 95.92 39 | 82.46 47 | 91.30 65 | 91.81 103 |
|
v15 | | | 77.51 189 | 76.12 190 | 81.66 182 | 84.09 233 | 64.65 171 | 87.14 145 | 88.96 174 | 72.76 136 | 68.90 267 | 81.91 283 | 62.74 118 | 91.73 195 | 72.32 134 | 66.29 317 | 88.61 220 |
|
v1 | | | 80.19 127 | 79.31 123 | 82.85 146 | 83.83 245 | 64.12 190 | 87.14 145 | 90.07 136 | 73.13 125 | 78.27 118 | 86.38 209 | 62.72 119 | 93.75 120 | 75.41 103 | 76.82 242 | 90.68 131 |
|
divwei89l23v2f112 | | | 80.19 127 | 79.31 123 | 82.85 146 | 83.84 243 | 64.11 192 | 87.13 148 | 90.08 134 | 73.13 125 | 78.27 118 | 86.39 205 | 62.69 120 | 93.75 120 | 75.40 104 | 76.82 242 | 90.68 131 |
|
V14 | | | 77.52 187 | 76.12 190 | 81.70 179 | 84.15 228 | 64.77 168 | 87.21 144 | 88.95 175 | 72.80 135 | 68.79 268 | 81.94 282 | 62.69 120 | 91.72 197 | 72.31 135 | 66.27 318 | 88.60 221 |
|
v1141 | | | 80.19 127 | 79.31 123 | 82.85 146 | 83.84 243 | 64.12 190 | 87.14 145 | 90.08 134 | 73.13 125 | 78.27 118 | 86.39 205 | 62.67 122 | 93.75 120 | 75.40 104 | 76.83 241 | 90.68 131 |
|
V9 | | | 77.52 187 | 76.11 193 | 81.73 178 | 84.19 227 | 64.89 165 | 87.26 142 | 88.94 178 | 72.87 134 | 68.65 271 | 81.96 281 | 62.65 123 | 91.72 197 | 72.27 136 | 66.24 319 | 88.60 221 |
|
HPM-MVS_fast | | | 85.35 50 | 84.95 51 | 86.57 46 | 93.69 27 | 70.58 59 | 92.15 23 | 91.62 82 | 73.89 105 | 82.67 66 | 94.09 26 | 62.60 124 | 95.54 45 | 80.93 53 | 92.93 51 | 93.57 50 |
|
PAPM | | | 77.68 182 | 76.40 181 | 81.51 187 | 87.29 182 | 61.85 227 | 83.78 241 | 89.59 148 | 64.74 250 | 71.23 236 | 88.70 129 | 62.59 125 | 93.66 127 | 52.66 275 | 87.03 114 | 89.01 203 |
|
1112_ss | | | 77.40 195 | 76.43 180 | 80.32 207 | 89.11 121 | 60.41 238 | 83.65 242 | 87.72 203 | 62.13 276 | 73.05 207 | 86.72 186 | 62.58 126 | 89.97 238 | 62.11 213 | 80.80 188 | 90.59 139 |
|
v12 | | | 77.51 189 | 76.09 194 | 81.76 177 | 84.22 223 | 64.99 162 | 87.30 140 | 88.93 179 | 72.92 131 | 68.48 275 | 81.97 279 | 62.54 127 | 91.70 200 | 72.24 137 | 66.21 321 | 88.58 224 |
|
v13 | | | 77.50 191 | 76.07 195 | 81.77 175 | 84.23 222 | 65.07 161 | 87.34 137 | 88.91 180 | 72.92 131 | 68.35 276 | 81.97 279 | 62.53 128 | 91.69 201 | 72.20 138 | 66.22 320 | 88.56 226 |
|
LCM-MVSNet-Re | | | 77.05 197 | 76.94 173 | 77.36 263 | 87.20 183 | 51.60 321 | 80.06 276 | 80.46 286 | 75.20 84 | 67.69 280 | 86.72 186 | 62.48 129 | 88.98 263 | 63.44 200 | 89.25 85 | 91.51 108 |
|
v148 | | | 78.72 157 | 77.80 157 | 81.47 188 | 82.73 271 | 61.96 226 | 86.30 177 | 88.08 197 | 73.26 121 | 76.18 164 | 85.47 235 | 62.46 130 | 92.36 176 | 71.92 141 | 73.82 278 | 90.09 160 |
|
MAR-MVS | | | 81.84 85 | 80.70 92 | 85.27 64 | 91.32 64 | 71.53 46 | 89.82 58 | 90.92 103 | 69.77 182 | 78.50 107 | 86.21 212 | 62.36 131 | 94.52 83 | 65.36 188 | 92.05 57 | 89.77 184 |
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020 |
MVS_111021_LR | | | 82.61 76 | 82.11 74 | 84.11 94 | 88.82 127 | 71.58 45 | 85.15 210 | 86.16 223 | 74.69 92 | 80.47 89 | 91.04 82 | 62.29 132 | 90.55 232 | 80.33 59 | 90.08 77 | 90.20 154 |
|
TAMVS | | | 78.89 156 | 77.51 164 | 83.03 134 | 87.80 165 | 67.79 114 | 84.72 218 | 85.05 233 | 67.63 220 | 76.75 148 | 87.70 158 | 62.25 133 | 90.82 228 | 58.53 243 | 87.13 112 | 90.49 145 |
|
CP-MVSNet | | | 78.22 165 | 78.34 147 | 77.84 254 | 87.83 163 | 54.54 302 | 87.94 118 | 91.17 98 | 77.65 33 | 73.48 202 | 88.49 137 | 62.24 134 | 88.43 271 | 62.19 210 | 74.07 273 | 90.55 143 |
|
OMC-MVS | | | 82.69 74 | 81.97 79 | 84.85 76 | 88.75 132 | 67.42 118 | 87.98 115 | 90.87 105 | 74.92 89 | 79.72 93 | 91.65 65 | 62.19 135 | 93.96 102 | 75.26 106 | 86.42 124 | 93.16 65 |
|
v11 | | | 77.45 192 | 76.06 196 | 81.59 186 | 84.22 223 | 64.52 174 | 87.11 150 | 89.02 166 | 72.76 136 | 68.76 269 | 81.90 284 | 62.09 136 | 91.71 199 | 71.98 139 | 66.73 312 | 88.56 226 |
|
testdata | | | | | 79.97 213 | 90.90 70 | 64.21 187 | | 84.71 234 | 59.27 297 | 85.40 25 | 92.91 47 | 62.02 137 | 89.08 260 | 68.95 161 | 91.37 64 | 86.63 269 |
|
MVSFormer | | | 82.85 73 | 82.05 76 | 85.24 65 | 87.35 177 | 70.21 62 | 90.50 44 | 90.38 118 | 68.55 206 | 81.32 78 | 89.47 113 | 61.68 138 | 93.46 134 | 78.98 66 | 90.26 74 | 92.05 97 |
|
lupinMVS | | | 81.39 95 | 80.27 101 | 84.76 78 | 87.35 177 | 70.21 62 | 85.55 202 | 86.41 218 | 62.85 268 | 81.32 78 | 88.61 133 | 61.68 138 | 92.24 180 | 78.41 72 | 90.26 74 | 91.83 101 |
|
cdsmvs_eth3d_5k | | | 19.96 336 | 26.61 336 | 0.00 353 | 0.00 367 | 0.00 368 | 0.00 360 | 89.26 160 | 0.00 363 | 0.00 364 | 88.61 133 | 61.62 140 | 0.00 366 | 0.00 363 | 0.00 364 | 0.00 364 |
|
CDS-MVSNet | | | 79.07 151 | 77.70 160 | 83.17 126 | 87.60 172 | 68.23 106 | 84.40 231 | 86.20 222 | 67.49 224 | 76.36 157 | 86.54 201 | 61.54 141 | 90.79 229 | 61.86 215 | 87.33 110 | 90.49 145 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
v10 | | | 79.74 138 | 78.67 136 | 82.97 141 | 84.06 238 | 64.95 163 | 87.88 121 | 90.62 111 | 73.11 128 | 75.11 190 | 86.56 200 | 61.46 142 | 94.05 100 | 73.68 116 | 75.55 258 | 89.90 174 |
|
v7 | | | 80.24 124 | 79.26 127 | 83.15 127 | 84.07 237 | 64.94 164 | 87.56 130 | 90.67 108 | 72.26 147 | 78.28 117 | 86.51 203 | 61.45 143 | 94.03 101 | 75.14 107 | 77.41 226 | 90.49 145 |
|
v1144 | | | 80.03 132 | 79.03 131 | 83.01 135 | 83.78 246 | 64.51 176 | 87.11 150 | 90.57 113 | 71.96 152 | 78.08 127 | 86.20 213 | 61.41 144 | 93.94 104 | 74.93 108 | 77.23 228 | 90.60 137 |
|
BH-w/o | | | 78.21 166 | 77.33 167 | 80.84 199 | 88.81 128 | 65.13 160 | 84.87 215 | 87.85 201 | 69.75 183 | 74.52 197 | 84.74 249 | 61.34 145 | 93.11 151 | 58.24 247 | 85.84 131 | 84.27 300 |
|
Test_1112_low_res | | | 76.40 211 | 75.44 204 | 79.27 227 | 89.28 112 | 58.09 254 | 81.69 264 | 87.07 212 | 59.53 295 | 72.48 214 | 86.67 192 | 61.30 146 | 89.33 249 | 60.81 225 | 80.15 198 | 90.41 149 |
|
Vis-MVSNet (Re-imp) | | | 78.36 163 | 78.45 142 | 78.07 252 | 88.64 134 | 51.78 320 | 86.70 165 | 79.63 295 | 74.14 99 | 75.11 190 | 90.83 88 | 61.29 147 | 89.75 241 | 58.10 248 | 91.60 60 | 92.69 77 |
|
PEN-MVS | | | 77.73 180 | 77.69 161 | 77.84 254 | 87.07 185 | 53.91 306 | 87.91 120 | 91.18 97 | 77.56 37 | 73.14 206 | 88.82 127 | 61.23 148 | 89.17 258 | 59.95 229 | 72.37 286 | 90.43 148 |
|
pm-mvs1 | | | 77.25 196 | 76.68 177 | 78.93 237 | 84.22 223 | 58.62 249 | 86.41 173 | 88.36 192 | 71.37 160 | 73.31 203 | 88.01 151 | 61.22 149 | 89.15 259 | 64.24 197 | 73.01 282 | 89.03 202 |
|
BH-untuned | | | 79.47 143 | 78.60 138 | 82.05 168 | 89.19 116 | 65.91 141 | 86.07 183 | 88.52 190 | 72.18 149 | 75.42 179 | 87.69 159 | 61.15 150 | 93.54 131 | 60.38 226 | 86.83 117 | 86.70 267 |
|
v2v482 | | | 80.23 125 | 79.29 126 | 83.05 133 | 83.62 248 | 64.14 188 | 87.04 152 | 89.97 138 | 73.61 113 | 78.18 124 | 87.22 172 | 61.10 151 | 93.82 113 | 76.11 92 | 76.78 244 | 91.18 116 |
|
jason | | | 81.39 95 | 80.29 100 | 84.70 79 | 86.63 191 | 69.90 69 | 85.95 185 | 86.77 214 | 63.24 262 | 81.07 84 | 89.47 113 | 61.08 152 | 92.15 181 | 78.33 73 | 90.07 78 | 92.05 97 |
jason: jason. |
Vis-MVSNet | | | 83.46 64 | 82.80 68 | 85.43 61 | 90.25 80 | 68.74 93 | 90.30 50 | 90.13 132 | 76.33 65 | 80.87 86 | 92.89 48 | 61.00 153 | 94.20 93 | 72.45 133 | 90.97 67 | 93.35 57 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
TAPA-MVS | | 73.13 9 | 79.15 149 | 77.94 154 | 82.79 154 | 89.59 96 | 62.99 216 | 88.16 113 | 91.51 87 | 65.77 240 | 77.14 145 | 91.09 80 | 60.91 154 | 93.21 143 | 50.26 285 | 87.05 113 | 92.17 94 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
PS-CasMVS | | | 78.01 173 | 78.09 151 | 77.77 256 | 87.71 170 | 54.39 304 | 88.02 114 | 91.22 95 | 77.50 40 | 73.26 204 | 88.64 132 | 60.73 155 | 88.41 272 | 61.88 214 | 73.88 277 | 90.53 144 |
|
OPM-MVS | | | 83.50 63 | 82.95 65 | 85.14 67 | 88.79 130 | 70.95 51 | 89.13 79 | 91.52 86 | 77.55 38 | 80.96 85 | 91.75 63 | 60.71 156 | 94.50 84 | 79.67 63 | 86.51 123 | 89.97 173 |
|
XVG-OURS-SEG-HR | | | 80.81 105 | 79.76 108 | 83.96 105 | 85.60 203 | 68.78 90 | 83.54 245 | 90.50 115 | 70.66 170 | 76.71 149 | 91.66 64 | 60.69 157 | 91.26 215 | 76.94 87 | 81.58 180 | 91.83 101 |
|
v144192 | | | 79.47 143 | 78.37 146 | 82.78 155 | 83.35 253 | 63.96 194 | 86.96 154 | 90.36 121 | 69.99 179 | 77.50 135 | 85.67 228 | 60.66 158 | 93.77 118 | 74.27 112 | 76.58 245 | 90.62 135 |
|
V42 | | | 79.38 146 | 78.24 150 | 82.83 149 | 81.10 294 | 65.50 149 | 85.55 202 | 89.82 143 | 71.57 158 | 78.21 122 | 86.12 214 | 60.66 158 | 93.18 147 | 75.64 100 | 75.46 260 | 89.81 179 |
|
CPTT-MVS | | | 83.73 59 | 83.33 59 | 84.92 75 | 93.28 35 | 70.86 55 | 92.09 24 | 90.38 118 | 68.75 203 | 79.57 94 | 92.83 50 | 60.60 160 | 93.04 156 | 80.92 54 | 91.56 62 | 90.86 125 |
|
DTE-MVSNet | | | 76.99 198 | 76.80 175 | 77.54 260 | 86.24 195 | 53.06 317 | 87.52 132 | 90.66 110 | 77.08 46 | 72.50 212 | 88.67 131 | 60.48 161 | 89.52 245 | 57.33 255 | 70.74 297 | 90.05 165 |
|
HQP_MVS | | | 83.64 61 | 83.14 61 | 85.14 67 | 90.08 84 | 68.71 95 | 91.25 32 | 92.44 46 | 79.12 23 | 78.92 101 | 91.00 85 | 60.42 162 | 95.38 53 | 78.71 68 | 86.32 125 | 91.33 112 |
|
plane_prior6 | | | | | | 89.84 89 | 68.70 97 | | | | | | 60.42 162 | | | | |
|
3Dnovator+ | | 77.84 4 | 85.48 46 | 84.47 54 | 88.51 2 | 91.08 66 | 73.49 14 | 93.18 4 | 93.78 7 | 80.79 11 | 76.66 150 | 93.37 37 | 60.40 164 | 96.75 14 | 77.20 83 | 93.73 48 | 95.29 1 |
|
HQP2-MVS | | | | | | | | | | | | | 60.17 165 | | | | |
|
HQP-MVS | | | 82.61 76 | 82.02 77 | 84.37 86 | 89.33 106 | 66.98 126 | 89.17 74 | 92.19 57 | 76.41 59 | 77.23 142 | 90.23 96 | 60.17 165 | 95.11 61 | 77.47 80 | 85.99 129 | 91.03 119 |
|
VPNet | | | 78.69 158 | 78.66 137 | 78.76 240 | 88.31 146 | 55.72 297 | 84.45 228 | 86.63 216 | 76.79 51 | 78.26 121 | 90.55 92 | 59.30 167 | 89.70 243 | 66.63 178 | 77.05 231 | 90.88 124 |
|
v1192 | | | 79.59 140 | 78.43 145 | 83.07 132 | 83.55 250 | 64.52 174 | 86.93 156 | 90.58 112 | 70.83 165 | 77.78 131 | 85.90 222 | 59.15 168 | 93.94 104 | 73.96 115 | 77.19 230 | 90.76 127 |
|
test222 | | | | | | 91.50 62 | 68.26 105 | 84.16 235 | 83.20 256 | 54.63 323 | 79.74 92 | 91.63 67 | 58.97 169 | | | 91.42 63 | 86.77 265 |
|
CHOSEN 1792x2688 | | | 77.63 185 | 75.69 198 | 83.44 115 | 89.98 86 | 68.58 100 | 78.70 291 | 87.50 207 | 56.38 317 | 75.80 170 | 86.84 182 | 58.67 170 | 91.40 211 | 61.58 218 | 85.75 132 | 90.34 151 |
|
3Dnovator | | 76.31 5 | 83.38 67 | 82.31 73 | 86.59 45 | 87.94 154 | 72.94 23 | 90.64 41 | 92.14 59 | 77.21 42 | 75.47 176 | 92.83 50 | 58.56 171 | 94.72 79 | 73.24 124 | 92.71 54 | 92.13 95 |
|
v1921920 | | | 79.22 148 | 78.03 152 | 82.80 152 | 83.30 255 | 63.94 195 | 86.80 160 | 90.33 123 | 69.91 180 | 77.48 136 | 85.53 233 | 58.44 172 | 93.75 120 | 73.60 119 | 76.85 239 | 90.71 130 |
|
v748 | | | 77.97 174 | 76.65 178 | 81.92 173 | 82.29 278 | 63.28 207 | 87.53 131 | 90.35 122 | 73.50 118 | 70.76 241 | 85.55 232 | 58.28 173 | 92.81 165 | 68.81 163 | 72.76 285 | 89.67 186 |
|
114514_t | | | 80.68 111 | 79.51 116 | 84.20 92 | 94.09 24 | 67.27 122 | 89.64 66 | 91.11 99 | 58.75 302 | 74.08 199 | 90.72 89 | 58.10 174 | 95.04 66 | 69.70 154 | 89.42 84 | 90.30 152 |
|
v7n | | | 78.97 154 | 77.58 163 | 83.14 128 | 83.45 252 | 65.51 148 | 88.32 106 | 91.21 96 | 73.69 112 | 72.41 221 | 86.32 210 | 57.93 175 | 93.81 114 | 69.18 159 | 75.65 256 | 90.11 158 |
|
QAPM | | | 80.88 100 | 79.50 117 | 85.03 70 | 88.01 153 | 68.97 87 | 91.59 28 | 92.00 65 | 66.63 231 | 75.15 189 | 92.16 55 | 57.70 176 | 95.45 48 | 63.52 199 | 88.76 90 | 90.66 134 |
|
HyFIR lowres test | | | 77.53 186 | 75.40 206 | 83.94 106 | 89.59 96 | 66.62 130 | 80.36 274 | 88.64 187 | 56.29 318 | 76.45 153 | 85.17 239 | 57.64 177 | 93.28 141 | 61.34 221 | 83.10 163 | 91.91 99 |
|
CNLPA | | | 78.08 170 | 76.79 176 | 81.97 171 | 90.40 78 | 71.07 49 | 87.59 125 | 84.55 236 | 66.03 238 | 72.38 222 | 89.64 108 | 57.56 178 | 86.04 289 | 59.61 232 | 83.35 159 | 88.79 212 |
|
0601test | | | 81.17 97 | 80.47 97 | 83.24 124 | 89.13 118 | 63.62 196 | 86.21 179 | 89.95 139 | 72.43 144 | 81.78 75 | 89.61 109 | 57.50 179 | 93.58 128 | 70.75 145 | 86.90 116 | 92.52 81 |
|
sss | | | 73.60 242 | 73.64 228 | 73.51 298 | 82.80 269 | 55.01 299 | 76.12 304 | 81.69 275 | 62.47 273 | 74.68 196 | 85.85 225 | 57.32 180 | 78.11 324 | 60.86 224 | 80.93 185 | 87.39 249 |
|
Effi-MVS+-dtu | | | 80.03 132 | 78.57 140 | 84.42 85 | 85.13 210 | 68.74 93 | 88.77 89 | 88.10 195 | 74.99 86 | 74.97 193 | 83.49 261 | 57.27 181 | 93.36 139 | 73.53 120 | 80.88 186 | 91.18 116 |
|
mvs-test1 | | | 80.88 100 | 79.40 119 | 85.29 63 | 85.13 210 | 69.75 72 | 89.28 70 | 88.10 195 | 74.99 86 | 76.44 156 | 86.72 186 | 57.27 181 | 94.26 92 | 73.53 120 | 83.18 162 | 91.87 100 |
|
DI_MVS_plusplus_test | | | 79.89 135 | 78.58 139 | 83.85 108 | 82.89 268 | 65.32 154 | 86.12 181 | 89.55 149 | 69.64 186 | 70.55 242 | 85.82 226 | 57.24 183 | 93.81 114 | 76.85 88 | 88.55 95 | 92.41 85 |
|
AdaColmap | | | 80.58 115 | 79.42 118 | 84.06 97 | 93.09 41 | 68.91 88 | 89.36 69 | 88.97 173 | 69.27 191 | 75.70 175 | 89.69 106 | 57.20 184 | 95.77 40 | 63.06 203 | 88.41 99 | 87.50 248 |
|
test_normal | | | 79.81 136 | 78.45 142 | 83.89 107 | 82.70 272 | 65.40 150 | 85.82 192 | 89.48 152 | 69.39 187 | 70.12 251 | 85.66 229 | 57.15 185 | 93.71 126 | 77.08 85 | 88.62 93 | 92.56 80 |
|
v1240 | | | 78.99 153 | 77.78 158 | 82.64 159 | 83.21 257 | 63.54 199 | 86.62 167 | 90.30 125 | 69.74 185 | 77.33 138 | 85.68 227 | 57.04 186 | 93.76 119 | 73.13 125 | 76.92 235 | 90.62 135 |
|
BH-RMVSNet | | | 79.61 139 | 78.44 144 | 83.14 128 | 89.38 105 | 65.93 140 | 84.95 214 | 87.15 211 | 73.56 115 | 78.19 123 | 89.79 105 | 56.67 187 | 93.36 139 | 59.53 234 | 86.74 118 | 90.13 157 |
|
test_djsdf | | | 80.30 123 | 79.32 122 | 83.27 122 | 83.98 240 | 65.37 153 | 90.50 44 | 90.38 118 | 68.55 206 | 76.19 163 | 88.70 129 | 56.44 188 | 93.46 134 | 78.98 66 | 80.14 199 | 90.97 122 |
|
pcd1.5k->3k | | | 34.07 332 | 35.26 332 | 30.50 347 | 86.92 186 | 0.00 368 | 0.00 360 | 91.58 84 | 0.00 363 | 0.00 364 | 0.00 365 | 56.23 189 | 0.00 366 | 0.00 363 | 82.60 170 | 91.49 110 |
|
EPNet_dtu | | | 75.46 226 | 74.86 215 | 77.23 266 | 82.57 275 | 54.60 301 | 86.89 157 | 83.09 257 | 71.64 154 | 66.25 294 | 85.86 224 | 55.99 190 | 88.04 276 | 54.92 265 | 86.55 122 | 89.05 200 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
V4 | | | 77.95 175 | 76.37 182 | 82.67 157 | 79.40 313 | 65.52 146 | 86.43 171 | 89.94 140 | 72.28 145 | 72.14 227 | 84.95 244 | 55.72 191 | 93.44 136 | 73.64 117 | 72.86 283 | 89.05 200 |
|
v52 | | | 77.94 177 | 76.37 182 | 82.67 157 | 79.39 314 | 65.52 146 | 86.43 171 | 89.94 140 | 72.28 145 | 72.15 226 | 84.94 245 | 55.70 192 | 93.44 136 | 73.64 117 | 72.84 284 | 89.06 196 |
|
CostFormer | | | 75.24 229 | 73.90 227 | 79.27 227 | 82.65 274 | 58.27 253 | 80.80 269 | 82.73 261 | 61.57 279 | 75.33 185 | 83.13 263 | 55.52 193 | 91.07 225 | 64.98 193 | 78.34 219 | 88.45 229 |
|
tpmrst | | | 72.39 260 | 72.13 247 | 73.18 300 | 80.54 299 | 49.91 330 | 79.91 279 | 79.08 298 | 63.11 263 | 71.69 232 | 79.95 299 | 55.32 194 | 82.77 307 | 65.66 187 | 73.89 276 | 86.87 262 |
|
1314 | | | 76.53 206 | 75.30 209 | 80.21 210 | 83.93 241 | 62.32 222 | 84.66 219 | 88.81 181 | 60.23 288 | 70.16 250 | 84.07 254 | 55.30 195 | 90.73 230 | 67.37 171 | 83.21 161 | 87.59 246 |
|
tfpnnormal | | | 74.39 232 | 73.16 232 | 78.08 251 | 86.10 197 | 58.05 255 | 84.65 222 | 87.53 206 | 70.32 175 | 71.22 238 | 85.63 230 | 54.97 196 | 89.86 239 | 43.03 328 | 75.02 266 | 86.32 275 |
|
GBi-Net | | | 78.40 161 | 77.40 165 | 81.40 190 | 87.60 172 | 63.01 213 | 88.39 103 | 89.28 157 | 71.63 155 | 75.34 182 | 87.28 168 | 54.80 197 | 91.11 219 | 62.72 204 | 79.57 207 | 90.09 160 |
|
test1 | | | 78.40 161 | 77.40 165 | 81.40 190 | 87.60 172 | 63.01 213 | 88.39 103 | 89.28 157 | 71.63 155 | 75.34 182 | 87.28 168 | 54.80 197 | 91.11 219 | 62.72 204 | 79.57 207 | 90.09 160 |
|
FMVSNet2 | | | 78.20 167 | 77.21 168 | 81.20 193 | 87.60 172 | 62.89 217 | 87.47 134 | 89.02 166 | 71.63 155 | 75.29 186 | 87.28 168 | 54.80 197 | 91.10 222 | 62.38 208 | 79.38 210 | 89.61 188 |
|
Fast-Effi-MVS+-dtu | | | 78.02 172 | 76.49 179 | 82.62 160 | 83.16 261 | 66.96 128 | 86.94 155 | 87.45 209 | 72.45 141 | 71.49 235 | 84.17 252 | 54.79 200 | 91.58 208 | 67.61 168 | 80.31 196 | 89.30 192 |
|
MVSTER | | | 79.01 152 | 77.88 155 | 82.38 163 | 83.07 262 | 64.80 167 | 84.08 238 | 88.95 175 | 69.01 200 | 78.69 103 | 87.17 175 | 54.70 201 | 92.43 172 | 74.69 109 | 80.57 192 | 89.89 175 |
|
OpenMVS | | 72.83 10 | 79.77 137 | 78.33 148 | 84.09 96 | 85.17 207 | 69.91 68 | 90.57 42 | 90.97 102 | 66.70 227 | 72.17 224 | 91.91 60 | 54.70 201 | 93.96 102 | 61.81 216 | 90.95 68 | 88.41 231 |
|
XVG-OURS | | | 80.41 117 | 79.23 128 | 83.97 104 | 85.64 202 | 69.02 84 | 83.03 255 | 90.39 117 | 71.09 163 | 77.63 134 | 91.49 72 | 54.62 203 | 91.35 213 | 75.71 99 | 83.47 154 | 91.54 107 |
|
Anonymous20240521 | | | 76.96 199 | 76.26 188 | 79.07 235 | 86.63 191 | 56.37 286 | 87.57 126 | 91.09 100 | 72.19 148 | 71.23 236 | 88.10 150 | 54.30 204 | 91.20 218 | 58.34 245 | 76.89 236 | 89.65 187 |
|
LPG-MVS_test | | | 82.08 80 | 81.27 84 | 84.50 82 | 89.23 114 | 68.76 91 | 90.22 52 | 91.94 69 | 75.37 80 | 76.64 151 | 91.51 70 | 54.29 205 | 94.91 70 | 78.44 70 | 83.78 145 | 89.83 177 |
|
LGP-MVS_train | | | | | 84.50 82 | 89.23 114 | 68.76 91 | | 91.94 69 | 75.37 80 | 76.64 151 | 91.51 70 | 54.29 205 | 94.91 70 | 78.44 70 | 83.78 145 | 89.83 177 |
|
TR-MVS | | | 77.44 193 | 76.18 189 | 81.20 193 | 88.24 147 | 63.24 208 | 84.61 223 | 86.40 219 | 67.55 223 | 77.81 130 | 86.48 204 | 54.10 207 | 93.15 148 | 57.75 251 | 82.72 168 | 87.20 255 |
|
FMVSNet3 | | | 77.88 178 | 76.85 174 | 80.97 198 | 86.84 188 | 62.36 220 | 86.52 170 | 88.77 182 | 71.13 161 | 75.34 182 | 86.66 193 | 54.07 208 | 91.10 222 | 62.72 204 | 79.57 207 | 89.45 190 |
|
DP-MVS | | | 76.78 202 | 74.57 218 | 83.42 116 | 93.29 34 | 69.46 80 | 88.55 98 | 83.70 244 | 63.98 259 | 70.20 247 | 88.89 125 | 54.01 209 | 94.80 77 | 46.66 308 | 81.88 177 | 86.01 283 |
|
ACMP | | 74.13 6 | 81.51 93 | 80.57 94 | 84.36 87 | 89.42 103 | 68.69 98 | 89.97 56 | 91.50 90 | 74.46 94 | 75.04 192 | 90.41 93 | 53.82 210 | 94.54 81 | 77.56 79 | 82.91 164 | 89.86 176 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
PLC | | 70.83 11 | 78.05 171 | 76.37 182 | 83.08 131 | 91.88 60 | 67.80 113 | 88.19 111 | 89.46 153 | 64.33 255 | 69.87 257 | 88.38 140 | 53.66 211 | 93.58 128 | 58.86 239 | 82.73 167 | 87.86 240 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
CANet_DTU | | | 80.61 112 | 79.87 105 | 82.83 149 | 85.60 203 | 63.17 212 | 87.36 136 | 88.65 186 | 76.37 63 | 75.88 168 | 88.44 139 | 53.51 212 | 93.07 153 | 73.30 123 | 89.74 81 | 92.25 90 |
|
ACMM | | 73.20 8 | 80.78 110 | 79.84 106 | 83.58 112 | 89.31 111 | 68.37 102 | 89.99 55 | 91.60 83 | 70.28 176 | 77.25 140 | 89.66 107 | 53.37 213 | 93.53 132 | 74.24 113 | 82.85 165 | 88.85 209 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
MVP-Stereo | | | 76.12 218 | 74.46 222 | 81.13 196 | 85.37 206 | 69.79 70 | 84.42 230 | 87.95 199 | 65.03 247 | 67.46 282 | 85.33 237 | 53.28 214 | 91.73 195 | 58.01 249 | 83.27 160 | 81.85 320 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
anonymousdsp | | | 78.60 159 | 77.15 169 | 82.98 137 | 80.51 300 | 67.08 124 | 87.24 143 | 89.53 150 | 65.66 242 | 75.16 188 | 87.19 174 | 52.52 215 | 92.25 179 | 77.17 84 | 79.34 211 | 89.61 188 |
|
CR-MVSNet | | | 73.37 250 | 71.27 256 | 79.67 219 | 81.32 292 | 65.19 158 | 75.92 306 | 80.30 288 | 59.92 291 | 72.73 210 | 81.19 287 | 52.50 216 | 86.69 283 | 59.84 230 | 77.71 221 | 87.11 259 |
|
Patchmtry | | | 70.74 270 | 69.16 269 | 75.49 285 | 80.72 296 | 54.07 305 | 74.94 315 | 80.30 288 | 58.34 303 | 70.01 252 | 81.19 287 | 52.50 216 | 86.54 285 | 53.37 272 | 71.09 295 | 85.87 286 |
|
pmmvs4 | | | 74.03 236 | 71.91 248 | 80.39 204 | 81.96 281 | 68.32 103 | 81.45 267 | 82.14 267 | 59.32 296 | 69.87 257 | 85.13 240 | 52.40 218 | 88.13 275 | 60.21 228 | 74.74 269 | 84.73 298 |
|
PatchFormer-LS_test | | | 74.50 231 | 73.05 233 | 78.86 238 | 82.95 266 | 59.55 244 | 81.65 265 | 82.30 265 | 67.44 225 | 71.62 233 | 78.15 312 | 52.34 219 | 88.92 267 | 65.05 192 | 75.90 253 | 88.12 234 |
|
RPMNet | | | 71.62 264 | 68.94 271 | 79.67 219 | 81.32 292 | 65.19 158 | 75.92 306 | 78.30 308 | 57.60 310 | 72.73 210 | 76.45 321 | 52.30 220 | 86.69 283 | 48.14 298 | 77.71 221 | 87.11 259 |
|
LFMVS | | | 81.82 86 | 81.23 85 | 83.57 113 | 91.89 59 | 63.43 204 | 89.84 57 | 81.85 274 | 77.04 47 | 83.21 56 | 93.10 42 | 52.26 221 | 93.43 138 | 71.98 139 | 89.95 79 | 93.85 34 |
|
VDD-MVS | | | 83.01 72 | 82.36 72 | 84.96 72 | 91.02 68 | 66.40 133 | 88.91 83 | 88.11 194 | 77.57 35 | 84.39 44 | 93.29 39 | 52.19 222 | 93.91 107 | 77.05 86 | 88.70 91 | 94.57 9 |
|
tfpn200view9 | | | 76.42 210 | 75.37 207 | 79.55 225 | 89.13 118 | 57.65 264 | 85.17 208 | 83.60 245 | 73.41 119 | 76.45 153 | 86.39 205 | 52.12 223 | 91.95 185 | 48.33 293 | 83.75 147 | 89.07 194 |
|
thres400 | | | 76.50 207 | 75.37 207 | 79.86 214 | 89.13 118 | 57.65 264 | 85.17 208 | 83.60 245 | 73.41 119 | 76.45 153 | 86.39 205 | 52.12 223 | 91.95 185 | 48.33 293 | 83.75 147 | 90.00 166 |
|
thres200 | | | 75.55 225 | 74.47 221 | 78.82 239 | 87.78 168 | 57.85 261 | 83.07 254 | 83.51 248 | 72.44 143 | 75.84 169 | 84.42 251 | 52.08 225 | 91.75 194 | 47.41 302 | 83.64 153 | 86.86 263 |
|
PMMVS | | | 69.34 282 | 68.67 272 | 71.35 308 | 75.67 328 | 62.03 225 | 75.17 310 | 73.46 333 | 50.00 338 | 68.68 270 | 79.05 305 | 52.07 226 | 78.13 323 | 61.16 222 | 82.77 166 | 73.90 341 |
|
tpm cat1 | | | 70.57 272 | 68.31 275 | 77.35 264 | 82.41 277 | 57.95 259 | 78.08 296 | 80.22 291 | 52.04 333 | 68.54 274 | 77.66 316 | 52.00 227 | 87.84 278 | 51.77 276 | 72.07 290 | 86.25 276 |
|
Patchmatch-test1 | | | 73.49 243 | 71.85 250 | 78.41 247 | 84.05 239 | 62.17 224 | 79.96 278 | 79.29 297 | 66.30 234 | 72.38 222 | 79.58 303 | 51.95 228 | 85.08 296 | 55.46 263 | 77.67 223 | 87.99 236 |
|
tfpn111 | | | 76.54 205 | 75.51 203 | 79.61 221 | 89.52 98 | 56.99 272 | 85.83 189 | 83.23 252 | 73.94 102 | 76.32 158 | 87.12 176 | 51.89 229 | 92.06 183 | 48.04 300 | 83.73 151 | 89.78 180 |
|
conf200view11 | | | 76.55 204 | 75.55 201 | 79.57 224 | 89.52 98 | 56.99 272 | 85.83 189 | 83.23 252 | 73.94 102 | 76.32 158 | 87.12 176 | 51.89 229 | 91.95 185 | 48.33 293 | 83.75 147 | 89.78 180 |
|
thres100view900 | | | 76.50 207 | 75.55 201 | 79.33 226 | 89.52 98 | 56.99 272 | 85.83 189 | 83.23 252 | 73.94 102 | 76.32 158 | 87.12 176 | 51.89 229 | 91.95 185 | 48.33 293 | 83.75 147 | 89.07 194 |
|
thres600view7 | | | 76.50 207 | 75.44 204 | 79.68 218 | 89.40 104 | 57.16 269 | 85.53 204 | 83.23 252 | 73.79 111 | 76.26 161 | 87.09 179 | 51.89 229 | 91.89 189 | 48.05 299 | 83.72 152 | 90.00 166 |
|
view600 | | | 76.20 214 | 75.21 210 | 79.16 231 | 89.64 91 | 55.82 292 | 85.74 194 | 82.06 269 | 73.88 106 | 75.74 171 | 87.85 153 | 51.84 233 | 91.66 202 | 46.75 304 | 83.42 155 | 90.00 166 |
|
view800 | | | 76.20 214 | 75.21 210 | 79.16 231 | 89.64 91 | 55.82 292 | 85.74 194 | 82.06 269 | 73.88 106 | 75.74 171 | 87.85 153 | 51.84 233 | 91.66 202 | 46.75 304 | 83.42 155 | 90.00 166 |
|
conf0.05thres1000 | | | 76.20 214 | 75.21 210 | 79.16 231 | 89.64 91 | 55.82 292 | 85.74 194 | 82.06 269 | 73.88 106 | 75.74 171 | 87.85 153 | 51.84 233 | 91.66 202 | 46.75 304 | 83.42 155 | 90.00 166 |
|
tfpn | | | 76.20 214 | 75.21 210 | 79.16 231 | 89.64 91 | 55.82 292 | 85.74 194 | 82.06 269 | 73.88 106 | 75.74 171 | 87.85 153 | 51.84 233 | 91.66 202 | 46.75 304 | 83.42 155 | 90.00 166 |
|
tpm2 | | | 73.26 253 | 71.46 253 | 78.63 241 | 83.34 254 | 56.71 279 | 80.65 272 | 80.40 287 | 56.63 316 | 73.55 201 | 82.02 277 | 51.80 237 | 91.24 216 | 56.35 260 | 78.42 218 | 87.95 237 |
|
LS3D | | | 76.95 200 | 74.82 216 | 83.37 119 | 90.45 76 | 67.36 121 | 89.15 78 | 86.94 213 | 61.87 278 | 69.52 260 | 90.61 91 | 51.71 238 | 94.53 82 | 46.38 311 | 86.71 119 | 88.21 233 |
|
IterMVS | | | 74.29 233 | 72.94 234 | 78.35 248 | 81.53 286 | 63.49 201 | 81.58 266 | 82.49 262 | 68.06 218 | 69.99 254 | 83.69 259 | 51.66 239 | 85.54 292 | 65.85 185 | 71.64 292 | 86.01 283 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
tpm | | | 72.37 262 | 71.71 252 | 74.35 294 | 82.19 279 | 52.00 318 | 79.22 285 | 77.29 314 | 64.56 252 | 72.95 208 | 83.68 260 | 51.35 240 | 83.26 306 | 58.33 246 | 75.80 254 | 87.81 241 |
|
sam_mvs1 | | | | | | | | | | | | | 51.32 241 | | | | 88.96 207 |
|
PatchmatchNet | | | 73.12 255 | 71.33 255 | 78.49 246 | 83.18 259 | 60.85 233 | 79.63 280 | 78.57 306 | 64.13 256 | 71.73 231 | 79.81 302 | 51.20 242 | 85.97 290 | 57.40 254 | 76.36 249 | 88.66 215 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
tpmp4_e23 | | | 73.45 244 | 71.17 258 | 80.31 208 | 83.55 250 | 59.56 243 | 81.88 260 | 82.33 264 | 57.94 307 | 70.51 244 | 81.62 285 | 51.19 243 | 91.63 206 | 53.96 269 | 77.51 225 | 89.75 185 |
|
patchmatchnet-post | | | | | | | | | | | | 74.00 328 | 51.12 244 | 88.60 270 | | | |
|
xiu_mvs_v1_base_debu | | | 80.80 107 | 79.72 109 | 84.03 100 | 87.35 177 | 70.19 64 | 85.56 199 | 88.77 182 | 69.06 196 | 81.83 71 | 88.16 145 | 50.91 245 | 92.85 161 | 78.29 74 | 87.56 106 | 89.06 196 |
|
xiu_mvs_v1_base | | | 80.80 107 | 79.72 109 | 84.03 100 | 87.35 177 | 70.19 64 | 85.56 199 | 88.77 182 | 69.06 196 | 81.83 71 | 88.16 145 | 50.91 245 | 92.85 161 | 78.29 74 | 87.56 106 | 89.06 196 |
|
xiu_mvs_v1_base_debi | | | 80.80 107 | 79.72 109 | 84.03 100 | 87.35 177 | 70.19 64 | 85.56 199 | 88.77 182 | 69.06 196 | 81.83 71 | 88.16 145 | 50.91 245 | 92.85 161 | 78.29 74 | 87.56 106 | 89.06 196 |
|
Patchmatch-test | | | 64.82 303 | 63.24 301 | 69.57 314 | 79.42 312 | 49.82 331 | 63.49 345 | 69.05 346 | 51.98 334 | 59.95 321 | 80.13 298 | 50.91 245 | 70.98 347 | 40.66 333 | 73.57 279 | 87.90 239 |
|
semantic-postprocess | | | | | 80.11 211 | 82.69 273 | 64.85 166 | | 83.47 249 | 69.16 194 | 70.49 245 | 84.15 253 | 50.83 249 | 88.15 274 | 69.23 158 | 72.14 289 | 87.34 251 |
|
Patchmatch-RL test | | | 70.24 276 | 67.78 285 | 77.61 258 | 77.43 321 | 59.57 242 | 71.16 321 | 70.33 339 | 62.94 267 | 68.65 271 | 72.77 331 | 50.62 250 | 85.49 293 | 69.58 156 | 66.58 315 | 87.77 242 |
|
conf0.01 | | | 73.67 240 | 72.42 240 | 77.42 261 | 87.85 156 | 53.28 311 | 83.38 246 | 79.08 298 | 68.40 209 | 72.45 215 | 86.08 216 | 50.60 251 | 89.19 252 | 44.25 319 | 79.66 201 | 89.78 180 |
|
conf0.002 | | | 73.67 240 | 72.42 240 | 77.42 261 | 87.85 156 | 53.28 311 | 83.38 246 | 79.08 298 | 68.40 209 | 72.45 215 | 86.08 216 | 50.60 251 | 89.19 252 | 44.25 319 | 79.66 201 | 89.78 180 |
|
thresconf0.02 | | | 73.39 246 | 72.42 240 | 76.31 272 | 87.85 156 | 53.28 311 | 83.38 246 | 79.08 298 | 68.40 209 | 72.45 215 | 86.08 216 | 50.60 251 | 89.19 252 | 44.25 319 | 79.66 201 | 86.48 270 |
|
tfpn_n400 | | | 73.39 246 | 72.42 240 | 76.31 272 | 87.85 156 | 53.28 311 | 83.38 246 | 79.08 298 | 68.40 209 | 72.45 215 | 86.08 216 | 50.60 251 | 89.19 252 | 44.25 319 | 79.66 201 | 86.48 270 |
|
tfpnconf | | | 73.39 246 | 72.42 240 | 76.31 272 | 87.85 156 | 53.28 311 | 83.38 246 | 79.08 298 | 68.40 209 | 72.45 215 | 86.08 216 | 50.60 251 | 89.19 252 | 44.25 319 | 79.66 201 | 86.48 270 |
|
tfpnview11 | | | 73.39 246 | 72.42 240 | 76.31 272 | 87.85 156 | 53.28 311 | 83.38 246 | 79.08 298 | 68.40 209 | 72.45 215 | 86.08 216 | 50.60 251 | 89.19 252 | 44.25 319 | 79.66 201 | 86.48 270 |
|
Anonymous20231211 | | | 78.97 154 | 77.69 161 | 82.81 151 | 90.54 75 | 64.29 186 | 90.11 54 | 91.51 87 | 65.01 248 | 76.16 167 | 88.13 149 | 50.56 257 | 93.03 157 | 69.68 155 | 77.56 224 | 91.11 118 |
|
VDDNet | | | 81.52 91 | 80.67 93 | 84.05 98 | 90.44 77 | 64.13 189 | 89.73 63 | 85.91 226 | 71.11 162 | 83.18 57 | 93.48 34 | 50.54 258 | 93.49 133 | 73.40 122 | 88.25 100 | 94.54 10 |
|
pmmvs6 | | | 74.69 230 | 73.39 229 | 78.61 242 | 81.38 289 | 57.48 267 | 86.64 166 | 87.95 199 | 64.99 249 | 70.18 248 | 86.61 197 | 50.43 259 | 89.52 245 | 62.12 212 | 70.18 299 | 88.83 210 |
|
test_post | | | | | | | | | | | | 5.46 360 | 50.36 260 | 84.24 299 | | | |
|
tfpn_ndepth | | | 73.70 238 | 72.75 235 | 76.52 270 | 87.78 168 | 54.92 300 | 84.32 233 | 80.28 290 | 67.57 222 | 72.50 212 | 84.82 246 | 50.12 261 | 89.44 248 | 45.73 314 | 81.66 179 | 85.20 290 |
|
sam_mvs | | | | | | | | | | | | | 50.01 262 | | | | |
|
Anonymous20240529 | | | 80.19 127 | 78.89 134 | 84.10 95 | 90.60 74 | 64.75 169 | 88.95 82 | 90.90 104 | 65.97 239 | 80.59 88 | 91.17 78 | 49.97 263 | 93.73 125 | 69.16 160 | 82.70 169 | 93.81 37 |
|
tfpn1000 | | | 73.44 245 | 72.49 238 | 76.29 276 | 87.81 164 | 53.69 308 | 84.05 239 | 78.81 305 | 67.99 219 | 72.09 228 | 86.27 211 | 49.95 264 | 89.04 261 | 44.09 325 | 81.38 181 | 86.15 278 |
|
PatchT | | | 68.46 288 | 67.85 282 | 70.29 312 | 80.70 297 | 43.93 340 | 72.47 319 | 74.88 324 | 60.15 289 | 70.55 242 | 76.57 320 | 49.94 265 | 81.59 310 | 50.58 281 | 74.83 268 | 85.34 289 |
|
tpmvs | | | 71.09 268 | 69.29 268 | 76.49 271 | 82.04 280 | 56.04 290 | 78.92 289 | 81.37 278 | 64.05 257 | 67.18 286 | 78.28 310 | 49.74 266 | 89.77 240 | 49.67 288 | 72.37 286 | 83.67 306 |
|
CVMVSNet | | | 72.99 257 | 72.58 237 | 74.25 295 | 84.28 220 | 50.85 326 | 86.41 173 | 83.45 250 | 44.56 342 | 73.23 205 | 87.54 164 | 49.38 267 | 85.70 291 | 65.90 184 | 78.44 217 | 86.19 277 |
|
MDTV_nov1_ep13_2view | | | | | | | 37.79 350 | 75.16 311 | | 55.10 321 | 66.53 291 | | 49.34 268 | | 53.98 268 | | 87.94 238 |
|
UGNet | | | 80.83 104 | 79.59 112 | 84.54 81 | 88.04 151 | 68.09 108 | 89.42 68 | 88.16 193 | 76.95 48 | 76.22 162 | 89.46 115 | 49.30 269 | 93.94 104 | 68.48 164 | 90.31 73 | 91.60 105 |
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 |
pmmvs5 | | | 71.55 265 | 70.20 265 | 75.61 282 | 77.83 319 | 56.39 284 | 81.74 263 | 80.89 279 | 57.76 308 | 67.46 282 | 84.49 250 | 49.26 270 | 85.32 295 | 57.08 257 | 75.29 263 | 85.11 294 |
|
LTVRE_ROB | | 69.57 13 | 76.25 213 | 74.54 220 | 81.41 189 | 88.60 135 | 64.38 185 | 79.24 284 | 89.12 164 | 70.76 168 | 69.79 259 | 87.86 152 | 49.09 271 | 93.20 145 | 56.21 261 | 80.16 197 | 86.65 268 |
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 |
FMVSNet1 | | | 77.44 193 | 76.12 190 | 81.40 190 | 86.81 189 | 63.01 213 | 88.39 103 | 89.28 157 | 70.49 173 | 74.39 198 | 87.28 168 | 49.06 272 | 91.11 219 | 60.91 223 | 78.52 215 | 90.09 160 |
|
MDTV_nov1_ep13 | | | | 69.97 266 | | 83.18 259 | 53.48 309 | 77.10 302 | 80.18 292 | 60.45 285 | 69.33 264 | 80.44 295 | 48.89 273 | 86.90 282 | 51.60 278 | 78.51 216 | |
|
test_post1 | | | | | | | | 78.90 290 | | | | 5.43 361 | 48.81 274 | 85.44 294 | 59.25 236 | | |
|
test-LLR | | | 72.94 258 | 72.43 239 | 74.48 292 | 81.35 290 | 58.04 256 | 78.38 292 | 77.46 312 | 66.66 228 | 69.95 255 | 79.00 307 | 48.06 275 | 79.24 318 | 66.13 180 | 84.83 136 | 86.15 278 |
|
test0.0.03 1 | | | 68.00 289 | 67.69 286 | 68.90 317 | 77.55 320 | 47.43 334 | 75.70 309 | 72.95 335 | 66.66 228 | 66.56 290 | 82.29 271 | 48.06 275 | 75.87 333 | 44.97 318 | 74.51 271 | 83.41 308 |
|
our_test_3 | | | 69.14 283 | 67.00 289 | 75.57 283 | 79.80 307 | 58.80 247 | 77.96 297 | 77.81 310 | 59.55 294 | 62.90 313 | 78.25 311 | 47.43 277 | 83.97 300 | 51.71 277 | 67.58 308 | 83.93 305 |
|
MS-PatchMatch | | | 73.83 237 | 72.67 236 | 77.30 265 | 83.87 242 | 66.02 138 | 81.82 261 | 84.66 235 | 61.37 282 | 68.61 273 | 82.82 266 | 47.29 278 | 88.21 273 | 59.27 235 | 84.32 142 | 77.68 333 |
|
cascas | | | 76.72 203 | 74.64 217 | 82.99 136 | 85.78 200 | 65.88 142 | 82.33 258 | 89.21 162 | 60.85 284 | 72.74 209 | 81.02 291 | 47.28 279 | 93.75 120 | 67.48 170 | 85.02 134 | 89.34 191 |
|
test20.03 | | | 67.45 291 | 66.95 290 | 68.94 316 | 75.48 331 | 44.84 338 | 77.50 299 | 77.67 311 | 66.66 228 | 63.01 311 | 83.80 256 | 47.02 280 | 78.40 322 | 42.53 330 | 68.86 305 | 83.58 307 |
|
test_0402 | | | 72.79 259 | 70.44 262 | 79.84 215 | 88.13 149 | 65.99 139 | 85.93 186 | 84.29 238 | 65.57 243 | 67.40 284 | 85.49 234 | 46.92 281 | 92.61 168 | 35.88 338 | 74.38 272 | 80.94 323 |
|
F-COLMAP | | | 76.38 212 | 74.33 223 | 82.50 161 | 89.28 112 | 66.95 129 | 88.41 102 | 89.03 165 | 64.05 257 | 66.83 288 | 88.61 133 | 46.78 282 | 92.89 160 | 57.48 252 | 78.55 214 | 87.67 243 |
|
ppachtmachnet_test | | | 70.04 278 | 67.34 288 | 78.14 250 | 79.80 307 | 61.13 229 | 79.19 286 | 80.59 283 | 59.16 298 | 65.27 299 | 79.29 304 | 46.75 283 | 87.29 280 | 49.33 289 | 66.72 313 | 86.00 285 |
|
Anonymous20231206 | | | 68.60 285 | 67.80 284 | 71.02 310 | 80.23 303 | 50.75 327 | 78.30 295 | 80.47 285 | 56.79 315 | 66.11 295 | 82.63 268 | 46.35 284 | 78.95 320 | 43.62 327 | 75.70 255 | 83.36 309 |
|
CHOSEN 280x420 | | | 66.51 297 | 64.71 296 | 71.90 303 | 81.45 287 | 63.52 200 | 57.98 350 | 68.95 347 | 53.57 328 | 62.59 314 | 76.70 319 | 46.22 285 | 75.29 336 | 55.25 264 | 79.68 200 | 76.88 339 |
|
GA-MVS | | | 76.87 201 | 75.17 214 | 81.97 171 | 82.75 270 | 62.58 219 | 81.44 268 | 86.35 221 | 72.16 151 | 74.74 195 | 82.89 264 | 46.20 286 | 92.02 184 | 68.85 162 | 81.09 184 | 91.30 114 |
|
MDA-MVSNet_test_wron | | | 65.03 301 | 62.92 302 | 71.37 306 | 75.93 326 | 56.73 277 | 69.09 333 | 74.73 327 | 57.28 313 | 54.03 336 | 77.89 313 | 45.88 287 | 74.39 339 | 49.89 287 | 61.55 330 | 82.99 315 |
|
YYNet1 | | | 65.03 301 | 62.91 303 | 71.38 305 | 75.85 327 | 56.60 281 | 69.12 332 | 74.66 330 | 57.28 313 | 54.12 335 | 77.87 314 | 45.85 288 | 74.48 338 | 49.95 286 | 61.52 331 | 83.05 313 |
|
EPMVS | | | 69.02 284 | 68.16 277 | 71.59 304 | 79.61 310 | 49.80 332 | 77.40 300 | 66.93 349 | 62.82 269 | 70.01 252 | 79.05 305 | 45.79 289 | 77.86 326 | 56.58 259 | 75.26 264 | 87.13 258 |
|
IB-MVS | | 68.01 15 | 75.85 222 | 73.36 230 | 83.31 120 | 84.76 214 | 66.03 137 | 83.38 246 | 85.06 232 | 70.21 178 | 69.40 261 | 81.05 290 | 45.76 290 | 94.66 80 | 65.10 191 | 75.49 259 | 89.25 193 |
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 |
jajsoiax | | | 79.29 147 | 77.96 153 | 83.27 122 | 84.68 216 | 66.57 132 | 89.25 73 | 90.16 131 | 69.20 193 | 75.46 177 | 89.49 112 | 45.75 291 | 93.13 150 | 76.84 89 | 80.80 188 | 90.11 158 |
|
PatchMatch-RL | | | 72.38 261 | 70.90 260 | 76.80 269 | 88.60 135 | 67.38 120 | 79.53 281 | 76.17 318 | 62.75 270 | 69.36 263 | 82.00 278 | 45.51 292 | 84.89 297 | 53.62 271 | 80.58 191 | 78.12 331 |
|
RPSCF | | | 73.23 254 | 71.46 253 | 78.54 244 | 82.50 276 | 59.85 240 | 82.18 259 | 82.84 260 | 58.96 299 | 71.15 239 | 89.41 119 | 45.48 293 | 84.77 298 | 58.82 240 | 71.83 291 | 91.02 121 |
|
MSDG | | | 73.36 252 | 70.99 259 | 80.49 203 | 84.51 218 | 65.80 143 | 80.71 271 | 86.13 224 | 65.70 241 | 65.46 297 | 83.74 258 | 44.60 294 | 90.91 227 | 51.13 280 | 76.89 236 | 84.74 297 |
|
PVSNet_0 | | 57.27 20 | 61.67 308 | 59.27 309 | 68.85 318 | 79.61 310 | 57.44 268 | 68.01 336 | 73.44 334 | 55.93 319 | 58.54 324 | 70.41 336 | 44.58 295 | 77.55 327 | 47.01 303 | 35.91 349 | 71.55 343 |
|
DWT-MVSNet_test | | | 73.70 238 | 71.86 249 | 79.21 229 | 82.91 267 | 58.94 246 | 82.34 257 | 82.17 266 | 65.21 244 | 71.05 240 | 78.31 309 | 44.21 296 | 90.17 237 | 63.29 202 | 77.28 227 | 88.53 228 |
|
mvs_tets | | | 79.13 150 | 77.77 159 | 83.22 125 | 84.70 215 | 66.37 134 | 89.17 74 | 90.19 130 | 69.38 189 | 75.40 180 | 89.46 115 | 44.17 297 | 93.15 148 | 76.78 90 | 80.70 190 | 90.14 156 |
|
MDA-MVSNet-bldmvs | | | 66.68 295 | 63.66 299 | 75.75 280 | 79.28 315 | 60.56 237 | 73.92 317 | 78.35 307 | 64.43 253 | 50.13 343 | 79.87 301 | 44.02 298 | 83.67 302 | 46.10 312 | 56.86 337 | 83.03 314 |
|
gg-mvs-nofinetune | | | 69.95 279 | 67.96 280 | 75.94 279 | 83.07 262 | 54.51 303 | 77.23 301 | 70.29 340 | 63.11 263 | 70.32 246 | 62.33 343 | 43.62 299 | 88.69 269 | 53.88 270 | 87.76 103 | 84.62 299 |
|
GG-mvs-BLEND | | | | | 75.38 286 | 81.59 285 | 55.80 296 | 79.32 283 | 69.63 342 | | 67.19 285 | 73.67 330 | 43.24 300 | 88.90 268 | 50.41 282 | 84.50 139 | 81.45 322 |
|
CMPMVS | | 51.72 21 | 70.19 277 | 68.16 277 | 76.28 277 | 73.15 337 | 57.55 266 | 79.47 282 | 83.92 241 | 48.02 340 | 56.48 332 | 84.81 247 | 43.13 301 | 86.42 287 | 62.67 207 | 81.81 178 | 84.89 295 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
Test4 | | | 77.83 179 | 75.90 197 | 83.62 110 | 80.24 302 | 65.25 156 | 85.27 207 | 90.67 108 | 69.03 199 | 66.48 292 | 83.75 257 | 43.07 302 | 93.00 158 | 75.93 95 | 88.66 92 | 92.62 79 |
|
dp | | | 66.80 294 | 65.43 294 | 70.90 311 | 79.74 309 | 48.82 333 | 75.12 313 | 74.77 326 | 59.61 293 | 64.08 307 | 77.23 317 | 42.89 303 | 80.72 313 | 48.86 291 | 66.58 315 | 83.16 311 |
|
PVSNet | | 64.34 18 | 72.08 263 | 70.87 261 | 75.69 281 | 86.21 196 | 56.44 283 | 74.37 316 | 80.73 282 | 62.06 277 | 70.17 249 | 82.23 272 | 42.86 304 | 83.31 305 | 54.77 266 | 84.45 141 | 87.32 252 |
|
pmmvs-eth3d | | | 70.50 274 | 67.83 283 | 78.52 245 | 77.37 322 | 66.18 136 | 81.82 261 | 81.51 276 | 58.90 300 | 63.90 308 | 80.42 296 | 42.69 305 | 86.28 288 | 58.56 242 | 65.30 323 | 83.11 312 |
|
UnsupCasMVSNet_eth | | | 67.33 292 | 65.99 293 | 71.37 306 | 73.48 334 | 51.47 323 | 75.16 311 | 85.19 231 | 65.20 245 | 60.78 317 | 80.93 294 | 42.35 306 | 77.20 328 | 57.12 256 | 53.69 342 | 85.44 288 |
|
ADS-MVSNet2 | | | 66.20 300 | 63.33 300 | 74.82 290 | 79.92 305 | 58.75 248 | 67.55 338 | 75.19 322 | 53.37 329 | 65.25 300 | 75.86 322 | 42.32 307 | 80.53 314 | 41.57 331 | 68.91 303 | 85.18 291 |
|
ADS-MVSNet | | | 64.36 304 | 62.88 304 | 68.78 319 | 79.92 305 | 47.17 335 | 67.55 338 | 71.18 338 | 53.37 329 | 65.25 300 | 75.86 322 | 42.32 307 | 73.99 341 | 41.57 331 | 68.91 303 | 85.18 291 |
|
SixPastTwentyTwo | | | 73.37 250 | 71.26 257 | 79.70 217 | 85.08 212 | 57.89 260 | 85.57 198 | 83.56 247 | 71.03 164 | 65.66 296 | 85.88 223 | 42.10 309 | 92.57 169 | 59.11 237 | 63.34 326 | 88.65 216 |
|
JIA-IIPM | | | 66.32 299 | 62.82 305 | 76.82 268 | 77.09 324 | 61.72 228 | 65.34 342 | 75.38 320 | 58.04 306 | 64.51 304 | 62.32 344 | 42.05 310 | 86.51 286 | 51.45 279 | 69.22 302 | 82.21 318 |
|
ACMH | | 67.68 16 | 75.89 221 | 73.93 226 | 81.77 175 | 88.71 133 | 66.61 131 | 88.62 95 | 89.01 168 | 69.81 181 | 66.78 289 | 86.70 191 | 41.95 311 | 91.51 209 | 55.64 262 | 78.14 220 | 87.17 256 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
ACMH+ | | 68.96 14 | 76.01 220 | 74.01 225 | 82.03 169 | 88.60 135 | 65.31 155 | 88.86 85 | 87.55 205 | 70.25 177 | 67.75 279 | 87.47 166 | 41.27 312 | 93.19 146 | 58.37 244 | 75.94 252 | 87.60 245 |
|
MIMVSNet | | | 70.69 271 | 69.30 267 | 74.88 289 | 84.52 217 | 56.35 287 | 75.87 308 | 79.42 296 | 64.59 251 | 67.76 278 | 82.41 269 | 41.10 313 | 81.54 311 | 46.64 310 | 81.34 182 | 86.75 266 |
|
Anonymous202405211 | | | 78.25 164 | 77.01 171 | 81.99 170 | 91.03 67 | 60.67 235 | 84.77 217 | 83.90 242 | 70.65 171 | 80.00 91 | 91.20 77 | 41.08 314 | 91.43 210 | 65.21 189 | 85.26 133 | 93.85 34 |
|
N_pmnet | | | 52.79 321 | 53.26 319 | 51.40 340 | 78.99 317 | 7.68 365 | 69.52 328 | 3.89 365 | 51.63 336 | 57.01 330 | 74.98 325 | 40.83 315 | 65.96 354 | 37.78 336 | 64.67 324 | 80.56 326 |
|
testing_2 | | | 75.73 223 | 73.34 231 | 82.89 145 | 77.37 322 | 65.22 157 | 84.10 237 | 90.54 114 | 69.09 195 | 60.46 318 | 81.15 289 | 40.48 316 | 92.84 164 | 76.36 91 | 80.54 194 | 90.60 137 |
|
EU-MVSNet | | | 68.53 287 | 67.61 287 | 71.31 309 | 78.51 318 | 47.01 336 | 84.47 225 | 84.27 239 | 42.27 343 | 66.44 293 | 84.79 248 | 40.44 317 | 83.76 301 | 58.76 241 | 68.54 307 | 83.17 310 |
|
DSMNet-mixed | | | 57.77 315 | 56.90 315 | 60.38 331 | 67.70 347 | 35.61 351 | 69.18 330 | 53.97 356 | 32.30 353 | 57.49 328 | 79.88 300 | 40.39 318 | 68.57 351 | 38.78 335 | 72.37 286 | 76.97 336 |
|
OurMVSNet-221017-0 | | | 74.26 234 | 72.42 240 | 79.80 216 | 83.76 247 | 59.59 241 | 85.92 187 | 86.64 215 | 66.39 233 | 66.96 287 | 87.58 161 | 39.46 319 | 91.60 207 | 65.76 186 | 69.27 301 | 88.22 232 |
|
K. test v3 | | | 71.19 267 | 68.51 273 | 79.21 229 | 83.04 264 | 57.78 263 | 84.35 232 | 76.91 316 | 72.90 133 | 62.99 312 | 82.86 265 | 39.27 320 | 91.09 224 | 61.65 217 | 52.66 343 | 88.75 213 |
|
lessismore_v0 | | | | | 78.97 236 | 81.01 295 | 57.15 270 | | 65.99 350 | | 61.16 316 | 82.82 266 | 39.12 321 | 91.34 214 | 59.67 231 | 46.92 347 | 88.43 230 |
|
UnsupCasMVSNet_bld | | | 63.70 306 | 61.53 308 | 70.21 313 | 73.69 333 | 51.39 324 | 72.82 318 | 81.89 273 | 55.63 320 | 57.81 326 | 71.80 333 | 38.67 322 | 78.61 321 | 49.26 290 | 52.21 344 | 80.63 324 |
|
new-patchmatchnet | | | 61.73 307 | 61.73 307 | 61.70 330 | 72.74 338 | 24.50 361 | 69.16 331 | 78.03 309 | 61.40 280 | 56.72 331 | 75.53 324 | 38.42 323 | 76.48 331 | 45.95 313 | 57.67 336 | 84.13 303 |
|
MVS-HIRNet | | | 59.14 311 | 57.67 313 | 63.57 327 | 81.65 284 | 43.50 341 | 71.73 320 | 65.06 352 | 39.59 347 | 51.43 341 | 57.73 347 | 38.34 324 | 82.58 308 | 39.53 334 | 73.95 275 | 64.62 348 |
|
COLMAP_ROB | | 66.92 17 | 73.01 256 | 70.41 263 | 80.81 200 | 87.13 184 | 65.63 145 | 88.30 107 | 84.19 240 | 62.96 266 | 63.80 309 | 87.69 159 | 38.04 325 | 92.56 170 | 46.66 308 | 74.91 267 | 84.24 301 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
TESTMET0.1,1 | | | 69.89 280 | 69.00 270 | 72.55 301 | 79.27 316 | 56.85 275 | 78.38 292 | 74.71 328 | 57.64 309 | 68.09 277 | 77.19 318 | 37.75 326 | 76.70 329 | 63.92 198 | 84.09 143 | 84.10 304 |
|
LP | | | 61.36 309 | 57.78 312 | 72.09 302 | 75.54 330 | 58.53 250 | 67.16 340 | 75.22 321 | 51.90 335 | 54.13 334 | 69.97 337 | 37.73 327 | 80.45 315 | 32.74 342 | 55.63 339 | 77.29 335 |
|
OpenMVS_ROB | | 64.09 19 | 70.56 273 | 68.19 276 | 77.65 257 | 80.26 301 | 59.41 245 | 85.01 213 | 82.96 259 | 58.76 301 | 65.43 298 | 82.33 270 | 37.63 328 | 91.23 217 | 45.34 317 | 76.03 251 | 82.32 317 |
|
FMVSNet5 | | | 69.50 281 | 67.96 280 | 74.15 296 | 82.97 265 | 55.35 298 | 80.01 277 | 82.12 268 | 62.56 272 | 63.02 310 | 81.53 286 | 36.92 329 | 81.92 309 | 48.42 292 | 74.06 274 | 85.17 293 |
|
MIMVSNet1 | | | 68.58 286 | 66.78 291 | 73.98 297 | 80.07 304 | 51.82 319 | 80.77 270 | 84.37 237 | 64.40 254 | 59.75 322 | 82.16 273 | 36.47 330 | 83.63 303 | 42.73 329 | 70.33 298 | 86.48 270 |
|
ITE_SJBPF | | | | | 78.22 249 | 81.77 283 | 60.57 236 | | 83.30 251 | 69.25 192 | 67.54 281 | 87.20 173 | 36.33 331 | 87.28 281 | 54.34 267 | 74.62 270 | 86.80 264 |
|
test-mter | | | 71.41 266 | 70.39 264 | 74.48 292 | 81.35 290 | 58.04 256 | 78.38 292 | 77.46 312 | 60.32 287 | 69.95 255 | 79.00 307 | 36.08 332 | 79.24 318 | 66.13 180 | 84.83 136 | 86.15 278 |
|
testgi | | | 66.67 296 | 66.53 292 | 67.08 322 | 75.62 329 | 41.69 345 | 75.93 305 | 76.50 317 | 66.11 235 | 65.20 302 | 86.59 198 | 35.72 333 | 74.71 337 | 43.71 326 | 73.38 281 | 84.84 296 |
|
EG-PatchMatch MVS | | | 74.04 235 | 71.82 251 | 80.71 202 | 84.92 213 | 67.42 118 | 85.86 188 | 88.08 197 | 66.04 237 | 64.22 306 | 83.85 255 | 35.10 334 | 92.56 170 | 57.44 253 | 80.83 187 | 82.16 319 |
|
XVG-ACMP-BASELINE | | | 76.11 219 | 74.27 224 | 81.62 184 | 83.20 258 | 64.67 170 | 83.60 244 | 89.75 145 | 69.75 183 | 71.85 230 | 87.09 179 | 32.78 335 | 92.11 182 | 69.99 152 | 80.43 195 | 88.09 235 |
|
AllTest | | | 70.96 269 | 68.09 279 | 79.58 222 | 85.15 208 | 63.62 196 | 84.58 224 | 79.83 293 | 62.31 274 | 60.32 319 | 86.73 184 | 32.02 336 | 88.96 265 | 50.28 283 | 71.57 293 | 86.15 278 |
|
TestCases | | | | | 79.58 222 | 85.15 208 | 63.62 196 | | 79.83 293 | 62.31 274 | 60.32 319 | 86.73 184 | 32.02 336 | 88.96 265 | 50.28 283 | 71.57 293 | 86.15 278 |
|
USDC | | | 70.33 275 | 68.37 274 | 76.21 278 | 80.60 298 | 56.23 288 | 79.19 286 | 86.49 217 | 60.89 283 | 61.29 315 | 85.47 235 | 31.78 338 | 89.47 247 | 53.37 272 | 76.21 250 | 82.94 316 |
|
tmp_tt | | | 18.61 337 | 21.40 338 | 10.23 350 | 4.82 364 | 10.11 364 | 34.70 357 | 30.74 363 | 1.48 360 | 23.91 355 | 26.07 358 | 28.42 339 | 13.41 363 | 27.12 351 | 15.35 359 | 7.17 359 |
|
testpf | | | 56.51 317 | 57.58 314 | 53.30 337 | 71.99 340 | 41.19 346 | 46.89 355 | 69.32 345 | 58.06 305 | 52.87 340 | 69.45 339 | 27.99 340 | 72.73 343 | 59.59 233 | 62.07 328 | 45.98 353 |
|
test2356 | | | 59.50 310 | 58.08 310 | 63.74 326 | 71.23 341 | 41.88 343 | 67.59 337 | 72.42 337 | 53.72 327 | 57.65 327 | 70.74 335 | 26.31 341 | 72.40 344 | 32.03 345 | 71.06 296 | 76.93 337 |
|
TDRefinement | | | 67.49 290 | 64.34 297 | 76.92 267 | 73.47 335 | 61.07 230 | 84.86 216 | 82.98 258 | 59.77 292 | 58.30 325 | 85.13 240 | 26.06 342 | 87.89 277 | 47.92 301 | 60.59 334 | 81.81 321 |
|
test1235678 | | | 58.74 313 | 56.89 316 | 64.30 324 | 69.70 343 | 41.87 344 | 71.05 322 | 74.87 325 | 54.06 324 | 50.63 342 | 71.53 334 | 25.30 343 | 74.10 340 | 31.80 346 | 63.10 327 | 76.93 337 |
|
TinyColmap | | | 67.30 293 | 64.81 295 | 74.76 291 | 81.92 282 | 56.68 280 | 80.29 275 | 81.49 277 | 60.33 286 | 56.27 333 | 83.22 262 | 24.77 344 | 87.66 279 | 45.52 315 | 69.47 300 | 79.95 327 |
|
LF4IMVS | | | 64.02 305 | 62.19 306 | 69.50 315 | 70.90 342 | 53.29 310 | 76.13 303 | 77.18 315 | 52.65 332 | 58.59 323 | 80.98 292 | 23.55 345 | 76.52 330 | 53.06 274 | 66.66 314 | 78.68 330 |
|
1111 | | | 57.11 316 | 56.82 317 | 57.97 334 | 69.10 344 | 28.28 356 | 68.90 334 | 74.54 331 | 54.01 325 | 53.71 337 | 74.51 326 | 23.09 346 | 67.90 352 | 32.28 343 | 61.26 332 | 77.73 332 |
|
.test1245 | | | 45.55 326 | 50.02 323 | 32.14 346 | 69.10 344 | 28.28 356 | 68.90 334 | 74.54 331 | 54.01 325 | 53.71 337 | 74.51 326 | 23.09 346 | 67.90 352 | 32.28 343 | 0.02 361 | 0.25 362 |
|
new_pmnet | | | 50.91 323 | 50.29 322 | 52.78 338 | 68.58 346 | 34.94 354 | 63.71 344 | 56.63 355 | 39.73 346 | 44.95 344 | 65.47 342 | 21.93 348 | 58.48 356 | 34.98 339 | 56.62 338 | 64.92 347 |
|
pmmvs3 | | | 57.79 314 | 54.26 318 | 68.37 320 | 64.02 349 | 56.72 278 | 75.12 313 | 65.17 351 | 40.20 345 | 52.93 339 | 69.86 338 | 20.36 349 | 75.48 335 | 45.45 316 | 55.25 341 | 72.90 342 |
|
PM-MVS | | | 66.41 298 | 64.14 298 | 73.20 299 | 73.92 332 | 56.45 282 | 78.97 288 | 64.96 353 | 63.88 261 | 64.72 303 | 80.24 297 | 19.84 350 | 83.44 304 | 66.24 179 | 64.52 325 | 79.71 328 |
|
no-one | | | 51.08 322 | 45.79 327 | 66.95 323 | 57.92 354 | 50.49 329 | 59.63 349 | 76.04 319 | 48.04 339 | 31.85 349 | 56.10 350 | 19.12 351 | 80.08 317 | 36.89 337 | 26.52 351 | 70.29 344 |
|
testus | | | 59.00 312 | 57.91 311 | 62.25 329 | 72.25 339 | 39.09 348 | 69.74 326 | 75.02 323 | 53.04 331 | 57.21 329 | 73.72 329 | 18.76 352 | 70.33 348 | 32.86 341 | 68.57 306 | 77.35 334 |
|
ambc | | | | | 75.24 287 | 73.16 336 | 50.51 328 | 63.05 346 | 87.47 208 | | 64.28 305 | 77.81 315 | 17.80 353 | 89.73 242 | 57.88 250 | 60.64 333 | 85.49 287 |
|
ANet_high | | | 50.57 324 | 46.10 326 | 63.99 325 | 48.67 359 | 39.13 347 | 70.99 324 | 80.85 280 | 61.39 281 | 31.18 351 | 57.70 348 | 17.02 354 | 73.65 342 | 31.22 347 | 15.89 358 | 79.18 329 |
|
FPMVS | | | 53.68 320 | 51.64 320 | 59.81 332 | 65.08 348 | 51.03 325 | 69.48 329 | 69.58 343 | 41.46 344 | 40.67 346 | 72.32 332 | 16.46 355 | 70.00 349 | 24.24 353 | 65.42 322 | 58.40 350 |
|
testmv | | | 53.85 319 | 51.03 321 | 62.31 328 | 61.46 351 | 38.88 349 | 70.95 325 | 74.69 329 | 51.11 337 | 41.26 345 | 66.85 340 | 14.28 356 | 72.13 345 | 29.19 348 | 49.51 346 | 75.93 340 |
|
test12356 | | | 49.28 325 | 48.51 325 | 51.59 339 | 62.06 350 | 19.11 362 | 60.40 347 | 72.45 336 | 47.60 341 | 40.64 347 | 65.68 341 | 13.84 357 | 68.72 350 | 27.29 350 | 46.67 348 | 66.94 346 |
|
EMVS | | | 30.81 334 | 29.65 335 | 34.27 345 | 50.96 358 | 25.95 360 | 56.58 353 | 46.80 360 | 24.01 356 | 15.53 359 | 30.68 357 | 12.47 358 | 54.43 359 | 12.81 359 | 17.05 357 | 22.43 358 |
|
E-PMN | | | 31.77 333 | 30.64 334 | 35.15 344 | 52.87 357 | 27.67 358 | 57.09 352 | 47.86 359 | 24.64 354 | 16.40 358 | 33.05 356 | 11.23 359 | 54.90 358 | 14.46 358 | 18.15 355 | 22.87 357 |
|
DeepMVS_CX | | | | | 27.40 348 | 40.17 363 | 26.90 359 | | 24.59 364 | 17.44 358 | 23.95 354 | 48.61 351 | 9.77 360 | 26.48 361 | 18.06 355 | 24.47 352 | 28.83 356 |
|
Gipuma | | | 45.18 327 | 41.86 328 | 55.16 336 | 77.03 325 | 51.52 322 | 32.50 358 | 80.52 284 | 32.46 351 | 27.12 352 | 35.02 354 | 9.52 361 | 75.50 334 | 22.31 354 | 60.21 335 | 38.45 355 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
LCM-MVSNet | | | 54.25 318 | 49.68 324 | 67.97 321 | 53.73 356 | 45.28 337 | 66.85 341 | 80.78 281 | 35.96 349 | 39.45 348 | 62.23 345 | 8.70 362 | 78.06 325 | 48.24 297 | 51.20 345 | 80.57 325 |
|
PMMVS2 | | | 40.82 329 | 38.86 330 | 46.69 342 | 53.84 355 | 16.45 363 | 48.61 354 | 49.92 358 | 37.49 348 | 31.67 350 | 60.97 346 | 8.14 363 | 56.42 357 | 28.42 349 | 30.72 350 | 67.19 345 |
|
PMVS | | 37.38 22 | 44.16 328 | 40.28 329 | 55.82 335 | 40.82 362 | 42.54 342 | 65.12 343 | 63.99 354 | 34.43 350 | 24.48 353 | 57.12 349 | 3.92 364 | 76.17 332 | 17.10 356 | 55.52 340 | 48.75 351 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
MVE | | 26.22 23 | 30.37 335 | 25.89 337 | 43.81 343 | 44.55 361 | 35.46 353 | 28.87 359 | 39.07 361 | 18.20 357 | 18.58 357 | 40.18 353 | 2.68 365 | 47.37 360 | 17.07 357 | 23.78 353 | 48.60 352 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
PNet_i23d | | | 38.26 331 | 35.42 331 | 46.79 341 | 58.74 352 | 35.48 352 | 59.65 348 | 51.25 357 | 32.45 352 | 23.44 356 | 47.53 352 | 2.04 366 | 58.96 355 | 25.60 352 | 18.09 356 | 45.92 354 |
|
wuyk23d | | | 16.82 338 | 15.94 339 | 19.46 349 | 58.74 352 | 31.45 355 | 39.22 356 | 3.74 366 | 6.84 359 | 6.04 361 | 2.70 362 | 1.27 367 | 24.29 362 | 10.54 360 | 14.40 360 | 2.63 360 |
|
wuykxyi23d | | | 39.76 330 | 33.18 333 | 59.51 333 | 46.98 360 | 44.01 339 | 57.70 351 | 67.74 348 | 24.13 355 | 13.98 360 | 34.33 355 | 1.27 367 | 71.33 346 | 34.23 340 | 18.23 354 | 63.18 349 |
|
test123 | | | 6.12 340 | 8.11 341 | 0.14 351 | 0.06 366 | 0.09 366 | 71.05 322 | 0.03 368 | 0.04 362 | 0.25 363 | 1.30 364 | 0.05 369 | 0.03 365 | 0.21 362 | 0.01 363 | 0.29 361 |
|
testmvs | | | 6.04 341 | 8.02 342 | 0.10 352 | 0.08 365 | 0.03 367 | 69.74 326 | 0.04 367 | 0.05 361 | 0.31 362 | 1.68 363 | 0.02 370 | 0.04 364 | 0.24 361 | 0.02 361 | 0.25 362 |
|
sosnet-low-res | | | 0.00 343 | 0.00 344 | 0.00 353 | 0.00 367 | 0.00 368 | 0.00 360 | 0.00 369 | 0.00 363 | 0.00 364 | 0.00 365 | 0.00 371 | 0.00 366 | 0.00 363 | 0.00 364 | 0.00 364 |
|
sosnet | | | 0.00 343 | 0.00 344 | 0.00 353 | 0.00 367 | 0.00 368 | 0.00 360 | 0.00 369 | 0.00 363 | 0.00 364 | 0.00 365 | 0.00 371 | 0.00 366 | 0.00 363 | 0.00 364 | 0.00 364 |
|
uncertanet | | | 0.00 343 | 0.00 344 | 0.00 353 | 0.00 367 | 0.00 368 | 0.00 360 | 0.00 369 | 0.00 363 | 0.00 364 | 0.00 365 | 0.00 371 | 0.00 366 | 0.00 363 | 0.00 364 | 0.00 364 |
|
Regformer | | | 0.00 343 | 0.00 344 | 0.00 353 | 0.00 367 | 0.00 368 | 0.00 360 | 0.00 369 | 0.00 363 | 0.00 364 | 0.00 365 | 0.00 371 | 0.00 366 | 0.00 363 | 0.00 364 | 0.00 364 |
|
ab-mvs-re | | | 7.23 339 | 9.64 340 | 0.00 353 | 0.00 367 | 0.00 368 | 0.00 360 | 0.00 369 | 0.00 363 | 0.00 364 | 86.72 186 | 0.00 371 | 0.00 366 | 0.00 363 | 0.00 364 | 0.00 364 |
|
uanet | | | 0.00 343 | 0.00 344 | 0.00 353 | 0.00 367 | 0.00 368 | 0.00 360 | 0.00 369 | 0.00 363 | 0.00 364 | 0.00 365 | 0.00 371 | 0.00 366 | 0.00 363 | 0.00 364 | 0.00 364 |
|
GSMVS | | | | | | | | | | | | | | | | | 88.96 207 |
|
test_part3 | | | | | | | | 92.22 19 | | 75.63 74 | | 95.29 3 | | 97.56 1 | 86.60 13 | | |
|
test_part2 | | | | | | 95.06 1 | 72.65 27 | | | | 91.80 2 | | | | | | |
|
MTGPA | | | | | | | | | 92.02 62 | | | | | | | | |
|
MTMP | | | | | | | | 92.18 22 | 32.83 362 | | | | | | | | |
|
gm-plane-assit | | | | | | 81.40 288 | 53.83 307 | | | 62.72 271 | | 80.94 293 | | 92.39 174 | 63.40 201 | | |
|
test9_res | | | | | | | | | | | | | | | 84.90 20 | 95.70 15 | 92.87 73 |
|
agg_prior2 | | | | | | | | | | | | | | | 82.91 42 | 95.45 17 | 92.70 75 |
|
agg_prior | | | | | | 92.85 44 | 71.94 42 | | 91.78 77 | | 84.41 42 | | | 94.93 68 | | | |
|
test_prior4 | | | | | | | 72.60 30 | 89.01 81 | | | | | | | | | |
|
test_prior | | | | | 86.33 48 | 92.61 49 | 69.59 75 | | 92.97 32 | | | | | 95.48 46 | | | 93.91 31 |
|
旧先验2 | | | | | | | | 86.56 169 | | 58.10 304 | 87.04 16 | | | 88.98 263 | 74.07 114 | | |
|
新几何2 | | | | | | | | 86.29 178 | | | | | | | | | |
|
无先验 | | | | | | | | 87.48 133 | 88.98 172 | 60.00 290 | | | | 94.12 97 | 67.28 172 | | 88.97 206 |
|
原ACMM2 | | | | | | | | 86.86 158 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 91.01 226 | 62.37 209 | | |
|
testdata1 | | | | | | | | 84.14 236 | | 75.71 71 | | | | | | | |
|
plane_prior7 | | | | | | 90.08 84 | 68.51 101 | | | | | | | | | | |
|
plane_prior5 | | | | | | | | | 92.44 46 | | | | | 95.38 53 | 78.71 68 | 86.32 125 | 91.33 112 |
|
plane_prior4 | | | | | | | | | | | | 91.00 85 | | | | | |
|
plane_prior3 | | | | | | | 68.60 99 | | | 78.44 30 | 78.92 101 | | | | | | |
|
plane_prior2 | | | | | | | | 91.25 32 | | 79.12 23 | | | | | | | |
|
plane_prior1 | | | | | | 89.90 88 | | | | | | | | | | | |
|
plane_prior | | | | | | | 68.71 95 | 90.38 48 | | 77.62 34 | | | | | | 86.16 127 | |
|
n2 | | | | | | | | | 0.00 369 | | | | | | | | |
|
nn | | | | | | | | | 0.00 369 | | | | | | | | |
|
door-mid | | | | | | | | | 69.98 341 | | | | | | | | |
|
test11 | | | | | | | | | 92.23 54 | | | | | | | | |
|
door | | | | | | | | | 69.44 344 | | | | | | | | |
|
HQP5-MVS | | | | | | | 66.98 126 | | | | | | | | | | |
|
HQP-NCC | | | | | | 89.33 106 | | 89.17 74 | | 76.41 59 | 77.23 142 | | | | | | |
|
ACMP_Plane | | | | | | 89.33 106 | | 89.17 74 | | 76.41 59 | 77.23 142 | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 77.47 80 | | |
|
HQP4-MVS | | | | | | | | | | | 77.24 141 | | | 95.11 61 | | | 91.03 119 |
|
HQP3-MVS | | | | | | | | | 92.19 57 | | | | | | | 85.99 129 | |
|
NP-MVS | | | | | | 89.62 95 | 68.32 103 | | | | | 90.24 95 | | | | | |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 81.95 176 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 81.25 183 | |
|