HSP-MVS | | | 82.45 2 | 83.62 1 | 78.96 36 | 82.99 91 | 52.71 126 | 85.04 110 | 89.99 10 | 66.08 45 | 86.77 1 | 92.75 13 | 72.05 1 | 91.46 44 | 83.35 5 | 93.53 1 | 92.72 17 |
|
MVP-Stereo | | | 70.97 104 | 70.44 92 | 72.59 172 | 76.03 208 | 51.36 153 | 85.02 112 | 86.99 51 | 60.31 126 | 56.53 202 | 78.92 199 | 40.11 149 | 90.00 73 | 60.00 134 | 90.01 2 | 76.41 285 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
DELS-MVS | | | 82.32 3 | 82.50 3 | 81.79 6 | 86.80 27 | 56.89 21 | 92.77 2 | 86.30 64 | 77.83 1 | 77.88 13 | 92.13 19 | 60.24 2 | 94.78 12 | 78.97 16 | 89.61 3 | 93.69 3 |
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++ | | | 80.50 7 | 80.71 7 | 79.88 25 | 87.34 24 | 55.20 49 | 89.93 20 | 87.55 45 | 66.04 48 | 79.46 9 | 93.00 12 | 53.10 17 | 91.76 41 | 80.40 10 | 89.56 4 | 92.68 18 |
|
MVS | | | 76.91 33 | 75.48 42 | 81.23 10 | 84.56 59 | 55.21 48 | 80.23 209 | 91.64 1 | 58.65 163 | 65.37 94 | 91.48 36 | 45.72 68 | 95.05 8 | 72.11 57 | 89.52 5 | 93.44 4 |
|
SMA-MVS | | | 78.93 14 | 78.55 14 | 80.10 22 | 84.42 60 | 55.81 34 | 87.58 49 | 86.47 59 | 61.29 112 | 79.34 10 | 93.10 7 | 46.02 65 | 92.41 31 | 79.97 11 | 88.72 6 | 92.08 24 |
|
3Dnovator | | 64.70 6 | 74.46 58 | 72.48 66 | 80.41 15 | 82.84 97 | 55.40 43 | 83.08 153 | 88.61 20 | 67.61 31 | 59.85 153 | 88.66 82 | 34.57 212 | 93.97 15 | 58.42 141 | 88.70 7 | 91.85 29 |
|
PHI-MVS | | | 77.49 28 | 77.00 28 | 78.95 37 | 85.33 50 | 50.69 162 | 88.57 35 | 88.59 22 | 58.14 173 | 73.60 30 | 93.31 5 | 43.14 106 | 93.79 18 | 73.81 44 | 88.53 8 | 92.37 20 |
|
CSCG | | | 80.41 9 | 79.72 9 | 82.49 4 | 89.12 11 | 57.67 12 | 89.29 30 | 91.54 2 | 59.19 144 | 71.82 50 | 90.05 64 | 59.72 3 | 96.04 1 | 78.37 19 | 88.40 9 | 93.75 2 |
|
MS-PatchMatch | | | 72.34 85 | 71.26 85 | 75.61 107 | 82.38 107 | 55.55 36 | 88.00 38 | 89.95 11 | 65.38 56 | 56.51 203 | 80.74 187 | 32.28 234 | 92.89 24 | 57.95 147 | 88.10 10 | 78.39 259 |
|
CNVR-MVS | | | 81.76 4 | 81.90 4 | 81.33 9 | 90.04 5 | 57.70 11 | 91.71 3 | 88.87 16 | 70.31 14 | 77.64 15 | 93.87 2 | 52.58 19 | 93.91 17 | 84.17 2 | 87.92 11 | 92.39 19 |
|
GG-mvs-BLEND | | | | | 77.77 65 | 86.68 28 | 50.61 163 | 68.67 302 | 88.45 24 | | 68.73 65 | 87.45 102 | 59.15 4 | 90.67 56 | 54.83 173 | 87.67 12 | 92.03 26 |
|
ACMMP_Plus | | | 76.43 41 | 75.66 40 | 78.73 41 | 81.92 117 | 54.67 67 | 84.06 131 | 85.35 78 | 61.10 115 | 72.99 36 | 91.50 35 | 40.25 145 | 91.00 49 | 76.84 28 | 86.98 13 | 90.51 59 |
|
PAPM | | | 76.76 36 | 76.07 38 | 78.81 39 | 80.20 156 | 59.11 5 | 86.86 65 | 86.23 65 | 68.60 21 | 70.18 60 | 88.84 81 | 51.57 24 | 87.16 162 | 65.48 90 | 86.68 14 | 90.15 70 |
|
gg-mvs-nofinetune | | | 67.43 168 | 64.53 189 | 76.13 98 | 85.95 33 | 47.79 218 | 64.38 310 | 88.28 31 | 39.34 310 | 66.62 80 | 41.27 338 | 58.69 5 | 89.00 99 | 49.64 206 | 86.62 15 | 91.59 33 |
|
test_part1 | | | | | | | | | 88.42 25 | | | | 58.18 6 | | | 86.59 16 | 91.53 36 |
|
ESAPD | | | 80.50 7 | 80.42 8 | 80.74 12 | 89.33 9 | 55.48 38 | 89.59 26 | 88.42 25 | 56.02 210 | 82.27 2 | 93.65 3 | 58.18 6 | 95.22 6 | 79.73 12 | 86.59 16 | 91.53 36 |
|
MAR-MVS | | | 76.76 36 | 75.60 41 | 80.21 18 | 90.87 3 | 54.68 66 | 89.14 31 | 89.11 14 | 62.95 92 | 70.54 58 | 92.33 17 | 41.05 139 | 94.95 9 | 57.90 148 | 86.55 18 | 91.00 48 |
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020 |
TSAR-MVS + MP. | | | 78.31 20 | 78.26 16 | 78.48 48 | 81.33 134 | 56.31 29 | 81.59 187 | 86.41 61 | 69.61 18 | 81.72 4 | 88.16 90 | 55.09 10 | 88.04 141 | 74.12 43 | 86.31 19 | 91.09 46 |
|
PS-MVSNAJ | | | 80.06 10 | 79.52 11 | 81.68 7 | 85.58 43 | 60.97 3 | 91.69 4 | 87.02 50 | 70.62 12 | 80.75 6 | 93.22 6 | 37.77 168 | 92.50 30 | 82.75 6 | 86.25 20 | 91.57 34 |
|
DeepPCF-MVS | | 69.37 1 | 80.65 6 | 81.56 5 | 77.94 64 | 85.46 48 | 49.56 184 | 90.99 11 | 86.66 57 | 70.58 13 | 80.07 7 | 95.30 1 | 56.18 9 | 90.97 51 | 82.57 8 | 86.22 21 | 93.28 7 |
|
test12 | | | | | 79.24 31 | 86.89 26 | 56.08 32 | | 85.16 85 | | 72.27 48 | | 47.15 48 | 91.10 48 | | 85.93 22 | 90.54 58 |
|
MCST-MVS | | | 83.01 1 | 83.30 2 | 82.15 5 | 92.84 2 | 57.58 13 | 93.77 1 | 91.10 4 | 75.95 2 | 77.10 16 | 93.09 9 | 54.15 13 | 95.57 3 | 85.80 1 | 85.87 23 | 93.31 6 |
|
MVS_0304 | | | 79.84 12 | 79.71 10 | 80.25 17 | 85.64 40 | 54.62 68 | 90.58 14 | 84.48 100 | 72.51 8 | 79.22 11 | 93.09 9 | 42.01 129 | 93.28 21 | 84.00 4 | 85.84 24 | 92.87 15 |
|
xiu_mvs_v2_base | | | 79.86 11 | 79.31 12 | 81.53 8 | 85.03 54 | 60.73 4 | 91.65 5 | 86.86 53 | 70.30 15 | 80.77 5 | 93.07 11 | 37.63 173 | 92.28 34 | 82.73 7 | 85.71 25 | 91.57 34 |
|
test_prior3 | | | 77.59 27 | 77.33 26 | 78.39 52 | 86.35 31 | 54.91 59 | 89.04 32 | 85.45 73 | 61.88 104 | 73.55 31 | 91.46 37 | 48.01 41 | 89.70 81 | 74.73 38 | 85.46 26 | 90.55 55 |
|
test_prior2 | | | | | | | | 89.04 32 | | 61.88 104 | 73.55 31 | 91.46 37 | 48.01 41 | | 74.73 38 | 85.46 26 | |
|
test9_res | | | | | | | | | | | | | | | 78.72 18 | 85.44 28 | 91.39 41 |
|
train_agg | | | 76.91 33 | 76.40 33 | 78.45 50 | 85.68 37 | 55.42 40 | 87.59 47 | 84.00 119 | 57.84 179 | 72.99 36 | 90.98 41 | 44.99 74 | 88.58 117 | 78.19 20 | 85.32 29 | 91.34 44 |
|
agg_prior3 | | | 76.73 38 | 76.15 37 | 78.48 48 | 85.66 39 | 55.59 35 | 87.54 51 | 83.95 123 | 57.78 181 | 71.78 51 | 90.81 49 | 44.33 82 | 88.52 122 | 78.19 20 | 85.32 29 | 91.34 44 |
|
DeepC-MVS_fast | | 67.50 3 | 78.00 23 | 77.63 22 | 79.13 34 | 88.52 13 | 55.12 51 | 89.95 19 | 85.98 67 | 68.31 23 | 71.33 54 | 92.75 13 | 45.52 70 | 90.37 64 | 71.15 60 | 85.14 31 | 91.91 27 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
agg_prior2 | | | | | | | | | | | | | | | 75.65 32 | 85.11 32 | 91.01 47 |
|
原ACMM1 | | | | | 76.13 98 | 84.89 56 | 54.59 69 | | 85.26 81 | 51.98 250 | 66.70 78 | 87.07 107 | 40.15 148 | 89.70 81 | 51.23 198 | 85.06 33 | 84.10 165 |
|
MP-MVS-pluss | | | 75.54 51 | 75.03 47 | 77.04 82 | 81.37 133 | 52.65 128 | 84.34 124 | 84.46 101 | 61.16 113 | 69.14 62 | 91.76 29 | 39.98 152 | 88.99 101 | 78.19 20 | 84.89 34 | 89.48 79 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
CANet | | | 80.90 5 | 81.17 6 | 80.09 23 | 87.62 22 | 54.21 75 | 91.60 6 | 86.47 59 | 73.13 5 | 79.89 8 | 93.10 7 | 49.88 34 | 92.98 23 | 84.09 3 | 84.75 35 | 93.08 11 |
|
agg_prior1 | | | 76.68 39 | 76.24 36 | 78.00 61 | 85.64 40 | 54.92 57 | 87.55 50 | 83.61 130 | 57.99 176 | 72.53 43 | 91.05 39 | 45.36 71 | 88.10 139 | 77.76 25 | 84.68 36 | 90.99 49 |
|
MG-MVS | | | 78.42 17 | 76.99 29 | 82.73 2 | 93.17 1 | 64.46 1 | 89.93 20 | 88.51 23 | 64.83 62 | 73.52 33 | 88.09 91 | 48.07 39 | 92.19 35 | 62.24 114 | 84.53 37 | 91.53 36 |
|
CDPH-MVS | | | 76.05 47 | 75.19 46 | 78.62 45 | 86.51 30 | 54.98 56 | 87.32 54 | 84.59 98 | 58.62 164 | 70.75 56 | 90.85 47 | 43.10 110 | 90.63 58 | 70.50 62 | 84.51 38 | 90.24 66 |
|
DeepC-MVS | | 67.15 4 | 76.90 35 | 76.27 35 | 78.80 40 | 80.70 143 | 55.02 54 | 86.39 69 | 86.71 55 | 66.96 36 | 67.91 71 | 89.97 66 | 48.03 40 | 91.41 45 | 75.60 33 | 84.14 39 | 89.96 73 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
NCCC | | | 79.57 13 | 79.23 13 | 80.59 13 | 89.50 8 | 56.99 19 | 91.38 8 | 88.17 32 | 67.71 29 | 73.81 29 | 92.75 13 | 46.88 51 | 93.28 21 | 78.79 17 | 84.07 40 | 91.50 39 |
|
OpenMVS | | 61.00 11 | 69.99 121 | 67.55 139 | 77.30 76 | 78.37 183 | 54.07 79 | 84.36 123 | 85.76 69 | 57.22 188 | 56.71 200 | 87.67 98 | 30.79 246 | 92.83 25 | 43.04 240 | 84.06 41 | 85.01 155 |
|
SteuartSystems-ACMMP | | | 77.08 31 | 76.33 34 | 79.34 30 | 80.98 137 | 55.31 44 | 89.76 24 | 86.91 52 | 62.94 93 | 71.65 52 | 91.56 34 | 42.33 121 | 92.56 29 | 77.14 27 | 83.69 42 | 90.15 70 |
Skip Steuart: Steuart Systems R&D Blog. |
APDe-MVS | | | 78.44 16 | 78.20 17 | 79.19 32 | 88.56 12 | 54.55 70 | 89.76 24 | 87.77 40 | 55.91 212 | 78.56 12 | 92.49 16 | 48.20 38 | 92.65 28 | 79.49 14 | 83.04 43 | 90.39 63 |
|
EPNet | | | 78.36 19 | 78.49 15 | 77.97 63 | 85.49 45 | 52.04 139 | 89.36 29 | 84.07 117 | 73.22 4 | 77.03 17 | 91.72 30 | 49.32 36 | 90.17 72 | 73.46 49 | 82.77 44 | 91.69 30 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
API-MVS | | | 74.17 62 | 72.07 75 | 80.49 14 | 90.02 6 | 58.55 7 | 87.30 56 | 84.27 105 | 57.51 185 | 65.77 91 | 87.77 97 | 41.61 136 | 95.97 2 | 51.71 196 | 82.63 45 | 86.94 123 |
|
MSLP-MVS++ | | | 74.21 61 | 72.25 72 | 80.11 21 | 81.45 131 | 56.47 26 | 86.32 71 | 79.65 198 | 58.19 172 | 66.36 83 | 92.29 18 | 36.11 196 | 90.66 57 | 67.39 76 | 82.49 46 | 93.18 10 |
|
zzz-MVS | | | 74.15 64 | 73.11 62 | 77.27 78 | 81.54 127 | 53.57 85 | 84.02 133 | 81.31 171 | 59.41 137 | 68.39 67 | 90.96 43 | 36.07 197 | 89.01 97 | 73.80 45 | 82.45 47 | 89.23 84 |
|
MTAPA | | | 72.73 79 | 71.22 86 | 77.27 78 | 81.54 127 | 53.57 85 | 67.06 305 | 81.31 171 | 59.41 137 | 68.39 67 | 90.96 43 | 36.07 197 | 89.01 97 | 73.80 45 | 82.45 47 | 89.23 84 |
|
MP-MVS | | | 74.99 56 | 74.33 52 | 76.95 85 | 82.89 95 | 53.05 119 | 85.63 85 | 83.50 133 | 57.86 178 | 67.25 75 | 90.24 59 | 43.38 103 | 88.85 110 | 76.03 30 | 82.23 49 | 88.96 92 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
3Dnovator+ | | 62.71 7 | 72.29 87 | 70.50 91 | 77.65 68 | 83.40 78 | 51.29 156 | 87.32 54 | 86.40 62 | 59.01 154 | 58.49 178 | 88.32 86 | 32.40 232 | 91.27 46 | 57.04 155 | 82.15 50 | 90.38 64 |
|
CHOSEN 1792x2688 | | | 76.24 43 | 74.03 54 | 82.88 1 | 83.09 86 | 62.84 2 | 85.73 83 | 85.39 76 | 69.79 16 | 64.87 102 | 83.49 148 | 41.52 137 | 93.69 19 | 70.55 61 | 81.82 51 | 92.12 23 |
|
APD-MVS | | | 76.15 45 | 75.68 39 | 77.54 70 | 88.52 13 | 53.44 90 | 87.26 58 | 85.03 88 | 53.79 227 | 74.91 23 | 91.68 32 | 43.80 91 | 90.31 65 | 74.36 41 | 81.82 51 | 88.87 94 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
QAPM | | | 71.88 92 | 69.33 109 | 79.52 27 | 82.20 109 | 54.30 72 | 86.30 72 | 88.77 17 | 56.61 199 | 59.72 155 | 87.48 101 | 33.90 219 | 95.36 4 | 47.48 218 | 81.49 53 | 88.90 93 |
|
PVSNet_Blended | | | 76.53 40 | 76.54 31 | 76.50 91 | 85.91 34 | 51.83 144 | 88.89 34 | 84.24 108 | 67.82 27 | 69.09 63 | 89.33 76 | 46.70 53 | 88.13 137 | 75.43 34 | 81.48 54 | 89.55 78 |
|
HFP-MVS | | | 74.37 59 | 73.13 61 | 78.10 59 | 84.30 62 | 53.68 82 | 85.58 86 | 84.36 102 | 56.82 193 | 65.78 89 | 90.56 53 | 40.70 142 | 90.90 52 | 69.18 68 | 80.88 55 | 89.71 74 |
|
#test# | | | 74.86 57 | 73.78 56 | 78.10 59 | 84.30 62 | 53.68 82 | 86.95 62 | 84.36 102 | 59.00 155 | 65.78 89 | 90.56 53 | 40.70 142 | 90.90 52 | 71.48 58 | 80.88 55 | 89.71 74 |
|
ACMMPR | | | 73.76 68 | 72.61 63 | 77.24 81 | 83.92 71 | 52.96 123 | 85.58 86 | 84.29 104 | 56.82 193 | 65.12 96 | 90.45 55 | 37.24 183 | 90.18 71 | 69.18 68 | 80.84 57 | 88.58 101 |
|
region2R | | | 73.75 69 | 72.55 65 | 77.33 75 | 83.90 72 | 52.98 122 | 85.54 89 | 84.09 110 | 56.83 192 | 65.10 97 | 90.45 55 | 37.34 181 | 90.24 69 | 68.89 70 | 80.83 58 | 88.77 97 |
|
MVS_Test | | | 75.85 49 | 74.93 49 | 78.62 45 | 84.08 67 | 55.20 49 | 83.99 134 | 85.17 84 | 68.07 25 | 73.38 34 | 82.76 157 | 50.44 29 | 89.00 99 | 65.90 86 | 80.61 59 | 91.64 31 |
|
Vis-MVSNet | | | 70.61 110 | 69.34 108 | 74.42 136 | 80.95 139 | 48.49 206 | 86.03 77 | 77.51 240 | 58.74 162 | 65.55 93 | 87.78 96 | 34.37 213 | 85.95 196 | 52.53 192 | 80.61 59 | 88.80 95 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
XVS | | | 72.92 77 | 71.62 79 | 76.81 87 | 83.41 75 | 52.48 129 | 84.88 115 | 83.20 140 | 58.03 174 | 63.91 114 | 89.63 71 | 35.50 205 | 89.78 78 | 65.50 88 | 80.50 61 | 88.16 104 |
|
X-MVStestdata | | | 65.85 194 | 62.20 203 | 76.81 87 | 83.41 75 | 52.48 129 | 84.88 115 | 83.20 140 | 58.03 174 | 63.91 114 | 4.82 356 | 35.50 205 | 89.78 78 | 65.50 88 | 80.50 61 | 88.16 104 |
|
1121 | | | 68.79 145 | 66.77 151 | 74.82 129 | 83.08 87 | 53.46 88 | 80.23 209 | 71.53 297 | 45.47 287 | 66.31 84 | 87.19 104 | 34.02 216 | 85.13 208 | 52.78 188 | 80.36 63 | 85.87 144 |
|
新几何1 | | | | | 73.30 161 | 83.10 84 | 53.48 87 | | 71.43 298 | 45.55 285 | 66.14 85 | 87.17 105 | 33.88 220 | 80.54 262 | 48.50 211 | 80.33 64 | 85.88 143 |
|
PGM-MVS | | | 72.60 81 | 71.20 87 | 76.80 89 | 82.95 92 | 52.82 125 | 83.07 154 | 82.14 153 | 56.51 205 | 63.18 123 | 89.81 68 | 35.68 204 | 89.76 80 | 67.30 77 | 80.19 65 | 87.83 111 |
|
MVSFormer | | | 73.53 71 | 72.19 73 | 77.57 69 | 83.02 89 | 55.24 46 | 81.63 184 | 81.44 168 | 50.28 260 | 76.67 18 | 90.91 45 | 44.82 78 | 86.11 188 | 60.83 123 | 80.09 66 | 91.36 42 |
|
lupinMVS | | | 78.38 18 | 78.11 18 | 79.19 32 | 83.02 89 | 55.24 46 | 91.57 7 | 84.82 92 | 69.12 19 | 76.67 18 | 92.02 23 | 44.82 78 | 90.23 70 | 80.83 9 | 80.09 66 | 92.08 24 |
|
HPM-MVS | | | 72.60 81 | 71.50 81 | 75.89 104 | 82.02 114 | 51.42 152 | 80.70 202 | 83.05 142 | 56.12 209 | 64.03 113 | 89.53 72 | 37.55 175 | 88.37 126 | 70.48 63 | 80.04 68 | 87.88 110 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
MVS_111021_HR | | | 76.39 42 | 75.38 45 | 79.42 29 | 85.33 50 | 56.47 26 | 88.15 37 | 84.97 89 | 65.15 61 | 66.06 86 | 89.88 67 | 43.79 92 | 92.16 36 | 75.03 37 | 80.03 69 | 89.64 77 |
|
TSAR-MVS + GP. | | | 77.82 25 | 77.59 23 | 78.49 47 | 85.25 52 | 50.27 175 | 90.02 17 | 90.57 5 | 56.58 200 | 74.26 27 | 91.60 33 | 54.26 11 | 92.16 36 | 75.87 31 | 79.91 70 | 93.05 12 |
|
LFMVS | | | 78.52 15 | 77.14 27 | 82.67 3 | 89.58 7 | 58.90 6 | 91.27 9 | 88.05 33 | 63.22 89 | 74.63 25 | 90.83 48 | 41.38 138 | 94.40 13 | 75.42 36 | 79.90 71 | 94.72 1 |
|
Effi-MVS+ | | | 75.24 53 | 73.61 57 | 80.16 20 | 81.92 117 | 57.42 14 | 85.21 94 | 76.71 249 | 60.68 122 | 73.32 35 | 89.34 74 | 47.30 46 | 91.63 42 | 68.28 72 | 79.72 72 | 91.42 40 |
|
PAPM_NR | | | 71.80 93 | 69.98 101 | 77.26 80 | 81.54 127 | 53.34 98 | 78.60 233 | 85.25 82 | 53.46 230 | 60.53 149 | 88.66 82 | 45.69 69 | 89.24 88 | 56.49 157 | 79.62 73 | 89.19 87 |
|
jason | | | 77.01 32 | 76.45 32 | 78.69 42 | 79.69 158 | 54.74 62 | 90.56 15 | 83.99 121 | 68.26 24 | 74.10 28 | 90.91 45 | 42.14 125 | 89.99 74 | 79.30 15 | 79.12 74 | 91.36 42 |
jason: jason. |
CANet_DTU | | | 73.71 70 | 73.14 59 | 75.40 112 | 82.61 104 | 50.05 177 | 84.67 120 | 79.36 204 | 69.72 17 | 75.39 21 | 90.03 65 | 29.41 253 | 85.93 197 | 67.99 74 | 79.11 75 | 90.22 67 |
|
TESTMET0.1,1 | | | 72.86 78 | 72.33 68 | 74.46 135 | 81.98 115 | 50.77 160 | 85.13 97 | 85.47 72 | 66.09 44 | 67.30 73 | 83.69 140 | 37.27 182 | 83.57 236 | 65.06 98 | 78.97 76 | 89.05 90 |
|
SD-MVS | | | 76.18 44 | 74.85 50 | 80.18 19 | 85.39 49 | 56.90 20 | 85.75 82 | 82.45 152 | 56.79 195 | 74.48 26 | 91.81 26 | 43.72 97 | 90.75 55 | 74.61 40 | 78.65 77 | 92.91 13 |
|
VNet | | | 77.99 24 | 77.92 20 | 78.19 57 | 87.43 23 | 50.12 176 | 90.93 12 | 91.41 3 | 67.48 32 | 75.12 22 | 90.15 63 | 46.77 52 | 91.00 49 | 73.52 48 | 78.46 78 | 93.44 4 |
|
旧先验1 | | | | | | 81.57 126 | 47.48 220 | | 71.83 292 | | | 88.66 82 | 36.94 187 | | | 78.34 79 | 88.67 98 |
|
mPP-MVS | | | 71.79 94 | 70.38 93 | 76.04 101 | 82.65 103 | 52.06 138 | 84.45 122 | 81.78 164 | 55.59 216 | 62.05 133 | 89.68 70 | 33.48 222 | 88.28 134 | 65.45 93 | 78.24 80 | 87.77 113 |
|
test_normal | | | 71.31 98 | 68.95 115 | 78.39 52 | 72.30 266 | 54.25 73 | 81.67 180 | 84.05 118 | 65.94 50 | 51.31 241 | 78.09 212 | 36.06 199 | 90.43 62 | 73.00 54 | 78.09 81 | 90.50 60 |
|
CP-MVS | | | 72.59 83 | 71.46 82 | 76.00 103 | 82.93 94 | 52.32 136 | 86.93 64 | 82.48 151 | 55.15 219 | 63.65 119 | 90.44 57 | 35.03 209 | 88.53 121 | 68.69 71 | 77.83 82 | 87.15 122 |
|
DI_MVS_plusplus_test | | | 71.30 99 | 68.98 114 | 78.26 56 | 72.76 248 | 54.08 78 | 81.72 179 | 83.22 138 | 65.75 51 | 51.94 237 | 78.47 206 | 36.01 202 | 90.31 65 | 73.33 50 | 77.60 83 | 90.40 62 |
|
PVSNet_Blended_VisFu | | | 73.40 73 | 72.44 67 | 76.30 93 | 81.32 135 | 54.70 65 | 85.81 78 | 78.82 211 | 63.70 81 | 64.53 106 | 85.38 122 | 47.11 49 | 87.38 159 | 67.75 75 | 77.55 84 | 86.81 129 |
|
canonicalmvs | | | 78.17 21 | 77.86 21 | 79.12 35 | 84.30 62 | 54.22 74 | 87.71 42 | 84.57 99 | 67.70 30 | 77.70 14 | 92.11 22 | 50.90 28 | 89.95 75 | 78.18 23 | 77.54 85 | 93.20 9 |
|
Test4 | | | 68.64 150 | 65.68 170 | 77.53 71 | 67.78 299 | 53.34 98 | 79.42 224 | 82.84 147 | 65.96 49 | 46.54 282 | 76.15 238 | 25.16 280 | 88.83 111 | 69.74 66 | 77.53 86 | 90.43 61 |
|
1314 | | | 71.11 102 | 69.41 106 | 76.22 96 | 79.32 162 | 50.49 167 | 80.23 209 | 85.14 87 | 59.44 136 | 58.93 168 | 88.89 80 | 33.83 221 | 89.60 85 | 61.49 119 | 77.42 87 | 88.57 102 |
|
Regformer-1 | | | 77.80 26 | 77.44 25 | 78.88 38 | 87.78 20 | 52.44 131 | 87.60 44 | 90.08 8 | 68.86 20 | 72.49 45 | 91.79 27 | 47.69 43 | 94.90 10 | 73.57 47 | 77.05 88 | 89.31 82 |
|
Regformer-2 | | | 77.15 30 | 76.82 30 | 78.14 58 | 87.78 20 | 51.84 143 | 87.60 44 | 89.12 13 | 67.23 33 | 71.93 49 | 91.79 27 | 46.03 64 | 93.53 20 | 72.85 55 | 77.05 88 | 89.05 90 |
|
PAPR | | | 75.20 54 | 74.13 53 | 78.41 51 | 88.31 17 | 55.10 53 | 84.31 125 | 85.66 70 | 63.76 80 | 67.55 72 | 90.73 50 | 43.48 102 | 89.40 86 | 66.36 83 | 77.03 90 | 90.73 53 |
|
alignmvs | | | 78.08 22 | 77.98 19 | 78.39 52 | 83.53 74 | 53.22 109 | 89.77 23 | 85.45 73 | 66.11 43 | 76.59 20 | 91.99 25 | 54.07 14 | 89.05 92 | 77.34 26 | 77.00 91 | 92.89 14 |
|
test222 | | | | | | 79.36 160 | 50.97 159 | 77.99 237 | 67.84 310 | 42.54 304 | 62.84 127 | 86.53 113 | 30.26 249 | | | 76.91 92 | 85.23 152 |
|
PMMVS | | | 72.98 76 | 72.05 76 | 75.78 106 | 83.57 73 | 48.60 201 | 84.08 129 | 82.85 146 | 61.62 108 | 68.24 69 | 90.33 58 | 28.35 258 | 87.78 150 | 72.71 56 | 76.69 93 | 90.95 50 |
|
UGNet | | | 68.71 148 | 67.11 147 | 73.50 159 | 80.55 154 | 47.61 219 | 84.08 129 | 78.51 219 | 59.45 135 | 65.68 92 | 82.73 160 | 23.78 286 | 85.08 210 | 52.80 187 | 76.40 94 | 87.80 112 |
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 |
xiu_mvs_v1_base_debu | | | 71.60 95 | 70.29 95 | 75.55 108 | 77.26 196 | 53.15 112 | 85.34 90 | 79.37 201 | 55.83 213 | 72.54 40 | 90.19 60 | 22.38 296 | 86.66 176 | 73.28 51 | 76.39 95 | 86.85 126 |
|
xiu_mvs_v1_base | | | 71.60 95 | 70.29 95 | 75.55 108 | 77.26 196 | 53.15 112 | 85.34 90 | 79.37 201 | 55.83 213 | 72.54 40 | 90.19 60 | 22.38 296 | 86.66 176 | 73.28 51 | 76.39 95 | 86.85 126 |
|
xiu_mvs_v1_base_debi | | | 71.60 95 | 70.29 95 | 75.55 108 | 77.26 196 | 53.15 112 | 85.34 90 | 79.37 201 | 55.83 213 | 72.54 40 | 90.19 60 | 22.38 296 | 86.66 176 | 73.28 51 | 76.39 95 | 86.85 126 |
|
Fast-Effi-MVS+ | | | 72.73 79 | 71.15 88 | 77.48 72 | 82.75 99 | 54.76 61 | 86.77 66 | 80.64 184 | 63.05 91 | 65.93 87 | 84.01 133 | 44.42 81 | 89.03 96 | 56.45 160 | 76.36 98 | 88.64 99 |
|
VDD-MVS | | | 76.08 46 | 74.97 48 | 79.44 28 | 84.27 65 | 53.33 100 | 91.13 10 | 85.88 68 | 65.33 58 | 72.37 46 | 89.34 74 | 32.52 231 | 92.76 26 | 77.90 24 | 75.96 99 | 92.22 22 |
|
testdata | | | | | 67.08 257 | 77.59 190 | 45.46 243 | | 69.20 309 | 44.47 292 | 71.50 53 | 88.34 85 | 31.21 244 | 70.76 324 | 52.20 193 | 75.88 100 | 85.03 154 |
|
mvs_anonymous | | | 72.29 87 | 70.74 89 | 76.94 86 | 82.85 96 | 54.72 64 | 78.43 235 | 81.54 167 | 63.77 79 | 61.69 135 | 79.32 194 | 51.11 26 | 85.31 205 | 62.15 116 | 75.79 101 | 90.79 52 |
|
VDDNet | | | 74.37 59 | 72.13 74 | 81.09 11 | 79.58 159 | 56.52 25 | 90.02 17 | 86.70 56 | 52.61 244 | 71.23 55 | 87.20 103 | 31.75 241 | 93.96 16 | 74.30 42 | 75.77 102 | 92.79 16 |
|
IS-MVSNet | | | 68.80 144 | 67.55 139 | 72.54 173 | 78.50 180 | 43.43 260 | 81.03 196 | 79.35 205 | 59.12 150 | 57.27 198 | 86.71 110 | 46.05 63 | 87.70 152 | 44.32 235 | 75.60 103 | 86.49 133 |
|
WTY-MVS | | | 77.47 29 | 77.52 24 | 77.30 76 | 88.33 16 | 46.25 236 | 88.46 36 | 90.32 6 | 71.40 10 | 72.32 47 | 91.72 30 | 53.44 15 | 92.37 32 | 66.28 84 | 75.42 104 | 93.28 7 |
|
Vis-MVSNet (Re-imp) | | | 65.52 195 | 65.63 171 | 65.17 278 | 77.49 192 | 30.54 324 | 75.49 259 | 77.73 237 | 59.34 140 | 52.26 235 | 86.69 111 | 49.38 35 | 80.53 263 | 37.07 258 | 75.28 105 | 84.42 162 |
|
test-LLR | | | 69.65 126 | 69.01 113 | 71.60 197 | 78.67 174 | 48.17 214 | 85.13 97 | 79.72 195 | 59.18 146 | 63.13 124 | 82.58 162 | 36.91 188 | 80.24 267 | 60.56 126 | 75.17 106 | 86.39 137 |
|
test-mter | | | 68.36 152 | 67.29 143 | 71.60 197 | 78.67 174 | 48.17 214 | 85.13 97 | 79.72 195 | 53.38 231 | 63.13 124 | 82.58 162 | 27.23 267 | 80.24 267 | 60.56 126 | 75.17 106 | 86.39 137 |
|
PVSNet | | 62.49 8 | 69.27 136 | 67.81 129 | 73.64 155 | 84.41 61 | 51.85 142 | 84.63 121 | 77.80 233 | 66.42 38 | 59.80 154 | 84.95 126 | 22.14 300 | 80.44 264 | 55.03 171 | 75.11 108 | 88.62 100 |
|
BH-w/o | | | 70.02 117 | 68.51 117 | 74.56 134 | 82.77 98 | 50.39 169 | 86.60 67 | 78.14 225 | 59.77 130 | 59.65 156 | 85.57 120 | 39.27 157 | 87.30 160 | 49.86 204 | 74.94 109 | 85.99 140 |
|
UA-Net | | | 67.32 170 | 66.23 158 | 70.59 216 | 78.85 170 | 41.23 280 | 73.60 268 | 75.45 266 | 61.54 109 | 66.61 81 | 84.53 128 | 38.73 161 | 86.57 181 | 42.48 244 | 74.24 110 | 83.98 170 |
|
CDS-MVSNet | | | 70.48 112 | 69.43 105 | 73.64 155 | 77.56 191 | 48.83 197 | 83.51 144 | 77.45 241 | 63.27 88 | 62.33 129 | 85.54 121 | 43.85 89 | 83.29 239 | 57.38 154 | 74.00 111 | 88.79 96 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
BH-RMVSNet | | | 70.08 115 | 68.01 124 | 76.27 94 | 84.21 66 | 51.22 158 | 87.29 57 | 79.33 206 | 58.96 157 | 63.63 120 | 86.77 109 | 33.29 224 | 90.30 68 | 44.63 234 | 73.96 112 | 87.30 121 |
|
CLD-MVS | | | 75.60 50 | 75.39 44 | 76.24 95 | 80.69 144 | 52.40 132 | 90.69 13 | 86.20 66 | 74.40 3 | 65.01 100 | 88.93 78 | 42.05 128 | 90.58 59 | 76.57 29 | 73.96 112 | 85.73 145 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
APD-MVS_3200maxsize | | | 69.62 127 | 68.23 122 | 73.80 152 | 81.58 125 | 48.22 213 | 81.91 173 | 79.50 200 | 48.21 270 | 64.24 112 | 89.75 69 | 31.91 240 | 87.55 155 | 63.08 104 | 73.85 114 | 85.64 148 |
|
HPM-MVS_fast | | | 67.86 158 | 66.28 157 | 72.61 171 | 80.67 145 | 48.34 211 | 81.18 193 | 75.95 260 | 50.81 259 | 59.55 160 | 88.05 93 | 27.86 262 | 85.98 193 | 58.83 137 | 73.58 115 | 83.51 179 |
|
ACMMP | | | 70.81 107 | 69.29 110 | 75.39 113 | 81.52 130 | 51.92 141 | 83.43 145 | 83.03 143 | 56.67 198 | 58.80 173 | 88.91 79 | 31.92 239 | 88.58 117 | 65.89 87 | 73.39 116 | 85.67 146 |
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence |
Regformer-3 | | | 76.02 48 | 75.47 43 | 77.70 66 | 85.49 45 | 51.47 150 | 85.12 100 | 90.19 7 | 68.52 22 | 69.36 61 | 90.66 51 | 46.45 61 | 94.81 11 | 70.25 64 | 73.16 117 | 86.81 129 |
|
Regformer-4 | | | 75.06 55 | 74.59 51 | 76.47 92 | 85.49 45 | 50.33 171 | 85.12 100 | 88.61 20 | 66.42 38 | 68.48 66 | 90.66 51 | 44.15 87 | 92.68 27 | 69.24 67 | 73.16 117 | 86.39 137 |
|
HQP3-MVS | | | | | | | | | 83.68 127 | | | | | | | 73.12 119 | |
|
HQP-MVS | | | 72.34 85 | 71.44 83 | 75.03 118 | 79.02 167 | 51.56 147 | 88.00 38 | 83.68 127 | 65.45 52 | 64.48 107 | 85.13 123 | 37.35 179 | 88.62 114 | 66.70 80 | 73.12 119 | 84.91 157 |
|
TAMVS | | | 69.51 129 | 68.16 123 | 73.56 158 | 76.30 204 | 48.71 198 | 82.57 162 | 77.17 245 | 62.10 101 | 61.32 137 | 84.23 131 | 41.90 130 | 83.46 237 | 54.80 175 | 73.09 121 | 88.50 103 |
|
BH-untuned | | | 68.28 154 | 66.40 155 | 73.91 147 | 81.62 122 | 50.01 178 | 85.56 88 | 77.39 242 | 57.63 184 | 57.47 195 | 83.69 140 | 36.36 194 | 87.08 164 | 44.81 233 | 73.08 122 | 84.65 159 |
|
plane_prior | | | | | | | 49.57 182 | 87.43 52 | | 64.57 66 | | | | | | 72.84 123 | |
|
PCF-MVS | | 61.03 10 | 70.10 114 | 68.40 119 | 75.22 117 | 77.15 200 | 51.99 140 | 79.30 229 | 82.12 157 | 56.47 206 | 61.88 134 | 86.48 115 | 43.98 88 | 87.24 161 | 55.37 168 | 72.79 124 | 86.43 136 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
DWT-MVSNet_test | | | 75.47 52 | 73.87 55 | 80.29 16 | 87.33 25 | 57.05 18 | 82.86 159 | 87.96 35 | 72.59 6 | 67.29 74 | 87.79 95 | 51.61 23 | 91.52 43 | 54.75 176 | 72.63 125 | 92.29 21 |
|
HY-MVS | | 67.03 5 | 73.90 66 | 73.14 59 | 76.18 97 | 84.70 58 | 47.36 221 | 75.56 256 | 86.36 63 | 66.27 41 | 70.66 57 | 83.91 135 | 51.05 27 | 89.31 87 | 67.10 78 | 72.61 126 | 91.88 28 |
|
DP-MVS Recon | | | 71.99 91 | 70.31 94 | 77.01 84 | 90.65 4 | 53.44 90 | 89.37 28 | 82.97 144 | 56.33 207 | 63.56 121 | 89.47 73 | 34.02 216 | 92.15 38 | 54.05 179 | 72.41 127 | 85.43 151 |
|
diffmvs | | | 70.02 117 | 68.35 120 | 75.03 118 | 79.19 165 | 51.48 149 | 78.50 234 | 76.65 251 | 59.71 131 | 67.10 77 | 80.32 189 | 42.81 117 | 87.12 163 | 58.48 139 | 72.37 128 | 86.49 133 |
|
HQP_MVS | | | 70.96 105 | 69.91 102 | 74.12 142 | 77.95 185 | 49.57 182 | 85.76 80 | 82.59 149 | 63.60 84 | 62.15 130 | 83.28 152 | 36.04 200 | 88.30 132 | 65.46 91 | 72.34 129 | 84.49 160 |
|
plane_prior5 | | | | | | | | | 82.59 149 | | | | | 88.30 132 | 65.46 91 | 72.34 129 | 84.49 160 |
|
MVS_111021_LR | | | 69.07 137 | 67.91 125 | 72.54 173 | 77.27 195 | 49.56 184 | 79.77 217 | 73.96 279 | 59.33 142 | 60.73 147 | 87.82 94 | 30.19 250 | 81.53 254 | 69.94 65 | 72.19 131 | 86.53 132 |
|
EPMVS | | | 68.45 151 | 65.44 177 | 77.47 73 | 84.91 55 | 56.17 30 | 71.89 287 | 81.91 162 | 61.72 107 | 60.85 145 | 72.49 266 | 36.21 195 | 87.06 165 | 47.32 219 | 71.62 132 | 89.17 88 |
|
TR-MVS | | | 69.71 124 | 67.85 128 | 75.27 115 | 82.94 93 | 48.48 207 | 87.40 53 | 80.86 181 | 57.15 189 | 64.61 105 | 87.08 106 | 32.67 230 | 89.64 84 | 46.38 226 | 71.55 133 | 87.68 115 |
|
abl_6 | | | 68.03 156 | 66.15 160 | 73.66 154 | 78.54 179 | 48.48 207 | 79.77 217 | 78.04 229 | 47.39 274 | 63.70 118 | 88.25 88 | 28.21 259 | 89.06 90 | 60.17 133 | 71.25 134 | 83.45 180 |
|
OPM-MVS | | | 70.75 108 | 69.58 104 | 74.26 140 | 75.55 213 | 51.34 154 | 86.05 76 | 83.29 137 | 61.94 103 | 62.95 126 | 85.77 118 | 34.15 215 | 88.44 124 | 65.44 94 | 71.07 135 | 82.99 189 |
|
114514_t | | | 69.87 123 | 67.88 127 | 75.85 105 | 88.38 15 | 52.35 135 | 86.94 63 | 83.68 127 | 53.70 228 | 55.68 211 | 85.60 119 | 30.07 251 | 91.20 47 | 55.84 163 | 71.02 136 | 83.99 169 |
|
sss | | | 70.49 111 | 70.13 99 | 71.58 199 | 81.59 124 | 39.02 290 | 80.78 201 | 84.71 97 | 59.34 140 | 66.61 81 | 88.09 91 | 37.17 184 | 85.52 201 | 61.82 118 | 71.02 136 | 90.20 68 |
|
cascas | | | 69.01 140 | 66.13 161 | 77.66 67 | 79.36 160 | 55.41 42 | 86.99 60 | 83.75 126 | 56.69 197 | 58.92 169 | 81.35 178 | 24.31 284 | 92.10 39 | 53.23 182 | 70.61 138 | 85.46 150 |
|
LCM-MVSNet-Re | | | 58.82 257 | 56.54 254 | 65.68 272 | 79.31 163 | 29.09 330 | 61.39 320 | 45.79 341 | 60.73 121 | 37.65 313 | 72.47 267 | 31.42 243 | 81.08 257 | 49.66 205 | 70.41 139 | 86.87 124 |
|
AdaColmap | | | 67.86 158 | 65.48 174 | 75.00 120 | 88.15 19 | 54.99 55 | 86.10 75 | 76.63 252 | 49.30 266 | 57.80 185 | 86.65 112 | 29.39 254 | 88.94 107 | 45.10 232 | 70.21 140 | 81.06 221 |
|
CPTT-MVS | | | 67.15 174 | 65.84 165 | 71.07 207 | 80.96 138 | 50.32 172 | 81.94 172 | 74.10 276 | 46.18 283 | 57.91 183 | 87.64 99 | 29.57 252 | 81.31 256 | 64.10 100 | 70.18 141 | 81.56 211 |
|
PatchmatchNet | | | 67.07 177 | 63.63 194 | 77.40 74 | 83.10 84 | 58.03 8 | 72.11 284 | 77.77 235 | 58.85 160 | 59.37 162 | 70.83 277 | 37.84 167 | 84.93 215 | 42.96 241 | 69.83 142 | 89.26 83 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
EPP-MVSNet | | | 71.14 100 | 70.07 100 | 74.33 138 | 79.18 166 | 46.52 230 | 83.81 136 | 86.49 58 | 56.32 208 | 57.95 182 | 84.90 127 | 54.23 12 | 89.14 89 | 58.14 145 | 69.65 143 | 87.33 119 |
|
EPNet_dtu | | | 66.25 190 | 66.71 153 | 64.87 280 | 78.66 176 | 34.12 311 | 82.80 160 | 75.51 264 | 61.75 106 | 64.47 110 | 86.90 108 | 37.06 185 | 72.46 317 | 43.65 238 | 69.63 144 | 88.02 109 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
MIMVSNet | | | 63.12 221 | 60.29 232 | 71.61 196 | 75.92 210 | 46.65 228 | 65.15 306 | 81.94 159 | 59.14 148 | 54.65 214 | 69.47 283 | 25.74 276 | 80.63 261 | 41.03 246 | 69.56 145 | 87.55 116 |
|
EI-MVSNet-Vis-set | | | 73.19 75 | 72.60 64 | 74.99 121 | 82.56 105 | 49.80 180 | 82.55 163 | 89.00 15 | 66.17 42 | 65.89 88 | 88.98 77 | 43.83 90 | 92.29 33 | 65.38 97 | 69.01 146 | 82.87 192 |
|
FIs | | | 70.00 120 | 70.24 98 | 69.30 233 | 77.93 187 | 38.55 292 | 83.99 134 | 87.72 42 | 66.86 37 | 57.66 189 | 84.17 132 | 52.28 21 | 85.31 205 | 52.72 191 | 68.80 147 | 84.02 167 |
|
CostFormer | | | 73.89 67 | 72.30 70 | 78.66 43 | 82.36 108 | 56.58 22 | 75.56 256 | 85.30 79 | 66.06 46 | 70.50 59 | 76.88 226 | 57.02 8 | 89.06 90 | 68.27 73 | 68.74 148 | 90.33 65 |
|
HyFIR lowres test | | | 69.94 122 | 67.58 137 | 77.04 82 | 77.11 201 | 57.29 15 | 81.49 190 | 79.11 209 | 58.27 170 | 58.86 171 | 80.41 188 | 42.33 121 | 86.96 168 | 61.91 117 | 68.68 149 | 86.87 124 |
|
PatchFormer-LS_test | | | 74.17 62 | 72.30 70 | 79.77 26 | 86.61 29 | 57.26 16 | 82.02 169 | 84.80 94 | 71.85 9 | 64.73 103 | 87.52 100 | 50.33 31 | 90.40 63 | 54.23 178 | 68.63 150 | 91.64 31 |
|
1112_ss | | | 70.05 116 | 69.37 107 | 72.10 178 | 80.77 142 | 42.78 267 | 85.12 100 | 76.75 248 | 59.69 132 | 61.19 138 | 92.12 20 | 47.48 44 | 83.84 232 | 53.04 185 | 68.21 151 | 89.66 76 |
|
ab-mvs | | | 70.65 109 | 69.11 112 | 75.29 114 | 80.87 140 | 46.23 237 | 73.48 269 | 85.24 83 | 59.99 129 | 66.65 79 | 80.94 185 | 43.13 107 | 88.69 112 | 63.58 102 | 68.07 152 | 90.95 50 |
|
tpm2 | | | 70.82 106 | 68.44 118 | 77.98 62 | 80.78 141 | 56.11 31 | 74.21 266 | 81.28 174 | 60.24 127 | 68.04 70 | 75.27 244 | 52.26 22 | 88.50 123 | 55.82 164 | 68.03 153 | 89.33 81 |
|
EI-MVSNet-UG-set | | | 72.37 84 | 71.73 78 | 74.29 139 | 81.60 123 | 49.29 189 | 81.85 175 | 88.64 19 | 65.29 60 | 65.05 98 | 88.29 87 | 43.18 104 | 91.83 40 | 63.74 101 | 67.97 154 | 81.75 209 |
|
thres200 | | | 68.71 148 | 67.27 144 | 73.02 163 | 84.73 57 | 46.76 227 | 85.03 111 | 87.73 41 | 62.34 98 | 59.87 152 | 83.45 149 | 43.15 105 | 88.32 131 | 31.25 283 | 67.91 155 | 83.98 170 |
|
tpmrst | | | 71.04 103 | 69.77 103 | 74.86 128 | 83.19 83 | 55.86 33 | 75.64 255 | 78.73 214 | 67.88 26 | 64.99 101 | 73.73 253 | 49.96 33 | 79.56 275 | 65.92 85 | 67.85 156 | 89.14 89 |
|
EG-PatchMatch MVS | | | 62.40 236 | 59.59 235 | 70.81 212 | 73.29 235 | 49.05 191 | 85.81 78 | 84.78 95 | 51.85 253 | 44.19 288 | 73.48 259 | 15.52 328 | 89.85 76 | 40.16 248 | 67.24 157 | 73.54 307 |
|
OMC-MVS | | | 65.97 193 | 65.06 183 | 68.71 245 | 72.97 239 | 42.58 271 | 78.61 232 | 75.35 267 | 54.72 222 | 59.31 163 | 86.25 116 | 33.30 223 | 77.88 287 | 57.99 146 | 67.05 158 | 85.66 147 |
|
TAPA-MVS | | 56.12 14 | 61.82 239 | 60.18 233 | 66.71 261 | 78.48 181 | 37.97 296 | 75.19 261 | 76.41 255 | 46.82 278 | 57.04 199 | 86.52 114 | 27.67 265 | 77.03 293 | 26.50 303 | 67.02 159 | 85.14 153 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
CMPMVS | | 40.41 21 | 55.34 278 | 52.64 279 | 63.46 286 | 60.88 327 | 43.84 257 | 61.58 319 | 71.06 300 | 30.43 335 | 36.33 315 | 74.63 248 | 24.14 285 | 75.44 299 | 48.05 215 | 66.62 160 | 71.12 319 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
FC-MVSNet-test | | | 67.49 166 | 67.91 125 | 66.21 265 | 76.06 206 | 33.06 316 | 80.82 200 | 87.18 47 | 64.44 67 | 54.81 213 | 82.87 155 | 50.40 30 | 82.60 248 | 48.05 215 | 66.55 161 | 82.98 190 |
|
GA-MVS | | | 69.04 138 | 66.70 154 | 76.06 100 | 75.11 216 | 52.36 134 | 83.12 152 | 80.23 188 | 63.32 87 | 60.65 148 | 79.22 196 | 30.98 245 | 88.37 126 | 61.25 120 | 66.41 162 | 87.46 117 |
|
conf200view11 | | | 66.80 182 | 65.42 178 | 70.95 210 | 83.29 79 | 43.15 262 | 81.67 180 | 87.78 36 | 59.04 151 | 55.92 207 | 82.18 169 | 43.73 93 | 87.80 146 | 28.80 289 | 66.36 163 | 81.89 203 |
|
thres100view900 | | | 66.87 180 | 65.42 178 | 71.24 203 | 83.29 79 | 43.15 262 | 81.67 180 | 87.78 36 | 59.04 151 | 55.92 207 | 82.18 169 | 43.73 93 | 87.80 146 | 28.80 289 | 66.36 163 | 82.78 193 |
|
tfpn200view9 | | | 67.57 164 | 66.13 161 | 71.89 191 | 84.05 68 | 45.07 246 | 83.40 147 | 87.71 43 | 60.79 119 | 57.79 186 | 82.76 157 | 43.53 100 | 87.80 146 | 28.80 289 | 66.36 163 | 82.78 193 |
|
thres400 | | | 67.40 169 | 66.13 161 | 71.19 205 | 84.05 68 | 45.07 246 | 83.40 147 | 87.71 43 | 60.79 119 | 57.79 186 | 82.76 157 | 43.53 100 | 87.80 146 | 28.80 289 | 66.36 163 | 80.71 227 |
|
tpmp4_e23 | | | 70.01 119 | 67.13 146 | 78.65 44 | 81.93 116 | 57.90 10 | 73.99 267 | 81.35 170 | 60.61 123 | 65.28 95 | 73.78 252 | 52.48 20 | 88.60 116 | 48.40 213 | 66.35 167 | 89.44 80 |
|
Test_1112_low_res | | | 67.18 173 | 66.23 158 | 70.02 230 | 78.75 172 | 41.02 281 | 83.43 145 | 73.69 282 | 57.29 187 | 58.45 179 | 82.39 166 | 45.30 72 | 80.88 259 | 50.50 201 | 66.26 168 | 88.16 104 |
|
mvs-test1 | | | 69.04 138 | 67.57 138 | 73.44 160 | 75.17 214 | 51.68 146 | 86.57 68 | 74.48 272 | 62.15 99 | 62.07 132 | 85.79 117 | 30.59 247 | 87.48 156 | 65.40 95 | 65.94 169 | 81.18 220 |
|
PVSNet_BlendedMVS | | | 73.42 72 | 73.30 58 | 73.76 153 | 85.91 34 | 51.83 144 | 86.18 74 | 84.24 108 | 65.40 55 | 69.09 63 | 80.86 186 | 46.70 53 | 88.13 137 | 75.43 34 | 65.92 170 | 81.33 216 |
|
XVG-OURS | | | 61.88 238 | 59.34 238 | 69.49 231 | 65.37 306 | 46.27 235 | 64.80 309 | 73.49 285 | 47.04 276 | 57.41 197 | 82.85 156 | 25.15 281 | 78.18 281 | 53.00 186 | 64.98 171 | 84.01 168 |
|
thres600view7 | | | 66.46 186 | 65.12 182 | 70.47 217 | 83.41 75 | 43.80 258 | 82.15 168 | 87.78 36 | 59.37 139 | 56.02 206 | 82.21 168 | 43.73 93 | 86.90 169 | 26.51 302 | 64.94 172 | 80.71 227 |
|
tfpn111 | | | 66.40 188 | 64.99 184 | 70.63 215 | 83.29 79 | 43.15 262 | 81.67 180 | 87.78 36 | 59.04 151 | 55.92 207 | 82.18 169 | 43.73 93 | 86.83 171 | 26.34 304 | 64.92 173 | 81.89 203 |
|
LPG-MVS_test | | | 66.44 187 | 64.58 188 | 72.02 182 | 74.42 224 | 48.60 201 | 83.07 154 | 80.64 184 | 54.69 223 | 53.75 222 | 83.83 136 | 25.73 277 | 86.98 166 | 60.33 131 | 64.71 174 | 80.48 233 |
|
LGP-MVS_train | | | | | 72.02 182 | 74.42 224 | 48.60 201 | | 80.64 184 | 54.69 223 | 53.75 222 | 83.83 136 | 25.73 277 | 86.98 166 | 60.33 131 | 64.71 174 | 80.48 233 |
|
MVSTER | | | 73.25 74 | 72.33 68 | 76.01 102 | 85.54 44 | 53.76 81 | 83.52 140 | 87.16 48 | 67.06 35 | 63.88 116 | 81.66 176 | 52.77 18 | 90.44 60 | 64.66 99 | 64.69 176 | 83.84 175 |
|
EI-MVSNet | | | 69.70 125 | 68.70 116 | 72.68 170 | 75.00 218 | 48.90 195 | 79.54 221 | 87.16 48 | 61.05 116 | 63.88 116 | 83.74 138 | 45.87 66 | 90.44 60 | 57.42 153 | 64.68 177 | 78.70 249 |
|
tpm cat1 | | | 66.28 189 | 62.78 200 | 76.77 90 | 81.40 132 | 57.14 17 | 70.03 296 | 77.19 244 | 53.00 235 | 58.76 174 | 70.73 279 | 46.17 62 | 86.73 174 | 43.27 239 | 64.46 178 | 86.44 135 |
|
testing_2 | | | 63.60 213 | 59.86 234 | 74.82 129 | 61.87 321 | 52.39 133 | 73.06 275 | 82.76 148 | 61.49 111 | 39.96 307 | 67.39 301 | 21.06 305 | 88.34 128 | 67.07 79 | 64.10 179 | 83.72 177 |
|
XVG-OURS-SEG-HR | | | 62.02 237 | 59.54 236 | 69.46 232 | 65.30 307 | 45.88 239 | 65.06 307 | 73.57 284 | 46.45 281 | 57.42 196 | 83.35 151 | 26.95 268 | 78.09 283 | 53.77 181 | 64.03 180 | 84.42 162 |
|
LS3D | | | 56.40 273 | 53.82 270 | 64.12 282 | 81.12 136 | 45.69 242 | 73.42 270 | 66.14 315 | 35.30 328 | 43.24 296 | 79.88 191 | 22.18 299 | 79.62 274 | 19.10 337 | 64.00 181 | 67.05 325 |
|
ACMP | | 61.11 9 | 66.24 191 | 64.33 190 | 72.00 184 | 74.89 220 | 49.12 190 | 83.18 151 | 79.83 193 | 55.41 218 | 52.29 233 | 82.68 161 | 25.83 275 | 86.10 190 | 60.89 122 | 63.94 182 | 80.78 225 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
tpm | | | 68.36 152 | 67.48 141 | 70.97 209 | 79.93 157 | 51.34 154 | 76.58 245 | 78.75 213 | 67.73 28 | 63.54 122 | 74.86 246 | 48.33 37 | 72.36 318 | 53.93 180 | 63.71 183 | 89.21 86 |
|
XXY-MVS | | | 70.18 113 | 69.28 111 | 72.89 167 | 77.64 189 | 42.88 266 | 85.06 109 | 87.50 46 | 62.58 96 | 62.66 128 | 82.34 167 | 43.64 99 | 89.83 77 | 58.42 141 | 63.70 184 | 85.96 142 |
|
GBi-Net | | | 67.09 175 | 65.47 175 | 71.96 185 | 82.71 100 | 46.36 232 | 83.52 140 | 83.31 134 | 58.55 165 | 57.58 190 | 76.23 234 | 36.72 191 | 86.20 184 | 47.25 220 | 63.40 185 | 83.32 182 |
|
test1 | | | 67.09 175 | 65.47 175 | 71.96 185 | 82.71 100 | 46.36 232 | 83.52 140 | 83.31 134 | 58.55 165 | 57.58 190 | 76.23 234 | 36.72 191 | 86.20 184 | 47.25 220 | 63.40 185 | 83.32 182 |
|
FMVSNet3 | | | 68.84 141 | 67.40 142 | 73.19 162 | 85.05 53 | 48.53 204 | 85.71 84 | 85.36 77 | 60.90 118 | 57.58 190 | 79.15 197 | 42.16 124 | 86.77 172 | 47.25 220 | 63.40 185 | 84.27 164 |
|
VPA-MVSNet | | | 71.12 101 | 70.66 90 | 72.49 175 | 78.75 172 | 44.43 252 | 87.64 43 | 90.02 9 | 63.97 75 | 65.02 99 | 81.58 177 | 42.14 125 | 87.42 158 | 63.42 103 | 63.38 188 | 85.63 149 |
|
pcd1.5k->3k | | | 27.74 322 | 27.68 322 | 27.93 340 | 73.75 232 | 0.00 362 | 0.00 354 | 85.50 71 | 0.00 356 | 0.00 359 | 0.00 360 | 26.52 271 | 0.00 359 | 0.00 358 | 63.37 189 | 83.79 176 |
|
Fast-Effi-MVS+-dtu | | | 66.53 185 | 64.10 193 | 73.84 150 | 72.41 262 | 52.30 137 | 84.73 117 | 75.66 263 | 59.51 134 | 56.34 204 | 79.11 198 | 28.11 261 | 85.85 198 | 57.74 150 | 63.29 190 | 83.35 181 |
|
CVMVSNet | | | 60.85 244 | 60.44 231 | 62.07 291 | 75.00 218 | 32.73 318 | 79.54 221 | 73.49 285 | 36.98 318 | 56.28 205 | 83.74 138 | 29.28 255 | 69.53 327 | 46.48 225 | 63.23 191 | 83.94 173 |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 63.20 192 | |
|
ACMM | | 58.35 12 | 64.35 205 | 62.01 205 | 71.38 201 | 74.21 227 | 48.51 205 | 82.25 167 | 79.66 197 | 47.61 272 | 54.54 215 | 80.11 190 | 25.26 279 | 86.00 192 | 51.26 197 | 63.16 193 | 79.64 241 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
CHOSEN 280x420 | | | 57.53 267 | 56.38 258 | 60.97 300 | 74.01 228 | 48.10 216 | 46.30 336 | 54.31 335 | 48.18 271 | 50.88 246 | 77.43 220 | 38.37 164 | 59.16 339 | 54.83 173 | 63.14 194 | 75.66 291 |
|
PS-MVSNAJss | | | 68.78 146 | 67.17 145 | 73.62 157 | 73.01 238 | 48.33 212 | 84.95 113 | 84.81 93 | 59.30 143 | 58.91 170 | 79.84 192 | 37.77 168 | 88.86 109 | 62.83 106 | 63.12 195 | 83.67 178 |
|
tfpn_ndepth | | | 64.50 202 | 63.34 199 | 67.99 249 | 81.84 119 | 38.30 294 | 79.26 230 | 83.57 132 | 53.69 229 | 52.86 231 | 84.51 129 | 46.96 50 | 84.79 216 | 24.28 309 | 63.09 196 | 80.87 224 |
|
MDTV_nov1_ep13 | | | | 61.56 218 | | 81.68 121 | 55.12 51 | 72.41 280 | 78.18 223 | 59.19 144 | 58.85 172 | 69.29 284 | 34.69 211 | 86.16 187 | 36.76 262 | 62.96 197 | |
|
FMVSNet2 | | | 67.57 164 | 65.79 166 | 72.90 165 | 82.71 100 | 47.97 217 | 85.15 96 | 84.93 90 | 58.55 165 | 56.71 200 | 78.26 207 | 36.72 191 | 86.67 175 | 46.15 228 | 62.94 198 | 84.07 166 |
|
view600 | | | 64.79 197 | 63.45 195 | 68.82 239 | 82.13 110 | 40.75 283 | 79.41 225 | 88.29 27 | 56.54 201 | 53.26 226 | 81.30 179 | 44.26 83 | 85.01 211 | 22.97 314 | 62.85 199 | 80.71 227 |
|
view800 | | | 64.79 197 | 63.45 195 | 68.82 239 | 82.13 110 | 40.75 283 | 79.41 225 | 88.29 27 | 56.54 201 | 53.26 226 | 81.30 179 | 44.26 83 | 85.01 211 | 22.97 314 | 62.85 199 | 80.71 227 |
|
conf0.05thres1000 | | | 64.79 197 | 63.45 195 | 68.82 239 | 82.13 110 | 40.75 283 | 79.41 225 | 88.29 27 | 56.54 201 | 53.26 226 | 81.30 179 | 44.26 83 | 85.01 211 | 22.97 314 | 62.85 199 | 80.71 227 |
|
tfpn | | | 64.79 197 | 63.45 195 | 68.82 239 | 82.13 110 | 40.75 283 | 79.41 225 | 88.29 27 | 56.54 201 | 53.26 226 | 81.30 179 | 44.26 83 | 85.01 211 | 22.97 314 | 62.85 199 | 80.71 227 |
|
MVS-HIRNet | | | 49.01 297 | 44.71 302 | 61.92 294 | 76.06 206 | 46.61 229 | 63.23 313 | 54.90 334 | 24.77 339 | 33.56 328 | 36.60 341 | 21.28 304 | 75.88 298 | 29.49 286 | 62.54 203 | 63.26 336 |
|
IB-MVS | | 68.87 2 | 74.01 65 | 72.03 77 | 79.94 24 | 83.04 88 | 55.50 37 | 90.24 16 | 88.65 18 | 67.14 34 | 61.38 136 | 81.74 175 | 53.21 16 | 94.28 14 | 60.45 128 | 62.41 204 | 90.03 72 |
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 |
nrg030 | | | 72.27 89 | 71.56 80 | 74.42 136 | 75.93 209 | 50.60 164 | 86.97 61 | 83.21 139 | 62.75 95 | 67.15 76 | 84.38 130 | 50.07 32 | 86.66 176 | 71.19 59 | 62.37 205 | 85.99 140 |
|
OpenMVS_ROB | | 53.19 17 | 59.20 253 | 56.00 260 | 68.83 238 | 71.13 279 | 44.30 253 | 83.64 139 | 75.02 269 | 46.42 282 | 46.48 283 | 73.03 262 | 18.69 314 | 88.14 136 | 27.74 298 | 61.80 206 | 74.05 303 |
|
dp | | | 64.41 203 | 61.58 216 | 72.90 165 | 82.40 106 | 54.09 77 | 72.53 278 | 76.59 253 | 60.39 125 | 55.68 211 | 70.39 280 | 35.18 208 | 76.90 295 | 39.34 250 | 61.71 207 | 87.73 114 |
|
FMVSNet1 | | | 64.57 201 | 62.11 204 | 71.96 185 | 77.32 194 | 46.36 232 | 83.52 140 | 83.31 134 | 52.43 248 | 54.42 216 | 76.23 234 | 27.80 263 | 86.20 184 | 42.59 243 | 61.34 208 | 83.32 182 |
|
tfpn1000 | | | 62.79 231 | 61.74 207 | 65.95 271 | 80.50 155 | 35.93 301 | 76.53 247 | 83.99 121 | 51.24 256 | 49.82 255 | 83.44 150 | 47.32 45 | 83.02 247 | 21.84 326 | 60.99 209 | 78.89 247 |
|
VPNet | | | 72.07 90 | 71.42 84 | 74.04 144 | 78.64 177 | 47.17 225 | 89.91 22 | 87.97 34 | 72.56 7 | 64.66 104 | 85.04 125 | 41.83 132 | 88.33 130 | 61.17 121 | 60.97 210 | 86.62 131 |
|
Effi-MVS+-dtu | | | 66.24 191 | 64.96 186 | 70.08 225 | 75.17 214 | 49.64 181 | 82.01 170 | 74.48 272 | 62.15 99 | 57.83 184 | 76.08 239 | 30.59 247 | 83.79 233 | 65.40 95 | 60.93 211 | 76.81 280 |
|
PLC | | 52.38 18 | 60.89 243 | 58.97 243 | 66.68 263 | 81.77 120 | 45.70 241 | 78.96 231 | 74.04 278 | 43.66 298 | 47.63 266 | 83.19 154 | 23.52 291 | 77.78 290 | 37.47 253 | 60.46 212 | 76.55 284 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
v7 | | | 68.76 147 | 66.79 150 | 74.68 132 | 72.60 255 | 53.37 96 | 84.72 118 | 80.88 180 | 63.80 78 | 60.43 150 | 78.21 208 | 40.05 151 | 88.89 108 | 60.34 130 | 60.07 213 | 81.77 208 |
|
v1neww | | | 69.43 133 | 67.62 134 | 74.89 125 | 72.90 243 | 53.31 101 | 85.12 100 | 81.11 175 | 64.29 69 | 61.00 139 | 78.53 202 | 42.88 114 | 88.98 102 | 62.66 108 | 60.06 214 | 82.37 199 |
|
v7new | | | 69.43 133 | 67.62 134 | 74.89 125 | 72.90 243 | 53.31 101 | 85.12 100 | 81.11 175 | 64.29 69 | 61.00 139 | 78.53 202 | 42.88 114 | 88.98 102 | 62.66 108 | 60.06 214 | 82.37 199 |
|
v6 | | | 69.43 133 | 67.61 136 | 74.88 127 | 72.87 247 | 53.30 105 | 85.12 100 | 81.10 177 | 64.29 69 | 60.99 141 | 78.52 204 | 42.88 114 | 88.98 102 | 62.67 107 | 60.06 214 | 82.37 199 |
|
CR-MVSNet | | | 62.47 234 | 59.04 242 | 72.77 168 | 73.97 230 | 56.57 23 | 60.52 321 | 71.72 293 | 60.04 128 | 57.49 193 | 65.86 306 | 38.94 158 | 80.31 265 | 42.86 242 | 59.93 217 | 81.42 213 |
|
RPMNet | | | 58.49 263 | 53.74 272 | 72.77 168 | 73.97 230 | 56.57 23 | 60.52 321 | 72.39 289 | 35.72 323 | 57.49 193 | 58.87 326 | 37.73 171 | 80.31 265 | 27.01 301 | 59.93 217 | 81.42 213 |
|
divwei89l23v2f112 | | | 69.50 130 | 67.67 130 | 74.98 122 | 72.72 251 | 53.41 93 | 85.08 106 | 82.14 153 | 64.79 64 | 60.88 142 | 78.19 209 | 43.11 108 | 89.04 93 | 62.51 110 | 59.62 219 | 82.48 196 |
|
v1141 | | | 69.50 130 | 67.67 130 | 74.98 122 | 72.73 250 | 53.41 93 | 85.08 106 | 82.14 153 | 64.79 64 | 60.88 142 | 78.19 209 | 43.09 111 | 89.04 93 | 62.51 110 | 59.61 220 | 82.47 197 |
|
v1 | | | 69.49 132 | 67.67 130 | 74.98 122 | 72.69 252 | 53.41 93 | 85.08 106 | 82.13 156 | 64.80 63 | 60.87 144 | 78.19 209 | 43.11 108 | 89.04 93 | 62.51 110 | 59.61 220 | 82.49 195 |
|
v1144 | | | 68.81 143 | 66.82 148 | 74.80 131 | 72.34 264 | 53.46 88 | 84.68 119 | 81.77 165 | 64.25 72 | 60.28 151 | 77.91 213 | 40.23 146 | 88.95 106 | 60.37 129 | 59.52 222 | 81.97 202 |
|
v2v482 | | | 69.55 128 | 67.64 133 | 75.26 116 | 72.32 265 | 53.83 80 | 84.93 114 | 81.94 159 | 65.37 57 | 60.80 146 | 79.25 195 | 41.62 135 | 88.98 102 | 63.03 105 | 59.51 223 | 82.98 190 |
|
CNLPA | | | 60.59 245 | 58.44 244 | 67.05 258 | 79.21 164 | 47.26 224 | 79.75 219 | 64.34 321 | 42.46 305 | 51.90 238 | 83.94 134 | 27.79 264 | 75.41 300 | 37.12 256 | 59.49 224 | 78.47 253 |
|
ACMMP++ | | | | | | | | | | | | | | | | 59.38 225 | |
|
PatchMatch-RL | | | 56.66 269 | 53.75 271 | 65.37 277 | 77.91 188 | 45.28 244 | 69.78 298 | 60.38 328 | 41.35 306 | 47.57 267 | 73.73 253 | 16.83 322 | 76.91 294 | 36.99 259 | 59.21 226 | 73.92 304 |
|
test0.0.03 1 | | | 62.54 232 | 62.44 202 | 62.86 290 | 72.28 267 | 29.51 327 | 82.93 157 | 78.78 212 | 59.18 146 | 53.07 230 | 82.41 165 | 36.91 188 | 77.39 291 | 37.45 254 | 58.96 227 | 81.66 210 |
|
v1192 | | | 67.96 157 | 65.74 168 | 74.63 133 | 71.79 269 | 53.43 92 | 84.06 131 | 80.99 179 | 63.19 90 | 59.56 159 | 77.46 219 | 37.50 178 | 88.65 113 | 58.20 144 | 58.93 228 | 81.79 207 |
|
conf0.01 | | | 63.04 222 | 61.74 207 | 66.95 259 | 80.60 146 | 35.92 302 | 76.01 248 | 84.09 110 | 52.62 238 | 50.87 247 | 83.60 142 | 46.49 55 | 83.04 241 | 22.59 319 | 58.77 229 | 81.89 203 |
|
conf0.002 | | | 63.04 222 | 61.74 207 | 66.95 259 | 80.60 146 | 35.92 302 | 76.01 248 | 84.09 110 | 52.62 238 | 50.87 247 | 83.60 142 | 46.49 55 | 83.04 241 | 22.59 319 | 58.77 229 | 81.89 203 |
|
thresconf0.02 | | | 62.84 225 | 61.74 207 | 66.14 266 | 80.60 146 | 35.92 302 | 76.01 248 | 84.09 110 | 52.62 238 | 50.87 247 | 83.60 142 | 46.49 55 | 83.04 241 | 22.59 319 | 58.77 229 | 79.44 242 |
|
tfpn_n400 | | | 62.84 225 | 61.74 207 | 66.14 266 | 80.60 146 | 35.92 302 | 76.01 248 | 84.09 110 | 52.62 238 | 50.87 247 | 83.60 142 | 46.49 55 | 83.04 241 | 22.59 319 | 58.77 229 | 79.44 242 |
|
tfpnconf | | | 62.84 225 | 61.74 207 | 66.14 266 | 80.60 146 | 35.92 302 | 76.01 248 | 84.09 110 | 52.62 238 | 50.87 247 | 83.60 142 | 46.49 55 | 83.04 241 | 22.59 319 | 58.77 229 | 79.44 242 |
|
tfpnview11 | | | 62.84 225 | 61.74 207 | 66.14 266 | 80.60 146 | 35.92 302 | 76.01 248 | 84.09 110 | 52.62 238 | 50.87 247 | 83.60 142 | 46.49 55 | 83.04 241 | 22.59 319 | 58.77 229 | 79.44 242 |
|
V42 | | | 67.66 162 | 65.60 173 | 73.86 149 | 70.69 281 | 53.63 84 | 81.50 188 | 78.61 217 | 63.85 77 | 59.49 161 | 77.49 218 | 37.98 165 | 87.65 153 | 62.33 113 | 58.43 235 | 80.29 236 |
|
tpmvs | | | 62.45 235 | 59.42 237 | 71.53 200 | 83.93 70 | 54.32 71 | 70.03 296 | 77.61 238 | 51.91 251 | 53.48 225 | 68.29 292 | 37.91 166 | 86.66 176 | 33.36 273 | 58.27 236 | 73.62 306 |
|
XVG-ACMP-BASELINE | | | 56.03 275 | 52.85 277 | 65.58 273 | 61.91 320 | 40.95 282 | 63.36 311 | 72.43 288 | 45.20 288 | 46.02 285 | 74.09 249 | 9.20 339 | 78.12 282 | 45.13 231 | 58.27 236 | 77.66 275 |
|
pmmvs5 | | | 62.80 230 | 61.18 220 | 67.66 252 | 69.53 288 | 42.37 274 | 82.65 161 | 75.19 268 | 54.30 226 | 52.03 236 | 78.51 205 | 31.64 242 | 80.67 260 | 48.60 210 | 58.15 238 | 79.95 239 |
|
v1240 | | | 66.99 178 | 64.68 187 | 73.93 146 | 71.38 277 | 52.66 127 | 83.39 149 | 79.98 191 | 61.97 102 | 58.44 180 | 77.11 224 | 35.25 207 | 87.81 145 | 56.46 159 | 58.15 238 | 81.33 216 |
|
v1921920 | | | 67.45 167 | 65.23 181 | 74.10 143 | 71.51 274 | 52.90 124 | 83.75 138 | 80.44 187 | 62.48 97 | 59.12 166 | 77.13 223 | 36.98 186 | 87.90 143 | 57.53 151 | 58.14 240 | 81.49 212 |
|
jajsoiax | | | 63.21 220 | 60.84 224 | 70.32 221 | 68.33 295 | 44.45 251 | 81.23 192 | 81.05 178 | 53.37 232 | 50.96 245 | 77.81 215 | 17.49 320 | 85.49 203 | 59.31 135 | 58.05 241 | 81.02 222 |
|
Anonymous20231206 | | | 59.08 255 | 57.59 248 | 63.55 285 | 68.77 291 | 32.14 321 | 80.26 207 | 79.78 194 | 50.00 263 | 49.39 256 | 72.39 269 | 26.64 270 | 78.36 279 | 33.12 276 | 57.94 242 | 80.14 237 |
|
mvs_tets | | | 62.96 224 | 60.55 229 | 70.19 222 | 68.22 297 | 44.24 255 | 80.90 199 | 80.74 183 | 52.99 236 | 50.82 253 | 77.56 216 | 16.74 323 | 85.44 204 | 59.04 136 | 57.94 242 | 80.89 223 |
|
LTVRE_ROB | | 45.45 19 | 52.73 290 | 49.74 290 | 61.69 295 | 69.78 286 | 34.99 308 | 44.52 338 | 67.60 312 | 43.11 302 | 43.79 291 | 74.03 250 | 18.54 315 | 81.45 255 | 28.39 296 | 57.94 242 | 68.62 323 |
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 |
v144192 | | | 67.86 158 | 65.76 167 | 74.16 141 | 71.68 271 | 53.09 116 | 84.14 128 | 80.83 182 | 62.85 94 | 59.21 165 | 77.28 222 | 39.30 156 | 88.00 142 | 58.67 138 | 57.88 245 | 81.40 215 |
|
IterMVS-LS | | | 66.63 183 | 65.36 180 | 70.42 219 | 75.10 217 | 48.90 195 | 81.45 191 | 76.69 250 | 61.05 116 | 55.71 210 | 77.10 225 | 45.86 67 | 83.65 235 | 57.44 152 | 57.88 245 | 78.70 249 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
ACMH | | 53.70 16 | 59.78 249 | 55.94 261 | 71.28 202 | 76.59 203 | 48.35 210 | 80.15 212 | 76.11 256 | 49.74 264 | 41.91 300 | 73.45 260 | 16.50 325 | 90.31 65 | 31.42 281 | 57.63 247 | 75.17 294 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
MSDG | | | 59.44 251 | 55.14 265 | 72.32 176 | 74.69 221 | 50.71 161 | 74.39 265 | 73.58 283 | 44.44 293 | 43.40 294 | 77.52 217 | 19.45 310 | 90.87 54 | 31.31 282 | 57.49 248 | 75.38 293 |
|
pmmvs4 | | | 63.34 218 | 61.07 222 | 70.16 223 | 70.14 283 | 50.53 166 | 79.97 214 | 71.41 299 | 55.08 220 | 54.12 219 | 78.58 201 | 32.79 229 | 82.09 252 | 50.33 202 | 57.22 249 | 77.86 272 |
|
UniMVSNet (Re) | | | 67.71 161 | 66.80 149 | 70.45 218 | 74.44 223 | 42.93 265 | 82.42 165 | 84.90 91 | 63.69 82 | 59.63 157 | 80.99 184 | 47.18 47 | 85.23 207 | 51.17 199 | 56.75 250 | 83.19 187 |
|
Patchmatch-test1 | | | 63.23 219 | 59.16 240 | 75.43 111 | 78.58 178 | 57.92 9 | 61.61 318 | 77.53 239 | 56.71 196 | 57.75 188 | 70.98 276 | 31.97 238 | 78.19 280 | 40.97 247 | 56.36 251 | 90.18 69 |
|
v8 | | | 67.25 171 | 64.99 184 | 74.04 144 | 72.89 245 | 53.31 101 | 82.37 166 | 80.11 190 | 61.54 109 | 54.29 218 | 76.02 240 | 42.89 113 | 88.41 125 | 58.43 140 | 56.36 251 | 80.39 235 |
|
DP-MVS | | | 59.24 252 | 56.12 259 | 68.63 246 | 88.24 18 | 50.35 170 | 82.51 164 | 64.43 320 | 41.10 307 | 46.70 279 | 78.77 200 | 24.75 283 | 88.57 120 | 22.26 325 | 56.29 253 | 66.96 326 |
|
NR-MVSNet | | | 67.25 171 | 65.99 164 | 71.04 208 | 73.27 236 | 43.91 256 | 85.32 93 | 84.75 96 | 66.05 47 | 53.65 224 | 82.11 172 | 45.05 73 | 85.97 195 | 47.55 217 | 56.18 254 | 83.24 185 |
|
v10 | | | 66.61 184 | 64.20 192 | 73.83 151 | 72.59 257 | 53.37 96 | 81.88 174 | 79.91 192 | 61.11 114 | 54.09 220 | 75.60 242 | 40.06 150 | 88.26 135 | 56.47 158 | 56.10 255 | 79.86 240 |
|
UniMVSNet_NR-MVSNet | | | 68.82 142 | 68.29 121 | 70.40 220 | 75.71 212 | 42.59 269 | 84.23 126 | 86.78 54 | 66.31 40 | 58.51 175 | 82.45 164 | 51.57 24 | 84.64 219 | 53.11 183 | 55.96 256 | 83.96 172 |
|
DU-MVS | | | 66.84 181 | 65.74 168 | 70.16 223 | 73.27 236 | 42.59 269 | 81.50 188 | 82.92 145 | 63.53 86 | 58.51 175 | 82.11 172 | 40.75 140 | 84.64 219 | 53.11 183 | 55.96 256 | 83.24 185 |
|
v148 | | | 68.24 155 | 66.35 156 | 73.88 148 | 71.76 270 | 51.47 150 | 84.23 126 | 81.90 163 | 63.69 82 | 58.94 167 | 76.44 230 | 43.72 97 | 87.78 150 | 60.63 125 | 55.86 258 | 82.39 198 |
|
test_djsdf | | | 63.84 209 | 61.56 218 | 70.70 213 | 68.78 290 | 44.69 250 | 81.63 184 | 81.44 168 | 50.28 260 | 52.27 234 | 76.26 233 | 26.72 269 | 86.11 188 | 60.83 123 | 55.84 259 | 81.29 219 |
|
tfpnnormal | | | 61.47 240 | 59.09 241 | 68.62 247 | 76.29 205 | 41.69 275 | 81.14 195 | 85.16 85 | 54.48 225 | 51.32 240 | 73.63 257 | 32.32 233 | 86.89 170 | 21.78 328 | 55.71 260 | 77.29 278 |
|
WR-MVS | | | 67.58 163 | 66.76 152 | 70.04 229 | 75.92 210 | 45.06 249 | 86.23 73 | 85.28 80 | 64.31 68 | 58.50 177 | 81.00 183 | 44.80 80 | 82.00 253 | 49.21 208 | 55.57 261 | 83.06 188 |
|
Baseline_NR-MVSNet | | | 65.49 196 | 64.27 191 | 69.13 234 | 74.37 226 | 41.65 276 | 83.39 149 | 78.85 210 | 59.56 133 | 59.62 158 | 76.88 226 | 40.75 140 | 87.44 157 | 49.99 203 | 55.05 262 | 78.28 266 |
|
v7n | | | 62.50 233 | 59.27 239 | 72.20 177 | 67.25 301 | 49.83 179 | 77.87 238 | 80.12 189 | 52.50 247 | 48.80 259 | 73.07 261 | 32.10 236 | 87.90 143 | 46.83 223 | 54.92 263 | 78.86 248 |
|
TranMVSNet+NR-MVSNet | | | 66.94 179 | 65.61 172 | 70.93 211 | 73.45 233 | 43.38 261 | 83.02 156 | 84.25 106 | 65.31 59 | 58.33 181 | 81.90 174 | 39.92 153 | 85.52 201 | 49.43 207 | 54.89 264 | 83.89 174 |
|
FMVSNet5 | | | 58.61 259 | 56.45 255 | 65.10 279 | 77.20 199 | 39.74 288 | 74.77 262 | 77.12 246 | 50.27 262 | 43.28 295 | 67.71 299 | 26.15 274 | 76.90 295 | 36.78 261 | 54.78 265 | 78.65 251 |
|
ACMH+ | | 54.58 15 | 58.55 261 | 55.24 263 | 68.50 248 | 74.68 222 | 45.80 240 | 80.27 206 | 70.21 306 | 47.15 275 | 42.77 297 | 75.48 243 | 16.73 324 | 85.98 193 | 35.10 269 | 54.78 265 | 73.72 305 |
|
IterMVS | | | 63.77 211 | 61.67 214 | 70.08 225 | 72.68 253 | 51.24 157 | 80.44 204 | 75.51 264 | 60.51 124 | 51.41 239 | 73.70 256 | 32.08 237 | 78.91 276 | 54.30 177 | 54.35 267 | 80.08 238 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
anonymousdsp | | | 60.46 246 | 57.65 247 | 68.88 236 | 63.63 315 | 45.09 245 | 72.93 276 | 78.63 216 | 46.52 280 | 51.12 242 | 72.80 265 | 21.46 303 | 83.07 240 | 57.79 149 | 53.97 268 | 78.47 253 |
|
F-COLMAP | | | 55.96 277 | 53.65 273 | 62.87 289 | 72.76 248 | 42.77 268 | 74.70 264 | 70.37 304 | 40.03 308 | 41.11 304 | 79.36 193 | 17.77 318 | 73.70 310 | 32.80 277 | 53.96 269 | 72.15 313 |
|
ADS-MVSNet2 | | | 55.21 280 | 51.44 283 | 66.51 264 | 80.60 146 | 49.56 184 | 55.03 329 | 65.44 317 | 44.72 290 | 51.00 243 | 61.19 317 | 22.83 292 | 75.41 300 | 28.54 294 | 53.63 270 | 74.57 298 |
|
ADS-MVSNet | | | 56.17 274 | 51.95 282 | 68.84 237 | 80.60 146 | 53.07 118 | 55.03 329 | 70.02 307 | 44.72 290 | 51.00 243 | 61.19 317 | 22.83 292 | 78.88 277 | 28.54 294 | 53.63 270 | 74.57 298 |
|
semantic-postprocess | | | | | 60.08 302 | 70.68 282 | 45.07 246 | | 74.25 275 | 43.54 299 | 50.02 254 | 73.73 253 | 32.22 235 | 56.74 340 | 51.06 200 | 53.60 272 | 78.42 258 |
|
pm-mvs1 | | | 64.12 208 | 62.56 201 | 68.78 244 | 71.68 271 | 38.87 291 | 82.89 158 | 81.57 166 | 55.54 217 | 53.89 221 | 77.82 214 | 37.73 171 | 86.74 173 | 48.46 212 | 53.49 273 | 80.72 226 |
|
PatchT | | | 56.60 270 | 52.97 275 | 67.48 253 | 72.94 241 | 46.16 238 | 57.30 327 | 73.78 281 | 38.77 312 | 54.37 217 | 57.26 329 | 37.52 176 | 78.06 284 | 32.02 278 | 52.79 274 | 78.23 269 |
|
test2356 | | | 53.94 285 | 52.37 281 | 58.64 307 | 61.58 322 | 27.53 335 | 78.20 236 | 74.33 274 | 46.92 277 | 44.01 289 | 66.04 305 | 18.91 313 | 74.11 305 | 28.80 289 | 52.55 275 | 74.28 300 |
|
JIA-IIPM | | | 52.33 293 | 47.77 296 | 66.03 270 | 71.20 278 | 46.92 226 | 40.00 344 | 76.48 254 | 37.10 317 | 46.73 278 | 37.02 340 | 32.96 226 | 77.88 287 | 35.97 263 | 52.45 276 | 73.29 310 |
|
Patchmatch-test | | | 53.33 289 | 48.17 293 | 68.81 243 | 73.31 234 | 42.38 273 | 42.98 340 | 58.23 330 | 32.53 331 | 38.79 312 | 70.77 278 | 39.66 154 | 73.51 311 | 25.18 306 | 52.06 277 | 90.55 55 |
|
testgi | | | 54.25 283 | 52.57 280 | 59.29 304 | 62.76 318 | 21.65 342 | 72.21 283 | 70.47 302 | 53.25 233 | 41.94 299 | 77.33 221 | 14.28 329 | 77.95 286 | 29.18 288 | 51.72 278 | 78.28 266 |
|
V4 | | | 59.82 247 | 56.41 256 | 70.05 228 | 61.49 324 | 48.67 199 | 69.46 300 | 75.79 262 | 52.55 245 | 47.49 271 | 68.83 288 | 28.60 256 | 85.70 199 | 52.13 194 | 51.35 279 | 75.80 288 |
|
v52 | | | 59.82 247 | 56.41 256 | 70.06 227 | 61.49 324 | 48.67 199 | 69.46 300 | 75.80 261 | 52.55 245 | 47.49 271 | 68.82 289 | 28.60 256 | 85.70 199 | 52.13 194 | 51.34 280 | 75.80 288 |
|
v748 | | | 61.35 241 | 58.24 245 | 70.69 214 | 66.28 302 | 47.35 222 | 76.58 245 | 79.17 208 | 53.09 234 | 46.37 284 | 71.50 274 | 33.18 225 | 86.33 183 | 46.78 224 | 51.19 281 | 78.39 259 |
|
test_0402 | | | 56.45 272 | 53.03 274 | 66.69 262 | 76.78 202 | 50.31 173 | 81.76 177 | 69.61 308 | 42.79 303 | 43.88 290 | 72.13 271 | 22.82 294 | 86.46 182 | 16.57 342 | 50.94 282 | 63.31 335 |
|
COLMAP_ROB | | 43.60 20 | 50.90 295 | 48.05 294 | 59.47 303 | 67.81 298 | 40.57 287 | 71.25 289 | 62.72 326 | 36.49 322 | 36.19 316 | 73.51 258 | 13.48 330 | 73.92 308 | 20.71 332 | 50.26 283 | 63.92 333 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
pmmvs6 | | | 59.64 250 | 57.15 251 | 67.09 256 | 66.01 303 | 36.86 300 | 80.50 203 | 78.64 215 | 45.05 289 | 49.05 258 | 73.94 251 | 27.28 266 | 86.10 190 | 43.96 237 | 49.94 284 | 78.31 264 |
|
USDC | | | 54.36 282 | 51.23 284 | 63.76 284 | 64.29 313 | 37.71 297 | 62.84 316 | 73.48 287 | 56.85 191 | 35.47 319 | 71.94 273 | 9.23 338 | 78.43 278 | 38.43 252 | 48.57 285 | 75.13 295 |
|
v18 | | | 64.36 204 | 61.80 206 | 72.05 179 | 72.97 239 | 53.31 101 | 81.16 194 | 77.76 236 | 59.14 148 | 48.50 260 | 68.97 287 | 42.91 112 | 84.38 221 | 56.62 156 | 48.17 286 | 78.47 253 |
|
v16 | | | 64.25 206 | 61.66 215 | 72.03 180 | 72.91 242 | 53.28 107 | 80.93 197 | 77.81 232 | 58.86 159 | 48.30 261 | 68.80 290 | 42.70 118 | 84.37 222 | 56.44 161 | 48.14 287 | 78.44 256 |
|
WR-MVS_H | | | 58.91 256 | 58.04 246 | 61.54 296 | 69.07 289 | 33.83 313 | 76.91 242 | 81.99 158 | 51.40 255 | 48.17 263 | 74.67 247 | 40.23 146 | 74.15 304 | 31.78 280 | 48.10 288 | 76.64 282 |
|
ITE_SJBPF | | | | | 51.84 320 | 58.03 329 | 31.94 322 | | 53.57 338 | 36.67 321 | 41.32 303 | 75.23 245 | 11.17 334 | 51.57 344 | 25.81 305 | 48.04 289 | 72.02 314 |
|
v17 | | | 64.19 207 | 61.58 216 | 72.03 180 | 72.89 245 | 53.28 107 | 80.91 198 | 77.80 233 | 58.87 158 | 48.22 262 | 68.77 291 | 42.69 119 | 84.37 222 | 56.43 162 | 47.66 290 | 78.43 257 |
|
CP-MVSNet | | | 58.54 262 | 57.57 249 | 61.46 298 | 68.50 293 | 33.96 312 | 76.90 243 | 78.60 218 | 51.67 254 | 47.83 264 | 76.60 229 | 34.99 210 | 72.79 315 | 35.45 265 | 47.58 291 | 77.64 276 |
|
MIMVSNet1 | | | 50.35 296 | 47.81 295 | 57.96 309 | 61.53 323 | 27.80 334 | 67.40 304 | 74.06 277 | 43.25 301 | 33.31 331 | 65.38 309 | 16.03 326 | 71.34 322 | 21.80 327 | 47.55 292 | 74.75 296 |
|
PS-CasMVS | | | 58.12 265 | 57.03 253 | 61.37 299 | 68.24 296 | 33.80 314 | 76.73 244 | 78.01 230 | 51.20 257 | 47.54 269 | 76.20 237 | 32.85 227 | 72.76 316 | 35.17 267 | 47.37 293 | 77.55 277 |
|
Patchmatch-RL test | | | 58.72 258 | 54.32 268 | 71.92 189 | 63.91 314 | 44.25 254 | 61.73 317 | 55.19 333 | 57.38 186 | 49.31 257 | 54.24 332 | 37.60 174 | 80.89 258 | 62.19 115 | 47.28 294 | 90.63 54 |
|
PEN-MVS | | | 58.35 264 | 57.15 251 | 61.94 293 | 67.55 300 | 34.39 310 | 77.01 241 | 78.35 220 | 51.87 252 | 47.72 265 | 76.73 228 | 33.91 218 | 73.75 309 | 34.03 272 | 47.17 295 | 77.68 274 |
|
FPMVS | | | 35.40 317 | 33.67 317 | 40.57 330 | 46.34 343 | 28.74 331 | 41.05 342 | 57.05 332 | 20.37 342 | 22.27 341 | 53.38 333 | 6.87 343 | 44.94 350 | 8.62 348 | 47.11 296 | 48.01 343 |
|
test20.03 | | | 55.22 279 | 54.07 269 | 58.68 306 | 63.14 317 | 25.00 336 | 77.69 239 | 74.78 270 | 52.64 237 | 43.43 293 | 72.39 269 | 26.21 273 | 74.76 303 | 29.31 287 | 47.05 297 | 76.28 286 |
|
DSMNet-mixed | | | 38.35 314 | 35.36 315 | 47.33 325 | 48.11 342 | 14.91 353 | 37.87 345 | 36.60 349 | 19.18 343 | 34.37 323 | 59.56 322 | 15.53 327 | 53.01 343 | 20.14 334 | 46.89 298 | 74.07 302 |
|
Patchmtry | | | 56.56 271 | 52.95 276 | 67.42 254 | 72.53 259 | 50.59 165 | 59.05 323 | 71.72 293 | 37.86 316 | 46.92 276 | 65.86 306 | 38.94 158 | 80.06 270 | 36.94 260 | 46.72 299 | 71.60 316 |
|
v11 | | | 63.44 216 | 60.66 228 | 71.79 195 | 72.61 254 | 53.02 121 | 79.80 216 | 78.08 228 | 58.30 169 | 47.27 274 | 67.91 296 | 40.67 144 | 84.14 229 | 54.93 172 | 46.39 300 | 78.23 269 |
|
EU-MVSNet | | | 52.63 291 | 50.72 285 | 58.37 308 | 62.69 319 | 28.13 332 | 72.60 277 | 75.97 259 | 30.94 334 | 40.76 306 | 72.11 272 | 20.16 308 | 70.80 323 | 35.11 268 | 46.11 301 | 76.19 287 |
|
v15 | | | 63.83 210 | 61.13 221 | 71.93 188 | 72.60 255 | 53.21 110 | 80.44 204 | 78.22 221 | 58.80 161 | 47.57 267 | 68.22 293 | 42.50 120 | 84.18 224 | 55.82 164 | 46.02 302 | 78.39 259 |
|
V14 | | | 63.72 212 | 60.99 223 | 71.91 190 | 72.58 258 | 53.18 111 | 80.24 208 | 78.19 222 | 58.53 168 | 47.35 273 | 68.10 294 | 42.28 123 | 84.18 224 | 55.68 166 | 45.97 303 | 78.36 262 |
|
RPSCF | | | 45.77 308 | 44.13 305 | 50.68 321 | 57.67 332 | 29.66 326 | 54.92 331 | 45.25 343 | 26.69 338 | 45.92 286 | 75.92 241 | 17.43 321 | 45.70 349 | 27.44 299 | 45.95 304 | 76.67 281 |
|
V9 | | | 63.60 213 | 60.84 224 | 71.87 192 | 72.51 260 | 53.12 115 | 80.04 213 | 78.15 224 | 58.25 171 | 47.14 275 | 67.98 295 | 42.08 127 | 84.18 224 | 55.47 167 | 45.92 305 | 78.32 263 |
|
v12 | | | 63.47 215 | 60.68 227 | 71.85 193 | 72.45 261 | 53.08 117 | 79.83 215 | 78.13 226 | 57.95 177 | 46.89 277 | 67.87 297 | 41.81 133 | 84.17 227 | 55.30 169 | 45.87 306 | 78.29 265 |
|
v13 | | | 63.36 217 | 60.54 230 | 71.82 194 | 72.41 262 | 53.03 120 | 79.64 220 | 78.10 227 | 57.66 183 | 46.67 280 | 67.75 298 | 41.68 134 | 84.17 227 | 55.11 170 | 45.82 307 | 78.25 268 |
|
our_test_3 | | | 59.11 254 | 55.08 266 | 71.18 206 | 71.42 275 | 53.29 106 | 81.96 171 | 74.52 271 | 48.32 269 | 42.08 298 | 69.28 285 | 28.14 260 | 82.15 250 | 34.35 271 | 45.68 308 | 78.11 271 |
|
testus | | | 48.97 298 | 46.53 299 | 56.31 314 | 57.39 333 | 24.08 338 | 73.40 271 | 70.45 303 | 43.37 300 | 35.52 318 | 63.95 311 | 4.77 350 | 71.36 321 | 24.88 307 | 45.02 309 | 73.50 308 |
|
DTE-MVSNet | | | 57.03 268 | 55.73 262 | 60.95 301 | 65.94 304 | 32.57 319 | 75.71 254 | 77.09 247 | 51.16 258 | 46.65 281 | 76.34 232 | 32.84 228 | 73.22 313 | 30.94 284 | 44.87 310 | 77.06 279 |
|
testpf | | | 45.92 307 | 45.81 301 | 46.27 326 | 69.56 287 | 27.86 333 | 23.18 349 | 73.91 280 | 44.10 296 | 36.99 314 | 57.16 330 | 20.56 306 | 71.77 319 | 42.17 245 | 44.64 311 | 39.18 345 |
|
pmmvs-eth3d | | | 55.97 276 | 52.78 278 | 65.54 274 | 61.02 326 | 46.44 231 | 75.36 260 | 67.72 311 | 49.61 265 | 43.65 292 | 67.58 300 | 21.63 302 | 77.04 292 | 44.11 236 | 44.33 312 | 73.15 312 |
|
AllTest | | | 47.32 302 | 44.66 303 | 55.32 315 | 65.08 309 | 37.50 298 | 62.96 315 | 54.25 336 | 35.45 326 | 33.42 329 | 72.82 263 | 9.98 335 | 59.33 337 | 24.13 310 | 43.84 313 | 69.13 321 |
|
TestCases | | | | | 55.32 315 | 65.08 309 | 37.50 298 | | 54.25 336 | 35.45 326 | 33.42 329 | 72.82 263 | 9.98 335 | 59.33 337 | 24.13 310 | 43.84 313 | 69.13 321 |
|
ppachtmachnet_test | | | 58.56 260 | 54.34 267 | 71.24 203 | 71.42 275 | 54.74 62 | 81.84 176 | 72.27 290 | 49.02 268 | 45.86 287 | 68.99 286 | 26.27 272 | 83.30 238 | 30.12 285 | 43.23 315 | 75.69 290 |
|
PM-MVS | | | 46.92 305 | 43.76 307 | 56.41 313 | 52.18 338 | 32.26 320 | 63.21 314 | 38.18 347 | 37.99 315 | 40.78 305 | 66.20 304 | 5.09 348 | 65.42 333 | 48.19 214 | 41.99 316 | 71.54 317 |
|
TinyColmap | | | 48.15 300 | 44.49 304 | 59.13 305 | 65.73 305 | 38.04 295 | 63.34 312 | 62.86 325 | 38.78 311 | 29.48 336 | 67.23 303 | 6.46 345 | 73.30 312 | 24.59 308 | 41.90 317 | 66.04 327 |
|
N_pmnet | | | 41.25 310 | 39.77 312 | 45.66 328 | 68.50 293 | 0.82 360 | 72.51 279 | 0.38 361 | 35.61 324 | 35.26 320 | 61.51 316 | 20.07 309 | 67.74 330 | 23.51 312 | 40.63 318 | 68.42 324 |
|
TransMVSNet (Re) | | | 62.82 229 | 60.76 226 | 69.02 235 | 73.98 229 | 41.61 277 | 86.36 70 | 79.30 207 | 56.90 190 | 52.53 232 | 76.44 230 | 41.85 131 | 87.60 154 | 38.83 251 | 40.61 319 | 77.86 272 |
|
OurMVSNet-221017-0 | | | 52.39 292 | 48.73 292 | 63.35 287 | 65.21 308 | 38.42 293 | 68.54 303 | 64.95 318 | 38.19 313 | 39.57 308 | 71.43 275 | 13.23 331 | 79.92 271 | 37.16 255 | 40.32 320 | 71.72 315 |
|
YYNet1 | | | 53.82 287 | 49.96 288 | 65.41 276 | 70.09 285 | 48.95 193 | 72.30 281 | 71.66 295 | 44.25 294 | 31.89 332 | 63.07 314 | 23.73 287 | 73.95 307 | 33.26 274 | 39.40 321 | 73.34 309 |
|
MDA-MVSNet_test_wron | | | 53.82 287 | 49.95 289 | 65.43 275 | 70.13 284 | 49.05 191 | 72.30 281 | 71.65 296 | 44.23 295 | 31.85 333 | 63.13 313 | 23.68 290 | 74.01 306 | 33.25 275 | 39.35 322 | 73.23 311 |
|
test1235678 | | | 47.09 303 | 43.82 306 | 56.91 312 | 53.18 337 | 24.90 337 | 71.93 285 | 70.31 305 | 39.54 309 | 31.44 334 | 56.59 331 | 9.50 337 | 71.55 320 | 22.63 318 | 39.24 323 | 74.28 300 |
|
1111 | | | 48.00 301 | 46.30 300 | 53.08 319 | 55.68 334 | 20.86 345 | 70.41 292 | 76.03 257 | 36.88 319 | 34.86 321 | 59.55 323 | 23.72 288 | 68.13 328 | 20.82 330 | 38.76 324 | 70.25 320 |
|
ambc | | | | | 62.06 292 | 53.98 336 | 29.38 328 | 35.08 346 | 79.65 198 | | 41.37 302 | 59.96 320 | 6.27 346 | 82.15 250 | 35.34 266 | 38.22 325 | 74.65 297 |
|
LP | | | 47.05 304 | 42.23 309 | 61.53 297 | 72.04 268 | 49.37 188 | 49.48 333 | 65.50 316 | 34.57 329 | 34.29 325 | 52.30 334 | 17.73 319 | 75.32 302 | 17.56 340 | 36.57 326 | 59.91 337 |
|
Gipuma | | | 27.47 323 | 24.26 324 | 37.12 333 | 60.55 328 | 29.17 329 | 11.68 352 | 60.00 329 | 14.18 346 | 10.52 349 | 15.12 352 | 2.20 356 | 63.01 335 | 8.39 349 | 35.65 327 | 19.18 350 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
UnsupCasMVSNet_eth | | | 57.56 266 | 55.15 264 | 64.79 281 | 64.57 312 | 33.12 315 | 73.17 273 | 83.87 125 | 58.98 156 | 41.75 301 | 70.03 281 | 22.54 295 | 79.92 271 | 46.12 229 | 35.31 328 | 81.32 218 |
|
TDRefinement | | | 40.91 311 | 38.37 313 | 48.55 324 | 50.45 339 | 33.03 317 | 58.98 324 | 50.97 339 | 28.50 336 | 29.89 335 | 67.39 301 | 6.21 347 | 54.51 341 | 17.67 339 | 35.25 329 | 58.11 338 |
|
Anonymous20231211 | | | 46.87 306 | 43.27 308 | 57.67 310 | 57.88 330 | 30.12 325 | 73.14 274 | 64.16 322 | 33.43 330 | 34.34 324 | 59.42 325 | 12.15 332 | 77.99 285 | 19.64 336 | 35.23 330 | 64.90 331 |
|
LF4IMVS | | | 33.04 320 | 32.55 318 | 34.52 335 | 40.96 346 | 22.03 341 | 44.45 339 | 35.62 350 | 20.42 341 | 28.12 337 | 62.35 315 | 5.03 349 | 31.88 355 | 21.61 329 | 34.42 331 | 49.63 342 |
|
new-patchmatchnet | | | 48.21 299 | 46.55 298 | 53.18 318 | 57.73 331 | 18.19 351 | 70.24 294 | 71.02 301 | 45.70 284 | 33.70 327 | 60.23 319 | 18.00 317 | 69.86 326 | 27.97 297 | 34.35 332 | 71.49 318 |
|
pmmvs3 | | | 45.53 309 | 41.55 311 | 57.44 311 | 48.97 341 | 39.68 289 | 70.06 295 | 57.66 331 | 28.32 337 | 34.06 326 | 57.29 328 | 8.50 340 | 66.85 332 | 34.86 270 | 34.26 333 | 65.80 328 |
|
SixPastTwentyTwo | | | 54.37 281 | 50.10 287 | 67.21 255 | 70.70 280 | 41.46 278 | 74.73 263 | 64.69 319 | 47.56 273 | 39.12 310 | 69.49 282 | 18.49 316 | 84.69 218 | 31.87 279 | 34.20 334 | 75.48 292 |
|
UnsupCasMVSNet_bld | | | 53.86 286 | 50.53 286 | 63.84 283 | 63.52 316 | 34.75 309 | 71.38 288 | 81.92 161 | 46.53 279 | 38.95 311 | 57.93 327 | 20.55 307 | 80.20 269 | 39.91 249 | 34.09 335 | 76.57 283 |
|
MDA-MVSNet-bldmvs | | | 51.56 294 | 47.75 297 | 63.00 288 | 71.60 273 | 47.32 223 | 69.70 299 | 72.12 291 | 43.81 297 | 27.65 338 | 63.38 312 | 21.97 301 | 75.96 297 | 27.30 300 | 32.19 336 | 65.70 329 |
|
PMVS | | 19.57 22 | 25.07 326 | 22.43 328 | 32.99 336 | 23.12 356 | 22.98 339 | 40.98 343 | 35.19 351 | 15.99 345 | 11.95 348 | 35.87 344 | 1.47 359 | 49.29 345 | 5.41 353 | 31.90 337 | 26.70 349 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
new_pmnet | | | 33.56 319 | 31.89 319 | 38.59 331 | 49.01 340 | 20.42 347 | 51.01 332 | 37.92 348 | 20.58 340 | 23.45 339 | 46.79 337 | 6.66 344 | 49.28 346 | 20.00 335 | 31.57 338 | 46.09 344 |
|
testmv | | | 39.64 312 | 36.01 314 | 50.55 322 | 42.18 345 | 21.56 343 | 64.81 308 | 66.88 314 | 32.22 332 | 22.25 342 | 47.47 336 | 4.33 352 | 64.81 334 | 17.71 338 | 26.22 339 | 65.29 330 |
|
K. test v3 | | | 54.04 284 | 49.42 291 | 67.92 251 | 68.55 292 | 42.57 272 | 75.51 258 | 63.07 324 | 52.07 249 | 39.21 309 | 64.59 310 | 19.34 311 | 82.21 249 | 37.11 257 | 25.31 340 | 78.97 246 |
|
LCM-MVSNet | | | 28.07 321 | 23.85 325 | 40.71 329 | 27.46 355 | 18.93 350 | 30.82 347 | 46.19 340 | 12.76 348 | 16.40 343 | 34.70 345 | 1.90 357 | 48.69 347 | 20.25 333 | 24.22 341 | 54.51 340 |
|
test12356 | | | 37.84 315 | 35.07 316 | 46.18 327 | 45.03 344 | 8.02 358 | 57.70 326 | 62.67 327 | 31.83 333 | 22.78 340 | 50.25 335 | 4.46 351 | 66.95 331 | 17.25 341 | 23.62 342 | 63.57 334 |
|
lessismore_v0 | | | | | 67.98 250 | 64.76 311 | 41.25 279 | | 45.75 342 | | 36.03 317 | 65.63 308 | 19.29 312 | 84.11 230 | 35.67 264 | 21.24 343 | 78.59 252 |
|
PVSNet_0 | | 57.04 13 | 61.19 242 | 57.24 250 | 73.02 163 | 77.45 193 | 50.31 173 | 79.43 223 | 77.36 243 | 63.96 76 | 47.51 270 | 72.45 268 | 25.03 282 | 83.78 234 | 52.76 190 | 19.22 344 | 84.96 156 |
|
no-one | | | 37.21 316 | 31.48 320 | 54.40 317 | 39.62 349 | 31.91 323 | 45.68 337 | 67.42 313 | 35.54 325 | 14.59 345 | 35.91 343 | 7.35 341 | 73.20 314 | 22.98 313 | 14.23 345 | 58.09 339 |
|
PMMVS2 | | | 26.71 324 | 22.98 327 | 37.87 332 | 36.89 350 | 8.51 357 | 42.51 341 | 29.32 355 | 19.09 344 | 13.01 346 | 37.54 339 | 2.23 355 | 53.11 342 | 14.54 343 | 11.71 346 | 51.99 341 |
|
MVE | | 16.60 23 | 17.34 331 | 13.39 332 | 29.16 339 | 28.43 354 | 19.72 349 | 13.73 351 | 23.63 356 | 7.23 353 | 7.96 351 | 21.41 348 | 0.80 360 | 36.08 354 | 6.97 350 | 10.39 347 | 31.69 347 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
PNet_i23d | | | 25.11 325 | 23.09 326 | 31.17 337 | 40.18 347 | 21.30 344 | 57.99 325 | 33.28 352 | 13.77 347 | 9.94 350 | 30.29 347 | 0.45 361 | 43.74 351 | 13.61 346 | 8.28 348 | 28.46 348 |
|
E-PMN | | | 19.16 328 | 18.40 329 | 21.44 341 | 36.19 351 | 13.63 354 | 47.59 334 | 30.89 353 | 10.73 349 | 5.91 353 | 16.59 350 | 3.66 354 | 39.77 352 | 5.95 352 | 8.14 349 | 10.92 352 |
|
wuykxyi23d | | | 19.94 327 | 14.87 331 | 35.13 334 | 22.47 357 | 19.80 348 | 25.80 348 | 38.64 346 | 7.61 352 | 4.88 355 | 13.58 355 | 0.23 362 | 48.42 348 | 13.11 347 | 7.53 350 | 37.18 346 |
|
DeepMVS_CX | | | | | 13.10 343 | 21.34 358 | 8.99 356 | | 10.02 359 | 10.59 350 | 7.53 352 | 30.55 346 | 1.82 358 | 14.55 356 | 6.83 351 | 7.52 351 | 15.75 351 |
|
EMVS | | | 18.42 329 | 17.66 330 | 20.71 342 | 34.13 352 | 12.64 355 | 46.94 335 | 29.94 354 | 10.46 351 | 5.58 354 | 14.93 353 | 4.23 353 | 38.83 353 | 5.24 354 | 7.51 352 | 10.67 353 |
|
wuyk23d | | | 9.11 333 | 8.77 335 | 10.15 344 | 40.18 347 | 16.76 352 | 20.28 350 | 1.01 360 | 2.58 354 | 2.66 356 | 0.98 357 | 0.23 362 | 12.49 357 | 4.08 355 | 6.90 353 | 1.19 355 |
|
tmp_tt | | | 9.44 332 | 10.68 333 | 5.73 345 | 2.49 359 | 4.21 359 | 10.48 353 | 18.04 357 | 0.34 355 | 12.59 347 | 20.49 349 | 11.39 333 | 7.03 358 | 13.84 345 | 6.46 354 | 5.95 354 |
|
ANet_high | | | 34.39 318 | 29.59 321 | 48.78 323 | 30.34 353 | 22.28 340 | 55.53 328 | 63.79 323 | 38.11 314 | 15.47 344 | 36.56 342 | 6.94 342 | 59.98 336 | 13.93 344 | 5.64 355 | 64.08 332 |
|
cdsmvs_eth3d_5k | | | 18.33 330 | 24.44 323 | 0.00 348 | 0.00 361 | 0.00 362 | 0.00 354 | 89.40 12 | 0.00 356 | 0.00 359 | 92.02 23 | 38.55 162 | 0.00 359 | 0.00 358 | 0.00 356 | 0.00 359 |
|
pcd_1.5k_mvsjas | | | 3.15 337 | 4.20 338 | 0.00 348 | 0.00 361 | 0.00 362 | 0.00 354 | 0.00 362 | 0.00 356 | 0.00 359 | 0.00 360 | 37.77 168 | 0.00 359 | 0.00 358 | 0.00 356 | 0.00 359 |
|
sosnet-low-res | | | 0.00 338 | 0.00 339 | 0.00 348 | 0.00 361 | 0.00 362 | 0.00 354 | 0.00 362 | 0.00 356 | 0.00 359 | 0.00 360 | 0.00 364 | 0.00 359 | 0.00 358 | 0.00 356 | 0.00 359 |
|
sosnet | | | 0.00 338 | 0.00 339 | 0.00 348 | 0.00 361 | 0.00 362 | 0.00 354 | 0.00 362 | 0.00 356 | 0.00 359 | 0.00 360 | 0.00 364 | 0.00 359 | 0.00 358 | 0.00 356 | 0.00 359 |
|
uncertanet | | | 0.00 338 | 0.00 339 | 0.00 348 | 0.00 361 | 0.00 362 | 0.00 354 | 0.00 362 | 0.00 356 | 0.00 359 | 0.00 360 | 0.00 364 | 0.00 359 | 0.00 358 | 0.00 356 | 0.00 359 |
|
Regformer | | | 0.00 338 | 0.00 339 | 0.00 348 | 0.00 361 | 0.00 362 | 0.00 354 | 0.00 362 | 0.00 356 | 0.00 359 | 0.00 360 | 0.00 364 | 0.00 359 | 0.00 358 | 0.00 356 | 0.00 359 |
|
.test1245 | | | 38.91 313 | 41.99 310 | 29.67 338 | 55.68 334 | 20.86 345 | 70.41 292 | 76.03 257 | 36.88 319 | 34.86 321 | 59.55 323 | 23.72 288 | 68.13 328 | 20.82 330 | 0.00 356 | 0.02 356 |
|
testmvs | | | 6.14 335 | 8.18 336 | 0.01 346 | 0.01 360 | 0.00 362 | 73.40 271 | 0.00 362 | 0.00 356 | 0.02 357 | 0.15 358 | 0.00 364 | 0.00 359 | 0.02 356 | 0.00 356 | 0.02 356 |
|
test123 | | | 6.01 336 | 8.01 337 | 0.01 346 | 0.00 361 | 0.01 361 | 71.93 285 | 0.00 362 | 0.00 356 | 0.02 357 | 0.11 359 | 0.00 364 | 0.00 359 | 0.02 356 | 0.00 356 | 0.02 356 |
|
ab-mvs-re | | | 7.68 334 | 10.24 334 | 0.00 348 | 0.00 361 | 0.00 362 | 0.00 354 | 0.00 362 | 0.00 356 | 0.00 359 | 92.12 20 | 0.00 364 | 0.00 359 | 0.00 358 | 0.00 356 | 0.00 359 |
|
uanet | | | 0.00 338 | 0.00 339 | 0.00 348 | 0.00 361 | 0.00 362 | 0.00 354 | 0.00 362 | 0.00 356 | 0.00 359 | 0.00 360 | 0.00 364 | 0.00 359 | 0.00 358 | 0.00 356 | 0.00 359 |
|
GSMVS | | | | | | | | | | | | | | | | | 88.13 107 |
|
test_part3 | | | | | | | | 89.59 26 | | 56.02 210 | | 93.65 3 | | 95.22 6 | 79.73 12 | | |
|
test_part2 | | | | | | 89.33 9 | 55.48 38 | | | | 82.27 2 | | | | | | |
|
sam_mvs1 | | | | | | | | | | | | | 38.86 160 | | | | 88.13 107 |
|
sam_mvs | | | | | | | | | | | | | 35.99 203 | | | | |
|
MTGPA | | | | | | | | | 81.31 171 | | | | | | | | |
|
test_post1 | | | | | | | | 70.84 291 | | | | 14.72 354 | 34.33 214 | 83.86 231 | 48.80 209 | | |
|
test_post | | | | | | | | | | | | 16.22 351 | 37.52 176 | 84.72 217 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 59.74 321 | 38.41 163 | 79.91 273 | | | |
|
MTMP | | | | | | | | | 15.34 358 | | | | | | | | |
|
gm-plane-assit | | | | | | 83.24 82 | 54.21 75 | | | 70.91 11 | | 88.23 89 | | 95.25 5 | 66.37 82 | | |
|
TEST9 | | | | | | 85.68 37 | 55.42 40 | 87.59 47 | 84.00 119 | 57.72 182 | 72.99 36 | 90.98 41 | 44.87 77 | 88.58 117 | | | |
|
test_8 | | | | | | 85.72 36 | 55.31 44 | 87.60 44 | 83.88 124 | 57.84 179 | 72.84 39 | 90.99 40 | 44.99 74 | 88.34 128 | | | |
|
agg_prior | | | | | | 85.64 40 | 54.92 57 | | 83.61 130 | | 72.53 43 | | | 88.10 139 | | | |
|
test_prior4 | | | | | | | 56.39 28 | 87.15 59 | | | | | | | | | |
|
test_prior | | | | | 78.39 52 | 86.35 31 | 54.91 59 | | 85.45 73 | | | | | 89.70 81 | | | 90.55 55 |
|
旧先验2 | | | | | | | | 81.73 178 | | 45.53 286 | 74.66 24 | | | 70.48 325 | 58.31 143 | | |
|
新几何2 | | | | | | | | 81.61 186 | | | | | | | | | |
|
无先验 | | | | | | | | 85.19 95 | 78.00 231 | 49.08 267 | | | | 85.13 208 | 52.78 188 | | 87.45 118 |
|
原ACMM2 | | | | | | | | 83.77 137 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 77.81 289 | 45.64 230 | | |
|
segment_acmp | | | | | | | | | | | | | 44.97 76 | | | | |
|
testdata1 | | | | | | | | 77.55 240 | | 64.14 73 | | | | | | | |
|
plane_prior7 | | | | | | 77.95 185 | 48.46 209 | | | | | | | | | | |
|
plane_prior6 | | | | | | 78.42 182 | 49.39 187 | | | | | | 36.04 200 | | | | |
|
plane_prior4 | | | | | | | | | | | | 83.28 152 | | | | | |
|
plane_prior3 | | | | | | | 48.95 193 | | | 64.01 74 | 62.15 130 | | | | | | |
|
plane_prior2 | | | | | | | | 85.76 80 | | 63.60 84 | | | | | | | |
|
plane_prior1 | | | | | | 78.31 184 | | | | | | | | | | | |
|
n2 | | | | | | | | | 0.00 362 | | | | | | | | |
|
nn | | | | | | | | | 0.00 362 | | | | | | | | |
|
door-mid | | | | | | | | | 41.31 345 | | | | | | | | |
|
test11 | | | | | | | | | 84.25 106 | | | | | | | | |
|
door | | | | | | | | | 43.27 344 | | | | | | | | |
|
HQP5-MVS | | | | | | | 51.56 147 | | | | | | | | | | |
|
HQP-NCC | | | | | | 79.02 167 | | 88.00 38 | | 65.45 52 | 64.48 107 | | | | | | |
|
ACMP_Plane | | | | | | 79.02 167 | | 88.00 38 | | 65.45 52 | 64.48 107 | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 66.70 80 | | |
|
HQP4-MVS | | | | | | | | | | | 64.47 110 | | | 88.61 115 | | | 84.91 157 |
|
HQP2-MVS | | | | | | | | | | | | | 37.35 179 | | | | |
|
NP-MVS | | | | | | 78.76 171 | 50.43 168 | | | | | 85.12 124 | | | | | |
|
MDTV_nov1_ep13_2view | | | | | | | 43.62 259 | 71.13 290 | | 54.95 221 | 59.29 164 | | 36.76 190 | | 46.33 227 | | 87.32 120 |
|
Test By Simon | | | | | | | | | | | | | 39.38 155 | | | | |
|