SMA-MVS | | | 89.03 4 | 89.17 4 | 88.60 2 | 94.25 18 | 73.68 7 | 92.40 14 | 93.59 9 | 74.72 90 | 91.86 1 | 95.97 2 | 74.27 21 | 97.24 4 | 88.58 4 | 96.91 1 | 94.87 5 |
|
HPM-MVS++ | | | 89.02 5 | 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 65 |
|
test_part1 | | | | | | | | | 94.09 1 | | | | 81.79 1 | | | 96.38 3 | 93.74 37 |
|
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 37 |
|
ACMMP_Plus | | | 88.05 11 | 88.08 11 | 87.94 13 | 93.70 26 | 73.05 19 | 90.86 36 | 93.59 9 | 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 58 | 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 8 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
CNVR-MVS | | | 88.93 6 | 89.13 6 | 88.33 4 | 94.77 4 | 73.82 6 | 90.51 42 | 93.00 27 | 80.90 10 | 88.06 12 | 94.06 27 | 76.43 6 | 96.84 10 | 88.48 5 | 95.99 7 | 94.34 16 |
|
PHI-MVS | | | 86.43 33 | 86.17 35 | 87.24 32 | 90.88 70 | 70.96 50 | 92.27 18 | 94.07 3 | 72.45 140 | 85.22 28 | 91.90 61 | 69.47 53 | 96.42 25 | 83.28 37 | 95.94 8 | 94.35 15 |
|
test_prior3 | | | 86.73 28 | 86.86 27 | 86.33 48 | 92.61 49 | 69.59 74 | 88.85 82 | 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 82 | | 75.41 78 | 84.91 32 | 93.54 32 | 74.28 19 | | 83.31 35 | 95.86 9 | |
|
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. |
DeepPCF-MVS | | 80.84 1 | 88.10 8 | 88.56 8 | 86.73 41 | 92.24 53 | 69.03 82 | 89.57 65 | 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 8 |
|
train_agg | | | 86.43 33 | 86.20 33 | 87.13 35 | 93.26 36 | 72.96 20 | 88.75 87 | 91.89 70 | 68.69 201 | 85.00 30 | 93.10 42 | 74.43 15 | 95.41 51 | 84.97 18 | 95.71 13 | 93.02 66 |
|
agg_prior3 | | | 86.16 39 | 85.85 40 | 87.10 36 | 93.31 33 | 72.86 24 | 88.77 85 | 91.68 80 | 68.29 213 | 84.26 45 | 92.83 50 | 72.83 28 | 95.42 50 | 84.97 18 | 95.71 13 | 93.02 66 |
|
test9_res | | | | | | | | | | | | | | | 84.90 20 | 95.70 15 | 92.87 71 |
|
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 12 |
|
agg_prior2 | | | | | | | | | | | | | | | 82.91 42 | 95.45 17 | 92.70 72 |
|
CDPH-MVS | | | 85.76 43 | 85.29 48 | 87.17 34 | 93.49 32 | 71.08 48 | 88.58 93 | 92.42 49 | 68.32 212 | 84.61 39 | 93.48 34 | 72.32 32 | 96.15 33 | 79.00 63 | 95.43 18 | 94.28 19 |
|
DeepC-MVS | | 79.81 2 | 87.08 26 | 86.88 26 | 87.69 26 | 91.16 65 | 72.32 38 | 90.31 48 | 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 44 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
zzz-MVS | | | 87.53 16 | 87.41 17 | 87.90 17 | 94.18 22 | 74.25 2 | 90.23 50 | 92.02 61 | 79.45 19 | 85.88 21 | 94.80 7 | 68.07 60 | 96.21 30 | 86.69 11 | 95.34 20 | 93.23 57 |
|
MTAPA | | | 87.23 22 | 87.00 22 | 87.90 17 | 94.18 22 | 74.25 2 | 86.58 164 | 92.02 61 | 79.45 19 | 85.88 21 | 94.80 7 | 68.07 60 | 96.21 30 | 86.69 11 | 95.34 20 | 93.23 57 |
|
agg_prior1 | | | 86.22 38 | 86.09 37 | 86.62 44 | 92.85 44 | 71.94 42 | 88.59 92 | 91.78 76 | 68.96 198 | 84.41 42 | 93.18 41 | 74.94 11 | 94.93 68 | 84.75 25 | 95.33 22 | 93.01 68 |
|
DeepC-MVS_fast | | 79.65 3 | 86.91 27 | 86.62 28 | 87.76 21 | 93.52 31 | 72.37 37 | 91.26 30 | 93.04 24 | 76.62 57 | 84.22 46 | 93.36 38 | 71.44 37 | 96.76 13 | 80.82 54 | 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 |
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 46 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
MCST-MVS | | | 87.37 20 | 87.25 18 | 87.73 22 | 94.53 9 | 72.46 34 | 89.82 56 | 93.82 6 | 73.07 128 | 84.86 37 | 92.89 48 | 76.22 7 | 96.33 26 | 84.89 22 | 95.13 25 | 94.40 13 |
|
APD-MVS | | | 87.44 17 | 87.52 15 | 87.19 33 | 94.24 19 | 72.39 35 | 91.86 25 | 92.83 36 | 73.01 129 | 88.58 9 | 94.52 10 | 73.36 24 | 96.49 24 | 84.26 30 | 95.01 26 | 92.70 72 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
NCCC | | | 88.06 9 | 88.01 12 | 88.24 6 | 94.41 14 | 73.62 8 | 91.22 33 | 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 |
|
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 58 | 96.65 17 | 84.53 26 | 94.90 28 | 94.00 29 |
|
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 39 |
|
#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 39 |
|
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 56 | 96.70 15 | 84.37 29 | 94.83 31 | 94.03 26 |
|
原ACMM1 | | | | | 84.35 87 | 93.01 42 | 68.79 88 | | 92.44 46 | 63.96 255 | 81.09 81 | 91.57 69 | 66.06 78 | 95.45 48 | 67.19 170 | 94.82 32 | 88.81 206 |
|
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 |
TSAR-MVS + MP. | | | 88.02 12 | 88.11 10 | 87.72 24 | 93.68 28 | 72.13 40 | 91.41 29 | 92.35 51 | 74.62 92 | 88.90 8 | 93.85 30 | 75.75 10 | 96.00 36 | 87.80 6 | 94.63 34 | 95.04 2 |
|
PGM-MVS | | | 86.68 29 | 86.27 32 | 87.90 17 | 94.22 20 | 73.38 16 | 90.22 51 | 93.04 24 | 75.53 76 | 83.86 49 | 94.42 17 | 67.87 64 | 96.64 18 | 82.70 44 | 94.57 35 | 93.66 39 |
|
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 67 | 96.60 21 | 83.06 39 | 94.50 36 | 94.07 24 |
|
X-MVStestdata | | | 80.37 119 | 77.83 153 | 88.00 10 | 94.42 12 | 73.33 17 | 92.78 9 | 92.99 29 | 79.14 21 | 83.67 53 | 12.47 355 | 67.45 67 | 96.60 21 | 83.06 39 | 94.50 36 | 94.07 24 |
|
test12 | | | | | 86.80 40 | 92.63 48 | 70.70 58 | | 91.79 75 | | 82.71 64 | | 71.67 35 | 96.16 32 | | 94.50 36 | 93.54 49 |
|
CP-MVS | | | 87.11 24 | 86.92 24 | 87.68 27 | 94.20 21 | 73.86 5 | 93.98 1 | 92.82 38 | 76.62 57 | 83.68 52 | 94.46 14 | 67.93 62 | 95.95 38 | 84.20 32 | 94.39 39 | 93.23 57 |
|
CSCG | | | 86.41 35 | 86.19 34 | 87.07 37 | 92.91 43 | 72.48 33 | 90.81 37 | 93.56 11 | 73.95 100 | 83.16 58 | 91.07 79 | 75.94 8 | 95.19 58 | 79.94 61 | 94.38 40 | 93.55 48 |
|
MSLP-MVS++ | | | 85.43 48 | 85.76 42 | 84.45 83 | 91.93 58 | 70.24 61 | 90.71 39 | 92.86 34 | 77.46 41 | 84.22 46 | 92.81 53 | 67.16 70 | 92.94 155 | 80.36 57 | 94.35 41 | 90.16 150 |
|
mPP-MVS | | | 86.67 30 | 86.32 31 | 87.72 24 | 94.41 14 | 73.55 10 | 92.74 11 | 92.22 54 | 76.87 50 | 82.81 63 | 94.25 20 | 66.44 74 | 96.24 29 | 82.88 43 | 94.28 42 | 93.38 52 |
|
SD-MVS | | | 88.06 9 | 88.50 9 | 86.71 42 | 92.60 51 | 72.71 25 | 91.81 26 | 93.19 21 | 77.87 32 | 90.32 6 | 94.00 28 | 74.83 12 | 93.78 115 | 87.63 8 | 94.27 43 | 93.65 44 |
|
MVS_0304 | | | 86.37 37 | 85.81 41 | 88.02 9 | 90.13 78 | 72.39 35 | 89.66 63 | 92.75 39 | 81.64 6 | 82.66 66 | 92.04 57 | 64.44 89 | 97.35 3 | 84.76 24 | 94.25 44 | 94.33 17 |
|
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 55 |
|
DELS-MVS | | | 85.41 49 | 85.30 47 | 85.77 57 | 88.49 134 | 67.93 110 | 85.52 201 | 93.44 14 | 78.70 28 | 83.63 55 | 89.03 120 | 74.57 13 | 95.71 41 | 80.26 59 | 94.04 46 | 93.66 39 |
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023 |
EPNet | | | 83.72 59 | 82.92 65 | 86.14 53 | 84.22 218 | 69.48 77 | 91.05 35 | 85.27 226 | 81.30 8 | 76.83 142 | 91.65 65 | 66.09 77 | 95.56 44 | 76.00 92 | 93.85 47 | 93.38 52 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
3Dnovator+ | | 77.84 4 | 85.48 46 | 84.47 54 | 88.51 3 | 91.08 66 | 73.49 14 | 93.18 4 | 93.78 7 | 80.79 11 | 76.66 145 | 93.37 37 | 60.40 163 | 96.75 14 | 77.20 81 | 93.73 48 | 95.29 1 |
|
CANet | | | 86.45 32 | 86.10 36 | 87.51 29 | 90.09 80 | 70.94 52 | 89.70 62 | 92.59 44 | 81.78 4 | 81.32 76 | 91.43 74 | 70.34 44 | 97.23 5 | 84.26 30 | 93.36 49 | 94.37 14 |
|
新几何1 | | | | | 83.42 114 | 93.13 38 | 70.71 57 | | 85.48 224 | 57.43 306 | 81.80 73 | 91.98 59 | 63.28 99 | 92.27 174 | 64.60 191 | 92.99 50 | 87.27 248 |
|
1121 | | | 80.84 99 | 79.77 104 | 84.05 96 | 93.11 40 | 70.78 56 | 84.66 213 | 85.42 225 | 57.37 307 | 81.76 74 | 92.02 58 | 63.41 97 | 94.12 96 | 67.28 168 | 92.93 51 | 87.26 249 |
|
HPM-MVS_fast | | | 85.35 50 | 84.95 51 | 86.57 46 | 93.69 27 | 70.58 59 | 92.15 22 | 91.62 81 | 73.89 104 | 82.67 65 | 94.09 26 | 62.60 123 | 95.54 45 | 80.93 52 | 92.93 51 | 93.57 47 |
|
旧先验1 | | | | | | 91.96 57 | 65.79 143 | | 86.37 216 | | | 93.08 46 | 69.31 55 | | | 92.74 53 | 88.74 209 |
|
3Dnovator | | 76.31 5 | 83.38 66 | 82.31 72 | 86.59 45 | 87.94 149 | 72.94 23 | 90.64 40 | 92.14 58 | 77.21 42 | 75.47 171 | 92.83 50 | 58.56 170 | 94.72 78 | 73.24 122 | 92.71 54 | 92.13 91 |
|
MVS_111021_HR | | | 85.14 53 | 84.75 53 | 86.32 50 | 91.65 61 | 72.70 26 | 85.98 179 | 90.33 120 | 76.11 68 | 82.08 69 | 91.61 68 | 71.36 38 | 94.17 95 | 81.02 51 | 92.58 55 | 92.08 92 |
|
APD-MVS_3200maxsize | | | 85.97 40 | 85.88 38 | 86.22 51 | 92.69 47 | 69.53 76 | 91.93 24 | 92.99 29 | 73.54 115 | 85.94 20 | 94.51 13 | 65.80 81 | 95.61 42 | 83.04 41 | 92.51 56 | 93.53 50 |
|
MAR-MVS | | | 81.84 84 | 80.70 90 | 85.27 63 | 91.32 64 | 71.53 46 | 89.82 56 | 90.92 101 | 69.77 179 | 78.50 103 | 86.21 207 | 62.36 130 | 94.52 82 | 65.36 184 | 92.05 57 | 89.77 179 |
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020 |
TSAR-MVS + GP. | | | 85.71 44 | 85.33 45 | 86.84 39 | 91.34 63 | 72.50 32 | 89.07 77 | 87.28 206 | 76.41 59 | 85.80 23 | 90.22 95 | 74.15 22 | 95.37 55 | 81.82 48 | 91.88 58 | 92.65 75 |
|
IS-MVSNet | | | 83.15 67 | 82.81 66 | 84.18 92 | 89.94 84 | 63.30 202 | 91.59 27 | 88.46 186 | 79.04 25 | 79.49 91 | 92.16 55 | 65.10 85 | 94.28 87 | 67.71 163 | 91.86 59 | 94.95 3 |
|
Vis-MVSNet (Re-imp) | | | 78.36 159 | 78.45 139 | 78.07 247 | 88.64 130 | 51.78 315 | 86.70 161 | 79.63 291 | 74.14 98 | 75.11 185 | 90.83 86 | 61.29 146 | 89.75 236 | 58.10 243 | 91.60 60 | 92.69 74 |
|
MG-MVS | | | 83.41 64 | 83.45 57 | 83.28 119 | 92.74 46 | 62.28 219 | 88.17 108 | 89.50 147 | 75.22 83 | 81.49 75 | 92.74 54 | 66.75 71 | 95.11 61 | 72.85 124 | 91.58 61 | 92.45 79 |
|
CPTT-MVS | | | 83.73 58 | 83.33 59 | 84.92 74 | 93.28 35 | 70.86 55 | 92.09 23 | 90.38 115 | 68.75 200 | 79.57 90 | 92.83 50 | 60.60 159 | 93.04 153 | 80.92 53 | 91.56 62 | 90.86 120 |
|
test222 | | | | | | 91.50 62 | 68.26 104 | 84.16 230 | 83.20 251 | 54.63 318 | 79.74 88 | 91.63 67 | 58.97 168 | | | 91.42 63 | 86.77 260 |
|
testdata | | | | | 79.97 208 | 90.90 69 | 64.21 184 | | 84.71 230 | 59.27 292 | 85.40 25 | 92.91 47 | 62.02 136 | 89.08 255 | 68.95 157 | 91.37 64 | 86.63 264 |
|
abl_6 | | | 85.23 51 | 84.95 51 | 86.07 54 | 92.23 54 | 70.48 60 | 90.80 38 | 92.08 59 | 73.51 116 | 85.26 27 | 94.16 23 | 62.75 116 | 95.92 39 | 82.46 47 | 91.30 65 | 91.81 99 |
|
API-MVS | | | 81.99 82 | 81.23 84 | 84.26 90 | 90.94 68 | 70.18 67 | 91.10 34 | 89.32 152 | 71.51 157 | 78.66 101 | 88.28 138 | 65.26 83 | 95.10 64 | 64.74 190 | 91.23 66 | 87.51 242 |
|
Vis-MVSNet | | | 83.46 63 | 82.80 67 | 85.43 60 | 90.25 77 | 68.74 92 | 90.30 49 | 90.13 129 | 76.33 65 | 80.87 84 | 92.89 48 | 61.00 152 | 94.20 92 | 72.45 131 | 90.97 67 | 93.35 54 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
OpenMVS | | 72.83 10 | 79.77 133 | 78.33 145 | 84.09 94 | 85.17 202 | 69.91 68 | 90.57 41 | 90.97 100 | 66.70 224 | 72.17 219 | 91.91 60 | 54.70 199 | 93.96 101 | 61.81 211 | 90.95 68 | 88.41 226 |
|
UA-Net | | | 85.08 54 | 84.96 50 | 85.45 59 | 92.07 56 | 68.07 108 | 89.78 59 | 90.86 103 | 82.48 2 | 84.60 40 | 93.20 40 | 69.35 54 | 95.22 57 | 71.39 143 | 90.88 69 | 93.07 64 |
|
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 85 | 93.82 31 | 64.33 90 | 96.29 27 | 82.67 45 | 90.69 70 | 93.23 57 |
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence |
Regformer-1 | | | 86.41 35 | 86.33 30 | 86.64 43 | 89.33 103 | 70.93 53 | 88.43 95 | 91.39 90 | 82.14 3 | 86.65 18 | 90.09 97 | 74.39 17 | 95.01 67 | 83.97 33 | 90.63 71 | 93.97 30 |
|
Regformer-2 | | | 86.63 31 | 86.53 29 | 86.95 38 | 89.33 103 | 71.24 47 | 88.43 95 | 92.05 60 | 82.50 1 | 86.88 17 | 90.09 97 | 74.45 14 | 95.61 42 | 84.38 28 | 90.63 71 | 94.01 28 |
|
UGNet | | | 80.83 101 | 79.59 109 | 84.54 80 | 88.04 146 | 68.09 107 | 89.42 66 | 88.16 188 | 76.95 48 | 76.22 157 | 89.46 111 | 49.30 265 | 93.94 103 | 68.48 160 | 90.31 73 | 91.60 101 |
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 |
MVSFormer | | | 82.85 72 | 82.05 75 | 85.24 64 | 87.35 172 | 70.21 62 | 90.50 43 | 90.38 115 | 68.55 203 | 81.32 76 | 89.47 109 | 61.68 137 | 93.46 131 | 78.98 64 | 90.26 74 | 92.05 93 |
|
lupinMVS | | | 81.39 93 | 80.27 98 | 84.76 77 | 87.35 172 | 70.21 62 | 85.55 197 | 86.41 214 | 62.85 263 | 81.32 76 | 88.61 128 | 61.68 137 | 92.24 176 | 78.41 70 | 90.26 74 | 91.83 97 |
|
DP-MVS Recon | | | 83.11 69 | 82.09 74 | 86.15 52 | 94.44 11 | 70.92 54 | 88.79 84 | 92.20 55 | 70.53 169 | 79.17 94 | 91.03 82 | 64.12 92 | 96.03 34 | 68.39 162 | 90.14 76 | 91.50 105 |
|
MVS_111021_LR | | | 82.61 75 | 82.11 73 | 84.11 93 | 88.82 123 | 71.58 45 | 85.15 206 | 86.16 219 | 74.69 91 | 80.47 86 | 91.04 80 | 62.29 131 | 90.55 227 | 80.33 58 | 90.08 77 | 90.20 149 |
|
jason | | | 81.39 93 | 80.29 97 | 84.70 78 | 86.63 186 | 69.90 69 | 85.95 180 | 86.77 210 | 63.24 257 | 81.07 82 | 89.47 109 | 61.08 151 | 92.15 177 | 78.33 71 | 90.07 78 | 92.05 93 |
jason: jason. |
LFMVS | | | 81.82 85 | 81.23 84 | 83.57 111 | 91.89 59 | 63.43 200 | 89.84 55 | 81.85 269 | 77.04 47 | 83.21 56 | 93.10 42 | 52.26 219 | 93.43 135 | 71.98 137 | 89.95 79 | 93.85 34 |
|
MVS | | | 78.19 163 | 76.99 167 | 81.78 169 | 85.66 196 | 66.99 124 | 84.66 213 | 90.47 113 | 55.08 317 | 72.02 224 | 85.27 233 | 63.83 95 | 94.11 98 | 66.10 178 | 89.80 80 | 84.24 296 |
|
CANet_DTU | | | 80.61 109 | 79.87 102 | 82.83 146 | 85.60 198 | 63.17 208 | 87.36 132 | 88.65 182 | 76.37 63 | 75.88 163 | 88.44 134 | 53.51 210 | 93.07 150 | 73.30 121 | 89.74 81 | 92.25 86 |
|
PVSNet_Blended | | | 80.98 96 | 80.34 95 | 82.90 140 | 88.85 120 | 65.40 149 | 84.43 223 | 92.00 64 | 67.62 218 | 78.11 120 | 85.05 238 | 66.02 79 | 94.27 88 | 71.52 141 | 89.50 82 | 89.01 198 |
|
PAPM_NR | | | 83.02 70 | 82.41 69 | 84.82 76 | 92.47 52 | 66.37 133 | 87.93 115 | 91.80 74 | 73.82 109 | 77.32 134 | 90.66 88 | 67.90 63 | 94.90 72 | 70.37 147 | 89.48 83 | 93.19 61 |
|
114514_t | | | 80.68 108 | 79.51 113 | 84.20 91 | 94.09 24 | 67.27 121 | 89.64 64 | 91.11 97 | 58.75 297 | 74.08 194 | 90.72 87 | 58.10 173 | 95.04 66 | 69.70 152 | 89.42 84 | 90.30 147 |
|
LCM-MVSNet-Re | | | 77.05 192 | 76.94 168 | 77.36 258 | 87.20 178 | 51.60 316 | 80.06 271 | 80.46 282 | 75.20 84 | 67.69 275 | 86.72 181 | 62.48 128 | 88.98 258 | 63.44 195 | 89.25 85 | 91.51 104 |
|
alignmvs | | | 85.48 46 | 85.32 46 | 85.96 56 | 89.51 98 | 69.47 78 | 89.74 60 | 92.47 45 | 76.17 67 | 87.73 15 | 91.46 73 | 70.32 45 | 93.78 115 | 81.51 49 | 88.95 86 | 94.63 7 |
|
VNet | | | 82.21 78 | 82.41 69 | 81.62 179 | 90.82 71 | 60.93 227 | 84.47 219 | 89.78 140 | 76.36 64 | 84.07 48 | 91.88 62 | 64.71 88 | 90.26 229 | 70.68 144 | 88.89 87 | 93.66 39 |
|
PS-MVSNAJ | | | 81.69 86 | 81.02 88 | 83.70 107 | 89.51 98 | 68.21 106 | 84.28 228 | 90.09 130 | 70.79 164 | 81.26 80 | 85.62 226 | 63.15 104 | 94.29 86 | 75.62 99 | 88.87 88 | 88.59 218 |
|
canonicalmvs | | | 85.91 41 | 85.87 39 | 86.04 55 | 89.84 86 | 69.44 80 | 90.45 46 | 93.00 27 | 76.70 56 | 88.01 13 | 91.23 76 | 73.28 25 | 93.91 106 | 81.50 50 | 88.80 89 | 94.77 6 |
|
QAPM | | | 80.88 97 | 79.50 114 | 85.03 69 | 88.01 148 | 68.97 86 | 91.59 27 | 92.00 64 | 66.63 228 | 75.15 184 | 92.16 55 | 57.70 175 | 95.45 48 | 63.52 194 | 88.76 90 | 90.66 129 |
|
VDD-MVS | | | 83.01 71 | 82.36 71 | 84.96 71 | 91.02 67 | 66.40 132 | 88.91 79 | 88.11 189 | 77.57 35 | 84.39 44 | 93.29 39 | 52.19 220 | 93.91 106 | 77.05 84 | 88.70 91 | 94.57 10 |
|
Test4 | | | 77.83 174 | 75.90 192 | 83.62 108 | 80.24 297 | 65.25 155 | 85.27 203 | 90.67 105 | 69.03 196 | 66.48 287 | 83.75 252 | 43.07 298 | 93.00 154 | 75.93 93 | 88.66 92 | 92.62 76 |
|
test_normal | | | 79.81 132 | 78.45 139 | 83.89 105 | 82.70 267 | 65.40 149 | 85.82 187 | 89.48 148 | 69.39 184 | 70.12 246 | 85.66 224 | 57.15 183 | 93.71 124 | 77.08 83 | 88.62 93 | 92.56 77 |
|
PVSNet_Blended_VisFu | | | 82.62 74 | 81.83 79 | 84.96 71 | 90.80 72 | 69.76 71 | 88.74 89 | 91.70 79 | 69.39 184 | 78.96 96 | 88.46 133 | 65.47 82 | 94.87 74 | 74.42 108 | 88.57 94 | 90.24 148 |
|
DI_MVS_plusplus_test | | | 79.89 131 | 78.58 136 | 83.85 106 | 82.89 263 | 65.32 153 | 86.12 176 | 89.55 145 | 69.64 183 | 70.55 237 | 85.82 221 | 57.24 181 | 93.81 113 | 76.85 86 | 88.55 95 | 92.41 81 |
|
xiu_mvs_v2_base | | | 81.69 86 | 81.05 87 | 83.60 109 | 89.15 114 | 68.03 109 | 84.46 221 | 90.02 134 | 70.67 167 | 81.30 79 | 86.53 197 | 63.17 103 | 94.19 93 | 75.60 100 | 88.54 96 | 88.57 220 |
|
PAPR | | | 81.66 88 | 80.89 89 | 83.99 101 | 90.27 76 | 64.00 190 | 86.76 160 | 91.77 78 | 68.84 199 | 77.13 141 | 89.50 107 | 67.63 65 | 94.88 73 | 67.55 165 | 88.52 97 | 93.09 63 |
|
MVS_Test | | | 83.15 67 | 83.06 62 | 83.41 116 | 86.86 182 | 63.21 205 | 86.11 177 | 92.00 64 | 74.31 95 | 82.87 61 | 89.44 114 | 70.03 47 | 93.21 140 | 77.39 80 | 88.50 98 | 93.81 36 |
|
AdaColmap | | | 80.58 112 | 79.42 115 | 84.06 95 | 93.09 41 | 68.91 87 | 89.36 67 | 88.97 169 | 69.27 188 | 75.70 170 | 89.69 103 | 57.20 182 | 95.77 40 | 63.06 198 | 88.41 99 | 87.50 243 |
|
VDDNet | | | 81.52 90 | 80.67 91 | 84.05 96 | 90.44 74 | 64.13 186 | 89.73 61 | 85.91 222 | 71.11 160 | 83.18 57 | 93.48 34 | 50.54 255 | 93.49 130 | 73.40 120 | 88.25 100 | 94.54 11 |
|
PCF-MVS | | 73.52 7 | 80.38 118 | 78.84 131 | 85.01 70 | 87.71 165 | 68.99 85 | 83.65 237 | 91.46 89 | 63.00 260 | 77.77 127 | 90.28 92 | 66.10 76 | 95.09 65 | 61.40 214 | 88.22 101 | 90.94 118 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
Effi-MVS+ | | | 83.62 61 | 83.08 61 | 85.24 64 | 88.38 139 | 67.45 116 | 88.89 80 | 89.15 159 | 75.50 77 | 82.27 67 | 88.28 138 | 69.61 52 | 94.45 84 | 77.81 75 | 87.84 102 | 93.84 35 |
|
gg-mvs-nofinetune | | | 69.95 274 | 67.96 275 | 75.94 274 | 83.07 257 | 54.51 298 | 77.23 296 | 70.29 336 | 63.11 258 | 70.32 241 | 62.33 339 | 43.62 295 | 88.69 264 | 53.88 265 | 87.76 103 | 84.62 294 |
|
Regformer-3 | | | 85.23 51 | 85.07 49 | 85.70 58 | 88.95 118 | 69.01 84 | 88.29 104 | 89.91 138 | 80.95 9 | 85.01 29 | 90.01 99 | 72.45 31 | 94.19 93 | 82.50 46 | 87.57 104 | 93.90 33 |
|
Regformer-4 | | | 85.68 45 | 85.45 43 | 86.35 47 | 88.95 118 | 69.67 73 | 88.29 104 | 91.29 92 | 81.73 5 | 85.36 26 | 90.01 99 | 72.62 30 | 95.35 56 | 83.28 37 | 87.57 104 | 94.03 26 |
|
xiu_mvs_v1_base_debu | | | 80.80 104 | 79.72 106 | 84.03 98 | 87.35 172 | 70.19 64 | 85.56 194 | 88.77 178 | 69.06 193 | 81.83 70 | 88.16 140 | 50.91 243 | 92.85 157 | 78.29 72 | 87.56 106 | 89.06 191 |
|
xiu_mvs_v1_base | | | 80.80 104 | 79.72 106 | 84.03 98 | 87.35 172 | 70.19 64 | 85.56 194 | 88.77 178 | 69.06 193 | 81.83 70 | 88.16 140 | 50.91 243 | 92.85 157 | 78.29 72 | 87.56 106 | 89.06 191 |
|
xiu_mvs_v1_base_debi | | | 80.80 104 | 79.72 106 | 84.03 98 | 87.35 172 | 70.19 64 | 85.56 194 | 88.77 178 | 69.06 193 | 81.83 70 | 88.16 140 | 50.91 243 | 92.85 157 | 78.29 72 | 87.56 106 | 89.06 191 |
|
CLD-MVS | | | 82.31 77 | 81.65 80 | 84.29 89 | 88.47 135 | 67.73 114 | 85.81 188 | 92.35 51 | 75.78 70 | 78.33 111 | 86.58 194 | 64.01 93 | 94.35 85 | 76.05 91 | 87.48 109 | 90.79 121 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
CDS-MVSNet | | | 79.07 148 | 77.70 157 | 83.17 123 | 87.60 167 | 68.23 105 | 84.40 225 | 86.20 218 | 67.49 221 | 76.36 152 | 86.54 196 | 61.54 140 | 90.79 224 | 61.86 210 | 87.33 110 | 90.49 140 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
EPP-MVSNet | | | 83.40 65 | 83.02 63 | 84.57 79 | 90.13 78 | 64.47 180 | 92.32 17 | 90.73 104 | 74.45 94 | 79.35 93 | 91.10 77 | 69.05 57 | 95.12 60 | 72.78 125 | 87.22 111 | 94.13 21 |
|
TAMVS | | | 78.89 152 | 77.51 160 | 83.03 131 | 87.80 160 | 67.79 113 | 84.72 212 | 85.05 229 | 67.63 217 | 76.75 143 | 87.70 152 | 62.25 132 | 90.82 223 | 58.53 238 | 87.13 112 | 90.49 140 |
|
TAPA-MVS | | 73.13 9 | 79.15 146 | 77.94 151 | 82.79 150 | 89.59 93 | 62.99 212 | 88.16 109 | 91.51 86 | 65.77 236 | 77.14 140 | 91.09 78 | 60.91 153 | 93.21 140 | 50.26 280 | 87.05 113 | 92.17 90 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
PAPM | | | 77.68 177 | 76.40 176 | 81.51 182 | 87.29 177 | 61.85 223 | 83.78 236 | 89.59 144 | 64.74 245 | 71.23 231 | 88.70 124 | 62.59 124 | 93.66 125 | 52.66 270 | 87.03 114 | 89.01 198 |
|
BH-untuned | | | 79.47 140 | 78.60 134 | 82.05 164 | 89.19 113 | 65.91 140 | 86.07 178 | 88.52 185 | 72.18 147 | 75.42 174 | 87.69 153 | 61.15 149 | 93.54 128 | 60.38 221 | 86.83 115 | 86.70 262 |
|
BH-RMVSNet | | | 79.61 135 | 78.44 141 | 83.14 125 | 89.38 102 | 65.93 139 | 84.95 209 | 87.15 207 | 73.56 114 | 78.19 118 | 89.79 102 | 56.67 185 | 93.36 136 | 59.53 229 | 86.74 116 | 90.13 152 |
|
LS3D | | | 76.95 195 | 74.82 211 | 83.37 117 | 90.45 73 | 67.36 120 | 89.15 75 | 86.94 209 | 61.87 273 | 69.52 255 | 90.61 89 | 51.71 236 | 94.53 81 | 46.38 306 | 86.71 117 | 88.21 228 |
|
Fast-Effi-MVS+ | | | 80.81 102 | 79.92 101 | 83.47 112 | 88.85 120 | 64.51 174 | 85.53 199 | 89.39 150 | 70.79 164 | 78.49 104 | 85.06 237 | 67.54 66 | 93.58 126 | 67.03 173 | 86.58 118 | 92.32 83 |
|
EPNet_dtu | | | 75.46 221 | 74.86 210 | 77.23 261 | 82.57 270 | 54.60 296 | 86.89 153 | 83.09 252 | 71.64 152 | 66.25 289 | 85.86 219 | 55.99 188 | 88.04 271 | 54.92 260 | 86.55 119 | 89.05 195 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
OPM-MVS | | | 83.50 62 | 82.95 64 | 85.14 66 | 88.79 126 | 70.95 51 | 89.13 76 | 91.52 85 | 77.55 38 | 80.96 83 | 91.75 63 | 60.71 155 | 94.50 83 | 79.67 62 | 86.51 120 | 89.97 168 |
|
OMC-MVS | | | 82.69 73 | 81.97 78 | 84.85 75 | 88.75 128 | 67.42 117 | 87.98 111 | 90.87 102 | 74.92 88 | 79.72 89 | 91.65 65 | 62.19 134 | 93.96 101 | 75.26 104 | 86.42 121 | 93.16 62 |
|
HQP_MVS | | | 83.64 60 | 83.14 60 | 85.14 66 | 90.08 81 | 68.71 94 | 91.25 31 | 92.44 46 | 79.12 23 | 78.92 97 | 91.00 83 | 60.42 161 | 95.38 53 | 78.71 66 | 86.32 122 | 91.33 108 |
|
plane_prior5 | | | | | | | | | 92.44 46 | | | | | 95.38 53 | 78.71 66 | 86.32 122 | 91.33 108 |
|
plane_prior | | | | | | | 68.71 94 | 90.38 47 | | 77.62 34 | | | | | | 86.16 124 | |
|
mvs_anonymous | | | 79.42 142 | 79.11 127 | 80.34 201 | 84.45 214 | 57.97 253 | 82.59 251 | 87.62 200 | 67.40 223 | 76.17 161 | 88.56 131 | 68.47 59 | 89.59 239 | 70.65 145 | 86.05 125 | 93.47 51 |
|
HQP3-MVS | | | | | | | | | 92.19 56 | | | | | | | 85.99 126 | |
|
HQP-MVS | | | 82.61 75 | 82.02 76 | 84.37 85 | 89.33 103 | 66.98 125 | 89.17 71 | 92.19 56 | 76.41 59 | 77.23 137 | 90.23 94 | 60.17 164 | 95.11 61 | 77.47 78 | 85.99 126 | 91.03 114 |
|
BH-w/o | | | 78.21 161 | 77.33 163 | 80.84 194 | 88.81 124 | 65.13 159 | 84.87 210 | 87.85 197 | 69.75 180 | 74.52 192 | 84.74 244 | 61.34 144 | 93.11 148 | 58.24 242 | 85.84 128 | 84.27 295 |
|
CHOSEN 1792x2688 | | | 77.63 180 | 75.69 193 | 83.44 113 | 89.98 83 | 68.58 99 | 78.70 286 | 87.50 203 | 56.38 312 | 75.80 165 | 86.84 177 | 58.67 169 | 91.40 206 | 61.58 213 | 85.75 129 | 90.34 146 |
|
cascas | | | 76.72 198 | 74.64 212 | 82.99 133 | 85.78 195 | 65.88 141 | 82.33 253 | 89.21 158 | 60.85 279 | 72.74 204 | 81.02 286 | 47.28 275 | 93.75 119 | 67.48 166 | 85.02 130 | 89.34 186 |
|
FIs | | | 82.07 80 | 82.42 68 | 81.04 192 | 88.80 125 | 58.34 247 | 88.26 106 | 93.49 13 | 76.93 49 | 78.47 105 | 91.04 80 | 69.92 49 | 92.34 173 | 69.87 151 | 84.97 131 | 92.44 80 |
|
test-LLR | | | 72.94 253 | 72.43 234 | 74.48 287 | 81.35 285 | 58.04 251 | 78.38 287 | 77.46 308 | 66.66 225 | 69.95 250 | 79.00 302 | 48.06 271 | 79.24 314 | 66.13 176 | 84.83 132 | 86.15 273 |
|
test-mter | | | 71.41 261 | 70.39 259 | 74.48 287 | 81.35 285 | 58.04 251 | 78.38 287 | 77.46 308 | 60.32 282 | 69.95 250 | 79.00 302 | 36.08 327 | 79.24 314 | 66.13 176 | 84.83 132 | 86.15 273 |
|
diffmvs | | | 79.51 137 | 78.59 135 | 82.25 161 | 83.31 250 | 62.66 214 | 84.17 229 | 88.11 189 | 67.64 216 | 76.09 162 | 87.47 160 | 64.01 93 | 91.15 213 | 71.71 140 | 84.82 134 | 92.94 70 |
|
EI-MVSNet-Vis-set | | | 84.19 55 | 83.81 55 | 85.31 61 | 88.18 143 | 67.85 111 | 87.66 119 | 89.73 142 | 80.05 17 | 82.95 59 | 89.59 106 | 70.74 43 | 94.82 75 | 80.66 56 | 84.72 135 | 93.28 56 |
|
GG-mvs-BLEND | | | | | 75.38 281 | 81.59 280 | 55.80 291 | 79.32 278 | 69.63 338 | | 67.19 280 | 73.67 326 | 43.24 296 | 88.90 263 | 50.41 277 | 84.50 136 | 81.45 318 |
|
FC-MVSNet-test | | | 81.52 90 | 82.02 76 | 80.03 207 | 88.42 138 | 55.97 286 | 87.95 113 | 93.42 15 | 77.10 45 | 77.38 132 | 90.98 85 | 69.96 48 | 91.79 187 | 68.46 161 | 84.50 136 | 92.33 82 |
|
PVSNet | | 64.34 18 | 72.08 258 | 70.87 256 | 75.69 276 | 86.21 191 | 56.44 278 | 74.37 311 | 80.73 278 | 62.06 272 | 70.17 244 | 82.23 267 | 42.86 300 | 83.31 301 | 54.77 261 | 84.45 138 | 87.32 247 |
|
MS-PatchMatch | | | 73.83 232 | 72.67 231 | 77.30 260 | 83.87 237 | 66.02 137 | 81.82 256 | 84.66 231 | 61.37 277 | 68.61 268 | 82.82 261 | 47.29 274 | 88.21 268 | 59.27 230 | 84.32 139 | 77.68 329 |
|
TESTMET0.1,1 | | | 69.89 275 | 69.00 265 | 72.55 297 | 79.27 311 | 56.85 270 | 78.38 287 | 74.71 324 | 57.64 304 | 68.09 272 | 77.19 314 | 37.75 321 | 76.70 325 | 63.92 193 | 84.09 140 | 84.10 299 |
|
EI-MVSNet-UG-set | | | 83.81 57 | 83.38 58 | 85.09 68 | 87.87 150 | 67.53 115 | 87.44 131 | 89.66 143 | 79.74 18 | 82.23 68 | 89.41 115 | 70.24 46 | 94.74 77 | 79.95 60 | 83.92 141 | 92.99 69 |
|
LPG-MVS_test | | | 82.08 79 | 81.27 83 | 84.50 81 | 89.23 111 | 68.76 90 | 90.22 51 | 91.94 68 | 75.37 80 | 76.64 146 | 91.51 70 | 54.29 203 | 94.91 70 | 78.44 68 | 83.78 142 | 89.83 172 |
|
LGP-MVS_train | | | | | 84.50 81 | 89.23 111 | 68.76 90 | | 91.94 68 | 75.37 80 | 76.64 146 | 91.51 70 | 54.29 203 | 94.91 70 | 78.44 68 | 83.78 142 | 89.83 172 |
|
conf200view11 | | | 76.55 199 | 75.55 196 | 79.57 219 | 89.52 95 | 56.99 267 | 85.83 184 | 83.23 247 | 73.94 101 | 76.32 153 | 87.12 171 | 51.89 227 | 91.95 181 | 48.33 288 | 83.75 144 | 89.78 175 |
|
thres100view900 | | | 76.50 202 | 75.55 196 | 79.33 221 | 89.52 95 | 56.99 267 | 85.83 184 | 83.23 247 | 73.94 101 | 76.32 153 | 87.12 171 | 51.89 227 | 91.95 181 | 48.33 288 | 83.75 144 | 89.07 189 |
|
tfpn200view9 | | | 76.42 205 | 75.37 202 | 79.55 220 | 89.13 115 | 57.65 259 | 85.17 204 | 83.60 240 | 73.41 118 | 76.45 148 | 86.39 200 | 52.12 221 | 91.95 181 | 48.33 288 | 83.75 144 | 89.07 189 |
|
thres400 | | | 76.50 202 | 75.37 202 | 79.86 209 | 89.13 115 | 57.65 259 | 85.17 204 | 83.60 240 | 73.41 118 | 76.45 148 | 86.39 200 | 52.12 221 | 91.95 181 | 48.33 288 | 83.75 144 | 90.00 161 |
|
tfpn111 | | | 76.54 200 | 75.51 198 | 79.61 216 | 89.52 95 | 56.99 267 | 85.83 184 | 83.23 247 | 73.94 101 | 76.32 153 | 87.12 171 | 51.89 227 | 92.06 179 | 48.04 295 | 83.73 148 | 89.78 175 |
|
thres600view7 | | | 76.50 202 | 75.44 199 | 79.68 213 | 89.40 101 | 57.16 264 | 85.53 199 | 83.23 247 | 73.79 110 | 76.26 156 | 87.09 174 | 51.89 227 | 91.89 185 | 48.05 294 | 83.72 149 | 90.00 161 |
|
thres200 | | | 75.55 220 | 74.47 216 | 78.82 234 | 87.78 163 | 57.85 256 | 83.07 249 | 83.51 243 | 72.44 142 | 75.84 164 | 84.42 246 | 52.08 223 | 91.75 190 | 47.41 297 | 83.64 150 | 86.86 258 |
|
XVG-OURS | | | 80.41 114 | 79.23 125 | 83.97 102 | 85.64 197 | 69.02 83 | 83.03 250 | 90.39 114 | 71.09 161 | 77.63 129 | 91.49 72 | 54.62 201 | 91.35 207 | 75.71 97 | 83.47 151 | 91.54 103 |
|
view600 | | | 76.20 209 | 75.21 205 | 79.16 226 | 89.64 88 | 55.82 287 | 85.74 189 | 82.06 264 | 73.88 105 | 75.74 166 | 87.85 147 | 51.84 231 | 91.66 198 | 46.75 299 | 83.42 152 | 90.00 161 |
|
view800 | | | 76.20 209 | 75.21 205 | 79.16 226 | 89.64 88 | 55.82 287 | 85.74 189 | 82.06 264 | 73.88 105 | 75.74 166 | 87.85 147 | 51.84 231 | 91.66 198 | 46.75 299 | 83.42 152 | 90.00 161 |
|
conf0.05thres1000 | | | 76.20 209 | 75.21 205 | 79.16 226 | 89.64 88 | 55.82 287 | 85.74 189 | 82.06 264 | 73.88 105 | 75.74 166 | 87.85 147 | 51.84 231 | 91.66 198 | 46.75 299 | 83.42 152 | 90.00 161 |
|
tfpn | | | 76.20 209 | 75.21 205 | 79.16 226 | 89.64 88 | 55.82 287 | 85.74 189 | 82.06 264 | 73.88 105 | 75.74 166 | 87.85 147 | 51.84 231 | 91.66 198 | 46.75 299 | 83.42 152 | 90.00 161 |
|
CNLPA | | | 78.08 165 | 76.79 171 | 81.97 166 | 90.40 75 | 71.07 49 | 87.59 121 | 84.55 232 | 66.03 235 | 72.38 217 | 89.64 105 | 57.56 177 | 86.04 285 | 59.61 227 | 83.35 156 | 88.79 207 |
|
MVP-Stereo | | | 76.12 213 | 74.46 217 | 81.13 191 | 85.37 201 | 69.79 70 | 84.42 224 | 87.95 195 | 65.03 243 | 67.46 277 | 85.33 232 | 53.28 212 | 91.73 191 | 58.01 244 | 83.27 157 | 81.85 316 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
1314 | | | 76.53 201 | 75.30 204 | 80.21 205 | 83.93 236 | 62.32 218 | 84.66 213 | 88.81 177 | 60.23 283 | 70.16 245 | 84.07 249 | 55.30 193 | 90.73 225 | 67.37 167 | 83.21 158 | 87.59 241 |
|
mvs-test1 | | | 80.88 97 | 79.40 116 | 85.29 62 | 85.13 205 | 69.75 72 | 89.28 68 | 88.10 191 | 74.99 86 | 76.44 151 | 86.72 181 | 57.27 179 | 94.26 91 | 73.53 118 | 83.18 159 | 91.87 96 |
|
HyFIR lowres test | | | 77.53 181 | 75.40 201 | 83.94 104 | 89.59 93 | 66.62 129 | 80.36 269 | 88.64 183 | 56.29 313 | 76.45 148 | 85.17 234 | 57.64 176 | 93.28 138 | 61.34 216 | 83.10 160 | 91.91 95 |
|
ACMP | | 74.13 6 | 81.51 92 | 80.57 92 | 84.36 86 | 89.42 100 | 68.69 97 | 89.97 54 | 91.50 88 | 74.46 93 | 75.04 187 | 90.41 91 | 53.82 208 | 94.54 80 | 77.56 77 | 82.91 161 | 89.86 171 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
ACMM | | 73.20 8 | 80.78 107 | 79.84 103 | 83.58 110 | 89.31 108 | 68.37 101 | 89.99 53 | 91.60 82 | 70.28 173 | 77.25 135 | 89.66 104 | 53.37 211 | 93.53 129 | 74.24 111 | 82.85 162 | 88.85 204 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
PMMVS | | | 69.34 277 | 68.67 267 | 71.35 304 | 75.67 324 | 62.03 221 | 75.17 305 | 73.46 329 | 50.00 334 | 68.68 265 | 79.05 300 | 52.07 224 | 78.13 319 | 61.16 217 | 82.77 163 | 73.90 337 |
|
PLC | | 70.83 11 | 78.05 166 | 76.37 177 | 83.08 128 | 91.88 60 | 67.80 112 | 88.19 107 | 89.46 149 | 64.33 250 | 69.87 252 | 88.38 135 | 53.66 209 | 93.58 126 | 58.86 234 | 82.73 164 | 87.86 235 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
TR-MVS | | | 77.44 188 | 76.18 184 | 81.20 188 | 88.24 142 | 63.24 204 | 84.61 217 | 86.40 215 | 67.55 220 | 77.81 125 | 86.48 199 | 54.10 205 | 93.15 145 | 57.75 246 | 82.72 165 | 87.20 250 |
|
pcd1.5k->3k | | | 34.07 328 | 35.26 328 | 30.50 343 | 86.92 181 | 0.00 364 | 0.00 355 | 91.58 83 | 0.00 359 | 0.00 360 | 0.00 361 | 56.23 187 | 0.00 362 | 0.00 359 | 82.60 166 | 91.49 106 |
|
ab-mvs | | | 79.51 137 | 78.97 130 | 81.14 190 | 88.46 136 | 60.91 228 | 83.84 235 | 89.24 157 | 70.36 171 | 79.03 95 | 88.87 122 | 63.23 102 | 90.21 231 | 65.12 185 | 82.57 167 | 92.28 85 |
|
HY-MVS | | 69.67 12 | 77.95 170 | 77.15 165 | 80.36 200 | 87.57 171 | 60.21 234 | 83.37 248 | 87.78 198 | 66.11 232 | 75.37 176 | 87.06 176 | 63.27 100 | 90.48 228 | 61.38 215 | 82.43 168 | 90.40 145 |
|
PS-MVSNAJss | | | 82.07 80 | 81.31 82 | 84.34 88 | 86.51 188 | 67.27 121 | 89.27 69 | 91.51 86 | 71.75 151 | 79.37 92 | 90.22 95 | 63.15 104 | 94.27 88 | 77.69 76 | 82.36 169 | 91.49 106 |
|
PVSNet_BlendedMVS | | | 80.60 110 | 80.02 99 | 82.36 160 | 88.85 120 | 65.40 149 | 86.16 175 | 92.00 64 | 69.34 187 | 78.11 120 | 86.09 210 | 66.02 79 | 94.27 88 | 71.52 141 | 82.06 170 | 87.39 244 |
|
WTY-MVS | | | 75.65 219 | 75.68 194 | 75.57 278 | 86.40 189 | 56.82 271 | 77.92 293 | 82.40 258 | 65.10 242 | 76.18 159 | 87.72 151 | 63.13 107 | 80.90 308 | 60.31 222 | 81.96 171 | 89.00 200 |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 81.95 172 | |
|
DP-MVS | | | 76.78 197 | 74.57 213 | 83.42 114 | 93.29 34 | 69.46 79 | 88.55 94 | 83.70 239 | 63.98 254 | 70.20 242 | 88.89 121 | 54.01 207 | 94.80 76 | 46.66 303 | 81.88 173 | 86.01 278 |
|
CMPMVS | | 51.72 21 | 70.19 272 | 68.16 272 | 76.28 272 | 73.15 333 | 57.55 261 | 79.47 277 | 83.92 237 | 48.02 336 | 56.48 328 | 84.81 242 | 43.13 297 | 86.42 283 | 62.67 202 | 81.81 174 | 84.89 290 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
tfpn_ndepth | | | 73.70 233 | 72.75 230 | 76.52 265 | 87.78 163 | 54.92 295 | 84.32 227 | 80.28 286 | 67.57 219 | 72.50 207 | 84.82 241 | 50.12 258 | 89.44 243 | 45.73 309 | 81.66 175 | 85.20 285 |
|
XVG-OURS-SEG-HR | | | 80.81 102 | 79.76 105 | 83.96 103 | 85.60 198 | 68.78 89 | 83.54 240 | 90.50 112 | 70.66 168 | 76.71 144 | 91.66 64 | 60.69 156 | 91.26 209 | 76.94 85 | 81.58 176 | 91.83 97 |
|
tfpn1000 | | | 73.44 240 | 72.49 233 | 76.29 271 | 87.81 159 | 53.69 303 | 84.05 234 | 78.81 301 | 67.99 215 | 72.09 223 | 86.27 206 | 49.95 260 | 89.04 256 | 44.09 320 | 81.38 177 | 86.15 273 |
|
MIMVSNet | | | 70.69 266 | 69.30 262 | 74.88 284 | 84.52 212 | 56.35 282 | 75.87 303 | 79.42 292 | 64.59 246 | 67.76 273 | 82.41 264 | 41.10 309 | 81.54 307 | 46.64 305 | 81.34 178 | 86.75 261 |
|
ACMMP++ | | | | | | | | | | | | | | | | 81.25 179 | |
|
GA-MVS | | | 76.87 196 | 75.17 209 | 81.97 166 | 82.75 265 | 62.58 215 | 81.44 263 | 86.35 217 | 72.16 149 | 74.74 190 | 82.89 259 | 46.20 282 | 92.02 180 | 68.85 158 | 81.09 180 | 91.30 110 |
|
sss | | | 73.60 237 | 73.64 223 | 73.51 294 | 82.80 264 | 55.01 294 | 76.12 299 | 81.69 270 | 62.47 268 | 74.68 191 | 85.85 220 | 57.32 178 | 78.11 320 | 60.86 219 | 80.93 181 | 87.39 244 |
|
Effi-MVS+-dtu | | | 80.03 128 | 78.57 137 | 84.42 84 | 85.13 205 | 68.74 92 | 88.77 85 | 88.10 191 | 74.99 86 | 74.97 188 | 83.49 256 | 57.27 179 | 93.36 136 | 73.53 118 | 80.88 182 | 91.18 112 |
|
EG-PatchMatch MVS | | | 74.04 230 | 71.82 246 | 80.71 197 | 84.92 208 | 67.42 117 | 85.86 183 | 88.08 193 | 66.04 234 | 64.22 301 | 83.85 250 | 35.10 329 | 92.56 166 | 57.44 248 | 80.83 183 | 82.16 315 |
|
jajsoiax | | | 79.29 144 | 77.96 150 | 83.27 120 | 84.68 211 | 66.57 131 | 89.25 70 | 90.16 128 | 69.20 190 | 75.46 172 | 89.49 108 | 45.75 287 | 93.13 147 | 76.84 87 | 80.80 184 | 90.11 153 |
|
1112_ss | | | 77.40 190 | 76.43 175 | 80.32 202 | 89.11 117 | 60.41 233 | 83.65 237 | 87.72 199 | 62.13 271 | 73.05 202 | 86.72 181 | 62.58 125 | 89.97 233 | 62.11 208 | 80.80 184 | 90.59 134 |
|
mvs_tets | | | 79.13 147 | 77.77 156 | 83.22 122 | 84.70 210 | 66.37 133 | 89.17 71 | 90.19 127 | 69.38 186 | 75.40 175 | 89.46 111 | 44.17 293 | 93.15 145 | 76.78 88 | 80.70 186 | 90.14 151 |
|
PatchMatch-RL | | | 72.38 256 | 70.90 255 | 76.80 264 | 88.60 131 | 67.38 119 | 79.53 276 | 76.17 314 | 62.75 265 | 69.36 258 | 82.00 273 | 45.51 288 | 84.89 293 | 53.62 266 | 80.58 187 | 78.12 327 |
|
EI-MVSNet | | | 80.52 113 | 79.98 100 | 82.12 162 | 84.28 215 | 63.19 207 | 86.41 169 | 88.95 171 | 74.18 97 | 78.69 99 | 87.54 158 | 66.62 72 | 92.43 168 | 72.57 130 | 80.57 188 | 90.74 124 |
|
MVSTER | | | 79.01 149 | 77.88 152 | 82.38 159 | 83.07 257 | 64.80 166 | 84.08 233 | 88.95 171 | 69.01 197 | 78.69 99 | 87.17 170 | 54.70 199 | 92.43 168 | 74.69 107 | 80.57 188 | 89.89 170 |
|
testing_2 | | | 75.73 218 | 73.34 226 | 82.89 142 | 77.37 317 | 65.22 156 | 84.10 232 | 90.54 111 | 69.09 192 | 60.46 313 | 81.15 284 | 40.48 311 | 92.84 160 | 76.36 89 | 80.54 190 | 90.60 132 |
|
XVG-ACMP-BASELINE | | | 76.11 214 | 74.27 219 | 81.62 179 | 83.20 253 | 64.67 168 | 83.60 239 | 89.75 141 | 69.75 180 | 71.85 225 | 87.09 174 | 32.78 330 | 92.11 178 | 69.99 150 | 80.43 191 | 88.09 230 |
|
Fast-Effi-MVS+-dtu | | | 78.02 167 | 76.49 174 | 82.62 156 | 83.16 256 | 66.96 127 | 86.94 151 | 87.45 205 | 72.45 140 | 71.49 230 | 84.17 247 | 54.79 198 | 91.58 204 | 67.61 164 | 80.31 192 | 89.30 187 |
|
LTVRE_ROB | | 69.57 13 | 76.25 208 | 74.54 215 | 81.41 184 | 88.60 131 | 64.38 183 | 79.24 279 | 89.12 160 | 70.76 166 | 69.79 254 | 87.86 146 | 49.09 267 | 93.20 142 | 56.21 256 | 80.16 193 | 86.65 263 |
Andreas Kuhn, Heiko Hirschmüller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016 |
Test_1112_low_res | | | 76.40 206 | 75.44 199 | 79.27 222 | 89.28 109 | 58.09 249 | 81.69 259 | 87.07 208 | 59.53 290 | 72.48 209 | 86.67 187 | 61.30 145 | 89.33 244 | 60.81 220 | 80.15 194 | 90.41 144 |
|
test_djsdf | | | 80.30 120 | 79.32 119 | 83.27 120 | 83.98 235 | 65.37 152 | 90.50 43 | 90.38 115 | 68.55 203 | 76.19 158 | 88.70 124 | 56.44 186 | 93.46 131 | 78.98 64 | 80.14 195 | 90.97 117 |
|
CHOSEN 280x420 | | | 66.51 292 | 64.71 291 | 71.90 299 | 81.45 282 | 63.52 196 | 57.98 345 | 68.95 343 | 53.57 324 | 62.59 309 | 76.70 315 | 46.22 281 | 75.29 332 | 55.25 259 | 79.68 196 | 76.88 335 |
|
conf0.01 | | | 73.67 235 | 72.42 235 | 77.42 256 | 87.85 151 | 53.28 306 | 83.38 241 | 79.08 294 | 68.40 206 | 72.45 210 | 86.08 211 | 50.60 249 | 89.19 247 | 44.25 314 | 79.66 197 | 89.78 175 |
|
conf0.002 | | | 73.67 235 | 72.42 235 | 77.42 256 | 87.85 151 | 53.28 306 | 83.38 241 | 79.08 294 | 68.40 206 | 72.45 210 | 86.08 211 | 50.60 249 | 89.19 247 | 44.25 314 | 79.66 197 | 89.78 175 |
|
thresconf0.02 | | | 73.39 241 | 72.42 235 | 76.31 267 | 87.85 151 | 53.28 306 | 83.38 241 | 79.08 294 | 68.40 206 | 72.45 210 | 86.08 211 | 50.60 249 | 89.19 247 | 44.25 314 | 79.66 197 | 86.48 265 |
|
tfpn_n400 | | | 73.39 241 | 72.42 235 | 76.31 267 | 87.85 151 | 53.28 306 | 83.38 241 | 79.08 294 | 68.40 206 | 72.45 210 | 86.08 211 | 50.60 249 | 89.19 247 | 44.25 314 | 79.66 197 | 86.48 265 |
|
tfpnconf | | | 73.39 241 | 72.42 235 | 76.31 267 | 87.85 151 | 53.28 306 | 83.38 241 | 79.08 294 | 68.40 206 | 72.45 210 | 86.08 211 | 50.60 249 | 89.19 247 | 44.25 314 | 79.66 197 | 86.48 265 |
|
tfpnview11 | | | 73.39 241 | 72.42 235 | 76.31 267 | 87.85 151 | 53.28 306 | 83.38 241 | 79.08 294 | 68.40 206 | 72.45 210 | 86.08 211 | 50.60 249 | 89.19 247 | 44.25 314 | 79.66 197 | 86.48 265 |
|
GBi-Net | | | 78.40 157 | 77.40 161 | 81.40 185 | 87.60 167 | 63.01 209 | 88.39 99 | 89.28 153 | 71.63 153 | 75.34 177 | 87.28 163 | 54.80 195 | 91.11 214 | 62.72 199 | 79.57 203 | 90.09 155 |
|
test1 | | | 78.40 157 | 77.40 161 | 81.40 185 | 87.60 167 | 63.01 209 | 88.39 99 | 89.28 153 | 71.63 153 | 75.34 177 | 87.28 163 | 54.80 195 | 91.11 214 | 62.72 199 | 79.57 203 | 90.09 155 |
|
FMVSNet3 | | | 77.88 173 | 76.85 169 | 80.97 193 | 86.84 183 | 62.36 216 | 86.52 166 | 88.77 178 | 71.13 159 | 75.34 177 | 86.66 188 | 54.07 206 | 91.10 217 | 62.72 199 | 79.57 203 | 89.45 185 |
|
FMVSNet2 | | | 78.20 162 | 77.21 164 | 81.20 188 | 87.60 167 | 62.89 213 | 87.47 130 | 89.02 162 | 71.63 153 | 75.29 181 | 87.28 163 | 54.80 195 | 91.10 217 | 62.38 203 | 79.38 206 | 89.61 183 |
|
anonymousdsp | | | 78.60 155 | 77.15 165 | 82.98 134 | 80.51 295 | 67.08 123 | 87.24 139 | 89.53 146 | 65.66 238 | 75.16 183 | 87.19 169 | 52.52 213 | 92.25 175 | 77.17 82 | 79.34 207 | 89.61 183 |
|
nrg030 | | | 83.88 56 | 83.53 56 | 84.96 71 | 86.77 185 | 69.28 81 | 90.46 45 | 92.67 41 | 74.79 89 | 82.95 59 | 91.33 75 | 72.70 29 | 93.09 149 | 80.79 55 | 79.28 208 | 92.50 78 |
|
VPA-MVSNet | | | 80.60 110 | 80.55 93 | 80.76 196 | 88.07 145 | 60.80 230 | 86.86 154 | 91.58 83 | 75.67 73 | 80.24 87 | 89.45 113 | 63.34 98 | 90.25 230 | 70.51 146 | 79.22 209 | 91.23 111 |
|
F-COLMAP | | | 76.38 207 | 74.33 218 | 82.50 157 | 89.28 109 | 66.95 128 | 88.41 98 | 89.03 161 | 64.05 252 | 66.83 283 | 88.61 128 | 46.78 278 | 92.89 156 | 57.48 247 | 78.55 210 | 87.67 238 |
|
FMVSNet1 | | | 77.44 188 | 76.12 185 | 81.40 185 | 86.81 184 | 63.01 209 | 88.39 99 | 89.28 153 | 70.49 170 | 74.39 193 | 87.28 163 | 49.06 268 | 91.11 214 | 60.91 218 | 78.52 211 | 90.09 155 |
|
MDTV_nov1_ep13 | | | | 69.97 261 | | 83.18 254 | 53.48 304 | 77.10 297 | 80.18 288 | 60.45 280 | 69.33 259 | 80.44 290 | 48.89 269 | 86.90 278 | 51.60 273 | 78.51 212 | |
|
CVMVSNet | | | 72.99 252 | 72.58 232 | 74.25 290 | 84.28 215 | 50.85 322 | 86.41 169 | 83.45 245 | 44.56 338 | 73.23 200 | 87.54 158 | 49.38 263 | 85.70 287 | 65.90 180 | 78.44 213 | 86.19 272 |
|
tpm2 | | | 73.26 248 | 71.46 248 | 78.63 236 | 83.34 249 | 56.71 274 | 80.65 267 | 80.40 283 | 56.63 311 | 73.55 196 | 82.02 272 | 51.80 235 | 91.24 210 | 56.35 255 | 78.42 214 | 87.95 232 |
|
CostFormer | | | 75.24 224 | 73.90 222 | 79.27 222 | 82.65 269 | 58.27 248 | 80.80 264 | 82.73 256 | 61.57 274 | 75.33 180 | 83.13 258 | 55.52 191 | 91.07 220 | 64.98 188 | 78.34 215 | 88.45 224 |
|
ACMH | | 67.68 16 | 75.89 216 | 73.93 221 | 81.77 170 | 88.71 129 | 66.61 130 | 88.62 91 | 89.01 164 | 69.81 178 | 66.78 284 | 86.70 186 | 41.95 307 | 91.51 205 | 55.64 257 | 78.14 216 | 87.17 251 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
CR-MVSNet | | | 73.37 245 | 71.27 251 | 79.67 214 | 81.32 287 | 65.19 157 | 75.92 301 | 80.30 284 | 59.92 286 | 72.73 205 | 81.19 282 | 52.50 214 | 86.69 279 | 59.84 225 | 77.71 217 | 87.11 254 |
|
RPMNet | | | 71.62 259 | 68.94 266 | 79.67 214 | 81.32 287 | 65.19 157 | 75.92 301 | 78.30 304 | 57.60 305 | 72.73 205 | 76.45 317 | 52.30 218 | 86.69 279 | 48.14 293 | 77.71 217 | 87.11 254 |
|
Patchmatch-test1 | | | 73.49 238 | 71.85 245 | 78.41 242 | 84.05 234 | 62.17 220 | 79.96 273 | 79.29 293 | 66.30 231 | 72.38 217 | 79.58 298 | 51.95 226 | 85.08 292 | 55.46 258 | 77.67 219 | 87.99 231 |
|
tpmp4_e23 | | | 73.45 239 | 71.17 253 | 80.31 203 | 83.55 245 | 59.56 238 | 81.88 255 | 82.33 259 | 57.94 302 | 70.51 239 | 81.62 280 | 51.19 241 | 91.63 202 | 53.96 264 | 77.51 220 | 89.75 180 |
|
v7 | | | 80.24 121 | 79.26 124 | 83.15 124 | 84.07 232 | 64.94 163 | 87.56 126 | 90.67 105 | 72.26 145 | 78.28 112 | 86.51 198 | 61.45 142 | 94.03 100 | 75.14 105 | 77.41 221 | 90.49 140 |
|
DWT-MVSNet_test | | | 73.70 233 | 71.86 244 | 79.21 224 | 82.91 262 | 58.94 241 | 82.34 252 | 82.17 261 | 65.21 240 | 71.05 235 | 78.31 304 | 44.21 292 | 90.17 232 | 63.29 197 | 77.28 222 | 88.53 223 |
|
v1144 | | | 80.03 128 | 79.03 128 | 83.01 132 | 83.78 241 | 64.51 174 | 87.11 146 | 90.57 110 | 71.96 150 | 78.08 122 | 86.20 208 | 61.41 143 | 93.94 103 | 74.93 106 | 77.23 223 | 90.60 132 |
|
WR-MVS | | | 79.49 139 | 79.22 126 | 80.27 204 | 88.79 126 | 58.35 246 | 85.06 207 | 88.61 184 | 78.56 29 | 77.65 128 | 88.34 136 | 63.81 96 | 90.66 226 | 64.98 188 | 77.22 224 | 91.80 100 |
|
v1192 | | | 79.59 136 | 78.43 142 | 83.07 129 | 83.55 245 | 64.52 172 | 86.93 152 | 90.58 109 | 70.83 163 | 77.78 126 | 85.90 217 | 59.15 167 | 93.94 103 | 73.96 113 | 77.19 225 | 90.76 122 |
|
VPNet | | | 78.69 154 | 78.66 133 | 78.76 235 | 88.31 141 | 55.72 292 | 84.45 222 | 86.63 212 | 76.79 51 | 78.26 116 | 90.55 90 | 59.30 166 | 89.70 238 | 66.63 174 | 77.05 226 | 90.88 119 |
|
v1neww | | | 80.40 115 | 79.54 110 | 82.98 134 | 84.10 226 | 64.51 174 | 87.57 122 | 90.22 124 | 73.25 121 | 78.47 105 | 86.65 189 | 62.83 112 | 93.86 109 | 75.72 95 | 77.02 227 | 90.58 135 |
|
v7new | | | 80.40 115 | 79.54 110 | 82.98 134 | 84.10 226 | 64.51 174 | 87.57 122 | 90.22 124 | 73.25 121 | 78.47 105 | 86.65 189 | 62.83 112 | 93.86 109 | 75.72 95 | 77.02 227 | 90.58 135 |
|
v6 | | | 80.40 115 | 79.54 110 | 82.98 134 | 84.09 228 | 64.50 178 | 87.57 122 | 90.22 124 | 73.25 121 | 78.47 105 | 86.63 191 | 62.84 111 | 93.86 109 | 75.73 94 | 77.02 227 | 90.58 135 |
|
v1240 | | | 78.99 150 | 77.78 155 | 82.64 155 | 83.21 252 | 63.54 195 | 86.62 163 | 90.30 122 | 69.74 182 | 77.33 133 | 85.68 222 | 57.04 184 | 93.76 118 | 73.13 123 | 76.92 230 | 90.62 130 |
|
Anonymous20240521 | | | 76.96 194 | 76.26 183 | 79.07 230 | 86.63 186 | 56.37 281 | 87.57 122 | 91.09 98 | 72.19 146 | 71.23 231 | 88.10 144 | 54.30 202 | 91.20 212 | 58.34 240 | 76.89 231 | 89.65 182 |
|
MSDG | | | 73.36 247 | 70.99 254 | 80.49 198 | 84.51 213 | 65.80 142 | 80.71 266 | 86.13 220 | 65.70 237 | 65.46 292 | 83.74 253 | 44.60 290 | 90.91 222 | 51.13 275 | 76.89 231 | 84.74 292 |
|
IterMVS-LS | | | 80.06 127 | 79.38 117 | 82.11 163 | 85.89 193 | 63.20 206 | 86.79 157 | 89.34 151 | 74.19 96 | 75.45 173 | 86.72 181 | 66.62 72 | 92.39 170 | 72.58 129 | 76.86 233 | 90.75 123 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
v1921920 | | | 79.22 145 | 78.03 149 | 82.80 148 | 83.30 251 | 63.94 192 | 86.80 156 | 90.33 120 | 69.91 177 | 77.48 131 | 85.53 228 | 58.44 171 | 93.75 119 | 73.60 117 | 76.85 234 | 90.71 125 |
|
XXY-MVS | | | 75.41 222 | 75.56 195 | 74.96 283 | 83.59 244 | 57.82 257 | 80.59 268 | 83.87 238 | 66.54 229 | 74.93 189 | 88.31 137 | 63.24 101 | 80.09 312 | 62.16 206 | 76.85 234 | 86.97 256 |
|
v1141 | | | 80.19 124 | 79.31 120 | 82.85 143 | 83.84 238 | 64.12 187 | 87.14 141 | 90.08 131 | 73.13 124 | 78.27 113 | 86.39 200 | 62.67 121 | 93.75 119 | 75.40 102 | 76.83 236 | 90.68 126 |
|
divwei89l23v2f112 | | | 80.19 124 | 79.31 120 | 82.85 143 | 83.84 238 | 64.11 189 | 87.13 144 | 90.08 131 | 73.13 124 | 78.27 113 | 86.39 200 | 62.69 119 | 93.75 119 | 75.40 102 | 76.82 237 | 90.68 126 |
|
v1 | | | 80.19 124 | 79.31 120 | 82.85 143 | 83.83 240 | 64.12 187 | 87.14 141 | 90.07 133 | 73.13 124 | 78.27 113 | 86.38 204 | 62.72 118 | 93.75 119 | 75.41 101 | 76.82 237 | 90.68 126 |
|
v2v482 | | | 80.23 122 | 79.29 123 | 83.05 130 | 83.62 243 | 64.14 185 | 87.04 148 | 89.97 135 | 73.61 112 | 78.18 119 | 87.22 167 | 61.10 150 | 93.82 112 | 76.11 90 | 76.78 239 | 91.18 112 |
|
v144192 | | | 79.47 140 | 78.37 143 | 82.78 151 | 83.35 248 | 63.96 191 | 86.96 150 | 90.36 118 | 69.99 176 | 77.50 130 | 85.67 223 | 60.66 157 | 93.77 117 | 74.27 110 | 76.58 240 | 90.62 130 |
|
UniMVSNet (Re) | | | 81.60 89 | 81.11 86 | 83.09 127 | 88.38 139 | 64.41 182 | 87.60 120 | 93.02 26 | 78.42 31 | 78.56 102 | 88.16 140 | 69.78 50 | 93.26 139 | 69.58 153 | 76.49 241 | 91.60 101 |
|
UniMVSNet_NR-MVSNet | | | 81.88 83 | 81.54 81 | 82.92 139 | 88.46 136 | 63.46 198 | 87.13 144 | 92.37 50 | 80.19 15 | 78.38 109 | 89.14 117 | 71.66 36 | 93.05 151 | 70.05 148 | 76.46 242 | 92.25 86 |
|
DU-MVS | | | 81.12 95 | 80.52 94 | 82.90 140 | 87.80 160 | 63.46 198 | 87.02 149 | 91.87 72 | 79.01 26 | 78.38 109 | 89.07 118 | 65.02 86 | 93.05 151 | 70.05 148 | 76.46 242 | 92.20 88 |
|
PatchmatchNet | | | 73.12 250 | 71.33 250 | 78.49 241 | 83.18 254 | 60.85 229 | 79.63 275 | 78.57 302 | 64.13 251 | 71.73 226 | 79.81 297 | 51.20 240 | 85.97 286 | 57.40 249 | 76.36 244 | 88.66 210 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
USDC | | | 70.33 270 | 68.37 269 | 76.21 273 | 80.60 293 | 56.23 283 | 79.19 281 | 86.49 213 | 60.89 278 | 61.29 310 | 85.47 230 | 31.78 333 | 89.47 242 | 53.37 267 | 76.21 245 | 82.94 312 |
|
OpenMVS_ROB | | 64.09 19 | 70.56 268 | 68.19 271 | 77.65 252 | 80.26 296 | 59.41 240 | 85.01 208 | 82.96 254 | 58.76 296 | 65.43 293 | 82.33 265 | 37.63 323 | 91.23 211 | 45.34 312 | 76.03 246 | 82.32 313 |
|
ACMH+ | | 68.96 14 | 76.01 215 | 74.01 220 | 82.03 165 | 88.60 131 | 65.31 154 | 88.86 81 | 87.55 201 | 70.25 174 | 67.75 274 | 87.47 160 | 41.27 308 | 93.19 143 | 58.37 239 | 75.94 247 | 87.60 240 |
|
PatchFormer-LS_test | | | 74.50 226 | 73.05 228 | 78.86 233 | 82.95 261 | 59.55 239 | 81.65 260 | 82.30 260 | 67.44 222 | 71.62 228 | 78.15 307 | 52.34 217 | 88.92 262 | 65.05 187 | 75.90 248 | 88.12 229 |
|
tpm | | | 72.37 257 | 71.71 247 | 74.35 289 | 82.19 274 | 52.00 313 | 79.22 280 | 77.29 310 | 64.56 247 | 72.95 203 | 83.68 255 | 51.35 238 | 83.26 302 | 58.33 241 | 75.80 249 | 87.81 236 |
|
Anonymous20231206 | | | 68.60 280 | 67.80 279 | 71.02 306 | 80.23 298 | 50.75 323 | 78.30 290 | 80.47 281 | 56.79 310 | 66.11 290 | 82.63 263 | 46.35 280 | 78.95 316 | 43.62 322 | 75.70 250 | 83.36 304 |
|
v7n | | | 78.97 151 | 77.58 159 | 83.14 125 | 83.45 247 | 65.51 147 | 88.32 102 | 91.21 94 | 73.69 111 | 72.41 216 | 86.32 205 | 57.93 174 | 93.81 113 | 69.18 156 | 75.65 251 | 90.11 153 |
|
NR-MVSNet | | | 80.23 122 | 79.38 117 | 82.78 151 | 87.80 160 | 63.34 201 | 86.31 172 | 91.09 98 | 79.01 26 | 72.17 219 | 89.07 118 | 67.20 69 | 92.81 161 | 66.08 179 | 75.65 251 | 92.20 88 |
|
v10 | | | 79.74 134 | 78.67 132 | 82.97 138 | 84.06 233 | 64.95 162 | 87.88 117 | 90.62 108 | 73.11 127 | 75.11 185 | 86.56 195 | 61.46 141 | 94.05 99 | 73.68 114 | 75.55 253 | 89.90 169 |
|
IB-MVS | | 68.01 15 | 75.85 217 | 73.36 225 | 83.31 118 | 84.76 209 | 66.03 136 | 83.38 241 | 85.06 228 | 70.21 175 | 69.40 256 | 81.05 285 | 45.76 286 | 94.66 79 | 65.10 186 | 75.49 254 | 89.25 188 |
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 |
V42 | | | 79.38 143 | 78.24 147 | 82.83 146 | 81.10 289 | 65.50 148 | 85.55 197 | 89.82 139 | 71.57 156 | 78.21 117 | 86.12 209 | 60.66 157 | 93.18 144 | 75.64 98 | 75.46 255 | 89.81 174 |
|
v8 | | | 79.97 130 | 79.02 129 | 82.80 148 | 84.09 228 | 64.50 178 | 87.96 112 | 90.29 123 | 74.13 99 | 75.24 182 | 86.81 178 | 62.88 109 | 93.89 108 | 74.39 109 | 75.40 256 | 90.00 161 |
|
Baseline_NR-MVSNet | | | 78.15 164 | 78.33 145 | 77.61 253 | 85.79 194 | 56.21 284 | 86.78 158 | 85.76 223 | 73.60 113 | 77.93 124 | 87.57 156 | 65.02 86 | 88.99 257 | 67.14 171 | 75.33 257 | 87.63 239 |
|
pmmvs5 | | | 71.55 260 | 70.20 260 | 75.61 277 | 77.83 314 | 56.39 279 | 81.74 258 | 80.89 275 | 57.76 303 | 67.46 277 | 84.49 245 | 49.26 266 | 85.32 291 | 57.08 252 | 75.29 258 | 85.11 289 |
|
EPMVS | | | 69.02 279 | 68.16 272 | 71.59 300 | 79.61 305 | 49.80 328 | 77.40 295 | 66.93 345 | 62.82 264 | 70.01 247 | 79.05 300 | 45.79 285 | 77.86 322 | 56.58 254 | 75.26 259 | 87.13 253 |
|
TranMVSNet+NR-MVSNet | | | 80.84 99 | 80.31 96 | 82.42 158 | 87.85 151 | 62.33 217 | 87.74 118 | 91.33 91 | 80.55 12 | 77.99 123 | 89.86 101 | 65.23 84 | 92.62 163 | 67.05 172 | 75.24 260 | 92.30 84 |
|
tfpnnormal | | | 74.39 227 | 73.16 227 | 78.08 246 | 86.10 192 | 58.05 250 | 84.65 216 | 87.53 202 | 70.32 172 | 71.22 233 | 85.63 225 | 54.97 194 | 89.86 234 | 43.03 323 | 75.02 261 | 86.32 270 |
|
COLMAP_ROB | | 66.92 17 | 73.01 251 | 70.41 258 | 80.81 195 | 87.13 179 | 65.63 144 | 88.30 103 | 84.19 236 | 62.96 261 | 63.80 304 | 87.69 153 | 38.04 320 | 92.56 166 | 46.66 303 | 74.91 262 | 84.24 296 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
PatchT | | | 68.46 283 | 67.85 277 | 70.29 308 | 80.70 292 | 43.93 336 | 72.47 314 | 74.88 320 | 60.15 284 | 70.55 237 | 76.57 316 | 49.94 261 | 81.59 306 | 50.58 276 | 74.83 263 | 85.34 284 |
|
pmmvs4 | | | 74.03 231 | 71.91 243 | 80.39 199 | 81.96 276 | 68.32 102 | 81.45 262 | 82.14 262 | 59.32 291 | 69.87 252 | 85.13 235 | 52.40 216 | 88.13 270 | 60.21 223 | 74.74 264 | 84.73 293 |
|
ITE_SJBPF | | | | | 78.22 244 | 81.77 278 | 60.57 231 | | 83.30 246 | 69.25 189 | 67.54 276 | 87.20 168 | 36.33 326 | 87.28 277 | 54.34 262 | 74.62 265 | 86.80 259 |
|
test0.0.03 1 | | | 68.00 284 | 67.69 281 | 68.90 313 | 77.55 315 | 47.43 330 | 75.70 304 | 72.95 331 | 66.66 225 | 66.56 285 | 82.29 266 | 48.06 271 | 75.87 329 | 44.97 313 | 74.51 266 | 83.41 303 |
|
test_0402 | | | 72.79 254 | 70.44 257 | 79.84 210 | 88.13 144 | 65.99 138 | 85.93 181 | 84.29 234 | 65.57 239 | 67.40 279 | 85.49 229 | 46.92 277 | 92.61 164 | 35.88 334 | 74.38 267 | 80.94 319 |
|
CP-MVSNet | | | 78.22 160 | 78.34 144 | 77.84 249 | 87.83 158 | 54.54 297 | 87.94 114 | 91.17 96 | 77.65 33 | 73.48 197 | 88.49 132 | 62.24 133 | 88.43 266 | 62.19 205 | 74.07 268 | 90.55 138 |
|
FMVSNet5 | | | 69.50 276 | 67.96 275 | 74.15 291 | 82.97 260 | 55.35 293 | 80.01 272 | 82.12 263 | 62.56 267 | 63.02 305 | 81.53 281 | 36.92 324 | 81.92 305 | 48.42 287 | 74.06 269 | 85.17 288 |
|
MVS-HIRNet | | | 59.14 307 | 57.67 309 | 63.57 323 | 81.65 279 | 43.50 337 | 71.73 315 | 65.06 348 | 39.59 343 | 51.43 337 | 57.73 343 | 38.34 319 | 82.58 304 | 39.53 329 | 73.95 270 | 64.62 344 |
|
tpmrst | | | 72.39 255 | 72.13 242 | 73.18 296 | 80.54 294 | 49.91 326 | 79.91 274 | 79.08 294 | 63.11 258 | 71.69 227 | 79.95 294 | 55.32 192 | 82.77 303 | 65.66 183 | 73.89 271 | 86.87 257 |
|
PS-CasMVS | | | 78.01 168 | 78.09 148 | 77.77 251 | 87.71 165 | 54.39 299 | 88.02 110 | 91.22 93 | 77.50 40 | 73.26 199 | 88.64 127 | 60.73 154 | 88.41 267 | 61.88 209 | 73.88 272 | 90.53 139 |
|
v148 | | | 78.72 153 | 77.80 154 | 81.47 183 | 82.73 266 | 61.96 222 | 86.30 173 | 88.08 193 | 73.26 120 | 76.18 159 | 85.47 230 | 62.46 129 | 92.36 172 | 71.92 139 | 73.82 273 | 90.09 155 |
|
Patchmatch-test | | | 64.82 298 | 63.24 296 | 69.57 310 | 79.42 307 | 49.82 327 | 63.49 340 | 69.05 342 | 51.98 330 | 59.95 316 | 80.13 293 | 50.91 243 | 70.98 343 | 40.66 328 | 73.57 274 | 87.90 234 |
|
WR-MVS_H | | | 78.51 156 | 78.49 138 | 78.56 238 | 88.02 147 | 56.38 280 | 88.43 95 | 92.67 41 | 77.14 43 | 73.89 195 | 87.55 157 | 66.25 75 | 89.24 246 | 58.92 233 | 73.55 275 | 90.06 159 |
|
testgi | | | 66.67 291 | 66.53 287 | 67.08 318 | 75.62 325 | 41.69 341 | 75.93 300 | 76.50 313 | 66.11 232 | 65.20 297 | 86.59 193 | 35.72 328 | 74.71 333 | 43.71 321 | 73.38 276 | 84.84 291 |
|
pm-mvs1 | | | 77.25 191 | 76.68 172 | 78.93 232 | 84.22 218 | 58.62 244 | 86.41 169 | 88.36 187 | 71.37 158 | 73.31 198 | 88.01 145 | 61.22 148 | 89.15 254 | 64.24 192 | 73.01 277 | 89.03 197 |
|
V4 | | | 77.95 170 | 76.37 177 | 82.67 153 | 79.40 308 | 65.52 145 | 86.43 167 | 89.94 136 | 72.28 143 | 72.14 222 | 84.95 239 | 55.72 189 | 93.44 133 | 73.64 115 | 72.86 278 | 89.05 195 |
|
v52 | | | 77.94 172 | 76.37 177 | 82.67 153 | 79.39 309 | 65.52 145 | 86.43 167 | 89.94 136 | 72.28 143 | 72.15 221 | 84.94 240 | 55.70 190 | 93.44 133 | 73.64 115 | 72.84 279 | 89.06 191 |
|
v748 | | | 77.97 169 | 76.65 173 | 81.92 168 | 82.29 273 | 63.28 203 | 87.53 127 | 90.35 119 | 73.50 117 | 70.76 236 | 85.55 227 | 58.28 172 | 92.81 161 | 68.81 159 | 72.76 280 | 89.67 181 |
|
tpmvs | | | 71.09 263 | 69.29 263 | 76.49 266 | 82.04 275 | 56.04 285 | 78.92 284 | 81.37 274 | 64.05 252 | 67.18 281 | 78.28 305 | 49.74 262 | 89.77 235 | 49.67 283 | 72.37 281 | 83.67 301 |
|
PEN-MVS | | | 77.73 175 | 77.69 158 | 77.84 249 | 87.07 180 | 53.91 301 | 87.91 116 | 91.18 95 | 77.56 37 | 73.14 201 | 88.82 123 | 61.23 147 | 89.17 253 | 59.95 224 | 72.37 281 | 90.43 143 |
|
DSMNet-mixed | | | 57.77 311 | 56.90 311 | 60.38 327 | 67.70 343 | 35.61 347 | 69.18 325 | 53.97 352 | 32.30 349 | 57.49 324 | 79.88 295 | 40.39 313 | 68.57 347 | 38.78 330 | 72.37 281 | 76.97 332 |
|
semantic-postprocess | | | | | 80.11 206 | 82.69 268 | 64.85 165 | | 83.47 244 | 69.16 191 | 70.49 240 | 84.15 248 | 50.83 247 | 88.15 269 | 69.23 155 | 72.14 284 | 87.34 246 |
|
tpm cat1 | | | 70.57 267 | 68.31 270 | 77.35 259 | 82.41 272 | 57.95 254 | 78.08 291 | 80.22 287 | 52.04 329 | 68.54 269 | 77.66 312 | 52.00 225 | 87.84 273 | 51.77 271 | 72.07 285 | 86.25 271 |
|
RPSCF | | | 73.23 249 | 71.46 248 | 78.54 239 | 82.50 271 | 59.85 235 | 82.18 254 | 82.84 255 | 58.96 294 | 71.15 234 | 89.41 115 | 45.48 289 | 84.77 294 | 58.82 235 | 71.83 286 | 91.02 116 |
|
IterMVS | | | 74.29 228 | 72.94 229 | 78.35 243 | 81.53 281 | 63.49 197 | 81.58 261 | 82.49 257 | 68.06 214 | 69.99 249 | 83.69 254 | 51.66 237 | 85.54 288 | 65.85 181 | 71.64 287 | 86.01 278 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
AllTest | | | 70.96 264 | 68.09 274 | 79.58 217 | 85.15 203 | 63.62 193 | 84.58 218 | 79.83 289 | 62.31 269 | 60.32 314 | 86.73 179 | 32.02 331 | 88.96 260 | 50.28 278 | 71.57 288 | 86.15 273 |
|
TestCases | | | | | 79.58 217 | 85.15 203 | 63.62 193 | | 79.83 289 | 62.31 269 | 60.32 314 | 86.73 179 | 32.02 331 | 88.96 260 | 50.28 278 | 71.57 288 | 86.15 273 |
|
Patchmtry | | | 70.74 265 | 69.16 264 | 75.49 280 | 80.72 291 | 54.07 300 | 74.94 310 | 80.30 284 | 58.34 298 | 70.01 247 | 81.19 282 | 52.50 214 | 86.54 281 | 53.37 267 | 71.09 290 | 85.87 281 |
|
test2356 | | | 59.50 306 | 58.08 306 | 63.74 322 | 71.23 337 | 41.88 339 | 67.59 332 | 72.42 333 | 53.72 323 | 57.65 323 | 70.74 331 | 26.31 337 | 72.40 340 | 32.03 341 | 71.06 291 | 76.93 333 |
|
DTE-MVSNet | | | 76.99 193 | 76.80 170 | 77.54 255 | 86.24 190 | 53.06 312 | 87.52 128 | 90.66 107 | 77.08 46 | 72.50 207 | 88.67 126 | 60.48 160 | 89.52 240 | 57.33 250 | 70.74 292 | 90.05 160 |
|
MIMVSNet1 | | | 68.58 281 | 66.78 286 | 73.98 292 | 80.07 299 | 51.82 314 | 80.77 265 | 84.37 233 | 64.40 249 | 59.75 317 | 82.16 268 | 36.47 325 | 83.63 299 | 42.73 324 | 70.33 293 | 86.48 265 |
|
pmmvs6 | | | 74.69 225 | 73.39 224 | 78.61 237 | 81.38 284 | 57.48 262 | 86.64 162 | 87.95 195 | 64.99 244 | 70.18 243 | 86.61 192 | 50.43 256 | 89.52 240 | 62.12 207 | 70.18 294 | 88.83 205 |
|
TinyColmap | | | 67.30 288 | 64.81 290 | 74.76 286 | 81.92 277 | 56.68 275 | 80.29 270 | 81.49 273 | 60.33 281 | 56.27 329 | 83.22 257 | 24.77 340 | 87.66 275 | 45.52 310 | 69.47 295 | 79.95 323 |
|
OurMVSNet-221017-0 | | | 74.26 229 | 72.42 235 | 79.80 211 | 83.76 242 | 59.59 236 | 85.92 182 | 86.64 211 | 66.39 230 | 66.96 282 | 87.58 155 | 39.46 314 | 91.60 203 | 65.76 182 | 69.27 296 | 88.22 227 |
|
JIA-IIPM | | | 66.32 294 | 62.82 300 | 76.82 263 | 77.09 320 | 61.72 224 | 65.34 337 | 75.38 316 | 58.04 301 | 64.51 299 | 62.32 340 | 42.05 306 | 86.51 282 | 51.45 274 | 69.22 297 | 82.21 314 |
|
ADS-MVSNet2 | | | 66.20 295 | 63.33 295 | 74.82 285 | 79.92 300 | 58.75 243 | 67.55 333 | 75.19 318 | 53.37 325 | 65.25 295 | 75.86 318 | 42.32 303 | 80.53 310 | 41.57 326 | 68.91 298 | 85.18 286 |
|
ADS-MVSNet | | | 64.36 300 | 62.88 299 | 68.78 315 | 79.92 300 | 47.17 331 | 67.55 333 | 71.18 334 | 53.37 325 | 65.25 295 | 75.86 318 | 42.32 303 | 73.99 337 | 41.57 326 | 68.91 298 | 85.18 286 |
|
test20.03 | | | 67.45 286 | 66.95 285 | 68.94 312 | 75.48 327 | 44.84 334 | 77.50 294 | 77.67 307 | 66.66 225 | 63.01 306 | 83.80 251 | 47.02 276 | 78.40 318 | 42.53 325 | 68.86 300 | 83.58 302 |
|
testus | | | 59.00 308 | 57.91 307 | 62.25 325 | 72.25 335 | 39.09 344 | 69.74 321 | 75.02 319 | 53.04 327 | 57.21 325 | 73.72 325 | 18.76 348 | 70.33 344 | 32.86 337 | 68.57 301 | 77.35 330 |
|
EU-MVSNet | | | 68.53 282 | 67.61 282 | 71.31 305 | 78.51 313 | 47.01 332 | 84.47 219 | 84.27 235 | 42.27 339 | 66.44 288 | 84.79 243 | 40.44 312 | 83.76 297 | 58.76 236 | 68.54 302 | 83.17 305 |
|
our_test_3 | | | 69.14 278 | 67.00 284 | 75.57 278 | 79.80 302 | 58.80 242 | 77.96 292 | 77.81 306 | 59.55 289 | 62.90 308 | 78.25 306 | 47.43 273 | 83.97 296 | 51.71 272 | 67.58 303 | 83.93 300 |
|
v16 | | | 77.69 176 | 76.36 180 | 81.68 176 | 84.15 223 | 64.63 171 | 87.33 134 | 88.99 166 | 72.69 138 | 69.31 260 | 82.08 269 | 62.80 115 | 91.79 187 | 72.70 127 | 67.23 304 | 88.63 212 |
|
v18 | | | 77.67 179 | 76.35 181 | 81.64 178 | 84.09 228 | 64.47 180 | 87.27 137 | 89.01 164 | 72.59 139 | 69.39 257 | 82.04 271 | 62.85 110 | 91.80 186 | 72.72 126 | 67.20 305 | 88.63 212 |
|
v17 | | | 77.68 177 | 76.35 181 | 81.69 175 | 84.15 223 | 64.65 169 | 87.33 134 | 88.99 166 | 72.70 137 | 69.25 261 | 82.07 270 | 62.82 114 | 91.79 187 | 72.69 128 | 67.15 306 | 88.63 212 |
|
v11 | | | 77.45 187 | 76.06 191 | 81.59 181 | 84.22 218 | 64.52 172 | 87.11 146 | 89.02 162 | 72.76 135 | 68.76 264 | 81.90 279 | 62.09 135 | 91.71 195 | 71.98 137 | 66.73 307 | 88.56 221 |
|
ppachtmachnet_test | | | 70.04 273 | 67.34 283 | 78.14 245 | 79.80 302 | 61.13 225 | 79.19 281 | 80.59 279 | 59.16 293 | 65.27 294 | 79.29 299 | 46.75 279 | 87.29 276 | 49.33 284 | 66.72 308 | 86.00 280 |
|
LF4IMVS | | | 64.02 301 | 62.19 301 | 69.50 311 | 70.90 338 | 53.29 305 | 76.13 298 | 77.18 311 | 52.65 328 | 58.59 318 | 80.98 287 | 23.55 341 | 76.52 326 | 53.06 269 | 66.66 309 | 78.68 326 |
|
Patchmatch-RL test | | | 70.24 271 | 67.78 280 | 77.61 253 | 77.43 316 | 59.57 237 | 71.16 316 | 70.33 335 | 62.94 262 | 68.65 266 | 72.77 327 | 50.62 248 | 85.49 289 | 69.58 153 | 66.58 310 | 87.77 237 |
|
dp | | | 66.80 289 | 65.43 289 | 70.90 307 | 79.74 304 | 48.82 329 | 75.12 308 | 74.77 322 | 59.61 288 | 64.08 302 | 77.23 313 | 42.89 299 | 80.72 309 | 48.86 286 | 66.58 310 | 83.16 306 |
|
v15 | | | 77.51 184 | 76.12 185 | 81.66 177 | 84.09 228 | 64.65 169 | 87.14 141 | 88.96 170 | 72.76 135 | 68.90 262 | 81.91 278 | 62.74 117 | 91.73 191 | 72.32 132 | 66.29 312 | 88.61 215 |
|
V14 | | | 77.52 182 | 76.12 185 | 81.70 174 | 84.15 223 | 64.77 167 | 87.21 140 | 88.95 171 | 72.80 134 | 68.79 263 | 81.94 277 | 62.69 119 | 91.72 193 | 72.31 133 | 66.27 313 | 88.60 216 |
|
V9 | | | 77.52 182 | 76.11 188 | 81.73 173 | 84.19 222 | 64.89 164 | 87.26 138 | 88.94 174 | 72.87 133 | 68.65 266 | 81.96 276 | 62.65 122 | 91.72 193 | 72.27 134 | 66.24 314 | 88.60 216 |
|
v13 | | | 77.50 186 | 76.07 190 | 81.77 170 | 84.23 217 | 65.07 160 | 87.34 133 | 88.91 176 | 72.92 130 | 68.35 271 | 81.97 274 | 62.53 127 | 91.69 197 | 72.20 136 | 66.22 315 | 88.56 221 |
|
v12 | | | 77.51 184 | 76.09 189 | 81.76 172 | 84.22 218 | 64.99 161 | 87.30 136 | 88.93 175 | 72.92 130 | 68.48 270 | 81.97 274 | 62.54 126 | 91.70 196 | 72.24 135 | 66.21 316 | 88.58 219 |
|
FPMVS | | | 53.68 316 | 51.64 316 | 59.81 328 | 65.08 344 | 51.03 320 | 69.48 324 | 69.58 339 | 41.46 340 | 40.67 342 | 72.32 328 | 16.46 351 | 70.00 345 | 24.24 349 | 65.42 317 | 58.40 346 |
|
pmmvs-eth3d | | | 70.50 269 | 67.83 278 | 78.52 240 | 77.37 317 | 66.18 135 | 81.82 256 | 81.51 272 | 58.90 295 | 63.90 303 | 80.42 291 | 42.69 301 | 86.28 284 | 58.56 237 | 65.30 318 | 83.11 307 |
|
N_pmnet | | | 52.79 317 | 53.26 315 | 51.40 336 | 78.99 312 | 7.68 361 | 69.52 323 | 3.89 361 | 51.63 332 | 57.01 326 | 74.98 321 | 40.83 310 | 65.96 350 | 37.78 332 | 64.67 319 | 80.56 322 |
|
PM-MVS | | | 66.41 293 | 64.14 293 | 73.20 295 | 73.92 328 | 56.45 277 | 78.97 283 | 64.96 349 | 63.88 256 | 64.72 298 | 80.24 292 | 19.84 346 | 83.44 300 | 66.24 175 | 64.52 320 | 79.71 324 |
|
SixPastTwentyTwo | | | 73.37 245 | 71.26 252 | 79.70 212 | 85.08 207 | 57.89 255 | 85.57 193 | 83.56 242 | 71.03 162 | 65.66 291 | 85.88 218 | 42.10 305 | 92.57 165 | 59.11 232 | 63.34 321 | 88.65 211 |
|
test1235678 | | | 58.74 309 | 56.89 312 | 64.30 320 | 69.70 339 | 41.87 340 | 71.05 317 | 74.87 321 | 54.06 320 | 50.63 338 | 71.53 330 | 25.30 339 | 74.10 336 | 31.80 342 | 63.10 322 | 76.93 333 |
|
testpf | | | 56.51 313 | 57.58 310 | 53.30 333 | 71.99 336 | 41.19 342 | 46.89 350 | 69.32 341 | 58.06 300 | 52.87 336 | 69.45 335 | 27.99 335 | 72.73 339 | 59.59 228 | 62.07 323 | 45.98 349 |
|
TransMVSNet (Re) | | | 75.39 223 | 74.56 214 | 77.86 248 | 85.50 200 | 57.10 266 | 86.78 158 | 86.09 221 | 72.17 148 | 71.53 229 | 87.34 162 | 63.01 108 | 89.31 245 | 56.84 253 | 61.83 324 | 87.17 251 |
|
MDA-MVSNet_test_wron | | | 65.03 296 | 62.92 297 | 71.37 302 | 75.93 322 | 56.73 272 | 69.09 328 | 74.73 323 | 57.28 308 | 54.03 332 | 77.89 309 | 45.88 283 | 74.39 335 | 49.89 282 | 61.55 325 | 82.99 310 |
|
YYNet1 | | | 65.03 296 | 62.91 298 | 71.38 301 | 75.85 323 | 56.60 276 | 69.12 327 | 74.66 326 | 57.28 308 | 54.12 331 | 77.87 310 | 45.85 284 | 74.48 334 | 49.95 281 | 61.52 326 | 83.05 308 |
|
1111 | | | 57.11 312 | 56.82 313 | 57.97 330 | 69.10 340 | 28.28 352 | 68.90 329 | 74.54 327 | 54.01 321 | 53.71 333 | 74.51 322 | 23.09 342 | 67.90 348 | 32.28 339 | 61.26 327 | 77.73 328 |
|
ambc | | | | | 75.24 282 | 73.16 332 | 50.51 324 | 63.05 341 | 87.47 204 | | 64.28 300 | 77.81 311 | 17.80 349 | 89.73 237 | 57.88 245 | 60.64 328 | 85.49 282 |
|
TDRefinement | | | 67.49 285 | 64.34 292 | 76.92 262 | 73.47 331 | 61.07 226 | 84.86 211 | 82.98 253 | 59.77 287 | 58.30 320 | 85.13 235 | 26.06 338 | 87.89 272 | 47.92 296 | 60.59 329 | 81.81 317 |
|
Gipuma | | | 45.18 323 | 41.86 324 | 55.16 332 | 77.03 321 | 51.52 317 | 32.50 353 | 80.52 280 | 32.46 347 | 27.12 348 | 35.02 350 | 9.52 357 | 75.50 330 | 22.31 350 | 60.21 330 | 38.45 351 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
new-patchmatchnet | | | 61.73 303 | 61.73 303 | 61.70 326 | 72.74 334 | 24.50 357 | 69.16 326 | 78.03 305 | 61.40 275 | 56.72 327 | 75.53 320 | 38.42 318 | 76.48 327 | 45.95 308 | 57.67 331 | 84.13 298 |
|
Anonymous20231211 | | | 64.82 298 | 61.79 302 | 73.91 293 | 77.11 319 | 50.92 321 | 85.29 202 | 81.53 271 | 54.19 319 | 57.98 321 | 78.03 308 | 26.90 336 | 87.83 274 | 37.92 331 | 57.12 332 | 82.99 310 |
|
MDA-MVSNet-bldmvs | | | 66.68 290 | 63.66 294 | 75.75 275 | 79.28 310 | 60.56 232 | 73.92 312 | 78.35 303 | 64.43 248 | 50.13 339 | 79.87 296 | 44.02 294 | 83.67 298 | 46.10 307 | 56.86 333 | 83.03 309 |
|
new_pmnet | | | 50.91 319 | 50.29 318 | 52.78 334 | 68.58 342 | 34.94 350 | 63.71 339 | 56.63 351 | 39.73 342 | 44.95 340 | 65.47 338 | 21.93 344 | 58.48 352 | 34.98 335 | 56.62 334 | 64.92 343 |
|
LP | | | 61.36 305 | 57.78 308 | 72.09 298 | 75.54 326 | 58.53 245 | 67.16 335 | 75.22 317 | 51.90 331 | 54.13 330 | 69.97 333 | 37.73 322 | 80.45 311 | 32.74 338 | 55.63 335 | 77.29 331 |
|
PMVS | | 37.38 22 | 44.16 324 | 40.28 325 | 55.82 331 | 40.82 358 | 42.54 338 | 65.12 338 | 63.99 350 | 34.43 346 | 24.48 349 | 57.12 345 | 3.92 360 | 76.17 328 | 17.10 352 | 55.52 336 | 48.75 347 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
pmmvs3 | | | 57.79 310 | 54.26 314 | 68.37 316 | 64.02 345 | 56.72 273 | 75.12 308 | 65.17 347 | 40.20 341 | 52.93 335 | 69.86 334 | 20.36 345 | 75.48 331 | 45.45 311 | 55.25 337 | 72.90 338 |
|
UnsupCasMVSNet_eth | | | 67.33 287 | 65.99 288 | 71.37 302 | 73.48 330 | 51.47 318 | 75.16 306 | 85.19 227 | 65.20 241 | 60.78 312 | 80.93 289 | 42.35 302 | 77.20 324 | 57.12 251 | 53.69 338 | 85.44 283 |
|
K. test v3 | | | 71.19 262 | 68.51 268 | 79.21 224 | 83.04 259 | 57.78 258 | 84.35 226 | 76.91 312 | 72.90 132 | 62.99 307 | 82.86 260 | 39.27 315 | 91.09 219 | 61.65 212 | 52.66 339 | 88.75 208 |
|
UnsupCasMVSNet_bld | | | 63.70 302 | 61.53 304 | 70.21 309 | 73.69 329 | 51.39 319 | 72.82 313 | 81.89 268 | 55.63 315 | 57.81 322 | 71.80 329 | 38.67 317 | 78.61 317 | 49.26 285 | 52.21 340 | 80.63 320 |
|
LCM-MVSNet | | | 54.25 314 | 49.68 320 | 67.97 317 | 53.73 352 | 45.28 333 | 66.85 336 | 80.78 277 | 35.96 345 | 39.45 344 | 62.23 341 | 8.70 358 | 78.06 321 | 48.24 292 | 51.20 341 | 80.57 321 |
|
testmv | | | 53.85 315 | 51.03 317 | 62.31 324 | 61.46 347 | 38.88 345 | 70.95 320 | 74.69 325 | 51.11 333 | 41.26 341 | 66.85 336 | 14.28 352 | 72.13 341 | 29.19 344 | 49.51 342 | 75.93 336 |
|
lessismore_v0 | | | | | 78.97 231 | 81.01 290 | 57.15 265 | | 65.99 346 | | 61.16 311 | 82.82 261 | 39.12 316 | 91.34 208 | 59.67 226 | 46.92 343 | 88.43 225 |
|
test12356 | | | 49.28 321 | 48.51 321 | 51.59 335 | 62.06 346 | 19.11 358 | 60.40 342 | 72.45 332 | 47.60 337 | 40.64 343 | 65.68 337 | 13.84 353 | 68.72 346 | 27.29 346 | 46.67 344 | 66.94 342 |
|
PVSNet_0 | | 57.27 20 | 61.67 304 | 59.27 305 | 68.85 314 | 79.61 305 | 57.44 263 | 68.01 331 | 73.44 330 | 55.93 314 | 58.54 319 | 70.41 332 | 44.58 291 | 77.55 323 | 47.01 298 | 35.91 345 | 71.55 339 |
|
PMMVS2 | | | 40.82 325 | 38.86 326 | 46.69 338 | 53.84 351 | 16.45 359 | 48.61 349 | 49.92 354 | 37.49 344 | 31.67 346 | 60.97 342 | 8.14 359 | 56.42 353 | 28.42 345 | 30.72 346 | 67.19 341 |
|
no-one | | | 51.08 318 | 45.79 323 | 66.95 319 | 57.92 350 | 50.49 325 | 59.63 344 | 76.04 315 | 48.04 335 | 31.85 345 | 56.10 346 | 19.12 347 | 80.08 313 | 36.89 333 | 26.52 347 | 70.29 340 |
|
DeepMVS_CX | | | | | 27.40 344 | 40.17 359 | 26.90 355 | | 24.59 360 | 17.44 354 | 23.95 350 | 48.61 347 | 9.77 356 | 26.48 357 | 18.06 351 | 24.47 348 | 28.83 352 |
|
MVE | | 26.22 23 | 30.37 331 | 25.89 333 | 43.81 339 | 44.55 357 | 35.46 349 | 28.87 354 | 39.07 357 | 18.20 353 | 18.58 353 | 40.18 349 | 2.68 361 | 47.37 356 | 17.07 353 | 23.78 349 | 48.60 348 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
wuykxyi23d | | | 39.76 326 | 33.18 329 | 59.51 329 | 46.98 356 | 44.01 335 | 57.70 346 | 67.74 344 | 24.13 351 | 13.98 356 | 34.33 351 | 1.27 363 | 71.33 342 | 34.23 336 | 18.23 350 | 63.18 345 |
|
E-PMN | | | 31.77 329 | 30.64 330 | 35.15 340 | 52.87 353 | 27.67 354 | 57.09 347 | 47.86 355 | 24.64 350 | 16.40 354 | 33.05 352 | 11.23 355 | 54.90 354 | 14.46 354 | 18.15 351 | 22.87 353 |
|
PNet_i23d | | | 38.26 327 | 35.42 327 | 46.79 337 | 58.74 348 | 35.48 348 | 59.65 343 | 51.25 353 | 32.45 348 | 23.44 352 | 47.53 348 | 2.04 362 | 58.96 351 | 25.60 348 | 18.09 352 | 45.92 350 |
|
EMVS | | | 30.81 330 | 29.65 331 | 34.27 341 | 50.96 354 | 25.95 356 | 56.58 348 | 46.80 356 | 24.01 352 | 15.53 355 | 30.68 353 | 12.47 354 | 54.43 355 | 12.81 355 | 17.05 353 | 22.43 354 |
|
ANet_high | | | 50.57 320 | 46.10 322 | 63.99 321 | 48.67 355 | 39.13 343 | 70.99 319 | 80.85 276 | 61.39 276 | 31.18 347 | 57.70 344 | 17.02 350 | 73.65 338 | 31.22 343 | 15.89 354 | 79.18 325 |
|
tmp_tt | | | 18.61 333 | 21.40 334 | 10.23 346 | 4.82 360 | 10.11 360 | 34.70 352 | 30.74 359 | 1.48 356 | 23.91 351 | 26.07 354 | 28.42 334 | 13.41 359 | 27.12 347 | 15.35 355 | 7.17 355 |
|
wuyk23d | | | 16.82 334 | 15.94 335 | 19.46 345 | 58.74 348 | 31.45 351 | 39.22 351 | 3.74 362 | 6.84 355 | 6.04 357 | 2.70 358 | 1.27 363 | 24.29 358 | 10.54 356 | 14.40 356 | 2.63 356 |
|
.test1245 | | | 45.55 322 | 50.02 319 | 32.14 342 | 69.10 340 | 28.28 352 | 68.90 329 | 74.54 327 | 54.01 321 | 53.71 333 | 74.51 322 | 23.09 342 | 67.90 348 | 32.28 339 | 0.02 357 | 0.25 358 |
|
testmvs | | | 6.04 337 | 8.02 338 | 0.10 348 | 0.08 361 | 0.03 363 | 69.74 321 | 0.04 363 | 0.05 357 | 0.31 358 | 1.68 359 | 0.02 366 | 0.04 360 | 0.24 357 | 0.02 357 | 0.25 358 |
|
test123 | | | 6.12 336 | 8.11 337 | 0.14 347 | 0.06 362 | 0.09 362 | 71.05 317 | 0.03 364 | 0.04 358 | 0.25 359 | 1.30 360 | 0.05 365 | 0.03 361 | 0.21 358 | 0.01 359 | 0.29 357 |
|
cdsmvs_eth3d_5k | | | 19.96 332 | 26.61 332 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 89.26 156 | 0.00 359 | 0.00 360 | 88.61 128 | 61.62 139 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
pcd_1.5k_mvsjas | | | 5.26 338 | 7.02 339 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 0.00 365 | 0.00 359 | 0.00 360 | 0.00 361 | 63.15 104 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
sosnet-low-res | | | 0.00 339 | 0.00 340 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 0.00 365 | 0.00 359 | 0.00 360 | 0.00 361 | 0.00 367 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
sosnet | | | 0.00 339 | 0.00 340 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 0.00 365 | 0.00 359 | 0.00 360 | 0.00 361 | 0.00 367 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
uncertanet | | | 0.00 339 | 0.00 340 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 0.00 365 | 0.00 359 | 0.00 360 | 0.00 361 | 0.00 367 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
Regformer | | | 0.00 339 | 0.00 340 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 0.00 365 | 0.00 359 | 0.00 360 | 0.00 361 | 0.00 367 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
ab-mvs-re | | | 7.23 335 | 9.64 336 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 0.00 365 | 0.00 359 | 0.00 360 | 86.72 181 | 0.00 367 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
uanet | | | 0.00 339 | 0.00 340 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 0.00 365 | 0.00 359 | 0.00 360 | 0.00 361 | 0.00 367 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
GSMVS | | | | | | | | | | | | | | | | | 88.96 202 |
|
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 | | | | | | |
|
sam_mvs1 | | | | | | | | | | | | | 51.32 239 | | | | 88.96 202 |
|
sam_mvs | | | | | | | | | | | | | 50.01 259 | | | | |
|
MTGPA | | | | | | | | | 92.02 61 | | | | | | | | |
|
test_post1 | | | | | | | | 78.90 285 | | | | 5.43 357 | 48.81 270 | 85.44 290 | 59.25 231 | | |
|
test_post | | | | | | | | | | | | 5.46 356 | 50.36 257 | 84.24 295 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 74.00 324 | 51.12 242 | 88.60 265 | | | |
|
MTMP | | | | | | | | | 32.83 358 | | | | | | | | |
|
gm-plane-assit | | | | | | 81.40 283 | 53.83 302 | | | 62.72 266 | | 80.94 288 | | 92.39 170 | 63.40 196 | | |
|
TEST9 | | | | | | 93.26 36 | 72.96 20 | 88.75 87 | 91.89 70 | 68.44 205 | 85.00 30 | 93.10 42 | 74.36 18 | 95.41 51 | | | |
|
test_8 | | | | | | 93.13 38 | 72.57 31 | 88.68 90 | 91.84 73 | 68.69 201 | 84.87 36 | 93.10 42 | 74.43 15 | 95.16 59 | | | |
|
agg_prior | | | | | | 92.85 44 | 71.94 42 | | 91.78 76 | | 84.41 42 | | | 94.93 68 | | | |
|
test_prior4 | | | | | | | 72.60 30 | 89.01 78 | | | | | | | | | |
|
test_prior | | | | | 86.33 48 | 92.61 49 | 69.59 74 | | 92.97 32 | | | | | 95.48 46 | | | 93.91 31 |
|
旧先验2 | | | | | | | | 86.56 165 | | 58.10 299 | 87.04 16 | | | 88.98 258 | 74.07 112 | | |
|
新几何2 | | | | | | | | 86.29 174 | | | | | | | | | |
|
无先验 | | | | | | | | 87.48 129 | 88.98 168 | 60.00 285 | | | | 94.12 96 | 67.28 168 | | 88.97 201 |
|
原ACMM2 | | | | | | | | 86.86 154 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 91.01 221 | 62.37 204 | | |
|
segment_acmp | | | | | | | | | | | | | 73.08 26 | | | | |
|
testdata1 | | | | | | | | 84.14 231 | | 75.71 71 | | | | | | | |
|
plane_prior7 | | | | | | 90.08 81 | 68.51 100 | | | | | | | | | | |
|
plane_prior6 | | | | | | 89.84 86 | 68.70 96 | | | | | | 60.42 161 | | | | |
|
plane_prior4 | | | | | | | | | | | | 91.00 83 | | | | | |
|
plane_prior3 | | | | | | | 68.60 98 | | | 78.44 30 | 78.92 97 | | | | | | |
|
plane_prior2 | | | | | | | | 91.25 31 | | 79.12 23 | | | | | | | |
|
plane_prior1 | | | | | | 89.90 85 | | | | | | | | | | | |
|
n2 | | | | | | | | | 0.00 365 | | | | | | | | |
|
nn | | | | | | | | | 0.00 365 | | | | | | | | |
|
door-mid | | | | | | | | | 69.98 337 | | | | | | | | |
|
test11 | | | | | | | | | 92.23 53 | | | | | | | | |
|
door | | | | | | | | | 69.44 340 | | | | | | | | |
|
HQP5-MVS | | | | | | | 66.98 125 | | | | | | | | | | |
|
HQP-NCC | | | | | | 89.33 103 | | 89.17 71 | | 76.41 59 | 77.23 137 | | | | | | |
|
ACMP_Plane | | | | | | 89.33 103 | | 89.17 71 | | 76.41 59 | 77.23 137 | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 77.47 78 | | |
|
HQP4-MVS | | | | | | | | | | | 77.24 136 | | | 95.11 61 | | | 91.03 114 |
|
HQP2-MVS | | | | | | | | | | | | | 60.17 164 | | | | |
|
NP-MVS | | | | | | 89.62 92 | 68.32 102 | | | | | 90.24 93 | | | | | |
|
MDTV_nov1_ep13_2view | | | | | | | 37.79 346 | 75.16 306 | | 55.10 316 | 66.53 286 | | 49.34 264 | | 53.98 263 | | 87.94 233 |
|
Test By Simon | | | | | | | | | | | | | 64.33 90 | | | | |
|