wuykxyi23d | | | 88.46 63 | 88.80 61 | 87.44 81 | 90.96 121 | 93.03 1 | 85.85 114 | 81.96 238 | 74.58 145 | 98.58 2 | 97.29 5 | 87.73 31 | 87.31 241 | 82.84 68 | 99.41 1 | 81.99 301 |
|
no-one | | | 71.52 273 | 70.43 278 | 74.81 269 | 78.45 307 | 63.41 190 | 57.73 341 | 77.03 261 | 51.46 321 | 77.17 268 | 90.33 181 | 54.96 278 | 80.35 300 | 47.41 320 | 99.29 2 | 80.68 322 |
|
LCM-MVSNet | | | 95.70 1 | 96.40 1 | 93.61 2 | 98.67 1 | 85.39 29 | 95.54 3 | 97.36 1 | 96.97 1 | 99.04 1 | 99.05 1 | 96.61 1 | 95.92 11 | 85.07 37 | 99.27 3 | 99.54 1 |
|
WR-MVS_H | | | 89.91 44 | 91.31 25 | 85.71 110 | 96.32 9 | 62.39 213 | 89.54 56 | 93.31 42 | 90.21 10 | 95.57 11 | 95.66 31 | 81.42 94 | 95.90 12 | 80.94 88 | 98.80 4 | 98.84 5 |
|
ACMP | | 79.16 10 | 90.54 29 | 90.60 37 | 90.35 41 | 94.36 41 | 80.98 57 | 89.16 62 | 94.05 23 | 79.03 86 | 92.87 41 | 93.74 94 | 90.60 12 | 95.21 42 | 82.87 66 | 98.76 5 | 94.87 77 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
ACMH+ | | 77.89 11 | 90.73 25 | 91.50 19 | 88.44 64 | 93.00 66 | 76.26 100 | 89.65 50 | 95.55 3 | 87.72 18 | 93.89 27 | 94.94 51 | 91.62 5 | 93.44 110 | 78.35 118 | 98.76 5 | 95.61 64 |
|
PS-CasMVS | | | 90.06 35 | 91.92 9 | 84.47 133 | 96.56 6 | 58.83 251 | 89.04 63 | 92.74 67 | 91.40 5 | 96.12 4 | 96.06 24 | 87.23 35 | 95.57 24 | 79.42 112 | 98.74 7 | 99.00 2 |
|
LPG-MVS_test | | | 91.47 16 | 91.68 15 | 90.82 34 | 94.75 36 | 81.69 51 | 90.00 40 | 94.27 13 | 82.35 48 | 93.67 30 | 94.82 55 | 91.18 6 | 95.52 28 | 85.36 35 | 98.73 8 | 95.23 72 |
|
LGP-MVS_train | | | | | 90.82 34 | 94.75 36 | 81.69 51 | | 94.27 13 | 82.35 48 | 93.67 30 | 94.82 55 | 91.18 6 | 95.52 28 | 85.36 35 | 98.73 8 | 95.23 72 |
|
PEN-MVS | | | 90.03 37 | 91.88 12 | 84.48 132 | 96.57 5 | 58.88 250 | 88.95 64 | 93.19 48 | 91.62 4 | 96.01 6 | 96.16 22 | 87.02 38 | 95.60 23 | 78.69 116 | 98.72 10 | 98.97 3 |
|
CP-MVSNet | | | 89.27 54 | 90.91 34 | 84.37 136 | 96.34 8 | 58.61 253 | 88.66 71 | 92.06 83 | 90.78 6 | 95.67 9 | 95.17 45 | 81.80 90 | 95.54 27 | 79.00 114 | 98.69 11 | 98.95 4 |
|
TranMVSNet+NR-MVSNet | | | 87.86 69 | 88.76 62 | 85.18 116 | 94.02 44 | 64.13 182 | 84.38 134 | 91.29 115 | 84.88 27 | 92.06 58 | 93.84 93 | 86.45 45 | 93.73 84 | 73.22 159 | 98.66 12 | 97.69 12 |
|
NR-MVSNet | | | 86.00 99 | 86.22 97 | 85.34 114 | 93.24 60 | 64.56 179 | 82.21 197 | 90.46 132 | 80.99 63 | 88.42 127 | 91.97 129 | 77.56 124 | 93.85 81 | 72.46 167 | 98.65 13 | 97.61 13 |
|
UA-Net | | | 91.49 14 | 91.53 18 | 91.39 22 | 94.98 32 | 82.95 50 | 93.52 5 | 92.79 65 | 88.22 16 | 88.53 124 | 97.64 3 | 83.45 66 | 94.55 60 | 86.02 34 | 98.60 14 | 96.67 36 |
|
FC-MVSNet-test | | | 85.93 103 | 87.05 82 | 82.58 178 | 92.25 86 | 56.44 264 | 85.75 115 | 93.09 51 | 77.33 112 | 91.94 62 | 94.65 60 | 74.78 156 | 93.41 112 | 75.11 143 | 98.58 15 | 97.88 10 |
|
DTE-MVSNet | | | 89.98 39 | 91.91 11 | 84.21 141 | 96.51 7 | 57.84 254 | 88.93 66 | 92.84 64 | 91.92 2 | 96.16 3 | 96.23 20 | 86.95 39 | 95.99 7 | 79.05 113 | 98.57 16 | 98.80 6 |
|
UniMVSNet (Re) | | | 86.87 80 | 86.98 84 | 86.55 89 | 93.11 64 | 68.48 157 | 83.80 149 | 92.87 61 | 80.37 67 | 89.61 106 | 91.81 136 | 77.72 122 | 94.18 67 | 75.00 145 | 98.53 17 | 96.99 34 |
|
Baseline_NR-MVSNet | | | 84.00 147 | 85.90 102 | 78.29 232 | 91.47 109 | 53.44 283 | 82.29 193 | 87.00 202 | 79.06 85 | 89.55 108 | 95.72 30 | 77.20 127 | 86.14 264 | 72.30 168 | 98.51 18 | 95.28 70 |
|
TDRefinement | | | 93.52 2 | 93.39 3 | 93.88 1 | 95.94 13 | 90.26 4 | 95.70 2 | 96.46 2 | 90.58 8 | 92.86 42 | 96.29 18 | 88.16 26 | 94.17 69 | 86.07 33 | 98.48 19 | 97.22 26 |
|
ACMM | | 79.39 9 | 90.65 26 | 90.99 31 | 89.63 50 | 95.03 31 | 83.53 44 | 89.62 53 | 93.35 39 | 79.20 82 | 93.83 28 | 93.60 96 | 90.81 9 | 92.96 131 | 85.02 39 | 98.45 20 | 92.41 147 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
MP-MVS-pluss | | | 90.81 24 | 91.08 29 | 89.99 46 | 95.97 12 | 79.88 63 | 88.13 76 | 94.51 11 | 75.79 131 | 92.94 38 | 94.96 50 | 88.36 20 | 95.01 47 | 90.70 2 | 98.40 21 | 95.09 75 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
Anonymous20231211 | | | 90.14 32 | 91.88 12 | 84.92 119 | 94.75 36 | 64.47 180 | 90.13 39 | 92.97 58 | 91.68 3 | 95.35 12 | 98.79 2 | 93.19 3 | 91.76 160 | 71.67 173 | 98.40 21 | 98.52 7 |
|
CP-MVS | | | 91.67 11 | 91.58 17 | 91.96 12 | 95.29 28 | 87.62 9 | 93.38 7 | 93.36 37 | 83.16 39 | 91.06 73 | 94.00 84 | 88.26 23 | 95.71 21 | 87.28 23 | 98.39 23 | 92.55 144 |
|
UniMVSNet_NR-MVSNet | | | 86.84 82 | 87.06 81 | 86.17 102 | 92.86 71 | 67.02 165 | 82.55 186 | 91.56 96 | 83.08 41 | 90.92 75 | 91.82 135 | 78.25 118 | 93.99 74 | 74.16 148 | 98.35 24 | 97.49 16 |
|
DU-MVS | | | 86.80 83 | 86.99 83 | 86.21 99 | 93.24 60 | 67.02 165 | 83.16 171 | 92.21 79 | 81.73 57 | 90.92 75 | 91.97 129 | 77.20 127 | 93.99 74 | 74.16 148 | 98.35 24 | 97.61 13 |
|
zzz-MVS | | | 91.27 19 | 91.26 27 | 91.29 25 | 96.59 3 | 86.29 14 | 88.94 65 | 91.81 91 | 84.07 32 | 92.00 59 | 94.40 70 | 86.63 41 | 95.28 38 | 88.59 4 | 98.31 26 | 92.30 152 |
|
MTAPA | | | 91.52 13 | 91.60 16 | 91.29 25 | 96.59 3 | 86.29 14 | 92.02 24 | 91.81 91 | 84.07 32 | 92.00 59 | 94.40 70 | 86.63 41 | 95.28 38 | 88.59 4 | 98.31 26 | 92.30 152 |
|
ACMH | | 76.49 14 | 89.34 53 | 91.14 28 | 83.96 147 | 92.50 78 | 70.36 144 | 89.55 54 | 93.84 29 | 81.89 56 | 94.70 16 | 95.44 39 | 90.69 10 | 88.31 233 | 83.33 59 | 98.30 28 | 93.20 124 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
COLMAP_ROB | | 83.01 3 | 91.97 8 | 91.95 8 | 92.04 10 | 93.68 51 | 86.15 18 | 93.37 8 | 95.10 7 | 90.28 9 | 92.11 56 | 95.03 48 | 89.75 15 | 94.93 49 | 79.95 104 | 98.27 29 | 95.04 76 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
ACMMP | | | 91.91 9 | 91.87 14 | 92.03 11 | 95.53 24 | 85.91 22 | 93.35 9 | 94.16 20 | 82.52 47 | 92.39 55 | 94.14 80 | 89.15 17 | 95.62 22 | 87.35 20 | 98.24 30 | 94.56 81 |
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 |
HPM-MVS | | | 92.13 6 | 92.20 7 | 91.91 15 | 95.58 23 | 84.67 38 | 93.51 6 | 94.85 9 | 82.88 43 | 91.77 64 | 93.94 91 | 90.55 13 | 95.73 20 | 88.50 8 | 98.23 31 | 95.33 69 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
MP-MVS | | | 91.14 23 | 90.91 34 | 91.83 18 | 96.18 10 | 86.88 11 | 92.20 22 | 93.03 56 | 82.59 46 | 88.52 125 | 94.37 73 | 86.74 40 | 95.41 33 | 86.32 27 | 98.21 32 | 93.19 125 |
|
SteuartSystems-ACMMP | | | 91.16 22 | 91.36 22 | 90.55 37 | 93.91 47 | 80.97 58 | 91.49 29 | 93.48 36 | 82.82 44 | 92.60 50 | 93.97 85 | 88.19 24 | 96.29 3 | 87.61 16 | 98.20 33 | 94.39 90 |
Skip Steuart: Steuart Systems R&D Blog. |
HPM-MVS_fast | | | 92.50 5 | 92.54 5 | 92.37 5 | 95.93 14 | 85.81 27 | 92.99 11 | 94.23 16 | 85.21 24 | 92.51 51 | 95.13 46 | 90.65 11 | 95.34 35 | 88.06 10 | 98.15 34 | 95.95 51 |
|
mPP-MVS | | | 91.69 10 | 91.47 20 | 92.37 5 | 96.04 11 | 88.48 8 | 92.72 14 | 92.60 72 | 83.09 40 | 91.54 67 | 94.25 76 | 87.67 33 | 95.51 30 | 87.21 24 | 98.11 35 | 93.12 126 |
|
WR-MVS | | | 83.56 155 | 84.40 137 | 81.06 202 | 93.43 56 | 54.88 275 | 78.67 261 | 85.02 223 | 81.24 61 | 90.74 79 | 91.56 142 | 72.85 192 | 91.08 175 | 68.00 201 | 98.04 36 | 97.23 25 |
|
XVG-ACMP-BASELINE | | | 89.98 39 | 89.84 46 | 90.41 39 | 94.91 34 | 84.50 41 | 89.49 58 | 93.98 24 | 79.68 75 | 92.09 57 | 93.89 92 | 83.80 62 | 93.10 126 | 82.67 70 | 98.04 36 | 93.64 113 |
|
DeepC-MVS | | 82.31 4 | 89.15 56 | 89.08 54 | 89.37 54 | 93.64 52 | 79.07 71 | 88.54 72 | 94.20 17 | 73.53 155 | 89.71 98 | 94.82 55 | 85.09 53 | 95.77 18 | 84.17 51 | 98.03 38 | 93.26 122 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
FIs | | | 85.35 110 | 86.27 96 | 82.60 177 | 91.86 97 | 57.31 258 | 85.10 123 | 93.05 53 | 75.83 130 | 91.02 74 | 93.97 85 | 73.57 177 | 92.91 135 | 73.97 151 | 98.02 39 | 97.58 15 |
|
abl_6 | | | 93.02 4 | 93.16 4 | 92.60 4 | 94.73 39 | 88.99 7 | 93.26 10 | 94.19 19 | 89.11 11 | 94.43 19 | 95.27 42 | 91.86 4 | 95.09 44 | 87.54 19 | 98.02 39 | 93.71 111 |
|
PGM-MVS | | | 91.20 21 | 90.95 33 | 91.93 13 | 95.67 20 | 85.85 25 | 90.00 40 | 93.90 28 | 80.32 69 | 91.74 65 | 94.41 69 | 88.17 25 | 95.98 8 | 86.37 26 | 97.99 41 | 93.96 101 |
|
APDe-MVS | | | 91.22 20 | 91.92 9 | 89.14 57 | 92.97 67 | 78.04 78 | 92.84 12 | 94.14 21 | 83.33 37 | 93.90 26 | 95.73 29 | 88.77 18 | 96.41 1 | 87.60 17 | 97.98 42 | 92.98 129 |
|
CLD-MVS | | | 83.18 161 | 82.64 163 | 84.79 123 | 89.05 149 | 67.82 162 | 77.93 266 | 92.52 73 | 68.33 219 | 85.07 173 | 81.54 307 | 82.06 82 | 92.96 131 | 69.35 188 | 97.91 43 | 93.57 116 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
SMA-MVS | | | 90.40 31 | 90.57 40 | 89.87 47 | 95.31 27 | 79.64 68 | 90.98 34 | 93.36 37 | 75.21 140 | 92.90 39 | 95.28 41 | 86.29 48 | 96.09 6 | 87.92 11 | 97.89 44 | 93.88 103 |
|
IS-MVSNet | | | 86.66 86 | 86.82 90 | 86.17 102 | 92.05 92 | 66.87 167 | 91.21 33 | 88.64 172 | 86.30 23 | 89.60 107 | 92.59 116 | 69.22 216 | 94.91 50 | 73.89 152 | 97.89 44 | 96.72 35 |
|
ACMMP_Plus | | | 90.65 26 | 91.07 30 | 89.42 53 | 95.93 14 | 79.54 69 | 89.95 43 | 93.68 31 | 77.65 103 | 91.97 61 | 94.89 52 | 88.38 19 | 95.45 31 | 89.27 3 | 97.87 46 | 93.27 121 |
|
OurMVSNet-221017-0 | | | 90.01 38 | 89.74 48 | 90.83 33 | 93.16 62 | 80.37 59 | 91.91 27 | 93.11 50 | 81.10 62 | 95.32 13 | 97.24 6 | 72.94 191 | 94.85 51 | 85.07 37 | 97.78 47 | 97.26 23 |
|
ACMMPR | | | 91.49 14 | 91.35 24 | 91.92 14 | 95.74 18 | 85.88 24 | 92.58 18 | 93.25 47 | 81.99 53 | 91.40 70 | 94.17 79 | 87.51 34 | 95.87 13 | 87.74 12 | 97.76 48 | 93.99 99 |
|
HFP-MVS | | | 91.30 18 | 91.39 21 | 91.02 29 | 95.43 25 | 84.66 39 | 92.58 18 | 93.29 45 | 81.99 53 | 91.47 68 | 93.96 87 | 88.35 21 | 95.56 25 | 87.74 12 | 97.74 49 | 92.85 130 |
|
#test# | | | 90.49 30 | 90.31 43 | 91.02 29 | 95.43 25 | 84.66 39 | 90.65 35 | 93.29 45 | 77.00 118 | 91.47 68 | 93.96 87 | 88.35 21 | 95.56 25 | 84.88 40 | 97.74 49 | 92.85 130 |
|
region2R | | | 91.44 17 | 91.30 26 | 91.87 16 | 95.75 17 | 85.90 23 | 92.63 17 | 93.30 43 | 81.91 55 | 90.88 78 | 94.21 77 | 87.75 30 | 95.87 13 | 87.60 17 | 97.71 51 | 93.83 104 |
|
v7n | | | 90.13 33 | 90.96 32 | 87.65 77 | 91.95 94 | 71.06 140 | 89.99 42 | 93.05 53 | 86.53 21 | 94.29 22 | 96.27 19 | 82.69 73 | 94.08 72 | 86.25 30 | 97.63 52 | 97.82 11 |
|
XVS | | | 91.54 12 | 91.36 22 | 92.08 8 | 95.64 21 | 86.25 16 | 92.64 15 | 93.33 40 | 85.07 25 | 89.99 87 | 94.03 83 | 86.57 43 | 95.80 16 | 87.35 20 | 97.62 53 | 94.20 92 |
|
X-MVStestdata | | | 85.04 114 | 82.70 161 | 92.08 8 | 95.64 21 | 86.25 16 | 92.64 15 | 93.33 40 | 85.07 25 | 89.99 87 | 16.05 353 | 86.57 43 | 95.80 16 | 87.35 20 | 97.62 53 | 94.20 92 |
|
test_part1 | | | | | | | | | 93.93 25 | | | | 87.19 36 | | | 97.61 55 | 91.48 173 |
|
ESAPD | | | 90.05 36 | 90.56 41 | 88.50 63 | 93.86 48 | 77.77 80 | 89.63 51 | 93.93 25 | 84.39 28 | 92.84 43 | 93.43 98 | 87.19 36 | 96.26 4 | 82.18 75 | 97.61 55 | 91.48 173 |
|
APD-MVS_3200maxsize | | | 92.05 7 | 92.24 6 | 91.48 20 | 93.02 65 | 85.17 31 | 92.47 21 | 95.05 8 | 87.65 19 | 93.21 36 | 94.39 72 | 90.09 14 | 95.08 45 | 86.67 25 | 97.60 57 | 94.18 94 |
|
OPM-MVS | | | 89.80 45 | 89.97 44 | 89.27 55 | 94.76 35 | 79.86 64 | 86.76 100 | 92.78 66 | 78.78 89 | 92.51 51 | 93.64 95 | 88.13 27 | 93.84 83 | 84.83 42 | 97.55 58 | 94.10 97 |
|
LTVRE_ROB | | 86.10 1 | 93.04 3 | 93.44 2 | 91.82 19 | 93.73 50 | 85.72 28 | 96.79 1 | 95.51 4 | 88.86 13 | 95.63 10 | 96.99 8 | 84.81 55 | 93.16 123 | 91.10 1 | 97.53 59 | 96.58 39 |
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 |
MIMVSNet1 | | | 83.63 154 | 84.59 131 | 80.74 204 | 94.06 43 | 62.77 204 | 82.72 182 | 84.53 227 | 77.57 105 | 90.34 81 | 95.92 26 | 76.88 139 | 85.83 268 | 61.88 236 | 97.42 60 | 93.62 114 |
|
ACMMP++ | | | | | | | | | | | | | | | | 97.35 61 | |
|
nrg030 | | | 87.85 70 | 88.49 63 | 85.91 105 | 90.07 136 | 69.73 146 | 87.86 79 | 94.20 17 | 74.04 150 | 92.70 48 | 94.66 59 | 85.88 52 | 91.50 164 | 79.72 106 | 97.32 62 | 96.50 40 |
|
pmmvs6 | | | 86.52 89 | 88.06 66 | 81.90 187 | 92.22 88 | 62.28 219 | 84.66 129 | 89.15 167 | 83.54 36 | 89.85 94 | 97.32 4 | 88.08 28 | 86.80 254 | 70.43 182 | 97.30 63 | 96.62 37 |
|
SD-MVS | | | 88.96 57 | 89.88 45 | 86.22 97 | 91.63 100 | 77.07 90 | 89.82 46 | 93.77 30 | 78.90 87 | 92.88 40 | 92.29 125 | 86.11 50 | 90.22 199 | 86.24 31 | 97.24 64 | 91.36 176 |
|
CPTT-MVS | | | 89.39 52 | 88.98 57 | 90.63 36 | 95.09 30 | 86.95 10 | 92.09 23 | 92.30 78 | 79.74 74 | 87.50 139 | 92.38 121 | 81.42 94 | 93.28 118 | 83.07 63 | 97.24 64 | 91.67 167 |
|
APD-MVS | | | 89.54 50 | 89.63 50 | 89.26 56 | 92.57 75 | 81.34 56 | 90.19 38 | 93.08 52 | 80.87 64 | 91.13 72 | 93.19 101 | 86.22 49 | 95.97 9 | 82.23 74 | 97.18 66 | 90.45 199 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
wuyk23d | | | 75.13 238 | 79.30 199 | 62.63 320 | 75.56 325 | 75.18 105 | 80.89 225 | 73.10 290 | 75.06 142 | 94.76 15 | 95.32 40 | 87.73 31 | 52.85 351 | 34.16 345 | 97.11 67 | 59.85 346 |
|
PMVS | | 80.48 6 | 90.08 34 | 90.66 36 | 88.34 66 | 96.71 2 | 92.97 2 | 90.31 37 | 89.57 162 | 88.51 15 | 90.11 83 | 95.12 47 | 90.98 8 | 88.92 222 | 77.55 126 | 97.07 68 | 83.13 288 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
OMC-MVS | | | 88.19 65 | 87.52 74 | 90.19 44 | 91.94 96 | 81.68 53 | 87.49 86 | 93.17 49 | 76.02 127 | 88.64 122 | 91.22 148 | 84.24 60 | 93.37 113 | 77.97 124 | 97.03 69 | 95.52 65 |
|
test_prior3 | | | 86.31 93 | 86.31 95 | 86.32 93 | 90.59 128 | 71.99 130 | 83.37 164 | 92.85 62 | 75.43 137 | 84.58 185 | 91.57 140 | 81.92 88 | 94.17 69 | 79.54 109 | 96.97 70 | 92.80 132 |
|
test_prior2 | | | | | | | | 83.37 164 | | 75.43 137 | 84.58 185 | 91.57 140 | 81.92 88 | | 79.54 109 | 96.97 70 | |
|
EPP-MVSNet | | | 85.47 109 | 85.04 115 | 86.77 86 | 91.52 107 | 69.37 149 | 91.63 28 | 87.98 184 | 81.51 60 | 87.05 147 | 91.83 134 | 66.18 228 | 95.29 36 | 70.75 177 | 96.89 72 | 95.64 59 |
|
VDDNet | | | 84.35 134 | 85.39 111 | 81.25 199 | 95.13 29 | 59.32 247 | 85.42 120 | 81.11 244 | 86.41 22 | 87.41 140 | 96.21 21 | 73.61 176 | 90.61 191 | 66.33 212 | 96.85 73 | 93.81 109 |
|
VPNet | | | 80.25 195 | 81.68 175 | 75.94 264 | 92.46 79 | 47.98 328 | 76.70 276 | 81.67 242 | 73.45 156 | 84.87 177 | 92.82 111 | 74.66 159 | 86.51 259 | 61.66 239 | 96.85 73 | 93.33 120 |
|
SixPastTwentyTwo | | | 87.20 76 | 87.45 76 | 86.45 91 | 92.52 77 | 69.19 154 | 87.84 81 | 88.05 181 | 81.66 58 | 94.64 17 | 96.53 14 | 65.94 229 | 94.75 53 | 83.02 65 | 96.83 75 | 95.41 67 |
|
VPA-MVSNet | | | 83.47 158 | 84.73 121 | 79.69 215 | 90.29 132 | 57.52 257 | 81.30 218 | 88.69 171 | 76.29 123 | 87.58 137 | 94.44 67 | 80.60 101 | 87.20 242 | 66.60 211 | 96.82 76 | 94.34 91 |
|
Gipuma | | | 84.44 128 | 86.33 94 | 78.78 224 | 84.20 260 | 73.57 113 | 89.55 54 | 90.44 133 | 84.24 31 | 84.38 189 | 94.89 52 | 76.35 142 | 80.40 299 | 76.14 137 | 96.80 77 | 82.36 296 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
CDPH-MVS | | | 86.17 98 | 85.54 108 | 88.05 73 | 92.25 86 | 75.45 103 | 83.85 146 | 92.01 84 | 65.91 238 | 86.19 160 | 91.75 138 | 83.77 63 | 94.98 48 | 77.43 129 | 96.71 78 | 93.73 110 |
|
v748 | | | 88.91 59 | 89.82 47 | 86.19 101 | 90.06 137 | 68.53 156 | 88.81 68 | 91.48 100 | 84.36 30 | 94.19 24 | 95.98 25 | 82.52 76 | 92.67 140 | 84.30 48 | 96.67 79 | 97.37 20 |
|
DP-MVS | | | 88.60 62 | 89.01 55 | 87.36 82 | 91.30 111 | 77.50 85 | 87.55 84 | 92.97 58 | 87.95 17 | 89.62 104 | 92.87 110 | 84.56 57 | 93.89 80 | 77.65 125 | 96.62 80 | 90.70 191 |
|
TransMVSNet (Re) | | | 84.02 146 | 85.74 104 | 78.85 223 | 91.00 120 | 55.20 274 | 82.29 193 | 87.26 192 | 79.65 76 | 88.38 129 | 95.52 37 | 83.00 70 | 86.88 246 | 67.97 202 | 96.60 81 | 94.45 88 |
|
ambc | | | | | 82.98 171 | 90.55 130 | 64.86 177 | 88.20 74 | 89.15 167 | | 89.40 111 | 93.96 87 | 71.67 209 | 91.38 171 | 78.83 115 | 96.55 82 | 92.71 135 |
|
train_agg | | | 85.98 102 | 85.28 112 | 88.07 70 | 92.34 83 | 79.70 66 | 83.94 142 | 90.32 137 | 65.79 239 | 84.49 187 | 90.97 162 | 81.93 86 | 93.63 89 | 81.21 83 | 96.54 83 | 90.88 186 |
|
agg_prior3 | | | 85.76 105 | 84.95 118 | 88.16 68 | 92.43 80 | 79.92 62 | 83.98 141 | 90.03 152 | 65.11 248 | 83.66 199 | 90.64 177 | 81.00 97 | 93.67 86 | 81.21 83 | 96.54 83 | 90.88 186 |
|
VDD-MVS | | | 84.23 138 | 84.58 132 | 83.20 168 | 91.17 117 | 65.16 176 | 83.25 168 | 84.97 225 | 79.79 73 | 87.18 142 | 94.27 74 | 74.77 157 | 90.89 182 | 69.24 189 | 96.54 83 | 93.55 119 |
|
HPM-MVS++ | | | 88.93 58 | 88.45 64 | 90.38 40 | 94.92 33 | 85.85 25 | 89.70 47 | 91.27 116 | 78.20 97 | 86.69 151 | 92.28 126 | 80.36 103 | 95.06 46 | 86.17 32 | 96.49 86 | 90.22 203 |
|
test_djsdf | | | 89.62 48 | 89.01 55 | 91.45 21 | 92.36 82 | 82.98 49 | 91.98 25 | 90.08 150 | 71.54 194 | 94.28 23 | 96.54 13 | 81.57 92 | 94.27 62 | 86.26 28 | 96.49 86 | 97.09 30 |
|
test9_res | | | | | | | | | | | | | | | 80.83 90 | 96.45 88 | 90.57 195 |
|
agg_prior1 | | | 85.72 106 | 85.20 113 | 87.28 83 | 91.58 104 | 77.69 82 | 83.69 153 | 90.30 141 | 66.29 234 | 84.32 191 | 91.07 159 | 82.13 81 | 93.18 121 | 81.02 86 | 96.36 89 | 90.98 181 |
|
anonymousdsp | | | 89.73 47 | 88.88 58 | 92.27 7 | 89.82 141 | 86.67 12 | 90.51 36 | 90.20 149 | 69.87 208 | 95.06 14 | 96.14 23 | 84.28 59 | 93.07 130 | 87.68 14 | 96.34 90 | 97.09 30 |
|
PHI-MVS | | | 86.38 91 | 85.81 103 | 88.08 69 | 88.44 164 | 77.34 87 | 89.35 61 | 93.05 53 | 73.15 166 | 84.76 179 | 87.70 224 | 78.87 113 | 94.18 67 | 80.67 93 | 96.29 91 | 92.73 134 |
|
PS-MVSNAJss | | | 88.31 64 | 87.90 68 | 89.56 52 | 93.31 58 | 77.96 79 | 87.94 78 | 91.97 86 | 70.73 200 | 94.19 24 | 96.67 11 | 76.94 133 | 94.57 58 | 83.07 63 | 96.28 92 | 96.15 42 |
|
v10 | | | 86.54 88 | 87.10 80 | 84.84 121 | 88.16 171 | 63.28 194 | 86.64 106 | 92.20 80 | 75.42 139 | 92.81 45 | 94.50 65 | 74.05 166 | 94.06 73 | 83.88 54 | 96.28 92 | 97.17 28 |
|
CNVR-MVS | | | 87.81 71 | 87.68 73 | 88.21 67 | 92.87 69 | 77.30 89 | 85.25 121 | 91.23 117 | 77.31 113 | 87.07 146 | 91.47 144 | 82.94 71 | 94.71 54 | 84.67 44 | 96.27 94 | 92.62 143 |
|
pcd1.5k->3k | | | 38.83 329 | 41.11 330 | 32.01 340 | 93.13 63 | 0.00 361 | 0.00 352 | 91.38 113 | 0.00 356 | 0.00 357 | 0.00 358 | 89.24 16 | 0.00 359 | 0.00 356 | 96.24 95 | 96.02 48 |
|
114514_t | | | 83.10 163 | 82.54 166 | 84.77 124 | 92.90 68 | 69.10 155 | 86.65 105 | 90.62 129 | 54.66 300 | 81.46 226 | 90.81 169 | 76.98 132 | 94.38 61 | 72.62 166 | 96.18 96 | 90.82 189 |
|
agg_prior2 | | | | | | | | | | | | | | | 79.68 107 | 96.16 97 | 90.22 203 |
|
v13 | | | 87.31 74 | 88.10 65 | 84.94 118 | 88.84 153 | 63.75 186 | 87.85 80 | 91.47 103 | 79.12 83 | 93.72 29 | 95.82 27 | 75.20 148 | 93.58 97 | 84.76 43 | 96.16 97 | 97.48 17 |
|
AllTest | | | 87.97 68 | 87.40 78 | 89.68 48 | 91.59 101 | 83.40 45 | 89.50 57 | 95.44 5 | 79.47 77 | 88.00 132 | 93.03 104 | 82.66 74 | 91.47 165 | 70.81 175 | 96.14 99 | 94.16 95 |
|
TestCases | | | | | 89.68 48 | 91.59 101 | 83.40 45 | | 95.44 5 | 79.47 77 | 88.00 132 | 93.03 104 | 82.66 74 | 91.47 165 | 70.81 175 | 96.14 99 | 94.16 95 |
|
EPNet | | | 80.37 193 | 78.41 205 | 86.23 96 | 76.75 317 | 73.28 115 | 87.18 91 | 77.45 259 | 76.24 124 | 68.14 318 | 88.93 203 | 65.41 231 | 93.85 81 | 69.47 187 | 96.12 101 | 91.55 171 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
v12 | | | 87.15 77 | 87.91 67 | 84.84 121 | 88.69 156 | 63.52 189 | 87.58 83 | 91.46 104 | 78.74 91 | 93.57 32 | 95.66 31 | 74.94 152 | 93.57 98 | 84.50 46 | 96.08 102 | 97.43 18 |
|
v11 | | | 86.96 78 | 87.78 71 | 84.51 130 | 88.50 162 | 62.60 209 | 87.21 89 | 91.63 95 | 78.08 100 | 93.40 34 | 95.56 36 | 75.07 149 | 93.57 98 | 84.46 47 | 96.08 102 | 97.36 21 |
|
pm-mvs1 | | | 83.69 152 | 84.95 118 | 79.91 211 | 90.04 139 | 59.66 244 | 82.43 188 | 87.44 189 | 75.52 136 | 87.85 134 | 95.26 43 | 81.25 96 | 85.65 270 | 68.74 196 | 96.04 104 | 94.42 89 |
|
V9 | | | 86.96 78 | 87.70 72 | 84.74 125 | 88.52 161 | 63.27 195 | 87.31 88 | 91.45 106 | 78.28 96 | 93.43 33 | 95.45 38 | 74.59 160 | 93.57 98 | 84.23 50 | 96.01 105 | 97.38 19 |
|
mvs_tets | | | 89.78 46 | 89.27 53 | 91.30 24 | 93.51 53 | 84.79 36 | 89.89 45 | 90.63 128 | 70.00 207 | 94.55 18 | 96.67 11 | 87.94 29 | 93.59 94 | 84.27 49 | 95.97 106 | 95.52 65 |
|
DeepPCF-MVS | | 81.24 5 | 87.28 75 | 86.21 98 | 90.49 38 | 91.48 108 | 84.90 34 | 83.41 163 | 92.38 77 | 70.25 205 | 89.35 112 | 90.68 173 | 82.85 72 | 94.57 58 | 79.55 108 | 95.95 107 | 92.00 160 |
|
V14 | | | 86.75 84 | 87.46 75 | 84.62 128 | 88.35 165 | 63.00 200 | 87.02 94 | 91.42 109 | 77.78 102 | 93.27 35 | 95.23 44 | 74.22 163 | 93.56 101 | 83.95 53 | 95.93 108 | 97.31 22 |
|
jajsoiax | | | 89.41 51 | 88.81 60 | 91.19 28 | 93.38 57 | 84.72 37 | 89.70 47 | 90.29 144 | 69.27 211 | 94.39 20 | 96.38 15 | 86.02 51 | 93.52 104 | 83.96 52 | 95.92 109 | 95.34 68 |
|
ANet_high | | | 83.17 162 | 85.68 106 | 75.65 266 | 81.24 284 | 45.26 333 | 79.94 235 | 92.91 60 | 83.83 35 | 91.33 71 | 96.88 10 | 80.25 104 | 85.92 266 | 68.89 194 | 95.89 110 | 95.76 53 |
|
3Dnovator+ | | 83.92 2 | 89.97 41 | 89.66 49 | 90.92 32 | 91.27 113 | 81.66 54 | 91.25 32 | 94.13 22 | 88.89 12 | 88.83 119 | 94.26 75 | 77.55 125 | 95.86 15 | 84.88 40 | 95.87 111 | 95.24 71 |
|
v15 | | | 86.56 87 | 87.25 79 | 84.51 130 | 88.15 172 | 62.72 205 | 86.72 104 | 91.40 111 | 77.38 107 | 93.11 37 | 95.00 49 | 73.93 168 | 93.55 102 | 83.67 57 | 95.86 112 | 97.26 23 |
|
HQP_MVS | | | 87.75 72 | 87.43 77 | 88.70 61 | 93.45 54 | 76.42 98 | 89.45 59 | 93.61 32 | 79.44 79 | 86.55 153 | 92.95 108 | 74.84 154 | 95.22 40 | 80.78 91 | 95.83 113 | 94.46 86 |
|
plane_prior5 | | | | | | | | | 93.61 32 | | | | | 95.22 40 | 80.78 91 | 95.83 113 | 94.46 86 |
|
DeepC-MVS_fast | | 80.27 8 | 86.23 96 | 85.65 107 | 87.96 74 | 91.30 111 | 76.92 91 | 87.19 90 | 91.99 85 | 70.56 201 | 84.96 174 | 90.69 172 | 80.01 106 | 95.14 43 | 78.37 117 | 95.78 115 | 91.82 164 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
LFMVS | | | 80.15 198 | 80.56 188 | 78.89 222 | 89.19 148 | 55.93 266 | 85.22 122 | 73.78 281 | 82.96 42 | 84.28 193 | 92.72 115 | 57.38 266 | 90.07 206 | 63.80 227 | 95.75 116 | 90.68 192 |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 95.74 117 | |
|
原ACMM1 | | | | | 84.60 129 | 92.81 73 | 74.01 111 | | 91.50 98 | 62.59 263 | 82.73 212 | 90.67 174 | 76.53 140 | 94.25 64 | 69.24 189 | 95.69 118 | 85.55 252 |
|
tfpnnormal | | | 81.79 178 | 82.95 159 | 78.31 231 | 88.93 152 | 55.40 270 | 80.83 227 | 82.85 233 | 76.81 119 | 85.90 165 | 94.14 80 | 74.58 161 | 86.51 259 | 66.82 210 | 95.68 119 | 93.01 128 |
|
TAPA-MVS | | 77.73 12 | 85.71 107 | 84.83 120 | 88.37 65 | 88.78 155 | 79.72 65 | 87.15 92 | 93.50 35 | 69.17 213 | 85.80 166 | 89.56 195 | 80.76 99 | 92.13 149 | 73.21 163 | 95.51 120 | 93.25 123 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
LS3D | | | 90.60 28 | 90.34 42 | 91.38 23 | 89.03 150 | 84.23 42 | 93.58 4 | 94.68 10 | 90.65 7 | 90.33 82 | 93.95 90 | 84.50 58 | 95.37 34 | 80.87 89 | 95.50 121 | 94.53 85 |
|
v8 | | | 86.22 97 | 86.83 89 | 84.36 137 | 87.82 182 | 62.35 214 | 86.42 109 | 91.33 114 | 76.78 120 | 92.73 47 | 94.48 66 | 73.41 180 | 93.72 85 | 83.10 62 | 95.41 122 | 97.01 33 |
|
Vis-MVSNet (Re-imp) | | | 77.82 209 | 77.79 208 | 77.92 238 | 88.82 154 | 51.29 303 | 83.28 166 | 71.97 297 | 74.04 150 | 82.23 214 | 89.78 191 | 57.38 266 | 89.41 212 | 57.22 274 | 95.41 122 | 93.05 127 |
|
FMVSNet1 | | | 84.55 125 | 85.45 110 | 81.85 190 | 90.27 133 | 61.05 234 | 86.83 97 | 88.27 178 | 78.57 93 | 89.66 100 | 95.64 33 | 75.43 145 | 90.68 188 | 69.09 192 | 95.33 124 | 93.82 106 |
|
test12 | | | | | 86.57 88 | 90.74 125 | 72.63 119 | | 90.69 126 | | 82.76 211 | | 79.20 110 | 94.80 52 | | 95.32 125 | 92.27 154 |
|
NCCC | | | 87.36 73 | 86.87 87 | 88.83 59 | 92.32 85 | 78.84 74 | 86.58 107 | 91.09 121 | 78.77 90 | 84.85 178 | 90.89 166 | 80.85 98 | 95.29 36 | 81.14 85 | 95.32 125 | 92.34 151 |
|
Patchmtry | | | 76.56 226 | 77.46 209 | 73.83 278 | 79.37 298 | 46.60 330 | 82.41 189 | 76.90 262 | 73.81 153 | 85.56 170 | 92.38 121 | 48.07 293 | 83.98 284 | 63.36 230 | 95.31 127 | 90.92 184 |
|
XVG-OURS | | | 89.18 55 | 88.83 59 | 90.23 43 | 94.28 42 | 86.11 20 | 85.91 112 | 93.60 34 | 80.16 71 | 89.13 115 | 93.44 97 | 83.82 61 | 90.98 177 | 83.86 55 | 95.30 128 | 93.60 115 |
|
TSAR-MVS + GP. | | | 83.95 148 | 82.69 162 | 87.72 75 | 89.27 146 | 81.45 55 | 83.72 152 | 81.58 243 | 74.73 144 | 85.66 167 | 86.06 245 | 72.56 200 | 92.69 139 | 75.44 141 | 95.21 129 | 89.01 220 |
|
test_0402 | | | 88.65 60 | 89.58 51 | 85.88 107 | 92.55 76 | 72.22 126 | 84.01 140 | 89.44 164 | 88.63 14 | 94.38 21 | 95.77 28 | 86.38 47 | 93.59 94 | 79.84 105 | 95.21 129 | 91.82 164 |
|
TinyColmap | | | 81.25 184 | 82.34 169 | 77.99 237 | 85.33 243 | 60.68 238 | 82.32 192 | 88.33 176 | 71.26 196 | 86.97 148 | 92.22 128 | 77.10 130 | 86.98 245 | 62.37 233 | 95.17 131 | 86.31 245 |
|
DP-MVS Recon | | | 84.05 145 | 83.22 155 | 86.52 90 | 91.73 99 | 75.27 104 | 83.23 170 | 92.40 75 | 72.04 182 | 82.04 217 | 88.33 214 | 77.91 121 | 93.95 79 | 66.17 213 | 95.12 132 | 90.34 202 |
|
PCF-MVS | | 74.62 15 | 82.15 173 | 80.92 186 | 85.84 108 | 89.43 143 | 72.30 124 | 80.53 228 | 91.82 90 | 57.36 288 | 87.81 135 | 89.92 189 | 77.67 123 | 93.63 89 | 58.69 262 | 95.08 133 | 91.58 170 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
CSCG | | | 86.26 94 | 86.47 93 | 85.60 112 | 90.87 123 | 74.26 110 | 87.98 77 | 91.85 89 | 80.35 68 | 89.54 110 | 88.01 218 | 79.09 111 | 92.13 149 | 75.51 140 | 95.06 134 | 90.41 200 |
|
plane_prior | | | | | | | 76.42 98 | 87.15 92 | | 75.94 129 | | | | | | 95.03 135 | |
|
new-patchmatchnet | | | 70.10 282 | 73.37 254 | 60.29 328 | 81.23 285 | 16.95 356 | 59.54 335 | 74.62 275 | 62.93 260 | 80.97 230 | 87.93 221 | 62.83 240 | 71.90 319 | 55.24 286 | 95.01 136 | 92.00 160 |
|
v1192 | | | 84.57 124 | 84.69 125 | 84.21 141 | 87.75 187 | 62.88 202 | 83.02 173 | 91.43 107 | 69.08 215 | 89.98 89 | 90.89 166 | 72.70 196 | 93.62 93 | 82.41 71 | 94.97 137 | 96.13 43 |
|
v1921920 | | | 84.23 138 | 84.37 141 | 83.79 150 | 87.64 192 | 61.71 222 | 82.91 176 | 91.20 118 | 67.94 224 | 90.06 85 | 90.34 180 | 72.04 205 | 93.59 94 | 82.32 73 | 94.91 138 | 96.07 45 |
|
v144192 | | | 84.24 137 | 84.41 136 | 83.71 156 | 87.59 193 | 61.57 227 | 82.95 175 | 91.03 122 | 67.82 227 | 89.80 96 | 90.49 178 | 73.28 184 | 93.51 107 | 81.88 80 | 94.89 139 | 96.04 47 |
|
testing_2 | | | 84.36 132 | 84.64 130 | 83.50 165 | 86.74 216 | 63.97 185 | 84.56 131 | 90.31 139 | 66.22 235 | 91.62 66 | 94.55 62 | 75.88 143 | 91.95 152 | 77.02 134 | 94.89 139 | 94.56 81 |
|
LCM-MVSNet-Re | | | 83.48 157 | 85.06 114 | 78.75 225 | 85.94 237 | 55.75 269 | 80.05 232 | 94.27 13 | 76.47 122 | 96.09 5 | 94.54 64 | 83.31 68 | 89.75 208 | 59.95 250 | 94.89 139 | 90.75 190 |
|
v17 | | | 86.32 92 | 86.95 85 | 84.44 134 | 88.00 174 | 62.62 208 | 86.74 102 | 91.48 100 | 77.17 115 | 92.74 46 | 94.56 61 | 73.74 172 | 93.53 103 | 83.27 60 | 94.87 142 | 97.18 27 |
|
v52 | | | 89.97 41 | 90.60 37 | 88.07 70 | 88.69 156 | 72.01 128 | 91.35 30 | 92.64 70 | 82.22 50 | 95.97 8 | 96.31 16 | 84.82 54 | 93.98 76 | 88.59 4 | 94.83 143 | 98.23 8 |
|
V4 | | | 89.97 41 | 90.60 37 | 88.07 70 | 88.69 156 | 72.01 128 | 91.35 30 | 92.64 70 | 82.22 50 | 95.98 7 | 96.31 16 | 84.80 56 | 93.98 76 | 88.59 4 | 94.83 143 | 98.23 8 |
|
v1240 | | | 84.30 135 | 84.51 134 | 83.65 157 | 87.65 191 | 61.26 231 | 82.85 177 | 91.54 97 | 67.94 224 | 90.68 80 | 90.65 175 | 71.71 208 | 93.64 88 | 82.84 68 | 94.78 145 | 96.07 45 |
|
MSLP-MVS++ | | | 85.00 116 | 86.03 101 | 81.90 187 | 91.84 98 | 71.56 138 | 86.75 101 | 93.02 57 | 75.95 128 | 87.12 143 | 89.39 197 | 77.98 119 | 89.40 213 | 77.46 127 | 94.78 145 | 84.75 264 |
|
IterMVS-LS | | | 84.73 121 | 84.98 117 | 83.96 147 | 87.35 197 | 63.66 187 | 83.25 168 | 89.88 155 | 76.06 125 | 89.62 104 | 92.37 124 | 73.40 182 | 92.52 143 | 78.16 121 | 94.77 147 | 95.69 57 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
AdaColmap | | | 83.66 153 | 83.69 151 | 83.57 160 | 90.05 138 | 72.26 125 | 86.29 111 | 90.00 153 | 78.19 98 | 81.65 224 | 87.16 230 | 83.40 67 | 94.24 65 | 61.69 238 | 94.76 148 | 84.21 271 |
|
MVS_0304 | | | 84.88 118 | 83.96 148 | 87.64 78 | 87.43 196 | 74.83 106 | 84.18 136 | 93.30 43 | 77.48 106 | 77.39 267 | 88.46 209 | 74.53 162 | 95.74 19 | 78.09 123 | 94.75 149 | 92.36 150 |
|
ITE_SJBPF | | | | | 90.11 45 | 90.72 126 | 84.97 33 | | 90.30 141 | 81.56 59 | 90.02 86 | 91.20 151 | 82.40 78 | 90.81 184 | 73.58 155 | 94.66 150 | 94.56 81 |
|
v1144 | | | 84.54 127 | 84.72 123 | 84.00 145 | 87.67 190 | 62.55 211 | 82.97 174 | 90.93 124 | 70.32 204 | 89.80 96 | 90.99 161 | 73.50 178 | 93.48 108 | 81.69 81 | 94.65 151 | 95.97 49 |
|
test20.03 | | | 73.75 252 | 74.59 238 | 71.22 294 | 81.11 286 | 51.12 305 | 70.15 312 | 72.10 296 | 70.42 202 | 80.28 249 | 91.50 143 | 64.21 233 | 74.72 315 | 46.96 324 | 94.58 152 | 87.82 232 |
|
TSAR-MVS + MP. | | | 88.14 66 | 87.82 70 | 89.09 58 | 95.72 19 | 76.74 94 | 92.49 20 | 91.19 119 | 67.85 226 | 86.63 152 | 94.84 54 | 79.58 109 | 95.96 10 | 87.62 15 | 94.50 153 | 94.56 81 |
|
HQP3-MVS | | | | | | | | | 92.68 68 | | | | | | | 94.47 154 | |
|
HQP-MVS | | | 84.61 123 | 84.06 145 | 86.27 95 | 91.19 114 | 70.66 142 | 84.77 125 | 92.68 68 | 73.30 161 | 80.55 245 | 90.17 186 | 72.10 202 | 94.61 56 | 77.30 130 | 94.47 154 | 93.56 117 |
|
MCST-MVS | | | 84.36 132 | 83.93 149 | 85.63 111 | 91.59 101 | 71.58 137 | 83.52 158 | 92.13 81 | 61.82 269 | 83.96 195 | 89.75 192 | 79.93 108 | 93.46 109 | 78.33 119 | 94.34 156 | 91.87 163 |
|
v16 | | | 86.24 95 | 86.85 88 | 84.43 135 | 87.96 176 | 62.59 210 | 86.73 103 | 91.48 100 | 77.17 115 | 92.67 49 | 94.55 62 | 73.63 173 | 93.52 104 | 83.26 61 | 94.16 157 | 97.17 28 |
|
alignmvs | | | 83.94 149 | 83.98 147 | 83.80 149 | 87.80 186 | 67.88 161 | 84.54 132 | 91.42 109 | 73.27 164 | 88.41 128 | 87.96 219 | 72.33 201 | 90.83 183 | 76.02 138 | 94.11 158 | 92.69 136 |
|
USDC | | | 76.63 224 | 76.73 215 | 76.34 259 | 83.46 268 | 57.20 259 | 80.02 233 | 88.04 182 | 52.14 315 | 83.65 200 | 91.25 147 | 63.24 239 | 86.65 258 | 54.66 291 | 94.11 158 | 85.17 255 |
|
MVS_111021_HR | | | 84.63 122 | 84.34 142 | 85.49 113 | 90.18 134 | 75.86 102 | 79.23 255 | 87.13 197 | 73.35 158 | 85.56 170 | 89.34 198 | 83.60 65 | 90.50 193 | 76.64 135 | 94.05 160 | 90.09 208 |
|
v18 | | | 85.99 101 | 86.55 92 | 84.30 139 | 87.73 188 | 62.29 218 | 86.40 110 | 91.49 99 | 76.64 121 | 92.40 54 | 94.20 78 | 73.28 184 | 93.52 104 | 82.87 66 | 93.99 161 | 97.09 30 |
|
VNet | | | 79.31 201 | 80.27 192 | 76.44 257 | 87.92 177 | 53.95 279 | 75.58 287 | 84.35 228 | 74.39 148 | 82.23 214 | 90.72 171 | 72.84 193 | 84.39 281 | 60.38 249 | 93.98 162 | 90.97 182 |
|
FMVSNet2 | | | 81.31 181 | 81.61 177 | 80.41 207 | 86.38 219 | 58.75 252 | 83.93 144 | 86.58 205 | 72.43 173 | 87.65 136 | 92.98 106 | 63.78 236 | 90.22 199 | 66.86 207 | 93.92 163 | 92.27 154 |
|
LF4IMVS | | | 82.75 165 | 81.93 174 | 85.19 115 | 82.08 277 | 80.15 61 | 85.53 118 | 88.76 170 | 68.01 221 | 85.58 169 | 87.75 223 | 71.80 207 | 86.85 247 | 74.02 150 | 93.87 164 | 88.58 222 |
|
divwei89l23v2f112 | | | 84.16 140 | 84.38 138 | 83.52 162 | 87.32 199 | 61.70 224 | 82.79 179 | 89.74 156 | 71.90 191 | 89.64 101 | 91.12 155 | 72.68 197 | 93.10 126 | 80.40 96 | 93.81 165 | 95.75 54 |
|
v1141 | | | 84.16 140 | 84.38 138 | 83.52 162 | 87.32 199 | 61.70 224 | 82.79 179 | 89.74 156 | 71.90 191 | 89.64 101 | 91.12 155 | 72.68 197 | 93.10 126 | 80.39 98 | 93.80 166 | 95.75 54 |
|
v1 | | | 84.16 140 | 84.38 138 | 83.52 162 | 87.33 198 | 61.71 222 | 82.79 179 | 89.73 158 | 71.89 193 | 89.64 101 | 91.11 157 | 72.72 194 | 93.10 126 | 80.40 96 | 93.79 167 | 95.75 54 |
|
canonicalmvs | | | 85.50 108 | 86.14 99 | 83.58 159 | 87.97 175 | 67.13 164 | 87.55 84 | 94.32 12 | 73.44 157 | 88.47 126 | 87.54 227 | 86.45 45 | 91.06 176 | 75.76 139 | 93.76 168 | 92.54 145 |
|
v2v482 | | | 84.09 143 | 84.24 143 | 83.62 158 | 87.13 207 | 61.40 228 | 82.71 183 | 89.71 159 | 72.19 181 | 89.55 108 | 91.41 145 | 70.70 213 | 93.20 120 | 81.02 86 | 93.76 168 | 96.25 41 |
|
UGNet | | | 82.78 164 | 81.64 176 | 86.21 99 | 86.20 228 | 76.24 101 | 86.86 95 | 85.68 213 | 77.07 117 | 73.76 293 | 92.82 111 | 69.64 214 | 91.82 158 | 69.04 193 | 93.69 170 | 90.56 196 |
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 |
v7 | | | 84.81 119 | 85.00 116 | 84.23 140 | 88.15 172 | 63.27 195 | 83.79 150 | 91.39 112 | 71.10 198 | 90.07 84 | 91.28 146 | 74.04 167 | 93.63 89 | 81.48 82 | 93.67 171 | 95.79 52 |
|
旧先验1 | | | | | | 91.97 93 | 71.77 132 | | 81.78 241 | | | 91.84 133 | 73.92 169 | | | 93.65 172 | 83.61 278 |
|
view600 | | | 76.79 219 | 76.54 216 | 77.56 242 | 87.91 178 | 50.77 309 | 81.92 203 | 71.35 304 | 77.38 107 | 84.62 181 | 88.40 210 | 45.18 316 | 89.26 215 | 58.58 263 | 93.49 173 | 92.66 137 |
|
view800 | | | 76.79 219 | 76.54 216 | 77.56 242 | 87.91 178 | 50.77 309 | 81.92 203 | 71.35 304 | 77.38 107 | 84.62 181 | 88.40 210 | 45.18 316 | 89.26 215 | 58.58 263 | 93.49 173 | 92.66 137 |
|
conf0.05thres1000 | | | 76.79 219 | 76.54 216 | 77.56 242 | 87.91 178 | 50.77 309 | 81.92 203 | 71.35 304 | 77.38 107 | 84.62 181 | 88.40 210 | 45.18 316 | 89.26 215 | 58.58 263 | 93.49 173 | 92.66 137 |
|
tfpn | | | 76.79 219 | 76.54 216 | 77.56 242 | 87.91 178 | 50.77 309 | 81.92 203 | 71.35 304 | 77.38 107 | 84.62 181 | 88.40 210 | 45.18 316 | 89.26 215 | 58.58 263 | 93.49 173 | 92.66 137 |
|
MVS_111021_LR | | | 84.28 136 | 83.76 150 | 85.83 109 | 89.23 147 | 83.07 48 | 80.99 224 | 83.56 229 | 72.71 171 | 86.07 161 | 89.07 201 | 81.75 91 | 86.19 263 | 77.11 132 | 93.36 177 | 88.24 223 |
|
GBi-Net | | | 82.02 175 | 82.07 171 | 81.85 190 | 86.38 219 | 61.05 234 | 86.83 97 | 88.27 178 | 72.43 173 | 86.00 162 | 95.64 33 | 63.78 236 | 90.68 188 | 65.95 214 | 93.34 178 | 93.82 106 |
|
test1 | | | 82.02 175 | 82.07 171 | 81.85 190 | 86.38 219 | 61.05 234 | 86.83 97 | 88.27 178 | 72.43 173 | 86.00 162 | 95.64 33 | 63.78 236 | 90.68 188 | 65.95 214 | 93.34 178 | 93.82 106 |
|
FMVSNet3 | | | 78.80 204 | 78.55 204 | 79.57 218 | 82.89 274 | 56.89 262 | 81.76 208 | 85.77 212 | 69.04 216 | 86.00 162 | 90.44 179 | 51.75 284 | 90.09 205 | 65.95 214 | 93.34 178 | 91.72 166 |
|
v6 | | | 84.43 129 | 84.66 127 | 83.75 152 | 87.81 183 | 62.34 215 | 83.59 154 | 90.26 147 | 72.33 177 | 89.94 90 | 91.19 152 | 73.30 183 | 93.29 115 | 80.26 99 | 93.26 181 | 95.62 60 |
|
v1neww | | | 84.43 129 | 84.66 127 | 83.75 152 | 87.81 183 | 62.34 215 | 83.59 154 | 90.27 145 | 72.33 177 | 89.93 91 | 91.22 148 | 73.28 184 | 93.29 115 | 80.25 100 | 93.25 182 | 95.62 60 |
|
v7new | | | 84.43 129 | 84.66 127 | 83.75 152 | 87.81 183 | 62.34 215 | 83.59 154 | 90.27 145 | 72.33 177 | 89.93 91 | 91.22 148 | 73.28 184 | 93.29 115 | 80.25 100 | 93.25 182 | 95.62 60 |
|
K. test v3 | | | 85.14 111 | 84.73 121 | 86.37 92 | 91.13 118 | 69.63 148 | 85.45 119 | 76.68 265 | 84.06 34 | 92.44 53 | 96.99 8 | 62.03 241 | 94.65 55 | 80.58 94 | 93.24 184 | 94.83 80 |
|
Anonymous20231206 | | | 71.38 274 | 71.88 268 | 69.88 295 | 86.31 223 | 54.37 276 | 70.39 311 | 74.62 275 | 52.57 311 | 76.73 269 | 88.76 204 | 59.94 251 | 72.06 318 | 44.35 329 | 93.23 185 | 83.23 286 |
|
新几何1 | | | | | 82.95 172 | 93.96 45 | 78.56 76 | | 80.24 248 | 55.45 296 | 83.93 196 | 91.08 158 | 71.19 211 | 88.33 232 | 65.84 217 | 93.07 186 | 81.95 303 |
|
1121 | | | 80.86 188 | 79.81 198 | 84.02 144 | 93.93 46 | 78.70 75 | 81.64 211 | 80.18 249 | 55.43 297 | 83.67 198 | 91.15 153 | 71.29 210 | 91.41 169 | 67.95 203 | 93.06 187 | 81.96 302 |
|
lessismore_v0 | | | | | 85.95 104 | 91.10 119 | 70.99 141 | | 70.91 308 | | 91.79 63 | 94.42 68 | 61.76 242 | 92.93 133 | 79.52 111 | 93.03 188 | 93.93 102 |
|
TAMVS | | | 78.08 208 | 76.36 221 | 83.23 167 | 90.62 127 | 72.87 117 | 79.08 256 | 80.01 251 | 61.72 271 | 81.35 228 | 86.92 233 | 63.96 235 | 88.78 227 | 50.61 306 | 93.01 189 | 88.04 227 |
|
EPNet_dtu | | | 72.87 261 | 71.33 273 | 77.49 247 | 77.72 311 | 60.55 239 | 82.35 191 | 75.79 267 | 66.49 233 | 58.39 348 | 81.06 310 | 53.68 280 | 85.98 265 | 53.55 295 | 92.97 190 | 85.95 248 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
Effi-MVS+-dtu | | | 85.82 104 | 83.38 153 | 93.14 3 | 87.13 207 | 91.15 3 | 87.70 82 | 88.42 174 | 74.57 146 | 83.56 201 | 85.65 247 | 78.49 115 | 94.21 66 | 72.04 170 | 92.88 191 | 94.05 98 |
|
CANet | | | 83.79 151 | 82.85 160 | 86.63 87 | 86.17 229 | 72.21 127 | 83.76 151 | 91.43 107 | 77.24 114 | 74.39 290 | 87.45 228 | 75.36 146 | 95.42 32 | 77.03 133 | 92.83 192 | 92.25 156 |
|
API-MVS | | | 82.28 171 | 82.61 164 | 81.30 197 | 86.29 224 | 69.79 145 | 88.71 70 | 87.67 187 | 78.42 95 | 82.15 216 | 84.15 273 | 77.98 119 | 91.59 163 | 65.39 219 | 92.75 193 | 82.51 295 |
|
Regformer-1 | | | 86.00 99 | 85.50 109 | 87.49 79 | 84.18 261 | 76.90 92 | 83.52 158 | 87.94 185 | 82.18 52 | 89.19 113 | 85.07 260 | 82.28 80 | 91.89 155 | 82.40 72 | 92.72 194 | 93.69 112 |
|
Regformer-2 | | | 86.74 85 | 86.08 100 | 88.73 60 | 84.18 261 | 79.20 70 | 83.52 158 | 89.33 165 | 83.33 37 | 89.92 93 | 85.07 260 | 83.23 69 | 93.16 123 | 83.39 58 | 92.72 194 | 93.83 104 |
|
testgi | | | 72.36 266 | 74.61 236 | 65.59 313 | 80.56 292 | 42.82 341 | 68.29 316 | 73.35 285 | 66.87 231 | 81.84 220 | 89.93 188 | 72.08 204 | 66.92 333 | 46.05 326 | 92.54 196 | 87.01 239 |
|
FMVSNet5 | | | 72.10 269 | 71.69 269 | 73.32 280 | 81.57 282 | 53.02 286 | 76.77 275 | 78.37 255 | 63.31 257 | 76.37 271 | 91.85 132 | 36.68 344 | 78.98 303 | 47.87 319 | 92.45 197 | 87.95 229 |
|
CDS-MVSNet | | | 77.32 214 | 75.40 230 | 83.06 170 | 89.00 151 | 72.48 122 | 77.90 267 | 82.17 237 | 60.81 276 | 78.94 258 | 83.49 278 | 59.30 256 | 88.76 228 | 54.64 292 | 92.37 198 | 87.93 230 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
CNLPA | | | 83.55 156 | 83.10 158 | 84.90 120 | 89.34 145 | 83.87 43 | 84.54 132 | 88.77 169 | 79.09 84 | 83.54 202 | 88.66 207 | 74.87 153 | 81.73 295 | 66.84 209 | 92.29 199 | 89.11 216 |
|
F-COLMAP | | | 84.97 117 | 83.42 152 | 89.63 50 | 92.39 81 | 83.40 45 | 88.83 67 | 91.92 88 | 73.19 165 | 80.18 250 | 89.15 200 | 77.04 131 | 93.28 118 | 65.82 218 | 92.28 200 | 92.21 157 |
|
tfpn111 | | | 76.03 231 | 75.53 229 | 77.53 246 | 87.27 201 | 51.88 295 | 81.07 221 | 73.26 286 | 75.68 132 | 83.25 204 | 86.37 238 | 45.54 306 | 89.38 214 | 55.07 288 | 92.26 201 | 91.34 177 |
|
thres600view7 | | | 75.97 232 | 75.35 232 | 77.85 240 | 87.01 212 | 51.84 299 | 80.45 229 | 73.26 286 | 75.20 141 | 83.10 208 | 86.31 243 | 45.54 306 | 89.05 219 | 55.03 289 | 92.24 202 | 92.66 137 |
|
PVSNet_BlendedMVS | | | 78.80 204 | 77.84 207 | 81.65 194 | 84.43 253 | 63.41 190 | 79.49 242 | 90.44 133 | 61.70 272 | 75.43 281 | 87.07 232 | 69.11 217 | 91.44 167 | 60.68 247 | 92.24 202 | 90.11 207 |
|
DELS-MVS | | | 81.44 180 | 81.25 181 | 82.03 185 | 84.27 257 | 62.87 203 | 76.47 280 | 92.49 74 | 70.97 199 | 81.64 225 | 83.83 274 | 75.03 150 | 92.70 138 | 74.29 146 | 92.22 204 | 90.51 198 |
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 |
testdata | | | | | 79.54 219 | 92.87 69 | 72.34 123 | | 80.14 250 | 59.91 281 | 85.47 172 | 91.75 138 | 67.96 221 | 85.24 272 | 68.57 199 | 92.18 205 | 81.06 320 |
|
v148 | | | 82.31 170 | 82.48 167 | 81.81 193 | 85.59 239 | 59.66 244 | 81.47 214 | 86.02 210 | 72.85 169 | 88.05 131 | 90.65 175 | 70.73 212 | 90.91 181 | 75.15 142 | 91.79 206 | 94.87 77 |
|
tfpn1000 | | | 73.63 257 | 73.58 245 | 73.79 279 | 85.46 242 | 50.31 315 | 79.99 234 | 68.18 323 | 72.33 177 | 80.66 241 | 83.05 282 | 39.80 341 | 86.74 257 | 60.96 245 | 91.78 207 | 84.32 269 |
|
test222 | | | | | | 93.31 58 | 76.54 95 | 79.38 249 | 77.79 257 | 52.59 310 | 82.36 213 | 90.84 168 | 66.83 225 | | | 91.69 208 | 81.25 315 |
|
pmmvs-eth3d | | | 78.42 206 | 77.04 212 | 82.57 180 | 87.44 195 | 74.41 109 | 80.86 226 | 79.67 252 | 55.68 295 | 84.69 180 | 90.31 183 | 60.91 245 | 85.42 271 | 62.20 234 | 91.59 209 | 87.88 231 |
|
Vis-MVSNet | | | 86.86 81 | 86.58 91 | 87.72 75 | 92.09 90 | 77.43 86 | 87.35 87 | 92.09 82 | 78.87 88 | 84.27 194 | 94.05 82 | 78.35 117 | 93.65 87 | 80.54 95 | 91.58 210 | 92.08 159 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
conf0.01 | | | 74.17 248 | 73.53 247 | 76.08 262 | 86.13 230 | 50.06 318 | 79.45 243 | 68.54 317 | 72.01 183 | 80.76 235 | 82.50 293 | 41.39 330 | 86.83 248 | 59.66 253 | 91.36 211 | 91.34 177 |
|
conf0.002 | | | 74.17 248 | 73.53 247 | 76.08 262 | 86.13 230 | 50.06 318 | 79.45 243 | 68.54 317 | 72.01 183 | 80.76 235 | 82.50 293 | 41.39 330 | 86.83 248 | 59.66 253 | 91.36 211 | 91.34 177 |
|
thresconf0.02 | | | 73.65 253 | 73.53 247 | 73.98 273 | 86.13 230 | 50.06 318 | 79.45 243 | 68.54 317 | 72.01 183 | 80.76 235 | 82.50 293 | 41.39 330 | 86.83 248 | 59.66 253 | 91.36 211 | 85.06 257 |
|
tfpn_n400 | | | 73.65 253 | 73.53 247 | 73.98 273 | 86.13 230 | 50.06 318 | 79.45 243 | 68.54 317 | 72.01 183 | 80.76 235 | 82.50 293 | 41.39 330 | 86.83 248 | 59.66 253 | 91.36 211 | 85.06 257 |
|
tfpnconf | | | 73.65 253 | 73.53 247 | 73.98 273 | 86.13 230 | 50.06 318 | 79.45 243 | 68.54 317 | 72.01 183 | 80.76 235 | 82.50 293 | 41.39 330 | 86.83 248 | 59.66 253 | 91.36 211 | 85.06 257 |
|
tfpnview11 | | | 73.65 253 | 73.53 247 | 73.98 273 | 86.13 230 | 50.06 318 | 79.45 243 | 68.54 317 | 72.01 183 | 80.76 235 | 82.50 293 | 41.39 330 | 86.83 248 | 59.66 253 | 91.36 211 | 85.06 257 |
|
OpenMVS | | 76.72 13 | 81.98 177 | 82.00 173 | 81.93 186 | 84.42 255 | 68.22 159 | 88.50 73 | 89.48 163 | 66.92 230 | 81.80 223 | 91.86 131 | 72.59 199 | 90.16 201 | 71.19 174 | 91.25 217 | 87.40 235 |
|
Test4 | | | 81.31 181 | 81.13 183 | 81.88 189 | 84.89 247 | 63.05 199 | 82.37 190 | 90.50 131 | 62.75 262 | 89.00 116 | 88.29 215 | 67.55 222 | 91.68 161 | 73.55 156 | 91.24 218 | 90.89 185 |
|
EG-PatchMatch MVS | | | 84.08 144 | 84.11 144 | 83.98 146 | 92.22 88 | 72.61 120 | 82.20 199 | 87.02 201 | 72.63 172 | 88.86 117 | 91.02 160 | 78.52 114 | 91.11 174 | 73.41 158 | 91.09 219 | 88.21 224 |
|
3Dnovator | | 80.37 7 | 84.80 120 | 84.71 124 | 85.06 117 | 86.36 222 | 74.71 107 | 88.77 69 | 90.00 153 | 75.65 135 | 84.96 174 | 93.17 102 | 74.06 165 | 91.19 172 | 78.28 120 | 91.09 219 | 89.29 215 |
|
Regformer-3 | | | 85.06 113 | 84.67 126 | 86.22 97 | 84.27 257 | 73.43 114 | 84.07 138 | 85.26 217 | 80.77 65 | 88.62 123 | 85.48 250 | 80.56 102 | 90.39 195 | 81.99 78 | 91.04 221 | 94.85 79 |
|
Regformer-4 | | | 86.41 90 | 85.71 105 | 88.52 62 | 84.27 257 | 77.57 84 | 84.07 138 | 88.00 183 | 82.82 44 | 89.84 95 | 85.48 250 | 82.06 82 | 92.77 137 | 83.83 56 | 91.04 221 | 95.22 74 |
|
conf200view11 | | | 75.62 235 | 75.05 233 | 77.34 248 | 87.27 201 | 51.88 295 | 81.07 221 | 73.26 286 | 75.68 132 | 83.25 204 | 86.37 238 | 45.54 306 | 88.80 223 | 51.98 301 | 90.99 223 | 91.34 177 |
|
thres100view900 | | | 75.45 236 | 75.05 233 | 76.66 256 | 87.27 201 | 51.88 295 | 81.07 221 | 73.26 286 | 75.68 132 | 83.25 204 | 86.37 238 | 45.54 306 | 88.80 223 | 51.98 301 | 90.99 223 | 89.31 213 |
|
tfpn200view9 | | | 74.86 243 | 74.23 241 | 76.74 255 | 86.24 225 | 52.12 292 | 79.24 252 | 73.87 279 | 73.34 159 | 81.82 221 | 84.60 268 | 46.02 300 | 88.80 223 | 51.98 301 | 90.99 223 | 89.31 213 |
|
thres400 | | | 75.14 237 | 74.23 241 | 77.86 239 | 86.24 225 | 52.12 292 | 79.24 252 | 73.87 279 | 73.34 159 | 81.82 221 | 84.60 268 | 46.02 300 | 88.80 223 | 51.98 301 | 90.99 223 | 92.66 137 |
|
cascas | | | 76.29 229 | 74.81 235 | 80.72 206 | 84.47 252 | 62.94 201 | 73.89 299 | 87.34 190 | 55.94 294 | 75.16 285 | 76.53 329 | 63.97 234 | 91.16 173 | 65.00 220 | 90.97 227 | 88.06 226 |
|
HSP-MVS | | | 88.63 61 | 87.84 69 | 91.02 29 | 95.76 16 | 86.14 19 | 92.75 13 | 91.01 123 | 78.43 94 | 89.16 114 | 92.25 127 | 72.03 206 | 96.36 2 | 88.21 9 | 90.93 228 | 90.55 197 |
|
ab-mvs | | | 79.67 200 | 80.56 188 | 76.99 250 | 88.48 163 | 56.93 260 | 84.70 128 | 86.06 209 | 68.95 217 | 80.78 234 | 93.08 103 | 75.30 147 | 84.62 279 | 56.78 276 | 90.90 229 | 89.43 212 |
|
MAR-MVS | | | 80.24 196 | 78.74 203 | 84.73 126 | 86.87 215 | 78.18 77 | 85.75 115 | 87.81 186 | 65.67 242 | 77.84 262 | 78.50 321 | 73.79 171 | 90.53 192 | 61.59 241 | 90.87 230 | 85.49 254 |
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 |
EI-MVSNet-Vis-set | | | 85.12 112 | 84.53 133 | 86.88 84 | 84.01 263 | 72.76 118 | 83.91 145 | 85.18 219 | 80.44 66 | 88.75 120 | 85.49 249 | 80.08 105 | 91.92 153 | 82.02 77 | 90.85 231 | 95.97 49 |
|
EI-MVSNet-UG-set | | | 85.04 114 | 84.44 135 | 86.85 85 | 83.87 266 | 72.52 121 | 83.82 147 | 85.15 220 | 80.27 70 | 88.75 120 | 85.45 253 | 79.95 107 | 91.90 154 | 81.92 79 | 90.80 232 | 96.13 43 |
|
XVG-OURS-SEG-HR | | | 89.59 49 | 89.37 52 | 90.28 42 | 94.47 40 | 85.95 21 | 86.84 96 | 93.91 27 | 80.07 72 | 86.75 150 | 93.26 100 | 93.64 2 | 90.93 179 | 84.60 45 | 90.75 233 | 93.97 100 |
|
test_normal | | | 81.23 185 | 81.16 182 | 81.43 196 | 84.77 250 | 61.99 221 | 81.46 215 | 86.95 203 | 63.16 259 | 87.22 141 | 89.63 193 | 73.62 174 | 91.65 162 | 72.92 164 | 90.70 234 | 90.65 194 |
|
EI-MVSNet | | | 82.61 166 | 82.42 168 | 83.20 168 | 83.25 270 | 63.66 187 | 83.50 161 | 85.07 221 | 76.06 125 | 86.55 153 | 85.10 258 | 73.41 180 | 90.25 196 | 78.15 122 | 90.67 235 | 95.68 58 |
|
MVSTER | | | 77.09 216 | 75.70 228 | 81.25 199 | 75.27 331 | 61.08 233 | 77.49 272 | 85.07 221 | 60.78 277 | 86.55 153 | 88.68 206 | 43.14 326 | 90.25 196 | 73.69 154 | 90.67 235 | 92.42 146 |
|
Patchmatch-RL test | | | 74.48 245 | 73.68 244 | 76.89 252 | 84.83 248 | 66.54 169 | 72.29 305 | 69.16 316 | 57.70 286 | 86.76 149 | 86.33 241 | 45.79 305 | 82.59 291 | 69.63 186 | 90.65 237 | 81.54 310 |
|
CMPMVS | | 59.41 20 | 75.12 239 | 73.57 246 | 79.77 212 | 75.84 323 | 67.22 163 | 81.21 219 | 82.18 236 | 50.78 325 | 76.50 270 | 87.66 225 | 55.20 276 | 82.99 289 | 62.17 235 | 90.64 238 | 89.09 217 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
DI_MVS_plusplus_test | | | 81.27 183 | 81.26 180 | 81.29 198 | 84.98 245 | 61.65 226 | 81.98 202 | 87.25 193 | 63.56 254 | 87.56 138 | 89.60 194 | 73.62 174 | 91.83 157 | 72.20 169 | 90.59 239 | 90.38 201 |
|
tfpn_ndepth | | | 72.54 264 | 72.30 265 | 73.24 282 | 84.81 249 | 51.42 301 | 79.24 252 | 70.49 310 | 69.26 212 | 78.48 260 | 79.80 316 | 40.16 340 | 86.77 255 | 58.08 272 | 90.43 240 | 81.53 311 |
|
V42 | | | 83.47 158 | 83.37 154 | 83.75 152 | 83.16 272 | 63.33 193 | 81.31 216 | 90.23 148 | 69.51 210 | 90.91 77 | 90.81 169 | 74.16 164 | 92.29 147 | 80.06 102 | 90.22 241 | 95.62 60 |
|
PM-MVS | | | 80.20 197 | 79.00 201 | 83.78 151 | 88.17 170 | 86.66 13 | 81.31 216 | 66.81 332 | 69.64 209 | 88.33 130 | 90.19 184 | 64.58 232 | 83.63 287 | 71.99 172 | 90.03 242 | 81.06 320 |
|
PLC | | 73.85 16 | 82.09 174 | 80.31 191 | 87.45 80 | 90.86 124 | 80.29 60 | 85.88 113 | 90.65 127 | 68.17 220 | 76.32 273 | 86.33 241 | 73.12 189 | 92.61 142 | 61.40 242 | 90.02 243 | 89.44 211 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
mvs-test1 | | | 84.55 125 | 82.12 170 | 91.84 17 | 87.13 207 | 89.54 5 | 85.05 124 | 88.42 174 | 74.57 146 | 80.60 242 | 82.98 284 | 78.49 115 | 93.98 76 | 72.04 170 | 89.77 244 | 92.00 160 |
|
CANet_DTU | | | 77.81 210 | 77.05 211 | 80.09 210 | 81.37 283 | 59.90 243 | 83.26 167 | 88.29 177 | 69.16 214 | 67.83 321 | 83.72 275 | 60.93 244 | 89.47 209 | 69.22 191 | 89.70 245 | 90.88 186 |
|
PMMVS2 | | | 55.64 324 | 59.27 321 | 44.74 339 | 64.30 354 | 12.32 357 | 40.60 349 | 49.79 354 | 53.19 307 | 65.06 333 | 84.81 264 | 53.60 281 | 49.76 352 | 32.68 348 | 89.41 246 | 72.15 335 |
|
Fast-Effi-MVS+-dtu | | | 82.54 168 | 81.41 179 | 85.90 106 | 85.60 238 | 76.53 97 | 83.07 172 | 89.62 161 | 73.02 168 | 79.11 257 | 83.51 277 | 80.74 100 | 90.24 198 | 68.76 195 | 89.29 247 | 90.94 183 |
|
thres200 | | | 72.34 267 | 71.55 271 | 74.70 271 | 83.48 267 | 51.60 300 | 75.02 291 | 73.71 282 | 70.14 206 | 78.56 259 | 80.57 311 | 46.20 298 | 88.20 234 | 46.99 323 | 89.29 247 | 84.32 269 |
|
jason | | | 77.42 213 | 75.75 227 | 82.43 183 | 87.10 210 | 69.27 150 | 77.99 265 | 81.94 240 | 51.47 320 | 77.84 262 | 85.07 260 | 60.32 248 | 89.00 220 | 70.74 178 | 89.27 249 | 89.03 218 |
jason: jason. |
MG-MVS | | | 80.32 194 | 80.94 185 | 78.47 230 | 88.18 169 | 52.62 290 | 82.29 193 | 85.01 224 | 72.01 183 | 79.24 256 | 92.54 119 | 69.36 215 | 93.36 114 | 70.65 179 | 89.19 250 | 89.45 210 |
|
BH-untuned | | | 80.96 187 | 80.99 184 | 80.84 203 | 88.55 160 | 68.23 158 | 80.33 230 | 88.46 173 | 72.79 170 | 86.55 153 | 86.76 234 | 74.72 158 | 91.77 159 | 61.79 237 | 88.99 251 | 82.52 294 |
|
PVSNet_Blended_VisFu | | | 81.55 179 | 80.49 190 | 84.70 127 | 91.58 104 | 73.24 116 | 84.21 135 | 91.67 94 | 62.86 261 | 80.94 231 | 87.16 230 | 67.27 223 | 92.87 136 | 69.82 185 | 88.94 252 | 87.99 228 |
|
MVSFormer | | | 82.23 172 | 81.57 178 | 84.19 143 | 85.54 240 | 69.26 151 | 91.98 25 | 90.08 150 | 71.54 194 | 76.23 274 | 85.07 260 | 58.69 259 | 94.27 62 | 86.26 28 | 88.77 253 | 89.03 218 |
|
lupinMVS | | | 76.37 228 | 74.46 239 | 82.09 184 | 85.54 240 | 69.26 151 | 76.79 274 | 80.77 247 | 50.68 327 | 76.23 274 | 82.82 288 | 58.69 259 | 88.94 221 | 69.85 184 | 88.77 253 | 88.07 225 |
|
RPSCF | | | 88.00 67 | 86.93 86 | 91.22 27 | 90.08 135 | 89.30 6 | 89.68 49 | 91.11 120 | 79.26 81 | 89.68 99 | 94.81 58 | 82.44 77 | 87.74 238 | 76.54 136 | 88.74 255 | 96.61 38 |
|
PAPM_NR | | | 83.23 160 | 83.19 157 | 83.33 166 | 90.90 122 | 65.98 171 | 88.19 75 | 90.78 125 | 78.13 99 | 80.87 233 | 87.92 222 | 73.49 179 | 92.42 144 | 70.07 183 | 88.40 256 | 91.60 169 |
|
xiu_mvs_v1_base_debu | | | 80.84 189 | 80.14 194 | 82.93 173 | 88.31 166 | 71.73 133 | 79.53 239 | 87.17 194 | 65.43 243 | 79.59 252 | 82.73 290 | 76.94 133 | 90.14 202 | 73.22 159 | 88.33 257 | 86.90 240 |
|
xiu_mvs_v1_base | | | 80.84 189 | 80.14 194 | 82.93 173 | 88.31 166 | 71.73 133 | 79.53 239 | 87.17 194 | 65.43 243 | 79.59 252 | 82.73 290 | 76.94 133 | 90.14 202 | 73.22 159 | 88.33 257 | 86.90 240 |
|
xiu_mvs_v1_base_debi | | | 80.84 189 | 80.14 194 | 82.93 173 | 88.31 166 | 71.73 133 | 79.53 239 | 87.17 194 | 65.43 243 | 79.59 252 | 82.73 290 | 76.94 133 | 90.14 202 | 73.22 159 | 88.33 257 | 86.90 240 |
|
XXY-MVS | | | 74.44 247 | 76.19 223 | 69.21 300 | 84.61 251 | 52.43 291 | 71.70 307 | 77.18 260 | 60.73 278 | 80.60 242 | 90.96 164 | 75.44 144 | 69.35 325 | 56.13 279 | 88.33 257 | 85.86 250 |
|
Fast-Effi-MVS+ | | | 81.04 186 | 80.57 187 | 82.46 182 | 87.50 194 | 63.22 197 | 78.37 262 | 89.63 160 | 68.01 221 | 81.87 219 | 82.08 302 | 82.31 79 | 92.65 141 | 67.10 205 | 88.30 261 | 91.51 172 |
|
MDA-MVSNet-bldmvs | | | 77.47 212 | 76.90 213 | 79.16 221 | 79.03 301 | 64.59 178 | 66.58 324 | 75.67 269 | 73.15 166 | 88.86 117 | 88.99 202 | 66.94 224 | 81.23 296 | 64.71 221 | 88.22 262 | 91.64 168 |
|
PAPR | | | 78.84 203 | 78.10 206 | 81.07 201 | 85.17 244 | 60.22 241 | 82.21 197 | 90.57 130 | 62.51 264 | 75.32 283 | 84.61 267 | 74.99 151 | 92.30 146 | 59.48 260 | 88.04 263 | 90.68 192 |
|
BH-RMVSNet | | | 80.53 192 | 80.22 193 | 81.49 195 | 87.19 206 | 66.21 170 | 77.79 268 | 86.23 207 | 74.21 149 | 83.69 197 | 88.50 208 | 73.25 188 | 90.75 185 | 63.18 232 | 87.90 264 | 87.52 233 |
|
Effi-MVS+ | | | 83.90 150 | 84.01 146 | 83.57 160 | 87.22 205 | 65.61 174 | 86.55 108 | 92.40 75 | 78.64 92 | 81.34 229 | 84.18 271 | 83.65 64 | 92.93 133 | 74.22 147 | 87.87 265 | 92.17 158 |
|
MVS_Test | | | 82.47 169 | 83.22 155 | 80.22 209 | 82.62 276 | 57.75 256 | 82.54 187 | 91.96 87 | 71.16 197 | 82.89 210 | 92.52 120 | 77.41 126 | 90.50 193 | 80.04 103 | 87.84 266 | 92.40 148 |
|
QAPM | | | 82.59 167 | 82.59 165 | 82.58 178 | 86.44 217 | 66.69 168 | 89.94 44 | 90.36 136 | 67.97 223 | 84.94 176 | 92.58 118 | 72.71 195 | 92.18 148 | 70.63 180 | 87.73 267 | 88.85 221 |
|
PVSNet_Blended | | | 76.49 227 | 75.40 230 | 79.76 213 | 84.43 253 | 63.41 190 | 75.14 290 | 90.44 133 | 57.36 288 | 75.43 281 | 78.30 322 | 69.11 217 | 91.44 167 | 60.68 247 | 87.70 268 | 84.42 267 |
|
pmmvs5 | | | 70.73 277 | 70.07 280 | 72.72 287 | 77.03 316 | 52.73 288 | 74.14 296 | 75.65 270 | 50.36 329 | 72.17 302 | 85.37 256 | 55.42 275 | 80.67 298 | 52.86 298 | 87.59 269 | 84.77 263 |
|
testmv | | | 70.47 279 | 70.70 274 | 69.77 297 | 86.22 227 | 53.89 280 | 67.32 321 | 71.91 298 | 63.32 256 | 78.16 261 | 89.47 196 | 56.12 271 | 73.10 316 | 36.43 342 | 87.33 270 | 82.33 297 |
|
IB-MVS | | 62.13 19 | 71.64 271 | 68.97 285 | 79.66 217 | 80.80 291 | 62.26 220 | 73.94 298 | 76.90 262 | 63.27 258 | 68.63 317 | 76.79 327 | 33.83 347 | 91.84 156 | 59.28 261 | 87.26 271 | 84.88 262 |
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 |
N_pmnet | | | 70.20 280 | 68.80 287 | 74.38 272 | 80.91 288 | 84.81 35 | 59.12 338 | 76.45 266 | 55.06 298 | 75.31 284 | 82.36 299 | 55.74 272 | 54.82 350 | 47.02 322 | 87.24 272 | 83.52 279 |
|
pmmvs4 | | | 74.92 242 | 72.98 258 | 80.73 205 | 84.95 246 | 71.71 136 | 76.23 282 | 77.59 258 | 52.83 309 | 77.73 265 | 86.38 237 | 56.35 269 | 84.97 275 | 57.72 273 | 87.05 273 | 85.51 253 |
|
MIMVSNet | | | 71.09 275 | 71.59 270 | 69.57 299 | 87.23 204 | 50.07 317 | 78.91 257 | 71.83 299 | 60.20 280 | 71.26 306 | 91.76 137 | 55.08 277 | 76.09 309 | 41.06 334 | 87.02 274 | 82.54 293 |
|
HyFIR lowres test | | | 75.12 239 | 72.66 260 | 82.50 181 | 91.44 110 | 65.19 175 | 72.47 304 | 87.31 191 | 46.79 336 | 80.29 248 | 84.30 270 | 52.70 282 | 92.10 151 | 51.88 305 | 86.73 275 | 90.22 203 |
|
diffmvs | | | 79.20 202 | 79.04 200 | 79.69 215 | 78.64 305 | 58.90 249 | 81.79 207 | 87.61 188 | 65.07 249 | 73.65 295 | 89.80 190 | 73.10 190 | 87.79 237 | 75.02 144 | 86.63 276 | 92.38 149 |
|
MSDG | | | 80.06 199 | 79.99 197 | 80.25 208 | 83.91 265 | 68.04 160 | 77.51 271 | 89.19 166 | 77.65 103 | 81.94 218 | 83.45 279 | 76.37 141 | 86.31 262 | 63.31 231 | 86.59 277 | 86.41 243 |
|
Patchmatch-test | | | 65.91 302 | 67.38 293 | 61.48 325 | 75.51 327 | 43.21 340 | 68.84 314 | 63.79 336 | 62.48 265 | 72.80 298 | 83.42 280 | 44.89 321 | 59.52 347 | 48.27 318 | 86.45 278 | 81.70 306 |
|
mvs_anonymous | | | 78.13 207 | 78.76 202 | 76.23 261 | 79.24 299 | 50.31 315 | 78.69 260 | 84.82 226 | 61.60 273 | 83.09 209 | 92.82 111 | 73.89 170 | 87.01 243 | 68.33 200 | 86.41 279 | 91.37 175 |
|
semantic-postprocess | | | | | 84.34 138 | 83.93 264 | 69.66 147 | | 81.09 245 | 72.43 173 | 86.47 159 | 90.19 184 | 57.56 265 | 93.15 125 | 77.45 128 | 86.39 280 | 90.22 203 |
|
E-PMN | | | 61.59 313 | 61.62 313 | 61.49 324 | 66.81 350 | 55.40 270 | 53.77 345 | 60.34 343 | 66.80 232 | 58.90 346 | 65.50 347 | 40.48 339 | 66.12 337 | 55.72 281 | 86.25 281 | 62.95 344 |
|
EMVS | | | 61.10 316 | 60.81 316 | 61.99 322 | 65.96 352 | 55.86 267 | 53.10 346 | 58.97 345 | 67.06 228 | 56.89 350 | 63.33 348 | 40.98 337 | 67.03 332 | 54.79 290 | 86.18 282 | 63.08 343 |
|
EU-MVSNet | | | 75.12 239 | 74.43 240 | 77.18 249 | 83.11 273 | 59.48 246 | 85.71 117 | 82.43 235 | 39.76 348 | 85.64 168 | 88.76 204 | 44.71 322 | 87.88 236 | 73.86 153 | 85.88 283 | 84.16 272 |
|
1111 | | | 61.71 311 | 63.77 307 | 55.55 334 | 78.05 308 | 25.74 353 | 60.62 332 | 67.52 324 | 66.09 236 | 74.68 287 | 86.50 235 | 16.00 359 | 59.22 348 | 38.79 337 | 85.65 284 | 81.70 306 |
|
GA-MVS | | | 75.83 233 | 74.61 236 | 79.48 220 | 81.87 279 | 59.25 248 | 73.42 302 | 82.88 232 | 68.68 218 | 79.75 251 | 81.80 304 | 50.62 286 | 89.46 210 | 66.85 208 | 85.64 285 | 89.72 209 |
|
MVS | | | 73.21 259 | 72.59 262 | 75.06 268 | 80.97 287 | 60.81 237 | 81.64 211 | 85.92 211 | 46.03 339 | 71.68 304 | 77.54 323 | 68.47 220 | 89.77 207 | 55.70 282 | 85.39 286 | 74.60 331 |
|
PatchT | | | 70.52 278 | 72.76 259 | 63.79 319 | 79.38 297 | 33.53 349 | 77.63 269 | 65.37 334 | 73.61 154 | 71.77 303 | 92.79 114 | 44.38 323 | 75.65 312 | 64.53 225 | 85.37 287 | 82.18 299 |
|
TR-MVS | | | 76.77 223 | 75.79 225 | 79.72 214 | 86.10 236 | 65.79 173 | 77.14 273 | 83.02 231 | 65.20 247 | 81.40 227 | 82.10 301 | 66.30 226 | 90.73 187 | 55.57 283 | 85.27 288 | 82.65 290 |
|
BH-w/o | | | 76.57 225 | 76.07 224 | 78.10 235 | 86.88 214 | 65.92 172 | 77.63 269 | 86.33 206 | 65.69 241 | 80.89 232 | 79.95 315 | 68.97 219 | 90.74 186 | 53.01 297 | 85.25 289 | 77.62 325 |
|
test1235678 | | | 65.57 304 | 65.73 301 | 65.06 316 | 82.84 275 | 50.90 307 | 62.90 329 | 69.26 314 | 57.17 291 | 72.36 300 | 83.04 283 | 46.02 300 | 70.10 322 | 32.79 347 | 85.24 290 | 74.19 332 |
|
IterMVS | | | 76.91 218 | 76.34 222 | 78.64 227 | 80.91 288 | 64.03 183 | 76.30 281 | 79.03 253 | 64.88 251 | 83.11 207 | 89.16 199 | 59.90 252 | 84.46 280 | 68.61 198 | 85.15 291 | 87.42 234 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
OpenMVS_ROB | | 70.19 17 | 77.77 211 | 77.46 209 | 78.71 226 | 84.39 256 | 61.15 232 | 81.18 220 | 82.52 234 | 62.45 266 | 83.34 203 | 87.37 229 | 66.20 227 | 88.66 230 | 64.69 222 | 85.02 292 | 86.32 244 |
|
test-LLR | | | 67.21 296 | 66.74 297 | 68.63 304 | 76.45 320 | 55.21 272 | 67.89 317 | 67.14 329 | 62.43 267 | 65.08 331 | 72.39 337 | 43.41 324 | 69.37 323 | 61.00 243 | 84.89 293 | 81.31 313 |
|
test-mter | | | 65.00 305 | 63.79 306 | 68.63 304 | 76.45 320 | 55.21 272 | 67.89 317 | 67.14 329 | 50.98 324 | 65.08 331 | 72.39 337 | 28.27 354 | 69.37 323 | 61.00 243 | 84.89 293 | 81.31 313 |
|
PS-MVSNAJ | | | 77.04 217 | 76.53 220 | 78.56 228 | 87.09 211 | 61.40 228 | 75.26 289 | 87.13 197 | 61.25 274 | 74.38 291 | 77.22 326 | 76.94 133 | 90.94 178 | 64.63 223 | 84.83 295 | 83.35 283 |
|
xiu_mvs_v2_base | | | 77.19 215 | 76.75 214 | 78.52 229 | 87.01 212 | 61.30 230 | 75.55 288 | 87.12 199 | 61.24 275 | 74.45 289 | 78.79 320 | 77.20 127 | 90.93 179 | 64.62 224 | 84.80 296 | 83.32 284 |
|
pmmvs3 | | | 62.47 307 | 60.02 320 | 69.80 296 | 71.58 347 | 64.00 184 | 70.52 310 | 58.44 346 | 39.77 347 | 66.05 325 | 75.84 330 | 27.10 356 | 72.28 317 | 46.15 325 | 84.77 297 | 73.11 334 |
|
MDTV_nov1_ep13 | | | | 68.29 291 | | 78.03 310 | 43.87 338 | 74.12 297 | 72.22 295 | 52.17 313 | 67.02 323 | 85.54 248 | 45.36 312 | 80.85 297 | 55.73 280 | 84.42 298 | |
|
1112_ss | | | 74.82 244 | 73.74 243 | 78.04 236 | 89.57 142 | 60.04 242 | 76.49 279 | 87.09 200 | 54.31 301 | 73.66 294 | 79.80 316 | 60.25 249 | 86.76 256 | 58.37 267 | 84.15 299 | 87.32 236 |
|
PatchMatch-RL | | | 74.48 245 | 73.22 255 | 78.27 233 | 87.70 189 | 85.26 30 | 75.92 283 | 70.09 311 | 64.34 253 | 76.09 276 | 81.25 309 | 65.87 230 | 78.07 304 | 53.86 294 | 83.82 300 | 71.48 336 |
|
MDA-MVSNet_test_wron | | | 70.05 284 | 70.44 276 | 68.88 302 | 73.84 336 | 53.47 282 | 58.93 340 | 67.28 327 | 58.43 283 | 87.09 145 | 85.40 254 | 59.80 254 | 67.25 331 | 59.66 253 | 83.54 301 | 85.92 249 |
|
YYNet1 | | | 70.06 283 | 70.44 276 | 68.90 301 | 73.76 337 | 53.42 284 | 58.99 339 | 67.20 328 | 58.42 284 | 87.10 144 | 85.39 255 | 59.82 253 | 67.32 330 | 59.79 251 | 83.50 302 | 85.96 247 |
|
Test_1112_low_res | | | 73.90 251 | 73.08 256 | 76.35 258 | 90.35 131 | 55.95 265 | 73.40 303 | 86.17 208 | 50.70 326 | 73.14 296 | 85.94 246 | 58.31 261 | 85.90 267 | 56.51 277 | 83.22 303 | 87.20 237 |
|
PVSNet | | 58.17 21 | 66.41 300 | 65.63 302 | 68.75 303 | 81.96 278 | 49.88 324 | 62.19 331 | 72.51 294 | 51.03 323 | 68.04 319 | 75.34 333 | 50.84 285 | 74.77 313 | 45.82 327 | 82.96 304 | 81.60 309 |
|
gg-mvs-nofinetune | | | 68.96 291 | 69.11 284 | 68.52 306 | 76.12 322 | 45.32 332 | 83.59 154 | 55.88 349 | 86.68 20 | 64.62 334 | 97.01 7 | 30.36 351 | 83.97 285 | 44.78 328 | 82.94 305 | 76.26 328 |
|
CR-MVSNet | | | 74.00 250 | 73.04 257 | 76.85 253 | 79.58 294 | 62.64 206 | 82.58 184 | 76.90 262 | 50.50 328 | 75.72 279 | 92.38 121 | 48.07 293 | 84.07 282 | 68.72 197 | 82.91 306 | 83.85 275 |
|
RPMNet | | | 76.06 230 | 75.79 225 | 76.85 253 | 79.58 294 | 62.64 206 | 82.58 184 | 71.75 301 | 74.80 143 | 75.72 279 | 92.59 116 | 48.69 291 | 84.07 282 | 73.48 157 | 82.91 306 | 83.85 275 |
|
test0.0.03 1 | | | 64.66 306 | 64.36 305 | 65.57 314 | 75.03 333 | 46.89 329 | 64.69 327 | 61.58 342 | 62.43 267 | 71.18 308 | 77.54 323 | 43.41 324 | 68.47 328 | 40.75 335 | 82.65 308 | 81.35 312 |
|
HY-MVS | | 64.64 18 | 73.03 260 | 72.47 264 | 74.71 270 | 83.36 269 | 54.19 277 | 82.14 200 | 81.96 238 | 56.76 293 | 69.57 314 | 86.21 244 | 60.03 250 | 84.83 278 | 49.58 312 | 82.65 308 | 85.11 256 |
|
Patchmatch-test1 | | | 72.75 262 | 72.61 261 | 73.19 283 | 81.62 281 | 55.86 267 | 78.89 258 | 71.37 303 | 61.73 270 | 74.93 286 | 82.15 300 | 60.46 247 | 81.80 293 | 59.68 252 | 82.63 310 | 81.92 304 |
|
testus | | | 62.33 309 | 63.03 309 | 60.20 329 | 78.78 303 | 40.74 342 | 59.14 336 | 69.80 313 | 49.26 332 | 71.41 305 | 74.72 335 | 52.33 283 | 63.52 342 | 29.84 349 | 82.01 311 | 76.36 327 |
|
CHOSEN 1792x2688 | | | 72.45 265 | 70.56 275 | 78.13 234 | 90.02 140 | 63.08 198 | 68.72 315 | 83.16 230 | 42.99 345 | 75.92 277 | 85.46 252 | 57.22 268 | 85.18 274 | 49.87 310 | 81.67 312 | 86.14 246 |
|
WTY-MVS | | | 67.91 294 | 68.35 289 | 66.58 311 | 80.82 290 | 48.12 327 | 65.96 325 | 72.60 292 | 53.67 305 | 71.20 307 | 81.68 306 | 58.97 258 | 69.06 327 | 48.57 315 | 81.67 312 | 82.55 292 |
|
TESTMET0.1,1 | | | 61.29 314 | 60.32 318 | 64.19 318 | 72.06 345 | 51.30 302 | 67.89 317 | 62.09 338 | 45.27 340 | 60.65 340 | 69.01 340 | 27.93 355 | 64.74 341 | 56.31 278 | 81.65 314 | 76.53 326 |
|
PAPM | | | 71.77 270 | 70.06 281 | 76.92 251 | 86.39 218 | 53.97 278 | 76.62 277 | 86.62 204 | 53.44 306 | 63.97 335 | 84.73 266 | 57.79 264 | 92.34 145 | 39.65 336 | 81.33 315 | 84.45 266 |
|
DSMNet-mixed | | | 60.98 317 | 61.61 314 | 59.09 331 | 72.88 343 | 45.05 335 | 74.70 293 | 46.61 355 | 26.20 351 | 65.34 329 | 90.32 182 | 55.46 274 | 63.12 345 | 41.72 333 | 81.30 316 | 69.09 340 |
|
sss | | | 66.92 297 | 67.26 295 | 65.90 312 | 77.23 313 | 51.10 306 | 64.79 326 | 71.72 302 | 52.12 316 | 70.13 312 | 80.18 313 | 57.96 262 | 65.36 340 | 50.21 307 | 81.01 317 | 81.25 315 |
|
tpm | | | 67.95 293 | 68.08 292 | 67.55 308 | 78.74 304 | 43.53 339 | 75.60 286 | 67.10 331 | 54.92 299 | 72.23 301 | 88.10 217 | 42.87 327 | 75.97 310 | 52.21 299 | 80.95 318 | 83.15 287 |
|
tpmp4_e23 | | | 69.43 287 | 67.33 294 | 75.72 265 | 78.53 306 | 52.75 287 | 82.13 201 | 74.91 272 | 49.23 333 | 66.37 324 | 84.17 272 | 41.28 336 | 88.67 229 | 49.73 311 | 79.63 319 | 85.75 251 |
|
tpm2 | | | 68.45 292 | 66.83 296 | 73.30 281 | 78.93 302 | 48.50 325 | 79.76 236 | 71.76 300 | 47.50 335 | 69.92 313 | 83.60 276 | 42.07 329 | 88.40 231 | 48.44 317 | 79.51 320 | 83.01 289 |
|
FPMVS | | | 72.29 268 | 72.00 267 | 73.14 284 | 88.63 159 | 85.00 32 | 74.65 294 | 67.39 326 | 71.94 190 | 77.80 264 | 87.66 225 | 50.48 287 | 75.83 311 | 49.95 308 | 79.51 320 | 58.58 348 |
|
UnsupCasMVSNet_bld | | | 69.21 290 | 69.68 282 | 67.82 307 | 79.42 296 | 51.15 304 | 67.82 320 | 75.79 267 | 54.15 302 | 77.47 266 | 85.36 257 | 59.26 257 | 70.64 321 | 48.46 316 | 79.35 322 | 81.66 308 |
|
CostFormer | | | 69.98 285 | 68.68 288 | 73.87 277 | 77.14 314 | 50.72 313 | 79.26 251 | 74.51 277 | 51.94 317 | 70.97 309 | 84.75 265 | 45.16 320 | 87.49 239 | 55.16 287 | 79.23 323 | 83.40 282 |
|
1314 | | | 73.22 258 | 72.56 263 | 75.20 267 | 80.41 293 | 57.84 254 | 81.64 211 | 85.36 216 | 51.68 318 | 73.10 297 | 76.65 328 | 61.45 243 | 85.19 273 | 63.54 228 | 79.21 324 | 82.59 291 |
|
PMMVS | | | 61.65 312 | 60.38 317 | 65.47 315 | 65.40 353 | 69.26 151 | 63.97 328 | 61.73 341 | 36.80 350 | 60.11 341 | 68.43 341 | 59.42 255 | 66.35 336 | 48.97 314 | 78.57 325 | 60.81 345 |
|
test2356 | | | 56.69 321 | 55.15 325 | 61.32 326 | 73.20 339 | 44.11 337 | 54.95 343 | 62.52 337 | 48.75 334 | 62.45 337 | 68.42 342 | 21.10 358 | 65.67 339 | 26.86 351 | 78.08 326 | 74.19 332 |
|
MS-PatchMatch | | | 70.93 276 | 70.22 279 | 73.06 285 | 81.85 280 | 62.50 212 | 73.82 300 | 77.90 256 | 52.44 312 | 75.92 277 | 81.27 308 | 55.67 273 | 81.75 294 | 55.37 285 | 77.70 327 | 74.94 330 |
|
UnsupCasMVSNet_eth | | | 71.63 272 | 72.30 265 | 69.62 298 | 76.47 319 | 52.70 289 | 70.03 313 | 80.97 246 | 59.18 282 | 79.36 255 | 88.21 216 | 60.50 246 | 69.12 326 | 58.33 269 | 77.62 328 | 87.04 238 |
|
CVMVSNet | | | 72.62 263 | 71.41 272 | 76.28 260 | 83.25 270 | 60.34 240 | 83.50 161 | 79.02 254 | 37.77 349 | 76.33 272 | 85.10 258 | 49.60 289 | 87.41 240 | 70.54 181 | 77.54 329 | 81.08 318 |
|
GG-mvs-BLEND | | | | | 67.16 309 | 73.36 338 | 46.54 331 | 84.15 137 | 55.04 350 | | 58.64 347 | 61.95 350 | 29.93 352 | 83.87 286 | 38.71 340 | 76.92 330 | 71.07 337 |
|
CHOSEN 280x420 | | | 59.08 319 | 56.52 324 | 66.76 310 | 76.51 318 | 64.39 181 | 49.62 348 | 59.00 344 | 43.86 343 | 55.66 351 | 68.41 343 | 35.55 346 | 68.21 329 | 43.25 330 | 76.78 331 | 67.69 341 |
|
tpmvs | | | 70.16 281 | 69.56 283 | 71.96 292 | 74.71 335 | 48.13 326 | 79.63 237 | 75.45 271 | 65.02 250 | 70.26 311 | 81.88 303 | 45.34 313 | 85.68 269 | 58.34 268 | 75.39 332 | 82.08 300 |
|
MVP-Stereo | | | 75.81 234 | 73.51 253 | 82.71 176 | 89.35 144 | 73.62 112 | 80.06 231 | 85.20 218 | 60.30 279 | 73.96 292 | 87.94 220 | 57.89 263 | 89.45 211 | 52.02 300 | 74.87 333 | 85.06 257 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
DWT-MVSNet_test | | | 66.43 299 | 64.37 304 | 72.63 288 | 74.86 334 | 50.86 308 | 76.52 278 | 72.74 291 | 54.06 303 | 65.50 328 | 68.30 344 | 32.13 349 | 84.84 277 | 61.63 240 | 73.59 334 | 82.19 298 |
|
PatchFormer-LS_test | | | 67.91 294 | 66.49 300 | 72.17 291 | 75.29 330 | 51.85 298 | 75.68 284 | 73.62 284 | 57.23 290 | 68.64 315 | 68.13 345 | 42.19 328 | 82.76 290 | 64.06 226 | 73.51 335 | 81.89 305 |
|
new_pmnet | | | 55.69 323 | 57.66 322 | 49.76 336 | 75.47 328 | 30.59 350 | 59.56 334 | 51.45 353 | 43.62 344 | 62.49 336 | 75.48 331 | 40.96 338 | 49.15 353 | 37.39 341 | 72.52 336 | 69.55 339 |
|
test12356 | | | 54.91 325 | 57.14 323 | 48.22 338 | 75.83 324 | 17.47 355 | 52.31 347 | 69.20 315 | 51.66 319 | 60.11 341 | 75.40 332 | 29.77 353 | 62.62 346 | 27.64 350 | 72.37 337 | 64.59 342 |
|
PatchmatchNet | | | 69.71 286 | 68.83 286 | 72.33 290 | 77.66 312 | 53.60 281 | 79.29 250 | 69.99 312 | 57.66 287 | 72.53 299 | 82.93 287 | 46.45 297 | 80.08 302 | 60.91 246 | 72.09 338 | 83.31 285 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
MVS-HIRNet | | | 61.16 315 | 62.92 310 | 55.87 332 | 79.09 300 | 35.34 348 | 71.83 306 | 57.98 348 | 46.56 337 | 59.05 345 | 91.14 154 | 49.95 288 | 76.43 308 | 38.74 339 | 71.92 339 | 55.84 349 |
|
tpmrst | | | 66.28 301 | 66.69 298 | 65.05 317 | 72.82 344 | 39.33 344 | 78.20 263 | 70.69 309 | 53.16 308 | 67.88 320 | 80.36 312 | 48.18 292 | 74.75 314 | 58.13 270 | 70.79 340 | 81.08 318 |
|
LP | | | 69.42 288 | 68.30 290 | 72.77 286 | 71.48 348 | 56.84 263 | 73.66 301 | 74.84 273 | 63.52 255 | 70.95 310 | 83.35 281 | 49.55 290 | 77.15 307 | 57.13 275 | 70.21 341 | 84.33 268 |
|
tpm cat1 | | | 66.76 298 | 65.21 303 | 71.42 293 | 77.09 315 | 50.62 314 | 78.01 264 | 73.68 283 | 44.89 341 | 68.64 315 | 79.00 319 | 45.51 310 | 82.42 292 | 49.91 309 | 70.15 342 | 81.23 317 |
|
ADS-MVSNet2 | | | 65.87 303 | 63.64 308 | 72.55 289 | 73.16 341 | 56.92 261 | 67.10 322 | 74.81 274 | 49.74 330 | 66.04 326 | 82.97 285 | 46.71 295 | 77.26 305 | 42.29 331 | 69.96 343 | 83.46 280 |
|
ADS-MVSNet | | | 61.90 310 | 62.19 312 | 61.03 327 | 73.16 341 | 36.42 347 | 67.10 322 | 61.75 340 | 49.74 330 | 66.04 326 | 82.97 285 | 46.71 295 | 63.21 344 | 42.29 331 | 69.96 343 | 83.46 280 |
|
JIA-IIPM | | | 69.41 289 | 66.64 299 | 77.70 241 | 73.19 340 | 71.24 139 | 75.67 285 | 65.56 333 | 70.42 202 | 65.18 330 | 92.97 107 | 33.64 348 | 83.06 288 | 53.52 296 | 69.61 345 | 78.79 324 |
|
testpf | | | 58.55 320 | 61.58 315 | 49.48 337 | 66.03 351 | 40.05 343 | 74.40 295 | 58.07 347 | 64.72 252 | 59.36 343 | 72.67 336 | 22.76 357 | 66.92 333 | 67.07 206 | 69.15 346 | 41.46 351 |
|
EPMVS | | | 62.47 307 | 62.63 311 | 62.01 321 | 70.63 349 | 38.74 345 | 74.76 292 | 52.86 351 | 53.91 304 | 67.71 322 | 80.01 314 | 39.40 342 | 66.60 335 | 55.54 284 | 68.81 347 | 80.68 322 |
|
MVE | | 40.22 23 | 51.82 327 | 50.47 329 | 55.87 332 | 62.66 355 | 51.91 294 | 31.61 351 | 39.28 356 | 40.65 346 | 50.76 352 | 74.98 334 | 56.24 270 | 44.67 354 | 33.94 346 | 64.11 348 | 71.04 338 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
dp | | | 60.70 318 | 60.29 319 | 61.92 323 | 72.04 346 | 38.67 346 | 70.83 308 | 64.08 335 | 51.28 322 | 60.75 339 | 77.28 325 | 36.59 345 | 71.58 320 | 47.41 320 | 62.34 349 | 75.52 329 |
|
PVSNet_0 | | 51.08 22 | 56.10 322 | 54.97 326 | 59.48 330 | 75.12 332 | 53.28 285 | 55.16 342 | 61.89 339 | 44.30 342 | 59.16 344 | 62.48 349 | 54.22 279 | 65.91 338 | 35.40 344 | 47.01 350 | 59.25 347 |
|
tmp_tt | | | 20.25 331 | 24.50 332 | 7.49 343 | 4.47 357 | 8.70 358 | 34.17 350 | 25.16 358 | 1.00 353 | 32.43 354 | 18.49 352 | 39.37 343 | 9.21 356 | 21.64 352 | 43.75 351 | 4.57 353 |
|
PNet_i23d | | | 52.13 326 | 51.24 328 | 54.79 335 | 75.56 325 | 45.26 333 | 54.54 344 | 52.55 352 | 66.95 229 | 57.19 349 | 65.82 346 | 13.15 361 | 63.40 343 | 36.39 343 | 39.04 352 | 55.71 350 |
|
DeepMVS_CX | | | | | 24.13 342 | 32.95 356 | 29.49 351 | | 21.63 359 | 12.07 352 | 37.95 353 | 45.07 351 | 30.84 350 | 19.21 355 | 17.94 353 | 33.06 353 | 23.69 352 |
|
.test1245 | | | 48.02 328 | 54.41 327 | 28.84 341 | 78.05 308 | 25.74 353 | 60.62 332 | 67.52 324 | 66.09 236 | 74.68 287 | 86.50 235 | 16.00 359 | 59.22 348 | 38.79 337 | 1.47 354 | 1.55 355 |
|
testmvs | | | 5.91 335 | 7.65 336 | 0.72 345 | 1.20 358 | 0.37 360 | 59.14 336 | 0.67 361 | 0.49 355 | 1.11 355 | 2.76 356 | 0.94 363 | 0.24 358 | 1.02 355 | 1.47 354 | 1.55 355 |
|
test123 | | | 6.27 334 | 8.08 335 | 0.84 344 | 1.11 359 | 0.57 359 | 62.90 329 | 0.82 360 | 0.54 354 | 1.07 356 | 2.75 357 | 1.26 362 | 0.30 357 | 1.04 354 | 1.26 356 | 1.66 354 |
|
cdsmvs_eth3d_5k | | | 20.81 330 | 27.75 331 | 0.00 346 | 0.00 360 | 0.00 361 | 0.00 352 | 85.44 215 | 0.00 356 | 0.00 357 | 82.82 288 | 81.46 93 | 0.00 359 | 0.00 356 | 0.00 357 | 0.00 357 |
|
pcd_1.5k_mvsjas | | | 6.41 333 | 8.55 334 | 0.00 346 | 0.00 360 | 0.00 361 | 0.00 352 | 0.00 362 | 0.00 356 | 0.00 357 | 0.00 358 | 76.94 133 | 0.00 359 | 0.00 356 | 0.00 357 | 0.00 357 |
|
sosnet-low-res | | | 0.00 336 | 0.00 337 | 0.00 346 | 0.00 360 | 0.00 361 | 0.00 352 | 0.00 362 | 0.00 356 | 0.00 357 | 0.00 358 | 0.00 364 | 0.00 359 | 0.00 356 | 0.00 357 | 0.00 357 |
|
sosnet | | | 0.00 336 | 0.00 337 | 0.00 346 | 0.00 360 | 0.00 361 | 0.00 352 | 0.00 362 | 0.00 356 | 0.00 357 | 0.00 358 | 0.00 364 | 0.00 359 | 0.00 356 | 0.00 357 | 0.00 357 |
|
uncertanet | | | 0.00 336 | 0.00 337 | 0.00 346 | 0.00 360 | 0.00 361 | 0.00 352 | 0.00 362 | 0.00 356 | 0.00 357 | 0.00 358 | 0.00 364 | 0.00 359 | 0.00 356 | 0.00 357 | 0.00 357 |
|
Regformer | | | 0.00 336 | 0.00 337 | 0.00 346 | 0.00 360 | 0.00 361 | 0.00 352 | 0.00 362 | 0.00 356 | 0.00 357 | 0.00 358 | 0.00 364 | 0.00 359 | 0.00 356 | 0.00 357 | 0.00 357 |
|
ab-mvs-re | | | 6.65 332 | 8.87 333 | 0.00 346 | 0.00 360 | 0.00 361 | 0.00 352 | 0.00 362 | 0.00 356 | 0.00 357 | 79.80 316 | 0.00 364 | 0.00 359 | 0.00 356 | 0.00 357 | 0.00 357 |
|
uanet | | | 0.00 336 | 0.00 337 | 0.00 346 | 0.00 360 | 0.00 361 | 0.00 352 | 0.00 362 | 0.00 356 | 0.00 357 | 0.00 358 | 0.00 364 | 0.00 359 | 0.00 356 | 0.00 357 | 0.00 357 |
|
GSMVS | | | | | | | | | | | | | | | | | 83.88 273 |
|
test_part3 | | | | | | | | 89.63 51 | | 84.39 28 | | 93.43 98 | | 96.26 4 | 82.18 75 | | |
|
test_part2 | | | | | | 93.86 48 | 77.77 80 | | | | 92.84 43 | | | | | | |
|
sam_mvs1 | | | | | | | | | | | | | 46.11 299 | | | | 83.88 273 |
|
sam_mvs | | | | | | | | | | | | | 45.92 304 | | | | |
|
MTGPA | | | | | | | | | 91.81 91 | | | | | | | | |
|
test_post1 | | | | | | | | 78.85 259 | | | | 3.13 354 | 45.19 315 | 80.13 301 | 58.11 271 | | |
|
test_post | | | | | | | | | | | | 3.10 355 | 45.43 311 | 77.22 306 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 81.71 305 | 45.93 303 | 87.01 243 | | | |
|
MTMP | | | | | | | | | 33.14 357 | | | | | | | | |
|
gm-plane-assit | | | | | | 75.42 329 | 44.97 336 | | | 52.17 313 | | 72.36 339 | | 87.90 235 | 54.10 293 | | |
|
TEST9 | | | | | | 92.34 83 | 79.70 66 | 83.94 142 | 90.32 137 | 65.41 246 | 84.49 187 | 90.97 162 | 82.03 84 | 93.63 89 | | | |
|
test_8 | | | | | | 92.09 90 | 78.87 73 | 83.82 147 | 90.31 139 | 65.79 239 | 84.36 190 | 90.96 164 | 81.93 86 | 93.44 110 | | | |
|
agg_prior | | | | | | 91.58 104 | 77.69 82 | | 90.30 141 | | 84.32 191 | | | 93.18 121 | | | |
|
test_prior4 | | | | | | | 78.97 72 | 84.59 130 | | | | | | | | | |
|
test_prior | | | | | 86.32 93 | 90.59 128 | 71.99 130 | | 92.85 62 | | | | | 94.17 69 | | | 92.80 132 |
|
旧先验2 | | | | | | | | 81.73 209 | | 56.88 292 | 86.54 158 | | | 84.90 276 | 72.81 165 | | |
|
新几何2 | | | | | | | | 81.72 210 | | | | | | | | | |
|
无先验 | | | | | | | | 82.81 178 | 85.62 214 | 58.09 285 | | | | 91.41 169 | 67.95 203 | | 84.48 265 |
|
原ACMM2 | | | | | | | | 82.26 196 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 86.43 261 | 63.52 229 | | |
|
segment_acmp | | | | | | | | | | | | | 81.94 85 | | | | |
|
testdata1 | | | | | | | | 79.62 238 | | 73.95 152 | | | | | | | |
|
plane_prior7 | | | | | | 93.45 54 | 77.31 88 | | | | | | | | | | |
|
plane_prior6 | | | | | | 92.61 74 | 76.54 95 | | | | | | 74.84 154 | | | | |
|
plane_prior4 | | | | | | | | | | | | 92.95 108 | | | | | |
|
plane_prior3 | | | | | | | 76.85 93 | | | 77.79 101 | 86.55 153 | | | | | | |
|
plane_prior2 | | | | | | | | 89.45 59 | | 79.44 79 | | | | | | | |
|
plane_prior1 | | | | | | 92.83 72 | | | | | | | | | | | |
|
n2 | | | | | | | | | 0.00 362 | | | | | | | | |
|
nn | | | | | | | | | 0.00 362 | | | | | | | | |
|
door-mid | | | | | | | | | 74.45 278 | | | | | | | | |
|
test11 | | | | | | | | | 91.46 104 | | | | | | | | |
|
door | | | | | | | | | 72.57 293 | | | | | | | | |
|
HQP5-MVS | | | | | | | 70.66 142 | | | | | | | | | | |
|
HQP-NCC | | | | | | 91.19 114 | | 84.77 125 | | 73.30 161 | 80.55 245 | | | | | | |
|
ACMP_Plane | | | | | | 91.19 114 | | 84.77 125 | | 73.30 161 | 80.55 245 | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 77.30 130 | | |
|
HQP4-MVS | | | | | | | | | | | 80.56 244 | | | 94.61 56 | | | 93.56 117 |
|
HQP2-MVS | | | | | | | | | | | | | 72.10 202 | | | | |
|
NP-MVS | | | | | | 91.95 94 | 74.55 108 | | | | | 90.17 186 | | | | | |
|
MDTV_nov1_ep13_2view | | | | | | | 27.60 352 | 70.76 309 | | 46.47 338 | 61.27 338 | | 45.20 314 | | 49.18 313 | | 83.75 277 |
|
Test By Simon | | | | | | | | | | | | | 79.09 111 | | | | |
|