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