APDe-MVS | | | 91.22 20 | 91.92 9 | 89.14 56 | 92.97 66 | 78.04 77 | 92.84 12 | 94.14 21 | 83.33 37 | 93.90 26 | 95.73 29 | 88.77 18 | 96.41 1 | 87.60 16 | 97.98 42 | 92.98 128 |
|
HSP-MVS | | | 88.63 60 | 87.84 68 | 91.02 29 | 95.76 16 | 86.14 19 | 92.75 13 | 91.01 122 | 78.43 94 | 89.16 113 | 92.25 126 | 72.03 205 | 96.36 2 | 88.21 9 | 90.93 225 | 90.55 194 |
|
SteuartSystems-ACMMP | | | 91.16 22 | 91.36 22 | 90.55 37 | 93.91 46 | 80.97 58 | 91.49 29 | 93.48 36 | 82.82 44 | 92.60 49 | 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. |
test_part3 | | | | | | | | 89.63 50 | | 84.39 28 | | 93.43 97 | | 96.26 4 | 82.18 74 | | |
|
ESAPD | | | 90.05 35 | 90.56 40 | 88.50 62 | 93.86 47 | 77.77 79 | 89.63 50 | 93.93 25 | 84.39 28 | 92.84 42 | 93.43 97 | 87.19 36 | 96.26 4 | 82.18 74 | 97.61 54 | 91.48 172 |
|
DTE-MVSNet | | | 89.98 38 | 91.91 11 | 84.21 140 | 96.51 7 | 57.84 253 | 88.93 65 | 92.84 63 | 91.92 2 | 96.16 3 | 96.23 20 | 86.95 39 | 95.99 6 | 79.05 112 | 98.57 16 | 98.80 6 |
|
PGM-MVS | | | 91.20 21 | 90.95 33 | 91.93 13 | 95.67 20 | 85.85 25 | 90.00 39 | 93.90 28 | 80.32 69 | 91.74 64 | 94.41 68 | 88.17 25 | 95.98 7 | 86.37 25 | 97.99 41 | 93.96 101 |
|
APD-MVS | | | 89.54 49 | 89.63 49 | 89.26 55 | 92.57 74 | 81.34 56 | 90.19 37 | 93.08 51 | 80.87 64 | 91.13 71 | 93.19 100 | 86.22 48 | 95.97 8 | 82.23 73 | 97.18 65 | 90.45 196 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
TSAR-MVS + MP. | | | 88.14 65 | 87.82 69 | 89.09 57 | 95.72 19 | 76.74 93 | 92.49 20 | 91.19 118 | 67.85 223 | 86.63 151 | 94.84 53 | 79.58 108 | 95.96 9 | 87.62 14 | 94.50 152 | 94.56 81 |
|
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 10 | 85.07 36 | 99.27 3 | 99.54 1 |
|
WR-MVS_H | | | 89.91 43 | 91.31 25 | 85.71 109 | 96.32 9 | 62.39 212 | 89.54 55 | 93.31 41 | 90.21 10 | 95.57 11 | 95.66 31 | 81.42 93 | 95.90 11 | 80.94 87 | 98.80 4 | 98.84 5 |
|
region2R | | | 91.44 17 | 91.30 26 | 91.87 16 | 95.75 17 | 85.90 23 | 92.63 17 | 93.30 42 | 81.91 55 | 90.88 77 | 94.21 76 | 87.75 30 | 95.87 12 | 87.60 16 | 97.71 50 | 93.83 103 |
|
ACMMPR | | | 91.49 14 | 91.35 24 | 91.92 14 | 95.74 18 | 85.88 24 | 92.58 18 | 93.25 46 | 81.99 53 | 91.40 69 | 94.17 78 | 87.51 34 | 95.87 12 | 87.74 11 | 97.76 47 | 93.99 99 |
|
3Dnovator+ | | 83.92 2 | 89.97 40 | 89.66 48 | 90.92 32 | 91.27 112 | 81.66 54 | 91.25 32 | 94.13 22 | 88.89 12 | 88.83 118 | 94.26 74 | 77.55 124 | 95.86 14 | 84.88 39 | 95.87 110 | 95.24 71 |
|
XVS | | | 91.54 12 | 91.36 22 | 92.08 8 | 95.64 21 | 86.25 16 | 92.64 15 | 93.33 39 | 85.07 25 | 89.99 86 | 94.03 82 | 86.57 43 | 95.80 15 | 87.35 19 | 97.62 52 | 94.20 92 |
|
X-MVStestdata | | | 85.04 113 | 82.70 160 | 92.08 8 | 95.64 21 | 86.25 16 | 92.64 15 | 93.33 39 | 85.07 25 | 89.99 86 | 16.05 350 | 86.57 43 | 95.80 15 | 87.35 19 | 97.62 52 | 94.20 92 |
|
DeepC-MVS | | 82.31 4 | 89.15 55 | 89.08 53 | 89.37 53 | 93.64 51 | 79.07 70 | 88.54 71 | 94.20 17 | 73.53 153 | 89.71 97 | 94.82 54 | 85.09 52 | 95.77 17 | 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 |
MVS_0304 | | | 84.88 117 | 83.96 147 | 87.64 77 | 87.43 195 | 74.83 105 | 84.18 135 | 93.30 42 | 77.48 106 | 77.39 264 | 88.46 208 | 74.53 161 | 95.74 18 | 78.09 122 | 94.75 148 | 92.36 149 |
|
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 63 | 93.94 90 | 90.55 13 | 95.73 19 | 88.50 8 | 98.23 31 | 95.33 69 |
|
CP-MVS | | | 91.67 11 | 91.58 17 | 91.96 12 | 95.29 27 | 87.62 9 | 93.38 7 | 93.36 37 | 83.16 39 | 91.06 72 | 94.00 83 | 88.26 23 | 95.71 20 | 87.28 22 | 98.39 23 | 92.55 143 |
|
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 54 | 94.14 79 | 89.15 17 | 95.62 21 | 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 |
PEN-MVS | | | 90.03 36 | 91.88 12 | 84.48 131 | 96.57 5 | 58.88 249 | 88.95 63 | 93.19 47 | 91.62 4 | 96.01 6 | 96.16 22 | 87.02 38 | 95.60 22 | 78.69 115 | 98.72 10 | 98.97 3 |
|
PS-CasMVS | | | 90.06 34 | 91.92 9 | 84.47 132 | 96.56 6 | 58.83 250 | 89.04 62 | 92.74 66 | 91.40 5 | 96.12 4 | 96.06 24 | 87.23 35 | 95.57 23 | 79.42 111 | 98.74 7 | 99.00 2 |
|
HFP-MVS | | | 91.30 18 | 91.39 21 | 91.02 29 | 95.43 25 | 84.66 39 | 92.58 18 | 93.29 44 | 81.99 53 | 91.47 67 | 93.96 86 | 88.35 21 | 95.56 24 | 87.74 11 | 97.74 48 | 92.85 129 |
|
#test# | | | 90.49 30 | 90.31 42 | 91.02 29 | 95.43 25 | 84.66 39 | 90.65 34 | 93.29 44 | 77.00 118 | 91.47 67 | 93.96 86 | 88.35 21 | 95.56 24 | 84.88 39 | 97.74 48 | 92.85 129 |
|
CP-MVSNet | | | 89.27 53 | 90.91 34 | 84.37 135 | 96.34 8 | 58.61 252 | 88.66 70 | 92.06 82 | 90.78 6 | 95.67 9 | 95.17 44 | 81.80 89 | 95.54 26 | 79.00 113 | 98.69 11 | 98.95 4 |
|
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 48 | 93.67 30 | 94.82 54 | 91.18 6 | 95.52 27 | 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 48 | 93.67 30 | 94.82 54 | 91.18 6 | 95.52 27 | 85.36 34 | 98.73 8 | 95.23 72 |
|
mPP-MVS | | | 91.69 10 | 91.47 20 | 92.37 5 | 96.04 11 | 88.48 8 | 92.72 14 | 92.60 71 | 83.09 40 | 91.54 66 | 94.25 75 | 87.67 33 | 95.51 29 | 87.21 23 | 98.11 35 | 93.12 125 |
|
ACMMP_Plus | | | 90.65 26 | 91.07 30 | 89.42 52 | 95.93 14 | 79.54 68 | 89.95 42 | 93.68 31 | 77.65 103 | 91.97 60 | 94.89 51 | 88.38 19 | 95.45 30 | 89.27 3 | 97.87 45 | 93.27 120 |
|
CANet | | | 83.79 150 | 82.85 159 | 86.63 86 | 86.17 227 | 72.21 126 | 83.76 150 | 91.43 106 | 77.24 114 | 74.39 287 | 87.45 227 | 75.36 145 | 95.42 31 | 77.03 132 | 92.83 191 | 92.25 155 |
|
MP-MVS | | | 91.14 23 | 90.91 34 | 91.83 18 | 96.18 10 | 86.88 11 | 92.20 22 | 93.03 55 | 82.59 46 | 88.52 124 | 94.37 72 | 86.74 40 | 95.41 32 | 86.32 26 | 98.21 32 | 93.19 124 |
|
LS3D | | | 90.60 28 | 90.34 41 | 91.38 23 | 89.03 149 | 84.23 42 | 93.58 4 | 94.68 10 | 90.65 7 | 90.33 81 | 93.95 89 | 84.50 57 | 95.37 33 | 80.87 88 | 95.50 120 | 94.53 85 |
|
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 50 | 95.13 45 | 90.65 11 | 95.34 34 | 88.06 10 | 98.15 34 | 95.95 51 |
|
NCCC | | | 87.36 72 | 86.87 86 | 88.83 58 | 92.32 84 | 78.84 73 | 86.58 106 | 91.09 120 | 78.77 90 | 84.85 177 | 90.89 165 | 80.85 97 | 95.29 35 | 81.14 84 | 95.32 124 | 92.34 150 |
|
EPP-MVSNet | | | 85.47 108 | 85.04 114 | 86.77 85 | 91.52 106 | 69.37 148 | 91.63 28 | 87.98 183 | 81.51 60 | 87.05 146 | 91.83 133 | 66.18 227 | 95.29 35 | 70.75 176 | 96.89 71 | 95.64 59 |
|
MPTG | | | 91.27 19 | 91.26 27 | 91.29 25 | 96.59 3 | 86.29 14 | 88.94 64 | 91.81 90 | 84.07 32 | 92.00 58 | 94.40 69 | 86.63 41 | 95.28 37 | 88.59 4 | 98.31 26 | 92.30 151 |
|
MTAPA | | | 91.52 13 | 91.60 16 | 91.29 25 | 96.59 3 | 86.29 14 | 92.02 24 | 91.81 90 | 84.07 32 | 92.00 58 | 94.40 69 | 86.63 41 | 95.28 37 | 88.59 4 | 98.31 26 | 92.30 151 |
|
HQP_MVS | | | 87.75 71 | 87.43 76 | 88.70 60 | 93.45 53 | 76.42 97 | 89.45 58 | 93.61 32 | 79.44 79 | 86.55 152 | 92.95 107 | 74.84 153 | 95.22 39 | 80.78 90 | 95.83 112 | 94.46 86 |
|
plane_prior5 | | | | | | | | | 93.61 32 | | | | | 95.22 39 | 80.78 90 | 95.83 112 | 94.46 86 |
|
ACMP | | 79.16 10 | 90.54 29 | 90.60 37 | 90.35 41 | 94.36 40 | 80.98 57 | 89.16 61 | 94.05 23 | 79.03 86 | 92.87 40 | 93.74 93 | 90.60 12 | 95.21 41 | 82.87 65 | 98.76 5 | 94.87 77 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
DeepC-MVS_fast | | 80.27 8 | 86.23 95 | 85.65 106 | 87.96 73 | 91.30 110 | 76.92 90 | 87.19 89 | 91.99 84 | 70.56 198 | 84.96 173 | 90.69 171 | 80.01 105 | 95.14 42 | 78.37 116 | 95.78 114 | 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 |
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 43 | 87.54 18 | 98.02 39 | 93.71 110 |
|
APD-MVS_3200maxsize | | | 92.05 7 | 92.24 6 | 91.48 20 | 93.02 64 | 85.17 31 | 92.47 21 | 95.05 8 | 87.65 19 | 93.21 36 | 94.39 71 | 90.09 14 | 95.08 44 | 86.67 24 | 97.60 56 | 94.18 94 |
|
HPM-MVS++ | | | 88.93 57 | 88.45 63 | 90.38 40 | 94.92 32 | 85.85 25 | 89.70 46 | 91.27 115 | 78.20 97 | 86.69 150 | 92.28 125 | 80.36 102 | 95.06 45 | 86.17 31 | 96.49 85 | 90.22 200 |
|
MP-MVS-pluss | | | 90.81 24 | 91.08 29 | 89.99 46 | 95.97 12 | 79.88 63 | 88.13 75 | 94.51 11 | 75.79 131 | 92.94 38 | 94.96 49 | 88.36 20 | 95.01 46 | 90.70 2 | 98.40 21 | 95.09 75 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
CDPH-MVS | | | 86.17 97 | 85.54 107 | 88.05 72 | 92.25 85 | 75.45 102 | 83.85 145 | 92.01 83 | 65.91 235 | 86.19 159 | 91.75 137 | 83.77 62 | 94.98 47 | 77.43 128 | 96.71 77 | 93.73 109 |
|
COLMAP_ROB | | 83.01 3 | 91.97 8 | 91.95 8 | 92.04 10 | 93.68 50 | 86.15 18 | 93.37 8 | 95.10 7 | 90.28 9 | 92.11 55 | 95.03 47 | 89.75 15 | 94.93 48 | 79.95 103 | 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 |
IS-MVSNet | | | 86.66 85 | 86.82 89 | 86.17 101 | 92.05 91 | 66.87 166 | 91.21 33 | 88.64 171 | 86.30 23 | 89.60 106 | 92.59 115 | 69.22 215 | 94.91 49 | 73.89 151 | 97.89 44 | 96.72 35 |
|
OurMVSNet-221017-0 | | | 90.01 37 | 89.74 47 | 90.83 33 | 93.16 61 | 80.37 59 | 91.91 27 | 93.11 49 | 81.10 62 | 95.32 13 | 97.24 6 | 72.94 190 | 94.85 50 | 85.07 36 | 97.78 46 | 97.26 23 |
|
test12 | | | | | 86.57 87 | 90.74 124 | 72.63 118 | | 90.69 125 | | 82.76 209 | | 79.20 109 | 94.80 51 | | 95.32 124 | 92.27 153 |
|
SixPastTwentyTwo | | | 87.20 75 | 87.45 75 | 86.45 90 | 92.52 76 | 69.19 153 | 87.84 80 | 88.05 180 | 81.66 58 | 94.64 17 | 96.53 14 | 65.94 228 | 94.75 52 | 83.02 64 | 96.83 74 | 95.41 67 |
|
CNVR-MVS | | | 87.81 70 | 87.68 72 | 88.21 66 | 92.87 68 | 77.30 88 | 85.25 120 | 91.23 116 | 77.31 113 | 87.07 145 | 91.47 143 | 82.94 70 | 94.71 53 | 84.67 43 | 96.27 93 | 92.62 142 |
|
K. test v3 | | | 85.14 110 | 84.73 120 | 86.37 91 | 91.13 117 | 69.63 147 | 85.45 118 | 76.68 264 | 84.06 34 | 92.44 52 | 96.99 8 | 62.03 240 | 94.65 54 | 80.58 93 | 93.24 183 | 94.83 80 |
|
HQP4-MVS | | | | | | | | | | | 80.56 241 | | | 94.61 55 | | | 93.56 116 |
|
HQP-MVS | | | 84.61 122 | 84.06 144 | 86.27 94 | 91.19 113 | 70.66 141 | 84.77 124 | 92.68 67 | 73.30 159 | 80.55 242 | 90.17 185 | 72.10 201 | 94.61 55 | 77.30 129 | 94.47 153 | 93.56 116 |
|
PS-MVSNAJss | | | 88.31 63 | 87.90 67 | 89.56 51 | 93.31 57 | 77.96 78 | 87.94 77 | 91.97 85 | 70.73 197 | 94.19 24 | 96.67 11 | 76.94 132 | 94.57 57 | 83.07 62 | 96.28 91 | 96.15 42 |
|
DeepPCF-MVS | | 81.24 5 | 87.28 74 | 86.21 97 | 90.49 38 | 91.48 107 | 84.90 34 | 83.41 162 | 92.38 76 | 70.25 202 | 89.35 111 | 90.68 172 | 82.85 71 | 94.57 57 | 79.55 107 | 95.95 106 | 92.00 159 |
|
UA-Net | | | 91.49 14 | 91.53 18 | 91.39 22 | 94.98 31 | 82.95 50 | 93.52 5 | 92.79 64 | 88.22 16 | 88.53 123 | 97.64 3 | 83.45 65 | 94.55 59 | 86.02 33 | 98.60 14 | 96.67 36 |
|
114514_t | | | 83.10 162 | 82.54 165 | 84.77 123 | 92.90 67 | 69.10 154 | 86.65 104 | 90.62 128 | 54.66 297 | 81.46 224 | 90.81 168 | 76.98 131 | 94.38 60 | 72.62 165 | 96.18 95 | 90.82 186 |
|
MVSFormer | | | 82.23 171 | 81.57 177 | 84.19 142 | 85.54 237 | 69.26 150 | 91.98 25 | 90.08 149 | 71.54 191 | 76.23 271 | 85.07 258 | 58.69 258 | 94.27 61 | 86.26 27 | 88.77 250 | 89.03 215 |
|
test_djsdf | | | 89.62 47 | 89.01 54 | 91.45 21 | 92.36 81 | 82.98 49 | 91.98 25 | 90.08 149 | 71.54 191 | 94.28 23 | 96.54 13 | 81.57 91 | 94.27 61 | 86.26 27 | 96.49 85 | 97.09 30 |
|
原ACMM1 | | | | | 84.60 128 | 92.81 72 | 74.01 110 | | 91.50 97 | 62.59 260 | 82.73 210 | 90.67 173 | 76.53 139 | 94.25 63 | 69.24 188 | 95.69 117 | 85.55 249 |
|
AdaColmap | | | 83.66 152 | 83.69 150 | 83.57 159 | 90.05 137 | 72.26 124 | 86.29 110 | 90.00 152 | 78.19 98 | 81.65 222 | 87.16 229 | 83.40 66 | 94.24 64 | 61.69 237 | 94.76 147 | 84.21 268 |
|
Effi-MVS+-dtu | | | 85.82 103 | 83.38 152 | 93.14 3 | 87.13 205 | 91.15 3 | 87.70 81 | 88.42 173 | 74.57 144 | 83.56 200 | 85.65 245 | 78.49 114 | 94.21 65 | 72.04 169 | 92.88 190 | 94.05 98 |
|
UniMVSNet (Re) | | | 86.87 79 | 86.98 83 | 86.55 88 | 93.11 63 | 68.48 156 | 83.80 148 | 92.87 60 | 80.37 67 | 89.61 105 | 91.81 135 | 77.72 121 | 94.18 66 | 75.00 144 | 98.53 17 | 96.99 34 |
|
PHI-MVS | | | 86.38 90 | 85.81 102 | 88.08 68 | 88.44 163 | 77.34 86 | 89.35 60 | 93.05 52 | 73.15 164 | 84.76 178 | 87.70 223 | 78.87 112 | 94.18 66 | 80.67 92 | 96.29 90 | 92.73 133 |
|
test_prior3 | | | 86.31 92 | 86.31 94 | 86.32 92 | 90.59 127 | 71.99 129 | 83.37 163 | 92.85 61 | 75.43 136 | 84.58 184 | 91.57 139 | 81.92 87 | 94.17 68 | 79.54 108 | 96.97 69 | 92.80 131 |
|
test_prior | | | | | 86.32 92 | 90.59 127 | 71.99 129 | | 92.85 61 | | | | | 94.17 68 | | | 92.80 131 |
|
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 68 | 86.07 32 | 98.48 19 | 97.22 26 |
|
v7n | | | 90.13 32 | 90.96 32 | 87.65 76 | 91.95 93 | 71.06 139 | 89.99 41 | 93.05 52 | 86.53 21 | 94.29 22 | 96.27 19 | 82.69 72 | 94.08 71 | 86.25 29 | 97.63 51 | 97.82 11 |
|
v10 | | | 86.54 87 | 87.10 79 | 84.84 120 | 88.16 170 | 63.28 193 | 86.64 105 | 92.20 79 | 75.42 138 | 92.81 44 | 94.50 64 | 74.05 165 | 94.06 72 | 83.88 53 | 96.28 91 | 97.17 28 |
|
UniMVSNet_NR-MVSNet | | | 86.84 81 | 87.06 80 | 86.17 101 | 92.86 70 | 67.02 164 | 82.55 185 | 91.56 95 | 83.08 41 | 90.92 74 | 91.82 134 | 78.25 117 | 93.99 73 | 74.16 147 | 98.35 24 | 97.49 16 |
|
DU-MVS | | | 86.80 82 | 86.99 82 | 86.21 98 | 93.24 59 | 67.02 164 | 83.16 170 | 92.21 78 | 81.73 57 | 90.92 74 | 91.97 128 | 77.20 126 | 93.99 73 | 74.16 147 | 98.35 24 | 97.61 13 |
|
mvs-test1 | | | 84.55 124 | 82.12 169 | 91.84 17 | 87.13 205 | 89.54 5 | 85.05 123 | 88.42 173 | 74.57 144 | 80.60 239 | 82.98 282 | 78.49 114 | 93.98 75 | 72.04 169 | 89.77 241 | 92.00 159 |
|
v52 | | | 89.97 40 | 90.60 37 | 88.07 69 | 88.69 155 | 72.01 127 | 91.35 30 | 92.64 69 | 82.22 50 | 95.97 8 | 96.31 16 | 84.82 53 | 93.98 75 | 88.59 4 | 94.83 142 | 98.23 8 |
|
V4 | | | 89.97 40 | 90.60 37 | 88.07 69 | 88.69 155 | 72.01 127 | 91.35 30 | 92.64 69 | 82.22 50 | 95.98 7 | 96.31 16 | 84.80 55 | 93.98 75 | 88.59 4 | 94.83 142 | 98.23 8 |
|
DP-MVS Recon | | | 84.05 144 | 83.22 154 | 86.52 89 | 91.73 98 | 75.27 103 | 83.23 169 | 92.40 74 | 72.04 180 | 82.04 215 | 88.33 213 | 77.91 120 | 93.95 78 | 66.17 212 | 95.12 131 | 90.34 199 |
|
DP-MVS | | | 88.60 61 | 89.01 54 | 87.36 81 | 91.30 110 | 77.50 84 | 87.55 83 | 92.97 57 | 87.95 17 | 89.62 103 | 92.87 109 | 84.56 56 | 93.89 79 | 77.65 124 | 96.62 79 | 90.70 188 |
|
NR-MVSNet | | | 86.00 98 | 86.22 96 | 85.34 113 | 93.24 59 | 64.56 178 | 82.21 196 | 90.46 131 | 80.99 63 | 88.42 126 | 91.97 128 | 77.56 123 | 93.85 80 | 72.46 166 | 98.65 13 | 97.61 13 |
|
EPNet | | | 80.37 192 | 78.41 204 | 86.23 95 | 76.75 314 | 73.28 114 | 87.18 90 | 77.45 258 | 76.24 124 | 68.14 315 | 88.93 202 | 65.41 230 | 93.85 80 | 69.47 186 | 96.12 100 | 91.55 170 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
OPM-MVS | | | 89.80 44 | 89.97 43 | 89.27 54 | 94.76 34 | 79.86 64 | 86.76 99 | 92.78 65 | 78.78 89 | 92.51 50 | 93.64 94 | 88.13 27 | 93.84 82 | 84.83 41 | 97.55 57 | 94.10 97 |
|
TranMVSNet+NR-MVSNet | | | 87.86 68 | 88.76 61 | 85.18 115 | 94.02 43 | 64.13 181 | 84.38 133 | 91.29 114 | 84.88 27 | 92.06 57 | 93.84 92 | 86.45 45 | 93.73 83 | 73.22 158 | 98.66 12 | 97.69 12 |
|
v8 | | | 86.22 96 | 86.83 88 | 84.36 136 | 87.82 181 | 62.35 213 | 86.42 108 | 91.33 113 | 76.78 120 | 92.73 46 | 94.48 65 | 73.41 179 | 93.72 84 | 83.10 61 | 95.41 121 | 97.01 33 |
|
agg_prior3 | | | 85.76 104 | 84.95 117 | 88.16 67 | 92.43 79 | 79.92 62 | 83.98 140 | 90.03 151 | 65.11 245 | 83.66 198 | 90.64 176 | 81.00 96 | 93.67 85 | 81.21 82 | 96.54 82 | 90.88 183 |
|
Vis-MVSNet | | | 86.86 80 | 86.58 90 | 87.72 74 | 92.09 89 | 77.43 85 | 87.35 86 | 92.09 81 | 78.87 88 | 84.27 193 | 94.05 81 | 78.35 116 | 93.65 86 | 80.54 94 | 91.58 208 | 92.08 158 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
v1240 | | | 84.30 134 | 84.51 133 | 83.65 156 | 87.65 190 | 61.26 230 | 82.85 176 | 91.54 96 | 67.94 221 | 90.68 79 | 90.65 174 | 71.71 207 | 93.64 87 | 82.84 67 | 94.78 144 | 96.07 45 |
|
TEST9 | | | | | | 92.34 82 | 79.70 66 | 83.94 141 | 90.32 136 | 65.41 243 | 84.49 186 | 90.97 161 | 82.03 83 | 93.63 88 | | | |
|
train_agg | | | 85.98 101 | 85.28 111 | 88.07 69 | 92.34 82 | 79.70 66 | 83.94 141 | 90.32 136 | 65.79 236 | 84.49 186 | 90.97 161 | 81.93 85 | 93.63 88 | 81.21 82 | 96.54 82 | 90.88 183 |
|
v7 | | | 84.81 118 | 85.00 115 | 84.23 139 | 88.15 171 | 63.27 194 | 83.79 149 | 91.39 111 | 71.10 195 | 90.07 83 | 91.28 145 | 74.04 166 | 93.63 88 | 81.48 81 | 93.67 170 | 95.79 52 |
|
PCF-MVS | | 74.62 15 | 82.15 172 | 80.92 185 | 85.84 107 | 89.43 142 | 72.30 123 | 80.53 226 | 91.82 89 | 57.36 285 | 87.81 134 | 89.92 188 | 77.67 122 | 93.63 88 | 58.69 260 | 95.08 132 | 91.58 169 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
v1192 | | | 84.57 123 | 84.69 124 | 84.21 140 | 87.75 186 | 62.88 201 | 83.02 172 | 91.43 106 | 69.08 212 | 89.98 88 | 90.89 165 | 72.70 195 | 93.62 92 | 82.41 70 | 94.97 136 | 96.13 43 |
|
v1921920 | | | 84.23 137 | 84.37 140 | 83.79 149 | 87.64 191 | 61.71 221 | 82.91 175 | 91.20 117 | 67.94 221 | 90.06 84 | 90.34 179 | 72.04 204 | 93.59 93 | 82.32 72 | 94.91 137 | 96.07 45 |
|
mvs_tets | | | 89.78 45 | 89.27 52 | 91.30 24 | 93.51 52 | 84.79 36 | 89.89 44 | 90.63 127 | 70.00 204 | 94.55 18 | 96.67 11 | 87.94 29 | 93.59 93 | 84.27 48 | 95.97 105 | 95.52 65 |
|
test_0402 | | | 88.65 59 | 89.58 50 | 85.88 106 | 92.55 75 | 72.22 125 | 84.01 139 | 89.44 163 | 88.63 14 | 94.38 21 | 95.77 28 | 86.38 47 | 93.59 93 | 79.84 104 | 95.21 128 | 91.82 163 |
|
v13 | | | 87.31 73 | 88.10 64 | 84.94 117 | 88.84 152 | 63.75 185 | 87.85 79 | 91.47 102 | 79.12 83 | 93.72 29 | 95.82 27 | 75.20 147 | 93.58 96 | 84.76 42 | 96.16 96 | 97.48 17 |
|
v12 | | | 87.15 76 | 87.91 66 | 84.84 120 | 88.69 155 | 63.52 188 | 87.58 82 | 91.46 103 | 78.74 91 | 93.57 32 | 95.66 31 | 74.94 151 | 93.57 97 | 84.50 45 | 96.08 101 | 97.43 18 |
|
v11 | | | 86.96 77 | 87.78 70 | 84.51 129 | 88.50 161 | 62.60 208 | 87.21 88 | 91.63 94 | 78.08 100 | 93.40 34 | 95.56 36 | 75.07 148 | 93.57 97 | 84.46 46 | 96.08 101 | 97.36 21 |
|
V9 | | | 86.96 77 | 87.70 71 | 84.74 124 | 88.52 160 | 63.27 194 | 87.31 87 | 91.45 105 | 78.28 96 | 93.43 33 | 95.45 38 | 74.59 159 | 93.57 97 | 84.23 49 | 96.01 104 | 97.38 19 |
|
V14 | | | 86.75 83 | 87.46 74 | 84.62 127 | 88.35 164 | 63.00 199 | 87.02 93 | 91.42 108 | 77.78 102 | 93.27 35 | 95.23 43 | 74.22 162 | 93.56 100 | 83.95 52 | 95.93 107 | 97.31 22 |
|
v15 | | | 86.56 86 | 87.25 78 | 84.51 129 | 88.15 171 | 62.72 204 | 86.72 103 | 91.40 110 | 77.38 107 | 93.11 37 | 95.00 48 | 73.93 167 | 93.55 101 | 83.67 56 | 95.86 111 | 97.26 23 |
|
v17 | | | 86.32 91 | 86.95 84 | 84.44 133 | 88.00 173 | 62.62 207 | 86.74 101 | 91.48 99 | 77.17 115 | 92.74 45 | 94.56 60 | 73.74 171 | 93.53 102 | 83.27 59 | 94.87 141 | 97.18 27 |
|
jajsoiax | | | 89.41 50 | 88.81 59 | 91.19 28 | 93.38 56 | 84.72 37 | 89.70 46 | 90.29 143 | 69.27 208 | 94.39 20 | 96.38 15 | 86.02 50 | 93.52 103 | 83.96 51 | 95.92 108 | 95.34 68 |
|
v18 | | | 85.99 100 | 86.55 91 | 84.30 138 | 87.73 187 | 62.29 217 | 86.40 109 | 91.49 98 | 76.64 121 | 92.40 53 | 94.20 77 | 73.28 183 | 93.52 103 | 82.87 65 | 93.99 160 | 97.09 30 |
|
v16 | | | 86.24 94 | 86.85 87 | 84.43 134 | 87.96 175 | 62.59 209 | 86.73 102 | 91.48 99 | 77.17 115 | 92.67 48 | 94.55 61 | 73.63 172 | 93.52 103 | 83.26 60 | 94.16 156 | 97.17 28 |
|
v144192 | | | 84.24 136 | 84.41 135 | 83.71 155 | 87.59 192 | 61.57 226 | 82.95 174 | 91.03 121 | 67.82 224 | 89.80 95 | 90.49 177 | 73.28 183 | 93.51 106 | 81.88 79 | 94.89 138 | 96.04 47 |
|
v1144 | | | 84.54 126 | 84.72 122 | 84.00 144 | 87.67 189 | 62.55 210 | 82.97 173 | 90.93 123 | 70.32 201 | 89.80 95 | 90.99 160 | 73.50 177 | 93.48 107 | 81.69 80 | 94.65 150 | 95.97 49 |
|
MCST-MVS | | | 84.36 131 | 83.93 148 | 85.63 110 | 91.59 100 | 71.58 136 | 83.52 157 | 92.13 80 | 61.82 266 | 83.96 194 | 89.75 191 | 79.93 107 | 93.46 108 | 78.33 118 | 94.34 155 | 91.87 162 |
|
test_8 | | | | | | 92.09 89 | 78.87 72 | 83.82 146 | 90.31 138 | 65.79 236 | 84.36 189 | 90.96 163 | 81.93 85 | 93.44 109 | | | |
|
ACMH+ | | 77.89 11 | 90.73 25 | 91.50 19 | 88.44 63 | 93.00 65 | 76.26 99 | 89.65 49 | 95.55 3 | 87.72 18 | 93.89 27 | 94.94 50 | 91.62 5 | 93.44 109 | 78.35 117 | 98.76 5 | 95.61 64 |
|
FC-MVSNet-test | | | 85.93 102 | 87.05 81 | 82.58 177 | 92.25 85 | 56.44 263 | 85.75 114 | 93.09 50 | 77.33 112 | 91.94 61 | 94.65 59 | 74.78 155 | 93.41 111 | 75.11 142 | 98.58 15 | 97.88 10 |
|
OMC-MVS | | | 88.19 64 | 87.52 73 | 90.19 44 | 91.94 95 | 81.68 53 | 87.49 85 | 93.17 48 | 76.02 127 | 88.64 121 | 91.22 147 | 84.24 59 | 93.37 112 | 77.97 123 | 97.03 68 | 95.52 65 |
|
MG-MVS | | | 80.32 193 | 80.94 184 | 78.47 229 | 88.18 168 | 52.62 289 | 82.29 192 | 85.01 223 | 72.01 181 | 79.24 253 | 92.54 118 | 69.36 214 | 93.36 113 | 70.65 178 | 89.19 247 | 89.45 207 |
|
v1neww | | | 84.43 128 | 84.66 126 | 83.75 151 | 87.81 182 | 62.34 214 | 83.59 153 | 90.27 144 | 72.33 175 | 89.93 90 | 91.22 147 | 73.28 183 | 93.29 114 | 80.25 99 | 93.25 181 | 95.62 60 |
|
v7new | | | 84.43 128 | 84.66 126 | 83.75 151 | 87.81 182 | 62.34 214 | 83.59 153 | 90.27 144 | 72.33 175 | 89.93 90 | 91.22 147 | 73.28 183 | 93.29 114 | 80.25 99 | 93.25 181 | 95.62 60 |
|
v6 | | | 84.43 128 | 84.66 126 | 83.75 151 | 87.81 182 | 62.34 214 | 83.59 153 | 90.26 146 | 72.33 175 | 89.94 89 | 91.19 151 | 73.30 182 | 93.29 114 | 80.26 98 | 93.26 180 | 95.62 60 |
|
CPTT-MVS | | | 89.39 51 | 88.98 56 | 90.63 36 | 95.09 29 | 86.95 10 | 92.09 23 | 92.30 77 | 79.74 74 | 87.50 138 | 92.38 120 | 81.42 93 | 93.28 117 | 83.07 62 | 97.24 63 | 91.67 166 |
|
F-COLMAP | | | 84.97 116 | 83.42 151 | 89.63 49 | 92.39 80 | 83.40 45 | 88.83 66 | 91.92 87 | 73.19 163 | 80.18 247 | 89.15 199 | 77.04 130 | 93.28 117 | 65.82 217 | 92.28 199 | 92.21 156 |
|
v2v482 | | | 84.09 142 | 84.24 142 | 83.62 157 | 87.13 205 | 61.40 227 | 82.71 182 | 89.71 158 | 72.19 179 | 89.55 107 | 91.41 144 | 70.70 212 | 93.20 119 | 81.02 85 | 93.76 167 | 96.25 41 |
|
agg_prior1 | | | 85.72 105 | 85.20 112 | 87.28 82 | 91.58 103 | 77.69 81 | 83.69 152 | 90.30 140 | 66.29 231 | 84.32 190 | 91.07 158 | 82.13 80 | 93.18 120 | 81.02 85 | 96.36 88 | 90.98 178 |
|
agg_prior | | | | | | 91.58 103 | 77.69 81 | | 90.30 140 | | 84.32 190 | | | 93.18 120 | | | |
|
Regformer-2 | | | 86.74 84 | 86.08 99 | 88.73 59 | 84.18 258 | 79.20 69 | 83.52 157 | 89.33 164 | 83.33 37 | 89.92 92 | 85.07 258 | 83.23 68 | 93.16 122 | 83.39 57 | 92.72 193 | 93.83 103 |
|
LTVRE_ROB | | 86.10 1 | 93.04 3 | 93.44 2 | 91.82 19 | 93.73 49 | 85.72 28 | 96.79 1 | 95.51 4 | 88.86 13 | 95.63 10 | 96.99 8 | 84.81 54 | 93.16 122 | 91.10 1 | 97.53 58 | 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 |
semantic-postprocess | | | | | 84.34 137 | 83.93 261 | 69.66 146 | | 81.09 244 | 72.43 171 | 86.47 158 | 90.19 183 | 57.56 264 | 93.15 124 | 77.45 127 | 86.39 277 | 90.22 200 |
|
v1141 | | | 84.16 139 | 84.38 137 | 83.52 161 | 87.32 198 | 61.70 223 | 82.79 178 | 89.74 155 | 71.90 188 | 89.64 100 | 91.12 154 | 72.68 196 | 93.10 125 | 80.39 97 | 93.80 165 | 95.75 54 |
|
divwei89l23v2f112 | | | 84.16 139 | 84.38 137 | 83.52 161 | 87.32 198 | 61.70 223 | 82.79 178 | 89.74 155 | 71.90 188 | 89.64 100 | 91.12 154 | 72.68 196 | 93.10 125 | 80.40 95 | 93.81 164 | 95.75 54 |
|
v1 | | | 84.16 139 | 84.38 137 | 83.52 161 | 87.33 197 | 61.71 221 | 82.79 178 | 89.73 157 | 71.89 190 | 89.64 100 | 91.11 156 | 72.72 193 | 93.10 125 | 80.40 95 | 93.79 166 | 95.75 54 |
|
XVG-ACMP-BASELINE | | | 89.98 38 | 89.84 45 | 90.41 39 | 94.91 33 | 84.50 41 | 89.49 57 | 93.98 24 | 79.68 75 | 92.09 56 | 93.89 91 | 83.80 61 | 93.10 125 | 82.67 69 | 98.04 36 | 93.64 112 |
|
anonymousdsp | | | 89.73 46 | 88.88 57 | 92.27 7 | 89.82 140 | 86.67 12 | 90.51 35 | 90.20 148 | 69.87 205 | 95.06 14 | 96.14 23 | 84.28 58 | 93.07 129 | 87.68 13 | 96.34 89 | 97.09 30 |
|
ACMM | | 79.39 9 | 90.65 26 | 90.99 31 | 89.63 49 | 95.03 30 | 83.53 44 | 89.62 52 | 93.35 38 | 79.20 82 | 93.83 28 | 93.60 95 | 90.81 9 | 92.96 130 | 85.02 38 | 98.45 20 | 92.41 146 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
CLD-MVS | | | 83.18 160 | 82.64 162 | 84.79 122 | 89.05 148 | 67.82 161 | 77.93 263 | 92.52 72 | 68.33 216 | 85.07 172 | 81.54 304 | 82.06 81 | 92.96 130 | 69.35 187 | 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 |
Effi-MVS+ | | | 83.90 149 | 84.01 145 | 83.57 159 | 87.22 203 | 65.61 173 | 86.55 107 | 92.40 74 | 78.64 92 | 81.34 227 | 84.18 269 | 83.65 63 | 92.93 132 | 74.22 146 | 87.87 262 | 92.17 157 |
|
lessismore_v0 | | | | | 85.95 103 | 91.10 118 | 70.99 140 | | 70.91 306 | | 91.79 62 | 94.42 67 | 61.76 241 | 92.93 132 | 79.52 110 | 93.03 187 | 93.93 102 |
|
FIs | | | 85.35 109 | 86.27 95 | 82.60 176 | 91.86 96 | 57.31 257 | 85.10 122 | 93.05 52 | 75.83 130 | 91.02 73 | 93.97 84 | 73.57 176 | 92.91 134 | 73.97 150 | 98.02 39 | 97.58 15 |
|
PVSNet_Blended_VisFu | | | 81.55 178 | 80.49 189 | 84.70 126 | 91.58 103 | 73.24 115 | 84.21 134 | 91.67 93 | 62.86 258 | 80.94 229 | 87.16 229 | 67.27 222 | 92.87 135 | 69.82 184 | 88.94 249 | 87.99 225 |
|
Regformer-4 | | | 86.41 89 | 85.71 104 | 88.52 61 | 84.27 254 | 77.57 83 | 84.07 137 | 88.00 182 | 82.82 44 | 89.84 94 | 85.48 248 | 82.06 81 | 92.77 136 | 83.83 55 | 91.04 218 | 95.22 74 |
|
DELS-MVS | | | 81.44 179 | 81.25 180 | 82.03 184 | 84.27 254 | 62.87 202 | 76.47 277 | 92.49 73 | 70.97 196 | 81.64 223 | 83.83 272 | 75.03 149 | 92.70 137 | 74.29 145 | 92.22 202 | 90.51 195 |
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 |
TSAR-MVS + GP. | | | 83.95 147 | 82.69 161 | 87.72 74 | 89.27 145 | 81.45 55 | 83.72 151 | 81.58 242 | 74.73 142 | 85.66 166 | 86.06 243 | 72.56 199 | 92.69 138 | 75.44 140 | 95.21 128 | 89.01 217 |
|
v748 | | | 88.91 58 | 89.82 46 | 86.19 100 | 90.06 136 | 68.53 155 | 88.81 67 | 91.48 99 | 84.36 30 | 94.19 24 | 95.98 25 | 82.52 75 | 92.67 139 | 84.30 47 | 96.67 78 | 97.37 20 |
|
Fast-Effi-MVS+ | | | 81.04 185 | 80.57 186 | 82.46 181 | 87.50 193 | 63.22 196 | 78.37 259 | 89.63 159 | 68.01 218 | 81.87 217 | 82.08 299 | 82.31 78 | 92.65 140 | 67.10 204 | 88.30 258 | 91.51 171 |
|
PLC | | 73.85 16 | 82.09 173 | 80.31 190 | 87.45 79 | 90.86 123 | 80.29 60 | 85.88 112 | 90.65 126 | 68.17 217 | 76.32 270 | 86.33 239 | 73.12 188 | 92.61 141 | 61.40 241 | 90.02 240 | 89.44 208 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
IterMVS-LS | | | 84.73 120 | 84.98 116 | 83.96 146 | 87.35 196 | 63.66 186 | 83.25 167 | 89.88 154 | 76.06 125 | 89.62 103 | 92.37 123 | 73.40 181 | 92.52 142 | 78.16 120 | 94.77 146 | 95.69 57 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
PAPM_NR | | | 83.23 159 | 83.19 156 | 83.33 165 | 90.90 121 | 65.98 170 | 88.19 74 | 90.78 124 | 78.13 99 | 80.87 231 | 87.92 221 | 73.49 178 | 92.42 143 | 70.07 182 | 88.40 253 | 91.60 168 |
|
PAPM | | | 71.77 267 | 70.06 278 | 76.92 249 | 86.39 216 | 53.97 277 | 76.62 274 | 86.62 203 | 53.44 303 | 63.97 332 | 84.73 264 | 57.79 263 | 92.34 144 | 39.65 333 | 81.33 312 | 84.45 263 |
|
PAPR | | | 78.84 202 | 78.10 205 | 81.07 200 | 85.17 241 | 60.22 240 | 82.21 196 | 90.57 129 | 62.51 261 | 75.32 280 | 84.61 265 | 74.99 150 | 92.30 145 | 59.48 258 | 88.04 260 | 90.68 189 |
|
V42 | | | 83.47 157 | 83.37 153 | 83.75 151 | 83.16 269 | 63.33 192 | 81.31 215 | 90.23 147 | 69.51 207 | 90.91 76 | 90.81 168 | 74.16 163 | 92.29 146 | 80.06 101 | 90.22 238 | 95.62 60 |
|
QAPM | | | 82.59 166 | 82.59 164 | 82.58 177 | 86.44 215 | 66.69 167 | 89.94 43 | 90.36 135 | 67.97 220 | 84.94 175 | 92.58 117 | 72.71 194 | 92.18 147 | 70.63 179 | 87.73 264 | 88.85 218 |
|
CSCG | | | 86.26 93 | 86.47 92 | 85.60 111 | 90.87 122 | 74.26 109 | 87.98 76 | 91.85 88 | 80.35 68 | 89.54 109 | 88.01 217 | 79.09 110 | 92.13 148 | 75.51 139 | 95.06 133 | 90.41 197 |
|
TAPA-MVS | | 77.73 12 | 85.71 106 | 84.83 119 | 88.37 64 | 88.78 154 | 79.72 65 | 87.15 91 | 93.50 35 | 69.17 210 | 85.80 165 | 89.56 194 | 80.76 98 | 92.13 148 | 73.21 162 | 95.51 119 | 93.25 122 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
HyFIR lowres test | | | 75.12 237 | 72.66 257 | 82.50 180 | 91.44 109 | 65.19 174 | 72.47 301 | 87.31 190 | 46.79 333 | 80.29 245 | 84.30 268 | 52.70 281 | 92.10 150 | 51.88 302 | 86.73 272 | 90.22 200 |
|
testing_2 | | | 84.36 131 | 84.64 129 | 83.50 164 | 86.74 214 | 63.97 184 | 84.56 130 | 90.31 138 | 66.22 232 | 91.62 65 | 94.55 61 | 75.88 142 | 91.95 151 | 77.02 133 | 94.89 138 | 94.56 81 |
|
EI-MVSNet-Vis-set | | | 85.12 111 | 84.53 132 | 86.88 83 | 84.01 260 | 72.76 117 | 83.91 144 | 85.18 218 | 80.44 66 | 88.75 119 | 85.49 247 | 80.08 104 | 91.92 152 | 82.02 76 | 90.85 228 | 95.97 49 |
|
EI-MVSNet-UG-set | | | 85.04 113 | 84.44 134 | 86.85 84 | 83.87 263 | 72.52 120 | 83.82 146 | 85.15 219 | 80.27 70 | 88.75 119 | 85.45 251 | 79.95 106 | 91.90 153 | 81.92 78 | 90.80 229 | 96.13 43 |
|
Regformer-1 | | | 86.00 98 | 85.50 108 | 87.49 78 | 84.18 258 | 76.90 91 | 83.52 157 | 87.94 184 | 82.18 52 | 89.19 112 | 85.07 258 | 82.28 79 | 91.89 154 | 82.40 71 | 92.72 193 | 93.69 111 |
|
IB-MVS | | 62.13 19 | 71.64 268 | 68.97 282 | 79.66 216 | 80.80 288 | 62.26 219 | 73.94 295 | 76.90 261 | 63.27 255 | 68.63 314 | 76.79 324 | 33.83 344 | 91.84 155 | 59.28 259 | 87.26 268 | 84.88 259 |
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 |
DI_MVS_plusplus_test | | | 81.27 182 | 81.26 179 | 81.29 197 | 84.98 242 | 61.65 225 | 81.98 201 | 87.25 192 | 63.56 251 | 87.56 137 | 89.60 193 | 73.62 173 | 91.83 156 | 72.20 168 | 90.59 236 | 90.38 198 |
|
UGNet | | | 82.78 163 | 81.64 175 | 86.21 98 | 86.20 226 | 76.24 100 | 86.86 94 | 85.68 212 | 77.07 117 | 73.76 290 | 92.82 110 | 69.64 213 | 91.82 157 | 69.04 192 | 93.69 169 | 90.56 193 |
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 |
BH-untuned | | | 80.96 186 | 80.99 183 | 80.84 202 | 88.55 159 | 68.23 157 | 80.33 228 | 88.46 172 | 72.79 168 | 86.55 152 | 86.76 233 | 74.72 157 | 91.77 158 | 61.79 236 | 88.99 248 | 82.52 291 |
|
Anonymous20231211 | | | 90.14 31 | 91.88 12 | 84.92 118 | 94.75 35 | 64.47 179 | 90.13 38 | 92.97 57 | 91.68 3 | 95.35 12 | 98.79 2 | 93.19 3 | 91.76 159 | 71.67 172 | 98.40 21 | 98.52 7 |
|
Test4 | | | 81.31 180 | 81.13 182 | 81.88 188 | 84.89 244 | 63.05 198 | 82.37 189 | 90.50 130 | 62.75 259 | 89.00 115 | 88.29 214 | 67.55 221 | 91.68 160 | 73.55 155 | 91.24 215 | 90.89 182 |
|
test_normal | | | 81.23 184 | 81.16 181 | 81.43 195 | 84.77 247 | 61.99 220 | 81.46 214 | 86.95 202 | 63.16 256 | 87.22 140 | 89.63 192 | 73.62 173 | 91.65 161 | 72.92 163 | 90.70 231 | 90.65 191 |
|
API-MVS | | | 82.28 170 | 82.61 163 | 81.30 196 | 86.29 222 | 69.79 144 | 88.71 69 | 87.67 186 | 78.42 95 | 82.15 214 | 84.15 271 | 77.98 118 | 91.59 162 | 65.39 218 | 92.75 192 | 82.51 292 |
|
nrg030 | | | 87.85 69 | 88.49 62 | 85.91 104 | 90.07 135 | 69.73 145 | 87.86 78 | 94.20 17 | 74.04 148 | 92.70 47 | 94.66 58 | 85.88 51 | 91.50 163 | 79.72 105 | 97.32 61 | 96.50 40 |
|
AllTest | | | 87.97 67 | 87.40 77 | 89.68 47 | 91.59 100 | 83.40 45 | 89.50 56 | 95.44 5 | 79.47 77 | 88.00 131 | 93.03 103 | 82.66 73 | 91.47 164 | 70.81 174 | 96.14 98 | 94.16 95 |
|
TestCases | | | | | 89.68 47 | 91.59 100 | 83.40 45 | | 95.44 5 | 79.47 77 | 88.00 131 | 93.03 103 | 82.66 73 | 91.47 164 | 70.81 174 | 96.14 98 | 94.16 95 |
|
PVSNet_BlendedMVS | | | 78.80 203 | 77.84 206 | 81.65 193 | 84.43 250 | 63.41 189 | 79.49 240 | 90.44 132 | 61.70 269 | 75.43 278 | 87.07 231 | 69.11 216 | 91.44 166 | 60.68 246 | 92.24 200 | 90.11 204 |
|
PVSNet_Blended | | | 76.49 226 | 75.40 228 | 79.76 212 | 84.43 250 | 63.41 189 | 75.14 287 | 90.44 132 | 57.36 285 | 75.43 278 | 78.30 319 | 69.11 216 | 91.44 166 | 60.68 246 | 87.70 265 | 84.42 264 |
|
æ— å…ˆéªŒ | | | | | | | | 82.81 177 | 85.62 213 | 58.09 282 | | | | 91.41 168 | 67.95 202 | | 84.48 262 |
|
1121 | | | 80.86 187 | 79.81 197 | 84.02 143 | 93.93 45 | 78.70 74 | 81.64 210 | 80.18 248 | 55.43 294 | 83.67 197 | 91.15 152 | 71.29 209 | 91.41 168 | 67.95 202 | 93.06 186 | 81.96 299 |
|
ambc | | | | | 82.98 170 | 90.55 129 | 64.86 176 | 88.20 73 | 89.15 166 | | 89.40 110 | 93.96 86 | 71.67 208 | 91.38 170 | 78.83 114 | 96.55 81 | 92.71 134 |
|
3Dnovator | | 80.37 7 | 84.80 119 | 84.71 123 | 85.06 116 | 86.36 220 | 74.71 106 | 88.77 68 | 90.00 152 | 75.65 134 | 84.96 173 | 93.17 101 | 74.06 164 | 91.19 171 | 78.28 119 | 91.09 216 | 89.29 212 |
|
cascas | | | 76.29 228 | 74.81 233 | 80.72 205 | 84.47 249 | 62.94 200 | 73.89 296 | 87.34 189 | 55.94 291 | 75.16 282 | 76.53 326 | 63.97 233 | 91.16 172 | 65.00 219 | 90.97 224 | 88.06 223 |
|
EG-PatchMatch MVS | | | 84.08 143 | 84.11 143 | 83.98 145 | 92.22 87 | 72.61 119 | 82.20 198 | 87.02 200 | 72.63 170 | 88.86 116 | 91.02 159 | 78.52 113 | 91.11 173 | 73.41 157 | 91.09 216 | 88.21 221 |
|
WR-MVS | | | 83.56 154 | 84.40 136 | 81.06 201 | 93.43 55 | 54.88 274 | 78.67 258 | 85.02 222 | 81.24 61 | 90.74 78 | 91.56 141 | 72.85 191 | 91.08 174 | 68.00 200 | 98.04 36 | 97.23 25 |
|
canonicalmvs | | | 85.50 107 | 86.14 98 | 83.58 158 | 87.97 174 | 67.13 163 | 87.55 83 | 94.32 12 | 73.44 155 | 88.47 125 | 87.54 226 | 86.45 45 | 91.06 175 | 75.76 138 | 93.76 167 | 92.54 144 |
|
XVG-OURS | | | 89.18 54 | 88.83 58 | 90.23 43 | 94.28 41 | 86.11 20 | 85.91 111 | 93.60 34 | 80.16 71 | 89.13 114 | 93.44 96 | 83.82 60 | 90.98 176 | 83.86 54 | 95.30 127 | 93.60 114 |
|
PS-MVSNAJ | | | 77.04 216 | 76.53 219 | 78.56 227 | 87.09 209 | 61.40 227 | 75.26 286 | 87.13 196 | 61.25 271 | 74.38 288 | 77.22 323 | 76.94 132 | 90.94 177 | 64.63 222 | 84.83 292 | 83.35 280 |
|
xiu_mvs_v2_base | | | 77.19 214 | 76.75 213 | 78.52 228 | 87.01 210 | 61.30 229 | 75.55 285 | 87.12 198 | 61.24 272 | 74.45 286 | 78.79 317 | 77.20 126 | 90.93 178 | 64.62 223 | 84.80 293 | 83.32 281 |
|
XVG-OURS-SEG-HR | | | 89.59 48 | 89.37 51 | 90.28 42 | 94.47 39 | 85.95 21 | 86.84 95 | 93.91 27 | 80.07 72 | 86.75 149 | 93.26 99 | 93.64 2 | 90.93 178 | 84.60 44 | 90.75 230 | 93.97 100 |
|
v148 | | | 82.31 169 | 82.48 166 | 81.81 192 | 85.59 236 | 59.66 243 | 81.47 213 | 86.02 209 | 72.85 167 | 88.05 130 | 90.65 174 | 70.73 211 | 90.91 180 | 75.15 141 | 91.79 204 | 94.87 77 |
|
VDD-MVS | | | 84.23 137 | 84.58 131 | 83.20 167 | 91.17 116 | 65.16 175 | 83.25 167 | 84.97 224 | 79.79 73 | 87.18 141 | 94.27 73 | 74.77 156 | 90.89 181 | 69.24 188 | 96.54 82 | 93.55 118 |
|
alignmvs | | | 83.94 148 | 83.98 146 | 83.80 148 | 87.80 185 | 67.88 160 | 84.54 131 | 91.42 108 | 73.27 162 | 88.41 127 | 87.96 218 | 72.33 200 | 90.83 182 | 76.02 137 | 94.11 157 | 92.69 135 |
|
ITE_SJBPF | | | | | 90.11 45 | 90.72 125 | 84.97 33 | | 90.30 140 | 81.56 59 | 90.02 85 | 91.20 150 | 82.40 77 | 90.81 183 | 73.58 154 | 94.66 149 | 94.56 81 |
|
BH-RMVSNet | | | 80.53 191 | 80.22 192 | 81.49 194 | 87.19 204 | 66.21 169 | 77.79 265 | 86.23 206 | 74.21 147 | 83.69 196 | 88.50 207 | 73.25 187 | 90.75 184 | 63.18 231 | 87.90 261 | 87.52 230 |
|
BH-w/o | | | 76.57 224 | 76.07 223 | 78.10 234 | 86.88 212 | 65.92 171 | 77.63 266 | 86.33 205 | 65.69 238 | 80.89 230 | 79.95 312 | 68.97 218 | 90.74 185 | 53.01 294 | 85.25 286 | 77.62 322 |
|
TR-MVS | | | 76.77 222 | 75.79 224 | 79.72 213 | 86.10 233 | 65.79 172 | 77.14 270 | 83.02 230 | 65.20 244 | 81.40 225 | 82.10 298 | 66.30 225 | 90.73 186 | 55.57 281 | 85.27 285 | 82.65 287 |
|
GBi-Net | | | 82.02 174 | 82.07 170 | 81.85 189 | 86.38 217 | 61.05 233 | 86.83 96 | 88.27 177 | 72.43 171 | 86.00 161 | 95.64 33 | 63.78 235 | 90.68 187 | 65.95 213 | 93.34 177 | 93.82 105 |
|
test1 | | | 82.02 174 | 82.07 170 | 81.85 189 | 86.38 217 | 61.05 233 | 86.83 96 | 88.27 177 | 72.43 171 | 86.00 161 | 95.64 33 | 63.78 235 | 90.68 187 | 65.95 213 | 93.34 177 | 93.82 105 |
|
FMVSNet1 | | | 84.55 124 | 85.45 109 | 81.85 189 | 90.27 132 | 61.05 233 | 86.83 96 | 88.27 177 | 78.57 93 | 89.66 99 | 95.64 33 | 75.43 144 | 90.68 187 | 69.09 191 | 95.33 123 | 93.82 105 |
|
VDDNet | | | 84.35 133 | 85.39 110 | 81.25 198 | 95.13 28 | 59.32 246 | 85.42 119 | 81.11 243 | 86.41 22 | 87.41 139 | 96.21 21 | 73.61 175 | 90.61 190 | 66.33 211 | 96.85 72 | 93.81 108 |
|
MAR-MVS | | | 80.24 195 | 78.74 202 | 84.73 125 | 86.87 213 | 78.18 76 | 85.75 114 | 87.81 185 | 65.67 239 | 77.84 259 | 78.50 318 | 73.79 170 | 90.53 191 | 61.59 240 | 90.87 227 | 85.49 251 |
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020 |
MVS_Test | | | 82.47 168 | 83.22 154 | 80.22 208 | 82.62 273 | 57.75 255 | 82.54 186 | 91.96 86 | 71.16 194 | 82.89 208 | 92.52 119 | 77.41 125 | 90.50 192 | 80.04 102 | 87.84 263 | 92.40 147 |
|
MVS_111021_HR | | | 84.63 121 | 84.34 141 | 85.49 112 | 90.18 133 | 75.86 101 | 79.23 252 | 87.13 196 | 73.35 156 | 85.56 169 | 89.34 197 | 83.60 64 | 90.50 192 | 76.64 134 | 94.05 159 | 90.09 205 |
|
Regformer-3 | | | 85.06 112 | 84.67 125 | 86.22 96 | 84.27 254 | 73.43 113 | 84.07 137 | 85.26 216 | 80.77 65 | 88.62 122 | 85.48 248 | 80.56 101 | 90.39 194 | 81.99 77 | 91.04 218 | 94.85 79 |
|
EI-MVSNet | | | 82.61 165 | 82.42 167 | 83.20 167 | 83.25 267 | 63.66 186 | 83.50 160 | 85.07 220 | 76.06 125 | 86.55 152 | 85.10 256 | 73.41 179 | 90.25 195 | 78.15 121 | 90.67 232 | 95.68 58 |
|
MVSTER | | | 77.09 215 | 75.70 227 | 81.25 198 | 75.27 328 | 61.08 232 | 77.49 269 | 85.07 220 | 60.78 274 | 86.55 152 | 88.68 205 | 43.14 324 | 90.25 195 | 73.69 153 | 90.67 232 | 92.42 145 |
|
Fast-Effi-MVS+-dtu | | | 82.54 167 | 81.41 178 | 85.90 105 | 85.60 235 | 76.53 96 | 83.07 171 | 89.62 160 | 73.02 166 | 79.11 254 | 83.51 275 | 80.74 99 | 90.24 197 | 68.76 194 | 89.29 244 | 90.94 180 |
|
SD-MVS | | | 88.96 56 | 89.88 44 | 86.22 96 | 91.63 99 | 77.07 89 | 89.82 45 | 93.77 30 | 78.90 87 | 92.88 39 | 92.29 124 | 86.11 49 | 90.22 198 | 86.24 30 | 97.24 63 | 91.36 175 |
|
FMVSNet2 | | | 81.31 180 | 81.61 176 | 80.41 206 | 86.38 217 | 58.75 251 | 83.93 143 | 86.58 204 | 72.43 171 | 87.65 135 | 92.98 105 | 63.78 235 | 90.22 198 | 66.86 206 | 93.92 162 | 92.27 153 |
|
OpenMVS | | 76.72 13 | 81.98 176 | 82.00 172 | 81.93 185 | 84.42 252 | 68.22 158 | 88.50 72 | 89.48 162 | 66.92 227 | 81.80 221 | 91.86 130 | 72.59 198 | 90.16 200 | 71.19 173 | 91.25 214 | 87.40 232 |
|
xiu_mvs_v1_base_debu | | | 80.84 188 | 80.14 193 | 82.93 172 | 88.31 165 | 71.73 132 | 79.53 237 | 87.17 193 | 65.43 240 | 79.59 249 | 82.73 288 | 76.94 132 | 90.14 201 | 73.22 158 | 88.33 254 | 86.90 237 |
|
xiu_mvs_v1_base | | | 80.84 188 | 80.14 193 | 82.93 172 | 88.31 165 | 71.73 132 | 79.53 237 | 87.17 193 | 65.43 240 | 79.59 249 | 82.73 288 | 76.94 132 | 90.14 201 | 73.22 158 | 88.33 254 | 86.90 237 |
|
xiu_mvs_v1_base_debi | | | 80.84 188 | 80.14 193 | 82.93 172 | 88.31 165 | 71.73 132 | 79.53 237 | 87.17 193 | 65.43 240 | 79.59 249 | 82.73 288 | 76.94 132 | 90.14 201 | 73.22 158 | 88.33 254 | 86.90 237 |
|
FMVSNet3 | | | 78.80 203 | 78.55 203 | 79.57 217 | 82.89 271 | 56.89 261 | 81.76 207 | 85.77 211 | 69.04 213 | 86.00 161 | 90.44 178 | 51.75 283 | 90.09 204 | 65.95 213 | 93.34 177 | 91.72 165 |
|
LFMVS | | | 80.15 197 | 80.56 187 | 78.89 221 | 89.19 147 | 55.93 265 | 85.22 121 | 73.78 280 | 82.96 42 | 84.28 192 | 92.72 114 | 57.38 265 | 90.07 205 | 63.80 226 | 95.75 115 | 90.68 189 |
|
MVS | | | 73.21 256 | 72.59 259 | 75.06 265 | 80.97 284 | 60.81 236 | 81.64 210 | 85.92 210 | 46.03 336 | 71.68 301 | 77.54 320 | 68.47 219 | 89.77 206 | 55.70 280 | 85.39 283 | 74.60 328 |
|
LCM-MVSNet-Re | | | 83.48 156 | 85.06 113 | 78.75 224 | 85.94 234 | 55.75 268 | 80.05 230 | 94.27 13 | 76.47 122 | 96.09 5 | 94.54 63 | 83.31 67 | 89.75 207 | 59.95 249 | 94.89 138 | 90.75 187 |
|
CANet_DTU | | | 77.81 209 | 77.05 210 | 80.09 209 | 81.37 280 | 59.90 242 | 83.26 166 | 88.29 176 | 69.16 211 | 67.83 318 | 83.72 273 | 60.93 243 | 89.47 208 | 69.22 190 | 89.70 242 | 90.88 183 |
|
GA-MVS | | | 75.83 231 | 74.61 234 | 79.48 219 | 81.87 276 | 59.25 247 | 73.42 299 | 82.88 231 | 68.68 215 | 79.75 248 | 81.80 301 | 50.62 285 | 89.46 209 | 66.85 207 | 85.64 282 | 89.72 206 |
|
MVP-Stereo | | | 75.81 232 | 73.51 250 | 82.71 175 | 89.35 143 | 73.62 111 | 80.06 229 | 85.20 217 | 60.30 276 | 73.96 289 | 87.94 219 | 57.89 262 | 89.45 210 | 52.02 297 | 74.87 330 | 85.06 254 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
Vis-MVSNet (Re-imp) | | | 77.82 208 | 77.79 207 | 77.92 237 | 88.82 153 | 51.29 301 | 83.28 165 | 71.97 295 | 74.04 148 | 82.23 212 | 89.78 190 | 57.38 265 | 89.41 211 | 57.22 272 | 95.41 121 | 93.05 126 |
|
MSLP-MVS++ | | | 85.00 115 | 86.03 100 | 81.90 186 | 91.84 97 | 71.56 137 | 86.75 100 | 93.02 56 | 75.95 128 | 87.12 142 | 89.39 196 | 77.98 118 | 89.40 212 | 77.46 126 | 94.78 144 | 84.75 261 |
|
view600 | | | 76.79 218 | 76.54 215 | 77.56 241 | 87.91 177 | 50.77 307 | 81.92 202 | 71.35 302 | 77.38 107 | 84.62 180 | 88.40 209 | 45.18 314 | 89.26 213 | 58.58 261 | 93.49 172 | 92.66 136 |
|
view800 | | | 76.79 218 | 76.54 215 | 77.56 241 | 87.91 177 | 50.77 307 | 81.92 202 | 71.35 302 | 77.38 107 | 84.62 180 | 88.40 209 | 45.18 314 | 89.26 213 | 58.58 261 | 93.49 172 | 92.66 136 |
|
conf0.05thres1000 | | | 76.79 218 | 76.54 215 | 77.56 241 | 87.91 177 | 50.77 307 | 81.92 202 | 71.35 302 | 77.38 107 | 84.62 180 | 88.40 209 | 45.18 314 | 89.26 213 | 58.58 261 | 93.49 172 | 92.66 136 |
|
tfpn | | | 76.79 218 | 76.54 215 | 77.56 241 | 87.91 177 | 50.77 307 | 81.92 202 | 71.35 302 | 77.38 107 | 84.62 180 | 88.40 209 | 45.18 314 | 89.26 213 | 58.58 261 | 93.49 172 | 92.66 136 |
|
thres600view7 | | | 75.97 230 | 75.35 230 | 77.85 239 | 87.01 210 | 51.84 297 | 80.45 227 | 73.26 285 | 75.20 139 | 83.10 206 | 86.31 241 | 45.54 305 | 89.05 217 | 55.03 286 | 92.24 200 | 92.66 136 |
|
jason | | | 77.42 212 | 75.75 226 | 82.43 182 | 87.10 208 | 69.27 149 | 77.99 262 | 81.94 239 | 51.47 317 | 77.84 259 | 85.07 258 | 60.32 247 | 89.00 218 | 70.74 177 | 89.27 246 | 89.03 215 |
jason: jason. |
lupinMVS | | | 76.37 227 | 74.46 237 | 82.09 183 | 85.54 237 | 69.26 150 | 76.79 271 | 80.77 246 | 50.68 324 | 76.23 271 | 82.82 286 | 58.69 258 | 88.94 219 | 69.85 183 | 88.77 250 | 88.07 222 |
|
PMVS | | 80.48 6 | 90.08 33 | 90.66 36 | 88.34 65 | 96.71 2 | 92.97 2 | 90.31 36 | 89.57 161 | 88.51 15 | 90.11 82 | 95.12 46 | 90.98 8 | 88.92 220 | 77.55 125 | 97.07 67 | 83.13 285 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
conf200view11 | | | 75.62 233 | 75.05 231 | 77.34 246 | 87.27 200 | 51.88 294 | 81.07 220 | 73.26 285 | 75.68 132 | 83.25 203 | 86.37 237 | 45.54 305 | 88.80 221 | 51.98 298 | 90.99 220 | 91.34 176 |
|
thres100view900 | | | 75.45 234 | 75.05 231 | 76.66 254 | 87.27 200 | 51.88 294 | 81.07 220 | 73.26 285 | 75.68 132 | 83.25 203 | 86.37 237 | 45.54 305 | 88.80 221 | 51.98 298 | 90.99 220 | 89.31 210 |
|
tfpn200view9 | | | 74.86 241 | 74.23 239 | 76.74 253 | 86.24 223 | 52.12 291 | 79.24 249 | 73.87 278 | 73.34 157 | 81.82 219 | 84.60 266 | 46.02 299 | 88.80 221 | 51.98 298 | 90.99 220 | 89.31 210 |
|
thres400 | | | 75.14 235 | 74.23 239 | 77.86 238 | 86.24 223 | 52.12 291 | 79.24 249 | 73.87 278 | 73.34 157 | 81.82 219 | 84.60 266 | 46.02 299 | 88.80 221 | 51.98 298 | 90.99 220 | 92.66 136 |
|
TAMVS | | | 78.08 207 | 76.36 220 | 83.23 166 | 90.62 126 | 72.87 116 | 79.08 253 | 80.01 250 | 61.72 268 | 81.35 226 | 86.92 232 | 63.96 234 | 88.78 225 | 50.61 303 | 93.01 188 | 88.04 224 |
|
CDS-MVSNet | | | 77.32 213 | 75.40 228 | 83.06 169 | 89.00 150 | 72.48 121 | 77.90 264 | 82.17 236 | 60.81 273 | 78.94 255 | 83.49 276 | 59.30 255 | 88.76 226 | 54.64 289 | 92.37 197 | 87.93 227 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
tpmp4_e23 | | | 69.43 284 | 67.33 291 | 75.72 262 | 78.53 303 | 52.75 286 | 82.13 200 | 74.91 271 | 49.23 330 | 66.37 321 | 84.17 270 | 41.28 333 | 88.67 227 | 49.73 308 | 79.63 316 | 85.75 248 |
|
OpenMVS_ROB | | 70.19 17 | 77.77 210 | 77.46 208 | 78.71 225 | 84.39 253 | 61.15 231 | 81.18 219 | 82.52 233 | 62.45 263 | 83.34 202 | 87.37 228 | 66.20 226 | 88.66 228 | 64.69 221 | 85.02 289 | 86.32 241 |
|
tpm2 | | | 68.45 289 | 66.83 293 | 73.30 278 | 78.93 299 | 48.50 322 | 79.76 234 | 71.76 298 | 47.50 332 | 69.92 310 | 83.60 274 | 42.07 327 | 88.40 229 | 48.44 314 | 79.51 317 | 83.01 286 |
|
æ–°å‡ ä½•1 | | | | | 82.95 171 | 93.96 44 | 78.56 75 | | 80.24 247 | 55.45 293 | 83.93 195 | 91.08 157 | 71.19 210 | 88.33 230 | 65.84 216 | 93.07 185 | 81.95 300 |
|
ACMH | | 76.49 14 | 89.34 52 | 91.14 28 | 83.96 146 | 92.50 77 | 70.36 143 | 89.55 53 | 93.84 29 | 81.89 56 | 94.70 16 | 95.44 39 | 90.69 10 | 88.31 231 | 83.33 58 | 98.30 28 | 93.20 123 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
thres200 | | | 72.34 264 | 71.55 268 | 74.70 268 | 83.48 264 | 51.60 298 | 75.02 288 | 73.71 281 | 70.14 203 | 78.56 256 | 80.57 308 | 46.20 297 | 88.20 232 | 46.99 320 | 89.29 244 | 84.32 266 |
|
gm-plane-assit | | | | | | 75.42 326 | 44.97 333 | | | 52.17 310 | | 72.36 336 | | 87.90 233 | 54.10 290 | | |
|
EU-MVSNet | | | 75.12 237 | 74.43 238 | 77.18 247 | 83.11 270 | 59.48 245 | 85.71 116 | 82.43 234 | 39.76 345 | 85.64 167 | 88.76 203 | 44.71 320 | 87.88 234 | 73.86 152 | 85.88 280 | 84.16 269 |
|
diffmvs | | | 79.20 201 | 79.04 199 | 79.69 214 | 78.64 302 | 58.90 248 | 81.79 206 | 87.61 187 | 65.07 246 | 73.65 292 | 89.80 189 | 73.10 189 | 87.79 235 | 75.02 143 | 86.63 273 | 92.38 148 |
|
RPSCF | | | 88.00 66 | 86.93 85 | 91.22 27 | 90.08 134 | 89.30 6 | 89.68 48 | 91.11 119 | 79.26 81 | 89.68 98 | 94.81 57 | 82.44 76 | 87.74 236 | 76.54 135 | 88.74 252 | 96.61 38 |
|
CostFormer | | | 69.98 282 | 68.68 285 | 73.87 274 | 77.14 311 | 50.72 311 | 79.26 248 | 74.51 276 | 51.94 314 | 70.97 306 | 84.75 263 | 45.16 318 | 87.49 237 | 55.16 285 | 79.23 320 | 83.40 279 |
|
CVMVSNet | | | 72.62 260 | 71.41 269 | 76.28 258 | 83.25 267 | 60.34 239 | 83.50 160 | 79.02 253 | 37.77 346 | 76.33 269 | 85.10 256 | 49.60 288 | 87.41 238 | 70.54 180 | 77.54 326 | 81.08 315 |
|
wuykxyi23d | | | 88.46 62 | 88.80 60 | 87.44 80 | 90.96 120 | 93.03 1 | 85.85 113 | 81.96 237 | 74.58 143 | 98.58 2 | 97.29 5 | 87.73 31 | 87.31 239 | 82.84 67 | 99.41 1 | 81.99 298 |
|
VPA-MVSNet | | | 83.47 157 | 84.73 120 | 79.69 214 | 90.29 131 | 57.52 256 | 81.30 217 | 88.69 170 | 76.29 123 | 87.58 136 | 94.44 66 | 80.60 100 | 87.20 240 | 66.60 210 | 96.82 75 | 94.34 91 |
|
patchmatchnet-post | | | | | | | | | | | | 81.71 302 | 45.93 302 | 87.01 241 | | | |
|
mvs_anonymous | | | 78.13 206 | 78.76 201 | 76.23 259 | 79.24 296 | 50.31 313 | 78.69 257 | 84.82 225 | 61.60 270 | 83.09 207 | 92.82 110 | 73.89 169 | 87.01 241 | 68.33 199 | 86.41 276 | 91.37 174 |
|
TinyColmap | | | 81.25 183 | 82.34 168 | 77.99 236 | 85.33 240 | 60.68 237 | 82.32 191 | 88.33 175 | 71.26 193 | 86.97 147 | 92.22 127 | 77.10 129 | 86.98 243 | 62.37 232 | 95.17 130 | 86.31 242 |
|
TransMVSNet (Re) | | | 84.02 145 | 85.74 103 | 78.85 222 | 91.00 119 | 55.20 273 | 82.29 192 | 87.26 191 | 79.65 76 | 88.38 128 | 95.52 37 | 83.00 69 | 86.88 244 | 67.97 201 | 96.60 80 | 94.45 88 |
|
LF4IMVS | | | 82.75 164 | 81.93 173 | 85.19 114 | 82.08 274 | 80.15 61 | 85.53 117 | 88.76 169 | 68.01 218 | 85.58 168 | 87.75 222 | 71.80 206 | 86.85 245 | 74.02 149 | 93.87 163 | 88.58 219 |
|
conf0.002 | | | 74.17 246 | 73.53 245 | 76.08 260 | 86.13 228 | 50.06 316 | 79.45 241 | 68.54 315 | 72.01 181 | 80.76 233 | 82.50 291 | 41.39 328 | 86.83 246 | 59.66 252 | 91.36 209 | 91.34 176 |
|
thresconf0.02 | | | 73.65 250 | 73.53 245 | 73.98 270 | 86.13 228 | 50.06 316 | 79.45 241 | 68.54 315 | 72.01 181 | 80.76 233 | 82.50 291 | 41.39 328 | 86.83 246 | 59.66 252 | 91.36 209 | 85.06 254 |
|
tfpn_n400 | | | 73.65 250 | 73.53 245 | 73.98 270 | 86.13 228 | 50.06 316 | 79.45 241 | 68.54 315 | 72.01 181 | 80.76 233 | 82.50 291 | 41.39 328 | 86.83 246 | 59.66 252 | 91.36 209 | 85.06 254 |
|
tfpnconf | | | 73.65 250 | 73.53 245 | 73.98 270 | 86.13 228 | 50.06 316 | 79.45 241 | 68.54 315 | 72.01 181 | 80.76 233 | 82.50 291 | 41.39 328 | 86.83 246 | 59.66 252 | 91.36 209 | 85.06 254 |
|
tfpnview11 | | | 73.65 250 | 73.53 245 | 73.98 270 | 86.13 228 | 50.06 316 | 79.45 241 | 68.54 315 | 72.01 181 | 80.76 233 | 82.50 291 | 41.39 328 | 86.83 246 | 59.66 252 | 91.36 209 | 85.06 254 |
|
pmmvs6 | | | 86.52 88 | 88.06 65 | 81.90 186 | 92.22 87 | 62.28 218 | 84.66 128 | 89.15 166 | 83.54 36 | 89.85 93 | 97.32 4 | 88.08 28 | 86.80 251 | 70.43 181 | 97.30 62 | 96.62 37 |
|
tfpn_ndepth | | | 72.54 261 | 72.30 262 | 73.24 279 | 84.81 246 | 51.42 299 | 79.24 249 | 70.49 308 | 69.26 209 | 78.48 257 | 79.80 313 | 40.16 337 | 86.77 252 | 58.08 270 | 90.43 237 | 81.53 308 |
|
1112_ss | | | 74.82 242 | 73.74 241 | 78.04 235 | 89.57 141 | 60.04 241 | 76.49 276 | 87.09 199 | 54.31 298 | 73.66 291 | 79.80 313 | 60.25 248 | 86.76 253 | 58.37 265 | 84.15 296 | 87.32 233 |
|
tfpn1000 | | | 73.63 254 | 73.58 243 | 73.79 276 | 85.46 239 | 50.31 313 | 79.99 232 | 68.18 320 | 72.33 175 | 80.66 238 | 83.05 280 | 39.80 338 | 86.74 254 | 60.96 244 | 91.78 205 | 84.32 266 |
|
USDC | | | 76.63 223 | 76.73 214 | 76.34 257 | 83.46 265 | 57.20 258 | 80.02 231 | 88.04 181 | 52.14 312 | 83.65 199 | 91.25 146 | 63.24 238 | 86.65 255 | 54.66 288 | 94.11 157 | 85.17 252 |
|
tfpnnormal | | | 81.79 177 | 82.95 158 | 78.31 230 | 88.93 151 | 55.40 269 | 80.83 225 | 82.85 232 | 76.81 119 | 85.90 164 | 94.14 79 | 74.58 160 | 86.51 256 | 66.82 209 | 95.68 118 | 93.01 127 |
|
VPNet | | | 80.25 194 | 81.68 174 | 75.94 261 | 92.46 78 | 47.98 325 | 76.70 273 | 81.67 241 | 73.45 154 | 84.87 176 | 92.82 110 | 74.66 158 | 86.51 256 | 61.66 238 | 96.85 72 | 93.33 119 |
|
testdata2 | | | | | | | | | | | | | | 86.43 258 | 63.52 228 | | |
|
MSDG | | | 80.06 198 | 79.99 196 | 80.25 207 | 83.91 262 | 68.04 159 | 77.51 268 | 89.19 165 | 77.65 103 | 81.94 216 | 83.45 277 | 76.37 140 | 86.31 259 | 63.31 230 | 86.59 274 | 86.41 240 |
|
MVS_111021_LR | | | 84.28 135 | 83.76 149 | 85.83 108 | 89.23 146 | 83.07 48 | 80.99 222 | 83.56 228 | 72.71 169 | 86.07 160 | 89.07 200 | 81.75 90 | 86.19 260 | 77.11 131 | 93.36 176 | 88.24 220 |
|
Baseline_NR-MVSNet | | | 84.00 146 | 85.90 101 | 78.29 231 | 91.47 108 | 53.44 282 | 82.29 192 | 87.00 201 | 79.06 85 | 89.55 107 | 95.72 30 | 77.20 126 | 86.14 261 | 72.30 167 | 98.51 18 | 95.28 70 |
|
EPNet_dtu | | | 72.87 258 | 71.33 270 | 77.49 245 | 77.72 308 | 60.55 238 | 82.35 190 | 75.79 266 | 66.49 230 | 58.39 345 | 81.06 307 | 53.68 279 | 85.98 262 | 53.55 292 | 92.97 189 | 85.95 245 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
ANet_high | | | 83.17 161 | 85.68 105 | 75.65 263 | 81.24 281 | 45.26 330 | 79.94 233 | 92.91 59 | 83.83 35 | 91.33 70 | 96.88 10 | 80.25 103 | 85.92 263 | 68.89 193 | 95.89 109 | 95.76 53 |
|
Test_1112_low_res | | | 73.90 248 | 73.08 253 | 76.35 256 | 90.35 130 | 55.95 264 | 73.40 300 | 86.17 207 | 50.70 323 | 73.14 293 | 85.94 244 | 58.31 260 | 85.90 264 | 56.51 275 | 83.22 300 | 87.20 234 |
|
MIMVSNet1 | | | 83.63 153 | 84.59 130 | 80.74 203 | 94.06 42 | 62.77 203 | 82.72 181 | 84.53 226 | 77.57 105 | 90.34 80 | 95.92 26 | 76.88 138 | 85.83 265 | 61.88 235 | 97.42 59 | 93.62 113 |
|
tpmvs | | | 70.16 278 | 69.56 280 | 71.96 289 | 74.71 332 | 48.13 323 | 79.63 235 | 75.45 270 | 65.02 247 | 70.26 308 | 81.88 300 | 45.34 311 | 85.68 266 | 58.34 266 | 75.39 329 | 82.08 297 |
|
pm-mvs1 | | | 83.69 151 | 84.95 117 | 79.91 210 | 90.04 138 | 59.66 243 | 82.43 187 | 87.44 188 | 75.52 135 | 87.85 133 | 95.26 42 | 81.25 95 | 85.65 267 | 68.74 195 | 96.04 103 | 94.42 89 |
|
pmmvs-eth3d | | | 78.42 205 | 77.04 211 | 82.57 179 | 87.44 194 | 74.41 108 | 80.86 224 | 79.67 251 | 55.68 292 | 84.69 179 | 90.31 182 | 60.91 244 | 85.42 268 | 62.20 233 | 91.59 207 | 87.88 228 |
|
testdata | | | | | 79.54 218 | 92.87 68 | 72.34 122 | | 80.14 249 | 59.91 278 | 85.47 171 | 91.75 137 | 67.96 220 | 85.24 269 | 68.57 198 | 92.18 203 | 81.06 317 |
|
1314 | | | 73.22 255 | 72.56 260 | 75.20 264 | 80.41 290 | 57.84 253 | 81.64 210 | 85.36 215 | 51.68 315 | 73.10 294 | 76.65 325 | 61.45 242 | 85.19 270 | 63.54 227 | 79.21 321 | 82.59 288 |
|
CHOSEN 1792x2688 | | | 72.45 262 | 70.56 272 | 78.13 233 | 90.02 139 | 63.08 197 | 68.72 312 | 83.16 229 | 42.99 342 | 75.92 274 | 85.46 250 | 57.22 267 | 85.18 271 | 49.87 307 | 81.67 309 | 86.14 243 |
|
pmmvs4 | | | 74.92 240 | 72.98 255 | 80.73 204 | 84.95 243 | 71.71 135 | 76.23 279 | 77.59 257 | 52.83 306 | 77.73 262 | 86.38 236 | 56.35 268 | 84.97 272 | 57.72 271 | 87.05 270 | 85.51 250 |
|
旧先验2 | | | | | | | | 81.73 208 | | 56.88 289 | 86.54 157 | | | 84.90 273 | 72.81 164 | | |
|
DWT-MVSNet_test | | | 66.43 296 | 64.37 301 | 72.63 285 | 74.86 331 | 50.86 306 | 76.52 275 | 72.74 289 | 54.06 300 | 65.50 325 | 68.30 341 | 32.13 346 | 84.84 274 | 61.63 239 | 73.59 331 | 82.19 295 |
|
HY-MVS | | 64.64 18 | 73.03 257 | 72.47 261 | 74.71 267 | 83.36 266 | 54.19 276 | 82.14 199 | 81.96 237 | 56.76 290 | 69.57 311 | 86.21 242 | 60.03 249 | 84.83 275 | 49.58 309 | 82.65 305 | 85.11 253 |
|
ab-mvs | | | 79.67 199 | 80.56 187 | 76.99 248 | 88.48 162 | 56.93 259 | 84.70 127 | 86.06 208 | 68.95 214 | 80.78 232 | 93.08 102 | 75.30 146 | 84.62 276 | 56.78 274 | 90.90 226 | 89.43 209 |
|
IterMVS | | | 76.91 217 | 76.34 221 | 78.64 226 | 80.91 285 | 64.03 182 | 76.30 278 | 79.03 252 | 64.88 248 | 83.11 205 | 89.16 198 | 59.90 251 | 84.46 277 | 68.61 197 | 85.15 288 | 87.42 231 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
VNet | | | 79.31 200 | 80.27 191 | 76.44 255 | 87.92 176 | 53.95 278 | 75.58 284 | 84.35 227 | 74.39 146 | 82.23 212 | 90.72 170 | 72.84 192 | 84.39 278 | 60.38 248 | 93.98 161 | 90.97 179 |
|
CR-MVSNet | | | 74.00 247 | 73.04 254 | 76.85 251 | 79.58 291 | 62.64 205 | 82.58 183 | 76.90 261 | 50.50 325 | 75.72 276 | 92.38 120 | 48.07 292 | 84.07 279 | 68.72 196 | 82.91 303 | 83.85 272 |
|
RPMNet | | | 76.06 229 | 75.79 224 | 76.85 251 | 79.58 291 | 62.64 205 | 82.58 183 | 71.75 299 | 74.80 141 | 75.72 276 | 92.59 115 | 48.69 290 | 84.07 279 | 73.48 156 | 82.91 303 | 83.85 272 |
|
Patchmtry | | | 76.56 225 | 77.46 208 | 73.83 275 | 79.37 295 | 46.60 327 | 82.41 188 | 76.90 261 | 73.81 151 | 85.56 169 | 92.38 120 | 48.07 292 | 83.98 281 | 63.36 229 | 95.31 126 | 90.92 181 |
|
gg-mvs-nofinetune | | | 68.96 288 | 69.11 281 | 68.52 303 | 76.12 319 | 45.32 329 | 83.59 153 | 55.88 346 | 86.68 20 | 64.62 331 | 97.01 7 | 30.36 348 | 83.97 282 | 44.78 325 | 82.94 302 | 76.26 325 |
|
GG-mvs-BLEND | | | | | 67.16 306 | 73.36 335 | 46.54 328 | 84.15 136 | 55.04 347 | | 58.64 344 | 61.95 347 | 29.93 349 | 83.87 283 | 38.71 337 | 76.92 327 | 71.07 334 |
|
PM-MVS | | | 80.20 196 | 79.00 200 | 83.78 150 | 88.17 169 | 86.66 13 | 81.31 215 | 66.81 329 | 69.64 206 | 88.33 129 | 90.19 183 | 64.58 231 | 83.63 284 | 71.99 171 | 90.03 239 | 81.06 317 |
|
JIA-IIPM | | | 69.41 286 | 66.64 296 | 77.70 240 | 73.19 337 | 71.24 138 | 75.67 282 | 65.56 330 | 70.42 199 | 65.18 327 | 92.97 106 | 33.64 345 | 83.06 285 | 53.52 293 | 69.61 342 | 78.79 321 |
|
CMPMVS | | 59.41 20 | 75.12 237 | 73.57 244 | 79.77 211 | 75.84 320 | 67.22 162 | 81.21 218 | 82.18 235 | 50.78 322 | 76.50 267 | 87.66 224 | 55.20 275 | 82.99 286 | 62.17 234 | 90.64 235 | 89.09 214 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
PatchFormer-LS_test | | | 67.91 291 | 66.49 297 | 72.17 288 | 75.29 327 | 51.85 296 | 75.68 281 | 73.62 283 | 57.23 287 | 68.64 312 | 68.13 342 | 42.19 326 | 82.76 287 | 64.06 225 | 73.51 332 | 81.89 302 |
|
Patchmatch-RL test | | | 74.48 243 | 73.68 242 | 76.89 250 | 84.83 245 | 66.54 168 | 72.29 302 | 69.16 314 | 57.70 283 | 86.76 148 | 86.33 239 | 45.79 304 | 82.59 288 | 69.63 185 | 90.65 234 | 81.54 307 |
|
tpm cat1 | | | 66.76 295 | 65.21 300 | 71.42 290 | 77.09 312 | 50.62 312 | 78.01 261 | 73.68 282 | 44.89 338 | 68.64 312 | 79.00 316 | 45.51 308 | 82.42 289 | 49.91 306 | 70.15 339 | 81.23 314 |
|
Patchmatch-test1 | | | 72.75 259 | 72.61 258 | 73.19 280 | 81.62 278 | 55.86 266 | 78.89 255 | 71.37 301 | 61.73 267 | 74.93 283 | 82.15 297 | 60.46 246 | 81.80 290 | 59.68 251 | 82.63 307 | 81.92 301 |
|
MS-PatchMatch | | | 70.93 273 | 70.22 276 | 73.06 282 | 81.85 277 | 62.50 211 | 73.82 297 | 77.90 255 | 52.44 309 | 75.92 274 | 81.27 305 | 55.67 272 | 81.75 291 | 55.37 283 | 77.70 324 | 74.94 327 |
|
CNLPA | | | 83.55 155 | 83.10 157 | 84.90 119 | 89.34 144 | 83.87 43 | 84.54 131 | 88.77 168 | 79.09 84 | 83.54 201 | 88.66 206 | 74.87 152 | 81.73 292 | 66.84 208 | 92.29 198 | 89.11 213 |
|
MDA-MVSNet-bldmvs | | | 77.47 211 | 76.90 212 | 79.16 220 | 79.03 298 | 64.59 177 | 66.58 321 | 75.67 268 | 73.15 164 | 88.86 116 | 88.99 201 | 66.94 223 | 81.23 293 | 64.71 220 | 88.22 259 | 91.64 167 |
|
MDTV_nov1_ep13 | | | | 68.29 288 | | 78.03 307 | 43.87 335 | 74.12 294 | 72.22 293 | 52.17 310 | 67.02 320 | 85.54 246 | 45.36 310 | 80.85 294 | 55.73 278 | 84.42 295 | |
|
pmmvs5 | | | 70.73 274 | 70.07 277 | 72.72 284 | 77.03 313 | 52.73 287 | 74.14 293 | 75.65 269 | 50.36 326 | 72.17 299 | 85.37 254 | 55.42 274 | 80.67 295 | 52.86 295 | 87.59 266 | 84.77 260 |
|
Gipuma | | | 84.44 127 | 86.33 93 | 78.78 223 | 84.20 257 | 73.57 112 | 89.55 53 | 90.44 132 | 84.24 31 | 84.38 188 | 94.89 51 | 76.35 141 | 80.40 296 | 76.14 136 | 96.80 76 | 82.36 293 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
no-one | | | 71.52 270 | 70.43 275 | 74.81 266 | 78.45 304 | 63.41 189 | 57.73 338 | 77.03 260 | 51.46 318 | 77.17 265 | 90.33 180 | 54.96 277 | 80.35 297 | 47.41 317 | 99.29 2 | 80.68 319 |
|
test_post1 | | | | | | | | 78.85 256 | | | | 3.13 351 | 45.19 313 | 80.13 298 | 58.11 269 | | |
|
PatchmatchNet | | | 69.71 283 | 68.83 283 | 72.33 287 | 77.66 309 | 53.60 280 | 79.29 247 | 69.99 310 | 57.66 284 | 72.53 296 | 82.93 285 | 46.45 296 | 80.08 299 | 60.91 245 | 72.09 335 | 83.31 282 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
FMVSNet5 | | | 72.10 266 | 71.69 266 | 73.32 277 | 81.57 279 | 53.02 285 | 76.77 272 | 78.37 254 | 63.31 254 | 76.37 268 | 91.85 131 | 36.68 341 | 78.98 300 | 47.87 316 | 92.45 196 | 87.95 226 |
|
PatchMatch-RL | | | 74.48 243 | 73.22 252 | 78.27 232 | 87.70 188 | 85.26 30 | 75.92 280 | 70.09 309 | 64.34 250 | 76.09 273 | 81.25 306 | 65.87 229 | 78.07 301 | 53.86 291 | 83.82 297 | 71.48 333 |
|
ADS-MVSNet2 | | | 65.87 300 | 63.64 305 | 72.55 286 | 73.16 338 | 56.92 260 | 67.10 319 | 74.81 273 | 49.74 327 | 66.04 323 | 82.97 283 | 46.71 294 | 77.26 302 | 42.29 328 | 69.96 340 | 83.46 277 |
|
test_post | | | | | | | | | | | | 3.10 352 | 45.43 309 | 77.22 303 | | | |
|
LP | | | 69.42 285 | 68.30 287 | 72.77 283 | 71.48 345 | 56.84 262 | 73.66 298 | 74.84 272 | 63.52 252 | 70.95 307 | 83.35 279 | 49.55 289 | 77.15 304 | 57.13 273 | 70.21 338 | 84.33 265 |
|
MVS-HIRNet | | | 61.16 312 | 62.92 307 | 55.87 329 | 79.09 297 | 35.34 345 | 71.83 303 | 57.98 345 | 46.56 334 | 59.05 342 | 91.14 153 | 49.95 287 | 76.43 305 | 38.74 336 | 71.92 336 | 55.84 346 |
|
MIMVSNet | | | 71.09 272 | 71.59 267 | 69.57 296 | 87.23 202 | 50.07 315 | 78.91 254 | 71.83 297 | 60.20 277 | 71.26 303 | 91.76 136 | 55.08 276 | 76.09 306 | 41.06 331 | 87.02 271 | 82.54 290 |
|
tpm | | | 67.95 290 | 68.08 289 | 67.55 305 | 78.74 301 | 43.53 336 | 75.60 283 | 67.10 328 | 54.92 296 | 72.23 298 | 88.10 216 | 42.87 325 | 75.97 307 | 52.21 296 | 80.95 315 | 83.15 284 |
|
FPMVS | | | 72.29 265 | 72.00 264 | 73.14 281 | 88.63 158 | 85.00 32 | 74.65 291 | 67.39 323 | 71.94 187 | 77.80 261 | 87.66 224 | 50.48 286 | 75.83 308 | 49.95 305 | 79.51 317 | 58.58 345 |
|
PatchT | | | 70.52 275 | 72.76 256 | 63.79 316 | 79.38 294 | 33.53 346 | 77.63 266 | 65.37 331 | 73.61 152 | 71.77 300 | 92.79 113 | 44.38 321 | 75.65 309 | 64.53 224 | 85.37 284 | 82.18 296 |
|
PVSNet | | 58.17 21 | 66.41 297 | 65.63 299 | 68.75 300 | 81.96 275 | 49.88 321 | 62.19 328 | 72.51 292 | 51.03 320 | 68.04 316 | 75.34 330 | 50.84 284 | 74.77 310 | 45.82 324 | 82.96 301 | 81.60 306 |
|
tpmrst | | | 66.28 298 | 66.69 295 | 65.05 314 | 72.82 341 | 39.33 341 | 78.20 260 | 70.69 307 | 53.16 305 | 67.88 317 | 80.36 309 | 48.18 291 | 74.75 311 | 58.13 268 | 70.79 337 | 81.08 315 |
|
test20.03 | | | 73.75 249 | 74.59 236 | 71.22 291 | 81.11 283 | 51.12 303 | 70.15 309 | 72.10 294 | 70.42 199 | 80.28 246 | 91.50 142 | 64.21 232 | 74.72 312 | 46.96 321 | 94.58 151 | 87.82 229 |
|
testmv | | | 70.47 276 | 70.70 271 | 69.77 294 | 86.22 225 | 53.89 279 | 67.32 318 | 71.91 296 | 63.32 253 | 78.16 258 | 89.47 195 | 56.12 270 | 73.10 313 | 36.43 339 | 87.33 267 | 82.33 294 |
|
pmmvs3 | | | 62.47 304 | 60.02 317 | 69.80 293 | 71.58 344 | 64.00 183 | 70.52 307 | 58.44 343 | 39.77 344 | 66.05 322 | 75.84 327 | 27.10 353 | 72.28 314 | 46.15 322 | 84.77 294 | 73.11 331 |
|
Anonymous20231206 | | | 71.38 271 | 71.88 265 | 69.88 292 | 86.31 221 | 54.37 275 | 70.39 308 | 74.62 274 | 52.57 308 | 76.73 266 | 88.76 203 | 59.94 250 | 72.06 315 | 44.35 326 | 93.23 184 | 83.23 283 |
|
new-patchmatchnet | | | 70.10 279 | 73.37 251 | 60.29 325 | 81.23 282 | 16.95 353 | 59.54 332 | 74.62 274 | 62.93 257 | 80.97 228 | 87.93 220 | 62.83 239 | 71.90 316 | 55.24 284 | 95.01 135 | 92.00 159 |
|
dp | | | 60.70 315 | 60.29 316 | 61.92 320 | 72.04 343 | 38.67 343 | 70.83 305 | 64.08 332 | 51.28 319 | 60.75 336 | 77.28 322 | 36.59 342 | 71.58 317 | 47.41 317 | 62.34 346 | 75.52 326 |
|
UnsupCasMVSNet_bld | | | 69.21 287 | 69.68 279 | 67.82 304 | 79.42 293 | 51.15 302 | 67.82 317 | 75.79 266 | 54.15 299 | 77.47 263 | 85.36 255 | 59.26 256 | 70.64 318 | 48.46 313 | 79.35 319 | 81.66 305 |
|
test1235678 | | | 65.57 301 | 65.73 298 | 65.06 313 | 82.84 272 | 50.90 305 | 62.90 326 | 69.26 312 | 57.17 288 | 72.36 297 | 83.04 281 | 46.02 299 | 70.10 319 | 32.79 344 | 85.24 287 | 74.19 329 |
|
test-LLR | | | 67.21 293 | 66.74 294 | 68.63 301 | 76.45 317 | 55.21 271 | 67.89 314 | 67.14 326 | 62.43 264 | 65.08 328 | 72.39 334 | 43.41 322 | 69.37 320 | 61.00 242 | 84.89 290 | 81.31 310 |
|
test-mter | | | 65.00 302 | 63.79 303 | 68.63 301 | 76.45 317 | 55.21 271 | 67.89 314 | 67.14 326 | 50.98 321 | 65.08 328 | 72.39 334 | 28.27 351 | 69.37 320 | 61.00 242 | 84.89 290 | 81.31 310 |
|
XXY-MVS | | | 74.44 245 | 76.19 222 | 69.21 297 | 84.61 248 | 52.43 290 | 71.70 304 | 77.18 259 | 60.73 275 | 80.60 239 | 90.96 163 | 75.44 143 | 69.35 322 | 56.13 277 | 88.33 254 | 85.86 247 |
|
UnsupCasMVSNet_eth | | | 71.63 269 | 72.30 262 | 69.62 295 | 76.47 316 | 52.70 288 | 70.03 310 | 80.97 245 | 59.18 279 | 79.36 252 | 88.21 215 | 60.50 245 | 69.12 323 | 58.33 267 | 77.62 325 | 87.04 235 |
|
WTY-MVS | | | 67.91 291 | 68.35 286 | 66.58 308 | 80.82 287 | 48.12 324 | 65.96 322 | 72.60 290 | 53.67 302 | 71.20 304 | 81.68 303 | 58.97 257 | 69.06 324 | 48.57 312 | 81.67 309 | 82.55 289 |
|
test0.0.03 1 | | | 64.66 303 | 64.36 302 | 65.57 311 | 75.03 330 | 46.89 326 | 64.69 324 | 61.58 339 | 62.43 264 | 71.18 305 | 77.54 320 | 43.41 322 | 68.47 325 | 40.75 332 | 82.65 305 | 81.35 309 |
|
CHOSEN 280x420 | | | 59.08 316 | 56.52 321 | 66.76 307 | 76.51 315 | 64.39 180 | 49.62 345 | 59.00 341 | 43.86 340 | 55.66 348 | 68.41 340 | 35.55 343 | 68.21 326 | 43.25 327 | 76.78 328 | 67.69 338 |
|
YYNet1 | | | 70.06 280 | 70.44 273 | 68.90 298 | 73.76 334 | 53.42 283 | 58.99 336 | 67.20 325 | 58.42 281 | 87.10 143 | 85.39 253 | 59.82 252 | 67.32 327 | 59.79 250 | 83.50 299 | 85.96 244 |
|
MDA-MVSNet_test_wron | | | 70.05 281 | 70.44 273 | 68.88 299 | 73.84 333 | 53.47 281 | 58.93 337 | 67.28 324 | 58.43 280 | 87.09 144 | 85.40 252 | 59.80 253 | 67.25 328 | 59.66 252 | 83.54 298 | 85.92 246 |
|
EMVS | | | 61.10 313 | 60.81 313 | 61.99 319 | 65.96 349 | 55.86 266 | 53.10 343 | 58.97 342 | 67.06 225 | 56.89 347 | 63.33 345 | 40.98 334 | 67.03 329 | 54.79 287 | 86.18 279 | 63.08 340 |
|
testgi | | | 72.36 263 | 74.61 234 | 65.59 310 | 80.56 289 | 42.82 338 | 68.29 313 | 73.35 284 | 66.87 228 | 81.84 218 | 89.93 187 | 72.08 203 | 66.92 330 | 46.05 323 | 92.54 195 | 87.01 236 |
|
testpf | | | 58.55 317 | 61.58 312 | 49.48 334 | 66.03 348 | 40.05 340 | 74.40 292 | 58.07 344 | 64.72 249 | 59.36 340 | 72.67 333 | 22.76 354 | 66.92 330 | 67.07 205 | 69.15 343 | 41.46 348 |
|
EPMVS | | | 62.47 304 | 62.63 308 | 62.01 318 | 70.63 346 | 38.74 342 | 74.76 289 | 52.86 348 | 53.91 301 | 67.71 319 | 80.01 311 | 39.40 339 | 66.60 332 | 55.54 282 | 68.81 344 | 80.68 319 |
|
PMMVS | | | 61.65 309 | 60.38 314 | 65.47 312 | 65.40 350 | 69.26 150 | 63.97 325 | 61.73 338 | 36.80 347 | 60.11 338 | 68.43 338 | 59.42 254 | 66.35 333 | 48.97 311 | 78.57 322 | 60.81 342 |
|
E-PMN | | | 61.59 310 | 61.62 310 | 61.49 321 | 66.81 347 | 55.40 269 | 53.77 342 | 60.34 340 | 66.80 229 | 58.90 343 | 65.50 344 | 40.48 336 | 66.12 334 | 55.72 279 | 86.25 278 | 62.95 341 |
|
PVSNet_0 | | 51.08 22 | 56.10 319 | 54.97 323 | 59.48 327 | 75.12 329 | 53.28 284 | 55.16 339 | 61.89 336 | 44.30 339 | 59.16 341 | 62.48 346 | 54.22 278 | 65.91 335 | 35.40 341 | 47.01 347 | 59.25 344 |
|
test2356 | | | 56.69 318 | 55.15 322 | 61.32 323 | 73.20 336 | 44.11 334 | 54.95 340 | 62.52 334 | 48.75 331 | 62.45 334 | 68.42 339 | 21.10 355 | 65.67 336 | 26.86 348 | 78.08 323 | 74.19 329 |
|
sss | | | 66.92 294 | 67.26 292 | 65.90 309 | 77.23 310 | 51.10 304 | 64.79 323 | 71.72 300 | 52.12 313 | 70.13 309 | 80.18 310 | 57.96 261 | 65.36 337 | 50.21 304 | 81.01 314 | 81.25 312 |
|
TESTMET0.1,1 | | | 61.29 311 | 60.32 315 | 64.19 315 | 72.06 342 | 51.30 300 | 67.89 314 | 62.09 335 | 45.27 337 | 60.65 337 | 69.01 337 | 27.93 352 | 64.74 338 | 56.31 276 | 81.65 311 | 76.53 323 |
|
testus | | | 62.33 306 | 63.03 306 | 60.20 326 | 78.78 300 | 40.74 339 | 59.14 333 | 69.80 311 | 49.26 329 | 71.41 302 | 74.72 332 | 52.33 282 | 63.52 339 | 29.84 346 | 82.01 308 | 76.36 324 |
|
PNet_i23d | | | 52.13 323 | 51.24 325 | 54.79 332 | 75.56 322 | 45.26 330 | 54.54 341 | 52.55 349 | 66.95 226 | 57.19 346 | 65.82 343 | 13.15 358 | 63.40 340 | 36.39 340 | 39.04 349 | 55.71 347 |
|
ADS-MVSNet | | | 61.90 307 | 62.19 309 | 61.03 324 | 73.16 338 | 36.42 344 | 67.10 319 | 61.75 337 | 49.74 327 | 66.04 323 | 82.97 283 | 46.71 294 | 63.21 341 | 42.29 328 | 69.96 340 | 83.46 277 |
|
DSMNet-mixed | | | 60.98 314 | 61.61 311 | 59.09 328 | 72.88 340 | 45.05 332 | 74.70 290 | 46.61 352 | 26.20 348 | 65.34 326 | 90.32 181 | 55.46 273 | 63.12 342 | 41.72 330 | 81.30 313 | 69.09 337 |
|
test12356 | | | 54.91 322 | 57.14 320 | 48.22 335 | 75.83 321 | 17.47 352 | 52.31 344 | 69.20 313 | 51.66 316 | 60.11 338 | 75.40 329 | 29.77 350 | 62.62 343 | 27.64 347 | 72.37 334 | 64.59 339 |
|
Patchmatch-test | | | 65.91 299 | 67.38 290 | 61.48 322 | 75.51 324 | 43.21 337 | 68.84 311 | 63.79 333 | 62.48 262 | 72.80 295 | 83.42 278 | 44.89 319 | 59.52 344 | 48.27 315 | 86.45 275 | 81.70 303 |
|
1111 | | | 61.71 308 | 63.77 304 | 55.55 331 | 78.05 305 | 25.74 350 | 60.62 329 | 67.52 321 | 66.09 233 | 74.68 284 | 86.50 234 | 16.00 356 | 59.22 345 | 38.79 334 | 85.65 281 | 81.70 303 |
|
.test1245 | | | 48.02 325 | 54.41 324 | 28.84 338 | 78.05 305 | 25.74 350 | 60.62 329 | 67.52 321 | 66.09 233 | 74.68 284 | 86.50 234 | 16.00 356 | 59.22 345 | 38.79 334 | 1.47 351 | 1.55 352 |
|
N_pmnet | | | 70.20 277 | 68.80 284 | 74.38 269 | 80.91 285 | 84.81 35 | 59.12 335 | 76.45 265 | 55.06 295 | 75.31 281 | 82.36 296 | 55.74 271 | 54.82 347 | 47.02 319 | 87.24 269 | 83.52 276 |
|
wuyk23d | | | 75.13 236 | 79.30 198 | 62.63 317 | 75.56 322 | 75.18 104 | 80.89 223 | 73.10 288 | 75.06 140 | 94.76 15 | 95.32 40 | 87.73 31 | 52.85 348 | 34.16 342 | 97.11 66 | 59.85 343 |
|
PMMVS2 | | | 55.64 321 | 59.27 318 | 44.74 336 | 64.30 351 | 12.32 354 | 40.60 346 | 49.79 351 | 53.19 304 | 65.06 330 | 84.81 262 | 53.60 280 | 49.76 349 | 32.68 345 | 89.41 243 | 72.15 332 |
|
new_pmnet | | | 55.69 320 | 57.66 319 | 49.76 333 | 75.47 325 | 30.59 347 | 59.56 331 | 51.45 350 | 43.62 341 | 62.49 333 | 75.48 328 | 40.96 335 | 49.15 350 | 37.39 338 | 72.52 333 | 69.55 336 |
|
MVE | | 40.22 23 | 51.82 324 | 50.47 326 | 55.87 329 | 62.66 352 | 51.91 293 | 31.61 348 | 39.28 353 | 40.65 343 | 50.76 349 | 74.98 331 | 56.24 269 | 44.67 351 | 33.94 343 | 64.11 345 | 71.04 335 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
DeepMVS_CX | | | | | 24.13 339 | 32.95 353 | 29.49 348 | | 21.63 356 | 12.07 349 | 37.95 350 | 45.07 348 | 30.84 347 | 19.21 352 | 17.94 350 | 33.06 350 | 23.69 349 |
|
tmp_tt | | | 20.25 328 | 24.50 329 | 7.49 340 | 4.47 354 | 8.70 355 | 34.17 347 | 25.16 355 | 1.00 350 | 32.43 351 | 18.49 349 | 39.37 340 | 9.21 353 | 21.64 349 | 43.75 348 | 4.57 350 |
|
test123 | | | 6.27 331 | 8.08 332 | 0.84 341 | 1.11 356 | 0.57 356 | 62.90 326 | 0.82 357 | 0.54 351 | 1.07 353 | 2.75 354 | 1.26 359 | 0.30 354 | 1.04 351 | 1.26 353 | 1.66 351 |
|
testmvs | | | 5.91 332 | 7.65 333 | 0.72 342 | 1.20 355 | 0.37 357 | 59.14 333 | 0.67 358 | 0.49 352 | 1.11 352 | 2.76 353 | 0.94 360 | 0.24 355 | 1.02 352 | 1.47 351 | 1.55 352 |
|
cdsmvs_eth3d_5k | | | 20.81 327 | 27.75 328 | 0.00 343 | 0.00 357 | 0.00 358 | 0.00 349 | 85.44 214 | 0.00 353 | 0.00 354 | 82.82 286 | 81.46 92 | 0.00 356 | 0.00 353 | 0.00 354 | 0.00 354 |
|
pcd_1.5k_mvsjas | | | 6.41 330 | 8.55 331 | 0.00 343 | 0.00 357 | 0.00 358 | 0.00 349 | 0.00 359 | 0.00 353 | 0.00 354 | 0.00 355 | 76.94 132 | 0.00 356 | 0.00 353 | 0.00 354 | 0.00 354 |
|
pcd1.5k->3k | | | 38.83 326 | 41.11 327 | 32.01 337 | 93.13 62 | 0.00 358 | 0.00 349 | 91.38 112 | 0.00 353 | 0.00 354 | 0.00 355 | 89.24 16 | 0.00 356 | 0.00 353 | 96.24 94 | 96.02 48 |
|
sosnet-low-res | | | 0.00 333 | 0.00 334 | 0.00 343 | 0.00 357 | 0.00 358 | 0.00 349 | 0.00 359 | 0.00 353 | 0.00 354 | 0.00 355 | 0.00 361 | 0.00 356 | 0.00 353 | 0.00 354 | 0.00 354 |
|
sosnet | | | 0.00 333 | 0.00 334 | 0.00 343 | 0.00 357 | 0.00 358 | 0.00 349 | 0.00 359 | 0.00 353 | 0.00 354 | 0.00 355 | 0.00 361 | 0.00 356 | 0.00 353 | 0.00 354 | 0.00 354 |
|
uncertanet | | | 0.00 333 | 0.00 334 | 0.00 343 | 0.00 357 | 0.00 358 | 0.00 349 | 0.00 359 | 0.00 353 | 0.00 354 | 0.00 355 | 0.00 361 | 0.00 356 | 0.00 353 | 0.00 354 | 0.00 354 |
|
Regformer | | | 0.00 333 | 0.00 334 | 0.00 343 | 0.00 357 | 0.00 358 | 0.00 349 | 0.00 359 | 0.00 353 | 0.00 354 | 0.00 355 | 0.00 361 | 0.00 356 | 0.00 353 | 0.00 354 | 0.00 354 |
|
ab-mvs-re | | | 6.65 329 | 8.87 330 | 0.00 343 | 0.00 357 | 0.00 358 | 0.00 349 | 0.00 359 | 0.00 353 | 0.00 354 | 79.80 313 | 0.00 361 | 0.00 356 | 0.00 353 | 0.00 354 | 0.00 354 |
|
uanet | | | 0.00 333 | 0.00 334 | 0.00 343 | 0.00 357 | 0.00 358 | 0.00 349 | 0.00 359 | 0.00 353 | 0.00 354 | 0.00 355 | 0.00 361 | 0.00 356 | 0.00 353 | 0.00 354 | 0.00 354 |
|
GSMVS | | | | | | | | | | | | | | | | | 83.88 270 |
|
test_part2 | | | | | | 93.86 47 | 77.77 79 | | | | 92.84 42 | | | | | | |
|
test_part1 | | | | | | | | | 93.93 25 | | | | 87.19 36 | | | 97.61 54 | 91.48 172 |
|
sam_mvs1 | | | | | | | | | | | | | 46.11 298 | | | | 83.88 270 |
|
sam_mvs | | | | | | | | | | | | | 45.92 303 | | | | |
|
MTGPA | | | | | | | | | 91.81 90 | | | | | | | | |
|
MTMP | | | | | | | | | 33.14 354 | | | | | | | | |
|
test9_res | | | | | | | | | | | | | | | 80.83 89 | 96.45 87 | 90.57 192 |
|
agg_prior2 | | | | | | | | | | | | | | | 79.68 106 | 96.16 96 | 90.22 200 |
|
test_prior4 | | | | | | | 78.97 71 | 84.59 129 | | | | | | | | | |
|
test_prior2 | | | | | | | | 83.37 163 | | 75.43 136 | 84.58 184 | 91.57 139 | 81.92 87 | | 79.54 108 | 96.97 69 | |
|
æ–°å‡ ä½•2 | | | | | | | | 81.72 209 | | | | | | | | | |
|
旧先验1 | | | | | | 91.97 92 | 71.77 131 | | 81.78 240 | | | 91.84 132 | 73.92 168 | | | 93.65 171 | 83.61 275 |
|
原ACMM2 | | | | | | | | 82.26 195 | | | | | | | | | |
|
test222 | | | | | | 93.31 57 | 76.54 94 | 79.38 246 | 77.79 256 | 52.59 307 | 82.36 211 | 90.84 167 | 66.83 224 | | | 91.69 206 | 81.25 312 |
|
segment_acmp | | | | | | | | | | | | | 81.94 84 | | | | |
|
testdata1 | | | | | | | | 79.62 236 | | 73.95 150 | | | | | | | |
|
plane_prior7 | | | | | | 93.45 53 | 77.31 87 | | | | | | | | | | |
|
plane_prior6 | | | | | | 92.61 73 | 76.54 94 | | | | | | 74.84 153 | | | | |
|
plane_prior4 | | | | | | | | | | | | 92.95 107 | | | | | |
|
plane_prior3 | | | | | | | 76.85 92 | | | 77.79 101 | 86.55 152 | | | | | | |
|
plane_prior2 | | | | | | | | 89.45 58 | | 79.44 79 | | | | | | | |
|
plane_prior1 | | | | | | 92.83 71 | | | | | | | | | | | |
|
plane_prior | | | | | | | 76.42 97 | 87.15 91 | | 75.94 129 | | | | | | 95.03 134 | |
|
n2 | | | | | | | | | 0.00 359 | | | | | | | | |
|
nn | | | | | | | | | 0.00 359 | | | | | | | | |
|
door-mid | | | | | | | | | 74.45 277 | | | | | | | | |
|
test11 | | | | | | | | | 91.46 103 | | | | | | | | |
|
door | | | | | | | | | 72.57 291 | | | | | | | | |
|
HQP5-MVS | | | | | | | 70.66 141 | | | | | | | | | | |
|
HQP-NCC | | | | | | 91.19 113 | | 84.77 124 | | 73.30 159 | 80.55 242 | | | | | | |
|
ACMP_Plane | | | | | | 91.19 113 | | 84.77 124 | | 73.30 159 | 80.55 242 | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 77.30 129 | | |
|
HQP3-MVS | | | | | | | | | 92.68 67 | | | | | | | 94.47 153 | |
|
HQP2-MVS | | | | | | | | | | | | | 72.10 201 | | | | |
|
NP-MVS | | | | | | 91.95 93 | 74.55 107 | | | | | 90.17 185 | | | | | |
|
MDTV_nov1_ep13_2view | | | | | | | 27.60 349 | 70.76 306 | | 46.47 335 | 61.27 335 | | 45.20 312 | | 49.18 310 | | 83.75 274 |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 95.74 116 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 97.35 60 | |
|
Test By Simon | | | | | | | | | | | | | 79.09 110 | | | | |
|