APDe-MVS | | | 91.22 20 | 91.92 9 | 89.14 57 | 92.97 67 | 78.04 78 | 92.84 12 | 94.14 21 | 83.33 37 | 93.90 26 | 95.73 30 | 88.77 18 | 96.41 1 | 87.60 17 | 97.98 42 | 92.98 130 |
|
HSP-MVS | | | 88.63 61 | 87.84 70 | 91.02 29 | 95.76 16 | 86.14 19 | 92.75 13 | 91.01 124 | 78.43 95 | 89.16 115 | 92.25 128 | 72.03 207 | 96.36 2 | 88.21 9 | 90.93 230 | 90.55 198 |
|
SteuartSystems-ACMMP | | | 91.16 22 | 91.36 22 | 90.55 37 | 93.91 47 | 80.97 58 | 91.49 29 | 93.48 36 | 82.82 45 | 92.60 51 | 93.97 86 | 88.19 24 | 96.29 3 | 87.61 16 | 98.20 33 | 94.39 91 |
Skip Steuart: Steuart Systems R&D Blog. |
test_part3 | | | | | | | | 89.63 51 | | 84.39 28 | | 93.43 99 | | 96.26 4 | 82.18 75 | | |
|
ESAPD | | | 90.05 36 | 90.56 41 | 88.50 63 | 93.86 48 | 77.77 80 | 89.63 51 | 93.93 25 | 84.39 28 | 92.84 44 | 93.43 99 | 87.19 36 | 96.26 4 | 82.18 75 | 97.61 56 | 91.48 174 |
|
SMA-MVS | | | 90.40 31 | 90.57 40 | 89.87 47 | 95.31 27 | 79.64 68 | 90.98 34 | 93.36 37 | 75.21 141 | 92.90 40 | 95.28 42 | 86.29 48 | 96.09 6 | 87.92 11 | 97.89 44 | 93.88 104 |
|
DTE-MVSNet | | | 89.98 39 | 91.91 11 | 84.21 142 | 96.51 7 | 57.84 255 | 88.93 67 | 92.84 64 | 91.92 2 | 96.16 3 | 96.23 20 | 86.95 39 | 95.99 7 | 79.05 113 | 98.57 16 | 98.80 6 |
|
PGM-MVS | | | 91.20 21 | 90.95 33 | 91.93 13 | 95.67 20 | 85.85 25 | 90.00 40 | 93.90 28 | 80.32 70 | 91.74 66 | 94.41 70 | 88.17 25 | 95.98 8 | 86.37 26 | 97.99 41 | 93.96 102 |
|
APD-MVS | | | 89.54 50 | 89.63 50 | 89.26 56 | 92.57 75 | 81.34 56 | 90.19 38 | 93.08 52 | 80.87 65 | 91.13 73 | 93.19 102 | 86.22 49 | 95.97 9 | 82.23 74 | 97.18 67 | 90.45 200 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
TSAR-MVS + MP. | | | 88.14 66 | 87.82 71 | 89.09 58 | 95.72 19 | 76.74 94 | 92.49 20 | 91.19 120 | 67.85 227 | 86.63 153 | 94.84 55 | 79.58 109 | 95.96 10 | 87.62 15 | 94.50 154 | 94.56 82 |
|
LCM-MVSNet | | | 95.70 1 | 96.40 1 | 93.61 2 | 98.67 1 | 85.39 29 | 95.54 3 | 97.36 1 | 96.97 1 | 99.04 1 | 99.05 1 | 96.61 1 | 95.92 11 | 85.07 37 | 99.27 3 | 99.54 1 |
|
WR-MVS_H | | | 89.91 44 | 91.31 25 | 85.71 110 | 96.32 9 | 62.39 214 | 89.54 56 | 93.31 42 | 90.21 10 | 95.57 11 | 95.66 32 | 81.42 94 | 95.90 12 | 80.94 88 | 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 43 | 81.91 56 | 90.88 79 | 94.21 78 | 87.75 30 | 95.87 13 | 87.60 17 | 97.71 52 | 93.83 105 |
|
ACMMPR | | | 91.49 14 | 91.35 24 | 91.92 14 | 95.74 18 | 85.88 24 | 92.58 18 | 93.25 47 | 81.99 54 | 91.40 71 | 94.17 80 | 87.51 34 | 95.87 13 | 87.74 12 | 97.76 48 | 93.99 100 |
|
3Dnovator+ | | 83.92 2 | 89.97 41 | 89.66 49 | 90.92 32 | 91.27 114 | 81.66 54 | 91.25 32 | 94.13 22 | 88.89 12 | 88.83 120 | 94.26 76 | 77.55 125 | 95.86 15 | 84.88 40 | 95.87 112 | 95.24 72 |
|
XVS | | | 91.54 12 | 91.36 22 | 92.08 8 | 95.64 21 | 86.25 16 | 92.64 15 | 93.33 40 | 85.07 25 | 89.99 88 | 94.03 84 | 86.57 43 | 95.80 16 | 87.35 20 | 97.62 54 | 94.20 93 |
|
X-MVStestdata | | | 85.04 115 | 82.70 162 | 92.08 8 | 95.64 21 | 86.25 16 | 92.64 15 | 93.33 40 | 85.07 25 | 89.99 88 | 16.05 356 | 86.57 43 | 95.80 16 | 87.35 20 | 97.62 54 | 94.20 93 |
|
DeepC-MVS | | 82.31 4 | 89.15 56 | 89.08 54 | 89.37 54 | 93.64 52 | 79.07 71 | 88.54 73 | 94.20 17 | 73.53 156 | 89.71 99 | 94.82 56 | 85.09 53 | 95.77 18 | 84.17 51 | 98.03 38 | 93.26 123 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
MVS_0304 | | | 84.88 119 | 83.96 149 | 87.64 78 | 87.43 197 | 74.83 106 | 84.18 137 | 93.30 43 | 77.48 107 | 77.39 270 | 88.46 210 | 74.53 163 | 95.74 19 | 78.09 123 | 94.75 150 | 92.36 151 |
|
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 44 | 91.77 65 | 93.94 92 | 90.55 13 | 95.73 20 | 88.50 8 | 98.23 31 | 95.33 70 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
CP-MVS | | | 91.67 11 | 91.58 17 | 91.96 12 | 95.29 28 | 87.62 9 | 93.38 7 | 93.36 37 | 83.16 39 | 91.06 74 | 94.00 85 | 88.26 23 | 95.71 21 | 87.28 23 | 98.39 23 | 92.55 145 |
|
ACMMP | | | 91.91 9 | 91.87 14 | 92.03 11 | 95.53 24 | 85.91 22 | 93.35 9 | 94.16 20 | 82.52 48 | 92.39 56 | 94.14 81 | 89.15 17 | 95.62 22 | 87.35 20 | 98.24 30 | 94.56 82 |
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 37 | 91.88 12 | 84.48 133 | 96.57 5 | 58.88 251 | 88.95 65 | 93.19 48 | 91.62 4 | 96.01 6 | 96.16 22 | 87.02 38 | 95.60 23 | 78.69 116 | 98.72 10 | 98.97 3 |
|
PS-CasMVS | | | 90.06 35 | 91.92 9 | 84.47 134 | 96.56 6 | 58.83 252 | 89.04 64 | 92.74 67 | 91.40 5 | 96.12 4 | 96.06 25 | 87.23 35 | 95.57 24 | 79.42 112 | 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 45 | 81.99 54 | 91.47 69 | 93.96 88 | 88.35 21 | 95.56 25 | 87.74 12 | 97.74 50 | 92.85 131 |
|
#test# | | | 90.49 30 | 90.31 43 | 91.02 29 | 95.43 25 | 84.66 39 | 90.65 35 | 93.29 45 | 77.00 119 | 91.47 69 | 93.96 88 | 88.35 21 | 95.56 25 | 84.88 40 | 97.74 50 | 92.85 131 |
|
CP-MVSNet | | | 89.27 54 | 90.91 34 | 84.37 137 | 96.34 8 | 58.61 254 | 88.66 72 | 92.06 83 | 90.78 6 | 95.67 9 | 95.17 46 | 81.80 90 | 95.54 27 | 79.00 114 | 98.69 11 | 98.95 4 |
|
LPG-MVS_test | | | 91.47 16 | 91.68 15 | 90.82 34 | 94.75 36 | 81.69 51 | 90.00 40 | 94.27 13 | 82.35 49 | 93.67 30 | 94.82 56 | 91.18 6 | 95.52 28 | 85.36 35 | 98.73 8 | 95.23 73 |
|
LGP-MVS_train | | | | | 90.82 34 | 94.75 36 | 81.69 51 | | 94.27 13 | 82.35 49 | 93.67 30 | 94.82 56 | 91.18 6 | 95.52 28 | 85.36 35 | 98.73 8 | 95.23 73 |
|
mPP-MVS | | | 91.69 10 | 91.47 20 | 92.37 5 | 96.04 11 | 88.48 8 | 92.72 14 | 92.60 72 | 83.09 40 | 91.54 68 | 94.25 77 | 87.67 33 | 95.51 30 | 87.21 24 | 98.11 35 | 93.12 127 |
|
ACMMP_Plus | | | 90.65 26 | 91.07 30 | 89.42 53 | 95.93 14 | 79.54 69 | 89.95 43 | 93.68 31 | 77.65 104 | 91.97 62 | 94.89 53 | 88.38 19 | 95.45 31 | 89.27 3 | 97.87 46 | 93.27 122 |
|
CANet | | | 83.79 152 | 82.85 161 | 86.63 87 | 86.17 230 | 72.21 127 | 83.76 152 | 91.43 108 | 77.24 115 | 74.39 293 | 87.45 229 | 75.36 147 | 95.42 32 | 77.03 134 | 92.83 193 | 92.25 157 |
|
MP-MVS | | | 91.14 23 | 90.91 34 | 91.83 18 | 96.18 10 | 86.88 11 | 92.20 22 | 93.03 56 | 82.59 47 | 88.52 126 | 94.37 74 | 86.74 40 | 95.41 33 | 86.32 27 | 98.21 32 | 93.19 126 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
LS3D | | | 90.60 28 | 90.34 42 | 91.38 23 | 89.03 151 | 84.23 42 | 93.58 4 | 94.68 10 | 90.65 7 | 90.33 83 | 93.95 91 | 84.50 58 | 95.37 34 | 80.87 89 | 95.50 122 | 94.53 86 |
|
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 52 | 95.13 47 | 90.65 11 | 95.34 35 | 88.06 10 | 98.15 34 | 95.95 52 |
|
NCCC | | | 87.36 74 | 86.87 88 | 88.83 59 | 92.32 85 | 78.84 74 | 86.58 108 | 91.09 122 | 78.77 91 | 84.85 179 | 90.89 167 | 80.85 98 | 95.29 36 | 81.14 85 | 95.32 126 | 92.34 152 |
|
EPP-MVSNet | | | 85.47 110 | 85.04 116 | 86.77 86 | 91.52 108 | 69.37 149 | 91.63 28 | 87.98 185 | 81.51 61 | 87.05 148 | 91.83 135 | 66.18 229 | 95.29 36 | 70.75 178 | 96.89 73 | 95.64 60 |
|
zzz-MVS | | | 91.27 19 | 91.26 27 | 91.29 25 | 96.59 3 | 86.29 14 | 88.94 66 | 91.81 91 | 84.07 32 | 92.00 60 | 94.40 71 | 86.63 41 | 95.28 38 | 88.59 4 | 98.31 26 | 92.30 153 |
|
MTAPA | | | 91.52 13 | 91.60 16 | 91.29 25 | 96.59 3 | 86.29 14 | 92.02 24 | 91.81 91 | 84.07 32 | 92.00 60 | 94.40 71 | 86.63 41 | 95.28 38 | 88.59 4 | 98.31 26 | 92.30 153 |
|
HQP_MVS | | | 87.75 72 | 87.43 78 | 88.70 61 | 93.45 54 | 76.42 98 | 89.45 59 | 93.61 32 | 79.44 80 | 86.55 154 | 92.95 109 | 74.84 155 | 95.22 40 | 80.78 91 | 95.83 114 | 94.46 87 |
|
plane_prior5 | | | | | | | | | 93.61 32 | | | | | 95.22 40 | 80.78 91 | 95.83 114 | 94.46 87 |
|
ACMP | | 79.16 10 | 90.54 29 | 90.60 37 | 90.35 41 | 94.36 41 | 80.98 57 | 89.16 63 | 94.05 23 | 79.03 87 | 92.87 42 | 93.74 95 | 90.60 12 | 95.21 42 | 82.87 66 | 98.76 5 | 94.87 78 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
DeepC-MVS_fast | | 80.27 8 | 86.23 97 | 85.65 108 | 87.96 74 | 91.30 112 | 76.92 91 | 87.19 91 | 91.99 85 | 70.56 202 | 84.96 175 | 90.69 173 | 80.01 106 | 95.14 43 | 78.37 117 | 95.78 116 | 91.82 165 |
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 39 | 88.99 7 | 93.26 10 | 94.19 19 | 89.11 11 | 94.43 19 | 95.27 43 | 91.86 4 | 95.09 44 | 87.54 19 | 98.02 39 | 93.71 112 |
|
APD-MVS_3200maxsize | | | 92.05 7 | 92.24 6 | 91.48 20 | 93.02 65 | 85.17 31 | 92.47 21 | 95.05 8 | 87.65 19 | 93.21 37 | 94.39 73 | 90.09 14 | 95.08 45 | 86.67 25 | 97.60 58 | 94.18 95 |
|
HPM-MVS++ | | | 88.93 58 | 88.45 65 | 90.38 40 | 94.92 33 | 85.85 25 | 89.70 47 | 91.27 117 | 78.20 98 | 86.69 152 | 92.28 127 | 80.36 103 | 95.06 46 | 86.17 32 | 96.49 87 | 90.22 204 |
|
MP-MVS-pluss | | | 90.81 24 | 91.08 29 | 89.99 46 | 95.97 12 | 79.88 63 | 88.13 77 | 94.51 11 | 75.79 132 | 92.94 39 | 94.96 51 | 88.36 20 | 95.01 47 | 90.70 2 | 98.40 21 | 95.09 76 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
CDPH-MVS | | | 86.17 99 | 85.54 109 | 88.05 73 | 92.25 86 | 75.45 103 | 83.85 147 | 92.01 84 | 65.91 239 | 86.19 161 | 91.75 139 | 83.77 63 | 94.98 48 | 77.43 129 | 96.71 79 | 93.73 111 |
|
COLMAP_ROB | | 83.01 3 | 91.97 8 | 91.95 8 | 92.04 10 | 93.68 51 | 86.15 18 | 93.37 8 | 95.10 7 | 90.28 9 | 92.11 57 | 95.03 49 | 89.75 15 | 94.93 49 | 79.95 104 | 98.27 29 | 95.04 77 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
IS-MVSNet | | | 86.66 87 | 86.82 91 | 86.17 102 | 92.05 92 | 66.87 167 | 91.21 33 | 88.64 173 | 86.30 23 | 89.60 108 | 92.59 117 | 69.22 217 | 94.91 50 | 73.89 153 | 97.89 44 | 96.72 36 |
|
OurMVSNet-221017-0 | | | 90.01 38 | 89.74 48 | 90.83 33 | 93.16 62 | 80.37 59 | 91.91 27 | 93.11 50 | 81.10 63 | 95.32 13 | 97.24 6 | 72.94 192 | 94.85 51 | 85.07 37 | 97.78 47 | 97.26 23 |
|
test12 | | | | | 86.57 88 | 90.74 126 | 72.63 119 | | 90.69 127 | | 82.76 212 | | 79.20 110 | 94.80 52 | | 95.32 126 | 92.27 155 |
|
SixPastTwentyTwo | | | 87.20 77 | 87.45 77 | 86.45 91 | 92.52 77 | 69.19 154 | 87.84 82 | 88.05 182 | 81.66 59 | 94.64 17 | 96.53 14 | 65.94 230 | 94.75 53 | 83.02 65 | 96.83 76 | 95.41 68 |
|
CNVR-MVS | | | 87.81 71 | 87.68 74 | 88.21 67 | 92.87 69 | 77.30 89 | 85.25 122 | 91.23 118 | 77.31 114 | 87.07 147 | 91.47 145 | 82.94 71 | 94.71 54 | 84.67 44 | 96.27 95 | 92.62 144 |
|
K. test v3 | | | 85.14 112 | 84.73 122 | 86.37 92 | 91.13 119 | 69.63 148 | 85.45 120 | 76.68 267 | 84.06 34 | 92.44 54 | 96.99 8 | 62.03 242 | 94.65 55 | 80.58 94 | 93.24 185 | 94.83 81 |
|
HQP4-MVS | | | | | | | | | | | 80.56 245 | | | 94.61 56 | | | 93.56 118 |
|
HQP-MVS | | | 84.61 124 | 84.06 146 | 86.27 95 | 91.19 115 | 70.66 142 | 84.77 126 | 92.68 68 | 73.30 162 | 80.55 246 | 90.17 187 | 72.10 203 | 94.61 56 | 77.30 130 | 94.47 155 | 93.56 118 |
|
PS-MVSNAJss | | | 88.31 64 | 87.90 69 | 89.56 52 | 93.31 58 | 77.96 79 | 87.94 79 | 91.97 86 | 70.73 201 | 94.19 24 | 96.67 11 | 76.94 134 | 94.57 58 | 83.07 63 | 96.28 93 | 96.15 43 |
|
DeepPCF-MVS | | 81.24 5 | 87.28 76 | 86.21 99 | 90.49 38 | 91.48 109 | 84.90 34 | 83.41 164 | 92.38 77 | 70.25 206 | 89.35 113 | 90.68 174 | 82.85 72 | 94.57 58 | 79.55 108 | 95.95 108 | 92.00 161 |
|
UA-Net | | | 91.49 14 | 91.53 18 | 91.39 22 | 94.98 32 | 82.95 50 | 93.52 5 | 92.79 65 | 88.22 16 | 88.53 125 | 97.64 3 | 83.45 66 | 94.55 60 | 86.02 34 | 98.60 14 | 96.67 37 |
|
114514_t | | | 83.10 164 | 82.54 167 | 84.77 125 | 92.90 68 | 69.10 155 | 86.65 106 | 90.62 130 | 54.66 303 | 81.46 227 | 90.81 170 | 76.98 133 | 94.38 61 | 72.62 167 | 96.18 97 | 90.82 190 |
|
MVSFormer | | | 82.23 173 | 81.57 179 | 84.19 144 | 85.54 241 | 69.26 151 | 91.98 25 | 90.08 151 | 71.54 195 | 76.23 277 | 85.07 261 | 58.69 260 | 94.27 62 | 86.26 28 | 88.77 255 | 89.03 220 |
|
test_djsdf | | | 89.62 48 | 89.01 55 | 91.45 21 | 92.36 82 | 82.98 49 | 91.98 25 | 90.08 151 | 71.54 195 | 94.28 23 | 96.54 13 | 81.57 92 | 94.27 62 | 86.26 28 | 96.49 87 | 97.09 30 |
|
原ACMM1 | | | | | 84.60 130 | 92.81 73 | 74.01 111 | | 91.50 99 | 62.59 264 | 82.73 213 | 90.67 175 | 76.53 141 | 94.25 64 | 69.24 190 | 95.69 119 | 85.55 254 |
|
AdaColmap | | | 83.66 154 | 83.69 152 | 83.57 161 | 90.05 139 | 72.26 125 | 86.29 112 | 90.00 154 | 78.19 99 | 81.65 225 | 87.16 231 | 83.40 67 | 94.24 65 | 61.69 241 | 94.76 149 | 84.21 274 |
|
Effi-MVS+-dtu | | | 85.82 105 | 83.38 154 | 93.14 3 | 87.13 208 | 91.15 3 | 87.70 83 | 88.42 175 | 74.57 147 | 83.56 202 | 85.65 248 | 78.49 115 | 94.21 66 | 72.04 171 | 92.88 192 | 94.05 99 |
|
UniMVSNet (Re) | | | 86.87 81 | 86.98 85 | 86.55 89 | 93.11 64 | 68.48 157 | 83.80 150 | 92.87 61 | 80.37 68 | 89.61 107 | 91.81 137 | 77.72 122 | 94.18 67 | 75.00 146 | 98.53 17 | 96.99 34 |
|
PHI-MVS | | | 86.38 92 | 85.81 104 | 88.08 69 | 88.44 165 | 77.34 87 | 89.35 61 | 93.05 53 | 73.15 167 | 84.76 180 | 87.70 225 | 78.87 113 | 94.18 67 | 80.67 93 | 96.29 92 | 92.73 135 |
|
test_prior3 | | | 86.31 94 | 86.31 96 | 86.32 93 | 90.59 129 | 71.99 130 | 83.37 165 | 92.85 62 | 75.43 138 | 84.58 186 | 91.57 141 | 81.92 88 | 94.17 69 | 79.54 109 | 96.97 71 | 92.80 133 |
|
test_prior | | | | | 86.32 93 | 90.59 129 | 71.99 130 | | 92.85 62 | | | | | 94.17 69 | | | 92.80 133 |
|
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 43 | 96.29 18 | 88.16 26 | 94.17 69 | 86.07 33 | 98.48 19 | 97.22 26 |
|
v7n | | | 90.13 33 | 90.96 32 | 87.65 77 | 91.95 94 | 71.06 140 | 89.99 42 | 93.05 53 | 86.53 21 | 94.29 22 | 96.27 19 | 82.69 73 | 94.08 72 | 86.25 30 | 97.63 53 | 97.82 11 |
|
v10 | | | 86.54 89 | 87.10 81 | 84.84 122 | 88.16 172 | 63.28 195 | 86.64 107 | 92.20 80 | 75.42 140 | 92.81 46 | 94.50 66 | 74.05 167 | 94.06 73 | 83.88 54 | 96.28 93 | 97.17 28 |
|
UniMVSNet_NR-MVSNet | | | 86.84 83 | 87.06 82 | 86.17 102 | 92.86 71 | 67.02 165 | 82.55 187 | 91.56 97 | 83.08 41 | 90.92 76 | 91.82 136 | 78.25 118 | 93.99 74 | 74.16 149 | 98.35 24 | 97.49 16 |
|
DU-MVS | | | 86.80 84 | 86.99 84 | 86.21 99 | 93.24 60 | 67.02 165 | 83.16 172 | 92.21 79 | 81.73 58 | 90.92 76 | 91.97 130 | 77.20 128 | 93.99 74 | 74.16 149 | 98.35 24 | 97.61 13 |
|
mvs-test1 | | | 84.55 126 | 82.12 171 | 91.84 17 | 87.13 208 | 89.54 5 | 85.05 125 | 88.42 175 | 74.57 147 | 80.60 243 | 82.98 285 | 78.49 115 | 93.98 76 | 72.04 171 | 89.77 246 | 92.00 161 |
|
v52 | | | 89.97 41 | 90.60 37 | 88.07 70 | 88.69 157 | 72.01 128 | 91.35 30 | 92.64 70 | 82.22 51 | 95.97 8 | 96.31 16 | 84.82 54 | 93.98 76 | 88.59 4 | 94.83 144 | 98.23 8 |
|
V4 | | | 89.97 41 | 90.60 37 | 88.07 70 | 88.69 157 | 72.01 128 | 91.35 30 | 92.64 70 | 82.22 51 | 95.98 7 | 96.31 16 | 84.80 56 | 93.98 76 | 88.59 4 | 94.83 144 | 98.23 8 |
|
DP-MVS Recon | | | 84.05 146 | 83.22 156 | 86.52 90 | 91.73 100 | 75.27 104 | 83.23 171 | 92.40 75 | 72.04 183 | 82.04 218 | 88.33 215 | 77.91 121 | 93.95 79 | 66.17 214 | 95.12 133 | 90.34 203 |
|
DP-MVS | | | 88.60 62 | 89.01 55 | 87.36 82 | 91.30 112 | 77.50 85 | 87.55 85 | 92.97 58 | 87.95 17 | 89.62 105 | 92.87 111 | 84.56 57 | 93.89 80 | 77.65 125 | 96.62 81 | 90.70 192 |
|
NR-MVSNet | | | 86.00 100 | 86.22 98 | 85.34 114 | 93.24 60 | 64.56 180 | 82.21 198 | 90.46 133 | 80.99 64 | 88.42 128 | 91.97 130 | 77.56 124 | 93.85 81 | 72.46 168 | 98.65 13 | 97.61 13 |
|
EPNet | | | 80.37 194 | 78.41 206 | 86.23 96 | 76.75 320 | 73.28 115 | 87.18 92 | 77.45 261 | 76.24 125 | 68.14 321 | 88.93 204 | 65.41 232 | 93.85 81 | 69.47 188 | 96.12 102 | 91.55 172 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
OPM-MVS | | | 89.80 45 | 89.97 44 | 89.27 55 | 94.76 35 | 79.86 64 | 86.76 101 | 92.78 66 | 78.78 90 | 92.51 52 | 93.64 96 | 88.13 27 | 93.84 83 | 84.83 42 | 97.55 59 | 94.10 98 |
|
TranMVSNet+NR-MVSNet | | | 87.86 69 | 88.76 62 | 85.18 116 | 94.02 44 | 64.13 183 | 84.38 135 | 91.29 116 | 84.88 27 | 92.06 59 | 93.84 94 | 86.45 45 | 93.73 84 | 73.22 160 | 98.66 12 | 97.69 12 |
|
v8 | | | 86.22 98 | 86.83 90 | 84.36 138 | 87.82 183 | 62.35 215 | 86.42 110 | 91.33 115 | 76.78 121 | 92.73 48 | 94.48 67 | 73.41 181 | 93.72 85 | 83.10 62 | 95.41 123 | 97.01 33 |
|
agg_prior3 | | | 85.76 106 | 84.95 119 | 88.16 68 | 92.43 80 | 79.92 62 | 83.98 142 | 90.03 153 | 65.11 249 | 83.66 200 | 90.64 178 | 81.00 97 | 93.67 86 | 81.21 83 | 96.54 84 | 90.88 187 |
|
Vis-MVSNet | | | 86.86 82 | 86.58 92 | 87.72 75 | 92.09 90 | 77.43 86 | 87.35 88 | 92.09 82 | 78.87 89 | 84.27 195 | 94.05 83 | 78.35 117 | 93.65 87 | 80.54 95 | 91.58 211 | 92.08 160 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
v1240 | | | 84.30 136 | 84.51 135 | 83.65 158 | 87.65 192 | 61.26 232 | 82.85 178 | 91.54 98 | 67.94 225 | 90.68 81 | 90.65 176 | 71.71 209 | 93.64 88 | 82.84 68 | 94.78 146 | 96.07 46 |
|
TEST9 | | | | | | 92.34 83 | 79.70 66 | 83.94 143 | 90.32 138 | 65.41 247 | 84.49 188 | 90.97 163 | 82.03 84 | 93.63 89 | | | |
|
train_agg | | | 85.98 103 | 85.28 113 | 88.07 70 | 92.34 83 | 79.70 66 | 83.94 143 | 90.32 138 | 65.79 240 | 84.49 188 | 90.97 163 | 81.93 86 | 93.63 89 | 81.21 83 | 96.54 84 | 90.88 187 |
|
v7 | | | 84.81 120 | 85.00 117 | 84.23 141 | 88.15 173 | 63.27 196 | 83.79 151 | 91.39 113 | 71.10 199 | 90.07 85 | 91.28 147 | 74.04 168 | 93.63 89 | 81.48 82 | 93.67 172 | 95.79 53 |
|
PCF-MVS | | 74.62 15 | 82.15 174 | 80.92 187 | 85.84 108 | 89.43 144 | 72.30 124 | 80.53 229 | 91.82 90 | 57.36 291 | 87.81 136 | 89.92 190 | 77.67 123 | 93.63 89 | 58.69 265 | 95.08 134 | 91.58 171 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
v1192 | | | 84.57 125 | 84.69 126 | 84.21 142 | 87.75 188 | 62.88 203 | 83.02 174 | 91.43 108 | 69.08 216 | 89.98 90 | 90.89 167 | 72.70 197 | 93.62 93 | 82.41 71 | 94.97 138 | 96.13 44 |
|
v1921920 | | | 84.23 139 | 84.37 142 | 83.79 151 | 87.64 193 | 61.71 223 | 82.91 177 | 91.20 119 | 67.94 225 | 90.06 86 | 90.34 181 | 72.04 206 | 93.59 94 | 82.32 73 | 94.91 139 | 96.07 46 |
|
mvs_tets | | | 89.78 46 | 89.27 53 | 91.30 24 | 93.51 53 | 84.79 36 | 89.89 45 | 90.63 129 | 70.00 208 | 94.55 18 | 96.67 11 | 87.94 29 | 93.59 94 | 84.27 49 | 95.97 107 | 95.52 66 |
|
test_0402 | | | 88.65 60 | 89.58 51 | 85.88 107 | 92.55 76 | 72.22 126 | 84.01 141 | 89.44 165 | 88.63 14 | 94.38 21 | 95.77 29 | 86.38 47 | 93.59 94 | 79.84 105 | 95.21 130 | 91.82 165 |
|
v13 | | | 87.31 75 | 88.10 66 | 84.94 118 | 88.84 154 | 63.75 187 | 87.85 81 | 91.47 104 | 79.12 84 | 93.72 29 | 95.82 28 | 75.20 149 | 93.58 97 | 84.76 43 | 96.16 98 | 97.48 17 |
|
v12 | | | 87.15 78 | 87.91 68 | 84.84 122 | 88.69 157 | 63.52 190 | 87.58 84 | 91.46 105 | 78.74 92 | 93.57 32 | 95.66 32 | 74.94 153 | 93.57 98 | 84.50 46 | 96.08 103 | 97.43 18 |
|
v11 | | | 86.96 79 | 87.78 72 | 84.51 131 | 88.50 163 | 62.60 210 | 87.21 90 | 91.63 96 | 78.08 101 | 93.40 34 | 95.56 37 | 75.07 150 | 93.57 98 | 84.46 47 | 96.08 103 | 97.36 21 |
|
V9 | | | 86.96 79 | 87.70 73 | 84.74 126 | 88.52 162 | 63.27 196 | 87.31 89 | 91.45 107 | 78.28 97 | 93.43 33 | 95.45 39 | 74.59 161 | 93.57 98 | 84.23 50 | 96.01 106 | 97.38 19 |
|
V14 | | | 86.75 85 | 87.46 76 | 84.62 129 | 88.35 166 | 63.00 201 | 87.02 95 | 91.42 110 | 77.78 103 | 93.27 36 | 95.23 45 | 74.22 164 | 93.56 101 | 83.95 53 | 95.93 109 | 97.31 22 |
|
v15 | | | 86.56 88 | 87.25 80 | 84.51 131 | 88.15 173 | 62.72 206 | 86.72 105 | 91.40 112 | 77.38 108 | 93.11 38 | 95.00 50 | 73.93 169 | 93.55 102 | 83.67 57 | 95.86 113 | 97.26 23 |
|
v17 | | | 86.32 93 | 86.95 86 | 84.44 135 | 88.00 175 | 62.62 209 | 86.74 103 | 91.48 101 | 77.17 116 | 92.74 47 | 94.56 62 | 73.74 173 | 93.53 103 | 83.27 60 | 94.87 143 | 97.18 27 |
|
jajsoiax | | | 89.41 51 | 88.81 60 | 91.19 28 | 93.38 57 | 84.72 37 | 89.70 47 | 90.29 145 | 69.27 212 | 94.39 20 | 96.38 15 | 86.02 51 | 93.52 104 | 83.96 52 | 95.92 110 | 95.34 69 |
|
v18 | | | 85.99 102 | 86.55 93 | 84.30 140 | 87.73 189 | 62.29 219 | 86.40 111 | 91.49 100 | 76.64 122 | 92.40 55 | 94.20 79 | 73.28 185 | 93.52 104 | 82.87 66 | 93.99 162 | 97.09 30 |
|
v16 | | | 86.24 96 | 86.85 89 | 84.43 136 | 87.96 177 | 62.59 211 | 86.73 104 | 91.48 101 | 77.17 116 | 92.67 50 | 94.55 63 | 73.63 174 | 93.52 104 | 83.26 61 | 94.16 158 | 97.17 28 |
|
v144192 | | | 84.24 138 | 84.41 137 | 83.71 157 | 87.59 194 | 61.57 228 | 82.95 176 | 91.03 123 | 67.82 228 | 89.80 97 | 90.49 179 | 73.28 185 | 93.51 107 | 81.88 80 | 94.89 140 | 96.04 48 |
|
v1144 | | | 84.54 128 | 84.72 124 | 84.00 146 | 87.67 191 | 62.55 212 | 82.97 175 | 90.93 125 | 70.32 205 | 89.80 97 | 90.99 162 | 73.50 179 | 93.48 108 | 81.69 81 | 94.65 152 | 95.97 50 |
|
MCST-MVS | | | 84.36 133 | 83.93 150 | 85.63 111 | 91.59 102 | 71.58 137 | 83.52 159 | 92.13 81 | 61.82 270 | 83.96 196 | 89.75 193 | 79.93 108 | 93.46 109 | 78.33 119 | 94.34 157 | 91.87 164 |
|
test_8 | | | | | | 92.09 90 | 78.87 73 | 83.82 148 | 90.31 140 | 65.79 240 | 84.36 191 | 90.96 165 | 81.93 86 | 93.44 110 | | | |
|
ACMH+ | | 77.89 11 | 90.73 25 | 91.50 19 | 88.44 64 | 93.00 66 | 76.26 100 | 89.65 50 | 95.55 3 | 87.72 18 | 93.89 27 | 94.94 52 | 91.62 5 | 93.44 110 | 78.35 118 | 98.76 5 | 95.61 65 |
|
FC-MVSNet-test | | | 85.93 104 | 87.05 83 | 82.58 179 | 92.25 86 | 56.44 266 | 85.75 116 | 93.09 51 | 77.33 113 | 91.94 63 | 94.65 61 | 74.78 157 | 93.41 112 | 75.11 144 | 98.58 15 | 97.88 10 |
|
OMC-MVS | | | 88.19 65 | 87.52 75 | 90.19 44 | 91.94 96 | 81.68 53 | 87.49 87 | 93.17 49 | 76.02 128 | 88.64 123 | 91.22 149 | 84.24 60 | 93.37 113 | 77.97 124 | 97.03 70 | 95.52 66 |
|
MG-MVS | | | 80.32 195 | 80.94 186 | 78.47 231 | 88.18 170 | 52.62 293 | 82.29 194 | 85.01 225 | 72.01 184 | 79.24 258 | 92.54 120 | 69.36 216 | 93.36 114 | 70.65 180 | 89.19 252 | 89.45 212 |
|
v1neww | | | 84.43 130 | 84.66 128 | 83.75 153 | 87.81 184 | 62.34 216 | 83.59 155 | 90.27 146 | 72.33 178 | 89.93 92 | 91.22 149 | 73.28 185 | 93.29 115 | 80.25 100 | 93.25 183 | 95.62 61 |
|
v7new | | | 84.43 130 | 84.66 128 | 83.75 153 | 87.81 184 | 62.34 216 | 83.59 155 | 90.27 146 | 72.33 178 | 89.93 92 | 91.22 149 | 73.28 185 | 93.29 115 | 80.25 100 | 93.25 183 | 95.62 61 |
|
v6 | | | 84.43 130 | 84.66 128 | 83.75 153 | 87.81 184 | 62.34 216 | 83.59 155 | 90.26 148 | 72.33 178 | 89.94 91 | 91.19 153 | 73.30 184 | 93.29 115 | 80.26 99 | 93.26 182 | 95.62 61 |
|
CPTT-MVS | | | 89.39 52 | 88.98 57 | 90.63 36 | 95.09 30 | 86.95 10 | 92.09 23 | 92.30 78 | 79.74 75 | 87.50 140 | 92.38 122 | 81.42 94 | 93.28 118 | 83.07 63 | 97.24 65 | 91.67 168 |
|
F-COLMAP | | | 84.97 118 | 83.42 153 | 89.63 50 | 92.39 81 | 83.40 45 | 88.83 68 | 91.92 88 | 73.19 166 | 80.18 251 | 89.15 201 | 77.04 132 | 93.28 118 | 65.82 219 | 92.28 201 | 92.21 158 |
|
v2v482 | | | 84.09 144 | 84.24 144 | 83.62 159 | 87.13 208 | 61.40 229 | 82.71 184 | 89.71 160 | 72.19 182 | 89.55 109 | 91.41 146 | 70.70 214 | 93.20 120 | 81.02 86 | 93.76 169 | 96.25 42 |
|
agg_prior1 | | | 85.72 107 | 85.20 114 | 87.28 83 | 91.58 105 | 77.69 82 | 83.69 154 | 90.30 142 | 66.29 235 | 84.32 192 | 91.07 160 | 82.13 81 | 93.18 121 | 81.02 86 | 96.36 90 | 90.98 182 |
|
agg_prior | | | | | | 91.58 105 | 77.69 82 | | 90.30 142 | | 84.32 192 | | | 93.18 121 | | | |
|
Regformer-2 | | | 86.74 86 | 86.08 101 | 88.73 60 | 84.18 262 | 79.20 70 | 83.52 159 | 89.33 166 | 83.33 37 | 89.92 94 | 85.07 261 | 83.23 69 | 93.16 123 | 83.39 58 | 92.72 195 | 93.83 105 |
|
LTVRE_ROB | | 86.10 1 | 93.04 3 | 93.44 2 | 91.82 19 | 93.73 50 | 85.72 28 | 96.79 1 | 95.51 4 | 88.86 13 | 95.63 10 | 96.99 8 | 84.81 55 | 93.16 123 | 91.10 1 | 97.53 60 | 96.58 40 |
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 | | | | | 84.34 139 | 83.93 265 | 69.66 147 | | 81.09 246 | 72.43 174 | 86.47 160 | 90.19 185 | 57.56 267 | 93.15 125 | 77.45 128 | 86.39 282 | 90.22 204 |
|
v1141 | | | 84.16 141 | 84.38 139 | 83.52 163 | 87.32 200 | 61.70 225 | 82.79 180 | 89.74 157 | 71.90 192 | 89.64 102 | 91.12 156 | 72.68 198 | 93.10 126 | 80.39 98 | 93.80 167 | 95.75 55 |
|
divwei89l23v2f112 | | | 84.16 141 | 84.38 139 | 83.52 163 | 87.32 200 | 61.70 225 | 82.79 180 | 89.74 157 | 71.90 192 | 89.64 102 | 91.12 156 | 72.68 198 | 93.10 126 | 80.40 96 | 93.81 166 | 95.75 55 |
|
v1 | | | 84.16 141 | 84.38 139 | 83.52 163 | 87.33 199 | 61.71 223 | 82.79 180 | 89.73 159 | 71.89 194 | 89.64 102 | 91.11 158 | 72.72 195 | 93.10 126 | 80.40 96 | 93.79 168 | 95.75 55 |
|
XVG-ACMP-BASELINE | | | 89.98 39 | 89.84 46 | 90.41 39 | 94.91 34 | 84.50 41 | 89.49 58 | 93.98 24 | 79.68 76 | 92.09 58 | 93.89 93 | 83.80 62 | 93.10 126 | 82.67 70 | 98.04 36 | 93.64 114 |
|
anonymousdsp | | | 89.73 47 | 88.88 58 | 92.27 7 | 89.82 142 | 86.67 12 | 90.51 36 | 90.20 150 | 69.87 209 | 95.06 14 | 96.14 23 | 84.28 59 | 93.07 130 | 87.68 14 | 96.34 91 | 97.09 30 |
|
ACMM | | 79.39 9 | 90.65 26 | 90.99 31 | 89.63 50 | 95.03 31 | 83.53 44 | 89.62 53 | 93.35 39 | 79.20 83 | 93.83 28 | 93.60 97 | 90.81 9 | 92.96 131 | 85.02 39 | 98.45 20 | 92.41 148 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
CLD-MVS | | | 83.18 162 | 82.64 164 | 84.79 124 | 89.05 150 | 67.82 162 | 77.93 267 | 92.52 73 | 68.33 220 | 85.07 174 | 81.54 309 | 82.06 82 | 92.96 131 | 69.35 189 | 97.91 43 | 93.57 117 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
Effi-MVS+ | | | 83.90 151 | 84.01 147 | 83.57 161 | 87.22 206 | 65.61 174 | 86.55 109 | 92.40 75 | 78.64 93 | 81.34 230 | 84.18 272 | 83.65 64 | 92.93 133 | 74.22 148 | 87.87 267 | 92.17 159 |
|
lessismore_v0 | | | | | 85.95 104 | 91.10 120 | 70.99 141 | | 70.91 311 | | 91.79 64 | 94.42 69 | 61.76 243 | 92.93 133 | 79.52 111 | 93.03 189 | 93.93 103 |
|
FIs | | | 85.35 111 | 86.27 97 | 82.60 178 | 91.86 97 | 57.31 259 | 85.10 124 | 93.05 53 | 75.83 131 | 91.02 75 | 93.97 86 | 73.57 178 | 92.91 135 | 73.97 152 | 98.02 39 | 97.58 15 |
|
Anonymous20240521 | | | 87.68 73 | 88.61 63 | 84.87 121 | 91.76 99 | 64.76 178 | 89.28 62 | 91.66 95 | 83.02 42 | 93.29 35 | 96.10 24 | 77.37 127 | 92.89 136 | 77.27 132 | 97.75 49 | 96.97 35 |
|
PVSNet_Blended_VisFu | | | 81.55 180 | 80.49 191 | 84.70 128 | 91.58 105 | 73.24 116 | 84.21 136 | 91.67 94 | 62.86 262 | 80.94 232 | 87.16 231 | 67.27 224 | 92.87 137 | 69.82 186 | 88.94 254 | 87.99 230 |
|
Regformer-4 | | | 86.41 91 | 85.71 106 | 88.52 62 | 84.27 258 | 77.57 84 | 84.07 139 | 88.00 184 | 82.82 45 | 89.84 96 | 85.48 251 | 82.06 82 | 92.77 138 | 83.83 56 | 91.04 223 | 95.22 75 |
|
DELS-MVS | | | 81.44 181 | 81.25 182 | 82.03 186 | 84.27 258 | 62.87 204 | 76.47 281 | 92.49 74 | 70.97 200 | 81.64 226 | 83.83 275 | 75.03 151 | 92.70 139 | 74.29 147 | 92.22 205 | 90.51 199 |
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. | | | 83.95 149 | 82.69 163 | 87.72 75 | 89.27 147 | 81.45 55 | 83.72 153 | 81.58 244 | 74.73 145 | 85.66 168 | 86.06 246 | 72.56 201 | 92.69 140 | 75.44 142 | 95.21 130 | 89.01 222 |
|
v748 | | | 88.91 59 | 89.82 47 | 86.19 101 | 90.06 138 | 68.53 156 | 88.81 69 | 91.48 101 | 84.36 30 | 94.19 24 | 95.98 26 | 82.52 76 | 92.67 141 | 84.30 48 | 96.67 80 | 97.37 20 |
|
Fast-Effi-MVS+ | | | 81.04 187 | 80.57 188 | 82.46 183 | 87.50 195 | 63.22 198 | 78.37 263 | 89.63 161 | 68.01 222 | 81.87 220 | 82.08 304 | 82.31 79 | 92.65 142 | 67.10 206 | 88.30 263 | 91.51 173 |
|
PLC | | 73.85 16 | 82.09 175 | 80.31 192 | 87.45 80 | 90.86 125 | 80.29 60 | 85.88 114 | 90.65 128 | 68.17 221 | 76.32 276 | 86.33 242 | 73.12 190 | 92.61 143 | 61.40 245 | 90.02 245 | 89.44 213 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
IterMVS-LS | | | 84.73 122 | 84.98 118 | 83.96 148 | 87.35 198 | 63.66 188 | 83.25 169 | 89.88 156 | 76.06 126 | 89.62 105 | 92.37 125 | 73.40 183 | 92.52 144 | 78.16 121 | 94.77 148 | 95.69 58 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
PAPM_NR | | | 83.23 161 | 83.19 158 | 83.33 167 | 90.90 123 | 65.98 171 | 88.19 76 | 90.78 126 | 78.13 100 | 80.87 234 | 87.92 223 | 73.49 180 | 92.42 145 | 70.07 184 | 88.40 258 | 91.60 170 |
|
PAPM | | | 71.77 273 | 70.06 284 | 76.92 252 | 86.39 219 | 53.97 280 | 76.62 278 | 86.62 205 | 53.44 309 | 63.97 338 | 84.73 267 | 57.79 266 | 92.34 146 | 39.65 339 | 81.33 318 | 84.45 269 |
|
PAPR | | | 78.84 204 | 78.10 207 | 81.07 202 | 85.17 245 | 60.22 242 | 82.21 198 | 90.57 131 | 62.51 265 | 75.32 286 | 84.61 268 | 74.99 152 | 92.30 147 | 59.48 263 | 88.04 265 | 90.68 193 |
|
V42 | | | 83.47 159 | 83.37 155 | 83.75 153 | 83.16 273 | 63.33 194 | 81.31 217 | 90.23 149 | 69.51 211 | 90.91 78 | 90.81 170 | 74.16 165 | 92.29 148 | 80.06 102 | 90.22 243 | 95.62 61 |
|
QAPM | | | 82.59 168 | 82.59 166 | 82.58 179 | 86.44 218 | 66.69 168 | 89.94 44 | 90.36 137 | 67.97 224 | 84.94 177 | 92.58 119 | 72.71 196 | 92.18 149 | 70.63 181 | 87.73 269 | 88.85 223 |
|
CSCG | | | 86.26 95 | 86.47 94 | 85.60 112 | 90.87 124 | 74.26 110 | 87.98 78 | 91.85 89 | 80.35 69 | 89.54 111 | 88.01 219 | 79.09 111 | 92.13 150 | 75.51 141 | 95.06 135 | 90.41 201 |
|
TAPA-MVS | | 77.73 12 | 85.71 108 | 84.83 121 | 88.37 65 | 88.78 156 | 79.72 65 | 87.15 93 | 93.50 35 | 69.17 214 | 85.80 167 | 89.56 196 | 80.76 99 | 92.13 150 | 73.21 164 | 95.51 121 | 93.25 124 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
HyFIR lowres test | | | 75.12 240 | 72.66 262 | 82.50 182 | 91.44 111 | 65.19 175 | 72.47 305 | 87.31 192 | 46.79 339 | 80.29 249 | 84.30 271 | 52.70 285 | 92.10 152 | 51.88 308 | 86.73 277 | 90.22 204 |
|
testing_2 | | | 84.36 133 | 84.64 131 | 83.50 166 | 86.74 217 | 63.97 186 | 84.56 132 | 90.31 140 | 66.22 236 | 91.62 67 | 94.55 63 | 75.88 144 | 91.95 153 | 77.02 135 | 94.89 140 | 94.56 82 |
|
EI-MVSNet-Vis-set | | | 85.12 113 | 84.53 134 | 86.88 84 | 84.01 264 | 72.76 118 | 83.91 146 | 85.18 220 | 80.44 67 | 88.75 121 | 85.49 250 | 80.08 105 | 91.92 154 | 82.02 77 | 90.85 233 | 95.97 50 |
|
EI-MVSNet-UG-set | | | 85.04 115 | 84.44 136 | 86.85 85 | 83.87 267 | 72.52 121 | 83.82 148 | 85.15 221 | 80.27 71 | 88.75 121 | 85.45 254 | 79.95 107 | 91.90 155 | 81.92 79 | 90.80 234 | 96.13 44 |
|
Regformer-1 | | | 86.00 100 | 85.50 110 | 87.49 79 | 84.18 262 | 76.90 92 | 83.52 159 | 87.94 186 | 82.18 53 | 89.19 114 | 85.07 261 | 82.28 80 | 91.89 156 | 82.40 72 | 92.72 195 | 93.69 113 |
|
IB-MVS | | 62.13 19 | 71.64 274 | 68.97 288 | 79.66 218 | 80.80 292 | 62.26 221 | 73.94 299 | 76.90 264 | 63.27 259 | 68.63 320 | 76.79 330 | 33.83 350 | 91.84 157 | 59.28 264 | 87.26 273 | 84.88 265 |
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 | | | 81.27 184 | 81.26 181 | 81.29 199 | 84.98 246 | 61.65 227 | 81.98 203 | 87.25 194 | 63.56 255 | 87.56 139 | 89.60 195 | 73.62 175 | 91.83 158 | 72.20 170 | 90.59 241 | 90.38 202 |
|
UGNet | | | 82.78 165 | 81.64 177 | 86.21 99 | 86.20 229 | 76.24 101 | 86.86 96 | 85.68 214 | 77.07 118 | 73.76 296 | 92.82 112 | 69.64 215 | 91.82 159 | 69.04 194 | 93.69 171 | 90.56 197 |
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 | | | 80.96 188 | 80.99 185 | 80.84 204 | 88.55 161 | 68.23 158 | 80.33 231 | 88.46 174 | 72.79 171 | 86.55 154 | 86.76 235 | 74.72 159 | 91.77 160 | 61.79 240 | 88.99 253 | 82.52 297 |
|
Anonymous20231211 | | | 90.14 32 | 91.88 12 | 84.92 119 | 94.75 36 | 64.47 181 | 90.13 39 | 92.97 58 | 91.68 3 | 95.35 12 | 98.79 2 | 93.19 3 | 91.76 161 | 71.67 174 | 98.40 21 | 98.52 7 |
|
Test4 | | | 81.31 182 | 81.13 184 | 81.88 190 | 84.89 248 | 63.05 200 | 82.37 191 | 90.50 132 | 62.75 263 | 89.00 117 | 88.29 216 | 67.55 223 | 91.68 162 | 73.55 157 | 91.24 220 | 90.89 186 |
|
test_normal | | | 81.23 186 | 81.16 183 | 81.43 197 | 84.77 251 | 61.99 222 | 81.46 216 | 86.95 204 | 63.16 260 | 87.22 142 | 89.63 194 | 73.62 175 | 91.65 163 | 72.92 165 | 90.70 236 | 90.65 195 |
|
API-MVS | | | 82.28 172 | 82.61 165 | 81.30 198 | 86.29 225 | 69.79 145 | 88.71 71 | 87.67 188 | 78.42 96 | 82.15 217 | 84.15 274 | 77.98 119 | 91.59 164 | 65.39 221 | 92.75 194 | 82.51 298 |
|
nrg030 | | | 87.85 70 | 88.49 64 | 85.91 105 | 90.07 137 | 69.73 146 | 87.86 80 | 94.20 17 | 74.04 151 | 92.70 49 | 94.66 60 | 85.88 52 | 91.50 165 | 79.72 106 | 97.32 63 | 96.50 41 |
|
AllTest | | | 87.97 68 | 87.40 79 | 89.68 48 | 91.59 102 | 83.40 45 | 89.50 57 | 95.44 5 | 79.47 78 | 88.00 133 | 93.03 105 | 82.66 74 | 91.47 166 | 70.81 176 | 96.14 100 | 94.16 96 |
|
TestCases | | | | | 89.68 48 | 91.59 102 | 83.40 45 | | 95.44 5 | 79.47 78 | 88.00 133 | 93.03 105 | 82.66 74 | 91.47 166 | 70.81 176 | 96.14 100 | 94.16 96 |
|
PVSNet_BlendedMVS | | | 78.80 205 | 77.84 208 | 81.65 195 | 84.43 254 | 63.41 191 | 79.49 243 | 90.44 134 | 61.70 273 | 75.43 284 | 87.07 233 | 69.11 218 | 91.44 168 | 60.68 250 | 92.24 203 | 90.11 208 |
|
PVSNet_Blended | | | 76.49 228 | 75.40 231 | 79.76 214 | 84.43 254 | 63.41 191 | 75.14 291 | 90.44 134 | 57.36 291 | 75.43 284 | 78.30 325 | 69.11 218 | 91.44 168 | 60.68 250 | 87.70 270 | 84.42 270 |
|
æ— å…ˆéªŒ | | | | | | | | 82.81 179 | 85.62 215 | 58.09 288 | | | | 91.41 170 | 67.95 204 | | 84.48 268 |
|
1121 | | | 80.86 189 | 79.81 199 | 84.02 145 | 93.93 46 | 78.70 75 | 81.64 212 | 80.18 250 | 55.43 300 | 83.67 199 | 91.15 154 | 71.29 211 | 91.41 170 | 67.95 204 | 93.06 188 | 81.96 305 |
|
ambc | | | | | 82.98 172 | 90.55 131 | 64.86 177 | 88.20 75 | 89.15 168 | | 89.40 112 | 93.96 88 | 71.67 210 | 91.38 172 | 78.83 115 | 96.55 83 | 92.71 136 |
|
3Dnovator | | 80.37 7 | 84.80 121 | 84.71 125 | 85.06 117 | 86.36 223 | 74.71 107 | 88.77 70 | 90.00 154 | 75.65 136 | 84.96 175 | 93.17 103 | 74.06 166 | 91.19 173 | 78.28 120 | 91.09 221 | 89.29 217 |
|
cascas | | | 76.29 230 | 74.81 236 | 80.72 207 | 84.47 253 | 62.94 202 | 73.89 300 | 87.34 191 | 55.94 297 | 75.16 288 | 76.53 332 | 63.97 235 | 91.16 174 | 65.00 222 | 90.97 229 | 88.06 228 |
|
EG-PatchMatch MVS | | | 84.08 145 | 84.11 145 | 83.98 147 | 92.22 88 | 72.61 120 | 82.20 200 | 87.02 202 | 72.63 173 | 88.86 118 | 91.02 161 | 78.52 114 | 91.11 175 | 73.41 159 | 91.09 221 | 88.21 226 |
|
WR-MVS | | | 83.56 156 | 84.40 138 | 81.06 203 | 93.43 56 | 54.88 277 | 78.67 262 | 85.02 224 | 81.24 62 | 90.74 80 | 91.56 143 | 72.85 193 | 91.08 176 | 68.00 202 | 98.04 36 | 97.23 25 |
|
canonicalmvs | | | 85.50 109 | 86.14 100 | 83.58 160 | 87.97 176 | 67.13 164 | 87.55 85 | 94.32 12 | 73.44 158 | 88.47 127 | 87.54 228 | 86.45 45 | 91.06 177 | 75.76 140 | 93.76 169 | 92.54 146 |
|
XVG-OURS | | | 89.18 55 | 88.83 59 | 90.23 43 | 94.28 42 | 86.11 20 | 85.91 113 | 93.60 34 | 80.16 72 | 89.13 116 | 93.44 98 | 83.82 61 | 90.98 178 | 83.86 55 | 95.30 129 | 93.60 116 |
|
PS-MVSNAJ | | | 77.04 218 | 76.53 221 | 78.56 229 | 87.09 212 | 61.40 229 | 75.26 290 | 87.13 198 | 61.25 275 | 74.38 294 | 77.22 329 | 76.94 134 | 90.94 179 | 64.63 225 | 84.83 298 | 83.35 286 |
|
xiu_mvs_v2_base | | | 77.19 216 | 76.75 215 | 78.52 230 | 87.01 213 | 61.30 231 | 75.55 289 | 87.12 200 | 61.24 276 | 74.45 292 | 78.79 323 | 77.20 128 | 90.93 180 | 64.62 226 | 84.80 299 | 83.32 287 |
|
XVG-OURS-SEG-HR | | | 89.59 49 | 89.37 52 | 90.28 42 | 94.47 40 | 85.95 21 | 86.84 97 | 93.91 27 | 80.07 73 | 86.75 151 | 93.26 101 | 93.64 2 | 90.93 180 | 84.60 45 | 90.75 235 | 93.97 101 |
|
v148 | | | 82.31 171 | 82.48 168 | 81.81 194 | 85.59 240 | 59.66 245 | 81.47 215 | 86.02 211 | 72.85 170 | 88.05 132 | 90.65 176 | 70.73 213 | 90.91 182 | 75.15 143 | 91.79 207 | 94.87 78 |
|
VDD-MVS | | | 84.23 139 | 84.58 133 | 83.20 169 | 91.17 118 | 65.16 176 | 83.25 169 | 84.97 226 | 79.79 74 | 87.18 143 | 94.27 75 | 74.77 158 | 90.89 183 | 69.24 190 | 96.54 84 | 93.55 120 |
|
alignmvs | | | 83.94 150 | 83.98 148 | 83.80 150 | 87.80 187 | 67.88 161 | 84.54 133 | 91.42 110 | 73.27 165 | 88.41 129 | 87.96 220 | 72.33 202 | 90.83 184 | 76.02 139 | 94.11 159 | 92.69 137 |
|
ITE_SJBPF | | | | | 90.11 45 | 90.72 127 | 84.97 33 | | 90.30 142 | 81.56 60 | 90.02 87 | 91.20 152 | 82.40 78 | 90.81 185 | 73.58 156 | 94.66 151 | 94.56 82 |
|
BH-RMVSNet | | | 80.53 193 | 80.22 194 | 81.49 196 | 87.19 207 | 66.21 170 | 77.79 269 | 86.23 208 | 74.21 150 | 83.69 198 | 88.50 209 | 73.25 189 | 90.75 186 | 63.18 234 | 87.90 266 | 87.52 235 |
|
BH-w/o | | | 76.57 226 | 76.07 225 | 78.10 236 | 86.88 215 | 65.92 172 | 77.63 270 | 86.33 207 | 65.69 242 | 80.89 233 | 79.95 318 | 68.97 220 | 90.74 187 | 53.01 300 | 85.25 292 | 77.62 328 |
|
TR-MVS | | | 76.77 224 | 75.79 226 | 79.72 215 | 86.10 237 | 65.79 173 | 77.14 274 | 83.02 232 | 65.20 248 | 81.40 228 | 82.10 303 | 66.30 227 | 90.73 188 | 55.57 286 | 85.27 291 | 82.65 293 |
|
GBi-Net | | | 82.02 176 | 82.07 172 | 81.85 191 | 86.38 220 | 61.05 235 | 86.83 98 | 88.27 179 | 72.43 174 | 86.00 163 | 95.64 34 | 63.78 237 | 90.68 189 | 65.95 215 | 93.34 179 | 93.82 107 |
|
test1 | | | 82.02 176 | 82.07 172 | 81.85 191 | 86.38 220 | 61.05 235 | 86.83 98 | 88.27 179 | 72.43 174 | 86.00 163 | 95.64 34 | 63.78 237 | 90.68 189 | 65.95 215 | 93.34 179 | 93.82 107 |
|
FMVSNet1 | | | 84.55 126 | 85.45 111 | 81.85 191 | 90.27 134 | 61.05 235 | 86.83 98 | 88.27 179 | 78.57 94 | 89.66 101 | 95.64 34 | 75.43 146 | 90.68 189 | 69.09 193 | 95.33 125 | 93.82 107 |
|
VDDNet | | | 84.35 135 | 85.39 112 | 81.25 200 | 95.13 29 | 59.32 248 | 85.42 121 | 81.11 245 | 86.41 22 | 87.41 141 | 96.21 21 | 73.61 177 | 90.61 192 | 66.33 213 | 96.85 74 | 93.81 110 |
|
MAR-MVS | | | 80.24 197 | 78.74 204 | 84.73 127 | 86.87 216 | 78.18 77 | 85.75 116 | 87.81 187 | 65.67 243 | 77.84 265 | 78.50 324 | 73.79 172 | 90.53 193 | 61.59 244 | 90.87 232 | 85.49 256 |
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 | | | 82.47 170 | 83.22 156 | 80.22 210 | 82.62 277 | 57.75 257 | 82.54 188 | 91.96 87 | 71.16 198 | 82.89 211 | 92.52 121 | 77.41 126 | 90.50 194 | 80.04 103 | 87.84 268 | 92.40 149 |
|
MVS_111021_HR | | | 84.63 123 | 84.34 143 | 85.49 113 | 90.18 135 | 75.86 102 | 79.23 256 | 87.13 198 | 73.35 159 | 85.56 171 | 89.34 199 | 83.60 65 | 90.50 194 | 76.64 136 | 94.05 161 | 90.09 209 |
|
Regformer-3 | | | 85.06 114 | 84.67 127 | 86.22 97 | 84.27 258 | 73.43 114 | 84.07 139 | 85.26 218 | 80.77 66 | 88.62 124 | 85.48 251 | 80.56 102 | 90.39 196 | 81.99 78 | 91.04 223 | 94.85 80 |
|
EI-MVSNet | | | 82.61 167 | 82.42 169 | 83.20 169 | 83.25 271 | 63.66 188 | 83.50 162 | 85.07 222 | 76.06 126 | 86.55 154 | 85.10 259 | 73.41 181 | 90.25 197 | 78.15 122 | 90.67 237 | 95.68 59 |
|
MVSTER | | | 77.09 217 | 75.70 229 | 81.25 200 | 75.27 334 | 61.08 234 | 77.49 273 | 85.07 222 | 60.78 278 | 86.55 154 | 88.68 207 | 43.14 329 | 90.25 197 | 73.69 155 | 90.67 237 | 92.42 147 |
|
Fast-Effi-MVS+-dtu | | | 82.54 169 | 81.41 180 | 85.90 106 | 85.60 239 | 76.53 97 | 83.07 173 | 89.62 162 | 73.02 169 | 79.11 259 | 83.51 278 | 80.74 100 | 90.24 199 | 68.76 196 | 89.29 249 | 90.94 184 |
|
SD-MVS | | | 88.96 57 | 89.88 45 | 86.22 97 | 91.63 101 | 77.07 90 | 89.82 46 | 93.77 30 | 78.90 88 | 92.88 41 | 92.29 126 | 86.11 50 | 90.22 200 | 86.24 31 | 97.24 65 | 91.36 177 |
|
FMVSNet2 | | | 81.31 182 | 81.61 178 | 80.41 208 | 86.38 220 | 58.75 253 | 83.93 145 | 86.58 206 | 72.43 174 | 87.65 137 | 92.98 107 | 63.78 237 | 90.22 200 | 66.86 208 | 93.92 164 | 92.27 155 |
|
OpenMVS | | 76.72 13 | 81.98 178 | 82.00 174 | 81.93 187 | 84.42 256 | 68.22 159 | 88.50 74 | 89.48 164 | 66.92 231 | 81.80 224 | 91.86 132 | 72.59 200 | 90.16 202 | 71.19 175 | 91.25 219 | 87.40 237 |
|
xiu_mvs_v1_base_debu | | | 80.84 190 | 80.14 195 | 82.93 174 | 88.31 167 | 71.73 133 | 79.53 240 | 87.17 195 | 65.43 244 | 79.59 253 | 82.73 292 | 76.94 134 | 90.14 203 | 73.22 160 | 88.33 259 | 86.90 242 |
|
xiu_mvs_v1_base | | | 80.84 190 | 80.14 195 | 82.93 174 | 88.31 167 | 71.73 133 | 79.53 240 | 87.17 195 | 65.43 244 | 79.59 253 | 82.73 292 | 76.94 134 | 90.14 203 | 73.22 160 | 88.33 259 | 86.90 242 |
|
xiu_mvs_v1_base_debi | | | 80.84 190 | 80.14 195 | 82.93 174 | 88.31 167 | 71.73 133 | 79.53 240 | 87.17 195 | 65.43 244 | 79.59 253 | 82.73 292 | 76.94 134 | 90.14 203 | 73.22 160 | 88.33 259 | 86.90 242 |
|
FMVSNet3 | | | 78.80 205 | 78.55 205 | 79.57 219 | 82.89 275 | 56.89 263 | 81.76 209 | 85.77 213 | 69.04 217 | 86.00 163 | 90.44 180 | 51.75 287 | 90.09 206 | 65.95 215 | 93.34 179 | 91.72 167 |
|
LFMVS | | | 80.15 199 | 80.56 189 | 78.89 223 | 89.19 149 | 55.93 268 | 85.22 123 | 73.78 283 | 82.96 43 | 84.28 194 | 92.72 116 | 57.38 268 | 90.07 207 | 63.80 229 | 95.75 117 | 90.68 193 |
|
MVS | | | 73.21 261 | 72.59 264 | 75.06 270 | 80.97 288 | 60.81 238 | 81.64 212 | 85.92 212 | 46.03 342 | 71.68 307 | 77.54 326 | 68.47 221 | 89.77 208 | 55.70 285 | 85.39 289 | 74.60 334 |
|
LCM-MVSNet-Re | | | 83.48 158 | 85.06 115 | 78.75 226 | 85.94 238 | 55.75 271 | 80.05 233 | 94.27 13 | 76.47 123 | 96.09 5 | 94.54 65 | 83.31 68 | 89.75 209 | 59.95 253 | 94.89 140 | 90.75 191 |
|
CANet_DTU | | | 77.81 211 | 77.05 212 | 80.09 211 | 81.37 284 | 59.90 244 | 83.26 168 | 88.29 178 | 69.16 215 | 67.83 324 | 83.72 276 | 60.93 245 | 89.47 210 | 69.22 192 | 89.70 247 | 90.88 187 |
|
GA-MVS | | | 75.83 234 | 74.61 237 | 79.48 221 | 81.87 280 | 59.25 249 | 73.42 303 | 82.88 233 | 68.68 219 | 79.75 252 | 81.80 306 | 50.62 289 | 89.46 211 | 66.85 209 | 85.64 288 | 89.72 210 |
|
MVP-Stereo | | | 75.81 235 | 73.51 255 | 82.71 177 | 89.35 145 | 73.62 112 | 80.06 232 | 85.20 219 | 60.30 280 | 73.96 295 | 87.94 221 | 57.89 265 | 89.45 212 | 52.02 303 | 74.87 336 | 85.06 260 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
Vis-MVSNet (Re-imp) | | | 77.82 210 | 77.79 209 | 77.92 239 | 88.82 155 | 51.29 306 | 83.28 167 | 71.97 299 | 74.04 151 | 82.23 215 | 89.78 192 | 57.38 268 | 89.41 213 | 57.22 277 | 95.41 123 | 93.05 128 |
|
MSLP-MVS++ | | | 85.00 117 | 86.03 102 | 81.90 188 | 91.84 98 | 71.56 138 | 86.75 102 | 93.02 57 | 75.95 129 | 87.12 144 | 89.39 198 | 77.98 119 | 89.40 214 | 77.46 127 | 94.78 146 | 84.75 267 |
|
tfpn111 | | | 76.03 232 | 75.53 230 | 77.53 247 | 87.27 202 | 51.88 298 | 81.07 222 | 73.26 288 | 75.68 133 | 83.25 205 | 86.37 239 | 45.54 309 | 89.38 215 | 55.07 291 | 92.26 202 | 91.34 178 |
|
view600 | | | 76.79 220 | 76.54 217 | 77.56 243 | 87.91 179 | 50.77 312 | 81.92 204 | 71.35 307 | 77.38 108 | 84.62 182 | 88.40 211 | 45.18 319 | 89.26 216 | 58.58 266 | 93.49 174 | 92.66 138 |
|
view800 | | | 76.79 220 | 76.54 217 | 77.56 243 | 87.91 179 | 50.77 312 | 81.92 204 | 71.35 307 | 77.38 108 | 84.62 182 | 88.40 211 | 45.18 319 | 89.26 216 | 58.58 266 | 93.49 174 | 92.66 138 |
|
conf0.05thres1000 | | | 76.79 220 | 76.54 217 | 77.56 243 | 87.91 179 | 50.77 312 | 81.92 204 | 71.35 307 | 77.38 108 | 84.62 182 | 88.40 211 | 45.18 319 | 89.26 216 | 58.58 266 | 93.49 174 | 92.66 138 |
|
tfpn | | | 76.79 220 | 76.54 217 | 77.56 243 | 87.91 179 | 50.77 312 | 81.92 204 | 71.35 307 | 77.38 108 | 84.62 182 | 88.40 211 | 45.18 319 | 89.26 216 | 58.58 266 | 93.49 174 | 92.66 138 |
|
thres600view7 | | | 75.97 233 | 75.35 233 | 77.85 241 | 87.01 213 | 51.84 302 | 80.45 230 | 73.26 288 | 75.20 142 | 83.10 209 | 86.31 244 | 45.54 309 | 89.05 220 | 55.03 292 | 92.24 203 | 92.66 138 |
|
jason | | | 77.42 214 | 75.75 228 | 82.43 184 | 87.10 211 | 69.27 150 | 77.99 266 | 81.94 241 | 51.47 323 | 77.84 265 | 85.07 261 | 60.32 249 | 89.00 221 | 70.74 179 | 89.27 251 | 89.03 220 |
jason: jason. |
lupinMVS | | | 76.37 229 | 74.46 240 | 82.09 185 | 85.54 241 | 69.26 151 | 76.79 275 | 80.77 248 | 50.68 330 | 76.23 277 | 82.82 290 | 58.69 260 | 88.94 222 | 69.85 185 | 88.77 255 | 88.07 227 |
|
PMVS | | 80.48 6 | 90.08 34 | 90.66 36 | 88.34 66 | 96.71 2 | 92.97 2 | 90.31 37 | 89.57 163 | 88.51 15 | 90.11 84 | 95.12 48 | 90.98 8 | 88.92 223 | 77.55 126 | 97.07 69 | 83.13 291 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
conf200view11 | | | 75.62 236 | 75.05 234 | 77.34 249 | 87.27 202 | 51.88 298 | 81.07 222 | 73.26 288 | 75.68 133 | 83.25 205 | 86.37 239 | 45.54 309 | 88.80 224 | 51.98 304 | 90.99 225 | 91.34 178 |
|
thres100view900 | | | 75.45 237 | 75.05 234 | 76.66 258 | 87.27 202 | 51.88 298 | 81.07 222 | 73.26 288 | 75.68 133 | 83.25 205 | 86.37 239 | 45.54 309 | 88.80 224 | 51.98 304 | 90.99 225 | 89.31 215 |
|
tfpn200view9 | | | 74.86 244 | 74.23 242 | 76.74 257 | 86.24 226 | 52.12 295 | 79.24 253 | 73.87 281 | 73.34 160 | 81.82 222 | 84.60 269 | 46.02 303 | 88.80 224 | 51.98 304 | 90.99 225 | 89.31 215 |
|
thres400 | | | 75.14 238 | 74.23 242 | 77.86 240 | 86.24 226 | 52.12 295 | 79.24 253 | 73.87 281 | 73.34 160 | 81.82 222 | 84.60 269 | 46.02 303 | 88.80 224 | 51.98 304 | 90.99 225 | 92.66 138 |
|
TAMVS | | | 78.08 209 | 76.36 222 | 83.23 168 | 90.62 128 | 72.87 117 | 79.08 257 | 80.01 252 | 61.72 272 | 81.35 229 | 86.92 234 | 63.96 236 | 88.78 228 | 50.61 309 | 93.01 190 | 88.04 229 |
|
CDS-MVSNet | | | 77.32 215 | 75.40 231 | 83.06 171 | 89.00 152 | 72.48 122 | 77.90 268 | 82.17 238 | 60.81 277 | 78.94 260 | 83.49 279 | 59.30 257 | 88.76 229 | 54.64 295 | 92.37 199 | 87.93 232 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
tpmp4_e23 | | | 69.43 290 | 67.33 297 | 75.72 267 | 78.53 309 | 52.75 290 | 82.13 202 | 74.91 274 | 49.23 336 | 66.37 327 | 84.17 273 | 41.28 339 | 88.67 230 | 49.73 314 | 79.63 322 | 85.75 253 |
|
OpenMVS_ROB | | 70.19 17 | 77.77 212 | 77.46 210 | 78.71 227 | 84.39 257 | 61.15 233 | 81.18 221 | 82.52 235 | 62.45 267 | 83.34 204 | 87.37 230 | 66.20 228 | 88.66 231 | 64.69 224 | 85.02 295 | 86.32 246 |
|
tpm2 | | | 68.45 295 | 66.83 299 | 73.30 283 | 78.93 305 | 48.50 328 | 79.76 237 | 71.76 302 | 47.50 338 | 69.92 316 | 83.60 277 | 42.07 332 | 88.40 232 | 48.44 320 | 79.51 323 | 83.01 292 |
|
æ–°å‡ ä½•1 | | | | | 82.95 173 | 93.96 45 | 78.56 76 | | 80.24 249 | 55.45 299 | 83.93 197 | 91.08 159 | 71.19 212 | 88.33 233 | 65.84 218 | 93.07 187 | 81.95 306 |
|
ACMH | | 76.49 14 | 89.34 53 | 91.14 28 | 83.96 148 | 92.50 78 | 70.36 144 | 89.55 54 | 93.84 29 | 81.89 57 | 94.70 16 | 95.44 40 | 90.69 10 | 88.31 234 | 83.33 59 | 98.30 28 | 93.20 125 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
thres200 | | | 72.34 269 | 71.55 274 | 74.70 273 | 83.48 268 | 51.60 303 | 75.02 292 | 73.71 284 | 70.14 207 | 78.56 261 | 80.57 313 | 46.20 301 | 88.20 235 | 46.99 326 | 89.29 249 | 84.32 272 |
|
gm-plane-assit | | | | | | 75.42 332 | 44.97 339 | | | 52.17 316 | | 72.36 342 | | 87.90 236 | 54.10 296 | | |
|
EU-MVSNet | | | 75.12 240 | 74.43 241 | 77.18 250 | 83.11 274 | 59.48 247 | 85.71 118 | 82.43 236 | 39.76 351 | 85.64 169 | 88.76 205 | 44.71 325 | 87.88 237 | 73.86 154 | 85.88 286 | 84.16 275 |
|
diffmvs | | | 79.20 203 | 79.04 201 | 79.69 216 | 78.64 308 | 58.90 250 | 81.79 208 | 87.61 189 | 65.07 250 | 73.65 298 | 89.80 191 | 73.10 191 | 87.79 238 | 75.02 145 | 86.63 278 | 92.38 150 |
|
RPSCF | | | 88.00 67 | 86.93 87 | 91.22 27 | 90.08 136 | 89.30 6 | 89.68 49 | 91.11 121 | 79.26 82 | 89.68 100 | 94.81 59 | 82.44 77 | 87.74 239 | 76.54 137 | 88.74 257 | 96.61 39 |
|
CostFormer | | | 69.98 288 | 68.68 291 | 73.87 279 | 77.14 317 | 50.72 316 | 79.26 252 | 74.51 279 | 51.94 320 | 70.97 312 | 84.75 266 | 45.16 323 | 87.49 240 | 55.16 290 | 79.23 326 | 83.40 285 |
|
CVMVSNet | | | 72.62 265 | 71.41 275 | 76.28 262 | 83.25 271 | 60.34 241 | 83.50 162 | 79.02 255 | 37.77 352 | 76.33 275 | 85.10 259 | 49.60 292 | 87.41 241 | 70.54 182 | 77.54 332 | 81.08 321 |
|
wuykxyi23d | | | 88.46 63 | 88.80 61 | 87.44 81 | 90.96 122 | 93.03 1 | 85.85 115 | 81.96 239 | 74.58 146 | 98.58 2 | 97.29 5 | 87.73 31 | 87.31 242 | 82.84 68 | 99.41 1 | 81.99 304 |
|
VPA-MVSNet | | | 83.47 159 | 84.73 122 | 79.69 216 | 90.29 133 | 57.52 258 | 81.30 219 | 88.69 172 | 76.29 124 | 87.58 138 | 94.44 68 | 80.60 101 | 87.20 243 | 66.60 212 | 96.82 77 | 94.34 92 |
|
patchmatchnet-post | | | | | | | | | | | | 81.71 307 | 45.93 306 | 87.01 244 | | | |
|
mvs_anonymous | | | 78.13 208 | 78.76 203 | 76.23 263 | 79.24 302 | 50.31 318 | 78.69 261 | 84.82 227 | 61.60 274 | 83.09 210 | 92.82 112 | 73.89 171 | 87.01 244 | 68.33 201 | 86.41 281 | 91.37 176 |
|
TinyColmap | | | 81.25 185 | 82.34 170 | 77.99 238 | 85.33 244 | 60.68 239 | 82.32 193 | 88.33 177 | 71.26 197 | 86.97 149 | 92.22 129 | 77.10 131 | 86.98 246 | 62.37 235 | 95.17 132 | 86.31 247 |
|
TransMVSNet (Re) | | | 84.02 147 | 85.74 105 | 78.85 224 | 91.00 121 | 55.20 276 | 82.29 194 | 87.26 193 | 79.65 77 | 88.38 130 | 95.52 38 | 83.00 70 | 86.88 247 | 67.97 203 | 96.60 82 | 94.45 89 |
|
LF4IMVS | | | 82.75 166 | 81.93 175 | 85.19 115 | 82.08 278 | 80.15 61 | 85.53 119 | 88.76 171 | 68.01 222 | 85.58 170 | 87.75 224 | 71.80 208 | 86.85 248 | 74.02 151 | 93.87 165 | 88.58 224 |
|
conf0.01 | | | 74.17 250 | 73.53 249 | 76.08 264 | 86.13 231 | 50.06 321 | 79.45 244 | 68.54 320 | 72.01 184 | 80.76 236 | 82.50 295 | 41.39 333 | 86.83 249 | 59.66 256 | 91.36 212 | 91.34 178 |
|
conf0.002 | | | 74.17 250 | 73.53 249 | 76.08 264 | 86.13 231 | 50.06 321 | 79.45 244 | 68.54 320 | 72.01 184 | 80.76 236 | 82.50 295 | 41.39 333 | 86.83 249 | 59.66 256 | 91.36 212 | 91.34 178 |
|
thresconf0.02 | | | 73.65 255 | 73.53 249 | 73.98 275 | 86.13 231 | 50.06 321 | 79.45 244 | 68.54 320 | 72.01 184 | 80.76 236 | 82.50 295 | 41.39 333 | 86.83 249 | 59.66 256 | 91.36 212 | 85.06 260 |
|
tfpn_n400 | | | 73.65 255 | 73.53 249 | 73.98 275 | 86.13 231 | 50.06 321 | 79.45 244 | 68.54 320 | 72.01 184 | 80.76 236 | 82.50 295 | 41.39 333 | 86.83 249 | 59.66 256 | 91.36 212 | 85.06 260 |
|
tfpnconf | | | 73.65 255 | 73.53 249 | 73.98 275 | 86.13 231 | 50.06 321 | 79.45 244 | 68.54 320 | 72.01 184 | 80.76 236 | 82.50 295 | 41.39 333 | 86.83 249 | 59.66 256 | 91.36 212 | 85.06 260 |
|
tfpnview11 | | | 73.65 255 | 73.53 249 | 73.98 275 | 86.13 231 | 50.06 321 | 79.45 244 | 68.54 320 | 72.01 184 | 80.76 236 | 82.50 295 | 41.39 333 | 86.83 249 | 59.66 256 | 91.36 212 | 85.06 260 |
|
pmmvs6 | | | 86.52 90 | 88.06 67 | 81.90 188 | 92.22 88 | 62.28 220 | 84.66 130 | 89.15 168 | 83.54 36 | 89.85 95 | 97.32 4 | 88.08 28 | 86.80 255 | 70.43 183 | 97.30 64 | 96.62 38 |
|
tfpn_ndepth | | | 72.54 266 | 72.30 267 | 73.24 284 | 84.81 250 | 51.42 304 | 79.24 253 | 70.49 313 | 69.26 213 | 78.48 262 | 79.80 319 | 40.16 343 | 86.77 256 | 58.08 275 | 90.43 242 | 81.53 314 |
|
1112_ss | | | 74.82 245 | 73.74 245 | 78.04 237 | 89.57 143 | 60.04 243 | 76.49 280 | 87.09 201 | 54.31 304 | 73.66 297 | 79.80 319 | 60.25 250 | 86.76 257 | 58.37 270 | 84.15 302 | 87.32 238 |
|
tfpn1000 | | | 73.63 259 | 73.58 247 | 73.79 281 | 85.46 243 | 50.31 318 | 79.99 235 | 68.18 326 | 72.33 178 | 80.66 242 | 83.05 283 | 39.80 344 | 86.74 258 | 60.96 248 | 91.78 208 | 84.32 272 |
|
USDC | | | 76.63 225 | 76.73 216 | 76.34 261 | 83.46 269 | 57.20 260 | 80.02 234 | 88.04 183 | 52.14 318 | 83.65 201 | 91.25 148 | 63.24 240 | 86.65 259 | 54.66 294 | 94.11 159 | 85.17 258 |
|
tfpnnormal | | | 81.79 179 | 82.95 160 | 78.31 232 | 88.93 153 | 55.40 272 | 80.83 228 | 82.85 234 | 76.81 120 | 85.90 166 | 94.14 81 | 74.58 162 | 86.51 260 | 66.82 211 | 95.68 120 | 93.01 129 |
|
VPNet | | | 80.25 196 | 81.68 176 | 75.94 266 | 92.46 79 | 47.98 331 | 76.70 277 | 81.67 243 | 73.45 157 | 84.87 178 | 92.82 112 | 74.66 160 | 86.51 260 | 61.66 242 | 96.85 74 | 93.33 121 |
|
testdata2 | | | | | | | | | | | | | | 86.43 262 | 63.52 231 | | |
|
MSDG | | | 80.06 200 | 79.99 198 | 80.25 209 | 83.91 266 | 68.04 160 | 77.51 272 | 89.19 167 | 77.65 104 | 81.94 219 | 83.45 280 | 76.37 142 | 86.31 263 | 63.31 233 | 86.59 279 | 86.41 245 |
|
MVS_111021_LR | | | 84.28 137 | 83.76 151 | 85.83 109 | 89.23 148 | 83.07 48 | 80.99 225 | 83.56 230 | 72.71 172 | 86.07 162 | 89.07 202 | 81.75 91 | 86.19 264 | 77.11 133 | 93.36 178 | 88.24 225 |
|
Baseline_NR-MVSNet | | | 84.00 148 | 85.90 103 | 78.29 233 | 91.47 110 | 53.44 286 | 82.29 194 | 87.00 203 | 79.06 86 | 89.55 109 | 95.72 31 | 77.20 128 | 86.14 265 | 72.30 169 | 98.51 18 | 95.28 71 |
|
EPNet_dtu | | | 72.87 263 | 71.33 276 | 77.49 248 | 77.72 314 | 60.55 240 | 82.35 192 | 75.79 269 | 66.49 234 | 58.39 351 | 81.06 312 | 53.68 283 | 85.98 266 | 53.55 298 | 92.97 191 | 85.95 250 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
ANet_high | | | 83.17 163 | 85.68 107 | 75.65 268 | 81.24 285 | 45.26 336 | 79.94 236 | 92.91 60 | 83.83 35 | 91.33 72 | 96.88 10 | 80.25 104 | 85.92 267 | 68.89 195 | 95.89 111 | 95.76 54 |
|
Test_1112_low_res | | | 73.90 253 | 73.08 258 | 76.35 260 | 90.35 132 | 55.95 267 | 73.40 304 | 86.17 209 | 50.70 329 | 73.14 299 | 85.94 247 | 58.31 262 | 85.90 268 | 56.51 280 | 83.22 306 | 87.20 239 |
|
MIMVSNet1 | | | 83.63 155 | 84.59 132 | 80.74 205 | 94.06 43 | 62.77 205 | 82.72 183 | 84.53 228 | 77.57 106 | 90.34 82 | 95.92 27 | 76.88 140 | 85.83 269 | 61.88 239 | 97.42 61 | 93.62 115 |
|
tpmvs | | | 70.16 284 | 69.56 286 | 71.96 295 | 74.71 338 | 48.13 329 | 79.63 238 | 75.45 273 | 65.02 251 | 70.26 314 | 81.88 305 | 45.34 316 | 85.68 270 | 58.34 271 | 75.39 335 | 82.08 303 |
|
pm-mvs1 | | | 83.69 153 | 84.95 119 | 79.91 212 | 90.04 140 | 59.66 245 | 82.43 189 | 87.44 190 | 75.52 137 | 87.85 135 | 95.26 44 | 81.25 96 | 85.65 271 | 68.74 197 | 96.04 105 | 94.42 90 |
|
pmmvs-eth3d | | | 78.42 207 | 77.04 213 | 82.57 181 | 87.44 196 | 74.41 109 | 80.86 227 | 79.67 253 | 55.68 298 | 84.69 181 | 90.31 184 | 60.91 246 | 85.42 272 | 62.20 236 | 91.59 210 | 87.88 233 |
|
testdata | | | | | 79.54 220 | 92.87 69 | 72.34 123 | | 80.14 251 | 59.91 282 | 85.47 173 | 91.75 139 | 67.96 222 | 85.24 273 | 68.57 200 | 92.18 206 | 81.06 323 |
|
1314 | | | 73.22 260 | 72.56 265 | 75.20 269 | 80.41 296 | 57.84 255 | 81.64 212 | 85.36 217 | 51.68 321 | 73.10 300 | 76.65 331 | 61.45 244 | 85.19 274 | 63.54 230 | 79.21 327 | 82.59 294 |
|
CHOSEN 1792x2688 | | | 72.45 267 | 70.56 278 | 78.13 235 | 90.02 141 | 63.08 199 | 68.72 318 | 83.16 231 | 42.99 348 | 75.92 280 | 85.46 253 | 57.22 270 | 85.18 275 | 49.87 313 | 81.67 315 | 86.14 248 |
|
pmmvs4 | | | 74.92 243 | 72.98 260 | 80.73 206 | 84.95 247 | 71.71 136 | 76.23 283 | 77.59 260 | 52.83 312 | 77.73 268 | 86.38 238 | 56.35 272 | 84.97 276 | 57.72 276 | 87.05 275 | 85.51 255 |
|
旧先验2 | | | | | | | | 81.73 210 | | 56.88 295 | 86.54 159 | | | 84.90 277 | 72.81 166 | | |
|
DWT-MVSNet_test | | | 66.43 302 | 64.37 307 | 72.63 290 | 74.86 337 | 50.86 311 | 76.52 279 | 72.74 293 | 54.06 306 | 65.50 331 | 68.30 347 | 32.13 352 | 84.84 278 | 61.63 243 | 73.59 337 | 82.19 301 |
|
HY-MVS | | 64.64 18 | 73.03 262 | 72.47 266 | 74.71 272 | 83.36 270 | 54.19 279 | 82.14 201 | 81.96 239 | 56.76 296 | 69.57 317 | 86.21 245 | 60.03 251 | 84.83 279 | 49.58 315 | 82.65 311 | 85.11 259 |
|
ab-mvs | | | 79.67 201 | 80.56 189 | 76.99 251 | 88.48 164 | 56.93 261 | 84.70 129 | 86.06 210 | 68.95 218 | 80.78 235 | 93.08 104 | 75.30 148 | 84.62 280 | 56.78 279 | 90.90 231 | 89.43 214 |
|
IterMVS | | | 76.91 219 | 76.34 223 | 78.64 228 | 80.91 289 | 64.03 184 | 76.30 282 | 79.03 254 | 64.88 252 | 83.11 208 | 89.16 200 | 59.90 253 | 84.46 281 | 68.61 199 | 85.15 294 | 87.42 236 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
VNet | | | 79.31 202 | 80.27 193 | 76.44 259 | 87.92 178 | 53.95 281 | 75.58 288 | 84.35 229 | 74.39 149 | 82.23 215 | 90.72 172 | 72.84 194 | 84.39 282 | 60.38 252 | 93.98 163 | 90.97 183 |
|
ppachtmachnet_test | | | 74.73 246 | 74.00 244 | 76.90 253 | 80.71 293 | 56.89 263 | 71.53 309 | 78.42 256 | 58.24 287 | 79.32 257 | 82.92 289 | 57.91 264 | 84.26 283 | 65.60 220 | 91.36 212 | 89.56 211 |
|
CR-MVSNet | | | 74.00 252 | 73.04 259 | 76.85 255 | 79.58 297 | 62.64 207 | 82.58 185 | 76.90 264 | 50.50 331 | 75.72 282 | 92.38 122 | 48.07 296 | 84.07 284 | 68.72 198 | 82.91 309 | 83.85 278 |
|
RPMNet | | | 76.06 231 | 75.79 226 | 76.85 255 | 79.58 297 | 62.64 207 | 82.58 185 | 71.75 303 | 74.80 144 | 75.72 282 | 92.59 117 | 48.69 294 | 84.07 284 | 73.48 158 | 82.91 309 | 83.85 278 |
|
Patchmtry | | | 76.56 227 | 77.46 210 | 73.83 280 | 79.37 301 | 46.60 333 | 82.41 190 | 76.90 264 | 73.81 154 | 85.56 171 | 92.38 122 | 48.07 296 | 83.98 286 | 63.36 232 | 95.31 128 | 90.92 185 |
|
gg-mvs-nofinetune | | | 68.96 294 | 69.11 287 | 68.52 309 | 76.12 325 | 45.32 335 | 83.59 155 | 55.88 352 | 86.68 20 | 64.62 337 | 97.01 7 | 30.36 354 | 83.97 287 | 44.78 331 | 82.94 308 | 76.26 331 |
|
GG-mvs-BLEND | | | | | 67.16 312 | 73.36 341 | 46.54 334 | 84.15 138 | 55.04 353 | | 58.64 350 | 61.95 353 | 29.93 355 | 83.87 288 | 38.71 343 | 76.92 333 | 71.07 340 |
|
PM-MVS | | | 80.20 198 | 79.00 202 | 83.78 152 | 88.17 171 | 86.66 13 | 81.31 217 | 66.81 335 | 69.64 210 | 88.33 131 | 90.19 185 | 64.58 233 | 83.63 289 | 71.99 173 | 90.03 244 | 81.06 323 |
|
JIA-IIPM | | | 69.41 292 | 66.64 302 | 77.70 242 | 73.19 343 | 71.24 139 | 75.67 286 | 65.56 336 | 70.42 203 | 65.18 333 | 92.97 108 | 33.64 351 | 83.06 290 | 53.52 299 | 69.61 348 | 78.79 327 |
|
CMPMVS | | 59.41 20 | 75.12 240 | 73.57 248 | 79.77 213 | 75.84 326 | 67.22 163 | 81.21 220 | 82.18 237 | 50.78 328 | 76.50 273 | 87.66 226 | 55.20 279 | 82.99 291 | 62.17 238 | 90.64 240 | 89.09 219 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
PatchFormer-LS_test | | | 67.91 297 | 66.49 303 | 72.17 294 | 75.29 333 | 51.85 301 | 75.68 285 | 73.62 286 | 57.23 293 | 68.64 318 | 68.13 348 | 42.19 331 | 82.76 292 | 64.06 228 | 73.51 338 | 81.89 308 |
|
Patchmatch-RL test | | | 74.48 247 | 73.68 246 | 76.89 254 | 84.83 249 | 66.54 169 | 72.29 306 | 69.16 319 | 57.70 289 | 86.76 150 | 86.33 242 | 45.79 308 | 82.59 293 | 69.63 187 | 90.65 239 | 81.54 313 |
|
tpm cat1 | | | 66.76 301 | 65.21 306 | 71.42 296 | 77.09 318 | 50.62 317 | 78.01 265 | 73.68 285 | 44.89 344 | 68.64 318 | 79.00 322 | 45.51 313 | 82.42 294 | 49.91 312 | 70.15 345 | 81.23 320 |
|
Patchmatch-test1 | | | 72.75 264 | 72.61 263 | 73.19 285 | 81.62 282 | 55.86 269 | 78.89 259 | 71.37 306 | 61.73 271 | 74.93 289 | 82.15 302 | 60.46 248 | 81.80 295 | 59.68 255 | 82.63 313 | 81.92 307 |
|
MS-PatchMatch | | | 70.93 279 | 70.22 282 | 73.06 287 | 81.85 281 | 62.50 213 | 73.82 301 | 77.90 258 | 52.44 315 | 75.92 280 | 81.27 310 | 55.67 276 | 81.75 296 | 55.37 288 | 77.70 330 | 74.94 333 |
|
CNLPA | | | 83.55 157 | 83.10 159 | 84.90 120 | 89.34 146 | 83.87 43 | 84.54 133 | 88.77 170 | 79.09 85 | 83.54 203 | 88.66 208 | 74.87 154 | 81.73 297 | 66.84 210 | 92.29 200 | 89.11 218 |
|
MDA-MVSNet-bldmvs | | | 77.47 213 | 76.90 214 | 79.16 222 | 79.03 304 | 64.59 179 | 66.58 327 | 75.67 271 | 73.15 167 | 88.86 118 | 88.99 203 | 66.94 225 | 81.23 298 | 64.71 223 | 88.22 264 | 91.64 169 |
|
MDTV_nov1_ep13 | | | | 68.29 294 | | 78.03 313 | 43.87 341 | 74.12 298 | 72.22 297 | 52.17 316 | 67.02 326 | 85.54 249 | 45.36 315 | 80.85 299 | 55.73 283 | 84.42 301 | |
|
pmmvs5 | | | 70.73 280 | 70.07 283 | 72.72 289 | 77.03 319 | 52.73 291 | 74.14 297 | 75.65 272 | 50.36 332 | 72.17 305 | 85.37 257 | 55.42 278 | 80.67 300 | 52.86 301 | 87.59 271 | 84.77 266 |
|
Gipuma | | | 84.44 129 | 86.33 95 | 78.78 225 | 84.20 261 | 73.57 113 | 89.55 54 | 90.44 134 | 84.24 31 | 84.38 190 | 94.89 53 | 76.35 143 | 80.40 301 | 76.14 138 | 96.80 78 | 82.36 299 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
no-one | | | 71.52 276 | 70.43 281 | 74.81 271 | 78.45 310 | 63.41 191 | 57.73 344 | 77.03 263 | 51.46 324 | 77.17 271 | 90.33 182 | 54.96 281 | 80.35 302 | 47.41 323 | 99.29 2 | 80.68 325 |
|
test_post1 | | | | | | | | 78.85 260 | | | | 3.13 357 | 45.19 318 | 80.13 303 | 58.11 274 | | |
|
PatchmatchNet | | | 69.71 289 | 68.83 289 | 72.33 293 | 77.66 315 | 53.60 284 | 79.29 251 | 69.99 315 | 57.66 290 | 72.53 302 | 82.93 288 | 46.45 300 | 80.08 304 | 60.91 249 | 72.09 341 | 83.31 288 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
FMVSNet5 | | | 72.10 271 | 71.69 271 | 73.32 282 | 81.57 283 | 53.02 289 | 76.77 276 | 78.37 257 | 63.31 258 | 76.37 274 | 91.85 133 | 36.68 347 | 78.98 305 | 47.87 322 | 92.45 198 | 87.95 231 |
|
our_test_3 | | | 71.85 272 | 71.59 272 | 72.62 291 | 80.71 293 | 53.78 283 | 69.72 316 | 71.71 305 | 58.80 284 | 78.03 264 | 80.51 314 | 56.61 271 | 78.84 306 | 62.20 236 | 86.04 285 | 85.23 257 |
|
PatchMatch-RL | | | 74.48 247 | 73.22 257 | 78.27 234 | 87.70 190 | 85.26 30 | 75.92 284 | 70.09 314 | 64.34 254 | 76.09 279 | 81.25 311 | 65.87 231 | 78.07 307 | 53.86 297 | 83.82 303 | 71.48 339 |
|
ADS-MVSNet2 | | | 65.87 306 | 63.64 311 | 72.55 292 | 73.16 344 | 56.92 262 | 67.10 325 | 74.81 276 | 49.74 333 | 66.04 329 | 82.97 286 | 46.71 298 | 77.26 308 | 42.29 334 | 69.96 346 | 83.46 283 |
|
test_post | | | | | | | | | | | | 3.10 358 | 45.43 314 | 77.22 309 | | | |
|
LP | | | 69.42 291 | 68.30 293 | 72.77 288 | 71.48 351 | 56.84 265 | 73.66 302 | 74.84 275 | 63.52 256 | 70.95 313 | 83.35 282 | 49.55 293 | 77.15 310 | 57.13 278 | 70.21 344 | 84.33 271 |
|
MVS-HIRNet | | | 61.16 318 | 62.92 313 | 55.87 335 | 79.09 303 | 35.34 351 | 71.83 307 | 57.98 351 | 46.56 340 | 59.05 348 | 91.14 155 | 49.95 291 | 76.43 311 | 38.74 342 | 71.92 342 | 55.84 352 |
|
MIMVSNet | | | 71.09 278 | 71.59 272 | 69.57 302 | 87.23 205 | 50.07 320 | 78.91 258 | 71.83 301 | 60.20 281 | 71.26 309 | 91.76 138 | 55.08 280 | 76.09 312 | 41.06 337 | 87.02 276 | 82.54 296 |
|
tpm | | | 67.95 296 | 68.08 295 | 67.55 311 | 78.74 307 | 43.53 342 | 75.60 287 | 67.10 334 | 54.92 302 | 72.23 304 | 88.10 218 | 42.87 330 | 75.97 313 | 52.21 302 | 80.95 321 | 83.15 290 |
|
FPMVS | | | 72.29 270 | 72.00 269 | 73.14 286 | 88.63 160 | 85.00 32 | 74.65 295 | 67.39 329 | 71.94 191 | 77.80 267 | 87.66 226 | 50.48 290 | 75.83 314 | 49.95 311 | 79.51 323 | 58.58 351 |
|
PatchT | | | 70.52 281 | 72.76 261 | 63.79 322 | 79.38 300 | 33.53 352 | 77.63 270 | 65.37 337 | 73.61 155 | 71.77 306 | 92.79 115 | 44.38 326 | 75.65 315 | 64.53 227 | 85.37 290 | 82.18 302 |
|
PVSNet | | 58.17 21 | 66.41 303 | 65.63 305 | 68.75 306 | 81.96 279 | 49.88 327 | 62.19 334 | 72.51 296 | 51.03 326 | 68.04 322 | 75.34 336 | 50.84 288 | 74.77 316 | 45.82 330 | 82.96 307 | 81.60 312 |
|
tpmrst | | | 66.28 304 | 66.69 301 | 65.05 320 | 72.82 347 | 39.33 347 | 78.20 264 | 70.69 312 | 53.16 311 | 67.88 323 | 80.36 315 | 48.18 295 | 74.75 317 | 58.13 273 | 70.79 343 | 81.08 321 |
|
test20.03 | | | 73.75 254 | 74.59 239 | 71.22 297 | 81.11 287 | 51.12 308 | 70.15 314 | 72.10 298 | 70.42 203 | 80.28 250 | 91.50 144 | 64.21 234 | 74.72 318 | 46.96 327 | 94.58 153 | 87.82 234 |
|
testmv | | | 70.47 282 | 70.70 277 | 69.77 300 | 86.22 228 | 53.89 282 | 67.32 324 | 71.91 300 | 63.32 257 | 78.16 263 | 89.47 197 | 56.12 274 | 73.10 319 | 36.43 345 | 87.33 272 | 82.33 300 |
|
pmmvs3 | | | 62.47 310 | 60.02 323 | 69.80 299 | 71.58 350 | 64.00 185 | 70.52 312 | 58.44 349 | 39.77 350 | 66.05 328 | 75.84 333 | 27.10 359 | 72.28 320 | 46.15 328 | 84.77 300 | 73.11 337 |
|
Anonymous20231206 | | | 71.38 277 | 71.88 270 | 69.88 298 | 86.31 224 | 54.37 278 | 70.39 313 | 74.62 277 | 52.57 314 | 76.73 272 | 88.76 205 | 59.94 252 | 72.06 321 | 44.35 332 | 93.23 186 | 83.23 289 |
|
new-patchmatchnet | | | 70.10 285 | 73.37 256 | 60.29 331 | 81.23 286 | 16.95 359 | 59.54 338 | 74.62 277 | 62.93 261 | 80.97 231 | 87.93 222 | 62.83 241 | 71.90 322 | 55.24 289 | 95.01 137 | 92.00 161 |
|
dp | | | 60.70 321 | 60.29 322 | 61.92 326 | 72.04 349 | 38.67 349 | 70.83 310 | 64.08 338 | 51.28 325 | 60.75 342 | 77.28 328 | 36.59 348 | 71.58 323 | 47.41 323 | 62.34 352 | 75.52 332 |
|
UnsupCasMVSNet_bld | | | 69.21 293 | 69.68 285 | 67.82 310 | 79.42 299 | 51.15 307 | 67.82 323 | 75.79 269 | 54.15 305 | 77.47 269 | 85.36 258 | 59.26 258 | 70.64 324 | 48.46 319 | 79.35 325 | 81.66 311 |
|
test1235678 | | | 65.57 307 | 65.73 304 | 65.06 319 | 82.84 276 | 50.90 310 | 62.90 332 | 69.26 317 | 57.17 294 | 72.36 303 | 83.04 284 | 46.02 303 | 70.10 325 | 32.79 350 | 85.24 293 | 74.19 335 |
|
test-LLR | | | 67.21 299 | 66.74 300 | 68.63 307 | 76.45 323 | 55.21 274 | 67.89 320 | 67.14 332 | 62.43 268 | 65.08 334 | 72.39 340 | 43.41 327 | 69.37 326 | 61.00 246 | 84.89 296 | 81.31 316 |
|
test-mter | | | 65.00 308 | 63.79 309 | 68.63 307 | 76.45 323 | 55.21 274 | 67.89 320 | 67.14 332 | 50.98 327 | 65.08 334 | 72.39 340 | 28.27 357 | 69.37 326 | 61.00 246 | 84.89 296 | 81.31 316 |
|
XXY-MVS | | | 74.44 249 | 76.19 224 | 69.21 303 | 84.61 252 | 52.43 294 | 71.70 308 | 77.18 262 | 60.73 279 | 80.60 243 | 90.96 165 | 75.44 145 | 69.35 328 | 56.13 282 | 88.33 259 | 85.86 252 |
|
UnsupCasMVSNet_eth | | | 71.63 275 | 72.30 267 | 69.62 301 | 76.47 322 | 52.70 292 | 70.03 315 | 80.97 247 | 59.18 283 | 79.36 256 | 88.21 217 | 60.50 247 | 69.12 329 | 58.33 272 | 77.62 331 | 87.04 240 |
|
WTY-MVS | | | 67.91 297 | 68.35 292 | 66.58 314 | 80.82 291 | 48.12 330 | 65.96 328 | 72.60 294 | 53.67 308 | 71.20 310 | 81.68 308 | 58.97 259 | 69.06 330 | 48.57 318 | 81.67 315 | 82.55 295 |
|
test0.0.03 1 | | | 64.66 309 | 64.36 308 | 65.57 317 | 75.03 336 | 46.89 332 | 64.69 330 | 61.58 345 | 62.43 268 | 71.18 311 | 77.54 326 | 43.41 327 | 68.47 331 | 40.75 338 | 82.65 311 | 81.35 315 |
|
CHOSEN 280x420 | | | 59.08 322 | 56.52 327 | 66.76 313 | 76.51 321 | 64.39 182 | 49.62 351 | 59.00 347 | 43.86 346 | 55.66 354 | 68.41 346 | 35.55 349 | 68.21 332 | 43.25 333 | 76.78 334 | 67.69 344 |
|
YYNet1 | | | 70.06 286 | 70.44 279 | 68.90 304 | 73.76 340 | 53.42 287 | 58.99 342 | 67.20 331 | 58.42 286 | 87.10 145 | 85.39 256 | 59.82 254 | 67.32 333 | 59.79 254 | 83.50 305 | 85.96 249 |
|
MDA-MVSNet_test_wron | | | 70.05 287 | 70.44 279 | 68.88 305 | 73.84 339 | 53.47 285 | 58.93 343 | 67.28 330 | 58.43 285 | 87.09 146 | 85.40 255 | 59.80 255 | 67.25 334 | 59.66 256 | 83.54 304 | 85.92 251 |
|
EMVS | | | 61.10 319 | 60.81 319 | 61.99 325 | 65.96 355 | 55.86 269 | 53.10 349 | 58.97 348 | 67.06 229 | 56.89 353 | 63.33 351 | 40.98 340 | 67.03 335 | 54.79 293 | 86.18 284 | 63.08 346 |
|
testgi | | | 72.36 268 | 74.61 237 | 65.59 316 | 80.56 295 | 42.82 344 | 68.29 319 | 73.35 287 | 66.87 232 | 81.84 221 | 89.93 189 | 72.08 205 | 66.92 336 | 46.05 329 | 92.54 197 | 87.01 241 |
|
testpf | | | 58.55 323 | 61.58 318 | 49.48 340 | 66.03 354 | 40.05 346 | 74.40 296 | 58.07 350 | 64.72 253 | 59.36 346 | 72.67 339 | 22.76 360 | 66.92 336 | 67.07 207 | 69.15 349 | 41.46 354 |
|
EPMVS | | | 62.47 310 | 62.63 314 | 62.01 324 | 70.63 352 | 38.74 348 | 74.76 293 | 52.86 354 | 53.91 307 | 67.71 325 | 80.01 317 | 39.40 345 | 66.60 338 | 55.54 287 | 68.81 350 | 80.68 325 |
|
PMMVS | | | 61.65 315 | 60.38 320 | 65.47 318 | 65.40 356 | 69.26 151 | 63.97 331 | 61.73 344 | 36.80 353 | 60.11 344 | 68.43 344 | 59.42 256 | 66.35 339 | 48.97 317 | 78.57 328 | 60.81 348 |
|
E-PMN | | | 61.59 316 | 61.62 316 | 61.49 327 | 66.81 353 | 55.40 272 | 53.77 348 | 60.34 346 | 66.80 233 | 58.90 349 | 65.50 350 | 40.48 342 | 66.12 340 | 55.72 284 | 86.25 283 | 62.95 347 |
|
PVSNet_0 | | 51.08 22 | 56.10 325 | 54.97 329 | 59.48 333 | 75.12 335 | 53.28 288 | 55.16 345 | 61.89 342 | 44.30 345 | 59.16 347 | 62.48 352 | 54.22 282 | 65.91 341 | 35.40 347 | 47.01 353 | 59.25 350 |
|
test2356 | | | 56.69 324 | 55.15 328 | 61.32 329 | 73.20 342 | 44.11 340 | 54.95 346 | 62.52 340 | 48.75 337 | 62.45 340 | 68.42 345 | 21.10 361 | 65.67 342 | 26.86 354 | 78.08 329 | 74.19 335 |
|
sss | | | 66.92 300 | 67.26 298 | 65.90 315 | 77.23 316 | 51.10 309 | 64.79 329 | 71.72 304 | 52.12 319 | 70.13 315 | 80.18 316 | 57.96 263 | 65.36 343 | 50.21 310 | 81.01 320 | 81.25 318 |
|
TESTMET0.1,1 | | | 61.29 317 | 60.32 321 | 64.19 321 | 72.06 348 | 51.30 305 | 67.89 320 | 62.09 341 | 45.27 343 | 60.65 343 | 69.01 343 | 27.93 358 | 64.74 344 | 56.31 281 | 81.65 317 | 76.53 329 |
|
testus | | | 62.33 312 | 63.03 312 | 60.20 332 | 78.78 306 | 40.74 345 | 59.14 339 | 69.80 316 | 49.26 335 | 71.41 308 | 74.72 338 | 52.33 286 | 63.52 345 | 29.84 352 | 82.01 314 | 76.36 330 |
|
PNet_i23d | | | 52.13 329 | 51.24 331 | 54.79 338 | 75.56 328 | 45.26 336 | 54.54 347 | 52.55 355 | 66.95 230 | 57.19 352 | 65.82 349 | 13.15 364 | 63.40 346 | 36.39 346 | 39.04 355 | 55.71 353 |
|
ADS-MVSNet | | | 61.90 313 | 62.19 315 | 61.03 330 | 73.16 344 | 36.42 350 | 67.10 325 | 61.75 343 | 49.74 333 | 66.04 329 | 82.97 286 | 46.71 298 | 63.21 347 | 42.29 334 | 69.96 346 | 83.46 283 |
|
DSMNet-mixed | | | 60.98 320 | 61.61 317 | 59.09 334 | 72.88 346 | 45.05 338 | 74.70 294 | 46.61 358 | 26.20 354 | 65.34 332 | 90.32 183 | 55.46 277 | 63.12 348 | 41.72 336 | 81.30 319 | 69.09 343 |
|
test12356 | | | 54.91 328 | 57.14 326 | 48.22 341 | 75.83 327 | 17.47 358 | 52.31 350 | 69.20 318 | 51.66 322 | 60.11 344 | 75.40 335 | 29.77 356 | 62.62 349 | 27.64 353 | 72.37 340 | 64.59 345 |
|
Patchmatch-test | | | 65.91 305 | 67.38 296 | 61.48 328 | 75.51 330 | 43.21 343 | 68.84 317 | 63.79 339 | 62.48 266 | 72.80 301 | 83.42 281 | 44.89 324 | 59.52 350 | 48.27 321 | 86.45 280 | 81.70 309 |
|
1111 | | | 61.71 314 | 63.77 310 | 55.55 337 | 78.05 311 | 25.74 356 | 60.62 335 | 67.52 327 | 66.09 237 | 74.68 290 | 86.50 236 | 16.00 362 | 59.22 351 | 38.79 340 | 85.65 287 | 81.70 309 |
|
.test1245 | | | 48.02 331 | 54.41 330 | 28.84 344 | 78.05 311 | 25.74 356 | 60.62 335 | 67.52 327 | 66.09 237 | 74.68 290 | 86.50 236 | 16.00 362 | 59.22 351 | 38.79 340 | 1.47 357 | 1.55 358 |
|
N_pmnet | | | 70.20 283 | 68.80 290 | 74.38 274 | 80.91 289 | 84.81 35 | 59.12 341 | 76.45 268 | 55.06 301 | 75.31 287 | 82.36 301 | 55.74 275 | 54.82 353 | 47.02 325 | 87.24 274 | 83.52 282 |
|
wuyk23d | | | 75.13 239 | 79.30 200 | 62.63 323 | 75.56 328 | 75.18 105 | 80.89 226 | 73.10 292 | 75.06 143 | 94.76 15 | 95.32 41 | 87.73 31 | 52.85 354 | 34.16 348 | 97.11 68 | 59.85 349 |
|
PMMVS2 | | | 55.64 327 | 59.27 324 | 44.74 342 | 64.30 357 | 12.32 360 | 40.60 352 | 49.79 357 | 53.19 310 | 65.06 336 | 84.81 265 | 53.60 284 | 49.76 355 | 32.68 351 | 89.41 248 | 72.15 338 |
|
new_pmnet | | | 55.69 326 | 57.66 325 | 49.76 339 | 75.47 331 | 30.59 353 | 59.56 337 | 51.45 356 | 43.62 347 | 62.49 339 | 75.48 334 | 40.96 341 | 49.15 356 | 37.39 344 | 72.52 339 | 69.55 342 |
|
MVE | | 40.22 23 | 51.82 330 | 50.47 332 | 55.87 335 | 62.66 358 | 51.91 297 | 31.61 354 | 39.28 359 | 40.65 349 | 50.76 355 | 74.98 337 | 56.24 273 | 44.67 357 | 33.94 349 | 64.11 351 | 71.04 341 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
DeepMVS_CX | | | | | 24.13 345 | 32.95 359 | 29.49 354 | | 21.63 362 | 12.07 355 | 37.95 356 | 45.07 354 | 30.84 353 | 19.21 358 | 17.94 356 | 33.06 356 | 23.69 355 |
|
tmp_tt | | | 20.25 334 | 24.50 335 | 7.49 346 | 4.47 360 | 8.70 361 | 34.17 353 | 25.16 361 | 1.00 356 | 32.43 357 | 18.49 355 | 39.37 346 | 9.21 359 | 21.64 355 | 43.75 354 | 4.57 356 |
|
test123 | | | 6.27 337 | 8.08 338 | 0.84 347 | 1.11 362 | 0.57 362 | 62.90 332 | 0.82 363 | 0.54 357 | 1.07 359 | 2.75 360 | 1.26 365 | 0.30 360 | 1.04 357 | 1.26 359 | 1.66 357 |
|
testmvs | | | 5.91 338 | 7.65 339 | 0.72 348 | 1.20 361 | 0.37 363 | 59.14 339 | 0.67 364 | 0.49 358 | 1.11 358 | 2.76 359 | 0.94 366 | 0.24 361 | 1.02 358 | 1.47 357 | 1.55 358 |
|
cdsmvs_eth3d_5k | | | 20.81 333 | 27.75 334 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 85.44 216 | 0.00 359 | 0.00 360 | 82.82 290 | 81.46 93 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
pcd_1.5k_mvsjas | | | 6.41 336 | 8.55 337 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 0.00 365 | 0.00 359 | 0.00 360 | 0.00 361 | 76.94 134 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
pcd1.5k->3k | | | 38.83 332 | 41.11 333 | 32.01 343 | 93.13 63 | 0.00 364 | 0.00 355 | 91.38 114 | 0.00 359 | 0.00 360 | 0.00 361 | 89.24 16 | 0.00 362 | 0.00 359 | 96.24 96 | 96.02 49 |
|
sosnet-low-res | | | 0.00 339 | 0.00 340 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 0.00 365 | 0.00 359 | 0.00 360 | 0.00 361 | 0.00 367 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
sosnet | | | 0.00 339 | 0.00 340 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 0.00 365 | 0.00 359 | 0.00 360 | 0.00 361 | 0.00 367 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
uncertanet | | | 0.00 339 | 0.00 340 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 0.00 365 | 0.00 359 | 0.00 360 | 0.00 361 | 0.00 367 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
Regformer | | | 0.00 339 | 0.00 340 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 0.00 365 | 0.00 359 | 0.00 360 | 0.00 361 | 0.00 367 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
ab-mvs-re | | | 6.65 335 | 8.87 336 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 0.00 365 | 0.00 359 | 0.00 360 | 79.80 319 | 0.00 367 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
uanet | | | 0.00 339 | 0.00 340 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 0.00 365 | 0.00 359 | 0.00 360 | 0.00 361 | 0.00 367 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
GSMVS | | | | | | | | | | | | | | | | | 83.88 276 |
|
test_part2 | | | | | | 93.86 48 | 77.77 80 | | | | 92.84 44 | | | | | | |
|
test_part1 | | | | | | | | | 93.93 25 | | | | 87.19 36 | | | 97.61 56 | 91.48 174 |
|
sam_mvs1 | | | | | | | | | | | | | 46.11 302 | | | | 83.88 276 |
|
sam_mvs | | | | | | | | | | | | | 45.92 307 | | | | |
|
MTGPA | | | | | | | | | 91.81 91 | | | | | | | | |
|
MTMP | | | | | | | | | 33.14 360 | | | | | | | | |
|
test9_res | | | | | | | | | | | | | | | 80.83 90 | 96.45 89 | 90.57 196 |
|
agg_prior2 | | | | | | | | | | | | | | | 79.68 107 | 96.16 98 | 90.22 204 |
|
test_prior4 | | | | | | | 78.97 72 | 84.59 131 | | | | | | | | | |
|
test_prior2 | | | | | | | | 83.37 165 | | 75.43 138 | 84.58 186 | 91.57 141 | 81.92 88 | | 79.54 109 | 96.97 71 | |
|
æ–°å‡ ä½•2 | | | | | | | | 81.72 211 | | | | | | | | | |
|
旧先验1 | | | | | | 91.97 93 | 71.77 132 | | 81.78 242 | | | 91.84 134 | 73.92 170 | | | 93.65 173 | 83.61 281 |
|
原ACMM2 | | | | | | | | 82.26 197 | | | | | | | | | |
|
test222 | | | | | | 93.31 58 | 76.54 95 | 79.38 250 | 77.79 259 | 52.59 313 | 82.36 214 | 90.84 169 | 66.83 226 | | | 91.69 209 | 81.25 318 |
|
segment_acmp | | | | | | | | | | | | | 81.94 85 | | | | |
|
testdata1 | | | | | | | | 79.62 239 | | 73.95 153 | | | | | | | |
|
plane_prior7 | | | | | | 93.45 54 | 77.31 88 | | | | | | | | | | |
|
plane_prior6 | | | | | | 92.61 74 | 76.54 95 | | | | | | 74.84 155 | | | | |
|
plane_prior4 | | | | | | | | | | | | 92.95 109 | | | | | |
|
plane_prior3 | | | | | | | 76.85 93 | | | 77.79 102 | 86.55 154 | | | | | | |
|
plane_prior2 | | | | | | | | 89.45 59 | | 79.44 80 | | | | | | | |
|
plane_prior1 | | | | | | 92.83 72 | | | | | | | | | | | |
|
plane_prior | | | | | | | 76.42 98 | 87.15 93 | | 75.94 130 | | | | | | 95.03 136 | |
|
n2 | | | | | | | | | 0.00 365 | | | | | | | | |
|
nn | | | | | | | | | 0.00 365 | | | | | | | | |
|
door-mid | | | | | | | | | 74.45 280 | | | | | | | | |
|
test11 | | | | | | | | | 91.46 105 | | | | | | | | |
|
door | | | | | | | | | 72.57 295 | | | | | | | | |
|
HQP5-MVS | | | | | | | 70.66 142 | | | | | | | | | | |
|
HQP-NCC | | | | | | 91.19 115 | | 84.77 126 | | 73.30 162 | 80.55 246 | | | | | | |
|
ACMP_Plane | | | | | | 91.19 115 | | 84.77 126 | | 73.30 162 | 80.55 246 | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 77.30 130 | | |
|
HQP3-MVS | | | | | | | | | 92.68 68 | | | | | | | 94.47 155 | |
|
HQP2-MVS | | | | | | | | | | | | | 72.10 203 | | | | |
|
NP-MVS | | | | | | 91.95 94 | 74.55 108 | | | | | 90.17 187 | | | | | |
|
MDTV_nov1_ep13_2view | | | | | | | 27.60 355 | 70.76 311 | | 46.47 341 | 61.27 341 | | 45.20 317 | | 49.18 316 | | 83.75 280 |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 95.74 118 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 97.35 62 | |
|
Test By Simon | | | | | | | | | | | | | 79.09 111 | | | | |
|