EPNet | | | 91.79 59 | 91.02 66 | 94.10 44 | 90.10 274 | 85.25 52 | 96.03 34 | 92.05 248 | 92.83 1 | 87.39 116 | 95.78 73 | 79.39 93 | 99.01 53 | 88.13 79 | 97.48 57 | 98.05 45 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
NCCC | | | 94.81 8 | 94.69 9 | 95.17 6 | 97.83 30 | 87.46 9 | 95.66 49 | 96.93 39 | 92.34 2 | 93.94 18 | 96.58 43 | 87.74 12 | 99.44 18 | 92.83 20 | 98.40 37 | 98.62 5 |
|
CNVR-MVS | | | 95.40 2 | 95.37 3 | 95.50 3 | 98.11 23 | 88.51 3 | 95.29 61 | 96.96 36 | 92.09 3 | 95.32 7 | 97.08 23 | 89.49 4 | 99.33 24 | 95.10 2 | 98.85 6 | 98.66 4 |
|
UA-Net | | | 92.83 51 | 92.54 51 | 93.68 55 | 96.10 78 | 84.71 57 | 95.66 49 | 96.39 76 | 91.92 4 | 93.22 30 | 96.49 47 | 83.16 53 | 98.87 65 | 84.47 119 | 95.47 86 | 97.45 71 |
|
CANet | | | 93.54 36 | 93.20 39 | 94.55 31 | 95.65 92 | 85.73 48 | 94.94 84 | 96.69 59 | 91.89 5 | 90.69 75 | 95.88 70 | 81.99 69 | 99.54 8 | 93.14 18 | 97.95 49 | 98.39 19 |
|
Regformer-2 | | | 94.33 18 | 94.22 16 | 94.68 26 | 95.54 95 | 86.75 18 | 94.57 108 | 96.70 57 | 91.84 6 | 94.41 10 | 96.56 45 | 87.19 18 | 99.13 38 | 93.50 11 | 97.65 55 | 98.16 36 |
|
MVS_0304 | | | 93.25 45 | 92.62 49 | 95.14 7 | 95.72 90 | 87.58 7 | 94.71 101 | 96.59 66 | 91.78 7 | 91.46 67 | 96.18 61 | 75.45 144 | 99.55 5 | 93.53 10 | 98.19 42 | 98.28 26 |
|
HPM-MVS++ | | | 95.14 5 | 94.91 7 | 95.83 1 | 98.25 19 | 89.65 1 | 95.92 38 | 96.96 36 | 91.75 8 | 94.02 17 | 96.83 30 | 88.12 9 | 99.55 5 | 93.41 15 | 98.94 3 | 98.28 26 |
|
HSP-MVS | | | 95.30 3 | 95.48 2 | 94.76 23 | 98.49 8 | 86.52 26 | 96.91 15 | 96.73 53 | 91.73 9 | 96.10 3 | 96.69 36 | 89.90 1 | 99.30 27 | 94.70 3 | 98.04 47 | 98.45 16 |
|
Regformer-1 | | | 94.22 22 | 94.13 21 | 94.51 33 | 95.54 95 | 86.36 31 | 94.57 108 | 96.44 71 | 91.69 10 | 94.32 12 | 96.56 45 | 87.05 20 | 99.03 48 | 93.35 16 | 97.65 55 | 98.15 37 |
|
Regformer-4 | | | 93.91 29 | 93.81 27 | 94.19 43 | 95.36 99 | 85.47 49 | 94.68 102 | 96.41 74 | 91.60 11 | 93.75 20 | 96.71 34 | 85.95 30 | 99.10 41 | 93.21 17 | 96.65 70 | 98.01 49 |
|
SteuartSystems-ACMMP | | | 95.20 4 | 95.32 5 | 94.85 15 | 96.99 53 | 86.33 32 | 97.33 3 | 97.30 16 | 91.38 12 | 95.39 6 | 97.46 7 | 88.98 8 | 99.40 19 | 94.12 7 | 98.89 5 | 98.82 2 |
Skip Steuart: Steuart Systems R&D Blog. |
Regformer-3 | | | 93.68 33 | 93.64 33 | 93.81 52 | 95.36 99 | 84.61 58 | 94.68 102 | 95.83 112 | 91.27 13 | 93.60 24 | 96.71 34 | 85.75 32 | 98.86 68 | 92.87 19 | 96.65 70 | 97.96 50 |
|
MPTG | | | 94.47 11 | 94.30 13 | 95.00 8 | 98.42 12 | 86.95 10 | 95.06 80 | 96.97 33 | 91.07 14 | 93.14 32 | 97.56 4 | 84.30 47 | 99.56 1 | 93.43 13 | 98.75 14 | 98.47 12 |
|
MTAPA | | | 94.42 16 | 94.22 16 | 95.00 8 | 98.42 12 | 86.95 10 | 94.36 128 | 96.97 33 | 91.07 14 | 93.14 32 | 97.56 4 | 84.30 47 | 99.56 1 | 93.43 13 | 98.75 14 | 98.47 12 |
|
EI-MVSNet-Vis-set | | | 93.01 50 | 92.92 45 | 93.29 58 | 95.01 109 | 83.51 88 | 94.48 111 | 95.77 115 | 90.87 16 | 92.52 47 | 96.67 38 | 84.50 46 | 99.00 56 | 91.99 36 | 94.44 105 | 97.36 72 |
|
3Dnovator+ | | 87.14 4 | 92.42 55 | 91.37 59 | 95.55 2 | 95.63 93 | 88.73 2 | 97.07 8 | 96.77 51 | 90.84 17 | 84.02 198 | 96.62 41 | 75.95 133 | 99.34 21 | 87.77 83 | 97.68 53 | 98.59 7 |
|
HQP_MVS | | | 90.60 82 | 90.19 77 | 91.82 115 | 94.70 123 | 82.73 110 | 95.85 40 | 96.22 85 | 90.81 18 | 86.91 123 | 94.86 95 | 74.23 156 | 98.12 106 | 88.15 77 | 89.99 157 | 94.63 162 |
|
plane_prior2 | | | | | | | | 95.85 40 | | 90.81 18 | | | | | | | |
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DELS-MVS | | | 93.43 39 | 93.25 37 | 93.97 45 | 95.42 98 | 85.04 53 | 93.06 199 | 97.13 24 | 90.74 20 | 91.84 60 | 95.09 90 | 86.32 26 | 99.21 30 | 91.22 50 | 98.45 36 | 97.65 63 |
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 |
EI-MVSNet-UG-set | | | 92.74 53 | 92.62 49 | 93.12 64 | 94.86 117 | 83.20 94 | 94.40 118 | 95.74 118 | 90.71 21 | 92.05 57 | 96.60 42 | 84.00 49 | 98.99 57 | 91.55 46 | 93.63 114 | 97.17 79 |
|
XVS | | | 94.45 12 | 94.32 12 | 94.85 15 | 98.54 5 | 86.60 24 | 96.93 12 | 97.19 21 | 90.66 22 | 92.85 34 | 97.16 21 | 85.02 41 | 99.49 14 | 91.99 36 | 98.56 33 | 98.47 12 |
|
X-MVStestdata | | | 88.31 135 | 86.13 178 | 94.85 15 | 98.54 5 | 86.60 24 | 96.93 12 | 97.19 21 | 90.66 22 | 92.85 34 | 23.41 336 | 85.02 41 | 99.49 14 | 91.99 36 | 98.56 33 | 98.47 12 |
|
SD-MVS | | | 94.96 6 | 95.33 4 | 93.88 48 | 97.25 50 | 86.69 19 | 96.19 29 | 97.11 27 | 90.42 24 | 96.95 1 | 97.27 11 | 89.53 3 | 96.91 209 | 94.38 5 | 98.85 6 | 98.03 47 |
|
plane_prior3 | | | | | | | 82.75 107 | | | 90.26 25 | 86.91 123 | | | | | | |
|
DeepPCF-MVS | | 89.96 1 | 94.20 24 | 94.77 8 | 92.49 87 | 96.52 64 | 80.00 169 | 94.00 152 | 97.08 28 | 90.05 26 | 95.65 5 | 97.29 10 | 89.66 2 | 98.97 59 | 93.95 8 | 98.71 17 | 98.50 9 |
|
MSLP-MVS++ | | | 93.72 32 | 94.08 22 | 92.65 81 | 97.31 44 | 83.43 89 | 95.79 42 | 97.33 13 | 90.03 27 | 93.58 25 | 96.96 26 | 84.87 43 | 97.76 133 | 92.19 31 | 98.66 26 | 96.76 92 |
|
canonicalmvs | | | 93.27 43 | 92.75 48 | 94.85 15 | 95.70 91 | 87.66 5 | 96.33 25 | 96.41 74 | 90.00 28 | 94.09 15 | 94.60 104 | 82.33 60 | 98.62 82 | 92.40 26 | 92.86 132 | 98.27 29 |
|
Vis-MVSNet | | | 91.75 61 | 91.23 62 | 93.29 58 | 95.32 102 | 83.78 80 | 96.14 30 | 95.98 100 | 89.89 29 | 90.45 77 | 96.58 43 | 75.09 148 | 98.31 99 | 84.75 116 | 96.90 64 | 97.78 62 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
TranMVSNet+NR-MVSNet | | | 88.84 124 | 87.95 127 | 91.49 124 | 92.68 186 | 83.01 101 | 94.92 86 | 96.31 79 | 89.88 30 | 85.53 157 | 93.85 131 | 76.63 118 | 96.96 206 | 81.91 156 | 79.87 278 | 94.50 173 |
|
UniMVSNet_NR-MVSNet | | | 89.92 95 | 89.29 94 | 91.81 117 | 93.39 167 | 83.72 81 | 94.43 116 | 97.12 25 | 89.80 31 | 86.46 130 | 93.32 140 | 83.16 53 | 97.23 187 | 84.92 112 | 81.02 260 | 94.49 175 |
|
alignmvs | | | 93.08 49 | 92.50 52 | 94.81 20 | 95.62 94 | 87.61 6 | 95.99 35 | 96.07 95 | 89.77 32 | 94.12 14 | 94.87 94 | 80.56 78 | 98.66 78 | 92.42 25 | 93.10 127 | 98.15 37 |
|
TSAR-MVS + GP. | | | 93.66 34 | 93.41 35 | 94.41 37 | 96.59 61 | 86.78 16 | 94.40 118 | 93.93 215 | 89.77 32 | 94.21 13 | 95.59 79 | 87.35 16 | 98.61 83 | 92.72 21 | 96.15 78 | 97.83 59 |
|
IS-MVSNet | | | 91.43 66 | 91.09 65 | 92.46 88 | 95.87 87 | 81.38 135 | 96.95 9 | 93.69 220 | 89.72 34 | 89.50 87 | 95.98 66 | 78.57 100 | 97.77 132 | 83.02 137 | 96.50 74 | 98.22 33 |
|
plane_prior | | | | | | | 82.73 110 | 95.21 71 | | 89.66 35 | | | | | | 89.88 160 | |
|
DU-MVS | | | 89.34 113 | 88.50 111 | 91.85 113 | 93.04 178 | 83.72 81 | 94.47 113 | 96.59 66 | 89.50 36 | 86.46 130 | 93.29 143 | 77.25 111 | 97.23 187 | 84.92 112 | 81.02 260 | 94.59 166 |
|
CANet_DTU | | | 90.26 87 | 89.41 91 | 92.81 76 | 93.46 166 | 83.01 101 | 93.48 179 | 94.47 192 | 89.43 37 | 87.76 111 | 94.23 115 | 70.54 209 | 99.03 48 | 84.97 111 | 96.39 76 | 96.38 100 |
|
DeepC-MVS_fast | | 89.43 2 | 94.04 25 | 93.79 28 | 94.80 21 | 97.48 39 | 86.78 16 | 95.65 51 | 96.89 41 | 89.40 38 | 92.81 37 | 96.97 25 | 85.37 36 | 99.24 29 | 90.87 56 | 98.69 19 | 98.38 20 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
UGNet | | | 89.95 93 | 88.95 102 | 92.95 72 | 94.51 130 | 83.31 92 | 95.70 46 | 95.23 162 | 89.37 39 | 87.58 113 | 93.94 124 | 64.00 263 | 98.78 75 | 83.92 129 | 96.31 77 | 96.74 94 |
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 |
FC-MVSNet-test | | | 90.27 86 | 90.18 78 | 90.53 152 | 93.71 160 | 79.85 173 | 95.77 43 | 97.59 2 | 89.31 40 | 86.27 136 | 94.67 101 | 81.93 70 | 97.01 202 | 84.26 124 | 88.09 189 | 94.71 161 |
|
UniMVSNet (Re) | | | 89.80 97 | 89.07 99 | 92.01 103 | 93.60 163 | 84.52 61 | 94.78 95 | 97.47 5 | 89.26 41 | 86.44 133 | 92.32 177 | 82.10 65 | 97.39 173 | 84.81 115 | 80.84 264 | 94.12 185 |
|
3Dnovator | | 86.66 5 | 91.73 62 | 90.82 70 | 94.44 34 | 94.59 127 | 86.37 30 | 97.18 6 | 97.02 30 | 89.20 42 | 84.31 194 | 96.66 39 | 73.74 167 | 99.17 33 | 86.74 98 | 97.96 48 | 97.79 61 |
|
VNet | | | 92.24 56 | 91.91 55 | 93.24 60 | 96.59 61 | 83.43 89 | 94.84 91 | 96.44 71 | 89.19 43 | 94.08 16 | 95.90 69 | 77.85 110 | 98.17 103 | 88.90 70 | 93.38 121 | 98.13 39 |
|
FIs | | | 90.51 83 | 90.35 74 | 90.99 143 | 93.99 150 | 80.98 146 | 95.73 44 | 97.54 3 | 89.15 44 | 86.72 127 | 94.68 100 | 81.83 71 | 97.24 185 | 85.18 109 | 88.31 186 | 94.76 160 |
|
NR-MVSNet | | | 88.58 130 | 87.47 134 | 91.93 109 | 93.04 178 | 84.16 74 | 94.77 96 | 96.25 83 | 89.05 45 | 80.04 253 | 93.29 143 | 79.02 94 | 97.05 200 | 81.71 160 | 80.05 273 | 94.59 166 |
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MP-MVS | | | 94.25 20 | 94.07 23 | 94.77 22 | 98.47 9 | 86.31 34 | 96.71 20 | 96.98 32 | 89.04 46 | 91.98 58 | 97.19 18 | 85.43 35 | 99.56 1 | 92.06 35 | 98.79 9 | 98.44 17 |
|
APDe-MVS | | | 95.46 1 | 95.64 1 | 94.91 11 | 98.26 18 | 86.29 36 | 97.46 2 | 97.40 8 | 89.03 47 | 96.20 2 | 98.10 1 | 89.39 5 | 99.34 21 | 95.88 1 | 99.03 1 | 99.10 1 |
|
DeepC-MVS | | 88.79 3 | 93.31 41 | 92.99 43 | 94.26 41 | 96.07 80 | 85.83 46 | 94.89 87 | 96.99 31 | 89.02 48 | 89.56 85 | 97.37 8 | 82.51 58 | 99.38 20 | 92.20 30 | 98.30 39 | 97.57 67 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
OPM-MVS | | | 90.12 88 | 89.56 87 | 91.82 115 | 93.14 174 | 83.90 77 | 94.16 135 | 95.74 118 | 88.96 49 | 87.86 102 | 95.43 81 | 72.48 184 | 97.91 128 | 88.10 80 | 90.18 156 | 93.65 213 |
|
HQP-NCC | | | | | | 94.17 140 | | 94.39 120 | | 88.81 50 | 85.43 166 | | | | | | |
|
ACMP_Plane | | | | | | 94.17 140 | | 94.39 120 | | 88.81 50 | 85.43 166 | | | | | | |
|
HQP-MVS | | | 89.80 97 | 89.28 95 | 91.34 128 | 94.17 140 | 81.56 127 | 94.39 120 | 96.04 98 | 88.81 50 | 85.43 166 | 93.97 123 | 73.83 165 | 97.96 124 | 87.11 95 | 89.77 162 | 94.50 173 |
|
MVS_111021_HR | | | 93.45 37 | 93.31 36 | 93.84 49 | 96.99 53 | 84.84 54 | 93.24 192 | 97.24 18 | 88.76 53 | 91.60 66 | 95.85 71 | 86.07 29 | 98.66 78 | 91.91 40 | 98.16 43 | 98.03 47 |
|
mPP-MVS | | | 93.99 27 | 93.78 29 | 94.63 28 | 98.50 7 | 85.90 45 | 96.87 16 | 96.91 40 | 88.70 54 | 91.83 62 | 97.17 20 | 83.96 50 | 99.55 5 | 91.44 49 | 98.64 29 | 98.43 18 |
|
VPNet | | | 88.20 138 | 87.47 134 | 90.39 163 | 93.56 164 | 79.46 183 | 94.04 148 | 95.54 132 | 88.67 55 | 86.96 121 | 94.58 105 | 69.33 221 | 97.15 191 | 84.05 128 | 80.53 268 | 94.56 169 |
|
HFP-MVS | | | 94.52 10 | 94.40 11 | 94.86 13 | 98.61 3 | 86.81 14 | 96.94 10 | 97.34 10 | 88.63 56 | 93.65 21 | 97.21 16 | 86.10 27 | 99.49 14 | 92.35 27 | 98.77 12 | 98.30 24 |
|
ACMMPR | | | 94.43 14 | 94.28 14 | 94.91 11 | 98.63 2 | 86.69 19 | 96.94 10 | 97.32 15 | 88.63 56 | 93.53 28 | 97.26 13 | 85.04 40 | 99.54 8 | 92.35 27 | 98.78 11 | 98.50 9 |
|
region2R | | | 94.43 14 | 94.27 15 | 94.92 10 | 98.65 1 | 86.67 21 | 96.92 14 | 97.23 20 | 88.60 58 | 93.58 25 | 97.27 11 | 85.22 37 | 99.54 8 | 92.21 29 | 98.74 16 | 98.56 8 |
|
WR-MVS | | | 88.38 132 | 87.67 131 | 90.52 158 | 93.30 170 | 80.18 162 | 93.26 190 | 95.96 102 | 88.57 59 | 85.47 162 | 92.81 164 | 76.12 122 | 96.91 209 | 81.24 163 | 82.29 238 | 94.47 176 |
|
CP-MVS | | | 94.34 17 | 94.21 18 | 94.74 25 | 98.39 14 | 86.64 23 | 97.60 1 | 97.24 18 | 88.53 60 | 92.73 41 | 97.23 14 | 85.20 38 | 99.32 25 | 92.15 32 | 98.83 8 | 98.25 32 |
|
CP-MVSNet | | | 87.63 161 | 87.26 140 | 88.74 221 | 93.12 175 | 76.59 250 | 95.29 61 | 96.58 68 | 88.43 61 | 83.49 211 | 92.98 158 | 75.28 145 | 95.83 251 | 78.97 204 | 81.15 257 | 93.79 203 |
|
VDD-MVS | | | 90.74 76 | 89.92 84 | 93.20 61 | 96.27 69 | 83.02 100 | 95.73 44 | 93.86 216 | 88.42 62 | 92.53 46 | 96.84 29 | 62.09 270 | 98.64 80 | 90.95 55 | 92.62 134 | 97.93 54 |
|
ACMMP | | | 93.24 46 | 92.88 47 | 94.30 40 | 98.09 25 | 85.33 51 | 96.86 17 | 97.45 7 | 88.33 63 | 90.15 81 | 97.03 24 | 81.44 72 | 99.51 12 | 90.85 57 | 95.74 81 | 98.04 46 |
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 |
nrg030 | | | 91.08 73 | 90.39 73 | 93.17 63 | 93.07 176 | 86.91 12 | 96.41 24 | 96.26 81 | 88.30 64 | 88.37 98 | 94.85 97 | 82.19 64 | 97.64 140 | 91.09 51 | 82.95 232 | 94.96 146 |
|
ACMMP_Plus | | | 94.74 9 | 94.56 10 | 95.28 4 | 98.02 28 | 87.70 4 | 95.68 47 | 97.34 10 | 88.28 65 | 95.30 8 | 97.67 3 | 85.90 31 | 99.54 8 | 93.91 9 | 98.95 2 | 98.60 6 |
|
PS-CasMVS | | | 87.32 174 | 86.88 152 | 88.63 224 | 92.99 181 | 76.33 253 | 95.33 56 | 96.61 65 | 88.22 66 | 83.30 214 | 93.07 152 | 73.03 176 | 95.79 254 | 78.36 209 | 81.00 262 | 93.75 209 |
|
MVS_111021_LR | | | 92.47 54 | 92.29 54 | 92.98 71 | 95.99 83 | 84.43 69 | 93.08 197 | 96.09 93 | 88.20 67 | 91.12 72 | 95.72 76 | 81.33 74 | 97.76 133 | 91.74 44 | 97.37 59 | 96.75 93 |
|
TSAR-MVS + MP. | | | 94.85 7 | 94.94 6 | 94.58 30 | 98.25 19 | 86.33 32 | 96.11 31 | 96.62 64 | 88.14 68 | 96.10 3 | 96.96 26 | 89.09 7 | 98.94 63 | 94.48 4 | 98.68 22 | 98.48 11 |
|
PEN-MVS | | | 86.80 187 | 86.27 176 | 88.40 237 | 92.32 191 | 75.71 257 | 95.18 72 | 96.38 77 | 87.97 69 | 82.82 218 | 93.15 148 | 73.39 172 | 95.92 247 | 76.15 231 | 79.03 281 | 93.59 220 |
|
testdata1 | | | | | | | | 92.15 227 | | 87.94 70 | | | | | | | |
|
VPA-MVSNet | | | 89.62 99 | 88.96 101 | 91.60 122 | 93.86 154 | 82.89 105 | 95.46 55 | 97.33 13 | 87.91 71 | 88.43 97 | 93.31 141 | 74.17 159 | 97.40 170 | 87.32 91 | 82.86 234 | 94.52 171 |
|
WR-MVS_H | | | 87.80 154 | 87.37 136 | 89.10 216 | 93.23 172 | 78.12 228 | 95.61 52 | 97.30 16 | 87.90 72 | 83.72 204 | 92.01 190 | 79.65 92 | 96.01 244 | 76.36 227 | 80.54 267 | 93.16 234 |
|
CLD-MVS | | | 89.47 105 | 88.90 104 | 91.18 133 | 94.22 139 | 82.07 122 | 92.13 228 | 96.09 93 | 87.90 72 | 85.37 172 | 92.45 172 | 74.38 154 | 97.56 143 | 87.15 93 | 90.43 152 | 93.93 193 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
abl_6 | | | 93.18 48 | 93.05 41 | 93.57 57 | 97.52 36 | 84.27 72 | 95.53 54 | 96.67 60 | 87.85 74 | 93.20 31 | 97.22 15 | 80.35 79 | 99.18 32 | 91.91 40 | 97.21 60 | 97.26 73 |
|
MG-MVS | | | 91.77 60 | 91.70 57 | 92.00 105 | 97.08 52 | 80.03 168 | 93.60 176 | 95.18 165 | 87.85 74 | 90.89 74 | 96.47 48 | 82.06 67 | 98.36 93 | 85.07 110 | 97.04 63 | 97.62 64 |
|
LCM-MVSNet-Re | | | 88.30 136 | 88.32 118 | 88.27 240 | 94.71 122 | 72.41 282 | 93.15 193 | 90.98 277 | 87.77 76 | 79.25 259 | 91.96 191 | 78.35 103 | 95.75 255 | 83.04 136 | 95.62 82 | 96.65 95 |
|
Effi-MVS+-dtu | | | 88.65 128 | 88.35 115 | 89.54 203 | 93.33 168 | 76.39 251 | 94.47 113 | 94.36 195 | 87.70 77 | 85.43 166 | 89.56 250 | 73.45 170 | 97.26 183 | 85.57 107 | 91.28 141 | 94.97 143 |
|
mvs-test1 | | | 89.45 106 | 89.14 97 | 90.38 165 | 93.33 168 | 77.63 242 | 94.95 83 | 94.36 195 | 87.70 77 | 87.10 120 | 92.81 164 | 73.45 170 | 98.03 121 | 85.57 107 | 93.04 128 | 95.48 130 |
|
test_prior3 | | | 93.60 35 | 93.53 34 | 93.82 50 | 97.29 46 | 84.49 62 | 94.12 136 | 96.88 42 | 87.67 79 | 92.63 43 | 96.39 50 | 86.62 23 | 98.87 65 | 91.50 47 | 98.67 24 | 98.11 41 |
|
test_prior2 | | | | | | | | 94.12 136 | | 87.67 79 | 92.63 43 | 96.39 50 | 86.62 23 | | 91.50 47 | 98.67 24 | |
|
Vis-MVSNet (Re-imp) | | | 89.59 101 | 89.44 90 | 90.03 186 | 95.74 89 | 75.85 256 | 95.61 52 | 90.80 282 | 87.66 81 | 87.83 108 | 95.40 82 | 76.79 115 | 96.46 227 | 78.37 208 | 96.73 67 | 97.80 60 |
|
#test# | | | 94.32 19 | 94.14 20 | 94.86 13 | 98.61 3 | 86.81 14 | 96.43 23 | 97.34 10 | 87.51 82 | 93.65 21 | 97.21 16 | 86.10 27 | 99.49 14 | 91.68 45 | 98.77 12 | 98.30 24 |
|
PGM-MVS | | | 93.96 28 | 93.72 31 | 94.68 26 | 98.43 11 | 86.22 37 | 95.30 59 | 97.78 1 | 87.45 83 | 93.26 29 | 97.33 9 | 84.62 45 | 99.51 12 | 90.75 58 | 98.57 32 | 98.32 23 |
|
DTE-MVSNet | | | 86.11 200 | 85.48 194 | 87.98 247 | 91.65 207 | 74.92 260 | 94.93 85 | 95.75 117 | 87.36 84 | 82.26 223 | 93.04 153 | 72.85 177 | 95.82 252 | 74.04 247 | 77.46 286 | 93.20 232 |
|
MCST-MVS | | | 94.45 12 | 94.20 19 | 95.19 5 | 98.46 10 | 87.50 8 | 95.00 81 | 97.12 25 | 87.13 85 | 92.51 48 | 96.30 52 | 89.24 6 | 99.34 21 | 93.46 12 | 98.62 30 | 98.73 3 |
|
Effi-MVS+ | | | 91.59 65 | 91.11 63 | 93.01 70 | 94.35 138 | 83.39 91 | 94.60 106 | 95.10 167 | 87.10 86 | 90.57 76 | 93.10 151 | 81.43 73 | 98.07 118 | 89.29 67 | 94.48 102 | 97.59 66 |
|
view600 | | | 87.62 162 | 86.65 163 | 90.53 152 | 96.19 71 | 78.52 214 | 95.29 61 | 91.09 269 | 87.08 87 | 87.84 104 | 93.03 154 | 68.86 228 | 98.11 108 | 69.44 272 | 91.02 146 | 94.96 146 |
|
view800 | | | 87.62 162 | 86.65 163 | 90.53 152 | 96.19 71 | 78.52 214 | 95.29 61 | 91.09 269 | 87.08 87 | 87.84 104 | 93.03 154 | 68.86 228 | 98.11 108 | 69.44 272 | 91.02 146 | 94.96 146 |
|
conf0.05thres1000 | | | 87.62 162 | 86.65 163 | 90.53 152 | 96.19 71 | 78.52 214 | 95.29 61 | 91.09 269 | 87.08 87 | 87.84 104 | 93.03 154 | 68.86 228 | 98.11 108 | 69.44 272 | 91.02 146 | 94.96 146 |
|
tfpn | | | 87.62 162 | 86.65 163 | 90.53 152 | 96.19 71 | 78.52 214 | 95.29 61 | 91.09 269 | 87.08 87 | 87.84 104 | 93.03 154 | 68.86 228 | 98.11 108 | 69.44 272 | 91.02 146 | 94.96 146 |
|
thres600view7 | | | 87.65 158 | 86.67 162 | 90.59 149 | 96.08 79 | 78.72 210 | 94.88 89 | 91.58 260 | 87.06 91 | 88.08 100 | 92.30 178 | 68.91 227 | 98.10 112 | 70.05 270 | 91.10 142 | 94.96 146 |
|
APD-MVS_3200maxsize | | | 93.78 31 | 93.77 30 | 93.80 53 | 97.92 29 | 84.19 73 | 96.30 26 | 96.87 44 | 86.96 92 | 93.92 19 | 97.47 6 | 83.88 51 | 98.96 62 | 92.71 22 | 97.87 50 | 98.26 31 |
|
OMC-MVS | | | 91.23 69 | 90.62 72 | 93.08 66 | 96.27 69 | 84.07 75 | 93.52 178 | 95.93 103 | 86.95 93 | 89.51 86 | 96.13 63 | 78.50 101 | 98.35 95 | 85.84 105 | 92.90 131 | 96.83 91 |
|
thres400 | | | 87.62 162 | 86.64 167 | 90.57 150 | 95.99 83 | 78.64 212 | 94.58 107 | 91.98 252 | 86.94 94 | 88.09 99 | 91.77 195 | 69.18 226 | 98.10 112 | 70.13 269 | 91.10 142 | 94.96 146 |
|
HPM-MVS | | | 94.02 26 | 93.88 26 | 94.43 36 | 98.39 14 | 85.78 47 | 97.25 5 | 97.07 29 | 86.90 95 | 92.62 45 | 96.80 33 | 84.85 44 | 99.17 33 | 92.43 24 | 98.65 28 | 98.33 22 |
|
LFMVS | | | 90.08 89 | 89.13 98 | 92.95 72 | 96.71 58 | 82.32 119 | 96.08 32 | 89.91 298 | 86.79 96 | 92.15 56 | 96.81 31 | 62.60 267 | 98.34 96 | 87.18 92 | 93.90 110 | 98.19 34 |
|
LPG-MVS_test | | | 89.45 106 | 88.90 104 | 91.12 134 | 94.47 131 | 81.49 130 | 95.30 59 | 96.14 89 | 86.73 97 | 85.45 163 | 95.16 87 | 69.89 214 | 98.10 112 | 87.70 84 | 89.23 171 | 93.77 207 |
|
LGP-MVS_train | | | | | 91.12 134 | 94.47 131 | 81.49 130 | | 96.14 89 | 86.73 97 | 85.45 163 | 95.16 87 | 69.89 214 | 98.10 112 | 87.70 84 | 89.23 171 | 93.77 207 |
|
EPNet_dtu | | | 86.49 196 | 85.94 185 | 88.14 245 | 90.24 272 | 72.82 274 | 94.11 138 | 92.20 244 | 86.66 99 | 79.42 258 | 92.36 176 | 73.52 168 | 95.81 253 | 71.26 260 | 93.66 113 | 95.80 122 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
ACMP | | 84.23 8 | 89.01 122 | 88.35 115 | 90.99 143 | 94.73 120 | 81.27 136 | 95.07 78 | 95.89 109 | 86.48 100 | 83.67 206 | 94.30 110 | 69.33 221 | 97.99 123 | 87.10 97 | 88.55 178 | 93.72 211 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
MVS_Test | | | 91.31 68 | 91.11 63 | 91.93 109 | 94.37 135 | 80.14 164 | 93.46 181 | 95.80 113 | 86.46 101 | 91.35 69 | 93.77 133 | 82.21 63 | 98.09 116 | 87.57 86 | 94.95 93 | 97.55 69 |
|
thres200 | | | 87.21 180 | 86.24 177 | 90.12 177 | 95.36 99 | 78.53 213 | 93.26 190 | 92.10 245 | 86.42 102 | 88.00 101 | 91.11 222 | 69.24 225 | 98.00 122 | 69.58 271 | 91.04 145 | 93.83 202 |
|
PAPM_NR | | | 91.22 70 | 90.78 71 | 92.52 86 | 97.60 33 | 81.46 132 | 94.37 124 | 96.24 84 | 86.39 103 | 87.41 114 | 94.80 99 | 82.06 67 | 98.48 89 | 82.80 141 | 95.37 88 | 97.61 65 |
|
PS-MVSNAJ | | | 91.18 71 | 90.92 67 | 91.96 107 | 95.26 105 | 82.60 116 | 92.09 230 | 95.70 120 | 86.27 104 | 91.84 60 | 92.46 171 | 79.70 88 | 98.99 57 | 89.08 68 | 95.86 80 | 94.29 179 |
|
MP-MVS-pluss | | | 94.21 23 | 94.00 25 | 94.85 15 | 98.17 22 | 86.65 22 | 94.82 92 | 97.17 23 | 86.26 105 | 92.83 36 | 97.87 2 | 85.57 34 | 99.56 1 | 94.37 6 | 98.92 4 | 98.34 21 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
PS-MVSNAJss | | | 89.97 92 | 89.62 86 | 91.02 141 | 91.90 197 | 80.85 151 | 95.26 68 | 95.98 100 | 86.26 105 | 86.21 137 | 94.29 111 | 79.70 88 | 97.65 138 | 88.87 71 | 88.10 187 | 94.57 168 |
|
EPP-MVSNet | | | 91.70 63 | 91.56 58 | 92.13 102 | 95.88 85 | 80.50 160 | 97.33 3 | 95.25 158 | 86.15 107 | 89.76 84 | 95.60 78 | 83.42 52 | 98.32 98 | 87.37 90 | 93.25 124 | 97.56 68 |
|
XVG-OURS | | | 89.40 111 | 88.70 107 | 91.52 123 | 94.06 143 | 81.46 132 | 91.27 245 | 96.07 95 | 86.14 108 | 88.89 93 | 95.77 74 | 68.73 233 | 97.26 183 | 87.39 89 | 89.96 159 | 95.83 120 |
|
xiu_mvs_v2_base | | | 91.13 72 | 90.89 69 | 91.86 112 | 94.97 112 | 82.42 117 | 92.24 224 | 95.64 126 | 86.11 109 | 91.74 65 | 93.14 149 | 79.67 91 | 98.89 64 | 89.06 69 | 95.46 87 | 94.28 180 |
|
Fast-Effi-MVS+-dtu | | | 87.44 171 | 86.72 160 | 89.63 201 | 92.04 196 | 77.68 241 | 94.03 149 | 93.94 214 | 85.81 110 | 82.42 221 | 91.32 212 | 70.33 211 | 97.06 199 | 80.33 181 | 90.23 155 | 94.14 184 |
|
XVG-OURS-SEG-HR | | | 89.95 93 | 89.45 89 | 91.47 125 | 94.00 149 | 81.21 140 | 91.87 232 | 96.06 97 | 85.78 111 | 88.55 95 | 95.73 75 | 74.67 152 | 97.27 181 | 88.71 72 | 89.64 164 | 95.91 115 |
|
HPM-MVS_fast | | | 93.40 40 | 93.22 38 | 93.94 47 | 98.36 16 | 84.83 55 | 97.15 7 | 96.80 48 | 85.77 112 | 92.47 49 | 97.13 22 | 82.38 59 | 99.07 42 | 90.51 60 | 98.40 37 | 97.92 55 |
|
EI-MVSNet | | | 89.10 116 | 88.86 106 | 89.80 196 | 91.84 199 | 78.30 224 | 93.70 171 | 95.01 170 | 85.73 113 | 87.15 118 | 95.28 83 | 79.87 85 | 97.21 189 | 83.81 131 | 87.36 195 | 93.88 197 |
|
IterMVS-LS | | | 88.36 134 | 87.91 129 | 89.70 199 | 93.80 157 | 78.29 225 | 93.73 167 | 95.08 169 | 85.73 113 | 84.75 180 | 91.90 193 | 79.88 84 | 96.92 208 | 83.83 130 | 82.51 236 | 93.89 195 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
APD-MVS | | | 94.24 21 | 94.07 23 | 94.75 24 | 98.06 26 | 86.90 13 | 95.88 39 | 96.94 38 | 85.68 115 | 95.05 9 | 97.18 19 | 87.31 17 | 99.07 42 | 91.90 43 | 98.61 31 | 98.28 26 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
K. test v3 | | | 81.59 260 | 80.15 261 | 85.91 279 | 89.89 280 | 69.42 301 | 92.57 214 | 87.71 314 | 85.56 116 | 73.44 296 | 89.71 247 | 55.58 297 | 95.52 261 | 77.17 222 | 69.76 311 | 92.78 246 |
|
SixPastTwentyTwo | | | 83.91 241 | 82.90 240 | 86.92 269 | 90.99 245 | 70.67 294 | 93.48 179 | 91.99 251 | 85.54 117 | 77.62 268 | 92.11 184 | 60.59 281 | 96.87 211 | 76.05 232 | 77.75 284 | 93.20 232 |
|
ITE_SJBPF | | | | | 88.24 242 | 91.88 198 | 77.05 246 | | 92.92 229 | 85.54 117 | 80.13 252 | 93.30 142 | 57.29 294 | 96.20 237 | 72.46 256 | 84.71 215 | 91.49 272 |
|
BH-RMVSNet | | | 88.37 133 | 87.48 133 | 91.02 141 | 95.28 103 | 79.45 185 | 92.89 205 | 93.07 228 | 85.45 119 | 86.91 123 | 94.84 98 | 70.35 210 | 97.76 133 | 73.97 248 | 94.59 99 | 95.85 118 |
|
semantic-postprocess | | | | | 88.18 244 | 91.71 204 | 76.87 248 | | 92.65 237 | 85.40 120 | 81.44 235 | 90.54 233 | 66.21 251 | 95.00 282 | 81.04 165 | 81.05 258 | 92.66 248 |
|
GA-MVS | | | 86.61 192 | 85.27 199 | 90.66 148 | 91.33 226 | 78.71 211 | 90.40 251 | 93.81 219 | 85.34 121 | 85.12 175 | 89.57 249 | 61.25 276 | 97.11 195 | 80.99 168 | 89.59 165 | 96.15 105 |
|
ACMM | | 84.12 9 | 89.14 115 | 88.48 114 | 91.12 134 | 94.65 126 | 81.22 139 | 95.31 57 | 96.12 92 | 85.31 122 | 85.92 141 | 94.34 107 | 70.19 213 | 98.06 119 | 85.65 106 | 88.86 176 | 94.08 189 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
xiu_mvs_v1_base_debu | | | 90.64 79 | 90.05 81 | 92.40 90 | 93.97 151 | 84.46 65 | 93.32 183 | 95.46 139 | 85.17 123 | 92.25 51 | 94.03 117 | 70.59 205 | 98.57 85 | 90.97 52 | 94.67 95 | 94.18 181 |
|
xiu_mvs_v1_base | | | 90.64 79 | 90.05 81 | 92.40 90 | 93.97 151 | 84.46 65 | 93.32 183 | 95.46 139 | 85.17 123 | 92.25 51 | 94.03 117 | 70.59 205 | 98.57 85 | 90.97 52 | 94.67 95 | 94.18 181 |
|
xiu_mvs_v1_base_debi | | | 90.64 79 | 90.05 81 | 92.40 90 | 93.97 151 | 84.46 65 | 93.32 183 | 95.46 139 | 85.17 123 | 92.25 51 | 94.03 117 | 70.59 205 | 98.57 85 | 90.97 52 | 94.67 95 | 94.18 181 |
|
PHI-MVS | | | 93.89 30 | 93.65 32 | 94.62 29 | 96.84 56 | 86.43 29 | 96.69 21 | 97.49 4 | 85.15 126 | 93.56 27 | 96.28 53 | 85.60 33 | 99.31 26 | 92.45 23 | 98.79 9 | 98.12 40 |
|
mvs_tets | | | 88.06 143 | 87.28 139 | 90.38 165 | 90.94 249 | 79.88 171 | 95.22 70 | 95.66 123 | 85.10 127 | 84.21 197 | 93.94 124 | 63.53 265 | 97.40 170 | 88.50 74 | 88.40 185 | 93.87 198 |
|
XVG-ACMP-BASELINE | | | 86.00 202 | 84.84 207 | 89.45 207 | 91.20 236 | 78.00 230 | 91.70 237 | 95.55 130 | 85.05 128 | 82.97 216 | 92.25 180 | 54.49 302 | 97.48 148 | 82.93 138 | 87.45 194 | 92.89 241 |
|
jajsoiax | | | 88.24 137 | 87.50 132 | 90.48 161 | 90.89 253 | 80.14 164 | 95.31 57 | 95.65 125 | 84.97 129 | 84.24 196 | 94.02 120 | 65.31 257 | 97.42 163 | 88.56 73 | 88.52 180 | 93.89 195 |
|
v2v482 | | | 87.84 150 | 87.06 148 | 90.17 170 | 90.99 245 | 79.23 205 | 94.00 152 | 95.13 166 | 84.87 130 | 85.53 157 | 92.07 188 | 74.45 153 | 97.45 152 | 84.71 117 | 81.75 250 | 93.85 201 |
|
v148 | | | 87.04 184 | 86.32 174 | 89.21 213 | 90.94 249 | 77.26 244 | 93.71 170 | 94.43 193 | 84.84 131 | 84.36 192 | 90.80 227 | 76.04 128 | 97.05 200 | 82.12 151 | 79.60 279 | 93.31 229 |
|
v8 | | | 87.50 170 | 86.71 161 | 89.89 191 | 91.37 220 | 79.40 191 | 94.50 110 | 95.38 149 | 84.81 132 | 83.60 208 | 91.33 210 | 76.05 126 | 97.42 163 | 82.84 140 | 80.51 270 | 92.84 243 |
|
BH-untuned | | | 88.60 129 | 88.13 124 | 90.01 188 | 95.24 106 | 78.50 219 | 93.29 188 | 94.15 202 | 84.75 133 | 84.46 186 | 93.40 137 | 75.76 138 | 97.40 170 | 77.59 217 | 94.52 101 | 94.12 185 |
|
OurMVSNet-221017-0 | | | 85.35 214 | 84.64 212 | 87.49 257 | 90.77 256 | 72.59 280 | 94.01 151 | 94.40 194 | 84.72 134 | 79.62 257 | 93.17 147 | 61.91 272 | 96.72 213 | 81.99 154 | 81.16 255 | 93.16 234 |
|
MVSFormer | | | 91.68 64 | 91.30 60 | 92.80 77 | 93.86 154 | 83.88 78 | 95.96 36 | 95.90 107 | 84.66 135 | 91.76 63 | 94.91 92 | 77.92 107 | 97.30 177 | 89.64 65 | 97.11 61 | 97.24 74 |
|
test_djsdf | | | 89.03 120 | 88.64 108 | 90.21 169 | 90.74 258 | 79.28 199 | 95.96 36 | 95.90 107 | 84.66 135 | 85.33 173 | 92.94 159 | 74.02 162 | 97.30 177 | 89.64 65 | 88.53 179 | 94.05 190 |
|
MVSTER | | | 88.84 124 | 88.29 120 | 90.51 159 | 92.95 182 | 80.44 161 | 93.73 167 | 95.01 170 | 84.66 135 | 87.15 118 | 93.12 150 | 72.79 178 | 97.21 189 | 87.86 82 | 87.36 195 | 93.87 198 |
|
v1neww | | | 87.98 144 | 87.25 141 | 90.16 171 | 91.38 218 | 79.41 187 | 94.37 124 | 95.28 154 | 84.48 138 | 85.77 144 | 91.53 206 | 76.12 122 | 97.45 152 | 84.45 121 | 81.89 245 | 93.61 218 |
|
v7new | | | 87.98 144 | 87.25 141 | 90.16 171 | 91.38 218 | 79.41 187 | 94.37 124 | 95.28 154 | 84.48 138 | 85.77 144 | 91.53 206 | 76.12 122 | 97.45 152 | 84.45 121 | 81.89 245 | 93.61 218 |
|
v6 | | | 87.98 144 | 87.25 141 | 90.16 171 | 91.36 221 | 79.39 192 | 94.37 124 | 95.27 157 | 84.48 138 | 85.78 143 | 91.51 208 | 76.15 121 | 97.46 150 | 84.46 120 | 81.88 247 | 93.62 217 |
|
v7n | | | 86.81 186 | 85.76 188 | 89.95 190 | 90.72 259 | 79.25 201 | 95.07 78 | 95.92 104 | 84.45 141 | 82.29 222 | 90.86 226 | 72.60 182 | 97.53 145 | 79.42 201 | 80.52 269 | 93.08 238 |
|
v1141 | | | 87.84 150 | 87.09 145 | 90.11 182 | 91.23 233 | 79.25 201 | 94.08 142 | 95.24 159 | 84.44 142 | 85.69 151 | 91.31 213 | 75.91 134 | 97.44 159 | 84.17 126 | 81.74 251 | 93.63 216 |
|
divwei89l23v2f112 | | | 87.84 150 | 87.09 145 | 90.10 184 | 91.23 233 | 79.24 203 | 94.09 140 | 95.24 159 | 84.44 142 | 85.70 149 | 91.31 213 | 75.91 134 | 97.44 159 | 84.17 126 | 81.73 252 | 93.64 214 |
|
v1 | | | 87.85 149 | 87.10 144 | 90.11 182 | 91.21 235 | 79.24 203 | 94.09 140 | 95.24 159 | 84.44 142 | 85.70 149 | 91.31 213 | 75.96 132 | 97.45 152 | 84.18 125 | 81.73 252 | 93.64 214 |
|
v748 | | | 86.27 198 | 85.28 198 | 89.25 212 | 90.26 271 | 77.58 243 | 94.89 87 | 95.50 137 | 84.28 145 | 81.41 236 | 90.46 236 | 72.57 183 | 97.32 176 | 79.81 193 | 78.36 282 | 92.84 243 |
|
CSCG | | | 93.23 47 | 93.05 41 | 93.76 54 | 98.04 27 | 84.07 75 | 96.22 28 | 97.37 9 | 84.15 146 | 90.05 82 | 95.66 77 | 87.77 11 | 99.15 36 | 89.91 63 | 98.27 40 | 98.07 43 |
|
Baseline_NR-MVSNet | | | 87.07 183 | 86.63 168 | 88.40 237 | 91.44 211 | 77.87 235 | 94.23 133 | 92.57 238 | 84.12 147 | 85.74 148 | 92.08 186 | 77.25 111 | 96.04 241 | 82.29 150 | 79.94 276 | 91.30 276 |
|
ab-mvs | | | 89.41 109 | 88.35 115 | 92.60 82 | 95.15 107 | 82.65 114 | 92.20 226 | 95.60 127 | 83.97 148 | 88.55 95 | 93.70 136 | 74.16 160 | 98.21 102 | 82.46 147 | 89.37 167 | 96.94 87 |
|
FMVSNet3 | | | 87.40 173 | 86.11 179 | 91.30 130 | 93.79 159 | 83.64 84 | 94.20 134 | 94.81 184 | 83.89 149 | 84.37 189 | 91.87 194 | 68.45 238 | 96.56 220 | 78.23 211 | 85.36 209 | 93.70 212 |
|
test_normal | | | 88.13 141 | 86.78 159 | 92.18 99 | 90.55 266 | 81.19 141 | 92.74 208 | 94.64 188 | 83.84 150 | 77.49 269 | 90.51 235 | 68.49 237 | 98.16 104 | 88.22 76 | 94.55 100 | 97.21 77 |
|
pm-mvs1 | | | 86.61 192 | 85.54 190 | 89.82 193 | 91.44 211 | 80.18 162 | 95.28 67 | 94.85 181 | 83.84 150 | 81.66 233 | 92.62 169 | 72.45 186 | 96.48 225 | 79.67 195 | 78.06 283 | 92.82 245 |
|
DI_MVS_plusplus_test | | | 88.15 140 | 86.82 155 | 92.14 101 | 90.67 261 | 81.07 143 | 93.01 200 | 94.59 189 | 83.83 152 | 77.78 266 | 90.63 230 | 68.51 236 | 98.16 104 | 88.02 81 | 94.37 106 | 97.17 79 |
|
v10 | | | 87.25 177 | 86.38 171 | 89.85 192 | 91.19 238 | 79.50 178 | 94.48 111 | 95.45 143 | 83.79 153 | 83.62 207 | 91.19 217 | 75.13 146 | 97.42 163 | 81.94 155 | 80.60 266 | 92.63 249 |
|
testgi | | | 80.94 270 | 80.20 260 | 83.18 294 | 87.96 299 | 66.29 309 | 91.28 244 | 90.70 285 | 83.70 154 | 78.12 263 | 92.84 161 | 51.37 308 | 90.82 312 | 63.34 299 | 82.46 237 | 92.43 254 |
|
V42 | | | 87.68 157 | 86.86 153 | 90.15 175 | 90.58 263 | 80.14 164 | 94.24 132 | 95.28 154 | 83.66 155 | 85.67 152 | 91.33 210 | 74.73 151 | 97.41 168 | 84.43 123 | 81.83 248 | 92.89 241 |
|
v52 | | | 86.50 194 | 85.53 193 | 89.39 208 | 89.17 284 | 78.99 208 | 94.72 100 | 95.54 132 | 83.59 156 | 82.10 226 | 90.60 232 | 71.59 191 | 97.45 152 | 82.52 143 | 79.99 275 | 91.73 268 |
|
V4 | | | 86.50 194 | 85.54 190 | 89.39 208 | 89.13 285 | 78.99 208 | 94.73 97 | 95.54 132 | 83.59 156 | 82.10 226 | 90.61 231 | 71.60 190 | 97.45 152 | 82.52 143 | 80.01 274 | 91.74 267 |
|
GBi-Net | | | 87.26 175 | 85.98 183 | 91.08 137 | 94.01 146 | 83.10 96 | 95.14 75 | 94.94 174 | 83.57 158 | 84.37 189 | 91.64 197 | 66.59 247 | 96.34 233 | 78.23 211 | 85.36 209 | 93.79 203 |
|
test1 | | | 87.26 175 | 85.98 183 | 91.08 137 | 94.01 146 | 83.10 96 | 95.14 75 | 94.94 174 | 83.57 158 | 84.37 189 | 91.64 197 | 66.59 247 | 96.34 233 | 78.23 211 | 85.36 209 | 93.79 203 |
|
FMVSNet2 | | | 87.19 181 | 85.82 187 | 91.30 130 | 94.01 146 | 83.67 83 | 94.79 94 | 94.94 174 | 83.57 158 | 83.88 200 | 92.05 189 | 66.59 247 | 96.51 223 | 77.56 218 | 85.01 213 | 93.73 210 |
|
Patchmatch-test1 | | | 85.81 207 | 84.71 209 | 89.12 214 | 92.15 192 | 76.60 249 | 91.12 248 | 91.69 258 | 83.53 161 | 85.50 160 | 88.56 262 | 66.79 245 | 95.00 282 | 72.69 255 | 90.35 154 | 95.76 123 |
|
PVSNet_BlendedMVS | | | 89.98 91 | 89.70 85 | 90.82 146 | 96.12 76 | 81.25 137 | 93.92 155 | 96.83 45 | 83.49 162 | 89.10 90 | 92.26 179 | 81.04 76 | 98.85 71 | 86.72 101 | 87.86 191 | 92.35 258 |
|
v7 | | | 87.75 155 | 86.96 151 | 90.12 177 | 91.20 236 | 79.50 178 | 94.28 130 | 95.46 139 | 83.45 163 | 85.75 146 | 91.56 205 | 75.13 146 | 97.43 161 | 83.60 132 | 82.18 240 | 93.42 227 |
|
test-LLR | | | 85.87 204 | 85.41 195 | 87.25 262 | 90.95 247 | 71.67 285 | 89.55 262 | 89.88 299 | 83.41 164 | 84.54 184 | 87.95 270 | 67.25 242 | 95.11 279 | 81.82 157 | 93.37 122 | 94.97 143 |
|
test0.0.03 1 | | | 82.41 254 | 81.69 247 | 84.59 288 | 88.23 294 | 72.89 273 | 90.24 253 | 87.83 313 | 83.41 164 | 79.86 254 | 89.78 246 | 67.25 242 | 88.99 315 | 65.18 294 | 83.42 230 | 91.90 265 |
|
Test4 | | | 85.75 209 | 83.72 223 | 91.83 114 | 88.08 297 | 81.03 145 | 92.48 216 | 95.54 132 | 83.38 166 | 73.40 297 | 88.57 261 | 50.99 309 | 97.37 174 | 86.61 103 | 94.47 103 | 97.09 83 |
|
v1144 | | | 87.61 167 | 86.79 158 | 90.06 185 | 91.01 244 | 79.34 195 | 93.95 154 | 95.42 148 | 83.36 167 | 85.66 153 | 91.31 213 | 74.98 150 | 97.42 163 | 83.37 133 | 82.06 241 | 93.42 227 |
|
PVSNet_Blended_VisFu | | | 91.38 67 | 90.91 68 | 92.80 77 | 96.39 66 | 83.17 95 | 94.87 90 | 96.66 61 | 83.29 168 | 89.27 88 | 94.46 106 | 80.29 81 | 99.17 33 | 87.57 86 | 95.37 88 | 96.05 112 |
|
IB-MVS | | 80.51 15 | 85.24 217 | 83.26 236 | 91.19 132 | 92.13 194 | 79.86 172 | 91.75 234 | 91.29 268 | 83.28 169 | 80.66 244 | 88.49 263 | 61.28 275 | 98.46 90 | 80.99 168 | 79.46 280 | 95.25 137 |
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 |
IterMVS | | | 84.88 226 | 83.98 218 | 87.60 253 | 91.44 211 | 76.03 255 | 90.18 255 | 92.41 240 | 83.24 170 | 81.06 240 | 90.42 237 | 66.60 246 | 94.28 287 | 79.46 197 | 80.98 263 | 92.48 252 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
Fast-Effi-MVS+ | | | 89.41 109 | 88.64 108 | 91.71 119 | 94.74 119 | 80.81 152 | 93.54 177 | 95.10 167 | 83.11 171 | 86.82 126 | 90.67 229 | 79.74 87 | 97.75 136 | 80.51 177 | 93.55 115 | 96.57 97 |
|
WTY-MVS | | | 89.60 100 | 88.92 103 | 91.67 120 | 95.47 97 | 81.15 142 | 92.38 220 | 94.78 185 | 83.11 171 | 89.06 92 | 94.32 109 | 78.67 98 | 96.61 219 | 81.57 161 | 90.89 150 | 97.24 74 |
|
V9 | | | 84.77 230 | 83.50 231 | 88.58 228 | 91.33 226 | 79.46 183 | 93.75 165 | 94.00 211 | 83.07 173 | 76.07 282 | 86.43 285 | 75.97 131 | 95.37 271 | 79.91 190 | 70.93 307 | 90.91 285 |
|
v15 | | | 84.79 228 | 83.53 229 | 88.57 231 | 91.30 232 | 79.41 187 | 93.70 171 | 94.01 208 | 83.06 174 | 76.27 276 | 86.42 288 | 76.03 129 | 95.38 270 | 80.01 185 | 71.00 304 | 90.92 284 |
|
v12 | | | 84.74 231 | 83.46 232 | 88.58 228 | 91.32 228 | 79.50 178 | 93.75 165 | 94.01 208 | 83.06 174 | 75.98 284 | 86.41 289 | 75.82 137 | 95.36 273 | 79.87 191 | 70.89 308 | 90.89 287 |
|
V14 | | | 84.79 228 | 83.52 230 | 88.57 231 | 91.32 228 | 79.43 186 | 93.72 169 | 94.01 208 | 83.06 174 | 76.22 277 | 86.43 285 | 76.01 130 | 95.37 271 | 79.96 187 | 70.99 305 | 90.91 285 |
|
v17 | | | 84.93 225 | 83.70 224 | 88.62 225 | 91.36 221 | 79.48 181 | 93.83 158 | 94.03 207 | 83.04 177 | 76.51 275 | 86.57 284 | 76.05 126 | 95.42 268 | 80.31 183 | 71.65 301 | 90.96 281 |
|
v16 | | | 84.96 223 | 83.74 222 | 88.62 225 | 91.40 216 | 79.48 181 | 93.83 158 | 94.04 205 | 83.03 178 | 76.54 274 | 86.59 283 | 76.11 125 | 95.42 268 | 80.33 181 | 71.80 299 | 90.95 283 |
|
v13 | | | 84.72 233 | 83.44 234 | 88.58 228 | 91.31 231 | 79.52 177 | 93.77 163 | 94.00 211 | 83.03 178 | 75.85 285 | 86.38 290 | 75.84 136 | 95.35 274 | 79.83 192 | 70.95 306 | 90.87 288 |
|
LTVRE_ROB | | 82.13 13 | 86.26 199 | 84.90 205 | 90.34 167 | 94.44 134 | 81.50 129 | 92.31 222 | 94.89 179 | 83.03 178 | 79.63 256 | 92.67 167 | 69.69 217 | 97.79 131 | 71.20 261 | 86.26 203 | 91.72 269 |
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 |
v18 | | | 84.97 222 | 83.76 220 | 88.60 227 | 91.36 221 | 79.41 187 | 93.82 160 | 94.04 205 | 83.00 181 | 76.61 273 | 86.60 282 | 76.19 120 | 95.43 267 | 80.39 178 | 71.79 300 | 90.96 281 |
|
UnsupCasMVSNet_eth | | | 80.07 273 | 78.27 275 | 85.46 282 | 85.24 307 | 72.63 279 | 88.45 279 | 94.87 180 | 82.99 182 | 71.64 305 | 88.07 269 | 56.34 296 | 91.75 309 | 73.48 252 | 63.36 322 | 92.01 264 |
|
XXY-MVS | | | 87.65 158 | 86.85 154 | 90.03 186 | 92.14 193 | 80.60 157 | 93.76 164 | 95.23 162 | 82.94 183 | 84.60 182 | 94.02 120 | 74.27 155 | 95.49 265 | 81.04 165 | 83.68 225 | 94.01 192 |
|
testing_2 | | | 83.40 247 | 81.02 252 | 90.56 151 | 85.06 308 | 80.51 159 | 91.37 243 | 95.57 128 | 82.92 184 | 67.06 313 | 85.54 296 | 49.47 312 | 97.24 185 | 86.74 98 | 85.44 208 | 93.93 193 |
|
mvs_anonymous | | | 89.37 112 | 89.32 93 | 89.51 206 | 93.47 165 | 74.22 262 | 91.65 239 | 94.83 183 | 82.91 185 | 85.45 163 | 93.79 132 | 81.23 75 | 96.36 232 | 86.47 104 | 94.09 108 | 97.94 51 |
|
BH-w/o | | | 87.57 168 | 87.05 149 | 89.12 214 | 94.90 116 | 77.90 233 | 92.41 218 | 93.51 222 | 82.89 186 | 83.70 205 | 91.34 209 | 75.75 139 | 97.07 198 | 75.49 234 | 93.49 117 | 92.39 256 |
|
v11 | | | 84.67 235 | 83.41 235 | 88.44 236 | 91.32 228 | 79.13 206 | 93.69 174 | 93.99 213 | 82.81 187 | 76.20 278 | 86.24 292 | 75.48 142 | 95.35 274 | 79.53 196 | 71.48 303 | 90.85 289 |
|
AdaColmap | | | 89.89 96 | 89.07 99 | 92.37 93 | 97.41 40 | 83.03 99 | 94.42 117 | 95.92 104 | 82.81 187 | 86.34 135 | 94.65 102 | 73.89 163 | 99.02 51 | 80.69 172 | 95.51 84 | 95.05 140 |
|
TransMVSNet (Re) | | | 84.43 237 | 83.06 238 | 88.54 233 | 91.72 203 | 78.44 220 | 95.18 72 | 92.82 232 | 82.73 189 | 79.67 255 | 92.12 182 | 73.49 169 | 95.96 246 | 71.10 264 | 68.73 315 | 91.21 277 |
|
PatchFormer-LS_test | | | 86.02 201 | 85.13 200 | 88.70 222 | 91.52 208 | 74.12 265 | 91.19 247 | 92.09 246 | 82.71 190 | 84.30 195 | 87.24 279 | 70.87 200 | 96.98 204 | 81.04 165 | 85.17 212 | 95.00 142 |
|
DP-MVS Recon | | | 91.95 58 | 91.28 61 | 93.96 46 | 98.33 17 | 85.92 42 | 94.66 105 | 96.66 61 | 82.69 191 | 90.03 83 | 95.82 72 | 82.30 61 | 99.03 48 | 84.57 118 | 96.48 75 | 96.91 88 |
|
v1192 | | | 87.25 177 | 86.33 173 | 90.00 189 | 90.76 257 | 79.04 207 | 93.80 161 | 95.48 138 | 82.57 192 | 85.48 161 | 91.18 218 | 73.38 173 | 97.42 163 | 82.30 149 | 82.06 241 | 93.53 222 |
|
API-MVS | | | 90.66 78 | 90.07 80 | 92.45 89 | 96.36 67 | 84.57 60 | 96.06 33 | 95.22 164 | 82.39 193 | 89.13 89 | 94.27 114 | 80.32 80 | 98.46 90 | 80.16 184 | 96.71 68 | 94.33 178 |
|
MAR-MVS | | | 90.30 85 | 89.37 92 | 93.07 68 | 96.61 60 | 84.48 64 | 95.68 47 | 95.67 121 | 82.36 194 | 87.85 103 | 92.85 160 | 76.63 118 | 98.80 74 | 80.01 185 | 96.68 69 | 95.91 115 |
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 |
TAMVS | | | 89.21 114 | 88.29 120 | 91.96 107 | 93.71 160 | 82.62 115 | 93.30 187 | 94.19 200 | 82.22 195 | 87.78 110 | 93.94 124 | 78.83 95 | 96.95 207 | 77.70 216 | 92.98 129 | 96.32 101 |
|
ACMH+ | | 81.04 14 | 85.05 220 | 83.46 232 | 89.82 193 | 94.66 125 | 79.37 193 | 94.44 115 | 94.12 204 | 82.19 196 | 78.04 264 | 92.82 163 | 58.23 291 | 97.54 144 | 73.77 250 | 82.90 233 | 92.54 250 |
|
ACMH | | 80.38 17 | 85.36 213 | 83.68 225 | 90.39 163 | 94.45 133 | 80.63 155 | 94.73 97 | 94.85 181 | 82.09 197 | 77.24 270 | 92.65 168 | 60.01 285 | 97.58 141 | 72.25 257 | 84.87 214 | 92.96 239 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
anonymousdsp | | | 87.84 150 | 87.09 145 | 90.12 177 | 89.13 285 | 80.54 158 | 94.67 104 | 95.55 130 | 82.05 198 | 83.82 202 | 92.12 182 | 71.47 194 | 97.15 191 | 87.15 93 | 87.80 192 | 92.67 247 |
|
PVSNet_Blended | | | 90.73 77 | 90.32 75 | 91.98 106 | 96.12 76 | 81.25 137 | 92.55 215 | 96.83 45 | 82.04 199 | 89.10 90 | 92.56 170 | 81.04 76 | 98.85 71 | 86.72 101 | 95.91 79 | 95.84 119 |
|
agg_prior1 | | | 93.29 42 | 92.97 44 | 94.26 41 | 97.38 41 | 85.92 42 | 93.92 155 | 96.72 55 | 81.96 200 | 92.16 54 | 96.23 55 | 87.85 10 | 98.97 59 | 91.95 39 | 98.55 35 | 97.90 56 |
|
CDS-MVSNet | | | 89.45 106 | 88.51 110 | 92.29 96 | 93.62 162 | 83.61 86 | 93.01 200 | 94.68 187 | 81.95 201 | 87.82 109 | 93.24 145 | 78.69 97 | 96.99 203 | 80.34 180 | 93.23 125 | 96.28 102 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
v144192 | | | 87.19 181 | 86.35 172 | 89.74 197 | 90.64 262 | 78.24 226 | 93.92 155 | 95.43 146 | 81.93 202 | 85.51 159 | 91.05 224 | 74.21 158 | 97.45 152 | 82.86 139 | 81.56 254 | 93.53 222 |
|
PAPR | | | 90.02 90 | 89.27 96 | 92.29 96 | 95.78 88 | 80.95 148 | 92.68 210 | 96.22 85 | 81.91 203 | 86.66 128 | 93.75 135 | 82.23 62 | 98.44 92 | 79.40 202 | 94.79 94 | 97.48 70 |
|
v1921920 | | | 86.97 185 | 86.06 182 | 89.69 200 | 90.53 267 | 78.11 229 | 93.80 161 | 95.43 146 | 81.90 204 | 85.33 173 | 91.05 224 | 72.66 180 | 97.41 168 | 82.05 153 | 81.80 249 | 93.53 222 |
|
CPTT-MVS | | | 91.99 57 | 91.80 56 | 92.55 84 | 98.24 21 | 81.98 124 | 96.76 19 | 96.49 70 | 81.89 205 | 90.24 79 | 96.44 49 | 78.59 99 | 98.61 83 | 89.68 64 | 97.85 51 | 97.06 84 |
|
train_agg | | | 93.44 38 | 93.08 40 | 94.52 32 | 97.53 34 | 86.49 27 | 94.07 144 | 96.78 49 | 81.86 206 | 92.77 38 | 96.20 57 | 87.63 14 | 99.12 39 | 92.14 33 | 98.69 19 | 97.94 51 |
|
test_8 | | | | | | 97.49 37 | 86.30 35 | 94.02 150 | 96.76 52 | 81.86 206 | 92.70 42 | 96.20 57 | 87.63 14 | 99.02 51 | | | |
|
v1240 | | | 86.78 188 | 85.85 186 | 89.56 202 | 90.45 268 | 77.79 237 | 93.61 175 | 95.37 151 | 81.65 208 | 85.43 166 | 91.15 220 | 71.50 193 | 97.43 161 | 81.47 162 | 82.05 243 | 93.47 226 |
|
FMVSNet1 | | | 85.85 205 | 84.11 215 | 91.08 137 | 92.81 184 | 83.10 96 | 95.14 75 | 94.94 174 | 81.64 209 | 82.68 219 | 91.64 197 | 59.01 289 | 96.34 233 | 75.37 236 | 83.78 222 | 93.79 203 |
|
PatchmatchNet | | | 85.85 205 | 84.70 210 | 89.29 211 | 91.76 202 | 75.54 258 | 88.49 278 | 91.30 267 | 81.63 210 | 85.05 176 | 88.70 259 | 71.71 188 | 96.24 236 | 74.61 244 | 89.05 174 | 96.08 109 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
TEST9 | | | | | | 97.53 34 | 86.49 27 | 94.07 144 | 96.78 49 | 81.61 211 | 92.77 38 | 96.20 57 | 87.71 13 | 99.12 39 | | | |
|
sss | | | 88.93 123 | 88.26 122 | 90.94 145 | 94.05 144 | 80.78 153 | 91.71 236 | 95.38 149 | 81.55 212 | 88.63 94 | 93.91 128 | 75.04 149 | 95.47 266 | 82.47 146 | 91.61 139 | 96.57 97 |
|
HY-MVS | | 83.01 12 | 89.03 120 | 87.94 128 | 92.29 96 | 94.86 117 | 82.77 106 | 92.08 231 | 94.49 191 | 81.52 213 | 86.93 122 | 92.79 166 | 78.32 104 | 98.23 100 | 79.93 188 | 90.55 151 | 95.88 117 |
|
agg_prior3 | | | 93.27 43 | 92.89 46 | 94.40 38 | 97.49 37 | 86.12 39 | 94.07 144 | 96.73 53 | 81.46 214 | 92.46 50 | 96.05 65 | 86.90 21 | 99.15 36 | 92.14 33 | 98.69 19 | 97.94 51 |
|
CNLPA | | | 89.07 117 | 87.98 126 | 92.34 94 | 96.87 55 | 84.78 56 | 94.08 142 | 93.24 225 | 81.41 215 | 84.46 186 | 95.13 89 | 75.57 141 | 96.62 217 | 77.21 221 | 93.84 112 | 95.61 128 |
|
EPMVS | | | 83.90 242 | 82.70 243 | 87.51 255 | 90.23 273 | 72.67 277 | 88.62 277 | 81.96 328 | 81.37 216 | 85.01 177 | 88.34 265 | 66.31 250 | 94.45 285 | 75.30 237 | 87.12 198 | 95.43 132 |
|
test20.03 | | | 79.95 274 | 79.08 271 | 82.55 297 | 85.79 305 | 67.74 306 | 91.09 249 | 91.08 273 | 81.23 217 | 74.48 292 | 89.96 244 | 61.63 273 | 90.15 313 | 60.08 307 | 76.38 288 | 89.76 295 |
|
TR-MVS | | | 86.78 188 | 85.76 188 | 89.82 193 | 94.37 135 | 78.41 221 | 92.47 217 | 92.83 231 | 81.11 218 | 86.36 134 | 92.40 174 | 68.73 233 | 97.48 148 | 73.75 251 | 89.85 161 | 93.57 221 |
|
VDDNet | | | 89.56 102 | 88.49 113 | 92.76 79 | 95.07 108 | 82.09 121 | 96.30 26 | 93.19 226 | 81.05 219 | 91.88 59 | 96.86 28 | 61.16 279 | 98.33 97 | 88.43 75 | 92.49 135 | 97.84 58 |
|
tpm | | | 84.73 232 | 84.02 216 | 86.87 272 | 90.33 269 | 68.90 302 | 89.06 272 | 89.94 297 | 80.85 220 | 85.75 146 | 89.86 245 | 68.54 235 | 95.97 245 | 77.76 215 | 84.05 221 | 95.75 124 |
|
DWT-MVSNet_test | | | 84.95 224 | 83.68 225 | 88.77 219 | 91.43 214 | 73.75 268 | 91.74 235 | 90.98 277 | 80.66 221 | 83.84 201 | 87.36 277 | 62.44 268 | 97.11 195 | 78.84 206 | 85.81 205 | 95.46 131 |
|
diffmvs | | | 89.07 117 | 88.32 118 | 91.34 128 | 93.24 171 | 79.79 174 | 92.29 223 | 94.98 173 | 80.24 222 | 87.38 117 | 92.45 172 | 78.02 105 | 97.33 175 | 83.29 134 | 92.93 130 | 96.91 88 |
|
jason | | | 90.80 75 | 90.10 79 | 92.90 74 | 93.04 178 | 83.53 87 | 93.08 197 | 94.15 202 | 80.22 223 | 91.41 68 | 94.91 92 | 76.87 113 | 97.93 127 | 90.28 62 | 96.90 64 | 97.24 74 |
jason: jason. |
tpmrst | | | 85.35 214 | 84.99 201 | 86.43 275 | 90.88 254 | 67.88 305 | 88.71 275 | 91.43 265 | 80.13 224 | 86.08 140 | 88.80 257 | 73.05 175 | 96.02 243 | 82.48 145 | 83.40 231 | 95.40 133 |
|
CDPH-MVS | | | 92.83 51 | 92.30 53 | 94.44 34 | 97.79 31 | 86.11 40 | 94.06 147 | 96.66 61 | 80.09 225 | 92.77 38 | 96.63 40 | 86.62 23 | 99.04 47 | 87.40 88 | 98.66 26 | 98.17 35 |
|
PM-MVS | | | 78.11 282 | 76.12 284 | 84.09 293 | 83.54 313 | 70.08 298 | 88.97 273 | 85.27 322 | 79.93 226 | 74.73 290 | 86.43 285 | 34.70 327 | 93.48 295 | 79.43 200 | 72.06 298 | 88.72 305 |
|
lupinMVS | | | 90.92 74 | 90.21 76 | 93.03 69 | 93.86 154 | 83.88 78 | 92.81 206 | 93.86 216 | 79.84 227 | 91.76 63 | 94.29 111 | 77.92 107 | 98.04 120 | 90.48 61 | 97.11 61 | 97.17 79 |
|
PatchMatch-RL | | | 86.77 190 | 85.54 190 | 90.47 162 | 95.88 85 | 82.71 112 | 90.54 250 | 92.31 241 | 79.82 228 | 84.32 193 | 91.57 204 | 68.77 232 | 96.39 230 | 73.16 253 | 93.48 119 | 92.32 259 |
|
PLC | | 84.53 7 | 89.06 119 | 88.03 125 | 92.15 100 | 97.27 48 | 82.69 113 | 94.29 129 | 95.44 145 | 79.71 229 | 84.01 199 | 94.18 116 | 76.68 117 | 98.75 76 | 77.28 220 | 93.41 120 | 95.02 141 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
F-COLMAP | | | 87.95 147 | 86.80 157 | 91.40 127 | 96.35 68 | 80.88 150 | 94.73 97 | 95.45 143 | 79.65 230 | 82.04 229 | 94.61 103 | 71.13 196 | 98.50 88 | 76.24 230 | 91.05 144 | 94.80 159 |
|
MIMVSNet | | | 82.59 253 | 80.53 256 | 88.76 220 | 91.51 209 | 78.32 223 | 86.57 292 | 90.13 292 | 79.32 231 | 80.70 243 | 88.69 260 | 52.98 306 | 93.07 302 | 66.03 291 | 88.86 176 | 94.90 154 |
|
test-mter | | | 84.54 236 | 83.64 227 | 87.25 262 | 90.95 247 | 71.67 285 | 89.55 262 | 89.88 299 | 79.17 232 | 84.54 184 | 87.95 270 | 55.56 298 | 95.11 279 | 81.82 157 | 93.37 122 | 94.97 143 |
|
MDA-MVSNet-bldmvs | | | 78.85 281 | 76.31 282 | 86.46 274 | 89.76 281 | 73.88 267 | 88.79 274 | 90.42 286 | 79.16 233 | 59.18 321 | 88.33 266 | 60.20 283 | 94.04 289 | 62.00 303 | 68.96 313 | 91.48 273 |
|
tpmvs | | | 83.35 248 | 82.07 245 | 87.20 266 | 91.07 243 | 71.00 292 | 88.31 280 | 91.70 257 | 78.91 234 | 80.49 247 | 87.18 280 | 69.30 224 | 97.08 197 | 68.12 284 | 83.56 227 | 93.51 225 |
|
原ACMM1 | | | | | 92.01 103 | 97.34 43 | 81.05 144 | | 96.81 47 | 78.89 235 | 90.45 77 | 95.92 68 | 82.65 57 | 98.84 73 | 80.68 173 | 98.26 41 | 96.14 106 |
|
MSDG | | | 84.86 227 | 83.09 237 | 90.14 176 | 93.80 157 | 80.05 167 | 89.18 271 | 93.09 227 | 78.89 235 | 78.19 262 | 91.91 192 | 65.86 256 | 97.27 181 | 68.47 279 | 88.45 182 | 93.11 236 |
|
PAPM | | | 86.68 191 | 85.39 196 | 90.53 152 | 93.05 177 | 79.33 198 | 89.79 261 | 94.77 186 | 78.82 237 | 81.95 230 | 93.24 145 | 76.81 114 | 97.30 177 | 66.94 286 | 93.16 126 | 94.95 153 |
|
PVSNet | | 78.82 18 | 85.55 211 | 84.65 211 | 88.23 243 | 94.72 121 | 71.93 283 | 87.12 289 | 92.75 234 | 78.80 238 | 84.95 178 | 90.53 234 | 64.43 262 | 96.71 215 | 74.74 242 | 93.86 111 | 96.06 111 |
|
MVP-Stereo | | | 85.97 203 | 84.86 206 | 89.32 210 | 90.92 251 | 82.19 120 | 92.11 229 | 94.19 200 | 78.76 239 | 78.77 261 | 91.63 200 | 68.38 239 | 96.56 220 | 75.01 241 | 93.95 109 | 89.20 300 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
OpenMVS | | 83.78 11 | 88.74 127 | 87.29 138 | 93.08 66 | 92.70 185 | 85.39 50 | 96.57 22 | 96.43 73 | 78.74 240 | 80.85 241 | 96.07 64 | 69.64 218 | 99.01 53 | 78.01 214 | 96.65 70 | 94.83 157 |
|
MDTV_nov1_ep13 | | | | 83.56 228 | | 91.69 206 | 69.93 299 | 87.75 285 | 91.54 262 | 78.60 241 | 84.86 179 | 88.90 255 | 69.54 219 | 96.03 242 | 70.25 266 | 88.93 175 | |
|
Patchmatch-RL test | | | 81.67 258 | 79.96 262 | 86.81 273 | 85.42 306 | 71.23 288 | 82.17 316 | 87.50 317 | 78.47 242 | 77.19 271 | 82.50 307 | 70.81 202 | 93.48 295 | 82.66 142 | 72.89 296 | 95.71 125 |
|
QAPM | | | 89.51 103 | 88.15 123 | 93.59 56 | 94.92 114 | 84.58 59 | 96.82 18 | 96.70 57 | 78.43 243 | 83.41 212 | 96.19 60 | 73.18 174 | 99.30 27 | 77.11 223 | 96.54 73 | 96.89 90 |
|
1314 | | | 87.51 169 | 86.57 169 | 90.34 167 | 92.42 189 | 79.74 176 | 92.63 211 | 95.35 153 | 78.35 244 | 80.14 251 | 91.62 201 | 74.05 161 | 97.15 191 | 81.05 164 | 93.53 116 | 94.12 185 |
|
CR-MVSNet | | | 85.35 214 | 83.76 220 | 90.12 177 | 90.58 263 | 79.34 195 | 85.24 301 | 91.96 253 | 78.27 245 | 85.55 155 | 87.87 273 | 71.03 198 | 95.61 257 | 73.96 249 | 89.36 168 | 95.40 133 |
|
USDC | | | 82.76 250 | 81.26 251 | 87.26 261 | 91.17 239 | 74.55 261 | 89.27 268 | 93.39 224 | 78.26 246 | 75.30 287 | 92.08 186 | 54.43 303 | 96.63 216 | 71.64 258 | 85.79 207 | 90.61 291 |
|
new-patchmatchnet | | | 76.41 285 | 75.17 285 | 80.13 300 | 82.65 317 | 59.61 319 | 87.66 286 | 91.08 273 | 78.23 247 | 69.85 307 | 83.22 304 | 54.76 301 | 91.63 311 | 64.14 298 | 64.89 318 | 89.16 301 |
|
1112_ss | | | 88.42 131 | 87.33 137 | 91.72 118 | 94.92 114 | 80.98 146 | 92.97 203 | 94.54 190 | 78.16 248 | 83.82 202 | 93.88 129 | 78.78 96 | 97.91 128 | 79.45 198 | 89.41 166 | 96.26 103 |
|
MIMVSNet1 | | | 79.38 278 | 77.28 278 | 85.69 280 | 86.35 304 | 73.67 269 | 91.61 240 | 92.75 234 | 78.11 249 | 72.64 301 | 88.12 268 | 48.16 314 | 91.97 308 | 60.32 306 | 77.49 285 | 91.43 274 |
|
MS-PatchMatch | | | 85.05 220 | 84.16 214 | 87.73 251 | 91.42 215 | 78.51 218 | 91.25 246 | 93.53 221 | 77.50 250 | 80.15 250 | 91.58 202 | 61.99 271 | 95.51 262 | 75.69 233 | 94.35 107 | 89.16 301 |
|
AllTest | | | 83.42 245 | 81.39 249 | 89.52 204 | 95.01 109 | 77.79 237 | 93.12 194 | 90.89 280 | 77.41 251 | 76.12 280 | 93.34 138 | 54.08 304 | 97.51 146 | 68.31 281 | 84.27 219 | 93.26 230 |
|
TestCases | | | | | 89.52 204 | 95.01 109 | 77.79 237 | | 90.89 280 | 77.41 251 | 76.12 280 | 93.34 138 | 54.08 304 | 97.51 146 | 68.31 281 | 84.27 219 | 93.26 230 |
|
TESTMET0.1,1 | | | 83.74 244 | 82.85 241 | 86.42 276 | 89.96 278 | 71.21 289 | 89.55 262 | 87.88 312 | 77.41 251 | 83.37 213 | 87.31 278 | 56.71 295 | 93.65 293 | 80.62 174 | 92.85 133 | 94.40 177 |
|
gm-plane-assit | | | | | | 89.60 283 | 68.00 304 | | | 77.28 254 | | 88.99 254 | | 97.57 142 | 79.44 199 | | |
|
EG-PatchMatch MVS | | | 82.37 255 | 80.34 257 | 88.46 235 | 90.27 270 | 79.35 194 | 92.80 207 | 94.33 197 | 77.14 255 | 73.26 298 | 90.18 240 | 47.47 316 | 96.72 213 | 70.25 266 | 87.32 197 | 89.30 298 |
|
FMVSNet5 | | | 81.52 262 | 79.60 266 | 87.27 260 | 91.17 239 | 77.95 231 | 91.49 241 | 92.26 243 | 76.87 256 | 76.16 279 | 87.91 272 | 51.67 307 | 92.34 304 | 67.74 285 | 81.16 255 | 91.52 271 |
|
TDRefinement | | | 79.81 275 | 77.34 277 | 87.22 265 | 79.24 323 | 75.48 259 | 93.12 194 | 92.03 249 | 76.45 257 | 75.01 288 | 91.58 202 | 49.19 313 | 96.44 228 | 70.22 268 | 69.18 312 | 89.75 296 |
|
LF4IMVS | | | 80.37 272 | 79.07 272 | 84.27 292 | 86.64 303 | 69.87 300 | 89.39 267 | 91.05 275 | 76.38 258 | 74.97 289 | 90.00 242 | 47.85 315 | 94.25 288 | 74.55 245 | 80.82 265 | 88.69 306 |
|
TAPA-MVS | | 84.62 6 | 88.16 139 | 87.01 150 | 91.62 121 | 96.64 59 | 80.65 154 | 94.39 120 | 96.21 88 | 76.38 258 | 86.19 138 | 95.44 80 | 79.75 86 | 98.08 117 | 62.75 302 | 95.29 90 | 96.13 107 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
dp | | | 81.47 263 | 80.23 259 | 85.17 285 | 89.92 279 | 65.49 312 | 86.74 290 | 90.10 293 | 76.30 260 | 81.10 239 | 87.12 281 | 62.81 266 | 95.92 247 | 68.13 283 | 79.88 277 | 94.09 188 |
|
CostFormer | | | 85.77 208 | 84.94 204 | 88.26 241 | 91.16 241 | 72.58 281 | 89.47 266 | 91.04 276 | 76.26 261 | 86.45 132 | 89.97 243 | 70.74 203 | 96.86 212 | 82.35 148 | 87.07 200 | 95.34 136 |
|
RPSCF | | | 85.07 219 | 84.27 213 | 87.48 258 | 92.91 183 | 70.62 295 | 91.69 238 | 92.46 239 | 76.20 262 | 82.67 220 | 95.22 86 | 63.94 264 | 97.29 180 | 77.51 219 | 85.80 206 | 94.53 170 |
|
Test_1112_low_res | | | 87.65 158 | 86.51 170 | 91.08 137 | 94.94 113 | 79.28 199 | 91.77 233 | 94.30 198 | 76.04 263 | 83.51 210 | 92.37 175 | 77.86 109 | 97.73 137 | 78.69 207 | 89.13 173 | 96.22 104 |
|
pmmvs4 | | | 85.43 212 | 83.86 219 | 90.16 171 | 90.02 277 | 82.97 103 | 90.27 252 | 92.67 236 | 75.93 264 | 80.73 242 | 91.74 196 | 71.05 197 | 95.73 256 | 78.85 205 | 83.46 229 | 91.78 266 |
|
LS3D | | | 87.89 148 | 86.32 174 | 92.59 83 | 96.07 80 | 82.92 104 | 95.23 69 | 94.92 178 | 75.66 265 | 82.89 217 | 95.98 66 | 72.48 184 | 99.21 30 | 68.43 280 | 95.23 92 | 95.64 127 |
|
pmmvs5 | | | 84.21 238 | 82.84 242 | 88.34 239 | 88.95 288 | 76.94 247 | 92.41 218 | 91.91 255 | 75.63 266 | 80.28 248 | 91.18 218 | 64.59 261 | 95.57 259 | 77.09 224 | 83.47 228 | 92.53 251 |
|
pmmvs-eth3d | | | 80.97 269 | 78.72 274 | 87.74 250 | 84.99 309 | 79.97 170 | 90.11 256 | 91.65 259 | 75.36 267 | 73.51 295 | 86.03 293 | 59.45 287 | 93.96 290 | 75.17 238 | 72.21 297 | 89.29 299 |
|
test_0402 | | | 81.30 266 | 79.17 270 | 87.67 252 | 93.19 173 | 78.17 227 | 92.98 202 | 91.71 256 | 75.25 268 | 76.02 283 | 90.31 238 | 59.23 288 | 96.37 231 | 50.22 319 | 83.63 226 | 88.47 311 |
|
COLMAP_ROB | | 80.39 16 | 83.96 240 | 82.04 246 | 89.74 197 | 95.28 103 | 79.75 175 | 94.25 131 | 92.28 242 | 75.17 269 | 78.02 265 | 93.77 133 | 58.60 290 | 97.84 130 | 65.06 295 | 85.92 204 | 91.63 270 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
TinyColmap | | | 79.76 276 | 77.69 276 | 85.97 278 | 91.71 204 | 73.12 271 | 89.55 262 | 90.36 288 | 75.03 270 | 72.03 303 | 90.19 239 | 46.22 318 | 96.19 238 | 63.11 300 | 81.03 259 | 88.59 307 |
|
DP-MVS | | | 87.25 177 | 85.36 197 | 92.90 74 | 97.65 32 | 83.24 93 | 94.81 93 | 92.00 250 | 74.99 271 | 81.92 231 | 95.00 91 | 72.66 180 | 99.05 44 | 66.92 288 | 92.33 136 | 96.40 99 |
|
PatchT | | | 82.68 252 | 81.27 250 | 86.89 271 | 90.09 275 | 70.94 293 | 84.06 308 | 90.15 291 | 74.91 272 | 85.63 154 | 83.57 302 | 69.37 220 | 94.87 284 | 65.19 293 | 88.50 181 | 94.84 156 |
|
CHOSEN 280x420 | | | 85.15 218 | 83.99 217 | 88.65 223 | 92.47 188 | 78.40 222 | 79.68 321 | 92.76 233 | 74.90 273 | 81.41 236 | 89.59 248 | 69.85 216 | 95.51 262 | 79.92 189 | 95.29 90 | 92.03 263 |
|
gg-mvs-nofinetune | | | 81.77 257 | 79.37 267 | 88.99 217 | 90.85 255 | 77.73 240 | 86.29 293 | 79.63 332 | 74.88 274 | 83.19 215 | 69.05 324 | 60.34 282 | 96.11 240 | 75.46 235 | 94.64 98 | 93.11 236 |
|
pmmvs6 | | | 83.42 245 | 81.60 248 | 88.87 218 | 88.01 298 | 77.87 235 | 94.96 82 | 94.24 199 | 74.67 275 | 78.80 260 | 91.09 223 | 60.17 284 | 96.49 224 | 77.06 225 | 75.40 291 | 92.23 261 |
|
CHOSEN 1792x2688 | | | 88.84 124 | 87.69 130 | 92.30 95 | 96.14 75 | 81.42 134 | 90.01 257 | 95.86 111 | 74.52 276 | 87.41 114 | 93.94 124 | 75.46 143 | 98.36 93 | 80.36 179 | 95.53 83 | 97.12 82 |
|
MDA-MVSNet_test_wron | | | 79.21 280 | 77.19 280 | 85.29 283 | 88.22 295 | 72.77 276 | 85.87 296 | 90.06 294 | 74.34 277 | 62.62 320 | 87.56 276 | 66.14 253 | 91.99 307 | 66.90 289 | 73.01 294 | 91.10 280 |
|
YYNet1 | | | 79.22 279 | 77.20 279 | 85.28 284 | 88.20 296 | 72.66 278 | 85.87 296 | 90.05 296 | 74.33 278 | 62.70 319 | 87.61 275 | 66.09 254 | 92.03 306 | 66.94 286 | 72.97 295 | 91.15 278 |
|
无先验 | | | | | | | | 93.28 189 | 96.26 81 | 73.95 279 | | | | 99.05 44 | 80.56 175 | | 96.59 96 |
|
Anonymous20231206 | | | 81.03 268 | 79.77 264 | 84.82 287 | 87.85 301 | 70.26 297 | 91.42 242 | 92.08 247 | 73.67 280 | 77.75 267 | 89.25 252 | 62.43 269 | 93.08 301 | 61.50 304 | 82.00 244 | 91.12 279 |
|
PCF-MVS | | 84.11 10 | 87.74 156 | 86.08 181 | 92.70 80 | 94.02 145 | 84.43 69 | 89.27 268 | 95.87 110 | 73.62 281 | 84.43 188 | 94.33 108 | 78.48 102 | 98.86 68 | 70.27 265 | 94.45 104 | 94.81 158 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
HyFIR lowres test | | | 88.09 142 | 86.81 156 | 91.93 109 | 96.00 82 | 80.63 155 | 90.01 257 | 95.79 114 | 73.42 282 | 87.68 112 | 92.10 185 | 73.86 164 | 97.96 124 | 80.75 171 | 91.70 138 | 97.19 78 |
|
MDTV_nov1_ep13_2view | | | | | | | 55.91 327 | 87.62 287 | | 73.32 283 | 84.59 183 | | 70.33 211 | | 74.65 243 | | 95.50 129 |
|
JIA-IIPM | | | 81.04 267 | 78.98 273 | 87.25 262 | 88.64 290 | 73.48 270 | 81.75 317 | 89.61 303 | 73.19 284 | 82.05 228 | 73.71 321 | 66.07 255 | 95.87 250 | 71.18 263 | 84.60 216 | 92.41 255 |
|
cascas | | | 86.43 197 | 84.98 202 | 90.80 147 | 92.10 195 | 80.92 149 | 90.24 253 | 95.91 106 | 73.10 285 | 83.57 209 | 88.39 264 | 65.15 258 | 97.46 150 | 84.90 114 | 91.43 140 | 94.03 191 |
|
ANet_high | | | 58.88 304 | 54.22 307 | 72.86 311 | 56.50 339 | 56.67 324 | 80.75 319 | 86.00 319 | 73.09 286 | 37.39 330 | 64.63 328 | 22.17 335 | 79.49 333 | 43.51 328 | 23.96 334 | 82.43 320 |
|
tpmp4_e23 | | | 83.87 243 | 82.33 244 | 88.48 234 | 91.46 210 | 72.82 274 | 89.82 260 | 91.57 261 | 73.02 287 | 81.86 232 | 89.05 253 | 66.20 252 | 96.97 205 | 71.57 259 | 86.39 202 | 95.66 126 |
|
ADS-MVSNet2 | | | 81.66 259 | 79.71 265 | 87.50 256 | 91.35 224 | 74.19 263 | 83.33 312 | 88.48 309 | 72.90 288 | 82.24 224 | 85.77 294 | 64.98 259 | 93.20 299 | 64.57 296 | 83.74 223 | 95.12 138 |
|
ADS-MVSNet | | | 81.56 261 | 79.78 263 | 86.90 270 | 91.35 224 | 71.82 284 | 83.33 312 | 89.16 305 | 72.90 288 | 82.24 224 | 85.77 294 | 64.98 259 | 93.76 291 | 64.57 296 | 83.74 223 | 95.12 138 |
|
PVSNet_0 | | 73.20 20 | 77.22 283 | 74.83 286 | 84.37 290 | 90.70 260 | 71.10 290 | 83.09 314 | 89.67 302 | 72.81 290 | 73.93 294 | 83.13 305 | 60.79 280 | 93.70 292 | 68.54 278 | 50.84 327 | 88.30 312 |
|
testdata | | | | | 90.49 160 | 96.40 65 | 77.89 234 | | 95.37 151 | 72.51 291 | 93.63 23 | 96.69 36 | 82.08 66 | 97.65 138 | 83.08 135 | 97.39 58 | 95.94 114 |
|
PMMVS | | | 85.71 210 | 84.96 203 | 87.95 248 | 88.90 289 | 77.09 245 | 88.68 276 | 90.06 294 | 72.32 292 | 86.47 129 | 90.76 228 | 72.15 187 | 94.40 286 | 81.78 159 | 93.49 117 | 92.36 257 |
|
testus | | | 74.41 289 | 73.35 287 | 77.59 306 | 82.49 318 | 57.08 322 | 86.02 294 | 90.21 290 | 72.28 293 | 72.89 300 | 84.32 299 | 37.08 325 | 86.96 321 | 52.24 315 | 82.65 235 | 88.73 304 |
|
Patchmtry | | | 82.71 251 | 80.93 254 | 88.06 246 | 90.05 276 | 76.37 252 | 84.74 303 | 91.96 253 | 72.28 293 | 81.32 238 | 87.87 273 | 71.03 198 | 95.50 264 | 68.97 277 | 80.15 272 | 92.32 259 |
|
tpm2 | | | 84.08 239 | 82.94 239 | 87.48 258 | 91.39 217 | 71.27 287 | 89.23 270 | 90.37 287 | 71.95 295 | 84.64 181 | 89.33 251 | 67.30 241 | 96.55 222 | 75.17 238 | 87.09 199 | 94.63 162 |
|
UnsupCasMVSNet_bld | | | 76.23 286 | 73.27 288 | 85.09 286 | 83.79 312 | 72.92 272 | 85.65 300 | 93.47 223 | 71.52 296 | 68.84 309 | 79.08 317 | 49.77 310 | 93.21 298 | 66.81 290 | 60.52 324 | 89.13 303 |
|
RPMNet | | | 83.18 249 | 80.87 255 | 90.12 177 | 90.58 263 | 79.34 195 | 85.24 301 | 90.78 283 | 71.44 297 | 85.55 155 | 82.97 306 | 70.87 200 | 95.61 257 | 61.01 305 | 89.36 168 | 95.40 133 |
|
test2356 | | | 74.50 288 | 73.27 288 | 78.20 302 | 80.81 319 | 59.84 317 | 83.76 311 | 88.33 311 | 71.43 298 | 72.37 302 | 81.84 310 | 45.60 319 | 86.26 323 | 50.97 317 | 84.32 217 | 88.50 308 |
|
旧先验2 | | | | | | | | 93.36 182 | | 71.25 299 | 94.37 11 | | | 97.13 194 | 86.74 98 | | |
|
testpf | | | 71.41 295 | 72.11 292 | 69.30 315 | 84.53 310 | 59.79 318 | 62.74 331 | 83.14 325 | 71.11 300 | 68.83 310 | 81.57 312 | 46.70 317 | 84.83 328 | 74.51 246 | 75.86 290 | 63.30 326 |
|
新几何1 | | | | | 93.10 65 | 97.30 45 | 84.35 71 | | 95.56 129 | 71.09 301 | 91.26 70 | 96.24 54 | 82.87 56 | 98.86 68 | 79.19 203 | 98.10 45 | 96.07 110 |
|
1121 | | | 90.42 84 | 89.49 88 | 93.20 61 | 97.27 48 | 84.46 65 | 92.63 211 | 95.51 136 | 71.01 302 | 91.20 71 | 96.21 56 | 82.92 55 | 99.05 44 | 80.56 175 | 98.07 46 | 96.10 108 |
|
Patchmatch-test | | | 81.37 264 | 79.30 268 | 87.58 254 | 90.92 251 | 74.16 264 | 80.99 318 | 87.68 315 | 70.52 303 | 76.63 272 | 88.81 256 | 71.21 195 | 92.76 303 | 60.01 309 | 86.93 201 | 95.83 120 |
|
114514_t | | | 89.51 103 | 88.50 111 | 92.54 85 | 98.11 23 | 81.99 123 | 95.16 74 | 96.36 78 | 70.19 304 | 85.81 142 | 95.25 85 | 76.70 116 | 98.63 81 | 82.07 152 | 96.86 66 | 97.00 85 |
|
test1235678 | | | 72.22 292 | 70.31 293 | 77.93 305 | 78.04 324 | 58.04 321 | 85.76 298 | 89.80 301 | 70.15 305 | 63.43 318 | 80.20 315 | 42.24 322 | 87.24 320 | 48.68 321 | 74.50 292 | 88.50 308 |
|
N_pmnet | | | 68.89 297 | 68.44 298 | 70.23 313 | 89.07 287 | 28.79 341 | 88.06 281 | 19.50 343 | 69.47 306 | 71.86 304 | 84.93 297 | 61.24 277 | 91.75 309 | 54.70 313 | 77.15 287 | 90.15 294 |
|
OpenMVS_ROB | | 74.94 19 | 79.51 277 | 77.03 281 | 86.93 268 | 87.00 302 | 76.23 254 | 92.33 221 | 90.74 284 | 68.93 307 | 74.52 291 | 88.23 267 | 49.58 311 | 96.62 217 | 57.64 311 | 84.29 218 | 87.94 313 |
|
test222 | | | | | | 96.55 63 | 81.70 126 | 92.22 225 | 95.01 170 | 68.36 308 | 90.20 80 | 96.14 62 | 80.26 82 | | | 97.80 52 | 96.05 112 |
|
LP | | | 75.51 287 | 72.15 291 | 85.61 281 | 87.86 300 | 73.93 266 | 80.20 320 | 88.43 310 | 67.39 309 | 70.05 306 | 80.56 314 | 58.18 292 | 93.18 300 | 46.28 325 | 70.36 310 | 89.71 297 |
|
1111 | | | 70.54 296 | 69.71 295 | 73.04 310 | 79.30 321 | 44.83 334 | 84.23 306 | 88.96 306 | 67.33 310 | 65.42 315 | 82.28 308 | 41.11 323 | 88.11 318 | 47.12 323 | 71.60 302 | 86.19 315 |
|
.test1245 | | | 57.63 306 | 61.79 303 | 45.14 324 | 79.30 321 | 44.83 334 | 84.23 306 | 88.96 306 | 67.33 310 | 65.42 315 | 82.28 308 | 41.11 323 | 88.11 318 | 47.12 323 | 0.39 338 | 2.46 337 |
|
MVS | | | 87.44 171 | 86.10 180 | 91.44 126 | 92.61 187 | 83.62 85 | 92.63 211 | 95.66 123 | 67.26 312 | 81.47 234 | 92.15 181 | 77.95 106 | 98.22 101 | 79.71 194 | 95.48 85 | 92.47 253 |
|
tpm cat1 | | | 81.96 256 | 80.27 258 | 87.01 267 | 91.09 242 | 71.02 291 | 87.38 288 | 91.53 263 | 66.25 313 | 80.17 249 | 86.35 291 | 68.22 240 | 96.15 239 | 69.16 276 | 82.29 238 | 93.86 200 |
|
CVMVSNet | | | 84.69 234 | 84.79 208 | 84.37 290 | 91.84 199 | 64.92 313 | 93.70 171 | 91.47 264 | 66.19 314 | 86.16 139 | 95.28 83 | 67.18 244 | 93.33 297 | 80.89 170 | 90.42 153 | 94.88 155 |
|
testmv | | | 65.49 299 | 62.66 300 | 73.96 309 | 68.78 330 | 53.14 329 | 84.70 304 | 88.56 308 | 65.94 315 | 52.35 324 | 74.65 320 | 25.02 333 | 85.14 326 | 43.54 327 | 60.40 325 | 83.60 316 |
|
test12356 | | | 64.99 300 | 63.78 299 | 68.61 317 | 72.69 327 | 39.14 337 | 78.46 322 | 87.61 316 | 64.91 316 | 55.77 322 | 77.48 318 | 28.10 330 | 85.59 325 | 44.69 326 | 64.35 319 | 81.12 321 |
|
CMPMVS | | 59.16 21 | 80.52 271 | 79.20 269 | 84.48 289 | 83.98 311 | 67.63 307 | 89.95 259 | 93.84 218 | 64.79 317 | 66.81 314 | 91.14 221 | 57.93 293 | 95.17 277 | 76.25 229 | 88.10 187 | 90.65 290 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
EU-MVSNet | | | 81.32 265 | 80.95 253 | 82.42 298 | 88.50 292 | 63.67 314 | 93.32 183 | 91.33 266 | 64.02 318 | 80.57 246 | 92.83 162 | 61.21 278 | 92.27 305 | 76.34 228 | 80.38 271 | 91.32 275 |
|
no-one | | | 61.56 302 | 56.58 304 | 76.49 308 | 67.80 333 | 62.76 316 | 78.13 323 | 86.11 318 | 63.16 319 | 43.24 328 | 64.70 327 | 26.12 332 | 88.95 316 | 50.84 318 | 29.15 330 | 77.77 323 |
|
new_pmnet | | | 72.15 293 | 70.13 294 | 78.20 302 | 82.95 316 | 65.68 310 | 83.91 309 | 82.40 327 | 62.94 320 | 64.47 317 | 79.82 316 | 42.85 321 | 86.26 323 | 57.41 312 | 74.44 293 | 82.65 319 |
|
Anonymous20231211 | | | 72.97 291 | 69.63 296 | 83.00 296 | 83.05 315 | 66.91 308 | 92.69 209 | 89.45 304 | 61.06 321 | 67.50 312 | 83.46 303 | 34.34 328 | 93.61 294 | 51.11 316 | 63.97 320 | 88.48 310 |
|
DSMNet-mixed | | | 76.94 284 | 76.29 283 | 78.89 301 | 83.10 314 | 56.11 326 | 87.78 284 | 79.77 331 | 60.65 322 | 75.64 286 | 88.71 258 | 61.56 274 | 88.34 317 | 60.07 308 | 89.29 170 | 92.21 262 |
|
pmmvs3 | | | 71.81 294 | 68.71 297 | 81.11 299 | 75.86 325 | 70.42 296 | 86.74 290 | 83.66 324 | 58.95 323 | 68.64 311 | 80.89 313 | 36.93 326 | 89.52 314 | 63.10 301 | 63.59 321 | 83.39 317 |
|
MVS-HIRNet | | | 73.70 290 | 72.20 290 | 78.18 304 | 91.81 201 | 56.42 325 | 82.94 315 | 82.58 326 | 55.24 324 | 68.88 308 | 66.48 325 | 55.32 300 | 95.13 278 | 58.12 310 | 88.42 184 | 83.01 318 |
|
PMMVS2 | | | 59.60 303 | 56.40 305 | 69.21 316 | 68.83 329 | 46.58 332 | 73.02 329 | 77.48 335 | 55.07 325 | 49.21 326 | 72.95 323 | 17.43 339 | 80.04 331 | 49.32 320 | 44.33 328 | 80.99 322 |
|
FPMVS | | | 64.63 301 | 62.55 301 | 70.88 312 | 70.80 328 | 56.71 323 | 84.42 305 | 84.42 323 | 51.78 326 | 49.57 325 | 81.61 311 | 23.49 334 | 81.48 330 | 40.61 330 | 76.25 289 | 74.46 325 |
|
LCM-MVSNet | | | 66.00 298 | 62.16 302 | 77.51 307 | 64.51 335 | 58.29 320 | 83.87 310 | 90.90 279 | 48.17 327 | 54.69 323 | 73.31 322 | 16.83 340 | 86.75 322 | 65.47 292 | 61.67 323 | 87.48 314 |
|
DeepMVS_CX | | | | | 56.31 322 | 74.23 326 | 51.81 330 | | 56.67 341 | 44.85 328 | 48.54 327 | 75.16 319 | 27.87 331 | 58.74 338 | 40.92 329 | 52.22 326 | 58.39 330 |
|
PNet_i23d | | | 50.48 309 | 47.18 309 | 60.36 320 | 68.59 331 | 44.56 336 | 72.75 330 | 72.61 336 | 43.92 329 | 33.91 332 | 60.19 330 | 6.16 341 | 73.52 334 | 38.50 331 | 28.04 331 | 63.01 327 |
|
Gipuma | | | 57.99 305 | 54.91 306 | 67.24 318 | 88.51 291 | 65.59 311 | 52.21 334 | 90.33 289 | 43.58 330 | 42.84 329 | 51.18 332 | 20.29 337 | 85.07 327 | 34.77 332 | 70.45 309 | 51.05 331 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
PMVS | | 47.18 22 | 52.22 307 | 48.46 308 | 63.48 319 | 45.72 340 | 46.20 333 | 73.41 327 | 78.31 333 | 41.03 331 | 30.06 333 | 65.68 326 | 6.05 342 | 83.43 329 | 30.04 333 | 65.86 316 | 60.80 328 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
wuykxyi23d | | | 50.55 308 | 44.13 310 | 69.81 314 | 56.77 337 | 54.58 328 | 73.22 328 | 80.78 329 | 39.79 332 | 22.08 337 | 46.69 334 | 4.03 344 | 79.71 332 | 47.65 322 | 26.13 332 | 75.14 324 |
|
E-PMN | | | 43.23 311 | 42.29 311 | 46.03 323 | 65.58 334 | 37.41 338 | 73.51 326 | 64.62 337 | 33.99 333 | 28.47 335 | 47.87 333 | 19.90 338 | 67.91 335 | 22.23 335 | 24.45 333 | 32.77 332 |
|
EMVS | | | 42.07 312 | 41.12 312 | 44.92 325 | 63.45 336 | 35.56 340 | 73.65 325 | 63.48 338 | 33.05 334 | 26.88 336 | 45.45 335 | 21.27 336 | 67.14 336 | 19.80 336 | 23.02 335 | 32.06 333 |
|
MVE | | 39.65 23 | 43.39 310 | 38.59 315 | 57.77 321 | 56.52 338 | 48.77 331 | 55.38 333 | 58.64 340 | 29.33 335 | 28.96 334 | 52.65 331 | 4.68 343 | 64.62 337 | 28.11 334 | 33.07 329 | 59.93 329 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
wuyk23d | | | 21.27 316 | 20.48 317 | 23.63 328 | 68.59 331 | 36.41 339 | 49.57 335 | 6.85 344 | 9.37 336 | 7.89 338 | 4.46 341 | 4.03 344 | 31.37 339 | 17.47 337 | 16.07 337 | 3.12 335 |
|
tmp_tt | | | 35.64 314 | 39.24 313 | 24.84 327 | 14.87 341 | 23.90 342 | 62.71 332 | 51.51 342 | 6.58 337 | 36.66 331 | 62.08 329 | 44.37 320 | 30.34 340 | 52.40 314 | 22.00 336 | 20.27 334 |
|
testmvs | | | 8.92 317 | 11.52 318 | 1.12 330 | 1.06 342 | 0.46 344 | 86.02 294 | 0.65 345 | 0.62 338 | 2.74 339 | 9.52 339 | 0.31 347 | 0.45 342 | 2.38 338 | 0.39 338 | 2.46 337 |
|
test123 | | | 8.76 318 | 11.22 319 | 1.39 329 | 0.85 343 | 0.97 343 | 85.76 298 | 0.35 346 | 0.54 339 | 2.45 340 | 8.14 340 | 0.60 346 | 0.48 341 | 2.16 339 | 0.17 340 | 2.71 336 |
|
cdsmvs_eth3d_5k | | | 22.14 315 | 29.52 316 | 0.00 331 | 0.00 344 | 0.00 345 | 0.00 336 | 95.76 116 | 0.00 340 | 0.00 341 | 94.29 111 | 75.66 140 | 0.00 343 | 0.00 340 | 0.00 341 | 0.00 339 |
|
pcd_1.5k_mvsjas | | | 6.64 320 | 8.86 321 | 0.00 331 | 0.00 344 | 0.00 345 | 0.00 336 | 0.00 347 | 0.00 340 | 0.00 341 | 0.00 342 | 79.70 88 | 0.00 343 | 0.00 340 | 0.00 341 | 0.00 339 |
|
pcd1.5k->3k | | | 37.02 313 | 38.84 314 | 31.53 326 | 92.33 190 | 0.00 345 | 0.00 336 | 96.13 91 | 0.00 340 | 0.00 341 | 0.00 342 | 72.70 179 | 0.00 343 | 0.00 340 | 88.43 183 | 94.60 165 |
|
sosnet-low-res | | | 0.00 321 | 0.00 322 | 0.00 331 | 0.00 344 | 0.00 345 | 0.00 336 | 0.00 347 | 0.00 340 | 0.00 341 | 0.00 342 | 0.00 348 | 0.00 343 | 0.00 340 | 0.00 341 | 0.00 339 |
|
sosnet | | | 0.00 321 | 0.00 322 | 0.00 331 | 0.00 344 | 0.00 345 | 0.00 336 | 0.00 347 | 0.00 340 | 0.00 341 | 0.00 342 | 0.00 348 | 0.00 343 | 0.00 340 | 0.00 341 | 0.00 339 |
|
uncertanet | | | 0.00 321 | 0.00 322 | 0.00 331 | 0.00 344 | 0.00 345 | 0.00 336 | 0.00 347 | 0.00 340 | 0.00 341 | 0.00 342 | 0.00 348 | 0.00 343 | 0.00 340 | 0.00 341 | 0.00 339 |
|
Regformer | | | 0.00 321 | 0.00 322 | 0.00 331 | 0.00 344 | 0.00 345 | 0.00 336 | 0.00 347 | 0.00 340 | 0.00 341 | 0.00 342 | 0.00 348 | 0.00 343 | 0.00 340 | 0.00 341 | 0.00 339 |
|
ab-mvs-re | | | 7.82 319 | 10.43 320 | 0.00 331 | 0.00 344 | 0.00 345 | 0.00 336 | 0.00 347 | 0.00 340 | 0.00 341 | 93.88 129 | 0.00 348 | 0.00 343 | 0.00 340 | 0.00 341 | 0.00 339 |
|
uanet | | | 0.00 321 | 0.00 322 | 0.00 331 | 0.00 344 | 0.00 345 | 0.00 336 | 0.00 347 | 0.00 340 | 0.00 341 | 0.00 342 | 0.00 348 | 0.00 343 | 0.00 340 | 0.00 341 | 0.00 339 |
|
ESAPD | | | | | | | | | 97.46 6 | | | | | | | | |
|
sam_mvs1 | | | | | | | | | | | | | 71.70 189 | | | | |
|
sam_mvs | | | | | | | | | | | | | 70.60 204 | | | | |
|
ambc | | | | | 83.06 295 | 79.99 320 | 63.51 315 | 77.47 324 | 92.86 230 | | 74.34 293 | 84.45 298 | 28.74 329 | 95.06 281 | 73.06 254 | 68.89 314 | 90.61 291 |
|
MTGPA | | | | | | | | | 96.97 33 | | | | | | | | |
|
test_post1 | | | | | | | | 88.00 282 | | | | 9.81 338 | 69.31 223 | 95.53 260 | 76.65 226 | | |
|
test_post | | | | | | | | | | | | 10.29 337 | 70.57 208 | 95.91 249 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 83.76 301 | 71.53 192 | 96.48 225 | | | |
|
GG-mvs-BLEND | | | | | 87.94 249 | 89.73 282 | 77.91 232 | 87.80 283 | 78.23 334 | | 80.58 245 | 83.86 300 | 59.88 286 | 95.33 276 | 71.20 261 | 92.22 137 | 90.60 293 |
|
MTMP | | | | | | | | | 60.64 339 | | | | | | | | |
|
test9_res | | | | | | | | | | | | | | | 91.91 40 | 98.71 17 | 98.07 43 |
|
agg_prior2 | | | | | | | | | | | | | | | 90.54 59 | 98.68 22 | 98.27 29 |
|
agg_prior | | | | | | 97.38 41 | 85.92 42 | | 96.72 55 | | 92.16 54 | | | 98.97 59 | | | |
|
test_prior4 | | | | | | | 85.96 41 | 94.11 138 | | | | | | | | | |
|
test_prior | | | | | 93.82 50 | 97.29 46 | 84.49 62 | | 96.88 42 | | | | | 98.87 65 | | | 98.11 41 |
|
新几何2 | | | | | | | | 93.11 196 | | | | | | | | | |
|
旧先验1 | | | | | | 96.79 57 | 81.81 125 | | 95.67 121 | | | 96.81 31 | 86.69 22 | | | 97.66 54 | 96.97 86 |
|
原ACMM2 | | | | | | | | 92.94 204 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 98.75 76 | 78.30 210 | | |
|
segment_acmp | | | | | | | | | | | | | 87.16 19 | | | | |
|
test12 | | | | | 94.34 39 | 97.13 51 | 86.15 38 | | 96.29 80 | | 91.04 73 | | 85.08 39 | 99.01 53 | | 98.13 44 | 97.86 57 |
|
plane_prior7 | | | | | | 94.70 123 | 82.74 109 | | | | | | | | | | |
|
plane_prior6 | | | | | | 94.52 129 | 82.75 107 | | | | | | 74.23 156 | | | | |
|
plane_prior5 | | | | | | | | | 96.22 85 | | | | | 98.12 106 | 88.15 77 | 89.99 157 | 94.63 162 |
|
plane_prior4 | | | | | | | | | | | | 94.86 95 | | | | | |
|
plane_prior1 | | | | | | 94.59 127 | | | | | | | | | | | |
|
n2 | | | | | | | | | 0.00 347 | | | | | | | | |
|
nn | | | | | | | | | 0.00 347 | | | | | | | | |
|
door-mid | | | | | | | | | 85.49 320 | | | | | | | | |
|
lessismore_v0 | | | | | 86.04 277 | 88.46 293 | 68.78 303 | | 80.59 330 | | 73.01 299 | 90.11 241 | 55.39 299 | 96.43 229 | 75.06 240 | 65.06 317 | 92.90 240 |
|
test11 | | | | | | | | | 96.57 69 | | | | | | | | |
|
door | | | | | | | | | 85.33 321 | | | | | | | | |
|
HQP5-MVS | | | | | | | 81.56 127 | | | | | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 87.11 95 | | |
|
HQP4-MVS | | | | | | | | | | | 85.43 166 | | | 97.96 124 | | | 94.51 172 |
|
HQP3-MVS | | | | | | | | | 96.04 98 | | | | | | | 89.77 162 | |
|
HQP2-MVS | | | | | | | | | | | | | 73.83 165 | | | | |
|
NP-MVS | | | | | | 94.37 135 | 82.42 117 | | | | | 93.98 122 | | | | | |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 87.47 193 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 88.01 190 | |
|
Test By Simon | | | | | | | | | | | | | 80.02 83 | | | | |
|