LTVRE_ROB | | 95.06 1 | 97.73 1 | 98.39 1 | 96.95 1 | 96.33 48 | 96.94 33 | 98.30 22 | 94.90 14 | 98.61 1 | 97.73 3 | 97.97 31 | 98.57 27 | 95.74 7 | 99.24 1 | 98.70 4 | 98.72 7 | 98.70 1 |
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 |
COLMAP_ROB | | 93.74 2 | 97.09 8 | 97.98 4 | 96.05 18 | 95.97 58 | 97.78 9 | 98.56 12 | 91.72 73 | 97.53 6 | 96.01 17 | 98.14 25 | 98.76 18 | 95.28 8 | 98.76 11 | 98.23 10 | 98.77 5 | 96.67 35 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
3Dnovator+ | | 92.82 3 | 95.22 49 | 95.16 61 | 95.29 35 | 96.17 52 | 96.55 39 | 97.64 45 | 94.02 30 | 94.16 38 | 94.29 49 | 92.09 143 | 93.71 155 | 91.90 57 | 96.68 64 | 96.51 46 | 97.70 50 | 96.40 38 |
|
DeepC-MVS | | 92.47 4 | 96.44 15 | 96.75 22 | 96.08 17 | 97.57 7 | 97.19 29 | 97.96 35 | 94.28 23 | 95.29 20 | 94.92 37 | 98.31 21 | 96.92 88 | 93.69 29 | 96.81 61 | 96.50 47 | 98.06 37 | 96.27 41 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
3Dnovator | | 91.81 5 | 93.36 98 | 94.27 88 | 92.29 115 | 92.99 156 | 95.03 90 | 95.76 105 | 87.79 164 | 93.82 43 | 92.38 95 | 92.19 142 | 93.37 159 | 88.14 123 | 95.26 93 | 94.85 88 | 96.69 77 | 95.40 56 |
|
DeepC-MVS_fast | | 91.38 6 | 94.73 57 | 94.98 64 | 94.44 52 | 96.83 34 | 96.12 53 | 96.69 70 | 92.17 60 | 92.98 56 | 93.72 64 | 94.14 119 | 95.45 127 | 90.49 104 | 95.73 82 | 95.30 76 | 96.71 76 | 95.13 63 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
DeepPCF-MVS | | 90.68 7 | 94.56 60 | 94.92 66 | 94.15 58 | 94.11 105 | 95.71 65 | 97.03 52 | 90.65 99 | 93.39 50 | 94.08 53 | 95.29 97 | 94.15 149 | 93.21 37 | 95.22 94 | 94.92 87 | 95.82 118 | 95.75 52 |
|
ACMH | | 90.17 8 | 96.61 11 | 97.69 10 | 95.35 31 | 95.29 75 | 96.94 33 | 98.43 16 | 92.05 65 | 98.04 4 | 95.38 22 | 98.07 28 | 99.25 6 | 93.23 36 | 98.35 15 | 97.16 36 | 97.72 48 | 96.00 45 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
ACMM | | 90.06 9 | 96.31 18 | 96.42 31 | 96.19 15 | 97.21 19 | 97.16 31 | 98.71 5 | 93.79 35 | 94.35 34 | 93.81 60 | 92.80 136 | 98.23 43 | 95.11 9 | 98.07 22 | 97.45 25 | 98.51 17 | 96.86 29 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
ACMH+ | | 89.90 10 | 96.27 19 | 97.52 11 | 94.81 48 | 95.19 77 | 97.18 30 | 97.97 34 | 92.52 49 | 96.72 8 | 90.50 133 | 97.31 59 | 99.11 8 | 94.10 23 | 98.67 12 | 97.90 14 | 98.56 15 | 95.79 50 |
|
ACMP | | 89.62 11 | 95.96 32 | 96.28 36 | 95.59 24 | 96.58 42 | 97.23 28 | 98.26 24 | 93.22 44 | 92.33 71 | 92.31 97 | 94.29 118 | 98.73 20 | 94.68 15 | 98.04 23 | 97.14 37 | 98.47 20 | 96.17 43 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
OpenMVS | | 89.22 12 | 91.09 140 | 91.42 145 | 90.71 133 | 92.79 162 | 93.61 139 | 92.74 173 | 85.47 192 | 86.10 172 | 90.73 126 | 85.71 202 | 93.07 162 | 86.69 133 | 94.07 123 | 93.34 121 | 95.86 114 | 94.02 82 |
|
TAPA-MVS | | 88.94 13 | 93.78 83 | 94.31 86 | 93.18 99 | 94.14 103 | 95.99 57 | 95.74 106 | 86.98 177 | 93.43 48 | 93.88 59 | 90.16 163 | 96.88 90 | 91.05 87 | 94.33 111 | 93.95 108 | 97.28 60 | 95.40 56 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
PCF-MVS | | 87.46 14 | 92.44 124 | 91.80 139 | 93.19 98 | 94.66 87 | 95.80 63 | 96.37 93 | 90.19 104 | 87.57 157 | 92.23 98 | 89.26 171 | 93.97 151 | 89.24 112 | 91.32 171 | 90.82 167 | 96.46 83 | 93.86 85 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
PLC | | 87.27 15 | 93.08 108 | 92.92 126 | 93.26 94 | 94.67 86 | 95.03 90 | 94.38 132 | 90.10 105 | 91.69 85 | 92.14 99 | 87.24 190 | 93.91 152 | 91.61 66 | 95.05 98 | 94.73 98 | 96.67 78 | 92.80 107 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
PMVS | | 87.16 16 | 95.88 36 | 96.47 30 | 95.19 38 | 97.00 26 | 96.02 55 | 96.70 68 | 91.57 77 | 94.43 32 | 95.33 23 | 97.16 63 | 95.37 129 | 92.39 50 | 98.89 10 | 98.72 3 | 98.17 32 | 94.71 69 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
IB-MVS | | 86.01 17 | 88.24 171 | 87.63 181 | 88.94 162 | 92.03 182 | 91.77 169 | 92.40 178 | 85.58 191 | 78.24 220 | 84.85 172 | 71.99 236 | 93.45 157 | 83.96 148 | 93.48 131 | 92.33 131 | 94.84 148 | 92.15 125 |
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 |
CMPMVS | | 66.55 18 | 85.55 191 | 87.46 184 | 83.32 207 | 84.99 227 | 81.97 216 | 79.19 238 | 75.93 224 | 79.32 213 | 88.82 147 | 85.09 203 | 91.07 169 | 82.12 164 | 92.56 147 | 89.63 177 | 88.84 199 | 92.56 116 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
MVE | | 60.41 19 | 73.21 231 | 80.84 213 | 64.30 234 | 56.34 242 | 57.24 243 | 75.28 242 | 72.76 228 | 87.14 164 | 41.39 242 | 86.31 198 | 85.30 191 | 80.66 176 | 86.17 211 | 83.36 205 | 59.35 240 | 80.38 205 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
Anonymous202405211 | | | | 94.63 75 | | 94.51 94 | 94.96 97 | 93.94 145 | 91.35 82 | 90.82 114 | | 95.60 88 | 95.85 119 | 81.74 172 | 96.47 69 | 95.84 65 | 97.39 56 | 92.85 105 |
|
Anonymous20240521 | | | 93.49 92 | 95.15 62 | 91.55 124 | 94.05 106 | 95.92 59 | 95.15 119 | 91.21 85 | 92.76 62 | 87.01 161 | 89.71 166 | 97.16 81 | 83.90 149 | 97.65 39 | 96.87 40 | 97.99 40 | 95.95 47 |
|
our_test_3 | | | | | | 91.78 183 | 88.87 188 | 94.37 133 | | | | | | | | | | |
|
SMA-MVS | | | 95.99 30 | 96.48 29 | 95.41 30 | 97.43 11 | 97.36 21 | 97.55 48 | 93.70 38 | 94.05 39 | 93.79 61 | 97.02 67 | 94.53 145 | 92.28 53 | 97.53 41 | 97.19 33 | 97.73 47 | 97.67 7 |
|
tfpn111 | | | 87.59 182 | 86.89 185 | 88.41 171 | 92.28 172 | 93.64 136 | 93.36 155 | 88.12 156 | 80.90 198 | 80.71 197 | 73.93 233 | 82.25 194 | 79.65 183 | 94.27 115 | 94.76 93 | 96.36 86 | 88.48 167 |
|
conf0.01 | | | 85.72 190 | 83.49 201 | 88.32 174 | 92.11 179 | 93.35 145 | 93.36 155 | 88.02 160 | 80.90 198 | 80.51 200 | 74.83 231 | 59.86 236 | 79.65 183 | 93.80 125 | 94.76 93 | 96.29 95 | 86.94 180 |
|
ESAPD | | | 96.00 29 | 96.80 21 | 95.06 43 | 95.87 64 | 97.47 19 | 98.25 25 | 93.73 37 | 92.38 68 | 91.57 113 | 97.55 53 | 97.97 60 | 92.98 39 | 97.49 42 | 97.61 19 | 97.96 42 | 97.16 15 |
|
conf0.002 | | | 84.82 193 | 81.84 208 | 88.30 175 | 92.05 181 | 93.28 147 | 93.36 155 | 88.00 163 | 80.90 198 | 80.48 201 | 73.43 235 | 52.48 241 | 79.65 183 | 93.72 126 | 92.82 127 | 96.28 96 | 86.22 184 |
|
thresconf0.02 | | | 84.34 197 | 82.02 207 | 87.06 186 | 92.23 176 | 90.93 176 | 91.05 195 | 86.43 185 | 88.83 144 | 77.65 215 | 73.93 233 | 55.81 237 | 79.68 182 | 90.62 176 | 90.28 170 | 95.30 126 | 83.73 192 |
|
tfpn_n400 | | | 89.03 159 | 89.39 161 | 88.61 167 | 93.98 111 | 92.33 159 | 91.83 183 | 88.97 141 | 92.97 57 | 78.90 205 | 84.93 204 | 78.24 211 | 81.77 170 | 95.00 99 | 93.67 113 | 96.22 102 | 88.59 165 |
|
tfpnconf | | | 89.03 159 | 89.39 161 | 88.61 167 | 93.98 111 | 92.33 159 | 91.83 183 | 88.97 141 | 92.97 57 | 78.90 205 | 84.93 204 | 78.24 211 | 81.77 170 | 95.00 99 | 93.67 113 | 96.22 102 | 88.59 165 |
|
tfpnview11 | | | 88.74 164 | 88.95 166 | 88.50 169 | 93.91 118 | 92.43 158 | 91.70 189 | 88.90 146 | 90.93 111 | 78.90 205 | 84.93 204 | 78.24 211 | 81.71 173 | 94.32 113 | 94.60 100 | 95.86 114 | 87.23 178 |
|
tfpn1000 | | | 88.13 176 | 88.68 172 | 87.49 184 | 93.94 116 | 92.64 155 | 91.50 191 | 88.70 150 | 90.12 121 | 74.35 220 | 86.74 196 | 75.27 217 | 80.14 179 | 94.16 121 | 94.66 99 | 96.33 93 | 87.16 179 |
|
tfpn_ndepth | | | 85.89 189 | 86.40 189 | 85.30 199 | 91.31 189 | 92.47 157 | 90.78 198 | 87.75 166 | 84.79 180 | 71.04 227 | 76.95 228 | 78.80 210 | 74.52 213 | 92.72 140 | 93.43 119 | 96.39 84 | 85.65 187 |
|
conf200view11 | | | 87.93 178 | 87.51 182 | 88.41 171 | 92.28 172 | 93.64 136 | 93.36 155 | 88.12 156 | 80.90 198 | 80.71 197 | 78.25 224 | 82.25 194 | 79.65 183 | 94.27 115 | 94.76 93 | 96.36 86 | 88.48 167 |
|
thres100view900 | | | 86.46 187 | 86.00 192 | 86.99 188 | 92.28 172 | 91.03 175 | 91.09 194 | 84.49 200 | 80.90 198 | 80.89 195 | 78.25 224 | 82.25 194 | 77.57 201 | 90.17 181 | 92.84 126 | 95.63 120 | 86.57 183 |
|
tfpnnormal | | | 92.45 123 | 94.77 72 | 89.74 150 | 93.95 114 | 93.44 144 | 93.25 162 | 88.49 152 | 95.27 22 | 83.20 182 | 96.51 74 | 96.23 109 | 83.17 154 | 95.47 84 | 94.52 103 | 96.38 85 | 91.97 129 |
|
tfpn200view9 | | | 87.94 177 | 87.51 182 | 88.44 170 | 92.28 172 | 93.63 138 | 93.35 159 | 88.11 158 | 80.90 198 | 80.89 195 | 78.25 224 | 82.25 194 | 79.65 183 | 94.27 115 | 94.76 93 | 96.36 86 | 88.48 167 |
|
view600 | | | 89.09 158 | 88.78 170 | 89.46 157 | 93.59 135 | 93.33 146 | 93.92 147 | 87.76 165 | 87.40 158 | 82.79 184 | 81.29 218 | 80.71 204 | 82.59 161 | 94.28 114 | 95.72 68 | 96.12 108 | 88.70 164 |
|
view800 | | | 89.42 154 | 89.11 164 | 89.78 148 | 94.00 107 | 93.71 133 | 93.96 144 | 88.47 153 | 88.10 150 | 82.91 183 | 82.61 216 | 79.85 207 | 83.10 155 | 94.92 101 | 95.38 74 | 96.26 100 | 89.19 157 |
|
conf0.05thres1000 | | | 91.24 139 | 91.85 138 | 90.53 135 | 94.59 92 | 94.56 111 | 94.33 138 | 89.52 128 | 93.67 44 | 83.77 180 | 91.04 150 | 79.10 209 | 83.98 146 | 96.66 66 | 95.56 72 | 96.98 73 | 92.36 121 |
|
tfpn | | | 87.65 181 | 85.66 193 | 89.96 146 | 94.36 97 | 93.94 129 | 93.85 148 | 89.02 139 | 88.71 145 | 82.78 185 | 83.79 211 | 53.79 238 | 83.43 153 | 95.35 87 | 94.54 102 | 96.35 90 | 89.51 155 |
|
v1.0 | | | 88.18 175 | 82.50 205 | 94.80 50 | 96.76 35 | 97.29 25 | 97.74 43 | 94.15 27 | 91.69 85 | 90.01 139 | 96.65 72 | 97.29 77 | 92.45 49 | 97.41 43 | 97.18 34 | 97.67 52 | 0.00 242 |
|
CHOSEN 280x420 | | | 79.24 217 | 78.26 222 | 80.38 213 | 79.60 234 | 68.80 241 | 89.32 212 | 75.38 225 | 77.25 224 | 78.02 214 | 75.57 230 | 76.17 216 | 81.19 175 | 88.61 194 | 81.39 211 | 78.79 229 | 80.03 208 |
|
CANet | | | 93.07 109 | 93.05 125 | 93.10 103 | 95.90 61 | 95.41 76 | 95.88 102 | 91.94 68 | 84.77 181 | 93.36 71 | 94.05 121 | 95.25 136 | 86.25 136 | 94.33 111 | 93.94 109 | 95.30 126 | 93.58 90 |
|
Fast-Effi-MVS+-dtu | | | 89.57 153 | 88.42 175 | 90.92 131 | 93.35 144 | 91.57 171 | 93.01 166 | 95.71 8 | 78.94 218 | 87.65 156 | 84.68 207 | 93.14 161 | 82.00 165 | 90.84 174 | 91.01 165 | 93.78 175 | 88.77 163 |
|
Effi-MVS+-dtu | | | 92.32 129 | 91.66 141 | 93.09 104 | 95.13 79 | 94.73 103 | 94.57 131 | 92.14 61 | 81.74 194 | 90.33 135 | 88.13 181 | 95.91 118 | 89.24 112 | 94.23 120 | 93.65 116 | 97.12 65 | 93.23 96 |
|
CANet_DTU | | | 88.95 162 | 89.51 160 | 88.29 176 | 93.12 153 | 91.22 174 | 93.61 150 | 83.47 207 | 80.07 209 | 90.71 130 | 89.19 172 | 93.68 156 | 76.27 209 | 91.44 170 | 91.17 164 | 92.59 186 | 89.83 150 |
|
MVS_0304 | | | 93.92 74 | 93.81 99 | 94.05 62 | 96.06 56 | 96.00 56 | 96.43 83 | 92.76 47 | 85.99 173 | 94.43 46 | 94.04 122 | 97.08 82 | 88.12 124 | 94.65 107 | 94.20 107 | 96.47 81 | 94.71 69 |
|
HSP-MVS | | | 95.04 52 | 95.45 57 | 94.57 51 | 96.87 28 | 97.77 10 | 98.71 5 | 93.88 33 | 91.21 105 | 91.48 114 | 95.36 94 | 98.37 37 | 90.73 95 | 94.37 110 | 92.98 124 | 95.77 119 | 98.08 3 |
|
TSAR-MVS + MP. | | | 95.99 30 | 96.57 27 | 95.31 33 | 96.87 28 | 96.50 44 | 98.71 5 | 91.58 76 | 93.25 51 | 92.71 85 | 96.86 69 | 96.57 100 | 93.92 24 | 98.09 20 | 97.91 13 | 98.08 35 | 96.81 31 |
|
OPM-MVS | | | 95.96 32 | 96.59 26 | 95.23 36 | 96.67 39 | 96.52 43 | 97.86 38 | 93.28 43 | 95.27 22 | 93.46 70 | 96.26 77 | 98.85 16 | 92.89 44 | 97.09 50 | 96.37 50 | 97.22 64 | 95.78 51 |
|
ACMMP_Plus | | | 95.86 37 | 96.18 38 | 95.47 29 | 97.11 22 | 97.26 26 | 98.37 21 | 93.48 42 | 93.49 46 | 93.99 55 | 95.61 86 | 94.11 150 | 92.49 48 | 97.87 30 | 97.44 26 | 97.40 55 | 97.52 8 |
|
ambc | | | | 94.61 77 | | 98.09 5 | 95.14 87 | 91.71 188 | | 94.18 37 | 96.46 14 | 96.26 77 | 96.30 106 | 91.26 76 | 94.70 105 | 92.00 143 | 93.45 177 | 93.67 87 |
|
zzz-MVS | | | 96.18 22 | 96.01 43 | 96.38 8 | 98.30 2 | 96.18 51 | 98.51 14 | 94.48 21 | 94.56 28 | 94.81 42 | 91.73 147 | 96.96 86 | 94.30 22 | 98.09 20 | 97.83 15 | 97.91 43 | 96.73 32 |
|
Effi-MVS+ | | | 92.93 112 | 92.16 136 | 93.83 74 | 94.29 98 | 93.53 142 | 95.04 121 | 92.98 46 | 85.27 178 | 94.46 44 | 90.24 162 | 95.34 131 | 89.99 108 | 93.72 126 | 94.23 106 | 96.22 102 | 92.79 108 |
|
new-patchmatchnet | | | 84.45 196 | 88.75 171 | 79.43 216 | 93.28 145 | 81.87 217 | 81.68 235 | 83.48 206 | 94.47 29 | 71.53 226 | 98.33 18 | 97.88 66 | 58.61 230 | 90.35 177 | 77.33 222 | 87.99 201 | 81.05 202 |
|
pmmvs6 | | | 94.58 59 | 97.30 15 | 91.40 127 | 94.84 85 | 94.61 107 | 93.40 154 | 92.43 53 | 98.51 2 | 85.61 169 | 98.73 9 | 99.53 2 | 84.40 145 | 97.88 29 | 97.03 38 | 97.72 48 | 94.79 67 |
|
pmmvs5 | | | 88.63 165 | 89.70 158 | 87.39 185 | 89.24 208 | 90.64 180 | 91.87 182 | 82.13 211 | 83.34 189 | 87.86 155 | 94.58 111 | 96.15 113 | 79.87 180 | 87.33 205 | 89.07 185 | 93.39 179 | 86.76 181 |
|
Fast-Effi-MVS+ | | | 92.93 112 | 92.64 130 | 93.27 93 | 93.81 124 | 93.88 130 | 95.90 101 | 90.61 100 | 83.98 186 | 92.71 85 | 92.81 135 | 96.22 110 | 90.67 97 | 94.90 103 | 93.92 110 | 95.92 113 | 92.77 109 |
|
Anonymous20231211 | | | 93.19 104 | 95.50 55 | 90.49 137 | 93.77 126 | 95.29 83 | 94.36 137 | 90.04 110 | 91.44 97 | 84.59 174 | 96.72 71 | 97.65 71 | 82.45 162 | 97.25 47 | 96.32 53 | 97.74 46 | 93.79 86 |
|
pmmvs-eth3d | | | 92.34 127 | 92.33 132 | 92.34 114 | 92.67 163 | 90.67 179 | 96.37 93 | 89.06 138 | 90.98 110 | 93.60 68 | 97.13 64 | 97.02 84 | 88.29 122 | 90.20 179 | 91.42 159 | 94.07 171 | 88.89 162 |
|
GG-mvs-BLEND | | | 54.28 236 | 77.89 226 | 26.72 238 | 0.37 246 | 83.31 212 | 70.04 243 | 0.39 243 | 74.71 230 | 5.36 245 | 68.78 238 | 83.06 193 | 0.62 243 | 83.73 220 | 78.99 220 | 83.55 221 | 72.68 230 |
|
Anonymous20231206 | | | 87.45 183 | 89.66 159 | 84.87 201 | 94.00 107 | 87.73 195 | 91.36 192 | 86.41 186 | 88.89 142 | 75.03 217 | 92.59 138 | 96.82 93 | 72.48 216 | 89.72 185 | 88.06 190 | 89.93 195 | 83.81 191 |
|
MTAPA | | | | | | | | | | | 94.88 39 | | 96.88 90 | | | | | |
|
MTMP | | | | | | | | | | | 95.43 21 | | 97.25 78 | | | | | |
|
gm-plane-assit | | | 86.15 188 | 82.51 204 | 90.40 138 | 95.81 65 | 92.29 161 | 97.99 32 | 84.66 199 | 92.15 76 | 93.15 79 | 97.84 37 | 44.65 243 | 78.60 190 | 88.02 202 | 85.95 198 | 92.20 188 | 76.69 218 |
|
train_agg | | | 93.89 77 | 93.46 114 | 94.40 54 | 97.35 14 | 93.78 132 | 97.63 46 | 92.19 59 | 88.12 149 | 90.52 132 | 93.57 128 | 95.78 120 | 92.31 52 | 94.78 104 | 93.46 117 | 96.36 86 | 94.70 71 |
|
gg-mvs-nofinetune | | | 88.32 168 | 88.81 168 | 87.75 182 | 93.07 155 | 89.37 186 | 89.06 213 | 95.94 7 | 95.29 20 | 87.15 158 | 97.38 58 | 76.38 215 | 68.05 222 | 91.04 173 | 89.10 184 | 93.24 181 | 83.10 196 |
|
MS-PatchMatch | | | 87.72 180 | 88.62 174 | 86.66 192 | 90.81 199 | 88.18 189 | 90.92 196 | 82.25 210 | 85.86 174 | 80.40 202 | 90.14 164 | 89.29 179 | 84.93 140 | 89.39 189 | 89.12 183 | 90.67 192 | 88.34 171 |
|
Patchmatch-RL test | | | | | | | | 8.96 246 | | | | | | | | | | |
|
tmp_tt | | | | | 28.44 237 | 36.05 243 | 15.86 245 | 21.29 245 | 6.40 240 | 54.52 242 | 51.96 240 | 50.37 241 | 38.68 246 | 9.55 240 | 61.75 240 | 59.66 238 | 45.36 242 | |
|
canonicalmvs | | | 93.38 97 | 94.36 84 | 92.24 116 | 93.94 116 | 96.41 48 | 94.18 141 | 90.47 103 | 93.07 55 | 88.47 151 | 88.66 175 | 93.78 154 | 88.80 116 | 95.74 81 | 95.75 67 | 97.57 53 | 97.13 16 |
|
anonymousdsp | | | 95.45 45 | 96.70 25 | 93.99 66 | 88.43 214 | 92.05 167 | 99.18 1 | 85.42 194 | 94.29 35 | 96.10 16 | 98.63 12 | 99.08 10 | 96.11 1 | 97.77 36 | 97.41 29 | 98.70 8 | 97.69 6 |
|
v144192 | | | 93.89 77 | 93.85 95 | 93.94 70 | 93.50 137 | 94.33 112 | 97.12 49 | 89.49 129 | 90.89 112 | 96.49 13 | 97.78 41 | 98.27 40 | 91.89 58 | 92.17 157 | 91.70 148 | 95.19 131 | 91.78 134 |
|
v1921920 | | | 93.90 76 | 93.82 97 | 94.00 65 | 93.74 129 | 94.31 113 | 97.12 49 | 89.33 135 | 91.13 106 | 96.77 10 | 97.90 33 | 98.06 54 | 91.95 56 | 91.93 165 | 91.54 153 | 95.10 135 | 91.85 131 |
|
FC-MVSNet-train | | | 92.75 118 | 95.40 58 | 89.66 153 | 95.21 76 | 94.82 100 | 97.00 54 | 89.40 132 | 91.13 106 | 81.71 193 | 97.72 43 | 96.43 104 | 77.57 201 | 96.89 57 | 96.72 43 | 97.05 70 | 94.09 80 |
|
UA-Net | | | 96.56 12 | 96.73 23 | 96.36 10 | 98.99 1 | 97.90 7 | 97.79 42 | 95.64 9 | 92.78 60 | 92.54 90 | 96.23 80 | 95.02 140 | 94.31 21 | 98.43 14 | 98.12 11 | 98.89 3 | 98.58 2 |
|
v1192 | | | 93.98 71 | 93.94 92 | 94.01 64 | 93.91 118 | 94.63 105 | 97.00 54 | 89.75 118 | 91.01 109 | 96.50 12 | 97.93 32 | 98.26 41 | 91.74 62 | 92.06 158 | 92.05 140 | 95.18 132 | 91.66 136 |
|
FC-MVSNet-test | | | 91.49 138 | 94.43 82 | 88.07 179 | 94.97 81 | 90.53 182 | 95.42 116 | 91.18 87 | 93.24 52 | 72.94 223 | 98.37 17 | 93.86 153 | 78.78 188 | 97.82 34 | 96.13 58 | 95.13 133 | 91.05 139 |
|
v1144 | | | 93.83 81 | 93.87 93 | 93.78 77 | 93.72 130 | 94.57 110 | 96.85 61 | 89.98 111 | 91.31 102 | 95.90 19 | 97.89 34 | 98.40 35 | 91.13 82 | 92.01 161 | 92.01 142 | 95.10 135 | 90.94 140 |
|
sosnet-low-res | | | 0.00 239 | 0.00 241 | 0.00 241 | 0.00 247 | 0.00 248 | 0.00 249 | 0.00 244 | 0.00 245 | 0.00 248 | 0.00 244 | 0.00 249 | 0.00 244 | 0.00 243 | 0.00 242 | 0.00 246 | 0.00 242 |
|
HFP-MVS | | | 96.18 22 | 96.53 28 | 95.77 21 | 97.34 16 | 97.26 26 | 98.16 30 | 94.54 17 | 94.45 30 | 92.52 91 | 95.05 103 | 96.95 87 | 93.89 26 | 97.28 45 | 97.46 24 | 98.19 30 | 97.25 9 |
|
v148 | | | 92.38 126 | 92.78 128 | 91.91 120 | 92.86 160 | 92.13 166 | 94.84 124 | 87.03 176 | 91.47 93 | 93.07 82 | 96.92 68 | 98.89 13 | 90.10 107 | 92.05 159 | 89.69 175 | 93.56 176 | 88.27 172 |
|
sosnet | | | 0.00 239 | 0.00 241 | 0.00 241 | 0.00 247 | 0.00 248 | 0.00 249 | 0.00 244 | 0.00 245 | 0.00 248 | 0.00 244 | 0.00 249 | 0.00 244 | 0.00 243 | 0.00 242 | 0.00 246 | 0.00 242 |
|
v748 | | | 96.05 28 | 97.00 19 | 94.95 46 | 94.41 95 | 94.77 102 | 96.72 67 | 91.03 91 | 96.12 15 | 96.71 11 | 98.74 7 | 99.59 1 | 93.55 31 | 97.97 28 | 95.96 60 | 97.28 60 | 95.84 49 |
|
v7n | | | 96.49 14 | 97.20 17 | 95.65 23 | 95.57 71 | 96.04 54 | 97.93 36 | 92.49 51 | 96.40 11 | 97.13 7 | 98.99 4 | 99.41 3 | 93.79 28 | 97.84 33 | 96.15 56 | 97.00 72 | 95.60 54 |
|
v1141 | | | 93.47 94 | 93.56 109 | 93.36 87 | 93.48 138 | 94.17 121 | 96.42 86 | 89.62 121 | 91.44 97 | 94.99 35 | 97.81 39 | 98.42 33 | 90.94 92 | 92.00 162 | 91.38 160 | 94.74 153 | 89.69 153 |
|
v1neww | | | 93.27 100 | 93.40 118 | 93.12 100 | 93.13 149 | 94.20 117 | 96.39 89 | 89.56 124 | 89.87 129 | 93.95 56 | 97.71 45 | 98.21 46 | 91.09 84 | 92.36 150 | 91.49 154 | 94.62 159 | 89.96 147 |
|
DI_MVS_plusplus_trai | | | 90.68 142 | 90.40 152 | 91.00 130 | 92.43 167 | 92.61 156 | 94.17 142 | 88.98 140 | 88.32 148 | 88.76 149 | 93.67 127 | 87.58 183 | 86.44 135 | 89.74 184 | 90.33 169 | 95.24 130 | 90.56 144 |
|
v7new | | | 93.27 100 | 93.40 118 | 93.12 100 | 93.13 149 | 94.20 117 | 96.39 89 | 89.56 124 | 89.87 129 | 93.95 56 | 97.71 45 | 98.21 46 | 91.09 84 | 92.36 150 | 91.49 154 | 94.62 159 | 89.96 147 |
|
HPM-MVS++ | | | 95.21 50 | 94.89 67 | 95.59 24 | 97.79 6 | 95.39 78 | 97.68 44 | 94.05 29 | 91.91 82 | 94.35 48 | 93.38 129 | 95.07 139 | 92.94 42 | 96.01 76 | 95.88 64 | 96.73 75 | 96.61 36 |
|
XVS | | | | | | 96.86 30 | 97.48 16 | 98.73 3 | | | 93.28 73 | | 96.82 93 | | | | 98.17 32 | |
|
v1240 | | | 93.89 77 | 93.72 102 | 94.09 59 | 93.98 111 | 94.31 113 | 97.12 49 | 89.37 133 | 90.74 117 | 96.92 9 | 98.05 29 | 97.89 63 | 92.15 55 | 91.53 169 | 91.60 151 | 94.99 141 | 91.93 130 |
|
v18 | | | 93.33 99 | 93.59 107 | 93.04 105 | 92.94 157 | 94.87 99 | 96.31 96 | 90.59 101 | 88.96 139 | 92.89 84 | 97.51 54 | 97.90 62 | 91.01 90 | 92.33 154 | 91.48 157 | 94.50 164 | 92.05 127 |
|
pm-mvs1 | | | 93.27 100 | 95.94 45 | 90.16 141 | 94.13 104 | 93.66 134 | 92.61 174 | 89.91 115 | 95.73 17 | 84.28 178 | 98.51 15 | 98.29 39 | 82.80 157 | 96.44 70 | 95.76 66 | 97.25 62 | 93.21 97 |
|
X-MVStestdata | | | | | | 96.86 30 | 97.48 16 | 98.73 3 | | | 93.28 73 | | 96.82 93 | | | | 98.17 32 | |
|
v17 | | | 93.60 86 | 93.85 95 | 93.30 92 | 93.15 148 | 94.99 94 | 96.46 81 | 90.81 95 | 89.58 134 | 93.61 67 | 97.66 49 | 98.15 50 | 91.19 78 | 92.60 145 | 91.61 150 | 94.61 161 | 92.37 120 |
|
v16 | | | 93.53 91 | 93.80 100 | 93.20 97 | 93.10 154 | 94.98 95 | 96.43 83 | 90.81 95 | 89.39 137 | 93.12 81 | 97.63 51 | 98.01 58 | 91.19 78 | 92.60 145 | 91.65 149 | 94.58 163 | 92.36 121 |
|
divwei89l23v2f112 | | | 93.47 94 | 93.56 109 | 93.37 85 | 93.48 138 | 94.17 121 | 96.42 86 | 89.62 121 | 91.46 94 | 95.00 33 | 97.81 39 | 98.42 33 | 90.94 92 | 92.00 162 | 91.38 160 | 94.75 151 | 89.70 151 |
|
v15 | | | 94.09 70 | 94.37 83 | 93.77 78 | 93.56 136 | 95.18 85 | 96.68 72 | 91.34 83 | 91.64 88 | 94.83 41 | 98.09 27 | 98.51 30 | 91.37 72 | 92.84 138 | 91.80 146 | 94.85 147 | 92.53 118 |
|
v13 | | | 94.54 62 | 94.93 65 | 94.09 59 | 93.81 124 | 95.44 73 | 96.99 56 | 91.67 74 | 92.43 67 | 95.20 27 | 98.33 18 | 98.73 20 | 91.87 59 | 93.67 128 | 92.26 133 | 95.00 140 | 93.63 89 |
|
v12 | | | 94.44 63 | 94.79 71 | 94.02 63 | 93.75 128 | 95.37 79 | 96.92 57 | 91.61 75 | 92.21 73 | 95.10 29 | 98.27 22 | 98.69 22 | 91.73 63 | 93.49 130 | 92.15 138 | 94.97 144 | 93.37 94 |
|
X-MVS | | | 95.33 48 | 95.13 63 | 95.57 26 | 97.35 14 | 97.48 16 | 98.43 16 | 94.28 23 | 92.30 72 | 93.28 73 | 86.89 194 | 96.82 93 | 91.87 59 | 97.85 31 | 97.59 21 | 98.19 30 | 96.95 25 |
|
v8 | | | 93.60 86 | 93.82 97 | 93.34 88 | 93.13 149 | 95.06 89 | 96.39 89 | 90.75 97 | 89.90 127 | 94.03 54 | 97.70 47 | 98.21 46 | 91.08 86 | 92.36 150 | 91.47 158 | 94.63 157 | 92.07 126 |
|
v7 | | | 93.65 84 | 93.73 101 | 93.57 82 | 93.38 142 | 94.60 108 | 96.83 63 | 89.92 114 | 89.69 132 | 95.02 31 | 97.89 34 | 98.24 42 | 91.27 74 | 92.38 149 | 92.18 136 | 94.99 141 | 91.12 138 |
|
v6 | | | 93.27 100 | 93.41 116 | 93.12 100 | 93.13 149 | 94.20 117 | 96.39 89 | 89.55 126 | 89.89 128 | 93.93 58 | 97.72 43 | 98.22 45 | 91.10 83 | 92.36 150 | 91.49 154 | 94.63 157 | 89.95 149 |
|
v11 | | | 94.32 66 | 94.62 76 | 93.97 67 | 93.95 114 | 95.31 80 | 96.83 63 | 91.30 84 | 91.95 80 | 95.51 20 | 98.32 20 | 98.61 25 | 91.44 70 | 92.83 139 | 92.23 135 | 94.77 150 | 93.08 101 |
|
v52 | | | 96.35 16 | 97.40 13 | 95.12 40 | 93.83 122 | 95.54 69 | 97.82 40 | 88.95 144 | 96.27 13 | 97.22 5 | 99.11 2 | 99.40 4 | 95.80 5 | 98.16 18 | 96.37 50 | 97.10 67 | 96.96 23 |
|
V14 | | | 94.21 69 | 94.52 79 | 93.85 73 | 93.62 134 | 95.25 84 | 96.76 66 | 91.42 79 | 91.83 83 | 94.91 38 | 98.15 24 | 98.57 27 | 91.49 69 | 93.06 137 | 91.93 144 | 94.90 146 | 92.82 106 |
|
v10 | | | 93.96 72 | 94.12 90 | 93.77 78 | 93.37 143 | 95.45 72 | 96.83 63 | 91.13 88 | 89.70 131 | 95.02 31 | 97.88 36 | 98.23 43 | 91.27 74 | 92.39 148 | 92.18 136 | 94.99 141 | 93.00 103 |
|
V4 | | | 96.35 16 | 97.40 13 | 95.12 40 | 93.83 122 | 95.54 69 | 97.82 40 | 88.95 144 | 96.27 13 | 97.21 6 | 99.10 3 | 99.40 4 | 95.79 6 | 98.17 17 | 96.37 50 | 97.10 67 | 96.96 23 |
|
v2v482 | | | 93.42 96 | 93.49 113 | 93.32 89 | 93.44 141 | 94.05 123 | 96.36 95 | 89.76 117 | 91.41 99 | 95.24 25 | 97.63 51 | 98.34 38 | 90.44 105 | 91.65 167 | 91.76 147 | 94.69 154 | 89.62 154 |
|
v1 | | | 93.48 93 | 93.57 108 | 93.37 85 | 93.48 138 | 94.18 120 | 96.41 88 | 89.61 123 | 91.46 94 | 95.03 30 | 97.82 38 | 98.43 32 | 90.95 91 | 92.00 162 | 91.37 162 | 94.75 151 | 89.70 151 |
|
V42 | | | 92.67 119 | 93.50 112 | 91.71 123 | 91.41 187 | 92.96 149 | 95.71 108 | 85.00 195 | 89.67 133 | 93.22 76 | 97.67 48 | 98.01 58 | 91.02 89 | 92.65 142 | 92.12 139 | 93.86 173 | 91.42 137 |
|
V9 | | | 94.33 65 | 94.66 74 | 93.94 70 | 93.69 132 | 95.31 80 | 96.84 62 | 91.53 78 | 92.04 79 | 95.00 33 | 98.22 23 | 98.64 23 | 91.62 65 | 93.29 132 | 92.05 140 | 94.93 145 | 93.10 99 |
|
SD-MVS | | | 95.77 40 | 96.17 39 | 95.30 34 | 96.72 37 | 96.19 50 | 97.01 53 | 93.04 45 | 94.03 40 | 92.71 85 | 96.45 75 | 96.78 97 | 93.91 25 | 96.79 62 | 95.89 63 | 98.42 24 | 97.09 18 |
|
GA-MVS | | | 88.76 163 | 88.04 178 | 89.59 154 | 92.32 171 | 91.46 172 | 92.28 179 | 86.62 181 | 83.82 188 | 89.84 141 | 92.51 139 | 81.94 199 | 83.53 152 | 89.41 188 | 89.27 181 | 92.95 184 | 87.90 173 |
|
MSLP-MVS++ | | | 93.91 75 | 94.30 87 | 93.45 84 | 95.51 72 | 95.83 62 | 93.12 164 | 91.93 69 | 91.45 96 | 91.40 116 | 87.42 189 | 96.12 114 | 93.27 34 | 96.57 68 | 96.40 49 | 95.49 123 | 96.29 39 |
|
APDe-MVS | | | 96.23 20 | 97.22 16 | 95.08 42 | 96.66 40 | 97.56 14 | 98.63 9 | 93.69 39 | 94.62 27 | 89.80 142 | 97.73 42 | 98.13 51 | 93.84 27 | 97.79 35 | 97.63 18 | 97.87 44 | 97.08 19 |
|
TSAR-MVS + COLMAP | | | 93.06 110 | 93.65 104 | 92.36 112 | 94.62 89 | 94.28 115 | 95.36 118 | 89.46 131 | 92.18 75 | 91.64 111 | 95.55 90 | 95.27 135 | 88.60 119 | 93.24 133 | 92.50 130 | 94.46 165 | 92.55 117 |
|
CVMVSNet | | | 88.97 161 | 89.73 157 | 88.10 178 | 87.33 222 | 85.22 202 | 94.68 129 | 78.68 218 | 88.94 141 | 86.98 162 | 95.55 90 | 85.71 189 | 89.87 109 | 91.19 172 | 89.69 175 | 91.05 191 | 91.78 134 |
|
TSAR-MVS + ACMM | | | 95.17 51 | 95.95 44 | 94.26 56 | 96.07 55 | 96.46 45 | 95.67 110 | 94.21 26 | 93.84 42 | 90.99 125 | 97.18 62 | 95.24 137 | 93.55 31 | 96.60 67 | 95.61 71 | 95.06 138 | 96.69 34 |
|
pmmvs4 | | | 89.95 150 | 89.32 163 | 90.69 134 | 91.60 186 | 89.17 187 | 94.37 133 | 87.63 167 | 88.07 153 | 91.02 124 | 94.50 113 | 90.50 174 | 86.13 137 | 86.33 209 | 89.40 179 | 93.39 179 | 87.29 177 |
|
EU-MVSNet | | | 91.63 137 | 92.73 129 | 90.35 139 | 88.36 215 | 87.89 193 | 96.53 77 | 81.51 215 | 92.45 66 | 91.82 108 | 96.44 76 | 97.05 83 | 93.26 35 | 94.10 122 | 88.94 186 | 90.61 193 | 92.24 124 |
|
test-LLR | | | 80.62 213 | 77.20 230 | 84.62 205 | 93.99 109 | 75.11 232 | 87.04 220 | 87.32 172 | 70.11 236 | 78.59 212 | 83.17 213 | 71.60 220 | 73.88 214 | 82.32 222 | 79.20 218 | 86.91 207 | 78.87 214 |
|
TESTMET0.1,1 | | | 77.47 227 | 77.20 230 | 77.78 224 | 81.94 231 | 75.11 232 | 87.04 220 | 58.33 238 | 70.11 236 | 78.59 212 | 83.17 213 | 71.60 220 | 73.88 214 | 82.32 222 | 79.20 218 | 86.91 207 | 78.87 214 |
|
test-mter | | | 78.71 222 | 78.35 221 | 79.12 221 | 84.03 229 | 76.58 228 | 88.51 216 | 59.06 236 | 71.06 234 | 78.87 208 | 83.73 212 | 71.83 219 | 76.44 208 | 83.41 221 | 80.61 213 | 87.79 204 | 81.24 200 |
|
ACMMPR | | | 96.54 13 | 96.71 24 | 96.35 11 | 97.55 9 | 97.63 11 | 98.62 10 | 94.54 17 | 94.45 30 | 94.19 50 | 95.04 105 | 97.35 76 | 94.92 13 | 97.85 31 | 97.50 23 | 98.26 28 | 97.17 14 |
|
testgi | | | 86.49 186 | 90.31 153 | 82.03 209 | 95.63 70 | 88.18 189 | 93.47 152 | 84.89 197 | 93.23 53 | 69.54 232 | 87.16 191 | 97.96 61 | 60.66 227 | 91.90 166 | 89.90 173 | 87.99 201 | 83.84 190 |
|
test20.03 | | | 88.20 173 | 91.26 146 | 84.63 204 | 96.64 41 | 89.39 185 | 90.73 200 | 89.97 112 | 91.07 108 | 72.02 225 | 94.98 106 | 95.45 127 | 69.35 218 | 92.70 141 | 91.19 163 | 89.06 198 | 84.02 189 |
|
thres600view7 | | | 89.14 157 | 88.83 167 | 89.51 156 | 93.71 131 | 93.55 140 | 93.93 146 | 88.02 160 | 87.30 160 | 82.40 188 | 81.18 219 | 80.63 205 | 82.69 160 | 94.27 115 | 95.90 62 | 96.27 98 | 88.94 161 |
|
1111 | | | 76.85 228 | 78.03 225 | 75.46 228 | 94.16 101 | 78.29 225 | 86.40 226 | 89.12 136 | 87.23 161 | 61.26 236 | 95.15 100 | 44.14 244 | 51.46 236 | 86.04 212 | 81.00 212 | 70.40 239 | 74.37 224 |
|
.test1245 | | | 60.07 235 | 56.75 238 | 63.93 235 | 94.16 101 | 78.29 225 | 86.40 226 | 89.12 136 | 87.23 161 | 61.26 236 | 95.15 100 | 44.14 244 | 51.46 236 | 86.04 212 | 2.51 240 | 1.21 244 | 3.92 240 |
|
ADS-MVSNet | | | 79.11 219 | 79.38 216 | 78.80 222 | 81.90 232 | 75.59 230 | 84.36 231 | 83.69 203 | 87.31 159 | 76.76 216 | 87.58 187 | 76.90 214 | 68.55 221 | 78.70 230 | 75.56 226 | 77.53 230 | 74.07 226 |
|
MP-MVS | | | 96.13 24 | 95.93 46 | 96.37 9 | 98.19 4 | 97.31 24 | 98.49 15 | 94.53 20 | 91.39 100 | 94.38 47 | 94.32 117 | 96.43 104 | 94.59 17 | 97.75 37 | 97.44 26 | 98.04 38 | 96.88 28 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
testmvs | | | 2.38 237 | 3.35 239 | 1.26 240 | 0.83 244 | 0.96 247 | 1.53 247 | 0.83 241 | 3.59 243 | 1.63 247 | 6.03 242 | 2.93 247 | 1.55 242 | 3.49 241 | 2.51 240 | 1.21 244 | 3.92 240 |
|
thres400 | | | 88.54 166 | 88.15 177 | 88.98 160 | 93.17 147 | 92.84 150 | 93.56 151 | 86.93 178 | 86.45 168 | 82.37 189 | 79.96 221 | 81.46 202 | 81.83 168 | 93.21 135 | 94.76 93 | 96.04 109 | 88.39 170 |
|
test123 | | | 2.16 238 | 2.82 240 | 1.41 239 | 0.62 245 | 1.18 246 | 1.53 247 | 0.82 242 | 2.78 244 | 2.27 246 | 4.18 243 | 1.98 248 | 1.64 241 | 2.58 242 | 3.01 239 | 1.56 243 | 4.00 239 |
|
thres200 | | | 88.29 170 | 87.88 179 | 88.76 164 | 92.50 166 | 93.55 140 | 92.47 177 | 88.02 160 | 84.80 179 | 81.44 194 | 79.28 223 | 82.20 198 | 81.83 168 | 94.27 115 | 93.67 113 | 96.27 98 | 87.40 176 |
|
test0.0.03 1 | | | 81.51 209 | 83.30 203 | 79.42 217 | 93.99 109 | 86.50 199 | 85.93 230 | 87.32 172 | 78.16 221 | 61.62 235 | 80.78 220 | 81.78 200 | 59.87 228 | 88.40 198 | 87.27 194 | 87.78 205 | 80.19 206 |
|
test12356 | | | 75.40 230 | 80.89 212 | 69.01 232 | 77.43 238 | 75.75 229 | 83.03 233 | 61.48 234 | 78.13 222 | 59.08 238 | 87.69 186 | 94.95 142 | 57.37 231 | 88.18 200 | 80.59 214 | 75.65 234 | 60.93 236 |
|
testus | | | 78.20 224 | 81.50 210 | 74.36 230 | 85.59 225 | 79.36 222 | 86.99 222 | 65.76 231 | 76.01 226 | 73.00 222 | 77.98 227 | 93.35 160 | 51.30 238 | 86.33 209 | 82.79 207 | 83.50 222 | 74.68 223 |
|
pmmvs3 | | | 81.69 207 | 83.83 198 | 79.19 220 | 78.33 236 | 78.57 224 | 89.53 210 | 58.71 237 | 78.88 219 | 84.34 177 | 88.36 178 | 91.96 165 | 77.69 200 | 87.48 203 | 82.42 208 | 86.54 209 | 79.18 213 |
|
testmv | | | 81.49 211 | 84.76 195 | 77.67 226 | 87.67 218 | 80.25 220 | 90.12 204 | 77.62 220 | 80.34 207 | 69.71 229 | 90.92 154 | 96.47 102 | 56.57 232 | 88.58 196 | 84.92 203 | 84.33 217 | 71.86 232 |
|
EMVS | | | 77.65 226 | 77.49 229 | 77.83 223 | 87.75 217 | 71.02 238 | 81.13 236 | 70.54 230 | 86.38 169 | 74.52 219 | 89.38 170 | 80.19 206 | 78.22 196 | 89.48 187 | 67.13 236 | 74.83 237 | 58.84 238 |
|
E-PMN | | | 77.81 225 | 77.88 227 | 77.73 225 | 88.26 216 | 70.48 239 | 80.19 237 | 71.20 229 | 86.66 166 | 72.89 224 | 88.09 182 | 81.74 201 | 78.75 189 | 90.02 183 | 68.30 235 | 75.10 235 | 59.85 237 |
|
test2356 | | | 72.95 232 | 71.24 236 | 74.95 229 | 84.89 228 | 75.49 231 | 82.67 234 | 75.38 225 | 68.02 239 | 68.65 233 | 74.40 232 | 52.81 240 | 55.61 235 | 81.50 225 | 79.80 216 | 82.50 224 | 66.70 235 |
|
test1235678 | | | 81.50 210 | 84.78 194 | 77.67 226 | 87.67 218 | 80.27 219 | 90.12 204 | 77.62 220 | 80.36 206 | 69.71 229 | 90.93 153 | 96.51 101 | 56.57 232 | 88.60 195 | 84.93 202 | 84.34 216 | 71.87 231 |
|
PGM-MVS | | | 95.90 35 | 95.72 49 | 96.10 16 | 97.53 10 | 97.45 20 | 98.55 13 | 94.12 28 | 90.25 119 | 93.71 65 | 93.20 131 | 97.18 80 | 94.63 16 | 97.68 38 | 97.34 32 | 98.08 35 | 96.97 22 |
|
MCST-MVS | | | 93.60 86 | 93.40 118 | 93.83 74 | 95.30 74 | 95.40 77 | 96.49 79 | 90.87 94 | 90.08 122 | 91.72 110 | 90.28 161 | 95.99 117 | 91.69 64 | 93.94 124 | 92.99 123 | 96.93 74 | 95.13 63 |
|
MVS_Test | | | 90.19 146 | 90.58 149 | 89.74 150 | 92.12 178 | 91.74 170 | 92.51 175 | 88.54 151 | 82.80 191 | 87.50 157 | 94.62 110 | 95.02 140 | 83.97 147 | 88.69 193 | 89.32 180 | 93.79 174 | 91.85 131 |
|
MDA-MVSNet-bldmvs | | | 89.75 151 | 91.67 140 | 87.50 183 | 74.25 240 | 90.88 177 | 94.68 129 | 85.89 188 | 91.64 88 | 91.03 123 | 95.86 83 | 94.35 148 | 89.10 114 | 96.87 59 | 86.37 197 | 90.04 194 | 85.72 186 |
|
CDPH-MVS | | | 93.96 72 | 93.86 94 | 94.08 61 | 96.31 49 | 95.84 61 | 96.92 57 | 91.85 70 | 87.21 163 | 91.25 121 | 92.83 134 | 96.06 115 | 91.05 87 | 95.57 83 | 94.81 89 | 97.12 65 | 94.72 68 |
|
casdiffmvs | | | 91.65 136 | 91.03 148 | 92.36 112 | 93.69 132 | 94.95 98 | 95.60 113 | 91.36 81 | 85.32 176 | 91.43 115 | 91.77 144 | 95.47 126 | 87.29 128 | 88.58 196 | 88.39 189 | 94.81 149 | 92.75 110 |
|
diffmvs | | | 90.35 144 | 91.54 143 | 88.96 161 | 90.90 197 | 92.20 164 | 91.93 181 | 88.45 154 | 86.34 170 | 86.36 164 | 93.13 133 | 96.99 85 | 83.54 151 | 90.32 178 | 88.69 188 | 92.59 186 | 93.09 100 |
|
casdiffmvs1 | | | 93.02 111 | 93.07 124 | 92.96 107 | 94.93 83 | 95.42 74 | 96.24 97 | 90.96 93 | 91.68 87 | 92.69 88 | 93.74 126 | 96.88 90 | 87.86 125 | 90.19 180 | 89.56 178 | 95.09 137 | 94.03 81 |
|
PMMVS2 | | | 69.86 234 | 82.14 206 | 55.52 236 | 75.19 239 | 63.08 242 | 75.52 241 | 60.97 235 | 88.50 147 | 25.11 244 | 91.77 144 | 96.44 103 | 25.43 239 | 88.70 192 | 79.34 217 | 70.93 238 | 67.17 233 |
|
PM-MVS | | | 92.65 120 | 93.20 123 | 92.00 119 | 92.11 179 | 90.16 183 | 95.99 100 | 84.81 198 | 91.31 102 | 92.41 94 | 95.87 82 | 96.64 99 | 92.35 51 | 93.65 129 | 92.91 125 | 94.34 168 | 91.85 131 |
|
PS-CasMVS | | | 97.22 5 | 97.84 6 | 96.50 5 | 97.08 23 | 97.92 6 | 98.17 29 | 97.02 2 | 94.71 26 | 95.32 24 | 98.52 14 | 98.97 11 | 92.91 43 | 99.04 4 | 98.47 5 | 98.49 18 | 97.24 11 |
|
UniMVSNet_NR-MVSNet | | | 95.34 47 | 95.51 54 | 95.14 39 | 95.80 66 | 96.55 39 | 96.61 75 | 94.79 15 | 90.04 124 | 93.78 62 | 97.51 54 | 97.25 78 | 91.19 78 | 96.68 64 | 96.31 54 | 98.65 11 | 94.22 77 |
|
PEN-MVS | | | 97.16 6 | 97.87 5 | 96.33 12 | 97.20 20 | 97.97 4 | 98.25 25 | 96.86 5 | 95.09 24 | 94.93 36 | 98.66 10 | 99.16 7 | 92.27 54 | 98.98 6 | 98.39 7 | 98.49 18 | 96.83 30 |
|
TransMVSNet (Re) | | | 93.55 90 | 96.32 34 | 90.32 140 | 94.38 96 | 94.05 123 | 93.30 161 | 89.53 127 | 97.15 7 | 85.12 171 | 98.83 5 | 97.89 63 | 82.21 163 | 96.75 63 | 96.14 57 | 97.35 58 | 93.46 92 |
|
DTE-MVSNet | | | 97.16 6 | 97.75 8 | 96.47 6 | 97.40 12 | 97.95 5 | 98.20 28 | 96.89 4 | 95.30 19 | 95.15 28 | 98.66 10 | 98.80 17 | 92.77 47 | 98.97 7 | 98.27 9 | 98.44 22 | 96.28 40 |
|
DU-MVS | | | 95.51 42 | 95.68 50 | 95.33 32 | 96.45 46 | 96.44 46 | 96.61 75 | 95.32 10 | 89.97 125 | 93.78 62 | 97.46 56 | 98.07 53 | 91.19 78 | 97.03 51 | 96.53 45 | 98.61 13 | 94.22 77 |
|
UniMVSNet (Re) | | | 95.46 43 | 95.86 47 | 95.00 45 | 96.09 53 | 96.60 38 | 96.68 72 | 94.99 12 | 90.36 118 | 92.13 100 | 97.64 50 | 98.13 51 | 91.38 71 | 96.90 56 | 96.74 42 | 98.73 6 | 94.63 72 |
|
CP-MVSNet | | | 96.97 10 | 97.42 12 | 96.44 7 | 97.06 24 | 97.82 8 | 98.12 31 | 96.98 3 | 93.50 45 | 95.21 26 | 97.98 30 | 98.44 31 | 92.83 46 | 98.93 8 | 98.37 8 | 98.46 21 | 96.91 27 |
|
WR-MVS_H | | | 97.06 9 | 97.78 7 | 96.23 14 | 96.74 36 | 98.04 3 | 98.25 25 | 97.32 1 | 94.40 33 | 93.71 65 | 98.55 13 | 98.89 13 | 92.97 40 | 98.91 9 | 98.45 6 | 98.38 27 | 97.19 13 |
|
WR-MVS | | | 97.53 3 | 98.20 3 | 96.76 3 | 96.93 27 | 98.17 1 | 98.60 11 | 96.67 6 | 96.39 12 | 94.46 44 | 99.14 1 | 98.92 12 | 94.57 18 | 99.06 3 | 98.80 2 | 99.32 1 | 96.92 26 |
|
NR-MVSNet | | | 94.55 61 | 95.66 52 | 93.25 96 | 94.26 100 | 96.44 46 | 96.69 70 | 95.32 10 | 89.97 125 | 91.79 109 | 97.46 56 | 98.39 36 | 82.85 156 | 96.87 59 | 96.48 48 | 98.57 14 | 93.98 83 |
|
Baseline_NR-MVSNet | | | 94.85 54 | 95.35 59 | 94.26 56 | 96.45 46 | 93.86 131 | 96.70 68 | 94.54 17 | 90.07 123 | 90.17 138 | 98.77 6 | 97.89 63 | 90.64 99 | 97.03 51 | 96.16 55 | 97.04 71 | 93.67 87 |
|
TranMVSNet+NR-MVSNet | | | 95.72 41 | 96.42 31 | 94.91 47 | 96.21 51 | 96.77 37 | 96.90 60 | 94.99 12 | 92.62 63 | 91.92 104 | 98.51 15 | 98.63 24 | 90.82 94 | 97.27 46 | 96.83 41 | 98.63 12 | 94.31 76 |
|
TSAR-MVS + GP. | | | 94.25 67 | 94.81 70 | 93.60 81 | 96.52 44 | 95.80 63 | 94.37 133 | 92.47 52 | 90.89 112 | 88.92 145 | 95.34 95 | 94.38 147 | 92.85 45 | 96.36 72 | 95.62 70 | 96.47 81 | 95.28 60 |
|
abl_6 | | | | | 91.88 121 | 93.76 127 | 94.98 95 | 95.64 111 | 88.97 141 | 86.20 171 | 90.00 140 | 86.31 198 | 94.50 146 | 87.31 127 | | | 95.60 121 | 92.48 119 |
|
mPP-MVS | | | | | | 98.24 3 | | | | | | | 97.65 71 | | | | | |
|
DWT-MVSNet_training | | | 79.22 218 | 73.99 235 | 85.33 198 | 88.57 212 | 84.41 207 | 90.56 202 | 80.96 217 | 73.90 233 | 85.72 168 | 75.62 229 | 50.09 242 | 81.30 174 | 76.91 235 | 77.02 223 | 84.88 212 | 79.97 209 |
|
testpf | | | 72.68 233 | 66.81 237 | 79.53 215 | 86.52 224 | 73.89 235 | 83.56 232 | 88.74 149 | 58.70 241 | 79.68 204 | 71.31 237 | 53.64 239 | 62.23 226 | 68.68 238 | 66.64 237 | 76.46 232 | 74.82 221 |
|
SixPastTwentyTwo | | | 97.36 4 | 97.73 9 | 96.92 2 | 97.36 13 | 96.15 52 | 98.29 23 | 94.43 22 | 96.50 10 | 96.96 8 | 98.74 7 | 98.74 19 | 96.04 3 | 99.03 5 | 97.74 16 | 98.44 22 | 97.22 12 |
|
LGP-MVS_train | | | 96.10 26 | 96.29 35 | 95.87 20 | 96.72 37 | 97.35 23 | 98.43 16 | 93.83 34 | 90.81 116 | 92.67 89 | 95.05 103 | 98.86 15 | 95.01 10 | 98.11 19 | 97.37 31 | 98.52 16 | 96.50 37 |
|
EPNet_dtu | | | 87.40 184 | 86.27 190 | 88.72 165 | 95.68 69 | 83.37 211 | 92.09 180 | 90.08 106 | 78.11 223 | 91.29 118 | 86.33 197 | 89.74 176 | 75.39 210 | 89.07 190 | 87.89 191 | 87.81 203 | 89.38 156 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
CHOSEN 1792x2688 | | | 86.64 185 | 86.62 187 | 86.65 193 | 90.33 202 | 87.86 194 | 93.19 163 | 83.30 208 | 83.95 187 | 82.32 190 | 87.93 183 | 89.34 178 | 86.92 131 | 85.64 215 | 84.95 201 | 83.85 220 | 86.68 182 |
|
EPNet | | | 90.17 147 | 89.07 165 | 91.45 126 | 97.25 18 | 90.62 181 | 94.84 124 | 93.54 41 | 80.96 197 | 91.85 106 | 86.98 193 | 85.88 188 | 77.79 198 | 92.30 155 | 92.58 129 | 93.41 178 | 94.20 79 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
APD-MVS | | | 95.38 46 | 95.68 50 | 95.03 44 | 97.30 17 | 96.90 35 | 97.83 39 | 93.92 31 | 89.40 135 | 90.35 134 | 95.41 93 | 97.69 70 | 92.97 40 | 97.24 48 | 97.17 35 | 97.83 45 | 95.96 46 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
LP | | | 84.09 198 | 84.31 196 | 83.85 206 | 79.40 235 | 84.34 208 | 90.26 203 | 84.02 201 | 87.99 154 | 84.66 173 | 91.61 148 | 79.13 208 | 80.58 177 | 85.90 214 | 81.59 210 | 84.16 219 | 79.59 210 |
|
CNVR-MVS | | | 94.24 68 | 94.47 81 | 93.96 69 | 96.56 43 | 95.67 66 | 96.43 83 | 91.95 67 | 92.08 77 | 91.28 119 | 90.51 159 | 95.35 130 | 91.20 77 | 96.34 73 | 95.50 73 | 96.34 91 | 95.88 48 |
|
NCCC | | | 93.87 80 | 93.42 115 | 94.40 54 | 96.84 32 | 95.42 74 | 96.47 80 | 92.62 48 | 92.36 70 | 92.05 101 | 83.83 210 | 95.55 122 | 91.84 61 | 95.89 78 | 95.23 79 | 96.56 79 | 95.63 53 |
|
CP-MVS | | | 96.21 21 | 96.16 41 | 96.27 13 | 97.56 8 | 97.13 32 | 98.43 16 | 94.70 16 | 92.62 63 | 94.13 52 | 92.71 137 | 98.03 57 | 94.54 19 | 98.00 26 | 97.60 20 | 98.23 29 | 97.05 20 |
|
NP-MVS | | | | | | | | | | 85.48 175 | | | | | | | | |
|
EG-PatchMatch MVS | | | 94.81 55 | 95.53 53 | 93.97 67 | 95.89 63 | 94.62 106 | 95.55 115 | 88.18 155 | 92.77 61 | 94.88 39 | 97.04 66 | 98.61 25 | 93.31 33 | 96.89 57 | 95.19 80 | 95.99 111 | 93.56 91 |
|
tpm cat1 | | | 80.03 214 | 75.93 233 | 84.81 202 | 89.31 207 | 83.26 213 | 88.86 214 | 86.55 184 | 79.24 215 | 86.10 165 | 84.22 209 | 63.62 233 | 77.37 203 | 73.43 236 | 70.88 234 | 80.67 227 | 76.87 217 |
|
SteuartSystems-ACMMP | | | 95.96 32 | 96.13 42 | 95.76 22 | 97.06 24 | 97.36 21 | 98.40 20 | 94.24 25 | 91.49 91 | 91.91 105 | 94.50 113 | 96.89 89 | 94.99 11 | 98.01 25 | 97.44 26 | 97.97 41 | 97.25 9 |
Skip Steuart: Steuart Systems R&D Blog. |
tpmp4_e23 | | | 82.16 204 | 78.26 222 | 86.70 191 | 89.92 203 | 84.82 204 | 91.17 193 | 89.95 113 | 81.21 196 | 87.10 159 | 81.91 217 | 64.01 232 | 77.88 197 | 79.89 229 | 74.99 229 | 84.18 218 | 81.00 203 |
|
CostFormer | | | 82.15 205 | 79.54 215 | 85.20 200 | 88.92 210 | 85.70 201 | 90.87 197 | 86.26 187 | 79.19 216 | 83.87 179 | 87.89 185 | 69.20 224 | 76.62 207 | 77.50 234 | 75.28 227 | 84.69 213 | 82.02 198 |
|
CR-MVSNet | | | 85.32 192 | 81.58 209 | 89.69 152 | 90.36 201 | 84.79 205 | 86.72 224 | 92.22 56 | 75.38 228 | 90.73 126 | 90.41 160 | 67.88 226 | 84.86 141 | 83.76 218 | 85.74 199 | 93.24 181 | 83.14 194 |
|
Patchmtry | | | | | | | 83.74 210 | 86.72 224 | 92.22 56 | | 90.73 126 | | | | | | | |
|
PatchT | | | 83.44 199 | 81.10 211 | 86.18 194 | 77.92 237 | 82.58 215 | 89.87 207 | 87.39 171 | 75.88 227 | 90.73 126 | 89.86 165 | 66.71 229 | 84.86 141 | 83.76 218 | 85.74 199 | 86.33 210 | 83.14 194 |
|
tpmrst | | | 78.81 221 | 76.18 232 | 81.87 210 | 88.56 213 | 77.45 227 | 86.74 223 | 81.52 214 | 80.08 208 | 83.48 181 | 90.84 155 | 66.88 227 | 74.54 211 | 73.04 237 | 71.02 233 | 76.38 233 | 73.95 227 |
|
tpm | | | 81.58 208 | 78.84 217 | 84.79 203 | 91.11 194 | 79.50 221 | 89.79 208 | 83.75 202 | 79.30 214 | 92.05 101 | 90.98 151 | 64.78 231 | 74.54 211 | 80.50 227 | 76.67 224 | 77.49 231 | 80.15 207 |
|
DELS-MVS | | | 92.33 128 | 93.61 106 | 90.83 132 | 92.84 161 | 95.13 88 | 94.76 127 | 87.22 175 | 87.78 156 | 88.42 153 | 95.78 85 | 95.28 134 | 85.71 139 | 94.44 109 | 93.91 111 | 96.01 110 | 92.97 104 |
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 |
RPMNet | | | 83.42 200 | 78.40 220 | 89.28 158 | 89.79 205 | 84.79 205 | 90.64 201 | 92.11 63 | 75.38 228 | 87.10 159 | 79.80 222 | 61.99 235 | 82.79 158 | 81.88 224 | 82.07 209 | 93.23 183 | 82.87 197 |
|
no-one | | | 92.05 134 | 94.57 78 | 89.12 159 | 85.55 226 | 87.65 196 | 94.21 140 | 77.34 222 | 93.43 48 | 89.64 143 | 95.11 102 | 99.11 8 | 95.86 4 | 95.38 86 | 95.24 78 | 92.08 190 | 96.11 44 |
|
MVSTER | | | 84.79 194 | 83.79 199 | 85.96 195 | 89.14 209 | 89.80 184 | 89.39 211 | 82.99 209 | 74.16 232 | 82.78 185 | 85.97 200 | 66.81 228 | 76.84 205 | 90.77 175 | 88.83 187 | 94.66 155 | 90.19 146 |
|
CPTT-MVS | | | 95.00 53 | 94.52 79 | 95.57 26 | 96.84 32 | 96.78 36 | 97.88 37 | 93.67 40 | 92.20 74 | 92.35 96 | 85.87 201 | 97.56 73 | 94.98 12 | 96.96 54 | 96.07 59 | 97.70 50 | 96.18 42 |
|
GBi-Net | | | 89.35 155 | 90.58 149 | 87.91 180 | 91.22 191 | 94.05 123 | 92.88 169 | 90.05 107 | 79.40 210 | 78.60 209 | 90.58 156 | 87.05 184 | 78.54 191 | 95.32 89 | 94.98 84 | 96.17 105 | 92.67 112 |
|
PVSNet_Blended_VisFu | | | 93.60 86 | 93.41 116 | 93.83 74 | 96.31 49 | 95.65 67 | 95.71 108 | 90.58 102 | 88.08 152 | 93.17 78 | 95.29 97 | 92.20 164 | 90.72 96 | 94.69 106 | 93.41 120 | 96.51 80 | 94.54 73 |
|
PVSNet_BlendedMVS | | | 90.09 148 | 90.12 154 | 90.05 144 | 92.40 168 | 92.74 153 | 91.74 185 | 85.89 188 | 80.54 204 | 90.30 136 | 88.54 176 | 95.51 123 | 84.69 143 | 92.64 143 | 90.25 171 | 95.28 128 | 90.61 142 |
|
PVSNet_Blended | | | 90.09 148 | 90.12 154 | 90.05 144 | 92.40 168 | 92.74 153 | 91.74 185 | 85.89 188 | 80.54 204 | 90.30 136 | 88.54 176 | 95.51 123 | 84.69 143 | 92.64 143 | 90.25 171 | 95.28 128 | 90.61 142 |
|
FMVSNet5 | | | 79.08 220 | 78.83 218 | 79.38 218 | 87.52 221 | 86.78 197 | 87.64 218 | 78.15 219 | 69.54 238 | 70.64 228 | 65.97 240 | 65.44 230 | 63.87 225 | 90.17 181 | 90.46 168 | 88.48 200 | 83.45 193 |
|
test1 | | | 89.35 155 | 90.58 149 | 87.91 180 | 91.22 191 | 94.05 123 | 92.88 169 | 90.05 107 | 79.40 210 | 78.60 209 | 90.58 156 | 87.05 184 | 78.54 191 | 95.32 89 | 94.98 84 | 96.17 105 | 92.67 112 |
|
new_pmnet | | | 76.65 229 | 83.52 200 | 68.63 233 | 82.60 230 | 72.08 237 | 76.76 240 | 64.17 232 | 84.41 183 | 49.73 241 | 91.77 144 | 91.53 167 | 56.16 234 | 86.59 207 | 83.26 206 | 82.37 225 | 75.02 220 |
|
FMVSNet3 | | | 87.90 179 | 88.63 173 | 87.04 187 | 89.78 206 | 93.46 143 | 91.62 190 | 90.05 107 | 79.40 210 | 78.60 209 | 90.58 156 | 87.05 184 | 77.07 204 | 88.03 201 | 89.86 174 | 95.12 134 | 92.04 128 |
|
dps | | | 81.42 212 | 77.88 227 | 85.56 196 | 87.67 218 | 85.17 203 | 88.37 217 | 87.46 169 | 74.37 231 | 84.55 175 | 86.80 195 | 62.18 234 | 80.20 178 | 81.13 226 | 77.52 221 | 85.10 211 | 77.98 216 |
|
FMVSNet2 | | | 90.28 145 | 92.04 137 | 88.23 177 | 91.22 191 | 94.05 123 | 92.88 169 | 90.69 98 | 86.53 167 | 79.89 203 | 94.38 116 | 92.73 163 | 78.54 191 | 91.64 168 | 92.26 133 | 96.17 105 | 92.67 112 |
|
FMVSNet1 | | | 92.86 115 | 95.26 60 | 90.06 143 | 92.40 168 | 95.16 86 | 94.37 133 | 92.22 56 | 93.18 54 | 82.16 192 | 96.76 70 | 97.48 74 | 81.85 167 | 95.32 89 | 94.98 84 | 97.34 59 | 93.93 84 |
|
N_pmnet | | | 79.33 215 | 84.22 197 | 73.62 231 | 91.72 184 | 73.72 236 | 86.11 228 | 76.36 223 | 92.38 68 | 53.38 239 | 95.54 92 | 95.62 121 | 59.14 229 | 84.23 217 | 74.84 230 | 75.03 236 | 73.25 228 |
|
UGNet | | | 92.31 130 | 94.70 73 | 89.53 155 | 90.99 195 | 95.53 71 | 96.19 98 | 92.10 64 | 91.35 101 | 85.76 166 | 95.31 96 | 95.48 125 | 76.84 205 | 95.22 94 | 94.79 91 | 95.32 125 | 95.19 61 |
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 |
MDTV_nov1_ep13_2view | | | 88.22 172 | 87.85 180 | 88.65 166 | 91.40 188 | 86.75 198 | 94.07 143 | 84.97 196 | 88.86 143 | 93.20 77 | 96.11 81 | 96.21 112 | 83.70 150 | 87.29 206 | 80.29 215 | 84.56 214 | 79.46 211 |
|
MDTV_nov1_ep13 | | | 82.33 203 | 79.66 214 | 85.45 197 | 88.83 211 | 83.88 209 | 90.09 206 | 81.98 212 | 79.07 217 | 88.82 147 | 88.70 174 | 73.77 218 | 78.41 195 | 80.29 228 | 76.08 225 | 84.56 214 | 75.83 219 |
|
MIMVSNet1 | | | 92.52 122 | 94.88 68 | 89.77 149 | 96.09 53 | 91.99 168 | 96.92 57 | 89.68 120 | 95.92 16 | 84.55 175 | 96.64 73 | 98.21 46 | 78.44 194 | 96.08 75 | 95.10 81 | 92.91 185 | 90.22 145 |
|
MIMVSNet | | | 84.76 195 | 86.75 186 | 82.44 208 | 91.71 185 | 85.95 200 | 89.74 209 | 89.49 129 | 85.28 177 | 69.69 231 | 87.93 183 | 90.88 172 | 64.85 224 | 88.26 199 | 87.74 192 | 89.18 197 | 81.24 200 |
|
IterMVS-LS | | | 92.10 132 | 92.33 132 | 91.82 122 | 93.18 146 | 93.66 134 | 92.80 172 | 92.27 55 | 90.82 114 | 90.59 131 | 97.19 61 | 90.97 171 | 87.76 126 | 89.60 186 | 90.94 166 | 94.34 168 | 93.16 98 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
CDS-MVSNet | | | 88.41 167 | 89.79 156 | 86.79 190 | 94.55 93 | 90.82 178 | 92.50 176 | 89.85 116 | 83.26 190 | 80.52 199 | 91.05 149 | 89.93 175 | 69.11 219 | 93.17 136 | 92.71 128 | 94.21 170 | 87.63 174 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
IterMVS | | | 88.32 168 | 88.25 176 | 88.41 171 | 90.83 198 | 91.24 173 | 93.07 165 | 81.69 213 | 86.77 165 | 88.55 150 | 95.61 86 | 86.91 187 | 87.01 129 | 87.38 204 | 83.77 204 | 89.29 196 | 86.06 185 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
MVS_111021_LR | | | 93.15 106 | 93.65 104 | 92.56 110 | 93.89 120 | 92.28 162 | 95.09 120 | 86.92 179 | 91.26 104 | 92.99 83 | 94.46 115 | 96.22 110 | 90.64 99 | 95.11 97 | 93.45 118 | 95.85 116 | 92.74 111 |
|
HQP-MVS | | | 92.87 114 | 92.49 131 | 93.31 90 | 95.75 67 | 95.01 93 | 95.64 111 | 91.06 90 | 88.54 146 | 91.62 112 | 88.16 180 | 96.25 108 | 89.47 111 | 92.26 156 | 91.81 145 | 96.34 91 | 95.40 56 |
|
QAPM | | | 92.57 121 | 93.51 111 | 91.47 125 | 92.91 159 | 94.82 100 | 93.01 166 | 87.51 168 | 91.49 91 | 91.21 122 | 92.24 140 | 91.70 166 | 88.74 117 | 94.54 108 | 94.39 105 | 95.41 124 | 95.37 59 |
|
Vis-MVSNet | | | 94.39 64 | 95.85 48 | 92.68 109 | 90.91 196 | 95.88 60 | 97.62 47 | 91.41 80 | 91.95 80 | 89.20 144 | 97.29 60 | 96.26 107 | 90.60 103 | 96.95 55 | 95.91 61 | 96.32 94 | 96.71 33 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
MVS-HIRNet | | | 78.28 223 | 75.28 234 | 81.79 211 | 80.33 233 | 69.38 240 | 76.83 239 | 86.59 182 | 70.76 235 | 86.66 163 | 89.57 168 | 81.04 203 | 77.74 199 | 77.81 233 | 71.65 232 | 82.62 223 | 66.73 234 |
|
HyFIR lowres test | | | 88.19 174 | 86.56 188 | 90.09 142 | 91.24 190 | 92.17 165 | 94.30 139 | 88.79 148 | 84.06 184 | 85.45 170 | 89.52 169 | 85.64 190 | 88.64 118 | 85.40 216 | 87.28 193 | 92.14 189 | 81.87 199 |
|
EPMVS | | | 79.26 216 | 78.20 224 | 80.49 212 | 87.04 223 | 78.86 223 | 86.08 229 | 83.51 205 | 82.63 192 | 73.94 221 | 89.59 167 | 68.67 225 | 72.03 217 | 78.17 232 | 75.08 228 | 80.37 228 | 74.37 224 |
|
TAMVS | | | 82.96 201 | 86.15 191 | 79.24 219 | 90.57 200 | 83.12 214 | 87.29 219 | 75.12 227 | 84.06 184 | 65.81 234 | 92.22 141 | 88.27 182 | 69.11 219 | 88.72 191 | 87.26 195 | 87.56 206 | 79.38 212 |
|
IS_MVSNet | | | 92.76 117 | 93.25 122 | 92.19 117 | 94.91 84 | 95.56 68 | 95.86 103 | 92.12 62 | 88.10 150 | 82.71 187 | 93.15 132 | 88.30 181 | 88.86 115 | 97.29 44 | 96.95 39 | 98.66 10 | 93.38 93 |
|
RPSCF | | | 95.46 43 | 96.95 20 | 93.73 80 | 95.72 68 | 95.94 58 | 95.58 114 | 88.08 159 | 95.31 18 | 91.34 117 | 96.26 77 | 98.04 56 | 93.63 30 | 98.28 16 | 97.67 17 | 98.01 39 | 97.13 16 |
|
Vis-MVSNet (Re-imp) | | | 90.68 142 | 92.18 134 | 88.92 163 | 94.63 88 | 92.75 152 | 92.91 168 | 91.20 86 | 89.21 138 | 75.01 218 | 93.96 125 | 89.07 180 | 82.72 159 | 95.88 79 | 95.30 76 | 97.08 69 | 89.08 160 |
|
MVS_111021_HR | | | 93.82 82 | 94.26 89 | 93.31 90 | 95.01 80 | 93.97 128 | 95.73 107 | 89.75 118 | 92.06 78 | 92.49 92 | 94.01 124 | 96.05 116 | 90.61 102 | 95.95 77 | 94.78 92 | 96.28 96 | 93.04 102 |
|
CSCG | | | 96.07 27 | 97.15 18 | 94.81 48 | 96.06 56 | 97.58 13 | 96.52 78 | 90.98 92 | 96.51 9 | 93.60 68 | 97.13 64 | 98.55 29 | 93.01 38 | 97.17 49 | 95.36 75 | 98.68 9 | 97.78 4 |
|
PatchMatch-RL | | | 89.59 152 | 88.80 169 | 90.51 136 | 92.20 177 | 88.00 192 | 91.72 187 | 86.64 180 | 84.75 182 | 88.25 154 | 87.10 192 | 90.66 173 | 89.85 110 | 93.23 134 | 92.28 132 | 94.41 167 | 85.60 188 |
|
TDRefinement | | | 97.59 2 | 98.32 2 | 96.73 4 | 95.90 61 | 98.10 2 | 99.08 2 | 93.92 31 | 98.24 3 | 96.44 15 | 98.12 26 | 97.86 68 | 96.06 2 | 99.24 1 | 98.93 1 | 99.00 2 | 97.77 5 |
|
USDC | | | 92.17 131 | 92.17 135 | 92.18 118 | 92.93 158 | 92.22 163 | 93.66 149 | 87.41 170 | 93.49 46 | 97.99 1 | 94.10 120 | 96.68 98 | 86.46 134 | 92.04 160 | 89.18 182 | 94.61 161 | 87.47 175 |
|
EPP-MVSNet | | | 93.63 85 | 93.95 91 | 93.26 94 | 95.15 78 | 96.54 42 | 96.18 99 | 91.97 66 | 91.74 84 | 85.76 166 | 94.95 107 | 84.27 192 | 91.60 67 | 97.61 40 | 97.38 30 | 98.87 4 | 95.18 62 |
|
PMMVS | | | 81.93 206 | 83.48 202 | 80.12 214 | 72.35 241 | 75.05 234 | 88.54 215 | 64.01 233 | 77.02 225 | 82.22 191 | 87.51 188 | 91.12 168 | 79.70 181 | 86.59 207 | 86.64 196 | 93.88 172 | 80.41 204 |
|
ACMMP | | | 96.12 25 | 96.27 37 | 95.93 19 | 97.20 20 | 97.60 12 | 98.64 8 | 93.74 36 | 92.47 65 | 93.13 80 | 93.23 130 | 98.06 54 | 94.51 20 | 97.99 27 | 97.57 22 | 98.39 26 | 96.99 21 |
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 |
CNLPA | | | 93.14 107 | 93.67 103 | 92.53 111 | 94.62 89 | 94.73 103 | 95.00 122 | 86.57 183 | 92.85 59 | 92.43 93 | 90.94 152 | 94.67 143 | 90.35 106 | 95.41 85 | 93.70 112 | 96.23 101 | 93.37 94 |
|
PatchmatchNet | | | 82.44 202 | 78.69 219 | 86.83 189 | 89.81 204 | 81.55 218 | 90.78 198 | 87.27 174 | 82.39 193 | 88.85 146 | 88.31 179 | 70.96 222 | 81.90 166 | 78.58 231 | 74.33 231 | 82.35 226 | 74.69 222 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
PHI-MVS | | | 94.65 58 | 94.84 69 | 94.44 52 | 94.95 82 | 96.55 39 | 96.46 81 | 91.10 89 | 88.96 139 | 96.00 18 | 94.55 112 | 95.32 132 | 90.67 97 | 96.97 53 | 96.69 44 | 97.44 54 | 94.84 65 |
|
OMC-MVS | | | 94.74 56 | 95.46 56 | 93.91 72 | 94.62 89 | 96.26 49 | 96.64 74 | 89.36 134 | 94.20 36 | 94.15 51 | 94.02 123 | 97.73 69 | 91.34 73 | 96.15 74 | 95.04 83 | 97.37 57 | 94.80 66 |
|
AdaColmap | | | 92.41 125 | 91.49 144 | 93.48 83 | 95.96 59 | 95.02 92 | 95.37 117 | 91.73 72 | 87.97 155 | 91.28 119 | 82.82 215 | 91.04 170 | 90.62 101 | 95.82 80 | 95.07 82 | 95.95 112 | 92.67 112 |
|
DeepMVS_CX | | | | | | | 47.68 244 | 53.20 244 | 19.21 239 | 63.24 240 | 26.96 243 | 66.50 239 | 69.82 223 | 66.91 223 | 64.27 239 | | 54.91 241 | 72.72 229 |
|
TinyColmap | | | 93.17 105 | 93.33 121 | 93.00 106 | 93.84 121 | 92.76 151 | 94.75 128 | 88.90 146 | 93.97 41 | 97.48 4 | 95.28 99 | 95.29 133 | 88.37 121 | 95.31 92 | 91.58 152 | 94.65 156 | 89.10 159 |
|
MAR-MVS | | | 91.86 135 | 91.14 147 | 92.71 108 | 94.29 98 | 94.24 116 | 94.91 123 | 91.82 71 | 81.66 195 | 93.32 72 | 84.51 208 | 93.42 158 | 86.86 132 | 95.16 96 | 94.44 104 | 95.05 139 | 94.53 74 |
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 |
MSDG | | | 92.09 133 | 92.84 127 | 91.22 128 | 92.55 164 | 92.97 148 | 93.42 153 | 85.43 193 | 90.24 120 | 91.83 107 | 94.70 109 | 94.59 144 | 88.48 120 | 94.91 102 | 93.31 122 | 95.59 122 | 89.15 158 |
|
LS3D | | | 95.83 39 | 96.35 33 | 95.22 37 | 96.47 45 | 97.49 15 | 97.99 32 | 92.35 54 | 94.92 25 | 94.58 43 | 94.88 108 | 95.11 138 | 91.52 68 | 98.48 13 | 98.05 12 | 98.42 24 | 95.49 55 |
|
CLD-MVS | | | 92.81 116 | 94.32 85 | 91.05 129 | 95.39 73 | 95.31 80 | 95.82 104 | 81.44 216 | 89.40 135 | 91.94 103 | 95.86 83 | 97.36 75 | 85.83 138 | 95.35 87 | 94.59 101 | 95.85 116 | 92.34 123 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
FPMVS | | | 90.81 141 | 91.60 142 | 89.88 147 | 92.52 165 | 88.18 189 | 93.31 160 | 83.62 204 | 91.59 90 | 88.45 152 | 88.96 173 | 89.73 177 | 86.96 130 | 96.42 71 | 95.69 69 | 94.43 166 | 90.65 141 |
|
Gipuma | | | 95.86 37 | 96.17 39 | 95.50 28 | 95.92 60 | 94.59 109 | 94.77 126 | 92.50 50 | 97.82 5 | 97.90 2 | 95.56 89 | 97.88 66 | 94.71 14 | 98.02 24 | 94.81 89 | 97.23 63 | 94.48 75 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |