LCM-MVSNet | | | 99.43 1 | 99.49 1 | 99.24 1 | 99.95 1 | 98.13 1 | 99.37 1 | 99.57 1 | 99.82 1 | 99.86 1 | 99.85 1 | 99.52 1 | 99.73 1 | 97.58 1 | 99.94 1 | 99.85 1 |
|
Anonymous20231211 | | | 97.78 3 | 98.31 2 | 96.16 47 | 99.55 2 | 89.37 81 | 98.40 5 | 98.89 4 | 98.75 2 | 99.48 3 | 99.62 2 | 98.70 2 | 99.40 36 | 91.60 107 | 99.84 5 | 99.71 3 |
|
DTE-MVSNet | | | 96.74 18 | 97.43 5 | 94.67 98 | 99.13 5 | 84.68 156 | 96.51 31 | 97.94 54 | 98.14 3 | 98.67 12 | 98.32 36 | 95.04 36 | 99.69 2 | 93.27 65 | 99.82 10 | 99.62 11 |
|
PEN-MVS | | | 96.69 20 | 97.39 8 | 94.61 100 | 99.16 3 | 84.50 157 | 96.54 30 | 98.05 37 | 98.06 4 | 98.64 13 | 98.25 39 | 95.01 39 | 99.65 3 | 92.95 74 | 99.83 8 | 99.68 5 |
|
PS-CasMVS | | | 96.69 20 | 97.43 5 | 94.49 110 | 99.13 5 | 84.09 164 | 96.61 26 | 97.97 48 | 97.91 5 | 98.64 13 | 98.13 41 | 95.24 31 | 99.65 3 | 93.39 61 | 99.84 5 | 99.72 2 |
|
CP-MVSNet | | | 96.19 45 | 96.80 19 | 94.38 116 | 98.99 13 | 83.82 166 | 96.31 42 | 97.53 88 | 97.60 6 | 98.34 22 | 97.52 69 | 91.98 93 | 99.63 6 | 93.08 72 | 99.81 11 | 99.70 4 |
|
WR-MVS_H | | | 96.60 25 | 97.05 15 | 95.24 83 | 99.02 11 | 86.44 132 | 96.78 23 | 98.08 32 | 97.42 7 | 98.48 18 | 97.86 56 | 91.76 97 | 99.63 6 | 94.23 37 | 99.84 5 | 99.66 7 |
|
TDRefinement | | | 97.68 4 | 97.60 4 | 97.93 2 | 99.02 11 | 95.95 6 | 98.61 3 | 98.81 5 | 97.41 8 | 97.28 49 | 98.46 29 | 94.62 47 | 98.84 123 | 94.64 26 | 99.53 44 | 98.99 71 |
|
LS3D | | | 96.11 47 | 95.83 59 | 96.95 33 | 94.75 250 | 94.20 14 | 97.34 12 | 97.98 45 | 97.31 9 | 95.32 128 | 96.77 106 | 93.08 71 | 99.20 65 | 91.79 102 | 98.16 179 | 97.44 177 |
|
VDDNet | | | 94.03 119 | 94.27 112 | 93.31 147 | 98.87 19 | 82.36 180 | 95.51 67 | 91.78 276 | 97.19 10 | 96.32 82 | 98.60 20 | 84.24 222 | 98.75 140 | 87.09 183 | 98.83 118 | 98.81 93 |
|
LTVRE_ROB | | 93.87 1 | 97.93 2 | 98.16 3 | 97.26 23 | 98.81 23 | 93.86 27 | 99.07 2 | 98.98 3 | 97.01 11 | 98.92 5 | 98.78 14 | 95.22 32 | 98.61 159 | 96.85 4 | 99.77 12 | 99.31 39 |
Andreas Kuhn, Heiko Hirschmüller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016 |
UA-Net | | | 97.35 5 | 97.24 13 | 97.69 5 | 98.22 62 | 93.87 26 | 98.42 4 | 98.19 24 | 96.95 12 | 95.46 125 | 99.23 4 | 93.45 60 | 99.57 13 | 95.34 17 | 99.89 4 | 99.63 10 |
|
DP-MVS | | | 95.62 57 | 95.84 58 | 94.97 90 | 97.16 114 | 88.62 94 | 94.54 104 | 97.64 75 | 96.94 13 | 96.58 75 | 97.32 82 | 93.07 72 | 98.72 145 | 90.45 119 | 98.84 115 | 97.57 171 |
|
test_0402 | | | 95.73 54 | 96.22 38 | 94.26 119 | 98.19 65 | 85.77 146 | 93.24 144 | 97.24 118 | 96.88 14 | 97.69 37 | 97.77 59 | 94.12 54 | 99.13 73 | 91.54 111 | 99.29 74 | 97.88 151 |
|
Gipuma | | | 95.31 70 | 95.80 60 | 93.81 134 | 97.99 80 | 90.91 64 | 96.42 37 | 97.95 51 | 96.69 15 | 91.78 224 | 98.85 12 | 91.77 96 | 95.49 304 | 91.72 103 | 99.08 94 | 95.02 265 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
COLMAP_ROB | | 91.06 5 | 96.75 17 | 96.62 25 | 97.13 26 | 98.38 51 | 94.31 12 | 96.79 22 | 98.32 13 | 96.69 15 | 96.86 63 | 97.56 66 | 95.48 22 | 98.77 139 | 90.11 133 | 99.44 55 | 98.31 121 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
v7n | | | 96.82 11 | 97.31 10 | 95.33 80 | 98.54 39 | 86.81 126 | 96.83 20 | 98.07 35 | 96.59 17 | 98.46 19 | 98.43 33 | 92.91 75 | 99.52 17 | 96.25 8 | 99.76 13 | 99.65 9 |
|
PMVS | | 87.21 14 | 94.97 83 | 95.33 78 | 93.91 130 | 98.97 14 | 97.16 2 | 95.54 66 | 95.85 194 | 96.47 18 | 93.40 184 | 97.46 73 | 95.31 28 | 95.47 305 | 86.18 198 | 98.78 126 | 89.11 339 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
gg-mvs-nofinetune | | | 82.10 306 | 81.02 310 | 85.34 312 | 87.46 344 | 71.04 318 | 94.74 91 | 67.56 357 | 96.44 19 | 79.43 346 | 98.99 6 | 45.24 356 | 96.15 293 | 67.18 335 | 92.17 322 | 88.85 340 |
|
ANet_high | | | 94.83 92 | 96.28 35 | 90.47 234 | 96.65 139 | 73.16 307 | 94.33 109 | 98.74 6 | 96.39 20 | 98.09 27 | 98.93 8 | 93.37 65 | 98.70 151 | 90.38 122 | 99.68 19 | 99.53 17 |
|
v748 | | | 96.51 28 | 97.05 15 | 94.89 92 | 98.35 56 | 85.82 145 | 96.58 28 | 97.47 94 | 96.25 21 | 98.46 19 | 98.35 34 | 93.27 68 | 99.33 52 | 95.13 19 | 99.59 35 | 99.52 20 |
|
IS-MVSNet | | | 94.49 106 | 94.35 107 | 94.92 91 | 98.25 61 | 86.46 131 | 97.13 16 | 94.31 231 | 96.24 22 | 96.28 88 | 96.36 137 | 82.88 229 | 99.35 48 | 88.19 169 | 99.52 46 | 98.96 77 |
|
3Dnovator+ | | 92.74 2 | 95.86 52 | 95.77 61 | 96.13 49 | 96.81 132 | 90.79 67 | 96.30 44 | 97.82 62 | 96.13 23 | 94.74 151 | 97.23 84 | 91.33 105 | 99.16 67 | 93.25 66 | 98.30 165 | 98.46 115 |
|
pmmvs6 | | | 96.80 14 | 97.36 9 | 95.15 87 | 99.12 7 | 87.82 113 | 96.68 24 | 97.86 58 | 96.10 24 | 98.14 26 | 99.28 3 | 97.94 4 | 98.21 201 | 91.38 114 | 99.69 16 | 99.42 27 |
|
ACMH+ | | 88.43 11 | 96.48 30 | 96.82 18 | 95.47 76 | 98.54 39 | 89.06 84 | 95.65 62 | 98.61 7 | 96.10 24 | 98.16 25 | 97.52 69 | 96.90 8 | 98.62 158 | 90.30 127 | 99.60 33 | 98.72 102 |
|
K. test v3 | | | 93.37 138 | 93.27 145 | 93.66 135 | 98.05 73 | 82.62 178 | 94.35 108 | 86.62 308 | 96.05 26 | 97.51 42 | 98.85 12 | 76.59 277 | 99.65 3 | 93.21 67 | 98.20 177 | 98.73 101 |
|
LFMVS | | | 91.33 191 | 91.16 191 | 91.82 203 | 96.27 175 | 79.36 233 | 95.01 83 | 85.61 318 | 96.04 27 | 94.82 148 | 97.06 94 | 72.03 285 | 98.46 183 | 84.96 211 | 98.70 132 | 97.65 167 |
|
TranMVSNet+NR-MVSNet | | | 96.07 49 | 96.26 36 | 95.50 74 | 98.26 60 | 87.69 114 | 93.75 128 | 97.86 58 | 95.96 28 | 97.48 43 | 97.14 89 | 95.33 27 | 99.44 24 | 90.79 117 | 99.76 13 | 99.38 32 |
|
abl_6 | | | 97.31 6 | 97.12 14 | 97.86 3 | 98.54 39 | 95.32 8 | 96.61 26 | 98.35 12 | 95.81 29 | 97.55 40 | 97.44 74 | 96.51 10 | 99.40 36 | 94.06 42 | 99.23 80 | 98.85 90 |
|
APD-MVS_3200maxsize | | | 96.82 11 | 96.65 23 | 97.32 22 | 97.95 81 | 93.82 29 | 96.31 42 | 98.25 19 | 95.51 30 | 96.99 61 | 97.05 95 | 95.63 20 | 99.39 41 | 93.31 64 | 98.88 110 | 98.75 98 |
|
V4 | | | 96.93 8 | 97.29 11 | 95.86 59 | 98.11 69 | 88.47 101 | 97.69 7 | 97.74 69 | 94.91 31 | 98.55 15 | 98.72 17 | 93.37 65 | 99.49 21 | 96.92 2 | 99.62 30 | 99.61 12 |
|
UniMVSNet_NR-MVSNet | | | 95.35 67 | 95.21 84 | 95.76 64 | 97.69 95 | 88.59 95 | 92.26 178 | 97.84 61 | 94.91 31 | 96.80 65 | 95.78 167 | 90.42 130 | 99.41 32 | 91.60 107 | 99.58 40 | 99.29 40 |
|
v52 | | | 96.93 8 | 97.29 11 | 95.86 59 | 98.12 68 | 88.48 100 | 97.69 7 | 97.74 69 | 94.90 33 | 98.55 15 | 98.72 17 | 93.39 64 | 99.49 21 | 96.92 2 | 99.62 30 | 99.61 12 |
|
SixPastTwentyTwo | | | 94.91 86 | 95.21 84 | 93.98 125 | 98.52 42 | 83.19 172 | 95.93 53 | 94.84 217 | 94.86 34 | 98.49 17 | 98.74 16 | 81.45 242 | 99.60 8 | 94.69 25 | 99.39 64 | 99.15 49 |
|
Anonymous20240521 | | | 96.37 40 | 96.66 22 | 95.50 74 | 98.49 46 | 87.84 112 | 97.47 10 | 97.77 68 | 94.75 35 | 98.22 24 | 98.49 26 | 90.93 118 | 99.28 56 | 94.12 41 | 99.74 15 | 99.38 32 |
|
ACMH | | 88.36 12 | 96.59 26 | 97.43 5 | 94.07 123 | 98.56 35 | 85.33 151 | 96.33 40 | 98.30 16 | 94.66 36 | 98.72 9 | 98.30 37 | 97.51 5 | 98.00 212 | 94.87 21 | 99.59 35 | 98.86 87 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
XVS | | | 96.49 29 | 96.18 39 | 97.44 13 | 98.56 35 | 93.99 22 | 96.50 32 | 97.95 51 | 94.58 37 | 94.38 159 | 96.49 120 | 94.56 48 | 99.39 41 | 93.57 51 | 99.05 97 | 98.93 80 |
|
X-MVStestdata | | | 90.70 198 | 88.45 226 | 97.44 13 | 98.56 35 | 93.99 22 | 96.50 32 | 97.95 51 | 94.58 37 | 94.38 159 | 26.89 356 | 94.56 48 | 99.39 41 | 93.57 51 | 99.05 97 | 98.93 80 |
|
Regformer-4 | | | 94.90 87 | 94.67 98 | 95.59 71 | 92.78 291 | 89.02 85 | 92.39 172 | 95.91 191 | 94.50 39 | 96.41 78 | 95.56 175 | 92.10 89 | 99.01 92 | 94.23 37 | 98.14 181 | 98.74 99 |
|
VDD-MVS | | | 94.37 108 | 94.37 106 | 94.40 115 | 97.49 105 | 86.07 140 | 93.97 118 | 93.28 249 | 94.49 40 | 96.24 89 | 97.78 57 | 87.99 172 | 98.79 132 | 88.92 157 | 99.14 88 | 98.34 118 |
|
zzz-MVS | | | 96.47 31 | 96.14 41 | 97.47 11 | 98.95 15 | 94.05 18 | 93.69 130 | 97.62 76 | 94.46 41 | 96.29 85 | 96.94 96 | 93.56 58 | 99.37 45 | 94.29 35 | 99.42 57 | 98.99 71 |
|
MTAPA | | | 96.65 22 | 96.38 32 | 97.47 11 | 98.95 15 | 94.05 18 | 95.88 56 | 97.62 76 | 94.46 41 | 96.29 85 | 96.94 96 | 93.56 58 | 99.37 45 | 94.29 35 | 99.42 57 | 98.99 71 |
|
Regformer-2 | | | 94.86 90 | 94.55 101 | 95.77 63 | 92.83 289 | 89.98 70 | 91.87 195 | 96.40 169 | 94.38 43 | 96.19 95 | 95.04 195 | 92.47 86 | 99.04 86 | 93.49 55 | 98.31 162 | 98.28 123 |
|
EPP-MVSNet | | | 93.91 121 | 93.68 133 | 94.59 105 | 98.08 72 | 85.55 149 | 97.44 11 | 94.03 236 | 94.22 44 | 94.94 145 | 96.19 150 | 82.07 237 | 99.57 13 | 87.28 182 | 98.89 108 | 98.65 104 |
|
OurMVSNet-221017-0 | | | 96.80 14 | 96.75 20 | 96.96 32 | 99.03 10 | 91.85 52 | 97.98 6 | 98.01 43 | 94.15 45 | 98.93 4 | 99.07 5 | 88.07 170 | 99.57 13 | 95.86 11 | 99.69 16 | 99.46 25 |
|
Regformer-1 | | | 94.55 104 | 94.33 108 | 95.19 85 | 92.83 289 | 88.54 98 | 91.87 195 | 95.84 195 | 93.99 46 | 95.95 104 | 95.04 195 | 92.00 91 | 98.79 132 | 93.14 69 | 98.31 162 | 98.23 125 |
|
DU-MVS | | | 95.28 72 | 95.12 88 | 95.75 65 | 97.75 87 | 88.59 95 | 92.58 160 | 97.81 63 | 93.99 46 | 96.80 65 | 95.90 159 | 90.10 138 | 99.41 32 | 91.60 107 | 99.58 40 | 99.26 41 |
|
TransMVSNet (Re) | | | 95.27 74 | 96.04 49 | 92.97 158 | 98.37 53 | 81.92 184 | 95.07 80 | 96.76 152 | 93.97 48 | 97.77 35 | 98.57 21 | 95.72 18 | 97.90 215 | 88.89 158 | 99.23 80 | 99.08 60 |
|
FC-MVSNet-test | | | 95.32 68 | 95.88 56 | 93.62 136 | 98.49 46 | 81.77 185 | 95.90 55 | 98.32 13 | 93.93 49 | 97.53 41 | 97.56 66 | 88.48 156 | 99.40 36 | 92.91 75 | 99.83 8 | 99.68 5 |
|
NR-MVSNet | | | 95.28 72 | 95.28 81 | 95.26 82 | 97.75 87 | 87.21 120 | 95.08 79 | 97.37 101 | 93.92 50 | 97.65 38 | 95.90 159 | 90.10 138 | 99.33 52 | 90.11 133 | 99.66 24 | 99.26 41 |
|
Baseline_NR-MVSNet | | | 94.47 107 | 95.09 89 | 92.60 179 | 98.50 45 | 80.82 197 | 92.08 183 | 96.68 155 | 93.82 51 | 96.29 85 | 98.56 22 | 90.10 138 | 97.75 238 | 90.10 135 | 99.66 24 | 99.24 43 |
|
MIMVSNet1 | | | 95.52 60 | 95.45 71 | 95.72 66 | 99.14 4 | 89.02 85 | 96.23 47 | 96.87 146 | 93.73 52 | 97.87 33 | 98.49 26 | 90.73 124 | 99.05 83 | 86.43 195 | 99.60 33 | 99.10 56 |
|
tfpnnormal | | | 94.27 113 | 94.87 93 | 92.48 185 | 97.71 92 | 80.88 196 | 94.55 103 | 95.41 209 | 93.70 53 | 96.67 71 | 97.72 60 | 91.40 103 | 98.18 206 | 87.45 178 | 99.18 85 | 98.36 117 |
|
EI-MVSNet-Vis-set | | | 94.36 109 | 94.28 110 | 94.61 100 | 92.55 293 | 85.98 142 | 92.44 170 | 94.69 224 | 93.70 53 | 96.12 98 | 95.81 164 | 91.24 109 | 98.86 120 | 93.76 49 | 98.22 174 | 98.98 76 |
|
WR-MVS | | | 93.49 132 | 93.72 130 | 92.80 169 | 97.57 101 | 80.03 213 | 90.14 250 | 95.68 198 | 93.70 53 | 96.62 73 | 95.39 184 | 87.21 187 | 99.04 86 | 87.50 177 | 99.64 27 | 99.33 37 |
|
Regformer-3 | | | 94.28 112 | 94.23 114 | 94.46 112 | 92.78 291 | 86.28 136 | 92.39 172 | 94.70 223 | 93.69 56 | 95.97 102 | 95.56 175 | 91.34 104 | 98.48 180 | 93.45 58 | 98.14 181 | 98.62 108 |
|
EI-MVSNet-UG-set | | | 94.35 110 | 94.27 112 | 94.59 105 | 92.46 294 | 85.87 143 | 92.42 171 | 94.69 224 | 93.67 57 | 96.13 97 | 95.84 163 | 91.20 112 | 98.86 120 | 93.78 46 | 98.23 172 | 99.03 67 |
|
UniMVSNet (Re) | | | 95.32 68 | 95.15 86 | 95.80 62 | 97.79 85 | 88.91 87 | 92.91 152 | 98.07 35 | 93.46 58 | 96.31 83 | 95.97 158 | 90.14 134 | 99.34 49 | 92.11 92 | 99.64 27 | 99.16 48 |
|
v13 | | | 95.39 65 | 96.12 43 | 93.18 150 | 97.22 111 | 80.81 198 | 95.55 65 | 97.57 83 | 93.42 59 | 98.02 30 | 98.49 26 | 89.62 143 | 99.18 66 | 95.54 12 | 99.68 19 | 99.54 16 |
|
VPA-MVSNet | | | 95.14 78 | 95.67 65 | 93.58 138 | 97.76 86 | 83.15 173 | 94.58 99 | 97.58 82 | 93.39 60 | 97.05 59 | 98.04 43 | 93.25 69 | 98.51 176 | 89.75 140 | 99.59 35 | 99.08 60 |
|
SteuartSystems-ACMMP | | | 96.40 37 | 96.30 34 | 96.71 38 | 98.63 28 | 91.96 50 | 95.70 60 | 98.01 43 | 93.34 61 | 96.64 72 | 96.57 118 | 94.99 40 | 99.36 47 | 93.48 56 | 99.34 67 | 98.82 92 |
Skip Steuart: Steuart Systems R&D Blog. |
v12 | | | 95.29 71 | 96.02 51 | 93.10 152 | 97.14 117 | 80.63 199 | 95.39 69 | 97.55 87 | 93.19 62 | 97.98 31 | 98.44 31 | 89.40 146 | 99.16 67 | 95.38 16 | 99.67 22 | 99.52 20 |
|
HPM-MVS_fast | | | 97.01 7 | 96.89 17 | 97.39 18 | 99.12 7 | 93.92 24 | 97.16 13 | 98.17 26 | 93.11 63 | 96.48 77 | 97.36 80 | 96.92 7 | 99.34 49 | 94.31 33 | 99.38 65 | 98.92 84 |
|
FIs | | | 94.90 87 | 95.35 75 | 93.55 139 | 98.28 58 | 81.76 186 | 95.33 71 | 98.14 28 | 93.05 64 | 97.07 55 | 97.18 87 | 87.65 176 | 99.29 54 | 91.72 103 | 99.69 16 | 99.61 12 |
|
view600 | | | 88.32 239 | 87.94 238 | 89.46 256 | 96.49 152 | 73.31 302 | 93.95 119 | 84.46 331 | 93.02 65 | 94.18 163 | 92.68 261 | 63.33 322 | 98.56 167 | 75.87 296 | 97.50 214 | 96.51 212 |
|
view800 | | | 88.32 239 | 87.94 238 | 89.46 256 | 96.49 152 | 73.31 302 | 93.95 119 | 84.46 331 | 93.02 65 | 94.18 163 | 92.68 261 | 63.33 322 | 98.56 167 | 75.87 296 | 97.50 214 | 96.51 212 |
|
conf0.05thres1000 | | | 88.32 239 | 87.94 238 | 89.46 256 | 96.49 152 | 73.31 302 | 93.95 119 | 84.46 331 | 93.02 65 | 94.18 163 | 92.68 261 | 63.33 322 | 98.56 167 | 75.87 296 | 97.50 214 | 96.51 212 |
|
tfpn | | | 88.32 239 | 87.94 238 | 89.46 256 | 96.49 152 | 73.31 302 | 93.95 119 | 84.46 331 | 93.02 65 | 94.18 163 | 92.68 261 | 63.33 322 | 98.56 167 | 75.87 296 | 97.50 214 | 96.51 212 |
|
V9 | | | 95.17 77 | 95.89 55 | 93.02 155 | 97.04 120 | 80.42 201 | 95.22 75 | 97.53 88 | 92.92 69 | 97.90 32 | 98.35 34 | 89.15 150 | 99.14 71 | 95.21 18 | 99.65 26 | 99.50 22 |
|
MP-MVS | | | 96.14 46 | 95.68 64 | 97.51 10 | 98.81 23 | 94.06 16 | 96.10 48 | 97.78 67 | 92.73 70 | 93.48 181 | 96.72 112 | 94.23 52 | 99.42 28 | 91.99 97 | 99.29 74 | 99.05 64 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
nrg030 | | | 96.32 41 | 96.55 28 | 95.62 70 | 97.83 84 | 88.55 97 | 95.77 59 | 98.29 18 | 92.68 71 | 98.03 28 | 97.91 53 | 95.13 33 | 98.95 101 | 93.85 44 | 99.49 48 | 99.36 36 |
|
CSCG | | | 94.69 98 | 94.75 94 | 94.52 108 | 97.55 102 | 87.87 110 | 95.01 83 | 97.57 83 | 92.68 71 | 96.20 93 | 93.44 247 | 91.92 94 | 98.78 135 | 89.11 154 | 99.24 78 | 96.92 200 |
|
CP-MVS | | | 96.44 35 | 96.08 46 | 97.54 9 | 98.29 57 | 94.62 10 | 96.80 21 | 98.08 32 | 92.67 73 | 95.08 141 | 96.39 132 | 94.77 44 | 99.42 28 | 93.17 68 | 99.44 55 | 98.58 112 |
|
v11 | | | 95.10 79 | 95.88 56 | 92.76 170 | 96.98 122 | 79.64 227 | 95.12 77 | 97.60 81 | 92.64 74 | 98.03 28 | 98.44 31 | 89.06 151 | 99.15 69 | 95.42 15 | 99.67 22 | 99.50 22 |
|
V14 | | | 95.05 80 | 95.75 62 | 92.94 161 | 96.94 124 | 80.21 204 | 95.03 82 | 97.50 92 | 92.62 75 | 97.84 34 | 98.28 38 | 88.87 153 | 99.13 73 | 95.03 20 | 99.64 27 | 99.48 24 |
|
mPP-MVS | | | 96.46 32 | 96.05 48 | 97.69 5 | 98.62 29 | 94.65 9 | 96.45 34 | 97.74 69 | 92.59 76 | 95.47 123 | 96.68 114 | 94.50 50 | 99.42 28 | 93.10 70 | 99.26 76 | 98.99 71 |
|
APDe-MVS | | | 96.46 32 | 96.64 24 | 95.93 56 | 97.68 96 | 89.38 80 | 96.90 19 | 98.41 11 | 92.52 77 | 97.43 46 | 97.92 51 | 95.11 34 | 99.50 18 | 94.45 30 | 99.30 72 | 98.92 84 |
|
RPSCF | | | 95.58 59 | 94.89 92 | 97.62 8 | 97.58 100 | 96.30 5 | 95.97 52 | 97.53 88 | 92.42 78 | 93.41 182 | 97.78 57 | 91.21 111 | 97.77 235 | 91.06 116 | 97.06 230 | 98.80 94 |
|
FMVSNet1 | | | 94.84 91 | 95.13 87 | 93.97 126 | 97.60 99 | 84.29 158 | 95.99 49 | 96.56 159 | 92.38 79 | 97.03 60 | 98.53 23 | 90.12 135 | 98.98 94 | 88.78 160 | 99.16 86 | 98.65 104 |
|
v15 | | | 94.93 85 | 95.62 67 | 92.86 166 | 96.83 130 | 80.01 217 | 94.84 89 | 97.48 93 | 92.36 80 | 97.76 36 | 98.20 40 | 88.61 154 | 99.11 76 | 94.86 22 | 99.62 30 | 99.46 25 |
|
Vis-MVSNet | | | 95.50 61 | 95.48 69 | 95.56 73 | 98.11 69 | 89.40 79 | 95.35 70 | 98.22 23 | 92.36 80 | 94.11 167 | 98.07 42 | 92.02 90 | 99.44 24 | 93.38 62 | 97.67 208 | 97.85 154 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
HFP-MVS | | | 96.39 38 | 96.17 40 | 97.04 28 | 98.51 43 | 93.37 35 | 96.30 44 | 97.98 45 | 92.35 82 | 95.63 119 | 96.47 122 | 95.37 24 | 99.27 59 | 93.78 46 | 99.14 88 | 98.48 113 |
|
ACMMPR | | | 96.46 32 | 96.14 41 | 97.41 17 | 98.60 32 | 93.82 29 | 96.30 44 | 97.96 49 | 92.35 82 | 95.57 121 | 96.61 116 | 94.93 42 | 99.41 32 | 93.78 46 | 99.15 87 | 99.00 69 |
|
HPM-MVS | | | 96.81 13 | 96.62 25 | 97.36 20 | 98.89 18 | 93.53 34 | 97.51 9 | 98.44 8 | 92.35 82 | 95.95 104 | 96.41 127 | 96.71 9 | 99.42 28 | 93.99 43 | 99.36 66 | 99.13 51 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
region2R | | | 96.41 36 | 96.09 45 | 97.38 19 | 98.62 29 | 93.81 31 | 96.32 41 | 97.96 49 | 92.26 85 | 95.28 130 | 96.57 118 | 95.02 38 | 99.41 32 | 93.63 50 | 99.11 91 | 98.94 79 |
|
ACMMP | | | 96.61 24 | 96.34 33 | 97.43 15 | 98.61 31 | 93.88 25 | 96.95 18 | 98.18 25 | 92.26 85 | 96.33 81 | 96.84 105 | 95.10 35 | 99.40 36 | 93.47 57 | 99.33 70 | 99.02 68 |
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 |
v17 | | | 94.80 93 | 95.46 70 | 92.83 167 | 96.76 135 | 80.02 215 | 94.85 87 | 97.40 99 | 92.23 87 | 97.45 45 | 98.04 43 | 88.46 158 | 99.06 81 | 94.56 27 | 99.40 62 | 99.41 28 |
|
v16 | | | 94.79 95 | 95.44 73 | 92.83 167 | 96.73 136 | 80.03 213 | 94.85 87 | 97.41 98 | 92.23 87 | 97.41 48 | 98.04 43 | 88.40 160 | 99.06 81 | 94.56 27 | 99.30 72 | 99.41 28 |
|
PatchT | | | 87.51 257 | 88.17 233 | 85.55 309 | 90.64 315 | 66.91 332 | 92.02 185 | 86.09 311 | 92.20 89 | 89.05 277 | 97.16 88 | 64.15 314 | 96.37 290 | 89.21 153 | 92.98 314 | 93.37 306 |
|
VNet | | | 92.67 163 | 92.96 148 | 91.79 204 | 96.27 175 | 80.15 206 | 91.95 187 | 94.98 214 | 92.19 90 | 94.52 157 | 96.07 154 | 87.43 181 | 97.39 254 | 84.83 212 | 98.38 153 | 97.83 155 |
|
tfpn111 | | | 87.60 255 | 87.12 253 | 89.04 269 | 96.14 185 | 73.09 308 | 93.00 147 | 85.31 321 | 92.13 91 | 93.26 190 | 90.96 292 | 63.42 318 | 98.48 180 | 72.87 311 | 96.98 234 | 95.56 251 |
|
conf200view11 | | | 87.41 259 | 86.89 257 | 88.97 270 | 96.14 185 | 73.09 308 | 93.00 147 | 85.31 321 | 92.13 91 | 93.26 190 | 90.96 292 | 63.42 318 | 98.28 194 | 71.27 323 | 96.54 248 | 95.56 251 |
|
thres100view900 | | | 87.35 261 | 86.89 257 | 88.72 275 | 96.14 185 | 73.09 308 | 93.00 147 | 85.31 321 | 92.13 91 | 93.26 190 | 90.96 292 | 63.42 318 | 98.28 194 | 71.27 323 | 96.54 248 | 94.79 269 |
|
LCM-MVSNet-Re | | | 94.20 116 | 94.58 100 | 93.04 153 | 95.91 207 | 83.13 174 | 93.79 127 | 99.19 2 | 92.00 94 | 98.84 6 | 98.04 43 | 93.64 57 | 99.02 90 | 81.28 242 | 98.54 141 | 96.96 198 |
|
ITE_SJBPF | | | | | 95.95 53 | 97.34 109 | 93.36 37 | | 96.55 162 | 91.93 95 | 94.82 148 | 95.39 184 | 91.99 92 | 97.08 264 | 85.53 202 | 97.96 195 | 97.41 178 |
|
RPMNet | | | 89.30 221 | 89.00 218 | 90.22 242 | 91.01 311 | 78.93 243 | 92.52 163 | 87.85 300 | 91.91 96 | 89.10 275 | 96.89 101 | 68.84 292 | 97.64 243 | 90.17 131 | 92.70 316 | 94.08 284 |
|
thres600view7 | | | 87.66 253 | 87.10 255 | 89.36 263 | 96.05 190 | 73.17 306 | 92.72 156 | 85.31 321 | 91.89 97 | 93.29 187 | 90.97 291 | 63.42 318 | 98.39 186 | 73.23 308 | 96.99 233 | 96.51 212 |
|
v18 | | | 94.63 101 | 95.26 83 | 92.74 171 | 96.60 146 | 79.81 221 | 94.64 96 | 97.37 101 | 91.87 98 | 97.26 51 | 97.91 53 | 88.13 165 | 99.04 86 | 94.30 34 | 99.24 78 | 99.38 32 |
|
v8 | | | 94.65 100 | 95.29 80 | 92.74 171 | 96.65 139 | 79.77 223 | 94.59 97 | 97.17 122 | 91.86 99 | 97.47 44 | 97.93 50 | 88.16 164 | 99.08 78 | 94.32 32 | 99.47 49 | 99.38 32 |
|
pm-mvs1 | | | 95.43 62 | 95.94 52 | 93.93 129 | 98.38 51 | 85.08 153 | 95.46 68 | 97.12 126 | 91.84 100 | 97.28 49 | 98.46 29 | 95.30 29 | 97.71 240 | 90.17 131 | 99.42 57 | 98.99 71 |
|
VPNet | | | 93.08 149 | 93.76 127 | 91.03 225 | 98.60 32 | 75.83 280 | 91.51 211 | 95.62 199 | 91.84 100 | 95.74 116 | 97.10 92 | 89.31 147 | 98.32 192 | 85.07 210 | 99.06 95 | 98.93 80 |
|
test_part3 | | | | | | | | 93.92 123 | | 91.83 102 | | 96.39 132 | | 99.44 24 | 89.00 155 | | |
|
ESAPD | | | 95.42 64 | 95.34 76 | 95.68 69 | 98.21 63 | 89.41 77 | 93.92 123 | 98.14 28 | 91.83 102 | 96.72 68 | 96.39 132 | 94.69 45 | 99.44 24 | 89.00 155 | 99.10 92 | 98.17 129 |
|
3Dnovator | | 92.54 3 | 94.80 93 | 94.90 91 | 94.47 111 | 95.47 227 | 87.06 122 | 96.63 25 | 97.28 116 | 91.82 104 | 94.34 162 | 97.41 75 | 90.60 128 | 98.65 157 | 92.47 87 | 98.11 185 | 97.70 163 |
|
LPG-MVS_test | | | 96.38 39 | 96.23 37 | 96.84 36 | 98.36 54 | 92.13 47 | 95.33 71 | 98.25 19 | 91.78 105 | 97.07 55 | 97.22 85 | 96.38 12 | 99.28 56 | 92.07 95 | 99.59 35 | 99.11 53 |
|
LGP-MVS_train | | | | | 96.84 36 | 98.36 54 | 92.13 47 | | 98.25 19 | 91.78 105 | 97.07 55 | 97.22 85 | 96.38 12 | 99.28 56 | 92.07 95 | 99.59 35 | 99.11 53 |
|
EI-MVSNet | | | 92.99 153 | 93.26 146 | 92.19 193 | 92.12 302 | 79.21 240 | 92.32 175 | 94.67 226 | 91.77 107 | 95.24 133 | 95.85 161 | 87.14 189 | 98.49 177 | 91.99 97 | 98.26 168 | 98.86 87 |
|
IterMVS-LS | | | 93.78 123 | 94.28 110 | 92.27 191 | 96.27 175 | 79.21 240 | 91.87 195 | 96.78 150 | 91.77 107 | 96.57 76 | 97.07 93 | 87.15 188 | 98.74 143 | 91.99 97 | 99.03 101 | 98.86 87 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
HQP_MVS | | | 94.26 114 | 93.93 118 | 95.23 84 | 97.71 92 | 88.12 106 | 94.56 101 | 97.81 63 | 91.74 109 | 93.31 185 | 95.59 170 | 86.93 195 | 98.95 101 | 89.26 150 | 98.51 144 | 98.60 110 |
|
plane_prior2 | | | | | | | | 94.56 101 | | 91.74 109 | | | | | | | |
|
wuyk23d | | | 87.83 249 | 90.79 198 | 78.96 335 | 90.46 320 | 88.63 93 | 92.72 156 | 90.67 283 | 91.65 111 | 98.68 11 | 97.64 63 | 96.06 15 | 77.53 356 | 59.84 346 | 99.41 61 | 70.73 353 |
|
alignmvs | | | 93.26 143 | 92.85 151 | 94.50 109 | 95.70 215 | 87.45 115 | 93.45 134 | 95.76 196 | 91.58 112 | 95.25 132 | 92.42 269 | 81.96 239 | 98.72 145 | 91.61 106 | 97.87 200 | 97.33 185 |
|
canonicalmvs | | | 94.59 102 | 94.69 96 | 94.30 118 | 95.60 223 | 87.03 123 | 95.59 63 | 98.24 22 | 91.56 113 | 95.21 135 | 92.04 277 | 94.95 41 | 98.66 155 | 91.45 112 | 97.57 212 | 97.20 190 |
|
semantic-postprocess | | | | | 91.94 200 | 93.89 273 | 79.22 239 | | 93.51 246 | 91.53 114 | 95.37 127 | 96.62 115 | 77.17 270 | 98.90 105 | 91.89 101 | 94.95 281 | 97.70 163 |
|
PGM-MVS | | | 96.32 41 | 95.94 52 | 97.43 15 | 98.59 34 | 93.84 28 | 95.33 71 | 98.30 16 | 91.40 115 | 95.76 115 | 96.87 102 | 95.26 30 | 99.45 23 | 92.77 76 | 99.21 82 | 99.00 69 |
|
Effi-MVS+ | | | 92.79 158 | 92.74 155 | 92.94 161 | 95.10 240 | 83.30 171 | 94.00 116 | 97.53 88 | 91.36 116 | 89.35 274 | 90.65 301 | 94.01 55 | 98.66 155 | 87.40 180 | 95.30 275 | 96.88 203 |
|
HSP-MVS | | | 95.18 76 | 94.49 103 | 97.23 24 | 98.67 27 | 94.05 18 | 96.41 38 | 97.00 130 | 91.26 117 | 95.12 136 | 95.15 188 | 86.60 204 | 99.50 18 | 93.43 60 | 96.81 237 | 98.13 134 |
|
SD-MVS | | | 95.19 75 | 95.73 63 | 93.55 139 | 96.62 145 | 88.88 90 | 94.67 93 | 98.05 37 | 91.26 117 | 97.25 52 | 96.40 128 | 95.42 23 | 94.36 321 | 92.72 80 | 99.19 83 | 97.40 180 |
|
Vis-MVSNet (Re-imp) | | | 90.42 203 | 90.16 206 | 91.20 223 | 97.66 98 | 77.32 263 | 94.33 109 | 87.66 301 | 91.20 119 | 92.99 197 | 95.13 190 | 75.40 279 | 98.28 194 | 77.86 278 | 99.19 83 | 97.99 140 |
|
API-MVS | | | 91.52 184 | 91.61 177 | 91.26 221 | 94.16 268 | 86.26 137 | 94.66 94 | 94.82 218 | 91.17 120 | 92.13 218 | 91.08 290 | 90.03 141 | 97.06 265 | 79.09 266 | 97.35 224 | 90.45 336 |
|
EPNet | | | 89.80 216 | 88.25 229 | 94.45 113 | 83.91 356 | 86.18 138 | 93.87 125 | 87.07 306 | 91.16 121 | 80.64 343 | 94.72 208 | 78.83 257 | 98.89 107 | 85.17 204 | 98.89 108 | 98.28 123 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
HPM-MVS++ | | | 95.02 81 | 94.39 104 | 96.91 34 | 97.88 82 | 93.58 33 | 94.09 114 | 96.99 132 | 91.05 122 | 92.40 209 | 95.22 187 | 91.03 117 | 99.25 61 | 92.11 92 | 98.69 133 | 97.90 149 |
|
wuykxyi23d | | | 96.76 16 | 96.57 27 | 97.34 21 | 97.75 87 | 96.73 3 | 94.37 107 | 96.48 165 | 91.00 123 | 99.72 2 | 98.99 6 | 96.06 15 | 98.21 201 | 94.86 22 | 99.90 2 | 97.09 192 |
|
tfpn200view9 | | | 87.05 270 | 86.52 266 | 88.67 276 | 95.77 211 | 72.94 311 | 91.89 192 | 86.00 313 | 90.84 124 | 92.61 204 | 89.80 306 | 63.93 315 | 98.28 194 | 71.27 323 | 96.54 248 | 94.79 269 |
|
thres400 | | | 87.20 266 | 86.52 266 | 89.24 267 | 95.77 211 | 72.94 311 | 91.89 192 | 86.00 313 | 90.84 124 | 92.61 204 | 89.80 306 | 63.93 315 | 98.28 194 | 71.27 323 | 96.54 248 | 96.51 212 |
|
ACMM | | 88.83 9 | 96.30 43 | 96.07 47 | 96.97 31 | 98.39 50 | 92.95 41 | 94.74 91 | 98.03 40 | 90.82 126 | 97.15 53 | 96.85 103 | 96.25 14 | 99.00 93 | 93.10 70 | 99.33 70 | 98.95 78 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
XVG-OURS | | | 94.72 97 | 94.12 115 | 96.50 45 | 98.00 77 | 94.23 13 | 91.48 212 | 98.17 26 | 90.72 127 | 95.30 129 | 96.47 122 | 87.94 173 | 96.98 267 | 91.41 113 | 97.61 211 | 98.30 122 |
|
XVG-OURS-SEG-HR | | | 95.38 66 | 95.00 90 | 96.51 43 | 98.10 71 | 94.07 15 | 92.46 169 | 98.13 31 | 90.69 128 | 93.75 175 | 96.25 142 | 98.03 3 | 97.02 266 | 92.08 94 | 95.55 267 | 98.45 116 |
|
v10 | | | 94.68 99 | 95.27 82 | 92.90 164 | 96.57 148 | 80.15 206 | 94.65 95 | 97.57 83 | 90.68 129 | 97.43 46 | 98.00 47 | 88.18 162 | 99.15 69 | 94.84 24 | 99.55 43 | 99.41 28 |
|
NCCC | | | 94.08 118 | 93.54 138 | 95.70 68 | 96.49 152 | 89.90 72 | 92.39 172 | 96.91 142 | 90.64 130 | 92.33 215 | 94.60 211 | 90.58 129 | 98.96 99 | 90.21 130 | 97.70 206 | 98.23 125 |
|
UGNet | | | 93.08 149 | 92.50 162 | 94.79 96 | 93.87 274 | 87.99 108 | 95.07 80 | 94.26 233 | 90.64 130 | 87.33 302 | 97.67 62 | 86.89 198 | 98.49 177 | 88.10 171 | 98.71 131 | 97.91 148 |
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 |
MSDG | | | 90.82 195 | 90.67 201 | 91.26 221 | 94.16 268 | 83.08 175 | 86.63 312 | 96.19 184 | 90.60 132 | 91.94 222 | 91.89 278 | 89.16 149 | 95.75 300 | 80.96 249 | 94.51 291 | 94.95 267 |
|
AllTest | | | 94.88 89 | 94.51 102 | 96.00 51 | 98.02 75 | 92.17 45 | 95.26 74 | 98.43 9 | 90.48 133 | 95.04 142 | 96.74 110 | 92.54 83 | 97.86 226 | 85.11 208 | 98.98 103 | 97.98 141 |
|
TestCases | | | | | 96.00 51 | 98.02 75 | 92.17 45 | | 98.43 9 | 90.48 133 | 95.04 142 | 96.74 110 | 92.54 83 | 97.86 226 | 85.11 208 | 98.98 103 | 97.98 141 |
|
XVG-ACMP-BASELINE | | | 95.68 56 | 95.34 76 | 96.69 39 | 98.40 49 | 93.04 38 | 94.54 104 | 98.05 37 | 90.45 135 | 96.31 83 | 96.76 108 | 92.91 75 | 98.72 145 | 91.19 115 | 99.42 57 | 98.32 119 |
|
ACMMP_Plus | | | 96.21 44 | 96.12 43 | 96.49 46 | 98.90 17 | 91.42 57 | 94.57 100 | 98.03 40 | 90.42 136 | 96.37 80 | 97.35 81 | 95.68 19 | 99.25 61 | 94.44 31 | 99.34 67 | 98.80 94 |
|
MDA-MVSNet-bldmvs | | | 91.04 193 | 90.88 194 | 91.55 212 | 94.68 255 | 80.16 205 | 85.49 318 | 92.14 271 | 90.41 137 | 94.93 146 | 95.79 165 | 85.10 217 | 96.93 269 | 85.15 206 | 94.19 298 | 97.57 171 |
|
plane_prior3 | | | | | | | 88.43 103 | | | 90.35 138 | 93.31 185 | | | | | | |
|
Patchmtry | | | 90.11 213 | 89.92 209 | 90.66 231 | 90.35 322 | 77.00 268 | 92.96 150 | 92.81 257 | 90.25 139 | 94.74 151 | 96.93 98 | 67.11 297 | 97.52 246 | 85.17 204 | 98.98 103 | 97.46 176 |
|
CNLPA | | | 91.72 180 | 91.20 189 | 93.26 148 | 96.17 183 | 91.02 61 | 91.14 220 | 95.55 205 | 90.16 140 | 90.87 243 | 93.56 243 | 86.31 207 | 94.40 320 | 79.92 260 | 97.12 228 | 94.37 280 |
|
OPM-MVS | | | 95.61 58 | 95.45 71 | 96.08 50 | 98.49 46 | 91.00 62 | 92.65 159 | 97.33 110 | 90.05 141 | 96.77 67 | 96.85 103 | 95.04 36 | 98.56 167 | 92.77 76 | 99.06 95 | 98.70 103 |
|
MVS_0304 | | | 92.99 153 | 92.54 160 | 94.35 117 | 94.67 256 | 86.06 141 | 91.16 219 | 97.92 55 | 90.01 142 | 88.33 290 | 94.41 215 | 87.02 191 | 99.22 63 | 90.36 124 | 99.00 102 | 97.76 159 |
|
Effi-MVS+-dtu | | | 93.90 122 | 92.60 159 | 97.77 4 | 94.74 251 | 96.67 4 | 94.00 116 | 95.41 209 | 89.94 143 | 91.93 223 | 92.13 275 | 90.12 135 | 98.97 98 | 87.68 175 | 97.48 218 | 97.67 166 |
|
mvs-test1 | | | 93.07 151 | 91.80 173 | 96.89 35 | 94.74 251 | 95.83 7 | 92.17 181 | 95.41 209 | 89.94 143 | 89.85 265 | 90.59 302 | 90.12 135 | 98.88 111 | 87.68 175 | 95.66 265 | 95.97 238 |
|
test20.03 | | | 90.80 196 | 90.85 196 | 90.63 232 | 95.63 221 | 79.24 235 | 89.81 264 | 92.87 256 | 89.90 145 | 94.39 158 | 96.40 128 | 85.77 212 | 95.27 312 | 73.86 305 | 99.05 97 | 97.39 181 |
|
CANet | | | 92.38 171 | 91.99 169 | 93.52 143 | 93.82 276 | 83.46 169 | 91.14 220 | 97.00 130 | 89.81 146 | 86.47 307 | 94.04 230 | 87.90 174 | 99.21 64 | 89.50 144 | 98.27 167 | 97.90 149 |
|
v148 | | | 92.87 157 | 93.29 142 | 91.62 209 | 96.25 178 | 77.72 259 | 91.28 217 | 95.05 213 | 89.69 147 | 95.93 107 | 96.04 155 | 87.34 184 | 98.38 188 | 90.05 136 | 97.99 194 | 98.78 96 |
|
CNVR-MVS | | | 94.58 103 | 94.29 109 | 95.46 77 | 96.94 124 | 89.35 82 | 91.81 204 | 96.80 149 | 89.66 148 | 93.90 173 | 95.44 180 | 92.80 79 | 98.72 145 | 92.74 78 | 98.52 143 | 98.32 119 |
|
Fast-Effi-MVS+-dtu | | | 92.77 160 | 92.16 165 | 94.58 107 | 94.66 257 | 88.25 104 | 92.05 184 | 96.65 156 | 89.62 149 | 90.08 257 | 91.23 287 | 92.56 82 | 98.60 161 | 86.30 197 | 96.27 255 | 96.90 201 |
|
ACMP | | 88.15 13 | 95.71 55 | 95.43 74 | 96.54 42 | 98.17 66 | 91.73 55 | 94.24 111 | 98.08 32 | 89.46 150 | 96.61 74 | 96.47 122 | 95.85 17 | 99.12 75 | 90.45 119 | 99.56 42 | 98.77 97 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
MSLP-MVS++ | | | 93.25 145 | 93.88 122 | 91.37 217 | 96.34 170 | 82.81 177 | 93.11 145 | 97.74 69 | 89.37 151 | 94.08 169 | 95.29 186 | 90.40 133 | 96.35 291 | 90.35 125 | 98.25 170 | 94.96 266 |
|
#test# | | | 95.89 51 | 95.51 68 | 97.04 28 | 98.51 43 | 93.37 35 | 95.14 76 | 97.98 45 | 89.34 152 | 95.63 119 | 96.47 122 | 95.37 24 | 99.27 59 | 91.99 97 | 99.14 88 | 98.48 113 |
|
test_prior3 | | | 93.29 140 | 92.85 151 | 94.61 100 | 95.95 204 | 87.23 118 | 90.21 246 | 97.36 107 | 89.33 153 | 90.77 244 | 94.81 202 | 90.41 131 | 98.68 153 | 88.21 167 | 98.55 139 | 97.93 145 |
|
test_prior2 | | | | | | | | 90.21 246 | | 89.33 153 | 90.77 244 | 94.81 202 | 90.41 131 | | 88.21 167 | 98.55 139 | |
|
tfpn1000 | | | 86.83 275 | 86.23 271 | 88.64 278 | 95.53 225 | 75.25 288 | 93.57 131 | 82.28 345 | 89.27 155 | 91.46 227 | 89.24 314 | 57.22 345 | 97.86 226 | 80.63 250 | 96.88 236 | 92.81 312 |
|
APD-MVS | | | 95.00 82 | 94.69 96 | 95.93 56 | 97.38 107 | 90.88 65 | 94.59 97 | 97.81 63 | 89.22 156 | 95.46 125 | 96.17 152 | 93.42 63 | 99.34 49 | 89.30 146 | 98.87 113 | 97.56 173 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
CPTT-MVS | | | 94.74 96 | 94.12 115 | 96.60 40 | 98.15 67 | 93.01 39 | 95.84 57 | 97.66 74 | 89.21 157 | 93.28 188 | 95.46 178 | 88.89 152 | 98.98 94 | 89.80 139 | 98.82 121 | 97.80 158 |
|
plane_prior | | | | | | | 88.12 106 | 93.01 146 | | 88.98 158 | | | | | | 98.06 189 | |
|
MVSFormer | | | 92.18 175 | 92.23 164 | 92.04 199 | 94.74 251 | 80.06 211 | 97.15 14 | 97.37 101 | 88.98 158 | 88.83 278 | 92.79 256 | 77.02 272 | 99.60 8 | 96.41 6 | 96.75 240 | 96.46 221 |
|
test_djsdf | | | 96.62 23 | 96.49 29 | 97.01 30 | 98.55 38 | 91.77 54 | 97.15 14 | 97.37 101 | 88.98 158 | 98.26 23 | 98.86 10 | 93.35 67 | 99.60 8 | 96.41 6 | 99.45 53 | 99.66 7 |
|
JIA-IIPM | | | 85.08 289 | 83.04 297 | 91.19 224 | 87.56 341 | 86.14 139 | 89.40 273 | 84.44 335 | 88.98 158 | 82.20 334 | 97.95 49 | 56.82 347 | 96.15 293 | 76.55 291 | 83.45 344 | 91.30 330 |
|
AdaColmap | | | 91.63 181 | 91.36 185 | 92.47 186 | 95.56 224 | 86.36 135 | 92.24 180 | 96.27 178 | 88.88 162 | 89.90 264 | 92.69 260 | 91.65 98 | 98.32 192 | 77.38 285 | 97.64 209 | 92.72 315 |
|
conf0.01 | | | 86.95 272 | 86.04 272 | 89.70 251 | 95.99 196 | 75.66 281 | 93.28 137 | 82.70 338 | 88.81 163 | 91.26 231 | 88.01 323 | 58.77 337 | 97.89 217 | 78.93 267 | 96.60 242 | 95.56 251 |
|
conf0.002 | | | 86.95 272 | 86.04 272 | 89.70 251 | 95.99 196 | 75.66 281 | 93.28 137 | 82.70 338 | 88.81 163 | 91.26 231 | 88.01 323 | 58.77 337 | 97.89 217 | 78.93 267 | 96.60 242 | 95.56 251 |
|
thresconf0.02 | | | 86.69 277 | 86.04 272 | 88.64 278 | 95.99 196 | 75.66 281 | 93.28 137 | 82.70 338 | 88.81 163 | 91.26 231 | 88.01 323 | 58.77 337 | 97.89 217 | 78.93 267 | 96.60 242 | 92.36 319 |
|
tfpn_n400 | | | 86.69 277 | 86.04 272 | 88.64 278 | 95.99 196 | 75.66 281 | 93.28 137 | 82.70 338 | 88.81 163 | 91.26 231 | 88.01 323 | 58.77 337 | 97.89 217 | 78.93 267 | 96.60 242 | 92.36 319 |
|
tfpnconf | | | 86.69 277 | 86.04 272 | 88.64 278 | 95.99 196 | 75.66 281 | 93.28 137 | 82.70 338 | 88.81 163 | 91.26 231 | 88.01 323 | 58.77 337 | 97.89 217 | 78.93 267 | 96.60 242 | 92.36 319 |
|
tfpnview11 | | | 86.69 277 | 86.04 272 | 88.64 278 | 95.99 196 | 75.66 281 | 93.28 137 | 82.70 338 | 88.81 163 | 91.26 231 | 88.01 323 | 58.77 337 | 97.89 217 | 78.93 267 | 96.60 242 | 92.36 319 |
|
MVS_Test | | | 92.57 167 | 93.29 142 | 90.40 236 | 93.53 280 | 75.85 278 | 92.52 163 | 96.96 134 | 88.73 169 | 92.35 212 | 96.70 113 | 90.77 120 | 98.37 191 | 92.53 86 | 95.49 269 | 96.99 197 |
|
PS-MVSNAJss | | | 96.01 50 | 96.04 49 | 95.89 58 | 98.82 22 | 88.51 99 | 95.57 64 | 97.88 56 | 88.72 170 | 98.81 7 | 98.86 10 | 90.77 120 | 99.60 8 | 95.43 14 | 99.53 44 | 99.57 15 |
|
GBi-Net | | | 93.21 146 | 92.96 148 | 93.97 126 | 95.40 229 | 84.29 158 | 95.99 49 | 96.56 159 | 88.63 171 | 95.10 138 | 98.53 23 | 81.31 245 | 98.98 94 | 86.74 186 | 98.38 153 | 98.65 104 |
|
test1 | | | 93.21 146 | 92.96 148 | 93.97 126 | 95.40 229 | 84.29 158 | 95.99 49 | 96.56 159 | 88.63 171 | 95.10 138 | 98.53 23 | 81.31 245 | 98.98 94 | 86.74 186 | 98.38 153 | 98.65 104 |
|
FMVSNet2 | | | 92.78 159 | 92.73 156 | 92.95 160 | 95.40 229 | 81.98 183 | 94.18 113 | 95.53 206 | 88.63 171 | 96.05 100 | 97.37 78 | 81.31 245 | 98.81 130 | 87.38 181 | 98.67 134 | 98.06 136 |
|
thres200 | | | 85.85 284 | 85.18 284 | 87.88 292 | 94.44 263 | 72.52 313 | 89.08 281 | 86.21 310 | 88.57 174 | 91.44 228 | 88.40 319 | 64.22 313 | 98.00 212 | 68.35 332 | 95.88 263 | 93.12 308 |
|
v1neww | | | 93.58 129 | 93.92 120 | 92.56 181 | 96.64 143 | 79.77 223 | 92.50 166 | 96.41 167 | 88.55 175 | 95.93 107 | 96.24 143 | 88.08 167 | 98.87 117 | 92.45 89 | 98.50 146 | 99.05 64 |
|
v7new | | | 93.58 129 | 93.92 120 | 92.56 181 | 96.64 143 | 79.77 223 | 92.50 166 | 96.41 167 | 88.55 175 | 95.93 107 | 96.24 143 | 88.08 167 | 98.87 117 | 92.45 89 | 98.50 146 | 99.05 64 |
|
v6 | | | 93.59 128 | 93.93 118 | 92.56 181 | 96.65 139 | 79.77 223 | 92.50 166 | 96.40 169 | 88.55 175 | 95.94 106 | 96.23 145 | 88.13 165 | 98.87 117 | 92.46 88 | 98.50 146 | 99.06 63 |
|
v2v482 | | | 93.29 140 | 93.63 134 | 92.29 190 | 96.35 169 | 78.82 246 | 91.77 207 | 96.28 177 | 88.45 178 | 95.70 118 | 96.26 141 | 86.02 211 | 98.90 105 | 93.02 73 | 98.81 124 | 99.14 50 |
|
testdata1 | | | | | | | | 88.96 284 | | 88.44 179 | | | | | | | |
|
testgi | | | 90.38 205 | 91.34 186 | 87.50 295 | 97.49 105 | 71.54 317 | 89.43 271 | 95.16 212 | 88.38 180 | 94.54 156 | 94.68 210 | 92.88 77 | 93.09 331 | 71.60 320 | 97.85 201 | 97.88 151 |
|
MVS_111021_HR | | | 93.63 127 | 93.42 141 | 94.26 119 | 96.65 139 | 86.96 124 | 89.30 276 | 96.23 181 | 88.36 181 | 93.57 179 | 94.60 211 | 93.45 60 | 97.77 235 | 90.23 129 | 98.38 153 | 98.03 138 |
|
v1141 | | | 93.42 136 | 93.76 127 | 92.40 189 | 96.37 161 | 79.24 235 | 91.84 200 | 96.38 172 | 88.33 182 | 95.86 112 | 96.23 145 | 87.41 182 | 98.89 107 | 92.61 83 | 98.82 121 | 99.08 60 |
|
divwei89l23v2f112 | | | 93.42 136 | 93.76 127 | 92.41 187 | 96.37 161 | 79.24 235 | 91.84 200 | 96.38 172 | 88.33 182 | 95.86 112 | 96.23 145 | 87.41 182 | 98.89 107 | 92.61 83 | 98.83 118 | 99.09 57 |
|
v1 | | | 93.43 134 | 93.77 126 | 92.41 187 | 96.37 161 | 79.24 235 | 91.84 200 | 96.38 172 | 88.33 182 | 95.87 111 | 96.22 148 | 87.45 180 | 98.89 107 | 92.61 83 | 98.83 118 | 99.09 57 |
|
BH-RMVSNet | | | 90.47 201 | 90.44 203 | 90.56 233 | 95.21 237 | 78.65 250 | 89.15 280 | 93.94 239 | 88.21 185 | 92.74 202 | 94.22 223 | 86.38 206 | 97.88 223 | 78.67 274 | 95.39 273 | 95.14 262 |
|
PAPM_NR | | | 91.03 194 | 90.81 197 | 91.68 208 | 96.73 136 | 81.10 194 | 93.72 129 | 96.35 176 | 88.19 186 | 88.77 284 | 92.12 276 | 85.09 218 | 97.25 258 | 82.40 234 | 93.90 300 | 96.68 209 |
|
EG-PatchMatch MVS | | | 94.54 105 | 94.67 98 | 94.14 121 | 97.87 83 | 86.50 128 | 92.00 186 | 96.74 153 | 88.16 187 | 96.93 62 | 97.61 64 | 93.04 73 | 97.90 215 | 91.60 107 | 98.12 184 | 98.03 138 |
|
TSAR-MVS + GP. | | | 93.07 151 | 92.41 163 | 95.06 89 | 95.82 209 | 90.87 66 | 90.97 224 | 92.61 263 | 88.04 188 | 94.61 154 | 93.79 238 | 88.08 167 | 97.81 231 | 89.41 145 | 98.39 152 | 96.50 219 |
|
BH-untuned | | | 90.68 199 | 90.90 193 | 90.05 247 | 95.98 202 | 79.57 230 | 90.04 254 | 94.94 216 | 87.91 189 | 94.07 170 | 93.00 253 | 87.76 175 | 97.78 234 | 79.19 265 | 95.17 278 | 92.80 313 |
|
MVS_111021_LR | | | 93.66 125 | 93.28 144 | 94.80 95 | 96.25 178 | 90.95 63 | 90.21 246 | 95.43 208 | 87.91 189 | 93.74 177 | 94.40 217 | 92.88 77 | 96.38 289 | 90.39 121 | 98.28 166 | 97.07 193 |
|
MP-MVS-pluss | | | 96.08 48 | 95.92 54 | 96.57 41 | 99.06 9 | 91.21 60 | 93.25 143 | 98.32 13 | 87.89 191 | 96.86 63 | 97.38 77 | 95.55 21 | 99.39 41 | 95.47 13 | 99.47 49 | 99.11 53 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
PHI-MVS | | | 94.34 111 | 93.80 124 | 95.95 53 | 95.65 219 | 91.67 56 | 94.82 90 | 97.86 58 | 87.86 192 | 93.04 196 | 94.16 226 | 91.58 99 | 98.78 135 | 90.27 128 | 98.96 106 | 97.41 178 |
|
EMVS | | | 80.35 320 | 80.28 317 | 80.54 332 | 84.73 355 | 69.07 326 | 72.54 350 | 80.73 349 | 87.80 193 | 81.66 339 | 81.73 348 | 62.89 326 | 89.84 345 | 75.79 300 | 94.65 289 | 82.71 349 |
|
E-PMN | | | 80.72 317 | 80.86 312 | 80.29 333 | 85.11 353 | 68.77 327 | 72.96 348 | 81.97 346 | 87.76 194 | 83.25 329 | 83.01 347 | 62.22 330 | 89.17 348 | 77.15 287 | 94.31 295 | 82.93 348 |
|
TinyColmap | | | 92.00 178 | 92.76 154 | 89.71 250 | 95.62 222 | 77.02 267 | 90.72 231 | 96.17 185 | 87.70 195 | 95.26 131 | 96.29 139 | 92.54 83 | 96.45 285 | 81.77 237 | 98.77 127 | 95.66 248 |
|
anonymousdsp | | | 96.74 18 | 96.42 30 | 97.68 7 | 98.00 77 | 94.03 21 | 96.97 17 | 97.61 79 | 87.68 196 | 98.45 21 | 98.77 15 | 94.20 53 | 99.50 18 | 96.70 5 | 99.40 62 | 99.53 17 |
|
mvs_tets | | | 96.83 10 | 96.71 21 | 97.17 25 | 98.83 21 | 92.51 43 | 96.58 28 | 97.61 79 | 87.57 197 | 98.80 8 | 98.90 9 | 96.50 11 | 99.59 12 | 96.15 9 | 99.47 49 | 99.40 31 |
|
v7 | | | 93.66 125 | 93.97 117 | 92.73 173 | 96.55 149 | 80.15 206 | 92.54 161 | 96.99 132 | 87.36 198 | 95.99 101 | 96.48 121 | 88.18 162 | 98.94 104 | 93.35 63 | 98.31 162 | 99.09 57 |
|
DeepC-MVS | | 91.39 4 | 95.43 62 | 95.33 78 | 95.71 67 | 97.67 97 | 90.17 68 | 93.86 126 | 98.02 42 | 87.35 199 | 96.22 91 | 97.99 48 | 94.48 51 | 99.05 83 | 92.73 79 | 99.68 19 | 97.93 145 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
DELS-MVS | | | 92.05 177 | 92.16 165 | 91.72 206 | 94.44 263 | 80.13 209 | 87.62 296 | 97.25 117 | 87.34 200 | 92.22 217 | 93.18 252 | 89.54 145 | 98.73 144 | 89.67 142 | 98.20 177 | 96.30 227 |
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 |
V42 | | | 93.43 134 | 93.58 136 | 92.97 158 | 95.34 234 | 81.22 192 | 92.67 158 | 96.49 164 | 87.25 201 | 96.20 93 | 96.37 136 | 87.32 185 | 98.85 122 | 92.39 91 | 98.21 175 | 98.85 90 |
|
HQP-NCC | | | | | | 96.36 166 | | 91.37 213 | | 87.16 202 | 88.81 280 | | | | | | |
|
ACMP_Plane | | | | | | 96.36 166 | | 91.37 213 | | 87.16 202 | 88.81 280 | | | | | | |
|
HQP-MVS | | | 92.09 176 | 91.49 181 | 93.88 131 | 96.36 166 | 84.89 154 | 91.37 213 | 97.31 111 | 87.16 202 | 88.81 280 | 93.40 248 | 84.76 219 | 98.60 161 | 86.55 192 | 97.73 203 | 98.14 133 |
|
OMC-MVS | | | 94.22 115 | 93.69 132 | 95.81 61 | 97.25 110 | 91.27 59 | 92.27 177 | 97.40 99 | 87.10 205 | 94.56 155 | 95.42 181 | 93.74 56 | 98.11 209 | 86.62 190 | 98.85 114 | 98.06 136 |
|
tfpn_ndepth | | | 85.85 284 | 85.15 285 | 87.98 289 | 95.19 239 | 75.36 287 | 92.79 155 | 83.18 337 | 86.97 206 | 89.92 262 | 86.43 335 | 57.44 344 | 97.85 229 | 78.18 276 | 96.22 256 | 90.72 334 |
|
jajsoiax | | | 96.59 26 | 96.42 30 | 97.12 27 | 98.76 25 | 92.49 44 | 96.44 36 | 97.42 97 | 86.96 207 | 98.71 10 | 98.72 17 | 95.36 26 | 99.56 16 | 95.92 10 | 99.45 53 | 99.32 38 |
|
v1144 | | | 93.50 131 | 93.81 123 | 92.57 180 | 96.28 174 | 79.61 229 | 91.86 199 | 96.96 134 | 86.95 208 | 95.91 110 | 96.32 138 | 87.65 176 | 98.96 99 | 93.51 54 | 98.88 110 | 99.13 51 |
|
ab-mvs | | | 92.40 170 | 92.62 158 | 91.74 205 | 97.02 121 | 81.65 187 | 95.84 57 | 95.50 207 | 86.95 208 | 92.95 199 | 97.56 66 | 90.70 126 | 97.50 247 | 79.63 261 | 97.43 220 | 96.06 236 |
|
1111 | | | 80.36 319 | 81.32 307 | 77.48 336 | 94.61 259 | 44.56 357 | 81.59 337 | 90.66 284 | 86.78 210 | 90.60 249 | 93.52 245 | 30.37 362 | 90.67 340 | 66.36 337 | 97.42 221 | 97.20 190 |
|
.test1245 | | | 64.72 331 | 70.88 332 | 46.22 344 | 94.61 259 | 44.56 357 | 81.59 337 | 90.66 284 | 86.78 210 | 90.60 249 | 93.52 245 | 30.37 362 | 90.67 340 | 66.36 337 | 3.45 358 | 3.44 358 |
|
SMA-MVS | | | 95.85 53 | 95.63 66 | 96.51 43 | 98.27 59 | 91.30 58 | 95.09 78 | 97.88 56 | 86.59 212 | 97.63 39 | 97.51 71 | 94.82 43 | 99.29 54 | 93.55 53 | 99.34 67 | 98.93 80 |
|
IterMVS | | | 90.18 211 | 90.16 206 | 90.21 244 | 93.15 285 | 75.98 277 | 87.56 299 | 92.97 255 | 86.43 213 | 94.09 168 | 96.40 128 | 78.32 262 | 97.43 250 | 87.87 174 | 94.69 288 | 97.23 188 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
new-patchmatchnet | | | 88.97 227 | 90.79 198 | 83.50 324 | 94.28 267 | 55.83 354 | 85.34 319 | 93.56 245 | 86.18 214 | 95.47 123 | 95.73 168 | 83.10 227 | 96.51 282 | 85.40 203 | 98.06 189 | 98.16 131 |
|
FMVSNet3 | | | 90.78 197 | 90.32 205 | 92.16 195 | 93.03 287 | 79.92 219 | 92.54 161 | 94.95 215 | 86.17 215 | 95.10 138 | 96.01 156 | 69.97 291 | 98.75 140 | 86.74 186 | 98.38 153 | 97.82 157 |
|
v1192 | | | 93.49 132 | 93.78 125 | 92.62 178 | 96.16 184 | 79.62 228 | 91.83 203 | 97.22 120 | 86.07 216 | 96.10 99 | 96.38 135 | 87.22 186 | 99.02 90 | 94.14 40 | 98.88 110 | 99.22 44 |
|
CANet_DTU | | | 89.85 215 | 89.17 214 | 91.87 202 | 92.20 300 | 80.02 215 | 90.79 229 | 95.87 193 | 86.02 217 | 82.53 332 | 91.77 280 | 80.01 253 | 98.57 166 | 85.66 201 | 97.70 206 | 97.01 196 |
|
XXY-MVS | | | 92.58 166 | 93.16 147 | 90.84 230 | 97.75 87 | 79.84 220 | 91.87 195 | 96.22 183 | 85.94 218 | 95.53 122 | 97.68 61 | 92.69 80 | 94.48 317 | 83.21 226 | 97.51 213 | 98.21 127 |
|
PM-MVS | | | 93.33 139 | 92.67 157 | 95.33 80 | 96.58 147 | 94.06 16 | 92.26 178 | 92.18 268 | 85.92 219 | 96.22 91 | 96.61 116 | 85.64 216 | 95.99 297 | 90.35 125 | 98.23 172 | 95.93 240 |
|
testing_2 | | | 94.03 119 | 94.38 105 | 93.00 156 | 96.79 134 | 81.41 191 | 92.87 154 | 96.96 134 | 85.88 220 | 97.06 58 | 97.92 51 | 91.18 115 | 98.71 150 | 91.72 103 | 99.04 100 | 98.87 86 |
|
MG-MVS | | | 89.54 218 | 89.80 210 | 88.76 274 | 94.88 243 | 72.47 314 | 89.60 267 | 92.44 266 | 85.82 221 | 89.48 272 | 95.98 157 | 82.85 230 | 97.74 239 | 81.87 236 | 95.27 276 | 96.08 235 |
|
UnsupCasMVSNet_eth | | | 90.33 208 | 90.34 204 | 90.28 239 | 94.64 258 | 80.24 203 | 89.69 266 | 95.88 192 | 85.77 222 | 93.94 172 | 95.69 169 | 81.99 238 | 92.98 332 | 84.21 218 | 91.30 327 | 97.62 169 |
|
Patchmatch-test | | | 86.10 283 | 86.01 278 | 86.38 305 | 90.63 316 | 74.22 297 | 89.57 268 | 86.69 307 | 85.73 223 | 89.81 267 | 92.83 255 | 65.24 310 | 91.04 339 | 77.82 281 | 95.78 264 | 93.88 293 |
|
DeepC-MVS_fast | | 89.96 7 | 93.73 124 | 93.44 140 | 94.60 104 | 96.14 185 | 87.90 109 | 93.36 136 | 97.14 123 | 85.53 224 | 93.90 173 | 95.45 179 | 91.30 107 | 98.59 163 | 89.51 143 | 98.62 135 | 97.31 186 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
TSAR-MVS + MP. | | | 94.96 84 | 94.75 94 | 95.57 72 | 98.86 20 | 88.69 91 | 96.37 39 | 96.81 148 | 85.23 225 | 94.75 150 | 97.12 91 | 91.85 95 | 99.40 36 | 93.45 58 | 98.33 160 | 98.62 108 |
|
v1921920 | | | 93.26 143 | 93.61 135 | 92.19 193 | 96.04 193 | 78.31 252 | 91.88 194 | 97.24 118 | 85.17 226 | 96.19 95 | 96.19 150 | 86.76 200 | 99.05 83 | 94.18 39 | 98.84 115 | 99.22 44 |
|
DeepPCF-MVS | | 90.46 6 | 94.20 116 | 93.56 137 | 96.14 48 | 95.96 203 | 92.96 40 | 89.48 270 | 97.46 95 | 85.14 227 | 96.23 90 | 95.42 181 | 93.19 70 | 98.08 210 | 90.37 123 | 98.76 128 | 97.38 183 |
|
v1240 | | | 93.29 140 | 93.71 131 | 92.06 198 | 96.01 194 | 77.89 257 | 91.81 204 | 97.37 101 | 85.12 228 | 96.69 70 | 96.40 128 | 86.67 201 | 99.07 80 | 94.51 29 | 98.76 128 | 99.22 44 |
|
GA-MVS | | | 87.70 251 | 86.82 259 | 90.31 238 | 93.27 283 | 77.22 265 | 84.72 324 | 92.79 259 | 85.11 229 | 89.82 266 | 90.07 303 | 66.80 300 | 97.76 237 | 84.56 216 | 94.27 296 | 95.96 239 |
|
LF4IMVS | | | 92.72 161 | 92.02 168 | 94.84 94 | 95.65 219 | 91.99 49 | 92.92 151 | 96.60 158 | 85.08 230 | 92.44 208 | 93.62 240 | 86.80 199 | 96.35 291 | 86.81 185 | 98.25 170 | 96.18 232 |
|
Fast-Effi-MVS+ | | | 91.28 192 | 90.86 195 | 92.53 184 | 95.45 228 | 82.53 179 | 89.25 279 | 96.52 163 | 85.00 231 | 89.91 263 | 88.55 318 | 92.94 74 | 98.84 123 | 84.72 215 | 95.44 272 | 96.22 230 |
|
v144192 | | | 93.20 148 | 93.54 138 | 92.16 195 | 96.05 190 | 78.26 253 | 91.95 187 | 97.14 123 | 84.98 232 | 95.96 103 | 96.11 153 | 87.08 190 | 99.04 86 | 93.79 45 | 98.84 115 | 99.17 47 |
|
DP-MVS Recon | | | 92.31 172 | 91.88 170 | 93.60 137 | 97.18 113 | 86.87 125 | 91.10 222 | 97.37 101 | 84.92 233 | 92.08 219 | 94.08 229 | 88.59 155 | 98.20 203 | 83.50 223 | 98.14 181 | 95.73 245 |
|
LP | | | 86.29 282 | 85.35 283 | 89.10 268 | 87.80 339 | 76.21 273 | 89.92 258 | 90.99 281 | 84.86 234 | 87.66 298 | 92.32 270 | 70.40 289 | 96.48 283 | 81.94 235 | 82.24 348 | 94.63 274 |
|
EPNet_dtu | | | 85.63 286 | 84.37 288 | 89.40 262 | 86.30 349 | 74.33 296 | 91.64 208 | 88.26 294 | 84.84 235 | 72.96 354 | 89.85 304 | 71.27 287 | 97.69 241 | 76.60 290 | 97.62 210 | 96.18 232 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
CLD-MVS | | | 91.82 179 | 91.41 183 | 93.04 153 | 96.37 161 | 83.65 168 | 86.82 309 | 97.29 114 | 84.65 236 | 92.27 216 | 89.67 311 | 92.20 87 | 97.85 229 | 83.95 220 | 99.47 49 | 97.62 169 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
testmv | | | 88.46 236 | 88.11 235 | 89.48 254 | 96.00 195 | 76.14 274 | 86.20 315 | 93.75 241 | 84.48 237 | 93.57 179 | 95.52 177 | 80.91 249 | 95.09 313 | 63.97 342 | 98.61 136 | 97.22 189 |
|
PMMVS2 | | | 81.31 311 | 83.44 294 | 74.92 339 | 90.52 318 | 46.49 356 | 69.19 352 | 85.23 326 | 84.30 238 | 87.95 295 | 94.71 209 | 76.95 274 | 84.36 354 | 64.07 341 | 98.09 187 | 93.89 292 |
|
F-COLMAP | | | 92.28 173 | 91.06 192 | 95.95 53 | 97.52 103 | 91.90 51 | 93.53 132 | 97.18 121 | 83.98 239 | 88.70 286 | 94.04 230 | 88.41 159 | 98.55 173 | 80.17 255 | 95.99 259 | 97.39 181 |
|
QAPM | | | 92.88 156 | 92.77 153 | 93.22 149 | 95.82 209 | 83.31 170 | 96.45 34 | 97.35 109 | 83.91 240 | 93.75 175 | 96.77 106 | 89.25 148 | 98.88 111 | 84.56 216 | 97.02 232 | 97.49 175 |
|
PNet_i23d | | | 72.03 330 | 70.91 331 | 75.38 338 | 90.46 320 | 57.84 352 | 71.73 351 | 81.53 348 | 83.86 241 | 82.21 333 | 83.49 345 | 29.97 364 | 87.80 351 | 60.78 345 | 54.12 356 | 80.51 351 |
|
mvs_anonymous | | | 90.37 206 | 91.30 187 | 87.58 294 | 92.17 301 | 68.00 328 | 89.84 263 | 94.73 222 | 83.82 242 | 93.22 194 | 97.40 76 | 87.54 178 | 97.40 253 | 87.94 172 | 95.05 280 | 97.34 184 |
|
FMVSNet5 | | | 87.82 250 | 86.56 264 | 91.62 209 | 92.31 296 | 79.81 221 | 93.49 133 | 94.81 220 | 83.26 243 | 91.36 229 | 96.93 98 | 52.77 351 | 97.49 248 | 76.07 293 | 98.03 192 | 97.55 174 |
|
xiu_mvs_v1_base_debu | | | 91.47 186 | 91.52 178 | 91.33 218 | 95.69 216 | 81.56 188 | 89.92 258 | 96.05 187 | 83.22 244 | 91.26 231 | 90.74 296 | 91.55 100 | 98.82 125 | 89.29 147 | 95.91 260 | 93.62 300 |
|
xiu_mvs_v1_base | | | 91.47 186 | 91.52 178 | 91.33 218 | 95.69 216 | 81.56 188 | 89.92 258 | 96.05 187 | 83.22 244 | 91.26 231 | 90.74 296 | 91.55 100 | 98.82 125 | 89.29 147 | 95.91 260 | 93.62 300 |
|
xiu_mvs_v1_base_debi | | | 91.47 186 | 91.52 178 | 91.33 218 | 95.69 216 | 81.56 188 | 89.92 258 | 96.05 187 | 83.22 244 | 91.26 231 | 90.74 296 | 91.55 100 | 98.82 125 | 89.29 147 | 95.91 260 | 93.62 300 |
|
FPMVS | | | 84.50 292 | 83.28 295 | 88.16 288 | 96.32 171 | 94.49 11 | 85.76 316 | 85.47 319 | 83.09 247 | 85.20 314 | 94.26 221 | 63.79 317 | 86.58 352 | 63.72 343 | 91.88 326 | 83.40 347 |
|
test-LLR | | | 83.58 296 | 83.17 296 | 84.79 317 | 89.68 327 | 66.86 334 | 83.08 331 | 84.52 329 | 83.07 248 | 82.85 330 | 84.78 341 | 62.86 327 | 93.49 328 | 82.85 228 | 94.86 282 | 94.03 287 |
|
test0.0.03 1 | | | 82.48 303 | 81.47 306 | 85.48 310 | 89.70 326 | 73.57 300 | 84.73 322 | 81.64 347 | 83.07 248 | 88.13 293 | 86.61 332 | 62.86 327 | 89.10 349 | 66.24 339 | 90.29 332 | 93.77 296 |
|
tpmvs | | | 84.22 294 | 83.97 291 | 84.94 315 | 87.09 346 | 65.18 339 | 91.21 218 | 88.35 293 | 82.87 250 | 85.21 313 | 90.96 292 | 65.24 310 | 96.75 275 | 79.60 263 | 85.25 340 | 92.90 311 |
|
diffmvs | | | 90.45 202 | 90.49 202 | 90.34 237 | 92.25 297 | 77.09 266 | 91.80 206 | 95.96 190 | 82.68 251 | 85.83 311 | 95.07 193 | 87.01 192 | 97.09 263 | 89.68 141 | 94.10 299 | 96.83 206 |
|
test_normal | | | 91.49 185 | 91.44 182 | 91.62 209 | 95.21 237 | 79.44 231 | 90.08 253 | 93.84 240 | 82.60 252 | 94.37 161 | 94.74 207 | 86.66 202 | 98.46 183 | 88.58 165 | 96.92 235 | 96.95 199 |
|
DI_MVS_plusplus_test | | | 91.42 189 | 91.41 183 | 91.46 214 | 95.34 234 | 79.06 242 | 90.58 237 | 93.74 242 | 82.59 253 | 94.69 153 | 94.76 206 | 86.54 205 | 98.44 185 | 87.93 173 | 96.49 253 | 96.87 204 |
|
MDA-MVSNet_test_wron | | | 88.16 244 | 88.23 231 | 87.93 290 | 92.22 298 | 73.71 298 | 80.71 341 | 88.84 289 | 82.52 254 | 94.88 147 | 95.14 189 | 82.70 232 | 93.61 327 | 83.28 225 | 93.80 302 | 96.46 221 |
|
YYNet1 | | | 88.17 243 | 88.24 230 | 87.93 290 | 92.21 299 | 73.62 299 | 80.75 340 | 88.77 290 | 82.51 255 | 94.99 144 | 95.11 191 | 82.70 232 | 93.70 326 | 83.33 224 | 93.83 301 | 96.48 220 |
|
OpenMVS | | 89.45 8 | 92.27 174 | 92.13 167 | 92.68 175 | 94.53 262 | 84.10 163 | 95.70 60 | 97.03 128 | 82.44 256 | 91.14 241 | 96.42 126 | 88.47 157 | 98.38 188 | 85.95 199 | 97.47 219 | 95.55 255 |
|
MVSTER | | | 89.32 220 | 88.75 223 | 91.03 225 | 90.10 324 | 76.62 270 | 90.85 227 | 94.67 226 | 82.27 257 | 95.24 133 | 95.79 165 | 61.09 332 | 98.49 177 | 90.49 118 | 98.26 168 | 97.97 144 |
|
Patchmatch-test1 | | | 87.28 262 | 87.30 248 | 87.22 298 | 92.01 304 | 71.98 316 | 89.43 271 | 88.11 298 | 82.26 258 | 88.71 285 | 92.20 272 | 78.65 259 | 95.81 299 | 80.99 248 | 93.30 307 | 93.87 294 |
|
Test4 | | | 91.41 190 | 91.25 188 | 91.89 201 | 95.35 233 | 80.32 202 | 90.97 224 | 96.92 139 | 81.96 259 | 95.11 137 | 93.81 237 | 81.34 244 | 98.48 180 | 88.71 162 | 97.08 229 | 96.87 204 |
|
TR-MVS | | | 87.70 251 | 87.17 251 | 89.27 265 | 94.11 270 | 79.26 234 | 88.69 288 | 91.86 274 | 81.94 260 | 90.69 247 | 89.79 308 | 82.82 231 | 97.42 251 | 72.65 313 | 91.98 324 | 91.14 331 |
|
BH-w/o | | | 87.21 265 | 87.02 256 | 87.79 293 | 94.77 249 | 77.27 264 | 87.90 294 | 93.21 253 | 81.74 261 | 89.99 261 | 88.39 320 | 83.47 224 | 96.93 269 | 71.29 322 | 92.43 318 | 89.15 338 |
|
testpf | | | 74.01 328 | 76.37 327 | 66.95 342 | 80.56 358 | 60.00 349 | 88.43 292 | 75.07 355 | 81.54 262 | 75.75 352 | 83.73 343 | 38.93 360 | 83.09 355 | 84.01 219 | 79.32 351 | 57.75 354 |
|
MIMVSNet | | | 87.13 269 | 86.54 265 | 88.89 272 | 96.05 190 | 76.11 275 | 94.39 106 | 88.51 292 | 81.37 263 | 88.27 292 | 96.75 109 | 72.38 283 | 95.52 303 | 65.71 340 | 95.47 271 | 95.03 264 |
|
MAR-MVS | | | 90.32 209 | 88.87 222 | 94.66 99 | 94.82 246 | 91.85 52 | 94.22 112 | 94.75 221 | 80.91 264 | 87.52 301 | 88.07 322 | 86.63 203 | 97.87 225 | 76.67 289 | 96.21 257 | 94.25 282 |
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 |
xiu_mvs_v2_base | | | 89.00 226 | 89.19 213 | 88.46 285 | 94.86 245 | 74.63 291 | 86.97 306 | 95.60 200 | 80.88 265 | 87.83 296 | 88.62 317 | 91.04 116 | 98.81 130 | 82.51 233 | 94.38 292 | 91.93 326 |
|
PS-MVSNAJ | | | 88.86 230 | 88.99 219 | 88.48 284 | 94.88 243 | 74.71 289 | 86.69 310 | 95.60 200 | 80.88 265 | 87.83 296 | 87.37 330 | 90.77 120 | 98.82 125 | 82.52 232 | 94.37 293 | 91.93 326 |
|
TAMVS | | | 90.16 212 | 89.05 216 | 93.49 144 | 96.49 152 | 86.37 134 | 90.34 243 | 92.55 264 | 80.84 267 | 92.99 197 | 94.57 213 | 81.94 240 | 98.20 203 | 73.51 306 | 98.21 175 | 95.90 241 |
|
PatchMatch-RL | | | 89.18 222 | 88.02 237 | 92.64 176 | 95.90 208 | 92.87 42 | 88.67 289 | 91.06 280 | 80.34 268 | 90.03 259 | 91.67 282 | 83.34 225 | 94.42 319 | 76.35 292 | 94.84 284 | 90.64 335 |
|
MCST-MVS | | | 92.91 155 | 92.51 161 | 94.10 122 | 97.52 103 | 85.72 147 | 91.36 216 | 97.13 125 | 80.33 269 | 92.91 200 | 94.24 222 | 91.23 110 | 98.72 145 | 89.99 137 | 97.93 197 | 97.86 153 |
|
PLC | | 85.34 15 | 90.40 204 | 88.92 220 | 94.85 93 | 96.53 150 | 90.02 69 | 91.58 209 | 96.48 165 | 80.16 270 | 86.14 309 | 92.18 273 | 85.73 213 | 98.25 199 | 76.87 288 | 94.61 290 | 96.30 227 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
MVP-Stereo | | | 90.07 214 | 88.92 220 | 93.54 141 | 96.31 172 | 86.49 129 | 90.93 226 | 95.59 203 | 79.80 271 | 91.48 226 | 95.59 170 | 80.79 250 | 97.39 254 | 78.57 275 | 91.19 328 | 96.76 208 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
our_test_3 | | | 87.55 256 | 87.59 245 | 87.44 296 | 91.76 305 | 70.48 321 | 83.83 330 | 90.55 286 | 79.79 272 | 92.06 220 | 92.17 274 | 78.63 260 | 95.63 301 | 84.77 213 | 94.73 286 | 96.22 230 |
|
CDS-MVSNet | | | 89.55 217 | 88.22 232 | 93.53 142 | 95.37 232 | 86.49 129 | 89.26 277 | 93.59 244 | 79.76 273 | 91.15 240 | 92.31 271 | 77.12 271 | 98.38 188 | 77.51 283 | 97.92 198 | 95.71 246 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
IB-MVS | | 77.21 19 | 83.11 297 | 81.05 309 | 89.29 264 | 91.15 309 | 75.85 278 | 85.66 317 | 86.00 313 | 79.70 274 | 82.02 337 | 86.61 332 | 48.26 355 | 98.39 186 | 77.84 279 | 92.22 321 | 93.63 299 |
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 |
agg_prior1 | | | 92.60 165 | 91.76 174 | 95.10 88 | 96.20 180 | 88.89 88 | 90.37 241 | 96.88 144 | 79.67 275 | 90.21 254 | 94.41 215 | 91.30 107 | 98.78 135 | 88.46 166 | 98.37 158 | 97.64 168 |
|
test1235678 | | | 84.54 291 | 83.85 293 | 86.59 302 | 93.81 277 | 73.41 301 | 82.38 334 | 91.79 275 | 79.43 276 | 89.50 271 | 91.61 284 | 70.59 288 | 92.94 333 | 58.14 348 | 97.40 222 | 93.44 304 |
|
PVSNet_BlendedMVS | | | 90.35 207 | 89.96 208 | 91.54 213 | 94.81 247 | 78.80 248 | 90.14 250 | 96.93 137 | 79.43 276 | 88.68 287 | 95.06 194 | 86.27 208 | 98.15 207 | 80.27 252 | 98.04 191 | 97.68 165 |
|
train_agg | | | 92.71 162 | 91.83 171 | 95.35 78 | 96.45 158 | 89.46 74 | 90.60 235 | 96.92 139 | 79.37 278 | 90.49 251 | 94.39 218 | 91.20 112 | 98.88 111 | 88.66 163 | 98.43 149 | 97.72 161 |
|
test_8 | | | | | | 96.37 161 | 89.14 83 | 90.51 239 | 96.89 143 | 79.37 278 | 90.42 253 | 94.36 220 | 91.20 112 | 98.82 125 | | | |
|
N_pmnet | | | 88.90 229 | 87.25 249 | 93.83 133 | 94.40 265 | 93.81 31 | 84.73 322 | 87.09 305 | 79.36 280 | 93.26 190 | 92.43 268 | 79.29 256 | 91.68 337 | 77.50 284 | 97.22 226 | 96.00 237 |
|
UnsupCasMVSNet_bld | | | 88.50 235 | 88.03 236 | 89.90 248 | 95.52 226 | 78.88 245 | 87.39 301 | 94.02 237 | 79.32 281 | 93.06 195 | 94.02 232 | 80.72 251 | 94.27 322 | 75.16 302 | 93.08 312 | 96.54 210 |
|
ppachtmachnet_test | | | 88.61 234 | 88.64 224 | 88.50 283 | 91.76 305 | 70.99 320 | 84.59 325 | 92.98 254 | 79.30 282 | 92.38 210 | 93.53 244 | 79.57 255 | 97.45 249 | 86.50 194 | 97.17 227 | 97.07 193 |
|
TEST9 | | | | | | 96.45 158 | 89.46 74 | 90.60 235 | 96.92 139 | 79.09 283 | 90.49 251 | 94.39 218 | 91.31 106 | 98.88 111 | | | |
|
PatchmatchNet | | | 85.22 287 | 84.64 287 | 86.98 300 | 89.51 330 | 69.83 325 | 90.52 238 | 87.34 304 | 78.87 284 | 87.22 303 | 92.74 258 | 66.91 299 | 96.53 280 | 81.77 237 | 86.88 339 | 94.58 275 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
PVSNet_Blended_VisFu | | | 91.63 181 | 91.20 189 | 92.94 161 | 97.73 91 | 83.95 165 | 92.14 182 | 97.46 95 | 78.85 285 | 92.35 212 | 94.98 198 | 84.16 223 | 99.08 78 | 86.36 196 | 96.77 239 | 95.79 243 |
|
agg_prior3 | | | 92.56 168 | 91.62 176 | 95.35 78 | 96.39 160 | 89.45 76 | 90.61 234 | 96.82 147 | 78.82 286 | 90.03 259 | 94.14 227 | 90.72 125 | 98.88 111 | 88.66 163 | 98.43 149 | 97.72 161 |
|
Patchmatch-RL test | | | 88.81 231 | 88.52 225 | 89.69 253 | 95.33 236 | 79.94 218 | 86.22 314 | 92.71 261 | 78.46 287 | 95.80 114 | 94.18 225 | 66.25 305 | 95.33 310 | 89.22 152 | 98.53 142 | 93.78 295 |
|
WTY-MVS | | | 86.93 274 | 86.50 268 | 88.24 287 | 94.96 242 | 74.64 290 | 87.19 304 | 92.07 273 | 78.29 288 | 88.32 291 | 91.59 285 | 78.06 264 | 94.27 322 | 74.88 303 | 93.15 310 | 95.80 242 |
|
pmmvs-eth3d | | | 91.54 183 | 90.73 200 | 93.99 124 | 95.76 213 | 87.86 111 | 90.83 228 | 93.98 238 | 78.23 289 | 94.02 171 | 96.22 148 | 82.62 234 | 96.83 273 | 86.57 191 | 98.33 160 | 97.29 187 |
|
TAPA-MVS | | 88.58 10 | 92.49 169 | 91.75 175 | 94.73 97 | 96.50 151 | 89.69 73 | 92.91 152 | 97.68 73 | 78.02 290 | 92.79 201 | 94.10 228 | 90.85 119 | 97.96 214 | 84.76 214 | 98.16 179 | 96.54 210 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
sss | | | 87.23 264 | 86.82 259 | 88.46 285 | 93.96 271 | 77.94 254 | 86.84 308 | 92.78 260 | 77.59 291 | 87.61 300 | 91.83 279 | 78.75 258 | 91.92 336 | 77.84 279 | 94.20 297 | 95.52 256 |
|
PatchFormer-LS_test | | | 82.62 302 | 81.71 303 | 85.32 313 | 87.92 338 | 67.31 330 | 89.03 282 | 88.20 296 | 77.58 292 | 83.79 324 | 80.50 351 | 60.96 334 | 96.42 286 | 83.86 222 | 83.59 343 | 92.23 323 |
|
CDPH-MVS | | | 92.67 163 | 91.83 171 | 95.18 86 | 96.94 124 | 88.46 102 | 90.70 232 | 97.07 127 | 77.38 293 | 92.34 214 | 95.08 192 | 92.67 81 | 98.88 111 | 85.74 200 | 98.57 138 | 98.20 128 |
|
EPMVS | | | 81.17 314 | 80.37 315 | 83.58 323 | 85.58 352 | 65.08 341 | 90.31 244 | 71.34 356 | 77.31 294 | 85.80 312 | 91.30 286 | 59.38 335 | 92.70 334 | 79.99 256 | 82.34 347 | 92.96 310 |
|
tpm | | | 84.38 293 | 84.08 290 | 85.30 314 | 90.47 319 | 63.43 347 | 89.34 274 | 85.63 317 | 77.24 295 | 87.62 299 | 95.03 197 | 61.00 333 | 97.30 257 | 79.26 264 | 91.09 330 | 95.16 260 |
|
OpenMVS_ROB | | 85.12 16 | 89.52 219 | 89.05 216 | 90.92 229 | 94.58 261 | 81.21 193 | 91.10 222 | 93.41 248 | 77.03 296 | 93.41 182 | 93.99 234 | 83.23 226 | 97.80 232 | 79.93 259 | 94.80 285 | 93.74 297 |
|
原ACMM1 | | | | | 92.87 165 | 96.91 127 | 84.22 161 | | 97.01 129 | 76.84 297 | 89.64 270 | 94.46 214 | 88.00 171 | 98.70 151 | 81.53 240 | 98.01 193 | 95.70 247 |
|
PAPR | | | 87.65 254 | 86.77 261 | 90.27 240 | 92.85 288 | 77.38 262 | 88.56 290 | 96.23 181 | 76.82 298 | 84.98 316 | 89.75 310 | 86.08 210 | 97.16 261 | 72.33 314 | 93.35 306 | 96.26 229 |
|
HY-MVS | | 82.50 18 | 86.81 276 | 85.93 279 | 89.47 255 | 93.63 278 | 77.93 255 | 94.02 115 | 91.58 277 | 75.68 299 | 83.64 325 | 93.64 239 | 77.40 268 | 97.42 251 | 71.70 319 | 92.07 323 | 93.05 309 |
|
tpmrst | | | 82.85 301 | 82.93 298 | 82.64 328 | 87.65 340 | 58.99 351 | 90.14 250 | 87.90 299 | 75.54 300 | 83.93 323 | 91.63 283 | 66.79 302 | 95.36 308 | 81.21 244 | 81.54 349 | 93.57 303 |
|
MS-PatchMatch | | | 88.05 245 | 87.75 242 | 88.95 271 | 93.28 282 | 77.93 255 | 87.88 295 | 92.49 265 | 75.42 301 | 92.57 206 | 93.59 242 | 80.44 252 | 94.24 324 | 81.28 242 | 92.75 315 | 94.69 273 |
|
PVSNet_Blended | | | 88.74 233 | 88.16 234 | 90.46 235 | 94.81 247 | 78.80 248 | 86.64 311 | 96.93 137 | 74.67 302 | 88.68 287 | 89.18 315 | 86.27 208 | 98.15 207 | 80.27 252 | 96.00 258 | 94.44 279 |
|
pmmvs4 | | | 88.95 228 | 87.70 244 | 92.70 174 | 94.30 266 | 85.60 148 | 87.22 303 | 92.16 270 | 74.62 303 | 89.75 269 | 94.19 224 | 77.97 265 | 96.41 287 | 82.71 230 | 96.36 254 | 96.09 234 |
|
test12356 | | | 76.35 325 | 77.41 326 | 73.19 341 | 90.70 314 | 38.86 360 | 74.56 346 | 91.14 279 | 74.55 304 | 80.54 344 | 88.18 321 | 52.36 352 | 90.49 344 | 52.38 353 | 92.26 320 | 90.21 337 |
|
no-one | | | 87.84 248 | 87.21 250 | 89.74 249 | 93.58 279 | 78.64 251 | 81.28 339 | 92.69 262 | 74.36 305 | 92.05 221 | 97.14 89 | 81.86 241 | 96.07 295 | 72.03 316 | 99.90 2 | 94.52 276 |
|
1314 | | | 86.46 281 | 86.33 269 | 86.87 301 | 91.65 307 | 74.54 292 | 91.94 189 | 94.10 235 | 74.28 306 | 84.78 318 | 87.33 331 | 83.03 228 | 95.00 314 | 78.72 273 | 91.16 329 | 91.06 332 |
|
Anonymous20231206 | | | 88.77 232 | 88.29 228 | 90.20 245 | 96.31 172 | 78.81 247 | 89.56 269 | 93.49 247 | 74.26 307 | 92.38 210 | 95.58 173 | 82.21 235 | 95.43 307 | 72.07 315 | 98.75 130 | 96.34 225 |
|
DWT-MVSNet_test | | | 80.74 316 | 79.18 321 | 85.43 311 | 87.51 343 | 66.87 333 | 89.87 262 | 86.01 312 | 74.20 308 | 80.86 341 | 80.62 350 | 48.84 354 | 96.68 279 | 81.54 239 | 83.14 346 | 92.75 314 |
|
MDTV_nov1_ep13 | | | | 83.88 292 | | 89.42 331 | 61.52 348 | 88.74 287 | 87.41 303 | 73.99 309 | 84.96 317 | 94.01 233 | 65.25 309 | 95.53 302 | 78.02 277 | 93.16 309 | |
|
test-mter | | | 81.21 313 | 80.01 319 | 84.79 317 | 89.68 327 | 66.86 334 | 83.08 331 | 84.52 329 | 73.85 310 | 82.85 330 | 84.78 341 | 43.66 359 | 93.49 328 | 82.85 228 | 94.86 282 | 94.03 287 |
|
pmmvs5 | | | 87.87 247 | 87.14 252 | 90.07 246 | 93.26 284 | 76.97 269 | 88.89 285 | 92.18 268 | 73.71 311 | 88.36 289 | 93.89 235 | 76.86 275 | 96.73 276 | 80.32 251 | 96.81 237 | 96.51 212 |
|
1112_ss | | | 88.42 237 | 87.41 246 | 91.45 215 | 96.69 138 | 80.99 195 | 89.72 265 | 96.72 154 | 73.37 312 | 87.00 305 | 90.69 299 | 77.38 269 | 98.20 203 | 81.38 241 | 93.72 303 | 95.15 261 |
|
USDC | | | 89.02 225 | 89.08 215 | 88.84 273 | 95.07 241 | 74.50 294 | 88.97 283 | 96.39 171 | 73.21 313 | 93.27 189 | 96.28 140 | 82.16 236 | 96.39 288 | 77.55 282 | 98.80 125 | 95.62 250 |
|
testus | | | 82.09 307 | 81.78 302 | 83.03 326 | 92.35 295 | 64.37 345 | 79.44 342 | 93.27 250 | 73.08 314 | 87.06 304 | 85.21 340 | 76.80 276 | 89.27 347 | 53.30 351 | 95.48 270 | 95.46 257 |
|
CR-MVSNet | | | 87.89 246 | 87.12 253 | 90.22 242 | 91.01 311 | 78.93 243 | 92.52 163 | 92.81 257 | 73.08 314 | 89.10 275 | 96.93 98 | 67.11 297 | 97.64 243 | 88.80 159 | 92.70 316 | 94.08 284 |
|
dp | | | 79.28 322 | 78.62 323 | 81.24 331 | 85.97 351 | 56.45 353 | 86.91 307 | 85.26 325 | 72.97 316 | 81.45 340 | 89.17 316 | 56.01 350 | 95.45 306 | 73.19 309 | 76.68 352 | 91.82 329 |
|
ADS-MVSNet2 | | | 84.01 295 | 82.20 301 | 89.41 261 | 89.04 334 | 76.37 272 | 87.57 297 | 90.98 282 | 72.71 317 | 84.46 319 | 92.45 265 | 68.08 293 | 96.48 283 | 70.58 328 | 83.97 341 | 95.38 258 |
|
ADS-MVSNet | | | 82.25 304 | 81.55 305 | 84.34 320 | 89.04 334 | 65.30 338 | 87.57 297 | 85.13 327 | 72.71 317 | 84.46 319 | 92.45 265 | 68.08 293 | 92.33 335 | 70.58 328 | 83.97 341 | 95.38 258 |
|
jason | | | 89.17 223 | 88.32 227 | 91.70 207 | 95.73 214 | 80.07 210 | 88.10 293 | 93.22 251 | 71.98 319 | 90.09 256 | 92.79 256 | 78.53 261 | 98.56 167 | 87.43 179 | 97.06 230 | 96.46 221 |
jason: jason. |
testdata | | | | | 91.03 225 | 96.87 129 | 82.01 182 | | 94.28 232 | 71.55 320 | 92.46 207 | 95.42 181 | 85.65 215 | 97.38 256 | 82.64 231 | 97.27 225 | 93.70 298 |
|
PVSNet | | 76.22 20 | 82.89 300 | 82.37 299 | 84.48 319 | 93.96 271 | 64.38 344 | 78.60 344 | 88.61 291 | 71.50 321 | 84.43 321 | 86.36 336 | 74.27 280 | 94.60 316 | 69.87 330 | 93.69 304 | 94.46 278 |
|
gm-plane-assit | | | | | | 87.08 347 | 59.33 350 | | | 71.22 322 | | 83.58 344 | | 97.20 260 | 73.95 304 | | |
|
lupinMVS | | | 88.34 238 | 87.31 247 | 91.45 215 | 94.74 251 | 80.06 211 | 87.23 302 | 92.27 267 | 71.10 323 | 88.83 278 | 91.15 288 | 77.02 272 | 98.53 174 | 86.67 189 | 96.75 240 | 95.76 244 |
|
cascas | | | 87.02 271 | 86.28 270 | 89.25 266 | 91.56 308 | 76.45 271 | 84.33 327 | 96.78 150 | 71.01 324 | 86.89 306 | 85.91 337 | 81.35 243 | 96.94 268 | 83.09 227 | 95.60 266 | 94.35 281 |
|
test2356 | | | 75.58 326 | 73.13 328 | 82.95 327 | 86.10 350 | 66.42 336 | 75.07 345 | 84.87 328 | 70.91 325 | 80.85 342 | 80.66 349 | 38.02 361 | 88.98 350 | 49.32 354 | 92.35 319 | 93.44 304 |
|
new_pmnet | | | 81.22 312 | 81.01 311 | 81.86 330 | 90.92 313 | 70.15 323 | 84.03 328 | 80.25 352 | 70.83 326 | 85.97 310 | 89.78 309 | 67.93 296 | 84.65 353 | 67.44 334 | 91.90 325 | 90.78 333 |
|
无先验 | | | | | | | | 89.94 257 | 95.75 197 | 70.81 327 | | | | 98.59 163 | 81.17 245 | | 94.81 268 |
|
CostFormer | | | 83.09 298 | 82.21 300 | 85.73 308 | 89.27 333 | 67.01 331 | 90.35 242 | 86.47 309 | 70.42 328 | 83.52 327 | 93.23 251 | 61.18 331 | 96.85 272 | 77.21 286 | 88.26 337 | 93.34 307 |
|
TESTMET0.1,1 | | | 79.09 323 | 78.04 324 | 82.25 329 | 87.52 342 | 64.03 346 | 83.08 331 | 80.62 350 | 70.28 329 | 80.16 345 | 83.22 346 | 44.13 358 | 90.56 342 | 79.95 257 | 93.36 305 | 92.15 324 |
|
CMPMVS | | 68.83 22 | 87.28 262 | 85.67 281 | 92.09 197 | 88.77 337 | 85.42 150 | 90.31 244 | 94.38 230 | 70.02 330 | 88.00 294 | 93.30 250 | 73.78 281 | 94.03 325 | 75.96 295 | 96.54 248 | 96.83 206 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
Test_1112_low_res | | | 87.50 258 | 86.58 263 | 90.25 241 | 96.80 133 | 77.75 258 | 87.53 300 | 96.25 179 | 69.73 331 | 86.47 307 | 93.61 241 | 75.67 278 | 97.88 223 | 79.95 257 | 93.20 308 | 95.11 263 |
|
PAPM | | | 81.91 308 | 80.11 318 | 87.31 297 | 93.87 274 | 72.32 315 | 84.02 329 | 93.22 251 | 69.47 332 | 76.13 351 | 89.84 305 | 72.15 284 | 97.23 259 | 53.27 352 | 89.02 333 | 92.37 318 |
|
MVS-HIRNet | | | 78.83 324 | 80.60 313 | 73.51 340 | 93.07 286 | 47.37 355 | 87.10 305 | 78.00 353 | 68.94 333 | 77.53 349 | 97.26 83 | 71.45 286 | 94.62 315 | 63.28 344 | 88.74 334 | 78.55 352 |
|
旧先验2 | | | | | | | | 90.00 256 | | 68.65 334 | 92.71 203 | | | 96.52 281 | 85.15 206 | | |
|
PCF-MVS | | 84.52 17 | 89.12 224 | 87.71 243 | 93.34 145 | 96.06 189 | 85.84 144 | 86.58 313 | 97.31 111 | 68.46 335 | 93.61 178 | 93.89 235 | 87.51 179 | 98.52 175 | 67.85 333 | 98.11 185 | 95.66 248 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
新几何1 | | | | | 93.17 151 | 97.16 114 | 87.29 117 | | 94.43 228 | 67.95 336 | 91.29 230 | 94.94 199 | 86.97 194 | 98.23 200 | 81.06 247 | 97.75 202 | 93.98 290 |
|
1121 | | | 90.26 210 | 89.23 212 | 93.34 145 | 97.15 116 | 87.40 116 | 91.94 189 | 94.39 229 | 67.88 337 | 91.02 242 | 94.91 200 | 86.91 197 | 98.59 163 | 81.17 245 | 97.71 205 | 94.02 289 |
|
tpmp4_e23 | | | 81.87 309 | 80.41 314 | 86.27 306 | 89.29 332 | 67.84 329 | 91.58 209 | 87.61 302 | 67.42 338 | 78.60 347 | 92.71 259 | 56.42 348 | 96.87 271 | 71.44 321 | 88.63 335 | 94.10 283 |
|
MVE | | 59.87 23 | 73.86 329 | 72.65 330 | 77.47 337 | 87.00 348 | 74.35 295 | 61.37 354 | 60.93 359 | 67.27 339 | 69.69 355 | 86.49 334 | 81.24 248 | 72.33 357 | 56.45 350 | 83.45 344 | 85.74 345 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
MDTV_nov1_ep13_2view | | | | | | | 42.48 359 | 88.45 291 | | 67.22 340 | 83.56 326 | | 66.80 300 | | 72.86 312 | | 94.06 286 |
|
CHOSEN 280x420 | | | 80.04 321 | 77.97 325 | 86.23 307 | 90.13 323 | 74.53 293 | 72.87 349 | 89.59 288 | 66.38 341 | 76.29 350 | 85.32 339 | 56.96 346 | 95.36 308 | 69.49 331 | 94.72 287 | 88.79 341 |
|
HyFIR lowres test | | | 87.19 267 | 85.51 282 | 92.24 192 | 97.12 119 | 80.51 200 | 85.03 320 | 96.06 186 | 66.11 342 | 91.66 225 | 92.98 254 | 70.12 290 | 99.14 71 | 75.29 301 | 95.23 277 | 97.07 193 |
|
114514_t | | | 90.51 200 | 89.80 210 | 92.63 177 | 98.00 77 | 82.24 181 | 93.40 135 | 97.29 114 | 65.84 343 | 89.40 273 | 94.80 205 | 86.99 193 | 98.75 140 | 83.88 221 | 98.61 136 | 96.89 202 |
|
tpm2 | | | 81.46 310 | 80.35 316 | 84.80 316 | 89.90 325 | 65.14 340 | 90.44 240 | 85.36 320 | 65.82 344 | 82.05 336 | 92.44 267 | 57.94 343 | 96.69 277 | 70.71 327 | 88.49 336 | 92.56 316 |
|
test222 | | | | | | 96.95 123 | 85.27 152 | 88.83 286 | 93.61 243 | 65.09 345 | 90.74 246 | 94.85 201 | 84.62 221 | | | 97.36 223 | 93.91 291 |
|
CHOSEN 1792x2688 | | | 87.19 267 | 85.92 280 | 91.00 228 | 97.13 118 | 79.41 232 | 84.51 326 | 95.60 200 | 64.14 346 | 90.07 258 | 94.81 202 | 78.26 263 | 97.14 262 | 73.34 307 | 95.38 274 | 96.46 221 |
|
pmmvs3 | | | 80.83 315 | 78.96 322 | 86.45 304 | 87.23 345 | 77.48 261 | 84.87 321 | 82.31 344 | 63.83 347 | 85.03 315 | 89.50 313 | 49.66 353 | 93.10 330 | 73.12 310 | 95.10 279 | 88.78 342 |
|
PVSNet_0 | | 70.34 21 | 74.58 327 | 72.96 329 | 79.47 334 | 90.63 316 | 66.24 337 | 73.26 347 | 83.40 336 | 63.67 348 | 78.02 348 | 78.35 352 | 72.53 282 | 89.59 346 | 56.68 349 | 60.05 355 | 82.57 350 |
|
tpm cat1 | | | 80.61 318 | 79.46 320 | 84.07 322 | 88.78 336 | 65.06 342 | 89.26 277 | 88.23 295 | 62.27 349 | 81.90 338 | 89.66 312 | 62.70 329 | 95.29 311 | 71.72 318 | 80.60 350 | 91.86 328 |
|
PMMVS | | | 83.00 299 | 81.11 308 | 88.66 277 | 83.81 357 | 86.44 132 | 82.24 336 | 85.65 316 | 61.75 350 | 82.07 335 | 85.64 338 | 79.75 254 | 91.59 338 | 75.99 294 | 93.09 311 | 87.94 343 |
|
MVS | | | 84.98 290 | 84.30 289 | 87.01 299 | 91.03 310 | 77.69 260 | 91.94 189 | 94.16 234 | 59.36 351 | 84.23 322 | 87.50 329 | 85.66 214 | 96.80 274 | 71.79 317 | 93.05 313 | 86.54 344 |
|
EU-MVSNet | | | 87.39 260 | 86.71 262 | 89.44 260 | 93.40 281 | 76.11 275 | 94.93 86 | 90.00 287 | 57.17 352 | 95.71 117 | 97.37 78 | 64.77 312 | 97.68 242 | 92.67 81 | 94.37 293 | 94.52 276 |
|
CVMVSNet | | | 85.16 288 | 84.72 286 | 86.48 303 | 92.12 302 | 70.19 322 | 92.32 175 | 88.17 297 | 56.15 353 | 90.64 248 | 95.85 161 | 67.97 295 | 96.69 277 | 88.78 160 | 90.52 331 | 92.56 316 |
|
DSMNet-mixed | | | 82.21 305 | 81.56 304 | 84.16 321 | 89.57 329 | 70.00 324 | 90.65 233 | 77.66 354 | 54.99 354 | 83.30 328 | 97.57 65 | 77.89 266 | 90.50 343 | 66.86 336 | 95.54 268 | 91.97 325 |
|
DeepMVS_CX | | | | | 53.83 343 | 70.38 359 | 64.56 343 | | 48.52 361 | 33.01 355 | 65.50 356 | 74.21 354 | 56.19 349 | 46.64 358 | 38.45 356 | 70.07 353 | 50.30 355 |
|
tmp_tt | | | 37.97 333 | 44.33 333 | 18.88 346 | 11.80 360 | 21.54 361 | 63.51 353 | 45.66 362 | 4.23 356 | 51.34 357 | 50.48 355 | 59.08 336 | 22.11 359 | 44.50 355 | 68.35 354 | 13.00 356 |
|
test123 | | | 9.49 335 | 12.01 336 | 1.91 347 | 2.87 361 | 1.30 362 | 82.38 334 | 1.34 364 | 1.36 357 | 2.84 358 | 6.56 358 | 2.45 365 | 0.97 360 | 2.73 357 | 5.56 357 | 3.47 357 |
|
testmvs | | | 9.02 336 | 11.42 337 | 1.81 348 | 2.77 362 | 1.13 363 | 79.44 342 | 1.90 363 | 1.18 358 | 2.65 359 | 6.80 357 | 1.95 366 | 0.87 361 | 2.62 358 | 3.45 358 | 3.44 358 |
|
cdsmvs_eth3d_5k | | | 23.35 334 | 31.13 335 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 95.58 204 | 0.00 359 | 0.00 360 | 91.15 288 | 93.43 62 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
pcd_1.5k_mvsjas | | | 7.56 337 | 10.09 338 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 0.00 365 | 0.00 359 | 0.00 360 | 0.00 361 | 90.77 120 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
pcd1.5k->3k | | | 41.03 332 | 43.65 334 | 33.18 345 | 98.74 26 | 0.00 364 | 0.00 355 | 97.57 83 | 0.00 359 | 0.00 360 | 0.00 361 | 97.01 6 | 0.00 362 | 0.00 359 | 99.52 46 | 99.53 17 |
|
sosnet-low-res | | | 0.00 339 | 0.00 340 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 0.00 365 | 0.00 359 | 0.00 360 | 0.00 361 | 0.00 367 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
sosnet | | | 0.00 339 | 0.00 340 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 0.00 365 | 0.00 359 | 0.00 360 | 0.00 361 | 0.00 367 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
uncertanet | | | 0.00 339 | 0.00 340 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 0.00 365 | 0.00 359 | 0.00 360 | 0.00 361 | 0.00 367 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
Regformer | | | 0.00 339 | 0.00 340 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 0.00 365 | 0.00 359 | 0.00 360 | 0.00 361 | 0.00 367 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
ab-mvs-re | | | 7.56 337 | 10.08 339 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 0.00 365 | 0.00 359 | 0.00 360 | 90.69 299 | 0.00 367 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
uanet | | | 0.00 339 | 0.00 340 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 0.00 365 | 0.00 359 | 0.00 360 | 0.00 361 | 0.00 367 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
GSMVS | | | | | | | | | | | | | | | | | 94.75 271 |
|
test_part2 | | | | | | 98.21 63 | 89.41 77 | | | | 96.72 68 | | | | | | |
|
test_part1 | | | | | | | | | 98.14 28 | | | | 94.69 45 | | | 99.10 92 | 98.17 129 |
|
sam_mvs1 | | | | | | | | | | | | | 66.64 303 | | | | 94.75 271 |
|
sam_mvs | | | | | | | | | | | | | 66.41 304 | | | | |
|
ambc | | | | | 92.98 157 | 96.88 128 | 83.01 176 | 95.92 54 | 96.38 172 | | 96.41 78 | 97.48 72 | 88.26 161 | 97.80 232 | 89.96 138 | 98.93 107 | 98.12 135 |
|
MTGPA | | | | | | | | | 97.62 76 | | | | | | | | |
|
test_post1 | | | | | | | | 90.21 246 | | | | 5.85 360 | 65.36 308 | 96.00 296 | 79.61 262 | | |
|
test_post | | | | | | | | | | | | 6.07 359 | 65.74 307 | 95.84 298 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 91.71 281 | 66.22 306 | 97.59 245 | | | |
|
GG-mvs-BLEND | | | | | 83.24 325 | 85.06 354 | 71.03 319 | 94.99 85 | 65.55 358 | | 74.09 353 | 75.51 353 | 44.57 357 | 94.46 318 | 59.57 347 | 87.54 338 | 84.24 346 |
|
MTMP | | | | | | | | | 54.62 360 | | | | | | | | |
|
test9_res | | | | | | | | | | | | | | | 88.16 170 | 98.40 151 | 97.83 155 |
|
agg_prior2 | | | | | | | | | | | | | | | 87.06 184 | 98.36 159 | 97.98 141 |
|
agg_prior | | | | | | 96.20 180 | 88.89 88 | | 96.88 144 | | 90.21 254 | | | 98.78 135 | | | |
|
test_prior4 | | | | | | | 89.91 71 | 90.74 230 | | | | | | | | | |
|
test_prior | | | | | 94.61 100 | 95.95 204 | 87.23 118 | | 97.36 107 | | | | | 98.68 153 | | | 97.93 145 |
|
新几何2 | | | | | | | | 90.02 255 | | | | | | | | | |
|
旧先验1 | | | | | | 96.20 180 | 84.17 162 | | 94.82 218 | | | 95.57 174 | 89.57 144 | | | 97.89 199 | 96.32 226 |
|
原ACMM2 | | | | | | | | 89.34 274 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 98.03 211 | 80.24 254 | | |
|
segment_acmp | | | | | | | | | | | | | 92.14 88 | | | | |
|
test12 | | | | | 94.43 114 | 95.95 204 | 86.75 127 | | 96.24 180 | | 89.76 268 | | 89.79 142 | 98.79 132 | | 97.95 196 | 97.75 160 |
|
plane_prior7 | | | | | | 97.71 92 | 88.68 92 | | | | | | | | | | |
|
plane_prior6 | | | | | | 97.21 112 | 88.23 105 | | | | | | 86.93 195 | | | | |
|
plane_prior5 | | | | | | | | | 97.81 63 | | | | | 98.95 101 | 89.26 150 | 98.51 144 | 98.60 110 |
|
plane_prior4 | | | | | | | | | | | | 95.59 170 | | | | | |
|
plane_prior1 | | | | | | 97.38 107 | | | | | | | | | | | |
|
n2 | | | | | | | | | 0.00 365 | | | | | | | | |
|
nn | | | | | | | | | 0.00 365 | | | | | | | | |
|
door-mid | | | | | | | | | 92.13 272 | | | | | | | | |
|
lessismore_v0 | | | | | 93.87 132 | 98.05 73 | 83.77 167 | | 80.32 351 | | 97.13 54 | 97.91 53 | 77.49 267 | 99.11 76 | 92.62 82 | 98.08 188 | 98.74 99 |
|
test11 | | | | | | | | | 96.65 156 | | | | | | | | |
|
door | | | | | | | | | 91.26 278 | | | | | | | | |
|
HQP5-MVS | | | | | | | 84.89 154 | | | | | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 86.55 192 | | |
|
HQP4-MVS | | | | | | | | | | | 88.81 280 | | | 98.61 159 | | | 98.15 132 |
|
HQP3-MVS | | | | | | | | | 97.31 111 | | | | | | | 97.73 203 | |
|
HQP2-MVS | | | | | | | | | | | | | 84.76 219 | | | | |
|
NP-MVS | | | | | | 96.82 131 | 87.10 121 | | | | | 93.40 248 | | | | | |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 98.82 121 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 99.25 77 | |
|
Test By Simon | | | | | | | | | | | | | 90.61 127 | | | | |
|