wuykxyi23d | | | 75.33 88 | 76.75 75 | 71.04 158 | 78.83 133 | 85.01 1 | 71.78 162 | 61.00 256 | 53.47 193 | 96.33 1 | 93.38 4 | 73.07 46 | 68.04 264 | 65.65 110 | 97.28 2 | 60.07 323 |
|
LCM-MVSNet | | | 86.90 1 | 88.67 1 | 81.57 20 | 91.50 1 | 63.30 104 | 84.80 24 | 87.77 7 | 86.18 1 | 96.26 2 | 96.06 2 | 90.32 1 | 84.49 49 | 68.08 82 | 97.05 3 | 96.93 1 |
|
V4 | | | 78.96 52 | 79.79 51 | 76.46 75 | 73.02 226 | 54.90 149 | 78.48 72 | 83.47 56 | 64.43 77 | 91.20 3 | 91.54 28 | 72.08 51 | 81.11 109 | 76.45 28 | 87.46 143 | 93.38 7 |
|
v52 | | | 78.96 52 | 79.79 51 | 76.46 75 | 73.03 225 | 54.90 149 | 78.48 72 | 83.48 55 | 64.43 77 | 91.19 4 | 91.54 28 | 72.08 51 | 81.11 109 | 76.45 28 | 87.47 141 | 93.38 7 |
|
DTE-MVSNet | | | 80.35 41 | 82.89 29 | 72.74 137 | 89.84 8 | 37.34 281 | 77.16 89 | 81.81 78 | 80.45 2 | 90.92 5 | 92.95 8 | 74.57 39 | 86.12 24 | 63.65 122 | 94.68 31 | 94.76 6 |
|
PS-CasMVS | | | 80.41 40 | 82.86 30 | 73.07 127 | 89.93 7 | 39.21 264 | 77.15 90 | 81.28 90 | 79.74 4 | 90.87 6 | 92.73 11 | 75.03 35 | 84.93 42 | 63.83 121 | 95.19 17 | 95.07 3 |
|
wuyk23d | | | 61.97 230 | 66.25 198 | 49.12 304 | 58.19 330 | 60.77 119 | 66.32 231 | 52.97 298 | 55.93 154 | 90.62 7 | 86.91 115 | 73.07 46 | 35.98 346 | 20.63 345 | 91.63 72 | 50.62 337 |
|
PEN-MVS | | | 80.46 39 | 82.91 28 | 73.11 126 | 89.83 9 | 39.02 267 | 77.06 92 | 82.61 68 | 80.04 3 | 90.60 8 | 92.85 9 | 74.93 36 | 85.21 38 | 63.15 124 | 95.15 19 | 95.09 2 |
|
CP-MVSNet | | | 79.48 48 | 81.65 38 | 72.98 130 | 89.66 13 | 39.06 266 | 76.76 94 | 80.46 111 | 78.91 6 | 90.32 9 | 91.70 25 | 68.49 78 | 84.89 43 | 63.40 123 | 95.12 20 | 95.01 4 |
|
LCM-MVSNet-Re | | | 69.10 173 | 71.57 153 | 61.70 247 | 70.37 249 | 34.30 303 | 61.45 280 | 79.62 124 | 56.81 147 | 89.59 10 | 88.16 105 | 68.44 79 | 72.94 213 | 42.30 258 | 87.33 147 | 77.85 214 |
|
WR-MVS_H | | | 80.22 43 | 82.17 35 | 74.39 96 | 89.46 15 | 42.69 243 | 78.24 77 | 82.24 71 | 78.21 8 | 89.57 11 | 92.10 18 | 68.05 83 | 85.59 31 | 66.04 107 | 95.62 11 | 94.88 5 |
|
anonymousdsp | | | 78.60 57 | 77.80 66 | 81.00 31 | 78.01 141 | 74.34 33 | 80.09 58 | 76.12 170 | 50.51 226 | 89.19 12 | 90.88 41 | 71.45 58 | 77.78 175 | 73.38 40 | 90.60 99 | 90.90 27 |
|
LTVRE_ROB | | 75.46 1 | 84.22 5 | 84.98 5 | 81.94 19 | 84.82 60 | 75.40 26 | 91.60 1 | 87.80 5 | 73.52 19 | 88.90 13 | 93.06 7 | 71.39 59 | 81.53 91 | 81.53 3 | 92.15 68 | 88.91 47 |
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 |
OurMVSNet-221017-0 | | | 78.57 58 | 78.53 61 | 78.67 54 | 80.48 111 | 64.16 96 | 80.24 56 | 82.06 73 | 61.89 101 | 88.77 14 | 93.32 5 | 57.15 194 | 82.60 77 | 70.08 67 | 92.80 59 | 89.25 37 |
|
test_0402 | | | 78.17 64 | 79.48 53 | 74.24 98 | 83.50 78 | 59.15 132 | 72.52 146 | 74.60 184 | 75.34 13 | 88.69 15 | 91.81 22 | 75.06 34 | 82.37 80 | 65.10 113 | 88.68 126 | 81.20 166 |
|
abl_6 | | | 84.92 3 | 85.70 3 | 82.57 14 | 86.72 39 | 79.27 8 | 87.56 5 | 86.08 16 | 77.48 9 | 88.12 16 | 91.53 30 | 81.18 6 | 84.31 54 | 78.12 21 | 94.47 35 | 84.15 112 |
|
TDRefinement | | | 86.32 2 | 86.33 2 | 86.29 1 | 88.64 28 | 81.19 6 | 88.84 2 | 90.72 1 | 78.27 7 | 87.95 17 | 92.53 13 | 79.37 12 | 84.79 46 | 74.51 34 | 96.15 4 | 92.88 9 |
|
LPG-MVS_test | | | 83.47 15 | 84.33 10 | 80.90 32 | 87.00 36 | 70.41 56 | 82.04 43 | 86.35 12 | 69.77 40 | 87.75 18 | 91.13 35 | 81.83 3 | 86.20 18 | 77.13 26 | 95.96 7 | 86.08 77 |
|
LGP-MVS_train | | | | | 80.90 32 | 87.00 36 | 70.41 56 | | 86.35 12 | 69.77 40 | 87.75 18 | 91.13 35 | 81.83 3 | 86.20 18 | 77.13 26 | 95.96 7 | 86.08 77 |
|
SixPastTwentyTwo | | | 75.77 81 | 76.34 80 | 74.06 101 | 81.69 102 | 54.84 151 | 76.47 97 | 75.49 176 | 64.10 82 | 87.73 20 | 92.24 17 | 50.45 224 | 81.30 103 | 67.41 92 | 91.46 77 | 86.04 79 |
|
ACMH+ | | 66.64 10 | 81.20 32 | 82.48 33 | 77.35 70 | 81.16 108 | 62.39 108 | 80.51 51 | 87.80 5 | 73.02 22 | 87.57 21 | 91.08 37 | 80.28 9 | 82.44 78 | 64.82 115 | 96.10 6 | 87.21 70 |
|
v7n | | | 79.37 50 | 80.41 45 | 76.28 78 | 78.67 135 | 55.81 146 | 79.22 66 | 82.51 70 | 70.72 34 | 87.54 22 | 92.44 14 | 68.00 85 | 81.34 101 | 72.84 42 | 91.72 70 | 91.69 12 |
|
ACMM | | 69.25 9 | 82.11 27 | 83.31 24 | 78.49 57 | 88.17 33 | 73.96 34 | 83.11 36 | 84.52 36 | 66.40 56 | 87.45 23 | 89.16 84 | 81.02 7 | 80.52 129 | 74.27 36 | 95.73 9 | 80.98 173 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
mvs_tets | | | 78.93 54 | 78.67 59 | 79.72 42 | 84.81 61 | 73.93 35 | 80.65 50 | 76.50 169 | 51.98 206 | 87.40 24 | 91.86 21 | 76.09 25 | 78.53 157 | 68.58 77 | 90.20 104 | 86.69 74 |
|
test_djsdf | | | 78.88 55 | 78.27 62 | 80.70 35 | 81.42 104 | 71.24 48 | 83.98 28 | 75.72 174 | 52.27 201 | 87.37 25 | 92.25 16 | 68.04 84 | 80.56 126 | 72.28 51 | 91.15 84 | 90.32 33 |
|
jajsoiax | | | 78.51 59 | 78.16 63 | 79.59 44 | 84.65 64 | 73.83 37 | 80.42 53 | 76.12 170 | 51.33 213 | 87.19 26 | 91.51 31 | 73.79 45 | 78.44 161 | 68.27 80 | 90.13 108 | 86.49 75 |
|
PMVS | | 70.70 6 | 81.70 28 | 83.15 27 | 77.36 69 | 90.35 6 | 82.82 3 | 82.15 41 | 79.22 129 | 74.08 17 | 87.16 27 | 91.97 19 | 84.80 2 | 76.97 180 | 64.98 114 | 93.61 51 | 72.28 253 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
ACMH | | 63.62 14 | 77.50 68 | 80.11 47 | 69.68 175 | 79.61 117 | 56.28 144 | 78.81 68 | 83.62 53 | 63.41 91 | 87.14 28 | 90.23 66 | 76.11 24 | 73.32 210 | 67.58 90 | 94.44 37 | 79.44 196 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
v13 | | | 76.23 76 | 77.02 73 | 73.86 105 | 74.61 185 | 48.80 184 | 76.91 93 | 81.10 97 | 62.66 95 | 87.02 29 | 91.01 39 | 59.76 151 | 81.41 96 | 71.29 54 | 88.78 125 | 91.38 14 |
|
v748 | | | 76.93 71 | 77.95 65 | 73.87 103 | 73.94 199 | 52.44 165 | 75.90 110 | 79.98 122 | 65.34 67 | 86.97 30 | 91.77 23 | 67.40 89 | 78.40 164 | 70.23 64 | 90.01 109 | 90.76 31 |
|
v12 | | | 76.03 78 | 76.79 74 | 73.76 107 | 74.45 187 | 48.60 190 | 76.59 95 | 81.11 94 | 62.22 100 | 86.79 31 | 90.74 47 | 59.51 152 | 81.40 98 | 71.01 57 | 88.67 127 | 91.29 16 |
|
ACMP | | 69.50 8 | 82.64 23 | 83.38 23 | 80.40 36 | 86.50 41 | 69.44 62 | 82.30 40 | 86.08 16 | 66.80 52 | 86.70 32 | 89.99 69 | 81.64 5 | 85.95 25 | 74.35 35 | 96.11 5 | 85.81 83 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
APDe-MVS | | | 82.88 22 | 84.14 12 | 79.08 49 | 84.80 62 | 66.72 77 | 86.54 16 | 85.11 26 | 72.00 28 | 86.65 33 | 91.75 24 | 78.20 17 | 87.04 7 | 77.93 22 | 94.32 40 | 83.47 123 |
|
V9 | | | 75.82 80 | 76.53 77 | 73.66 108 | 74.28 191 | 48.37 191 | 76.26 103 | 81.10 97 | 61.73 103 | 86.59 34 | 90.43 55 | 59.16 158 | 81.42 95 | 70.71 60 | 88.56 128 | 91.21 19 |
|
APD-MVS_3200maxsize | | | 83.57 12 | 84.33 10 | 81.31 26 | 82.83 88 | 73.53 40 | 85.50 21 | 87.45 8 | 74.11 16 | 86.45 35 | 90.52 54 | 80.02 10 | 84.48 50 | 77.73 23 | 94.34 39 | 85.93 81 |
|
v11 | | | 75.76 82 | 76.51 78 | 73.48 115 | 74.28 191 | 47.81 203 | 76.16 105 | 81.28 90 | 61.56 104 | 86.39 36 | 90.38 61 | 59.32 156 | 81.41 96 | 70.85 58 | 88.41 130 | 91.23 17 |
|
V14 | | | 75.58 85 | 76.26 83 | 73.55 113 | 74.10 198 | 48.13 196 | 75.91 109 | 81.07 100 | 61.19 107 | 86.34 37 | 90.11 68 | 58.80 162 | 81.40 98 | 70.40 62 | 88.43 129 | 91.12 20 |
|
PS-MVSNAJss | | | 77.54 67 | 77.35 69 | 78.13 63 | 84.88 59 | 66.37 81 | 78.55 71 | 79.59 126 | 53.48 192 | 86.29 38 | 92.43 15 | 62.39 123 | 80.25 133 | 67.90 89 | 90.61 98 | 87.77 64 |
|
HPM-MVS_fast | | | 84.59 4 | 85.10 4 | 83.06 4 | 88.60 29 | 75.83 23 | 86.27 19 | 86.89 11 | 73.69 18 | 86.17 39 | 91.70 25 | 78.23 16 | 85.20 39 | 79.45 11 | 94.91 26 | 88.15 61 |
|
SD-MVS | | | 80.28 42 | 81.55 40 | 76.47 74 | 83.57 77 | 67.83 74 | 83.39 33 | 85.35 25 | 64.42 79 | 86.14 40 | 87.07 112 | 74.02 42 | 80.97 116 | 77.70 24 | 92.32 67 | 80.62 181 |
|
v15 | | | 75.37 87 | 76.01 85 | 73.44 116 | 73.91 202 | 47.87 202 | 75.55 117 | 81.04 101 | 60.76 112 | 86.11 41 | 89.76 73 | 58.53 168 | 81.40 98 | 70.11 65 | 88.32 131 | 91.04 23 |
|
COLMAP_ROB | | 72.78 3 | 83.75 10 | 84.11 13 | 82.68 11 | 82.97 86 | 74.39 32 | 87.18 7 | 88.18 4 | 78.98 5 | 86.11 41 | 91.47 32 | 79.70 11 | 85.76 30 | 66.91 98 | 95.46 13 | 87.89 63 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
Anonymous20231211 | | | 77.74 65 | 80.26 46 | 70.19 168 | 83.05 83 | 43.39 237 | 75.86 112 | 76.74 168 | 75.91 12 | 85.92 43 | 96.14 1 | 80.85 8 | 75.59 195 | 53.58 188 | 94.27 41 | 91.58 13 |
|
v10 | | | 75.69 84 | 76.20 84 | 74.16 99 | 74.44 189 | 48.69 186 | 75.84 113 | 82.93 64 | 59.02 126 | 85.92 43 | 89.17 83 | 58.56 167 | 82.74 75 | 70.73 59 | 89.14 121 | 91.05 21 |
|
ACMMP | | | 84.22 5 | 84.84 6 | 82.35 17 | 89.23 22 | 76.66 22 | 87.65 4 | 85.89 18 | 71.03 32 | 85.85 45 | 90.58 50 | 78.77 14 | 85.78 29 | 79.37 14 | 95.17 18 | 84.62 99 |
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 |
v8 | | | 75.07 94 | 75.64 89 | 73.35 118 | 73.42 208 | 47.46 212 | 75.20 122 | 81.45 85 | 60.05 118 | 85.64 46 | 89.26 78 | 58.08 177 | 81.80 88 | 69.71 71 | 87.97 137 | 90.79 29 |
|
XVG-ACMP-BASELINE | | | 80.54 38 | 81.06 41 | 78.98 51 | 87.01 35 | 72.91 41 | 80.23 57 | 85.56 20 | 66.56 55 | 85.64 46 | 89.57 74 | 69.12 73 | 80.55 128 | 72.51 47 | 93.37 53 | 83.48 122 |
|
SteuartSystems-ACMMP | | | 83.07 19 | 83.64 19 | 81.35 25 | 85.14 56 | 71.00 50 | 85.53 20 | 84.78 31 | 70.91 33 | 85.64 46 | 90.41 59 | 75.55 28 | 87.69 3 | 79.75 7 | 95.08 21 | 85.36 88 |
Skip Steuart: Steuart Systems R&D Blog. |
v17 | | | 75.03 95 | 75.59 90 | 73.36 117 | 73.56 204 | 47.66 207 | 75.48 118 | 81.45 85 | 60.58 114 | 85.55 49 | 89.02 88 | 58.36 170 | 81.47 92 | 69.69 72 | 86.59 158 | 90.96 24 |
|
OPM-MVS | | | 80.99 36 | 81.63 39 | 79.07 50 | 86.86 38 | 69.39 63 | 79.41 65 | 84.00 49 | 65.64 60 | 85.54 50 | 89.28 77 | 76.32 23 | 83.47 64 | 74.03 37 | 93.57 52 | 84.35 109 |
|
v16 | | | 74.89 100 | 75.41 94 | 73.35 118 | 73.54 205 | 47.62 208 | 75.47 119 | 81.45 85 | 60.58 114 | 85.46 51 | 88.97 91 | 58.27 171 | 81.47 92 | 69.66 73 | 85.25 180 | 90.95 25 |
|
MP-MVS-pluss | | | 82.54 24 | 83.46 22 | 79.76 40 | 88.88 27 | 68.44 70 | 81.57 46 | 86.33 14 | 63.17 92 | 85.38 52 | 91.26 34 | 76.33 22 | 84.67 48 | 83.30 1 | 94.96 24 | 86.17 76 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
HPM-MVS | | | 84.12 7 | 84.63 7 | 82.60 12 | 88.21 32 | 74.40 31 | 85.24 22 | 87.21 9 | 70.69 35 | 85.14 53 | 90.42 58 | 78.99 13 | 86.62 11 | 80.83 6 | 94.93 25 | 86.79 72 |
|
v18 | | | 74.60 104 | 75.06 95 | 73.22 123 | 73.29 214 | 47.36 216 | 75.02 123 | 81.47 84 | 60.01 119 | 85.13 54 | 88.44 97 | 57.93 185 | 81.47 92 | 69.26 74 | 85.02 184 | 90.84 28 |
|
mPP-MVS | | | 84.01 9 | 84.39 9 | 82.88 5 | 90.65 4 | 81.38 5 | 87.08 9 | 82.79 65 | 72.41 25 | 85.11 55 | 90.85 43 | 76.65 21 | 84.89 43 | 79.30 15 | 94.63 32 | 82.35 148 |
|
SMA-MVS | | | 83.01 21 | 83.63 20 | 81.13 29 | 91.16 2 | 78.16 12 | 82.72 39 | 80.63 106 | 72.08 26 | 84.93 56 | 90.79 44 | 74.65 37 | 84.42 51 | 80.98 4 | 94.75 28 | 80.82 176 |
|
MTAPA | | | 83.19 17 | 83.87 16 | 81.13 29 | 91.16 2 | 78.16 12 | 84.87 23 | 80.63 106 | 72.08 26 | 84.93 56 | 90.79 44 | 74.65 37 | 84.42 51 | 80.98 4 | 94.75 28 | 80.82 176 |
|
PGM-MVS | | | 83.07 19 | 83.25 26 | 82.54 15 | 89.57 14 | 77.21 20 | 82.04 43 | 85.40 23 | 67.96 47 | 84.91 58 | 90.88 41 | 75.59 27 | 86.57 12 | 78.16 20 | 94.71 30 | 83.82 116 |
|
K. test v3 | | | 73.67 113 | 73.61 114 | 73.87 103 | 79.78 115 | 55.62 147 | 74.69 132 | 62.04 253 | 66.16 58 | 84.76 59 | 93.23 6 | 49.47 226 | 80.97 116 | 65.66 109 | 86.67 157 | 85.02 93 |
|
CP-MVS | | | 84.12 7 | 84.55 8 | 82.80 9 | 89.42 18 | 79.74 7 | 88.19 3 | 84.43 37 | 71.96 29 | 84.70 60 | 90.56 51 | 77.12 18 | 86.18 20 | 79.24 16 | 95.36 14 | 82.49 146 |
|
test_part2 | | | | | | 85.90 45 | 66.44 79 | | | | 84.61 61 | | | | | | |
|
ESAPD | | | 81.57 29 | 82.55 32 | 78.63 55 | 85.90 45 | 66.44 79 | 83.39 33 | 84.94 29 | 73.27 20 | 84.61 61 | 89.25 80 | 75.17 31 | 86.96 9 | 72.56 45 | 93.83 48 | 82.50 144 |
|
ACMMPR | | | 83.62 11 | 83.93 15 | 82.69 10 | 89.78 11 | 77.51 18 | 87.01 11 | 84.19 44 | 70.23 36 | 84.49 63 | 90.67 49 | 75.15 33 | 86.37 14 | 79.58 9 | 94.26 42 | 84.18 111 |
|
HFP-MVS | | | 83.39 16 | 84.03 14 | 81.48 22 | 89.25 20 | 75.69 24 | 87.01 11 | 84.27 40 | 70.23 36 | 84.47 64 | 90.43 55 | 76.79 19 | 85.94 26 | 79.58 9 | 94.23 44 | 82.82 136 |
|
#test# | | | 82.40 25 | 82.71 31 | 81.48 22 | 89.25 20 | 75.69 24 | 84.47 26 | 84.27 40 | 64.45 76 | 84.47 64 | 90.43 55 | 76.79 19 | 85.94 26 | 76.01 30 | 94.23 44 | 82.82 136 |
|
testing_2 | | | 72.01 146 | 72.36 141 | 70.95 159 | 70.79 243 | 48.70 185 | 72.81 142 | 78.09 154 | 48.79 236 | 84.46 66 | 89.15 85 | 57.90 186 | 78.55 156 | 61.55 130 | 87.74 138 | 85.61 87 |
|
ACMMP_Plus | | | 82.33 26 | 83.28 25 | 79.46 45 | 89.28 19 | 69.09 68 | 83.62 31 | 84.98 27 | 64.77 73 | 83.97 67 | 91.02 38 | 75.53 29 | 85.93 28 | 82.00 2 | 94.36 38 | 83.35 129 |
|
region2R | | | 83.54 13 | 83.86 17 | 82.58 13 | 89.82 10 | 77.53 16 | 87.06 10 | 84.23 43 | 70.19 38 | 83.86 68 | 90.72 48 | 75.20 30 | 86.27 17 | 79.41 13 | 94.25 43 | 83.95 115 |
|
lessismore_v0 | | | | | 72.75 136 | 79.60 118 | 56.83 143 | | 57.37 271 | | 83.80 69 | 89.01 89 | 47.45 236 | 78.74 153 | 64.39 118 | 86.49 159 | 82.69 140 |
|
APD-MVS | | | 81.13 33 | 81.73 37 | 79.36 47 | 84.47 68 | 70.53 55 | 83.85 30 | 83.70 51 | 69.43 42 | 83.67 70 | 88.96 92 | 75.89 26 | 86.41 13 | 72.62 44 | 92.95 58 | 81.14 169 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
ITE_SJBPF | | | | | 80.35 37 | 76.94 154 | 73.60 38 | | 80.48 110 | 66.87 50 | 83.64 71 | 86.18 142 | 70.25 66 | 79.90 139 | 61.12 134 | 88.95 123 | 87.56 67 |
|
nrg030 | | | 74.87 101 | 75.99 86 | 71.52 156 | 74.90 174 | 49.88 179 | 74.10 136 | 82.58 69 | 54.55 178 | 83.50 72 | 89.21 82 | 71.51 56 | 75.74 193 | 61.24 132 | 92.34 66 | 88.94 46 |
|
V42 | | | 71.06 152 | 70.83 160 | 71.72 153 | 67.25 278 | 47.14 219 | 65.94 238 | 80.35 114 | 51.35 212 | 83.40 73 | 83.23 185 | 59.25 157 | 78.80 151 | 65.91 108 | 80.81 236 | 89.23 38 |
|
TranMVSNet+NR-MVSNet | | | 76.13 77 | 77.66 67 | 71.56 155 | 84.61 66 | 42.57 244 | 70.98 177 | 78.29 149 | 68.67 45 | 83.04 74 | 89.26 78 | 72.99 48 | 80.75 125 | 55.58 173 | 95.47 12 | 91.35 15 |
|
XVS | | | 83.51 14 | 83.73 18 | 82.85 7 | 89.43 16 | 77.61 14 | 86.80 13 | 84.66 33 | 72.71 23 | 82.87 75 | 90.39 60 | 73.86 43 | 86.31 15 | 78.84 17 | 94.03 46 | 84.64 97 |
|
X-MVStestdata | | | 76.81 72 | 74.79 97 | 82.85 7 | 89.43 16 | 77.61 14 | 86.80 13 | 84.66 33 | 72.71 23 | 82.87 75 | 9.95 351 | 73.86 43 | 86.31 15 | 78.84 17 | 94.03 46 | 84.64 97 |
|
XVG-OURS | | | 79.51 47 | 79.82 49 | 78.58 56 | 86.11 44 | 74.96 29 | 76.33 102 | 84.95 28 | 66.89 49 | 82.75 77 | 88.99 90 | 66.82 93 | 78.37 166 | 74.80 31 | 90.76 97 | 82.40 147 |
|
FC-MVSNet-test | | | 73.32 121 | 74.78 98 | 68.93 186 | 79.21 125 | 36.57 283 | 71.82 161 | 79.54 127 | 57.63 138 | 82.57 78 | 90.38 61 | 59.38 155 | 78.99 147 | 57.91 151 | 94.56 33 | 91.23 17 |
|
ANet_high | | | 67.08 194 | 69.94 165 | 58.51 272 | 57.55 332 | 27.09 336 | 58.43 297 | 76.80 166 | 63.56 87 | 82.40 79 | 91.93 20 | 59.82 149 | 64.98 283 | 50.10 207 | 88.86 124 | 83.46 124 |
|
v1240 | | | 73.06 124 | 73.14 123 | 72.84 134 | 74.74 178 | 47.27 218 | 71.88 160 | 81.11 94 | 51.80 207 | 82.28 80 | 84.21 169 | 56.22 202 | 82.34 81 | 68.82 75 | 87.17 152 | 88.91 47 |
|
v7 | | | 73.59 115 | 73.69 110 | 73.28 122 | 74.42 190 | 48.68 187 | 72.74 145 | 81.98 75 | 54.76 174 | 82.07 81 | 85.05 159 | 58.53 168 | 82.22 84 | 67.99 85 | 85.66 168 | 88.95 45 |
|
LS3D | | | 80.99 36 | 80.85 42 | 81.41 24 | 78.37 136 | 71.37 46 | 87.45 6 | 85.87 19 | 77.48 9 | 81.98 82 | 89.95 70 | 69.14 72 | 85.26 36 | 66.15 105 | 91.24 82 | 87.61 66 |
|
v1192 | | | 73.40 119 | 73.42 116 | 73.32 121 | 74.65 184 | 48.67 188 | 72.21 149 | 81.73 79 | 52.76 199 | 81.85 83 | 84.56 166 | 57.12 195 | 82.24 83 | 68.58 77 | 87.33 147 | 89.06 41 |
|
v1144 | | | 73.29 122 | 73.39 117 | 73.01 128 | 74.12 197 | 48.11 197 | 72.01 154 | 81.08 99 | 53.83 189 | 81.77 84 | 84.68 164 | 58.07 178 | 81.91 86 | 68.10 81 | 86.86 155 | 88.99 44 |
|
OMC-MVS | | | 79.41 49 | 78.79 57 | 81.28 27 | 80.62 110 | 70.71 54 | 80.91 49 | 84.76 32 | 62.54 97 | 81.77 84 | 86.65 129 | 71.46 57 | 83.53 63 | 67.95 88 | 92.44 64 | 89.60 35 |
|
UniMVSNet_NR-MVSNet | | | 74.90 99 | 75.65 88 | 72.64 139 | 83.04 84 | 45.79 229 | 69.26 195 | 78.81 138 | 66.66 54 | 81.74 86 | 86.88 116 | 63.26 115 | 81.07 113 | 56.21 166 | 94.98 22 | 91.05 21 |
|
DU-MVS | | | 74.91 98 | 75.57 91 | 72.93 132 | 83.50 78 | 45.79 229 | 69.47 193 | 80.14 119 | 65.22 68 | 81.74 86 | 87.08 110 | 61.82 129 | 81.07 113 | 56.21 166 | 94.98 22 | 91.93 10 |
|
v1921920 | | | 72.96 129 | 72.98 131 | 72.89 133 | 74.67 181 | 47.58 209 | 71.92 158 | 80.69 105 | 51.70 209 | 81.69 88 | 83.89 173 | 56.58 200 | 82.25 82 | 68.34 79 | 87.36 145 | 88.82 49 |
|
v6 | | | 72.93 130 | 73.08 126 | 72.48 142 | 73.42 208 | 47.47 211 | 72.17 150 | 80.25 117 | 55.63 156 | 81.65 89 | 85.04 160 | 57.95 184 | 81.28 104 | 66.56 102 | 85.01 185 | 88.70 52 |
|
v1neww | | | 72.93 130 | 73.07 127 | 72.48 142 | 73.41 210 | 47.46 212 | 72.17 150 | 80.26 115 | 55.63 156 | 81.63 90 | 85.07 157 | 57.97 181 | 81.28 104 | 66.55 103 | 84.98 186 | 88.70 52 |
|
v7new | | | 72.93 130 | 73.07 127 | 72.48 142 | 73.41 210 | 47.46 212 | 72.17 150 | 80.26 115 | 55.63 156 | 81.63 90 | 85.07 157 | 57.97 181 | 81.28 104 | 66.55 103 | 84.98 186 | 88.70 52 |
|
WR-MVS | | | 71.20 151 | 72.48 139 | 67.36 201 | 84.98 58 | 35.70 292 | 64.43 255 | 68.66 226 | 65.05 71 | 81.49 92 | 86.43 137 | 57.57 189 | 76.48 186 | 50.36 205 | 93.32 55 | 89.90 34 |
|
v144192 | | | 72.99 127 | 73.06 129 | 72.77 135 | 74.58 186 | 47.48 210 | 71.90 159 | 80.44 112 | 51.57 210 | 81.46 93 | 84.11 171 | 58.04 179 | 82.12 85 | 67.98 86 | 87.47 141 | 88.70 52 |
|
MP-MVS | | | 83.19 17 | 83.54 21 | 82.14 18 | 90.54 5 | 79.00 9 | 86.42 18 | 83.59 54 | 71.31 30 | 81.26 94 | 90.96 40 | 74.57 39 | 84.69 47 | 78.41 19 | 94.78 27 | 82.74 139 |
|
v1 | | | 72.60 138 | 72.73 134 | 72.19 149 | 73.12 220 | 47.01 221 | 71.48 165 | 79.10 133 | 55.01 165 | 81.24 95 | 84.92 163 | 57.46 190 | 80.90 121 | 66.59 99 | 85.67 166 | 88.68 56 |
|
v1141 | | | 72.59 140 | 72.73 134 | 72.19 149 | 73.10 221 | 47.00 222 | 71.48 165 | 79.11 131 | 55.01 165 | 81.23 96 | 84.94 162 | 57.45 191 | 80.89 122 | 66.58 100 | 85.65 169 | 88.68 56 |
|
divwei89l23v2f112 | | | 72.60 138 | 72.73 134 | 72.19 149 | 73.10 221 | 47.00 222 | 71.48 165 | 79.11 131 | 55.01 165 | 81.23 96 | 84.95 161 | 57.45 191 | 80.89 122 | 66.58 100 | 85.67 166 | 88.68 56 |
|
v2v482 | | | 72.55 142 | 72.58 138 | 72.43 145 | 72.92 231 | 46.72 226 | 71.41 170 | 79.13 130 | 55.27 160 | 81.17 98 | 85.25 155 | 55.41 204 | 81.13 108 | 67.25 97 | 85.46 174 | 89.43 36 |
|
HSP-MVS | | | 79.69 45 | 79.17 55 | 81.27 28 | 89.70 12 | 77.46 19 | 87.16 8 | 80.58 109 | 64.94 72 | 81.05 99 | 88.38 100 | 57.10 196 | 87.10 6 | 79.75 7 | 83.87 196 | 79.24 198 |
|
Test4 | | | 69.04 175 | 68.95 176 | 69.32 180 | 69.52 255 | 48.10 198 | 70.69 181 | 78.25 151 | 45.90 257 | 80.99 100 | 82.24 196 | 51.91 216 | 78.11 173 | 58.46 148 | 82.58 207 | 81.74 161 |
|
MDA-MVSNet-bldmvs | | | 62.34 229 | 61.73 226 | 64.16 222 | 61.64 308 | 49.90 176 | 48.11 322 | 57.24 274 | 53.31 195 | 80.95 101 | 79.39 228 | 49.00 229 | 61.55 294 | 45.92 236 | 80.05 243 | 81.03 171 |
|
CPTT-MVS | | | 81.51 31 | 81.76 36 | 80.76 34 | 89.20 23 | 78.75 10 | 86.48 17 | 82.03 74 | 68.80 43 | 80.92 102 | 88.52 96 | 72.00 54 | 82.39 79 | 74.80 31 | 93.04 57 | 81.14 169 |
|
DeepC-MVS | | 72.44 4 | 81.00 35 | 80.83 43 | 81.50 21 | 86.70 40 | 70.03 60 | 82.06 42 | 87.00 10 | 59.89 120 | 80.91 103 | 90.53 52 | 72.19 50 | 88.56 1 | 73.67 39 | 94.52 34 | 85.92 82 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
FIs | | | 72.56 141 | 73.80 109 | 68.84 190 | 78.74 134 | 37.74 277 | 71.02 176 | 79.83 123 | 56.12 152 | 80.88 104 | 89.45 75 | 58.18 172 | 78.28 168 | 56.63 159 | 93.36 54 | 90.51 32 |
|
3Dnovator+ | | 73.19 2 | 81.08 34 | 80.48 44 | 82.87 6 | 81.41 105 | 72.03 42 | 84.38 27 | 86.23 15 | 77.28 11 | 80.65 105 | 90.18 67 | 59.80 150 | 87.58 4 | 73.06 41 | 91.34 80 | 89.01 42 |
|
IterMVS-LS | | | 73.01 125 | 73.12 125 | 72.66 138 | 73.79 203 | 49.90 176 | 71.63 164 | 78.44 146 | 58.22 130 | 80.51 106 | 86.63 130 | 58.15 174 | 79.62 141 | 62.51 126 | 88.20 132 | 88.48 59 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
Regformer-2 | | | 75.32 89 | 74.47 101 | 77.88 64 | 74.22 194 | 66.65 78 | 72.77 143 | 77.54 159 | 68.47 46 | 80.44 107 | 72.08 292 | 70.60 63 | 80.97 116 | 70.08 67 | 84.02 194 | 86.01 80 |
|
DP-MVS | | | 78.44 62 | 79.29 54 | 75.90 83 | 81.86 100 | 65.33 87 | 79.05 67 | 84.63 35 | 74.83 15 | 80.41 108 | 86.27 139 | 71.68 55 | 83.45 65 | 62.45 128 | 92.40 65 | 78.92 202 |
|
XVG-OURS-SEG-HR | | | 79.62 46 | 79.99 48 | 78.49 57 | 86.46 42 | 74.79 30 | 77.15 90 | 85.39 24 | 66.73 53 | 80.39 109 | 88.85 94 | 74.43 41 | 78.33 167 | 74.73 33 | 85.79 164 | 82.35 148 |
|
DeepPCF-MVS | | 71.07 5 | 78.48 61 | 77.14 71 | 82.52 16 | 84.39 72 | 77.04 21 | 76.35 100 | 84.05 47 | 56.66 150 | 80.27 110 | 85.31 154 | 68.56 77 | 87.03 8 | 67.39 93 | 91.26 81 | 83.50 121 |
|
Regformer-4 | | | 74.64 103 | 73.67 111 | 77.55 67 | 74.74 178 | 64.49 95 | 72.91 140 | 75.42 179 | 67.45 48 | 80.24 111 | 72.07 295 | 68.98 74 | 80.19 136 | 70.29 63 | 80.91 232 | 87.98 62 |
|
AllTest | | | 77.66 66 | 77.43 68 | 78.35 59 | 79.19 126 | 70.81 51 | 78.60 70 | 88.64 2 | 65.37 65 | 80.09 112 | 88.17 103 | 70.33 64 | 78.43 162 | 55.60 170 | 90.90 93 | 85.81 83 |
|
TestCases | | | | | 78.35 59 | 79.19 126 | 70.81 51 | | 88.64 2 | 65.37 65 | 80.09 112 | 88.17 103 | 70.33 64 | 78.43 162 | 55.60 170 | 90.90 93 | 85.81 83 |
|
UA-Net | | | 81.56 30 | 82.28 34 | 79.40 46 | 88.91 26 | 69.16 66 | 84.67 25 | 80.01 121 | 75.34 13 | 79.80 114 | 94.91 3 | 69.79 69 | 80.25 133 | 72.63 43 | 94.46 36 | 88.78 51 |
|
PCF-MVS | | 63.80 13 | 72.70 136 | 71.69 149 | 75.72 85 | 78.10 139 | 60.01 124 | 73.04 139 | 81.50 82 | 45.34 263 | 79.66 115 | 84.35 168 | 65.15 105 | 82.65 76 | 48.70 216 | 89.38 118 | 84.50 105 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
UniMVSNet (Re) | | | 75.00 96 | 75.48 92 | 73.56 112 | 83.14 82 | 47.92 201 | 70.41 183 | 81.04 101 | 63.67 86 | 79.54 116 | 86.37 138 | 62.83 117 | 81.82 87 | 57.10 157 | 95.25 16 | 90.94 26 |
|
Baseline_NR-MVSNet | | | 70.62 157 | 73.19 122 | 62.92 235 | 76.97 153 | 34.44 302 | 68.84 198 | 70.88 216 | 60.25 117 | 79.50 117 | 90.53 52 | 61.82 129 | 69.11 249 | 54.67 179 | 95.27 15 | 85.22 89 |
|
FMVSNet1 | | | 71.06 152 | 72.48 139 | 66.81 206 | 77.65 147 | 40.68 254 | 71.96 155 | 73.03 190 | 61.14 108 | 79.45 118 | 90.36 63 | 60.44 143 | 75.20 199 | 50.20 206 | 88.05 134 | 84.54 101 |
|
Regformer-1 | | | 74.28 107 | 73.63 113 | 76.21 81 | 74.22 194 | 64.12 97 | 72.77 143 | 75.46 178 | 66.86 51 | 79.27 119 | 72.08 292 | 69.29 71 | 78.74 153 | 68.73 76 | 84.02 194 | 85.77 86 |
|
ambc | | | | | 70.10 171 | 77.74 145 | 50.21 174 | 74.28 135 | 77.93 156 | | 79.26 120 | 88.29 102 | 54.11 209 | 79.77 140 | 64.43 117 | 91.10 86 | 80.30 186 |
|
IS-MVSNet | | | 75.10 93 | 75.42 93 | 74.15 100 | 79.23 124 | 48.05 199 | 79.43 63 | 78.04 155 | 70.09 39 | 79.17 121 | 88.02 107 | 53.04 211 | 83.60 61 | 58.05 150 | 93.76 50 | 90.79 29 |
|
CSCG | | | 74.12 109 | 74.39 102 | 73.33 120 | 79.35 121 | 61.66 114 | 77.45 85 | 81.98 75 | 62.47 99 | 79.06 122 | 80.19 217 | 61.83 128 | 78.79 152 | 59.83 141 | 87.35 146 | 79.54 195 |
|
RPSCF | | | 75.76 82 | 74.37 103 | 79.93 39 | 74.81 176 | 77.53 16 | 77.53 84 | 79.30 128 | 59.44 122 | 78.88 123 | 89.80 72 | 71.26 60 | 73.09 212 | 57.45 153 | 80.89 234 | 89.17 40 |
|
v148 | | | 69.38 169 | 69.39 168 | 69.36 177 | 69.14 259 | 44.56 233 | 68.83 199 | 72.70 195 | 54.79 172 | 78.59 124 | 84.12 170 | 54.69 206 | 76.74 185 | 59.40 144 | 82.20 208 | 86.79 72 |
|
EI-MVSNet-Vis-set | | | 72.78 135 | 71.87 146 | 75.54 87 | 74.77 177 | 59.02 133 | 72.24 148 | 71.56 205 | 63.92 83 | 78.59 124 | 71.59 302 | 66.22 98 | 78.60 155 | 67.58 90 | 80.32 240 | 89.00 43 |
|
EI-MVSNet-UG-set | | | 72.63 137 | 71.68 150 | 75.47 88 | 74.67 181 | 58.64 137 | 72.02 153 | 71.50 206 | 63.53 89 | 78.58 126 | 71.39 305 | 65.98 99 | 78.53 157 | 67.30 96 | 80.18 241 | 89.23 38 |
|
旧先验2 | | | | | | | | 71.17 175 | | 45.11 265 | 78.54 127 | | | 61.28 295 | 59.19 145 | | |
|
Regformer-3 | | | 72.86 134 | 72.28 143 | 74.62 93 | 74.74 178 | 60.18 122 | 72.91 140 | 71.76 202 | 64.74 74 | 78.42 128 | 72.07 295 | 67.00 90 | 76.28 188 | 67.97 87 | 80.91 232 | 87.39 68 |
|
MIMVSNet1 | | | 66.57 196 | 69.23 171 | 58.59 271 | 81.26 107 | 37.73 278 | 64.06 258 | 57.62 268 | 57.02 145 | 78.40 129 | 90.75 46 | 62.65 118 | 58.10 302 | 41.77 264 | 89.58 115 | 79.95 193 |
|
test_normal | | | 68.88 177 | 68.88 177 | 68.88 189 | 69.43 257 | 47.03 220 | 69.85 189 | 74.83 183 | 46.06 256 | 78.30 130 | 83.29 183 | 58.76 166 | 78.23 169 | 57.51 152 | 81.90 213 | 81.36 165 |
|
DI_MVS_plusplus_test | | | 69.01 176 | 69.04 173 | 68.93 186 | 69.54 254 | 46.74 225 | 70.14 184 | 75.49 176 | 46.64 253 | 78.30 130 | 83.18 188 | 58.80 162 | 78.86 149 | 57.14 155 | 82.15 209 | 81.18 167 |
|
HQP_MVS | | | 78.77 56 | 78.78 58 | 78.72 53 | 85.18 54 | 65.18 89 | 82.74 37 | 85.49 21 | 65.45 62 | 78.23 132 | 89.11 86 | 60.83 141 | 86.15 21 | 71.09 55 | 90.94 89 | 84.82 94 |
|
plane_prior3 | | | | | | | 65.67 85 | | | 63.82 85 | 78.23 132 | | | | | | |
|
HPM-MVS++ | | | 79.89 44 | 79.80 50 | 80.18 38 | 89.02 24 | 78.44 11 | 83.49 32 | 80.18 118 | 64.71 75 | 78.11 134 | 88.39 99 | 65.46 103 | 83.14 69 | 77.64 25 | 91.20 83 | 78.94 201 |
|
PM-MVS | | | 64.49 207 | 63.61 211 | 67.14 203 | 76.68 156 | 75.15 28 | 68.49 208 | 42.85 334 | 51.17 216 | 77.85 135 | 80.51 213 | 45.76 238 | 66.31 279 | 52.83 191 | 76.35 270 | 59.96 325 |
|
BH-untuned | | | 69.39 168 | 69.46 167 | 69.18 181 | 77.96 142 | 56.88 142 | 68.47 209 | 77.53 160 | 56.77 148 | 77.79 136 | 79.63 225 | 60.30 144 | 80.20 135 | 46.04 235 | 80.65 237 | 70.47 267 |
|
MSLP-MVS++ | | | 74.48 106 | 75.78 87 | 70.59 162 | 84.66 63 | 62.40 107 | 78.65 69 | 84.24 42 | 60.55 116 | 77.71 137 | 81.98 200 | 63.12 116 | 77.64 176 | 62.95 125 | 88.14 133 | 71.73 258 |
|
CDPH-MVS | | | 77.33 69 | 77.06 72 | 78.14 62 | 84.21 73 | 63.98 98 | 76.07 107 | 83.45 57 | 54.20 181 | 77.68 138 | 87.18 109 | 69.98 67 | 85.37 33 | 68.01 84 | 92.72 62 | 85.08 92 |
|
CNVR-MVS | | | 78.49 60 | 78.59 60 | 78.16 61 | 85.86 49 | 67.40 76 | 78.12 80 | 81.50 82 | 63.92 83 | 77.51 139 | 86.56 133 | 68.43 80 | 84.82 45 | 73.83 38 | 91.61 73 | 82.26 151 |
|
TinyColmap | | | 67.98 186 | 69.28 169 | 64.08 224 | 67.98 274 | 46.82 224 | 70.04 185 | 75.26 180 | 53.05 196 | 77.36 140 | 86.79 119 | 59.39 154 | 72.59 224 | 45.64 237 | 88.01 136 | 72.83 246 |
|
TSAR-MVS + MP. | | | 79.05 51 | 78.81 56 | 79.74 41 | 88.94 25 | 67.52 75 | 86.61 15 | 81.38 89 | 51.71 208 | 77.15 141 | 91.42 33 | 65.49 102 | 87.20 5 | 79.44 12 | 87.17 152 | 84.51 104 |
|
TEST9 | | | | | | 85.47 52 | 69.32 64 | 76.42 98 | 78.69 140 | 53.73 190 | 76.97 142 | 86.74 123 | 66.84 92 | 81.10 111 | | | |
|
train_agg | | | 76.38 74 | 76.55 76 | 75.86 84 | 85.47 52 | 69.32 64 | 76.42 98 | 78.69 140 | 54.00 185 | 76.97 142 | 86.74 123 | 66.60 94 | 81.10 111 | 72.50 48 | 91.56 74 | 77.15 217 |
|
agg_prior1 | | | 75.89 79 | 76.41 79 | 74.31 97 | 84.44 70 | 66.02 83 | 76.12 106 | 78.62 143 | 54.40 179 | 76.95 144 | 86.85 117 | 66.44 97 | 80.34 131 | 72.45 50 | 91.42 78 | 76.57 222 |
|
agg_prior | | | | | | 84.44 70 | 66.02 83 | | 78.62 143 | | 76.95 144 | | | 80.34 131 | | | |
|
semantic-postprocess | | | | | 72.49 141 | 73.34 213 | 58.20 139 | | 65.55 238 | 48.10 242 | 76.91 146 | 82.64 190 | 42.25 258 | 78.84 150 | 61.20 133 | 77.89 265 | 80.44 185 |
|
test_8 | | | | | | 85.09 57 | 67.89 73 | 76.26 103 | 78.66 142 | 54.00 185 | 76.89 147 | 86.72 125 | 66.60 94 | 80.89 122 | | | |
|
MVS_111021_LR | | | 72.10 144 | 71.82 148 | 72.95 131 | 79.53 119 | 73.90 36 | 70.45 182 | 66.64 233 | 56.87 146 | 76.81 148 | 81.76 204 | 68.78 75 | 71.76 235 | 61.81 129 | 83.74 198 | 73.18 243 |
|
CLD-MVS | | | 72.88 133 | 72.36 141 | 74.43 95 | 77.03 152 | 54.30 155 | 68.77 203 | 83.43 58 | 52.12 203 | 76.79 149 | 74.44 276 | 69.54 70 | 83.91 56 | 55.88 169 | 93.25 56 | 85.09 91 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
FMVSNet2 | | | 67.48 192 | 68.21 187 | 65.29 217 | 73.14 217 | 38.94 268 | 68.81 200 | 71.21 214 | 54.81 169 | 76.73 150 | 86.48 136 | 48.63 231 | 74.60 204 | 47.98 223 | 86.11 162 | 82.35 148 |
|
canonicalmvs | | | 72.29 143 | 73.38 118 | 69.04 183 | 74.23 193 | 47.37 215 | 73.93 137 | 83.18 59 | 54.36 180 | 76.61 151 | 81.64 206 | 72.03 53 | 75.34 197 | 57.12 156 | 87.28 149 | 84.40 107 |
|
EG-PatchMatch MVS | | | 70.70 156 | 70.88 159 | 70.16 169 | 82.64 90 | 58.80 134 | 71.48 165 | 73.64 188 | 54.98 168 | 76.55 152 | 81.77 203 | 61.10 139 | 78.94 148 | 54.87 177 | 80.84 235 | 72.74 248 |
|
alignmvs | | | 70.54 158 | 71.00 158 | 69.15 182 | 73.50 206 | 48.04 200 | 69.85 189 | 79.62 124 | 53.94 188 | 76.54 153 | 82.00 199 | 59.00 160 | 74.68 203 | 57.32 154 | 87.21 150 | 84.72 96 |
|
test_prior3 | | | 76.71 73 | 77.19 70 | 75.27 90 | 82.15 96 | 59.85 125 | 75.57 115 | 84.33 38 | 58.92 127 | 76.53 154 | 86.78 120 | 67.83 86 | 83.39 66 | 69.81 69 | 92.76 60 | 82.58 141 |
|
test_prior2 | | | | | | | | 75.57 115 | | 58.92 127 | 76.53 154 | 86.78 120 | 67.83 86 | | 69.81 69 | 92.76 60 | |
|
EPP-MVSNet | | | 73.86 111 | 73.38 118 | 75.31 89 | 78.19 138 | 53.35 162 | 80.45 52 | 77.32 163 | 65.11 70 | 76.47 156 | 86.80 118 | 49.47 226 | 83.77 58 | 53.89 185 | 92.72 62 | 88.81 50 |
|
pmmvs6 | | | 71.82 147 | 73.66 112 | 66.31 212 | 75.94 165 | 42.01 246 | 66.99 224 | 72.53 197 | 63.45 90 | 76.43 157 | 92.78 10 | 72.95 49 | 69.69 247 | 51.41 197 | 90.46 101 | 87.22 69 |
|
testdata | | | | | 64.13 223 | 85.87 48 | 63.34 103 | | 61.80 254 | 47.83 246 | 76.42 158 | 86.60 132 | 48.83 230 | 62.31 292 | 54.46 182 | 81.26 229 | 66.74 300 |
|
TAPA-MVS | | 65.27 12 | 75.16 92 | 74.29 105 | 77.77 66 | 74.86 175 | 68.08 71 | 77.89 81 | 84.04 48 | 55.15 164 | 76.19 159 | 83.39 177 | 66.91 91 | 80.11 137 | 60.04 139 | 90.14 107 | 85.13 90 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
agg_prior3 | | | 76.32 75 | 76.33 81 | 76.28 78 | 85.86 49 | 70.13 59 | 76.50 96 | 78.26 150 | 53.41 194 | 75.78 160 | 86.49 135 | 66.58 96 | 81.57 90 | 72.50 48 | 91.56 74 | 77.15 217 |
|
MVS_111021_HR | | | 72.98 128 | 72.97 132 | 72.99 129 | 80.82 109 | 65.47 86 | 68.81 200 | 72.77 194 | 57.67 136 | 75.76 161 | 82.38 195 | 71.01 61 | 77.17 178 | 61.38 131 | 86.15 160 | 76.32 223 |
|
CNLPA | | | 73.44 117 | 73.03 130 | 74.66 92 | 78.27 137 | 75.29 27 | 75.99 108 | 78.49 145 | 65.39 64 | 75.67 162 | 83.22 187 | 61.23 137 | 66.77 276 | 53.70 187 | 85.33 178 | 81.92 159 |
|
NR-MVSNet | | | 73.62 114 | 74.05 107 | 72.33 148 | 83.50 78 | 43.71 236 | 65.65 242 | 77.32 163 | 64.32 80 | 75.59 163 | 87.08 110 | 62.45 122 | 81.34 101 | 54.90 176 | 95.63 10 | 91.93 10 |
|
NCCC | | | 78.25 63 | 78.04 64 | 78.89 52 | 85.61 51 | 69.45 61 | 79.80 62 | 80.99 103 | 65.77 59 | 75.55 164 | 86.25 141 | 67.42 88 | 85.42 32 | 70.10 66 | 90.88 95 | 81.81 160 |
|
YYNet1 | | | 52.58 289 | 53.50 285 | 49.85 298 | 54.15 347 | 36.45 285 | 40.53 336 | 46.55 324 | 38.09 299 | 75.52 165 | 73.31 287 | 41.08 264 | 43.88 332 | 41.10 266 | 71.14 297 | 69.21 284 |
|
MDA-MVSNet_test_wron | | | 52.57 290 | 53.49 286 | 49.81 299 | 54.24 346 | 36.47 284 | 40.48 337 | 46.58 323 | 38.13 298 | 75.47 166 | 73.32 286 | 41.05 265 | 43.85 333 | 40.98 267 | 71.20 296 | 69.10 286 |
|
EI-MVSNet | | | 69.61 166 | 69.01 175 | 71.41 157 | 73.94 199 | 49.90 176 | 71.31 173 | 71.32 208 | 58.22 130 | 75.40 167 | 70.44 306 | 58.16 173 | 75.85 189 | 62.51 126 | 79.81 246 | 88.48 59 |
|
MVSTER | | | 63.29 217 | 61.60 230 | 68.36 194 | 59.77 319 | 46.21 228 | 60.62 286 | 71.32 208 | 41.83 284 | 75.40 167 | 79.12 234 | 30.25 320 | 75.85 189 | 56.30 165 | 79.81 246 | 83.03 131 |
|
TransMVSNet (Re) | | | 69.62 165 | 71.63 151 | 63.57 229 | 76.51 157 | 35.93 290 | 65.75 241 | 71.29 210 | 61.05 109 | 75.02 169 | 89.90 71 | 65.88 100 | 70.41 245 | 49.79 208 | 89.48 116 | 84.38 108 |
|
新几何1 | | | | | 69.99 173 | 88.37 30 | 71.34 47 | | 62.08 250 | 43.85 271 | 74.99 170 | 86.11 146 | 52.85 213 | 70.57 242 | 50.99 200 | 83.23 203 | 68.05 290 |
|
Effi-MVS+-dtu | | | 75.43 86 | 72.28 143 | 84.91 2 | 77.05 150 | 83.58 2 | 78.47 74 | 77.70 157 | 57.68 134 | 74.89 171 | 78.13 240 | 64.80 108 | 84.26 55 | 56.46 163 | 85.32 179 | 86.88 71 |
|
DeepC-MVS_fast | | 69.89 7 | 77.17 70 | 76.33 81 | 79.70 43 | 83.90 76 | 67.94 72 | 80.06 60 | 83.75 50 | 56.73 149 | 74.88 172 | 85.32 153 | 65.54 101 | 87.79 2 | 65.61 111 | 91.14 85 | 83.35 129 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
1121 | | | 69.23 170 | 68.26 186 | 72.12 152 | 88.36 31 | 71.40 45 | 68.59 204 | 62.06 251 | 43.80 272 | 74.75 173 | 86.18 142 | 52.92 212 | 76.85 183 | 54.47 180 | 83.27 202 | 68.12 289 |
|
VDDNet | | | 71.60 149 | 73.13 124 | 67.02 204 | 86.29 43 | 41.11 252 | 69.97 186 | 66.50 234 | 68.72 44 | 74.74 174 | 91.70 25 | 59.90 147 | 75.81 191 | 48.58 218 | 91.72 70 | 84.15 112 |
|
GBi-Net | | | 68.30 184 | 68.79 179 | 66.81 206 | 73.14 217 | 40.68 254 | 71.96 155 | 73.03 190 | 54.81 169 | 74.72 175 | 90.36 63 | 48.63 231 | 75.20 199 | 47.12 228 | 85.37 175 | 84.54 101 |
|
test1 | | | 68.30 184 | 68.79 179 | 66.81 206 | 73.14 217 | 40.68 254 | 71.96 155 | 73.03 190 | 54.81 169 | 74.72 175 | 90.36 63 | 48.63 231 | 75.20 199 | 47.12 228 | 85.37 175 | 84.54 101 |
|
FMVSNet3 | | | 65.00 203 | 65.16 202 | 64.52 221 | 69.47 256 | 37.56 280 | 66.63 228 | 70.38 219 | 51.55 211 | 74.72 175 | 83.27 184 | 37.89 280 | 74.44 206 | 47.12 228 | 85.37 175 | 81.57 163 |
|
Patchmatch-RL test | | | 59.95 245 | 59.12 245 | 62.44 243 | 72.46 233 | 54.61 154 | 59.63 291 | 47.51 321 | 41.05 288 | 74.58 178 | 74.30 278 | 31.06 314 | 65.31 280 | 51.61 195 | 79.85 245 | 67.39 293 |
|
TSAR-MVS + GP. | | | 73.08 123 | 71.60 152 | 77.54 68 | 78.99 132 | 70.73 53 | 74.96 124 | 69.38 223 | 60.73 113 | 74.39 179 | 78.44 237 | 57.72 188 | 82.78 74 | 60.16 137 | 89.60 114 | 79.11 200 |
|
原ACMM1 | | | | | 73.90 102 | 85.90 45 | 65.15 91 | | 81.67 80 | 50.97 223 | 74.25 180 | 86.16 144 | 61.60 131 | 83.54 62 | 56.75 158 | 91.08 87 | 73.00 244 |
|
pmmvs-eth3d | | | 64.41 210 | 63.27 214 | 67.82 198 | 75.81 167 | 60.18 122 | 69.49 192 | 62.05 252 | 38.81 295 | 74.13 181 | 82.23 197 | 43.76 250 | 68.65 259 | 42.53 257 | 80.63 239 | 74.63 234 |
|
VPA-MVSNet | | | 68.71 181 | 70.37 163 | 63.72 228 | 76.13 162 | 38.06 275 | 64.10 257 | 71.48 207 | 56.60 151 | 74.10 182 | 88.31 101 | 64.78 110 | 69.72 246 | 47.69 226 | 90.15 106 | 83.37 128 |
|
VDD-MVS | | | 70.81 155 | 71.44 155 | 68.91 188 | 79.07 131 | 46.51 227 | 67.82 214 | 70.83 217 | 61.23 106 | 74.07 183 | 88.69 95 | 59.86 148 | 75.62 194 | 51.11 199 | 90.28 103 | 84.61 100 |
|
pm-mvs1 | | | 68.40 183 | 69.85 166 | 64.04 225 | 73.10 221 | 39.94 261 | 64.61 253 | 70.50 218 | 55.52 159 | 73.97 184 | 89.33 76 | 63.91 114 | 68.38 261 | 49.68 210 | 88.02 135 | 83.81 117 |
|
BH-RMVSNet | | | 68.69 182 | 68.20 188 | 70.14 170 | 76.40 158 | 53.90 159 | 64.62 252 | 73.48 189 | 58.01 132 | 73.91 185 | 81.78 202 | 59.09 159 | 78.22 170 | 48.59 217 | 77.96 264 | 78.31 206 |
|
test12 | | | | | 76.51 72 | 82.28 94 | 60.94 118 | | 81.64 81 | | 73.60 186 | | 64.88 107 | 85.19 40 | | 90.42 102 | 83.38 126 |
|
QAPM | | | 69.18 172 | 69.26 170 | 68.94 185 | 71.61 241 | 52.58 164 | 80.37 55 | 78.79 139 | 49.63 231 | 73.51 187 | 85.14 156 | 53.66 210 | 79.12 145 | 55.11 175 | 75.54 275 | 75.11 232 |
|
Gipuma | | | 69.55 167 | 72.83 133 | 59.70 265 | 63.63 299 | 53.97 157 | 80.08 59 | 75.93 172 | 64.24 81 | 73.49 188 | 88.93 93 | 57.89 187 | 62.46 290 | 59.75 143 | 91.55 76 | 62.67 317 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
mvs_anonymous | | | 65.08 202 | 65.49 201 | 63.83 227 | 63.79 297 | 37.60 279 | 66.52 230 | 69.82 222 | 43.44 277 | 73.46 189 | 86.08 147 | 58.79 165 | 71.75 236 | 51.90 194 | 75.63 274 | 82.15 152 |
|
Vis-MVSNet | | | 74.85 102 | 74.56 99 | 75.72 85 | 81.63 103 | 64.64 93 | 76.35 100 | 79.06 134 | 62.85 94 | 73.33 190 | 88.41 98 | 62.54 121 | 79.59 143 | 63.94 120 | 82.92 204 | 82.94 133 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
PAPM_NR | | | 73.91 110 | 74.16 106 | 73.16 125 | 81.90 99 | 53.50 160 | 81.28 47 | 81.40 88 | 66.17 57 | 73.30 191 | 83.31 182 | 59.96 146 | 83.10 70 | 58.45 149 | 81.66 220 | 82.87 134 |
|
PHI-MVS | | | 74.92 97 | 74.36 104 | 76.61 71 | 76.40 158 | 62.32 109 | 80.38 54 | 83.15 60 | 54.16 183 | 73.23 192 | 80.75 211 | 62.19 126 | 83.86 57 | 68.02 83 | 90.92 92 | 83.65 120 |
|
test222 | | | | | | 87.30 34 | 69.15 67 | 67.85 213 | 59.59 261 | 41.06 287 | 73.05 193 | 85.72 152 | 48.03 234 | | | 80.65 237 | 66.92 296 |
|
MCST-MVS | | | 73.42 118 | 73.34 120 | 73.63 111 | 81.28 106 | 59.17 131 | 74.80 129 | 83.13 61 | 45.50 260 | 72.84 194 | 83.78 175 | 65.15 105 | 80.99 115 | 64.54 116 | 89.09 122 | 80.73 179 |
|
tfpnnormal | | | 66.48 197 | 67.93 189 | 62.16 245 | 73.40 212 | 36.65 282 | 63.45 262 | 64.99 241 | 55.97 153 | 72.82 195 | 87.80 108 | 57.06 197 | 69.10 250 | 48.31 221 | 87.54 140 | 80.72 180 |
|
view600 | | | 62.88 223 | 62.90 218 | 62.82 236 | 72.97 227 | 33.66 308 | 66.10 233 | 55.01 285 | 57.05 141 | 72.66 196 | 82.56 191 | 31.60 305 | 72.78 215 | 42.64 253 | 85.55 170 | 82.02 153 |
|
view800 | | | 62.88 223 | 62.90 218 | 62.82 236 | 72.97 227 | 33.66 308 | 66.10 233 | 55.01 285 | 57.05 141 | 72.66 196 | 82.56 191 | 31.60 305 | 72.78 215 | 42.64 253 | 85.55 170 | 82.02 153 |
|
conf0.05thres1000 | | | 62.88 223 | 62.90 218 | 62.82 236 | 72.97 227 | 33.66 308 | 66.10 233 | 55.01 285 | 57.05 141 | 72.66 196 | 82.56 191 | 31.60 305 | 72.78 215 | 42.64 253 | 85.55 170 | 82.02 153 |
|
tfpn | | | 62.88 223 | 62.90 218 | 62.82 236 | 72.97 227 | 33.66 308 | 66.10 233 | 55.01 285 | 57.05 141 | 72.66 196 | 82.56 191 | 31.60 305 | 72.78 215 | 42.64 253 | 85.55 170 | 82.02 153 |
|
114514_t | | | 73.40 119 | 73.33 121 | 73.64 110 | 84.15 75 | 57.11 141 | 78.20 78 | 80.02 120 | 43.76 273 | 72.55 200 | 86.07 148 | 64.00 113 | 83.35 68 | 60.14 138 | 91.03 88 | 80.45 184 |
|
AdaColmap | | | 74.22 108 | 74.56 99 | 73.20 124 | 81.95 98 | 60.97 117 | 79.43 63 | 80.90 104 | 65.57 61 | 72.54 201 | 81.76 204 | 70.98 62 | 85.26 36 | 47.88 224 | 90.00 110 | 73.37 241 |
|
LF4IMVS | | | 67.50 191 | 67.31 194 | 68.08 195 | 58.86 324 | 61.93 110 | 71.43 169 | 75.90 173 | 44.67 268 | 72.42 202 | 80.20 216 | 57.16 193 | 70.44 243 | 58.99 146 | 86.12 161 | 71.88 256 |
|
F-COLMAP | | | 75.29 90 | 73.99 108 | 79.18 48 | 81.73 101 | 71.90 43 | 81.86 45 | 82.98 62 | 59.86 121 | 72.27 203 | 84.00 172 | 64.56 111 | 83.07 71 | 51.48 196 | 87.19 151 | 82.56 143 |
|
USDC | | | 62.80 227 | 63.10 216 | 61.89 246 | 65.19 291 | 43.30 238 | 67.42 218 | 74.20 186 | 35.80 310 | 72.25 204 | 84.48 167 | 45.67 239 | 71.95 233 | 37.95 286 | 84.97 188 | 70.42 269 |
|
3Dnovator | | 65.95 11 | 71.50 150 | 71.22 157 | 72.34 147 | 73.16 216 | 63.09 105 | 78.37 75 | 78.32 147 | 57.67 136 | 72.22 205 | 84.61 165 | 54.77 205 | 78.47 159 | 60.82 135 | 81.07 231 | 75.45 228 |
|
Patchmtry | | | 60.91 239 | 63.01 217 | 54.62 289 | 66.10 287 | 26.27 340 | 67.47 217 | 56.40 280 | 54.05 184 | 72.04 206 | 86.66 127 | 33.19 292 | 60.17 297 | 43.69 242 | 87.45 144 | 77.42 215 |
|
HQP4-MVS | | | | | | | | | | | 71.59 207 | | | 85.31 34 | | | 83.74 118 |
|
HQP-NCC | | | | | | 82.37 91 | | 77.32 86 | | 59.08 123 | 71.58 208 | | | | | | |
|
ACMP_Plane | | | | | | 82.37 91 | | 77.32 86 | | 59.08 123 | 71.58 208 | | | | | | |
|
HQP-MVS | | | 75.24 91 | 75.01 96 | 75.94 82 | 82.37 91 | 58.80 134 | 77.32 86 | 84.12 45 | 59.08 123 | 71.58 208 | 85.96 150 | 58.09 175 | 85.30 35 | 67.38 94 | 89.16 119 | 83.73 119 |
|
MVS_Test | | | 69.84 164 | 70.71 161 | 67.24 202 | 67.49 277 | 43.25 239 | 69.87 188 | 81.22 93 | 52.69 200 | 71.57 211 | 86.68 126 | 62.09 127 | 74.51 205 | 66.05 106 | 78.74 255 | 83.96 114 |
|
TR-MVS | | | 64.59 205 | 63.54 212 | 67.73 199 | 75.75 168 | 50.83 171 | 63.39 263 | 70.29 220 | 49.33 233 | 71.55 212 | 74.55 274 | 50.94 222 | 78.46 160 | 40.43 271 | 75.69 273 | 73.89 239 |
|
IterMVS | | | 63.12 219 | 62.48 225 | 65.02 218 | 66.34 285 | 52.86 163 | 63.81 259 | 62.25 248 | 46.57 254 | 71.51 213 | 80.40 215 | 44.60 245 | 66.82 275 | 51.38 198 | 75.47 276 | 75.38 230 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
Fast-Effi-MVS+ | | | 68.81 179 | 68.30 185 | 70.35 164 | 74.66 183 | 48.61 189 | 66.06 237 | 78.32 147 | 50.62 225 | 71.48 214 | 75.54 262 | 68.75 76 | 79.59 143 | 50.55 204 | 78.73 256 | 82.86 135 |
|
VPNet | | | 65.58 199 | 67.56 191 | 59.65 266 | 79.72 116 | 30.17 329 | 60.27 288 | 62.14 249 | 54.19 182 | 71.24 215 | 86.63 130 | 58.80 162 | 67.62 267 | 44.17 241 | 90.87 96 | 81.18 167 |
|
API-MVS | | | 70.97 154 | 71.51 154 | 69.37 176 | 75.20 170 | 55.94 145 | 80.99 48 | 76.84 165 | 62.48 98 | 71.24 215 | 77.51 243 | 61.51 133 | 80.96 120 | 52.04 192 | 85.76 165 | 71.22 262 |
|
mvs-test1 | | | 73.81 112 | 70.69 162 | 83.18 3 | 77.05 150 | 81.39 4 | 75.39 120 | 77.70 157 | 57.68 134 | 71.19 217 | 74.72 272 | 64.80 108 | 83.66 60 | 56.46 163 | 81.19 230 | 84.50 105 |
|
LFMVS | | | 67.06 195 | 67.89 190 | 64.56 220 | 78.02 140 | 38.25 273 | 70.81 180 | 59.60 260 | 65.18 69 | 71.06 218 | 86.56 133 | 43.85 249 | 75.22 198 | 46.35 234 | 89.63 113 | 80.21 187 |
|
BH-w/o | | | 64.81 204 | 64.29 206 | 66.36 211 | 76.08 164 | 54.71 152 | 65.61 243 | 75.23 181 | 50.10 229 | 71.05 219 | 71.86 301 | 54.33 208 | 79.02 146 | 38.20 284 | 76.14 271 | 65.36 305 |
|
Effi-MVS+ | | | 72.10 144 | 72.28 143 | 71.58 154 | 74.21 196 | 50.33 172 | 74.72 131 | 82.73 66 | 62.62 96 | 70.77 220 | 76.83 247 | 69.96 68 | 80.97 116 | 60.20 136 | 78.43 259 | 83.45 125 |
|
tfpn111 | | | 61.91 231 | 61.65 228 | 62.68 241 | 72.14 235 | 35.01 296 | 65.42 245 | 56.99 275 | 55.23 161 | 70.71 221 | 79.90 219 | 32.07 300 | 72.85 214 | 38.80 277 | 83.61 199 | 80.18 188 |
|
conf200view11 | | | 61.42 236 | 61.09 234 | 62.43 244 | 72.14 235 | 35.01 296 | 65.42 245 | 56.99 275 | 55.23 161 | 70.71 221 | 79.90 219 | 32.07 300 | 72.09 228 | 35.61 299 | 81.73 215 | 80.18 188 |
|
thres100view900 | | | 61.17 238 | 61.09 234 | 61.39 251 | 72.14 235 | 35.01 296 | 65.42 245 | 56.99 275 | 55.23 161 | 70.71 221 | 79.90 219 | 32.07 300 | 72.09 228 | 35.61 299 | 81.73 215 | 77.08 220 |
|
OpenMVS_ROB | | 54.93 17 | 63.23 218 | 63.28 213 | 63.07 234 | 69.81 251 | 45.34 231 | 68.52 207 | 67.14 230 | 43.74 274 | 70.61 224 | 79.22 231 | 47.90 235 | 72.66 220 | 48.75 215 | 73.84 286 | 71.21 263 |
|
MSDG | | | 67.47 193 | 67.48 192 | 67.46 200 | 70.70 246 | 54.69 153 | 66.90 226 | 78.17 152 | 60.88 111 | 70.41 225 | 74.76 270 | 61.22 138 | 73.18 211 | 47.38 227 | 76.87 268 | 74.49 235 |
|
DP-MVS Recon | | | 73.57 116 | 72.69 137 | 76.23 80 | 82.85 87 | 63.39 102 | 74.32 134 | 82.96 63 | 57.75 133 | 70.35 226 | 81.98 200 | 64.34 112 | 84.41 53 | 49.69 209 | 89.95 111 | 80.89 174 |
|
thres600view7 | | | 61.82 232 | 61.38 233 | 63.12 233 | 71.81 240 | 34.93 299 | 64.64 251 | 56.99 275 | 54.78 173 | 70.33 227 | 79.74 224 | 32.07 300 | 72.42 225 | 38.61 280 | 83.46 200 | 82.02 153 |
|
OpenMVS | | 62.51 15 | 68.76 180 | 68.75 181 | 68.78 191 | 70.56 247 | 53.91 158 | 78.29 76 | 77.35 162 | 48.85 235 | 70.22 228 | 83.52 176 | 52.65 214 | 76.93 181 | 55.31 174 | 81.99 211 | 75.49 227 |
|
Vis-MVSNet (Re-imp) | | | 62.74 228 | 63.21 215 | 61.34 252 | 72.19 234 | 31.56 327 | 67.31 222 | 53.87 292 | 53.60 191 | 69.88 229 | 83.37 179 | 40.52 267 | 70.98 239 | 41.40 265 | 86.78 156 | 81.48 164 |
|
TAMVS | | | 65.31 201 | 63.75 209 | 69.97 174 | 82.23 95 | 59.76 127 | 66.78 227 | 63.37 246 | 45.20 264 | 69.79 230 | 79.37 229 | 47.42 237 | 72.17 227 | 34.48 304 | 85.15 183 | 77.99 213 |
|
FPMVS | | | 59.43 249 | 60.07 240 | 57.51 277 | 77.62 148 | 71.52 44 | 62.33 268 | 50.92 309 | 57.40 139 | 69.40 231 | 80.00 218 | 39.14 271 | 61.92 293 | 37.47 290 | 66.36 318 | 39.09 347 |
|
GA-MVS | | | 62.91 221 | 61.66 227 | 66.66 210 | 67.09 280 | 44.49 234 | 61.18 284 | 69.36 224 | 51.33 213 | 69.33 232 | 74.47 275 | 36.83 281 | 74.94 202 | 50.60 203 | 74.72 281 | 80.57 183 |
|
EU-MVSNet | | | 60.82 240 | 60.80 237 | 60.86 257 | 68.37 268 | 41.16 251 | 72.27 147 | 68.27 228 | 26.96 344 | 69.08 233 | 75.71 260 | 32.09 299 | 67.44 268 | 55.59 172 | 78.90 254 | 73.97 237 |
|
HyFIR lowres test | | | 63.01 220 | 60.47 238 | 70.61 161 | 83.04 84 | 54.10 156 | 59.93 290 | 72.24 201 | 33.67 323 | 69.00 234 | 75.63 261 | 38.69 273 | 76.93 181 | 36.60 294 | 75.45 277 | 80.81 178 |
|
DELS-MVS | | | 68.83 178 | 68.31 184 | 70.38 163 | 70.55 248 | 48.31 192 | 63.78 260 | 82.13 72 | 54.00 185 | 68.96 235 | 75.17 268 | 58.95 161 | 80.06 138 | 58.55 147 | 82.74 205 | 82.76 138 |
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 |
Fast-Effi-MVS+-dtu | | | 70.00 162 | 68.74 182 | 73.77 106 | 73.47 207 | 64.53 94 | 71.36 171 | 78.14 153 | 55.81 155 | 68.84 236 | 74.71 273 | 65.36 104 | 75.75 192 | 52.00 193 | 79.00 253 | 81.03 171 |
|
MG-MVS | | | 70.47 159 | 71.34 156 | 67.85 197 | 79.26 123 | 40.42 259 | 74.67 133 | 75.15 182 | 58.41 129 | 68.74 237 | 88.14 106 | 56.08 203 | 83.69 59 | 59.90 140 | 81.71 219 | 79.43 197 |
|
tfpn200view9 | | | 60.35 244 | 59.97 241 | 61.51 249 | 70.78 244 | 35.35 294 | 63.27 265 | 57.47 269 | 53.00 197 | 68.31 238 | 77.09 245 | 32.45 297 | 72.09 228 | 35.61 299 | 81.73 215 | 77.08 220 |
|
thres400 | | | 60.77 242 | 59.97 241 | 63.15 232 | 70.78 244 | 35.35 294 | 63.27 265 | 57.47 269 | 53.00 197 | 68.31 238 | 77.09 245 | 32.45 297 | 72.09 228 | 35.61 299 | 81.73 215 | 82.02 153 |
|
testgi | | | 54.00 282 | 56.86 268 | 45.45 315 | 58.20 329 | 25.81 341 | 49.05 318 | 49.50 314 | 45.43 262 | 67.84 240 | 81.17 209 | 51.81 219 | 43.20 335 | 29.30 325 | 79.41 251 | 67.34 295 |
|
xiu_mvs_v1_base_debu | | | 67.87 187 | 67.07 195 | 70.26 165 | 79.13 128 | 61.90 111 | 67.34 219 | 71.25 211 | 47.98 243 | 67.70 241 | 74.19 281 | 61.31 134 | 72.62 221 | 56.51 160 | 78.26 261 | 76.27 224 |
|
xiu_mvs_v1_base | | | 67.87 187 | 67.07 195 | 70.26 165 | 79.13 128 | 61.90 111 | 67.34 219 | 71.25 211 | 47.98 243 | 67.70 241 | 74.19 281 | 61.31 134 | 72.62 221 | 56.51 160 | 78.26 261 | 76.27 224 |
|
xiu_mvs_v1_base_debi | | | 67.87 187 | 67.07 195 | 70.26 165 | 79.13 128 | 61.90 111 | 67.34 219 | 71.25 211 | 47.98 243 | 67.70 241 | 74.19 281 | 61.31 134 | 72.62 221 | 56.51 160 | 78.26 261 | 76.27 224 |
|
conf0.01 | | | 59.26 250 | 58.88 248 | 60.40 260 | 68.66 260 | 31.96 321 | 62.04 270 | 51.95 301 | 50.99 217 | 67.57 244 | 75.91 254 | 28.59 330 | 69.07 251 | 42.77 247 | 81.40 223 | 80.18 188 |
|
conf0.002 | | | 59.26 250 | 58.88 248 | 60.40 260 | 68.66 260 | 31.96 321 | 62.04 270 | 51.95 301 | 50.99 217 | 67.57 244 | 75.91 254 | 28.59 330 | 69.07 251 | 42.77 247 | 81.40 223 | 80.18 188 |
|
thresconf0.02 | | | 58.38 257 | 58.88 248 | 56.91 280 | 68.66 260 | 31.96 321 | 62.04 270 | 51.95 301 | 50.99 217 | 67.57 244 | 75.91 254 | 28.59 330 | 69.07 251 | 42.77 247 | 81.40 223 | 69.70 275 |
|
tfpn_n400 | | | 58.38 257 | 58.88 248 | 56.91 280 | 68.66 260 | 31.96 321 | 62.04 270 | 51.95 301 | 50.99 217 | 67.57 244 | 75.91 254 | 28.59 330 | 69.07 251 | 42.77 247 | 81.40 223 | 69.70 275 |
|
tfpnconf | | | 58.38 257 | 58.88 248 | 56.91 280 | 68.66 260 | 31.96 321 | 62.04 270 | 51.95 301 | 50.99 217 | 67.57 244 | 75.91 254 | 28.59 330 | 69.07 251 | 42.77 247 | 81.40 223 | 69.70 275 |
|
tfpnview11 | | | 58.38 257 | 58.88 248 | 56.91 280 | 68.66 260 | 31.96 321 | 62.04 270 | 51.95 301 | 50.99 217 | 67.57 244 | 75.91 254 | 28.59 330 | 69.07 251 | 42.77 247 | 81.40 223 | 69.70 275 |
|
tfpn1000 | | | 58.28 261 | 58.86 254 | 56.53 284 | 68.05 273 | 32.26 318 | 62.58 267 | 51.67 308 | 51.25 215 | 67.38 250 | 75.95 253 | 27.24 337 | 68.83 257 | 43.51 245 | 82.11 210 | 68.49 288 |
|
CDS-MVSNet | | | 64.33 211 | 62.66 224 | 69.35 178 | 80.44 112 | 58.28 138 | 65.26 248 | 65.66 236 | 44.36 269 | 67.30 251 | 75.54 262 | 43.27 252 | 71.77 234 | 37.68 287 | 84.44 189 | 78.01 212 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
PVSNet_Blended_VisFu | | | 70.04 161 | 68.88 177 | 73.53 114 | 82.71 89 | 63.62 101 | 74.81 127 | 81.95 77 | 48.53 238 | 67.16 252 | 79.18 233 | 51.42 221 | 78.38 165 | 54.39 183 | 79.72 249 | 78.60 204 |
|
PLC | | 62.01 16 | 71.79 148 | 70.28 164 | 76.33 77 | 80.31 113 | 68.63 69 | 78.18 79 | 81.24 92 | 54.57 177 | 67.09 253 | 80.63 212 | 59.44 153 | 81.74 89 | 46.91 231 | 84.17 191 | 78.63 203 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
VNet | | | 64.01 215 | 65.15 203 | 60.57 258 | 73.28 215 | 35.61 293 | 57.60 299 | 67.08 231 | 54.61 176 | 66.76 254 | 83.37 179 | 56.28 201 | 66.87 272 | 42.19 259 | 85.20 182 | 79.23 199 |
|
PAPR | | | 69.20 171 | 68.66 183 | 70.82 160 | 75.15 171 | 47.77 204 | 75.31 121 | 81.11 94 | 49.62 232 | 66.33 255 | 79.27 230 | 61.53 132 | 82.96 72 | 48.12 222 | 81.50 222 | 81.74 161 |
|
MVS_0304 | | | 74.55 105 | 73.47 115 | 77.80 65 | 77.41 149 | 63.88 99 | 75.75 114 | 83.67 52 | 63.55 88 | 66.12 256 | 82.16 198 | 60.20 145 | 86.15 21 | 65.37 112 | 86.98 154 | 83.38 126 |
|
pmmvs4 | | | 60.78 241 | 59.04 246 | 66.00 214 | 73.06 224 | 57.67 140 | 64.53 254 | 60.22 258 | 36.91 305 | 65.96 257 | 77.27 244 | 39.66 269 | 68.54 260 | 38.87 276 | 74.89 280 | 71.80 257 |
|
CMPMVS | | 48.73 20 | 61.54 235 | 60.89 236 | 63.52 230 | 61.08 311 | 51.55 167 | 68.07 212 | 68.00 229 | 33.88 319 | 65.87 258 | 81.25 208 | 37.91 279 | 67.71 265 | 49.32 212 | 82.60 206 | 71.31 261 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
MAR-MVS | | | 67.72 190 | 66.16 199 | 72.40 146 | 74.45 187 | 64.99 92 | 74.87 125 | 77.50 161 | 48.67 237 | 65.78 259 | 68.58 320 | 57.01 198 | 77.79 174 | 46.68 233 | 81.92 212 | 74.42 236 |
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 |
ab-mvs | | | 64.11 213 | 65.13 204 | 61.05 254 | 71.99 239 | 38.03 276 | 67.59 215 | 68.79 225 | 49.08 234 | 65.32 260 | 86.26 140 | 58.02 180 | 66.85 274 | 39.33 273 | 79.79 248 | 78.27 207 |
|
jason | | | 64.47 208 | 62.84 222 | 69.34 179 | 76.91 155 | 59.20 128 | 67.15 223 | 65.67 235 | 35.29 312 | 65.16 261 | 76.74 248 | 44.67 244 | 70.68 240 | 54.74 178 | 79.28 252 | 78.14 209 |
jason: jason. |
test20.03 | | | 55.74 273 | 57.51 263 | 50.42 297 | 59.89 318 | 32.09 319 | 50.63 315 | 49.01 315 | 50.11 228 | 65.07 262 | 83.23 185 | 45.61 240 | 48.11 317 | 30.22 320 | 83.82 197 | 71.07 266 |
|
testmv | | | 52.91 287 | 54.31 282 | 48.71 306 | 72.13 238 | 36.18 286 | 50.26 316 | 47.78 319 | 44.15 270 | 64.61 263 | 79.78 223 | 38.18 275 | 50.20 313 | 21.96 342 | 69.93 304 | 59.75 326 |
|
new-patchmatchnet | | | 52.89 288 | 55.76 275 | 44.26 321 | 59.94 317 | 6.31 355 | 37.36 345 | 50.76 311 | 41.10 286 | 64.28 264 | 79.82 222 | 44.77 243 | 48.43 316 | 36.24 296 | 87.61 139 | 78.03 211 |
|
tfpn_ndepth | | | 56.91 268 | 57.30 265 | 55.71 285 | 67.22 279 | 33.26 313 | 61.72 278 | 53.98 291 | 48.49 239 | 64.16 265 | 71.94 299 | 27.65 336 | 68.71 258 | 40.49 270 | 80.08 242 | 65.17 307 |
|
thres200 | | | 57.55 266 | 57.02 266 | 59.17 267 | 67.89 276 | 34.93 299 | 58.91 295 | 57.25 273 | 50.24 227 | 64.01 266 | 71.46 304 | 32.49 296 | 71.39 237 | 31.31 315 | 79.57 250 | 71.19 264 |
|
XXY-MVS | | | 55.19 275 | 57.40 264 | 48.56 307 | 64.45 295 | 34.84 301 | 51.54 314 | 53.59 294 | 38.99 294 | 63.79 267 | 79.43 227 | 56.59 199 | 45.57 322 | 36.92 293 | 71.29 295 | 65.25 306 |
|
cascas | | | 64.59 205 | 62.77 223 | 70.05 172 | 75.27 169 | 50.02 175 | 61.79 277 | 71.61 203 | 42.46 281 | 63.68 268 | 68.89 317 | 49.33 228 | 80.35 130 | 47.82 225 | 84.05 193 | 79.78 194 |
|
MVSFormer | | | 69.93 163 | 69.03 174 | 72.63 140 | 74.93 172 | 59.19 129 | 83.98 28 | 75.72 174 | 52.27 201 | 63.53 269 | 76.74 248 | 43.19 253 | 80.56 126 | 72.28 51 | 78.67 257 | 78.14 209 |
|
lupinMVS | | | 63.36 216 | 61.49 232 | 68.97 184 | 74.93 172 | 59.19 129 | 65.80 240 | 64.52 243 | 34.68 317 | 63.53 269 | 74.25 279 | 43.19 253 | 70.62 241 | 53.88 186 | 78.67 257 | 77.10 219 |
|
UnsupCasMVSNet_eth | | | 52.26 292 | 53.29 287 | 49.16 303 | 55.08 343 | 33.67 307 | 50.03 317 | 58.79 264 | 37.67 301 | 63.43 271 | 74.75 271 | 41.82 260 | 45.83 321 | 38.59 281 | 59.42 333 | 67.98 291 |
|
Anonymous20231206 | | | 54.13 279 | 55.82 274 | 49.04 305 | 70.89 242 | 35.96 289 | 51.73 313 | 50.87 310 | 34.86 313 | 62.49 272 | 79.22 231 | 42.52 257 | 44.29 331 | 27.95 329 | 81.88 214 | 66.88 297 |
|
CANet | | | 73.00 126 | 71.84 147 | 76.48 73 | 75.82 166 | 61.28 115 | 74.81 127 | 80.37 113 | 63.17 92 | 62.43 273 | 80.50 214 | 61.10 139 | 85.16 41 | 64.00 119 | 84.34 190 | 83.01 132 |
|
diffmvs | | | 66.15 198 | 65.86 200 | 67.01 205 | 62.31 304 | 44.43 235 | 68.81 200 | 72.93 193 | 48.13 241 | 62.12 274 | 83.33 181 | 57.96 183 | 72.29 226 | 59.83 141 | 77.31 267 | 84.33 110 |
|
xiu_mvs_v2_base | | | 64.43 209 | 63.96 207 | 65.85 216 | 77.72 146 | 51.32 169 | 63.63 261 | 72.31 200 | 45.06 267 | 61.70 275 | 69.66 311 | 62.56 119 | 73.93 209 | 49.06 214 | 73.91 284 | 72.31 252 |
|
PS-MVSNAJ | | | 64.27 212 | 63.73 210 | 65.90 215 | 77.82 144 | 51.42 168 | 63.33 264 | 72.33 199 | 45.09 266 | 61.60 276 | 68.04 321 | 62.39 123 | 73.95 208 | 49.07 213 | 73.87 285 | 72.34 251 |
|
CHOSEN 1792x2688 | | | 58.09 262 | 56.30 271 | 63.45 231 | 79.95 114 | 50.93 170 | 54.07 308 | 65.59 237 | 28.56 341 | 61.53 277 | 74.33 277 | 41.09 263 | 66.52 278 | 33.91 308 | 67.69 316 | 72.92 245 |
|
CR-MVSNet | | | 58.96 253 | 58.49 258 | 60.36 262 | 66.37 283 | 48.24 194 | 70.93 178 | 56.40 280 | 32.87 327 | 61.35 278 | 86.66 127 | 33.19 292 | 63.22 287 | 48.50 219 | 70.17 302 | 69.62 280 |
|
RPMNet | | | 61.25 237 | 61.55 231 | 60.36 262 | 66.37 283 | 48.24 194 | 70.93 178 | 54.45 290 | 54.66 175 | 61.35 278 | 86.77 122 | 33.29 291 | 63.22 287 | 55.93 168 | 70.17 302 | 69.62 280 |
|
PatchMatch-RL | | | 58.68 256 | 57.72 261 | 61.57 248 | 76.21 161 | 73.59 39 | 61.83 276 | 49.00 316 | 47.30 251 | 61.08 280 | 68.97 315 | 50.16 225 | 59.01 300 | 36.06 298 | 68.84 310 | 52.10 336 |
|
FMVSNet5 | | | 55.08 276 | 55.54 277 | 53.71 290 | 65.80 288 | 33.50 312 | 56.22 301 | 52.50 300 | 43.72 275 | 61.06 281 | 83.38 178 | 25.46 342 | 54.87 305 | 30.11 321 | 81.64 221 | 72.75 247 |
|
1314 | | | 59.83 246 | 58.86 254 | 62.74 240 | 65.71 289 | 44.78 232 | 68.59 204 | 72.63 196 | 33.54 326 | 61.05 282 | 67.29 325 | 43.62 251 | 71.26 238 | 49.49 211 | 67.84 315 | 72.19 254 |
|
Patchmatch-test1 | | | 57.81 264 | 58.04 260 | 57.13 278 | 70.17 250 | 41.07 253 | 65.19 249 | 53.38 296 | 43.34 280 | 61.00 283 | 71.94 299 | 45.20 241 | 62.69 289 | 41.81 263 | 70.31 301 | 67.63 292 |
|
no-one | | | 56.11 270 | 55.62 276 | 57.60 276 | 62.68 301 | 49.23 182 | 39.12 341 | 58.99 263 | 33.72 321 | 60.98 284 | 80.90 210 | 36.07 284 | 60.36 296 | 30.68 317 | 97.40 1 | 63.22 314 |
|
UGNet | | | 70.20 160 | 69.05 172 | 73.65 109 | 76.24 160 | 63.64 100 | 75.87 111 | 72.53 197 | 61.48 105 | 60.93 285 | 86.14 145 | 52.37 215 | 77.12 179 | 50.67 202 | 85.21 181 | 80.17 192 |
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 |
UnsupCasMVSNet_bld | | | 50.01 298 | 51.03 298 | 46.95 308 | 58.61 326 | 32.64 316 | 48.31 320 | 53.27 297 | 34.27 318 | 60.47 286 | 71.53 303 | 41.40 261 | 47.07 319 | 30.68 317 | 60.78 329 | 61.13 321 |
|
CVMVSNet | | | 59.21 252 | 58.44 259 | 61.51 249 | 73.94 199 | 47.76 205 | 71.31 173 | 64.56 242 | 26.91 345 | 60.34 287 | 70.44 306 | 36.24 283 | 67.65 266 | 53.57 189 | 68.66 312 | 69.12 285 |
|
1111 | | | 45.08 312 | 47.96 307 | 36.43 334 | 59.56 321 | 14.82 351 | 43.56 331 | 45.65 327 | 45.60 258 | 60.04 288 | 75.47 265 | 9.31 358 | 34.46 347 | 23.66 338 | 68.76 311 | 60.02 324 |
|
.test1245 | | | 34.47 328 | 40.38 324 | 16.73 340 | 59.56 321 | 14.82 351 | 43.56 331 | 45.65 327 | 45.60 258 | 60.04 288 | 75.47 265 | 9.31 358 | 34.46 347 | 23.66 338 | 0.55 354 | 0.90 353 |
|
PVSNet_BlendedMVS | | | 65.38 200 | 64.30 205 | 68.61 192 | 69.81 251 | 49.36 180 | 65.60 244 | 78.96 135 | 45.50 260 | 59.98 290 | 78.61 236 | 51.82 217 | 78.20 171 | 44.30 239 | 84.11 192 | 78.27 207 |
|
PVSNet_Blended | | | 62.90 222 | 61.64 229 | 66.69 209 | 69.81 251 | 49.36 180 | 61.23 283 | 78.96 135 | 42.04 283 | 59.98 290 | 68.86 318 | 51.82 217 | 78.20 171 | 44.30 239 | 77.77 266 | 72.52 249 |
|
MVS | | | 60.62 243 | 59.97 241 | 62.58 242 | 68.13 272 | 47.28 217 | 68.59 204 | 73.96 187 | 32.19 328 | 59.94 292 | 68.86 318 | 50.48 223 | 77.64 176 | 41.85 262 | 75.74 272 | 62.83 315 |
|
1112_ss | | | 59.48 248 | 58.99 247 | 60.96 256 | 77.84 143 | 42.39 245 | 61.42 281 | 68.45 227 | 37.96 300 | 59.93 293 | 67.46 323 | 45.11 242 | 65.07 282 | 40.89 268 | 71.81 293 | 75.41 229 |
|
Test_1112_low_res | | | 58.78 255 | 58.69 256 | 59.04 269 | 79.41 120 | 38.13 274 | 57.62 298 | 66.98 232 | 34.74 315 | 59.62 294 | 77.56 242 | 42.92 255 | 63.65 286 | 38.66 279 | 70.73 299 | 75.35 231 |
|
CostFormer | | | 57.35 267 | 56.14 272 | 60.97 255 | 63.76 298 | 38.43 270 | 67.50 216 | 60.22 258 | 37.14 304 | 59.12 295 | 76.34 250 | 32.78 294 | 71.99 232 | 39.12 275 | 69.27 308 | 72.47 250 |
|
test1235678 | | | 48.41 302 | 49.60 302 | 44.83 319 | 68.52 266 | 33.81 306 | 46.33 328 | 45.89 326 | 38.72 296 | 58.46 296 | 72.08 292 | 29.85 325 | 47.82 318 | 19.67 346 | 66.91 317 | 52.88 334 |
|
PatchmatchNet | | | 54.60 277 | 54.27 283 | 55.59 286 | 65.17 293 | 39.08 265 | 66.92 225 | 51.80 307 | 39.89 291 | 58.39 297 | 73.12 289 | 31.69 304 | 58.33 301 | 43.01 246 | 58.38 339 | 69.38 283 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
MS-PatchMatch | | | 55.59 274 | 54.89 280 | 57.68 275 | 69.18 258 | 49.05 183 | 61.00 285 | 62.93 247 | 35.98 308 | 58.36 298 | 68.93 316 | 36.71 282 | 66.59 277 | 37.62 289 | 63.30 324 | 57.39 329 |
|
tpm2 | | | 56.12 269 | 54.64 281 | 60.55 259 | 66.24 286 | 36.01 288 | 68.14 211 | 56.77 279 | 33.60 325 | 58.25 299 | 75.52 264 | 30.25 320 | 74.33 207 | 33.27 310 | 69.76 307 | 71.32 260 |
|
testus | | | 45.03 313 | 46.49 311 | 40.65 329 | 62.53 302 | 25.24 342 | 42.54 333 | 46.23 325 | 31.16 338 | 57.69 300 | 62.90 332 | 34.60 287 | 42.33 336 | 17.72 348 | 63.01 325 | 54.37 333 |
|
N_pmnet | | | 52.06 293 | 51.11 297 | 54.92 288 | 59.64 320 | 71.03 49 | 37.42 344 | 61.62 255 | 33.68 322 | 57.12 301 | 72.10 291 | 37.94 278 | 31.03 350 | 29.13 328 | 71.35 294 | 62.70 316 |
|
tpm | | | 50.60 295 | 52.42 291 | 45.14 317 | 65.18 292 | 26.29 339 | 60.30 287 | 43.50 331 | 37.41 302 | 57.01 302 | 79.09 235 | 30.20 322 | 42.32 337 | 32.77 312 | 66.36 318 | 66.81 299 |
|
LP | | | 53.02 286 | 52.27 292 | 55.27 287 | 55.76 341 | 40.55 257 | 55.64 304 | 55.07 283 | 42.46 281 | 56.95 303 | 73.21 288 | 33.67 290 | 54.18 309 | 38.41 282 | 59.29 334 | 71.08 265 |
|
tpm cat1 | | | 54.02 281 | 52.63 289 | 58.19 273 | 64.85 294 | 39.86 262 | 66.26 232 | 57.28 272 | 32.16 329 | 56.90 304 | 70.39 308 | 32.75 295 | 65.30 281 | 34.29 306 | 58.79 335 | 69.41 282 |
|
Patchmatch-test | | | 47.93 303 | 49.96 301 | 41.84 326 | 57.42 333 | 24.26 344 | 48.75 319 | 41.49 341 | 39.30 292 | 56.79 305 | 73.48 285 | 30.48 319 | 33.87 349 | 29.29 326 | 72.61 289 | 67.39 293 |
|
EPNet | | | 69.10 173 | 67.32 193 | 74.46 94 | 68.33 270 | 61.27 116 | 77.56 83 | 63.57 245 | 60.95 110 | 56.62 306 | 82.75 189 | 51.53 220 | 81.24 107 | 54.36 184 | 90.20 104 | 80.88 175 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
MVP-Stereo | | | 61.56 234 | 59.22 244 | 68.58 193 | 79.28 122 | 60.44 120 | 69.20 196 | 71.57 204 | 43.58 276 | 56.42 307 | 78.37 238 | 39.57 270 | 76.46 187 | 34.86 303 | 60.16 330 | 68.86 287 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
tpmvs | | | 55.84 271 | 55.45 279 | 57.01 279 | 60.33 315 | 33.20 314 | 65.89 239 | 59.29 262 | 47.52 250 | 56.04 308 | 73.60 284 | 31.05 315 | 68.06 263 | 40.64 269 | 64.64 321 | 69.77 274 |
|
MIMVSNet | | | 54.39 278 | 56.12 273 | 49.20 302 | 72.57 232 | 30.91 328 | 59.98 289 | 48.43 318 | 41.66 285 | 55.94 309 | 83.86 174 | 41.19 262 | 50.42 311 | 26.05 331 | 75.38 278 | 66.27 301 |
|
IB-MVS | | 49.67 18 | 59.69 247 | 56.96 267 | 67.90 196 | 68.19 271 | 50.30 173 | 61.42 281 | 65.18 240 | 47.57 249 | 55.83 310 | 67.15 326 | 23.77 345 | 79.60 142 | 43.56 244 | 79.97 244 | 73.79 240 |
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 |
test0.0.03 1 | | | 47.72 304 | 48.31 305 | 45.93 313 | 55.53 342 | 29.39 330 | 46.40 327 | 41.21 343 | 43.41 278 | 55.81 311 | 67.65 322 | 29.22 327 | 43.77 334 | 25.73 334 | 69.87 305 | 64.62 310 |
|
pmmvs5 | | | 52.49 291 | 52.58 290 | 52.21 295 | 54.99 344 | 32.38 317 | 55.45 305 | 53.84 293 | 32.15 330 | 55.49 312 | 74.81 269 | 38.08 277 | 57.37 303 | 34.02 307 | 74.40 282 | 66.88 297 |
|
tpmp4_e23 | | | 57.57 265 | 55.46 278 | 63.93 226 | 66.48 282 | 41.56 250 | 71.68 163 | 60.65 257 | 35.64 311 | 55.35 313 | 76.25 251 | 29.53 326 | 75.41 196 | 34.40 305 | 69.12 309 | 74.83 233 |
|
CANet_DTU | | | 64.04 214 | 63.83 208 | 64.66 219 | 68.39 267 | 42.97 241 | 73.45 138 | 74.50 185 | 52.05 205 | 54.78 314 | 75.44 267 | 43.99 248 | 70.42 244 | 53.49 190 | 78.41 260 | 80.59 182 |
|
PatchT | | | 53.35 284 | 56.47 270 | 43.99 322 | 64.19 296 | 17.46 349 | 59.15 292 | 43.10 332 | 52.11 204 | 54.74 315 | 86.95 114 | 29.97 323 | 49.98 314 | 43.62 243 | 74.40 282 | 64.53 312 |
|
HY-MVS | | 49.31 19 | 57.96 263 | 57.59 262 | 59.10 268 | 66.85 281 | 36.17 287 | 65.13 250 | 65.39 239 | 39.24 293 | 54.69 316 | 78.14 239 | 44.28 247 | 67.18 271 | 33.75 309 | 70.79 298 | 73.95 238 |
|
PatchFormer-LS_test | | | 53.94 283 | 52.64 288 | 57.85 274 | 61.87 306 | 39.59 263 | 61.60 279 | 57.63 267 | 40.65 289 | 54.52 317 | 58.64 340 | 29.07 329 | 64.18 284 | 46.78 232 | 62.98 326 | 69.78 273 |
|
PVSNet | | 43.83 21 | 51.56 294 | 51.17 295 | 52.73 292 | 68.34 269 | 38.27 272 | 48.22 321 | 53.56 295 | 36.41 306 | 54.29 318 | 64.94 329 | 34.60 287 | 54.20 308 | 30.34 319 | 69.87 305 | 65.71 304 |
|
WTY-MVS | | | 49.39 299 | 50.31 300 | 46.62 311 | 61.22 310 | 32.00 320 | 46.61 326 | 49.77 313 | 33.87 320 | 54.12 319 | 69.55 313 | 41.96 259 | 45.40 324 | 31.28 316 | 64.42 322 | 62.47 318 |
|
PAPM | | | 61.79 233 | 60.37 239 | 66.05 213 | 76.09 163 | 41.87 247 | 69.30 194 | 76.79 167 | 40.64 290 | 53.80 320 | 79.62 226 | 44.38 246 | 82.92 73 | 29.64 324 | 73.11 288 | 73.36 242 |
|
tpmrst | | | 50.15 297 | 51.38 294 | 46.45 312 | 56.05 337 | 24.77 343 | 64.40 256 | 49.98 312 | 36.14 307 | 53.32 321 | 69.59 312 | 35.16 286 | 48.69 315 | 39.24 274 | 58.51 338 | 65.89 302 |
|
MDTV_nov1_ep13 | | | | 54.05 284 | | 65.54 290 | 29.30 331 | 59.00 294 | 55.22 282 | 35.96 309 | 52.44 322 | 75.98 252 | 30.77 317 | 59.62 298 | 38.21 283 | 73.33 287 | |
|
sss | | | 47.59 305 | 48.32 304 | 45.40 316 | 56.73 336 | 33.96 304 | 45.17 330 | 48.51 317 | 32.11 332 | 52.37 323 | 65.79 327 | 40.39 268 | 41.91 340 | 31.85 313 | 61.97 327 | 60.35 322 |
|
DWT-MVSNet_test | | | 53.04 285 | 51.12 296 | 58.77 270 | 61.23 309 | 38.67 269 | 62.16 269 | 57.74 266 | 38.24 297 | 51.76 324 | 59.07 339 | 21.36 347 | 67.40 269 | 44.80 238 | 63.76 323 | 70.25 270 |
|
EPMVS | | | 45.74 307 | 46.53 310 | 43.39 323 | 54.14 348 | 22.33 346 | 55.02 306 | 35.00 350 | 34.69 316 | 51.09 325 | 70.20 310 | 25.92 340 | 42.04 339 | 37.19 291 | 55.50 343 | 65.78 303 |
|
gg-mvs-nofinetune | | | 55.75 272 | 56.75 269 | 52.72 293 | 62.87 300 | 28.04 335 | 68.92 197 | 41.36 342 | 71.09 31 | 50.80 326 | 92.63 12 | 20.74 348 | 66.86 273 | 29.97 322 | 72.41 290 | 63.25 313 |
|
ADS-MVSNet2 | | | 48.76 300 | 47.25 309 | 53.29 291 | 55.90 339 | 40.54 258 | 47.34 324 | 54.99 289 | 31.41 336 | 50.48 327 | 72.06 297 | 31.23 311 | 54.26 307 | 25.93 332 | 55.93 341 | 65.07 308 |
|
ADS-MVSNet | | | 44.62 315 | 45.58 312 | 41.73 327 | 55.90 339 | 20.83 347 | 47.34 324 | 39.94 346 | 31.41 336 | 50.48 327 | 72.06 297 | 31.23 311 | 39.31 343 | 25.93 332 | 55.93 341 | 65.07 308 |
|
pmmvs3 | | | 46.71 306 | 45.09 314 | 51.55 296 | 56.76 335 | 48.25 193 | 55.78 303 | 39.53 347 | 24.13 348 | 50.35 329 | 63.40 331 | 15.90 355 | 51.08 310 | 29.29 326 | 70.69 300 | 55.33 332 |
|
JIA-IIPM | | | 54.03 280 | 51.62 293 | 61.25 253 | 59.14 323 | 55.21 148 | 59.10 293 | 47.72 320 | 50.85 224 | 50.31 330 | 85.81 151 | 20.10 350 | 63.97 285 | 36.16 297 | 55.41 344 | 64.55 311 |
|
test-LLR | | | 50.43 296 | 50.69 299 | 49.64 300 | 60.76 312 | 41.87 247 | 53.18 310 | 45.48 329 | 43.41 278 | 49.41 331 | 60.47 337 | 29.22 327 | 44.73 328 | 42.09 260 | 72.14 291 | 62.33 319 |
|
test-mter | | | 48.56 301 | 48.20 306 | 49.64 300 | 60.76 312 | 41.87 247 | 53.18 310 | 45.48 329 | 31.91 334 | 49.41 331 | 60.47 337 | 18.34 351 | 44.73 328 | 42.09 260 | 72.14 291 | 62.33 319 |
|
test2356 | | | 40.85 321 | 40.47 323 | 41.98 325 | 58.78 325 | 28.65 334 | 39.45 339 | 40.98 345 | 31.95 333 | 48.47 333 | 56.63 341 | 12.54 357 | 44.41 330 | 15.84 350 | 59.58 332 | 52.88 334 |
|
PMMVS2 | | | 37.74 323 | 40.87 320 | 28.36 339 | 42.41 354 | 5.35 356 | 24.61 348 | 27.75 353 | 32.15 330 | 47.85 334 | 70.27 309 | 35.85 285 | 29.51 351 | 19.08 347 | 67.85 314 | 50.22 338 |
|
EPNet_dtu | | | 58.93 254 | 58.52 257 | 60.16 264 | 67.91 275 | 47.70 206 | 69.97 186 | 58.02 265 | 49.73 230 | 47.28 335 | 73.02 290 | 38.14 276 | 62.34 291 | 36.57 295 | 85.99 163 | 70.43 268 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
DSMNet-mixed | | | 43.18 318 | 44.66 317 | 38.75 332 | 54.75 345 | 28.88 333 | 57.06 300 | 27.42 354 | 13.47 350 | 47.27 336 | 77.67 241 | 38.83 272 | 39.29 344 | 25.32 336 | 60.12 331 | 48.08 339 |
|
testpf | | | 45.32 309 | 48.47 303 | 35.88 335 | 53.56 349 | 26.84 337 | 58.86 296 | 42.95 333 | 47.78 247 | 46.18 337 | 63.70 330 | 13.73 356 | 50.29 312 | 50.81 201 | 58.61 337 | 30.51 350 |
|
GG-mvs-BLEND | | | | | 52.24 294 | 60.64 314 | 29.21 332 | 69.73 191 | 42.41 335 | | 45.47 338 | 52.33 345 | 20.43 349 | 68.16 262 | 25.52 335 | 65.42 320 | 59.36 327 |
|
new_pmnet | | | 37.55 324 | 39.80 326 | 30.79 337 | 56.83 334 | 16.46 350 | 39.35 340 | 30.65 352 | 25.59 346 | 45.26 339 | 61.60 335 | 24.54 343 | 28.02 352 | 21.60 343 | 52.80 346 | 47.90 340 |
|
MDTV_nov1_ep13_2view | | | | | | | 18.41 348 | 53.74 309 | | 31.57 335 | 44.89 340 | | 29.90 324 | | 32.93 311 | | 71.48 259 |
|
TESTMET0.1,1 | | | 45.17 310 | 44.93 315 | 45.89 314 | 56.02 338 | 38.31 271 | 53.18 310 | 41.94 340 | 27.85 342 | 44.86 341 | 56.47 342 | 17.93 352 | 41.50 342 | 38.08 285 | 68.06 313 | 57.85 328 |
|
PVSNet_0 | | 36.71 22 | 41.12 320 | 40.78 322 | 42.14 324 | 59.97 316 | 40.13 260 | 40.97 335 | 42.24 339 | 30.81 339 | 44.86 341 | 49.41 348 | 40.70 266 | 45.12 326 | 23.15 340 | 34.96 349 | 41.16 346 |
|
test12356 | | | 38.35 322 | 40.80 321 | 31.01 336 | 58.31 328 | 9.09 354 | 36.67 346 | 46.65 322 | 33.65 324 | 44.39 343 | 60.94 336 | 17.56 353 | 39.23 345 | 16.01 349 | 53.03 345 | 44.72 344 |
|
dp | | | 44.09 317 | 44.88 316 | 41.72 328 | 58.53 327 | 23.18 345 | 54.70 307 | 42.38 337 | 34.80 314 | 44.25 344 | 65.61 328 | 24.48 344 | 44.80 327 | 29.77 323 | 49.42 347 | 57.18 330 |
|
PMMVS | | | 44.69 314 | 43.95 319 | 46.92 309 | 50.05 351 | 53.47 161 | 48.08 323 | 42.40 336 | 22.36 349 | 44.01 345 | 53.05 344 | 42.60 256 | 45.49 323 | 31.69 314 | 61.36 328 | 41.79 345 |
|
MVS-HIRNet | | | 45.53 308 | 47.29 308 | 40.24 330 | 62.29 305 | 26.82 338 | 56.02 302 | 37.41 348 | 29.74 340 | 43.69 346 | 81.27 207 | 33.96 289 | 55.48 304 | 24.46 337 | 56.79 340 | 38.43 348 |
|
PNet_i23d | | | 36.76 325 | 36.63 328 | 37.12 333 | 58.19 330 | 33.00 315 | 39.86 338 | 32.55 351 | 48.44 240 | 39.64 347 | 51.31 346 | 6.89 360 | 41.83 341 | 22.29 341 | 30.55 350 | 36.54 349 |
|
E-PMN | | | 45.17 310 | 45.36 313 | 44.60 320 | 50.07 350 | 42.75 242 | 38.66 342 | 42.29 338 | 46.39 255 | 39.55 348 | 51.15 347 | 26.00 339 | 45.37 325 | 37.68 287 | 76.41 269 | 45.69 343 |
|
MVE | | 27.91 23 | 36.69 326 | 35.64 329 | 39.84 331 | 43.37 353 | 35.85 291 | 19.49 349 | 24.61 355 | 24.68 347 | 39.05 349 | 62.63 334 | 38.67 274 | 27.10 353 | 21.04 344 | 47.25 348 | 56.56 331 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
EMVS | | | 44.61 316 | 44.45 318 | 45.10 318 | 48.91 352 | 43.00 240 | 37.92 343 | 41.10 344 | 46.75 252 | 38.00 350 | 48.43 349 | 26.42 338 | 46.27 320 | 37.11 292 | 75.38 278 | 46.03 342 |
|
CHOSEN 280x420 | | | 41.62 319 | 39.89 325 | 46.80 310 | 61.81 307 | 51.59 166 | 33.56 347 | 35.74 349 | 27.48 343 | 37.64 351 | 53.53 343 | 23.24 346 | 42.09 338 | 27.39 330 | 58.64 336 | 46.72 341 |
|
tmp_tt | | | 11.98 330 | 14.73 331 | 3.72 342 | 2.28 356 | 4.62 357 | 19.44 350 | 14.50 357 | 0.47 352 | 21.55 352 | 9.58 352 | 25.78 341 | 4.57 355 | 11.61 351 | 27.37 351 | 1.96 352 |
|
DeepMVS_CX | | | | | 11.83 341 | 15.51 355 | 13.86 353 | | 11.25 358 | 5.76 351 | 20.85 353 | 26.46 350 | 17.06 354 | 9.22 354 | 9.69 352 | 13.82 352 | 12.42 351 |
|
test123 | | | 4.43 333 | 5.78 334 | 0.39 344 | 0.97 357 | 0.28 358 | 46.33 328 | 0.45 359 | 0.31 353 | 0.62 354 | 1.50 355 | 0.61 362 | 0.11 357 | 0.56 353 | 0.63 353 | 0.77 355 |
|
testmvs | | | 4.06 334 | 5.28 335 | 0.41 343 | 0.64 358 | 0.16 359 | 42.54 333 | 0.31 360 | 0.26 354 | 0.50 355 | 1.40 356 | 0.77 361 | 0.17 356 | 0.56 353 | 0.55 354 | 0.90 353 |
|
cdsmvs_eth3d_5k | | | 17.71 329 | 23.62 330 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 70.17 221 | 0.00 355 | 0.00 356 | 74.25 279 | 68.16 82 | 0.00 358 | 0.00 355 | 0.00 356 | 0.00 356 |
|
pcd_1.5k_mvsjas | | | 5.20 332 | 6.93 333 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 0.00 357 | 62.39 123 | 0.00 358 | 0.00 355 | 0.00 356 | 0.00 356 |
|
pcd1.5k->3k | | | 35.00 327 | 36.93 327 | 29.21 338 | 84.62 65 | 0.00 360 | 0.00 351 | 78.90 137 | 0.00 355 | 0.00 356 | 0.00 357 | 78.26 15 | 0.00 358 | 0.00 355 | 90.55 100 | 87.62 65 |
|
sosnet-low-res | | | 0.00 335 | 0.00 336 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 0.00 357 | 0.00 363 | 0.00 358 | 0.00 355 | 0.00 356 | 0.00 356 |
|
sosnet | | | 0.00 335 | 0.00 336 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 0.00 357 | 0.00 363 | 0.00 358 | 0.00 355 | 0.00 356 | 0.00 356 |
|
uncertanet | | | 0.00 335 | 0.00 336 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 0.00 357 | 0.00 363 | 0.00 358 | 0.00 355 | 0.00 356 | 0.00 356 |
|
Regformer | | | 0.00 335 | 0.00 336 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 0.00 357 | 0.00 363 | 0.00 358 | 0.00 355 | 0.00 356 | 0.00 356 |
|
ab-mvs-re | | | 5.62 331 | 7.50 332 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 67.46 323 | 0.00 363 | 0.00 358 | 0.00 355 | 0.00 356 | 0.00 356 |
|
uanet | | | 0.00 335 | 0.00 336 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 0.00 357 | 0.00 363 | 0.00 358 | 0.00 355 | 0.00 356 | 0.00 356 |
|
GSMVS | | | | | | | | | | | | | | | | | 70.05 271 |
|
test_part3 | | | | | | | | 83.39 33 | | 73.27 20 | | 89.25 80 | | 86.96 9 | 72.56 45 | | |
|
test_part1 | | | | | | | | | 84.94 29 | | | | 75.17 31 | | | 93.83 48 | 82.50 144 |
|
sam_mvs1 | | | | | | | | | | | | | 31.41 309 | | | | 70.05 271 |
|
sam_mvs | | | | | | | | | | | | | 31.21 313 | | | | |
|
MTGPA | | | | | | | | | 80.63 106 | | | | | | | | |
|
test_post1 | | | | | | | | 66.63 228 | | | | 2.08 353 | 30.66 318 | 59.33 299 | 40.34 272 | | |
|
test_post | | | | | | | | | | | | 1.99 354 | 30.91 316 | 54.76 306 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 68.99 314 | 31.32 310 | 69.38 248 | | | |
|
MTMP | | | | | | | | | 19.26 356 | | | | | | | | |
|
gm-plane-assit | | | | | | 62.51 303 | 33.91 305 | | | 37.25 303 | | 62.71 333 | | 72.74 219 | 38.70 278 | | |
|
test9_res | | | | | | | | | | | | | | | 72.12 53 | 91.37 79 | 77.40 216 |
|
agg_prior2 | | | | | | | | | | | | | | | 70.70 61 | 90.93 91 | 78.55 205 |
|
test_prior4 | | | | | | | 70.14 58 | 77.57 82 | | | | | | | | | |
|
test_prior | | | | | 75.27 90 | 82.15 96 | 59.85 125 | | 84.33 38 | | | | | 83.39 66 | | | 82.58 141 |
|
新几何2 | | | | | | | | 71.33 172 | | | | | | | | | |
|
旧先验1 | | | | | | 84.55 67 | 60.36 121 | | 63.69 244 | | | 87.05 113 | 54.65 207 | | | 83.34 201 | 69.66 279 |
|
无先验 | | | | | | | | 74.82 126 | 70.94 215 | 47.75 248 | | | | 76.85 183 | 54.47 180 | | 72.09 255 |
|
原ACMM2 | | | | | | | | 74.78 130 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 67.30 270 | 48.34 220 | | |
|
segment_acmp | | | | | | | | | | | | | 68.30 81 | | | | |
|
testdata1 | | | | | | | | 68.34 210 | | 57.24 140 | | | | | | | |
|
plane_prior7 | | | | | | 85.18 54 | 66.21 82 | | | | | | | | | | |
|
plane_prior6 | | | | | | 84.18 74 | 65.31 88 | | | | | | 60.83 141 | | | | |
|
plane_prior5 | | | | | | | | | 85.49 21 | | | | | 86.15 21 | 71.09 55 | 90.94 89 | 84.82 94 |
|
plane_prior4 | | | | | | | | | | | | 89.11 86 | | | | | |
|
plane_prior2 | | | | | | | | 82.74 37 | | 65.45 62 | | | | | | | |
|
plane_prior1 | | | | | | 84.46 69 | | | | | | | | | | | |
|
plane_prior | | | | | | | 65.18 89 | 80.06 60 | | 61.88 102 | | | | | | 89.91 112 | |
|
n2 | | | | | | | | | 0.00 361 | | | | | | | | |
|
nn | | | | | | | | | 0.00 361 | | | | | | | | |
|
door-mid | | | | | | | | | 55.02 284 | | | | | | | | |
|
test11 | | | | | | | | | 82.71 67 | | | | | | | | |
|
door | | | | | | | | | 52.91 299 | | | | | | | | |
|
HQP5-MVS | | | | | | | 58.80 134 | | | | | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 67.38 94 | | |
|
HQP3-MVS | | | | | | | | | 84.12 45 | | | | | | | 89.16 119 | |
|
HQP2-MVS | | | | | | | | | | | | | 58.09 175 | | | | |
|
NP-MVS | | | | | | 83.34 81 | 63.07 106 | | | | | 85.97 149 | | | | | |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 89.47 117 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 91.96 69 | |
|
Test By Simon | | | | | | | | | | | | | 62.56 119 | | | | |
|