Anonymous20231211 | | | 77.74 66 | 80.26 47 | 70.19 169 | 83.05 84 | 43.39 239 | 75.86 113 | 76.74 169 | 75.91 12 | 85.92 43 | 96.14 1 | 80.85 8 | 75.59 196 | 53.58 189 | 94.27 42 | 91.58 13 |
|
LCM-MVSNet | | | 86.90 1 | 88.67 1 | 81.57 20 | 91.50 1 | 63.30 105 | 84.80 25 | 87.77 7 | 86.18 1 | 96.26 2 | 96.06 2 | 90.32 1 | 84.49 50 | 68.08 83 | 97.05 3 | 96.93 1 |
|
UA-Net | | | 81.56 31 | 82.28 35 | 79.40 47 | 88.91 26 | 69.16 67 | 84.67 26 | 80.01 122 | 75.34 13 | 79.80 115 | 94.91 3 | 69.79 70 | 80.25 134 | 72.63 44 | 94.46 37 | 88.78 51 |
|
wuykxyi23d | | | 75.33 89 | 76.75 76 | 71.04 159 | 78.83 134 | 85.01 1 | 71.78 163 | 61.00 257 | 53.47 194 | 96.33 1 | 93.38 4 | 73.07 46 | 68.04 265 | 65.65 111 | 97.28 2 | 60.07 325 |
|
OurMVSNet-221017-0 | | | 78.57 59 | 78.53 62 | 78.67 55 | 80.48 112 | 64.16 97 | 80.24 57 | 82.06 74 | 61.89 102 | 88.77 14 | 93.32 5 | 57.15 195 | 82.60 78 | 70.08 68 | 92.80 60 | 89.25 37 |
|
K. test v3 | | | 73.67 114 | 73.61 115 | 73.87 104 | 79.78 116 | 55.62 148 | 74.69 133 | 62.04 254 | 66.16 58 | 84.76 59 | 93.23 6 | 49.47 227 | 80.97 117 | 65.66 110 | 86.67 158 | 85.02 93 |
|
LTVRE_ROB | | 75.46 1 | 84.22 5 | 84.98 5 | 81.94 19 | 84.82 61 | 75.40 26 | 91.60 1 | 87.80 5 | 73.52 19 | 88.90 13 | 93.06 7 | 71.39 60 | 81.53 92 | 81.53 3 | 92.15 69 | 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 |
DTE-MVSNet | | | 80.35 42 | 82.89 29 | 72.74 138 | 89.84 8 | 37.34 283 | 77.16 90 | 81.81 79 | 80.45 2 | 90.92 5 | 92.95 8 | 74.57 39 | 86.12 25 | 63.65 123 | 94.68 31 | 94.76 6 |
|
PEN-MVS | | | 80.46 40 | 82.91 28 | 73.11 127 | 89.83 9 | 39.02 269 | 77.06 93 | 82.61 69 | 80.04 3 | 90.60 8 | 92.85 9 | 74.93 36 | 85.21 39 | 63.15 125 | 95.15 19 | 95.09 2 |
|
pmmvs6 | | | 71.82 148 | 73.66 113 | 66.31 213 | 75.94 166 | 42.01 248 | 66.99 225 | 72.53 198 | 63.45 91 | 76.43 158 | 92.78 10 | 72.95 49 | 69.69 248 | 51.41 198 | 90.46 102 | 87.22 69 |
|
PS-CasMVS | | | 80.41 41 | 82.86 30 | 73.07 128 | 89.93 7 | 39.21 266 | 77.15 91 | 81.28 91 | 79.74 4 | 90.87 6 | 92.73 11 | 75.03 35 | 84.93 43 | 63.83 122 | 95.19 17 | 95.07 3 |
|
gg-mvs-nofinetune | | | 55.75 274 | 56.75 271 | 52.72 295 | 62.87 302 | 28.04 337 | 68.92 198 | 41.36 344 | 71.09 31 | 50.80 328 | 92.63 12 | 20.74 350 | 66.86 274 | 29.97 324 | 72.41 292 | 63.25 315 |
|
TDRefinement | | | 86.32 2 | 86.33 2 | 86.29 1 | 88.64 29 | 81.19 6 | 88.84 2 | 90.72 1 | 78.27 7 | 87.95 17 | 92.53 13 | 79.37 12 | 84.79 47 | 74.51 35 | 96.15 4 | 92.88 9 |
|
v7n | | | 79.37 51 | 80.41 46 | 76.28 79 | 78.67 136 | 55.81 147 | 79.22 67 | 82.51 71 | 70.72 34 | 87.54 22 | 92.44 14 | 68.00 86 | 81.34 102 | 72.84 43 | 91.72 71 | 91.69 12 |
|
PS-MVSNAJss | | | 77.54 68 | 77.35 70 | 78.13 64 | 84.88 60 | 66.37 82 | 78.55 72 | 79.59 127 | 53.48 193 | 86.29 38 | 92.43 15 | 62.39 124 | 80.25 134 | 67.90 90 | 90.61 99 | 87.77 64 |
|
test_djsdf | | | 78.88 56 | 78.27 63 | 80.70 35 | 81.42 105 | 71.24 48 | 83.98 29 | 75.72 175 | 52.27 202 | 87.37 25 | 92.25 16 | 68.04 85 | 80.56 127 | 72.28 52 | 91.15 85 | 90.32 33 |
|
SixPastTwentyTwo | | | 75.77 82 | 76.34 81 | 74.06 102 | 81.69 103 | 54.84 152 | 76.47 98 | 75.49 177 | 64.10 82 | 87.73 20 | 92.24 17 | 50.45 225 | 81.30 104 | 67.41 93 | 91.46 78 | 86.04 79 |
|
WR-MVS_H | | | 80.22 44 | 82.17 36 | 74.39 97 | 89.46 15 | 42.69 245 | 78.24 78 | 82.24 72 | 78.21 8 | 89.57 11 | 92.10 18 | 68.05 84 | 85.59 32 | 66.04 108 | 95.62 11 | 94.88 5 |
|
PMVS | | 70.70 6 | 81.70 29 | 83.15 27 | 77.36 70 | 90.35 6 | 82.82 3 | 82.15 42 | 79.22 130 | 74.08 17 | 87.16 27 | 91.97 19 | 84.80 2 | 76.97 181 | 64.98 115 | 93.61 52 | 72.28 255 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
ANet_high | | | 67.08 195 | 69.94 166 | 58.51 274 | 57.55 334 | 27.09 338 | 58.43 298 | 76.80 167 | 63.56 87 | 82.40 80 | 91.93 20 | 59.82 150 | 64.98 284 | 50.10 208 | 88.86 125 | 83.46 125 |
|
mvs_tets | | | 78.93 55 | 78.67 60 | 79.72 43 | 84.81 62 | 73.93 35 | 80.65 51 | 76.50 170 | 51.98 207 | 87.40 24 | 91.86 21 | 76.09 25 | 78.53 158 | 68.58 78 | 90.20 105 | 86.69 74 |
|
test_0402 | | | 78.17 65 | 79.48 54 | 74.24 99 | 83.50 79 | 59.15 133 | 72.52 147 | 74.60 185 | 75.34 13 | 88.69 15 | 91.81 22 | 75.06 34 | 82.37 81 | 65.10 114 | 88.68 127 | 81.20 167 |
|
v748 | | | 76.93 72 | 77.95 66 | 73.87 104 | 73.94 200 | 52.44 166 | 75.90 111 | 79.98 123 | 65.34 67 | 86.97 30 | 91.77 23 | 67.40 90 | 78.40 165 | 70.23 65 | 90.01 110 | 90.76 31 |
|
APDe-MVS | | | 82.88 22 | 84.14 12 | 79.08 50 | 84.80 63 | 66.72 78 | 86.54 16 | 85.11 26 | 72.00 28 | 86.65 33 | 91.75 24 | 78.20 17 | 87.04 8 | 77.93 23 | 94.32 41 | 83.47 124 |
|
VDDNet | | | 71.60 150 | 73.13 125 | 67.02 205 | 86.29 44 | 41.11 254 | 69.97 187 | 66.50 235 | 68.72 44 | 74.74 175 | 91.70 25 | 59.90 148 | 75.81 192 | 48.58 219 | 91.72 71 | 84.15 113 |
|
CP-MVSNet | | | 79.48 49 | 81.65 39 | 72.98 131 | 89.66 13 | 39.06 268 | 76.76 95 | 80.46 112 | 78.91 6 | 90.32 9 | 91.70 25 | 68.49 79 | 84.89 44 | 63.40 124 | 95.12 20 | 95.01 4 |
|
HPM-MVS_fast | | | 84.59 4 | 85.10 4 | 83.06 4 | 88.60 30 | 75.83 23 | 86.27 19 | 86.89 11 | 73.69 18 | 86.17 39 | 91.70 25 | 78.23 16 | 85.20 40 | 79.45 12 | 94.91 26 | 88.15 61 |
|
v52 | | | 78.96 53 | 79.79 52 | 76.46 76 | 73.03 226 | 54.90 150 | 78.48 73 | 83.48 56 | 64.43 77 | 91.19 4 | 91.54 28 | 72.08 52 | 81.11 110 | 76.45 29 | 87.47 142 | 93.38 7 |
|
V4 | | | 78.96 53 | 79.79 52 | 76.46 76 | 73.02 227 | 54.90 150 | 78.48 73 | 83.47 57 | 64.43 77 | 91.20 3 | 91.54 28 | 72.08 52 | 81.11 110 | 76.45 29 | 87.46 144 | 93.38 7 |
|
abl_6 | | | 84.92 3 | 85.70 3 | 82.57 14 | 86.72 40 | 79.27 8 | 87.56 5 | 86.08 16 | 77.48 9 | 88.12 16 | 91.53 30 | 81.18 6 | 84.31 55 | 78.12 22 | 94.47 35 | 84.15 113 |
|
jajsoiax | | | 78.51 60 | 78.16 64 | 79.59 45 | 84.65 65 | 73.83 37 | 80.42 54 | 76.12 171 | 51.33 214 | 87.19 26 | 91.51 31 | 73.79 45 | 78.44 162 | 68.27 81 | 90.13 109 | 86.49 75 |
|
SMA-MVS | | | 82.23 27 | 82.82 31 | 80.48 36 | 88.90 27 | 69.66 61 | 85.12 23 | 84.95 28 | 63.53 89 | 84.31 67 | 91.47 32 | 72.87 50 | 87.16 6 | 79.74 9 | 94.47 35 | 84.61 100 |
|
COLMAP_ROB | | 72.78 3 | 83.75 10 | 84.11 13 | 82.68 11 | 82.97 87 | 74.39 32 | 87.18 7 | 88.18 4 | 78.98 5 | 86.11 41 | 91.47 32 | 79.70 11 | 85.76 31 | 66.91 99 | 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 |
TSAR-MVS + MP. | | | 79.05 52 | 78.81 57 | 79.74 42 | 88.94 25 | 67.52 76 | 86.61 15 | 81.38 90 | 51.71 209 | 77.15 142 | 91.42 34 | 65.49 103 | 87.20 5 | 79.44 13 | 87.17 153 | 84.51 105 |
|
MP-MVS-pluss | | | 82.54 24 | 83.46 22 | 79.76 41 | 88.88 28 | 68.44 71 | 81.57 47 | 86.33 14 | 63.17 93 | 85.38 52 | 91.26 35 | 76.33 22 | 84.67 49 | 83.30 1 | 94.96 24 | 86.17 76 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
LPG-MVS_test | | | 83.47 15 | 84.33 10 | 80.90 32 | 87.00 37 | 70.41 56 | 82.04 44 | 86.35 12 | 69.77 40 | 87.75 18 | 91.13 36 | 81.83 3 | 86.20 19 | 77.13 27 | 95.96 7 | 86.08 77 |
|
LGP-MVS_train | | | | | 80.90 32 | 87.00 37 | 70.41 56 | | 86.35 12 | 69.77 40 | 87.75 18 | 91.13 36 | 81.83 3 | 86.20 19 | 77.13 27 | 95.96 7 | 86.08 77 |
|
ACMH+ | | 66.64 10 | 81.20 33 | 82.48 34 | 77.35 71 | 81.16 109 | 62.39 109 | 80.51 52 | 87.80 5 | 73.02 22 | 87.57 21 | 91.08 38 | 80.28 9 | 82.44 79 | 64.82 116 | 96.10 6 | 87.21 70 |
|
ACMMP_Plus | | | 82.33 26 | 83.28 25 | 79.46 46 | 89.28 19 | 69.09 69 | 83.62 32 | 84.98 27 | 64.77 73 | 83.97 68 | 91.02 39 | 75.53 29 | 85.93 29 | 82.00 2 | 94.36 39 | 83.35 130 |
|
v13 | | | 76.23 77 | 77.02 74 | 73.86 106 | 74.61 186 | 48.80 185 | 76.91 94 | 81.10 98 | 62.66 96 | 87.02 29 | 91.01 40 | 59.76 152 | 81.41 97 | 71.29 55 | 88.78 126 | 91.38 14 |
|
MP-MVS | | | 83.19 17 | 83.54 21 | 82.14 18 | 90.54 5 | 79.00 9 | 86.42 18 | 83.59 55 | 71.31 30 | 81.26 95 | 90.96 41 | 74.57 39 | 84.69 48 | 78.41 20 | 94.78 27 | 82.74 140 |
|
anonymousdsp | | | 78.60 58 | 77.80 67 | 81.00 31 | 78.01 142 | 74.34 33 | 80.09 59 | 76.12 171 | 50.51 227 | 89.19 12 | 90.88 42 | 71.45 59 | 77.78 176 | 73.38 41 | 90.60 100 | 90.90 27 |
|
PGM-MVS | | | 83.07 19 | 83.25 26 | 82.54 15 | 89.57 14 | 77.21 20 | 82.04 44 | 85.40 23 | 67.96 47 | 84.91 58 | 90.88 42 | 75.59 27 | 86.57 13 | 78.16 21 | 94.71 30 | 83.82 117 |
|
mPP-MVS | | | 84.01 9 | 84.39 9 | 82.88 5 | 90.65 4 | 81.38 5 | 87.08 9 | 82.79 66 | 72.41 25 | 85.11 55 | 90.85 44 | 76.65 21 | 84.89 44 | 79.30 16 | 94.63 32 | 82.35 149 |
|
zzz-MVS | | | 83.01 21 | 83.63 20 | 81.13 29 | 91.16 2 | 78.16 12 | 82.72 40 | 80.63 107 | 72.08 26 | 84.93 56 | 90.79 45 | 74.65 37 | 84.42 52 | 80.98 4 | 94.75 28 | 80.82 177 |
|
MTAPA | | | 83.19 17 | 83.87 16 | 81.13 29 | 91.16 2 | 78.16 12 | 84.87 24 | 80.63 107 | 72.08 26 | 84.93 56 | 90.79 45 | 74.65 37 | 84.42 52 | 80.98 4 | 94.75 28 | 80.82 177 |
|
MIMVSNet1 | | | 66.57 197 | 69.23 172 | 58.59 273 | 81.26 108 | 37.73 280 | 64.06 259 | 57.62 270 | 57.02 146 | 78.40 130 | 90.75 47 | 62.65 119 | 58.10 304 | 41.77 266 | 89.58 116 | 79.95 194 |
|
v12 | | | 76.03 79 | 76.79 75 | 73.76 108 | 74.45 188 | 48.60 191 | 76.59 96 | 81.11 95 | 62.22 101 | 86.79 31 | 90.74 48 | 59.51 153 | 81.40 99 | 71.01 58 | 88.67 128 | 91.29 16 |
|
region2R | | | 83.54 13 | 83.86 17 | 82.58 13 | 89.82 10 | 77.53 16 | 87.06 10 | 84.23 44 | 70.19 38 | 83.86 69 | 90.72 49 | 75.20 30 | 86.27 18 | 79.41 14 | 94.25 44 | 83.95 116 |
|
ACMMPR | | | 83.62 11 | 83.93 15 | 82.69 10 | 89.78 11 | 77.51 18 | 87.01 11 | 84.19 45 | 70.23 36 | 84.49 63 | 90.67 50 | 75.15 33 | 86.37 15 | 79.58 10 | 94.26 43 | 84.18 112 |
|
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 51 | 78.77 14 | 85.78 30 | 79.37 15 | 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 |
CP-MVS | | | 84.12 7 | 84.55 8 | 82.80 9 | 89.42 18 | 79.74 7 | 88.19 3 | 84.43 38 | 71.96 29 | 84.70 60 | 90.56 52 | 77.12 18 | 86.18 21 | 79.24 17 | 95.36 14 | 82.49 147 |
|
Baseline_NR-MVSNet | | | 70.62 158 | 73.19 123 | 62.92 236 | 76.97 154 | 34.44 304 | 68.84 199 | 70.88 217 | 60.25 118 | 79.50 118 | 90.53 53 | 61.82 130 | 69.11 250 | 54.67 180 | 95.27 15 | 85.22 89 |
|
DeepC-MVS | | 72.44 4 | 81.00 36 | 80.83 44 | 81.50 21 | 86.70 41 | 70.03 60 | 82.06 43 | 87.00 10 | 59.89 121 | 80.91 104 | 90.53 53 | 72.19 51 | 88.56 1 | 73.67 40 | 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 |
APD-MVS_3200maxsize | | | 83.57 12 | 84.33 10 | 81.31 26 | 82.83 89 | 73.53 40 | 85.50 21 | 87.45 8 | 74.11 16 | 86.45 35 | 90.52 55 | 80.02 10 | 84.48 51 | 77.73 24 | 94.34 40 | 85.93 81 |
|
HFP-MVS | | | 83.39 16 | 84.03 14 | 81.48 22 | 89.25 20 | 75.69 24 | 87.01 11 | 84.27 41 | 70.23 36 | 84.47 64 | 90.43 56 | 76.79 19 | 85.94 27 | 79.58 10 | 94.23 45 | 82.82 137 |
|
#test# | | | 82.40 25 | 82.71 32 | 81.48 22 | 89.25 20 | 75.69 24 | 84.47 27 | 84.27 41 | 64.45 76 | 84.47 64 | 90.43 56 | 76.79 19 | 85.94 27 | 76.01 31 | 94.23 45 | 82.82 137 |
|
V9 | | | 75.82 81 | 76.53 78 | 73.66 109 | 74.28 192 | 48.37 192 | 76.26 104 | 81.10 98 | 61.73 104 | 86.59 34 | 90.43 56 | 59.16 159 | 81.42 96 | 70.71 61 | 88.56 129 | 91.21 19 |
|
HPM-MVS | | | 84.12 7 | 84.63 7 | 82.60 12 | 88.21 33 | 74.40 31 | 85.24 22 | 87.21 9 | 70.69 35 | 85.14 53 | 90.42 59 | 78.99 13 | 86.62 12 | 80.83 6 | 94.93 25 | 86.79 72 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
SteuartSystems-ACMMP | | | 83.07 19 | 83.64 19 | 81.35 25 | 85.14 57 | 71.00 50 | 85.53 20 | 84.78 32 | 70.91 33 | 85.64 46 | 90.41 60 | 75.55 28 | 87.69 3 | 79.75 7 | 95.08 21 | 85.36 88 |
Skip Steuart: Steuart Systems R&D Blog. |
XVS | | | 83.51 14 | 83.73 18 | 82.85 7 | 89.43 16 | 77.61 14 | 86.80 13 | 84.66 34 | 72.71 23 | 82.87 76 | 90.39 61 | 73.86 43 | 86.31 16 | 78.84 18 | 94.03 47 | 84.64 97 |
|
FC-MVSNet-test | | | 73.32 122 | 74.78 99 | 68.93 187 | 79.21 126 | 36.57 285 | 71.82 162 | 79.54 128 | 57.63 139 | 82.57 79 | 90.38 62 | 59.38 156 | 78.99 148 | 57.91 152 | 94.56 33 | 91.23 17 |
|
v11 | | | 75.76 83 | 76.51 79 | 73.48 116 | 74.28 192 | 47.81 204 | 76.16 106 | 81.28 91 | 61.56 105 | 86.39 36 | 90.38 62 | 59.32 157 | 81.41 97 | 70.85 59 | 88.41 131 | 91.23 17 |
|
GBi-Net | | | 68.30 185 | 68.79 180 | 66.81 207 | 73.14 218 | 40.68 256 | 71.96 156 | 73.03 191 | 54.81 170 | 74.72 176 | 90.36 64 | 48.63 232 | 75.20 200 | 47.12 229 | 85.37 176 | 84.54 102 |
|
test1 | | | 68.30 185 | 68.79 180 | 66.81 207 | 73.14 218 | 40.68 256 | 71.96 156 | 73.03 191 | 54.81 170 | 74.72 176 | 90.36 64 | 48.63 232 | 75.20 200 | 47.12 229 | 85.37 176 | 84.54 102 |
|
FMVSNet1 | | | 71.06 153 | 72.48 140 | 66.81 207 | 77.65 148 | 40.68 256 | 71.96 156 | 73.03 191 | 61.14 109 | 79.45 119 | 90.36 64 | 60.44 144 | 75.20 200 | 50.20 207 | 88.05 135 | 84.54 102 |
|
ACMH | | 63.62 14 | 77.50 69 | 80.11 48 | 69.68 176 | 79.61 118 | 56.28 145 | 78.81 69 | 83.62 54 | 63.41 92 | 87.14 28 | 90.23 67 | 76.11 24 | 73.32 211 | 67.58 91 | 94.44 38 | 79.44 197 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
3Dnovator+ | | 73.19 2 | 81.08 35 | 80.48 45 | 82.87 6 | 81.41 106 | 72.03 42 | 84.38 28 | 86.23 15 | 77.28 11 | 80.65 106 | 90.18 68 | 59.80 151 | 87.58 4 | 73.06 42 | 91.34 81 | 89.01 42 |
|
V14 | | | 75.58 86 | 76.26 84 | 73.55 114 | 74.10 199 | 48.13 197 | 75.91 110 | 81.07 101 | 61.19 108 | 86.34 37 | 90.11 69 | 58.80 163 | 81.40 99 | 70.40 63 | 88.43 130 | 91.12 20 |
|
ACMP | | 69.50 8 | 82.64 23 | 83.38 23 | 80.40 37 | 86.50 42 | 69.44 63 | 82.30 41 | 86.08 16 | 66.80 52 | 86.70 32 | 89.99 70 | 81.64 5 | 85.95 26 | 74.35 36 | 96.11 5 | 85.81 83 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
LS3D | | | 80.99 37 | 80.85 43 | 81.41 24 | 78.37 137 | 71.37 46 | 87.45 6 | 85.87 19 | 77.48 9 | 81.98 83 | 89.95 71 | 69.14 73 | 85.26 37 | 66.15 106 | 91.24 83 | 87.61 66 |
|
TransMVSNet (Re) | | | 69.62 166 | 71.63 152 | 63.57 230 | 76.51 158 | 35.93 292 | 65.75 242 | 71.29 211 | 61.05 110 | 75.02 170 | 89.90 72 | 65.88 101 | 70.41 246 | 49.79 209 | 89.48 117 | 84.38 109 |
|
RPSCF | | | 75.76 83 | 74.37 104 | 79.93 40 | 74.81 177 | 77.53 16 | 77.53 85 | 79.30 129 | 59.44 123 | 78.88 124 | 89.80 73 | 71.26 61 | 73.09 213 | 57.45 154 | 80.89 236 | 89.17 40 |
|
v15 | | | 75.37 88 | 76.01 86 | 73.44 117 | 73.91 203 | 47.87 203 | 75.55 118 | 81.04 102 | 60.76 113 | 86.11 41 | 89.76 74 | 58.53 169 | 81.40 99 | 70.11 66 | 88.32 132 | 91.04 23 |
|
XVG-ACMP-BASELINE | | | 80.54 39 | 81.06 42 | 78.98 52 | 87.01 36 | 72.91 41 | 80.23 58 | 85.56 20 | 66.56 55 | 85.64 46 | 89.57 75 | 69.12 74 | 80.55 129 | 72.51 48 | 93.37 54 | 83.48 123 |
|
FIs | | | 72.56 142 | 73.80 110 | 68.84 191 | 78.74 135 | 37.74 279 | 71.02 177 | 79.83 124 | 56.12 153 | 80.88 105 | 89.45 76 | 58.18 173 | 78.28 169 | 56.63 160 | 93.36 55 | 90.51 32 |
|
pm-mvs1 | | | 68.40 184 | 69.85 167 | 64.04 226 | 73.10 222 | 39.94 263 | 64.61 254 | 70.50 219 | 55.52 160 | 73.97 185 | 89.33 77 | 63.91 115 | 68.38 262 | 49.68 211 | 88.02 136 | 83.81 118 |
|
OPM-MVS | | | 80.99 37 | 81.63 40 | 79.07 51 | 86.86 39 | 69.39 64 | 79.41 66 | 84.00 50 | 65.64 60 | 85.54 50 | 89.28 78 | 76.32 23 | 83.47 65 | 74.03 38 | 93.57 53 | 84.35 110 |
|
v8 | | | 75.07 95 | 75.64 90 | 73.35 119 | 73.42 209 | 47.46 213 | 75.20 123 | 81.45 86 | 60.05 119 | 85.64 46 | 89.26 79 | 58.08 178 | 81.80 89 | 69.71 72 | 87.97 138 | 90.79 29 |
|
TranMVSNet+NR-MVSNet | | | 76.13 78 | 77.66 68 | 71.56 156 | 84.61 67 | 42.57 246 | 70.98 178 | 78.29 150 | 68.67 45 | 83.04 75 | 89.26 79 | 72.99 48 | 80.75 126 | 55.58 174 | 95.47 12 | 91.35 15 |
|
test_part3 | | | | | | | | 83.39 34 | | 73.27 20 | | 89.25 81 | | 86.96 10 | 72.56 46 | | |
|
ESAPD | | | 81.57 30 | 82.55 33 | 78.63 56 | 85.90 46 | 66.44 80 | 83.39 34 | 84.94 30 | 73.27 20 | 84.61 61 | 89.25 81 | 75.17 31 | 86.96 10 | 72.56 46 | 93.83 49 | 82.50 145 |
|
nrg030 | | | 74.87 102 | 75.99 87 | 71.52 157 | 74.90 175 | 49.88 180 | 74.10 137 | 82.58 70 | 54.55 179 | 83.50 73 | 89.21 83 | 71.51 57 | 75.74 194 | 61.24 133 | 92.34 67 | 88.94 46 |
|
v10 | | | 75.69 85 | 76.20 85 | 74.16 100 | 74.44 190 | 48.69 187 | 75.84 114 | 82.93 65 | 59.02 127 | 85.92 43 | 89.17 84 | 58.56 168 | 82.74 76 | 70.73 60 | 89.14 122 | 91.05 21 |
|
ACMM | | 69.25 9 | 82.11 28 | 83.31 24 | 78.49 58 | 88.17 34 | 73.96 34 | 83.11 37 | 84.52 37 | 66.40 56 | 87.45 23 | 89.16 85 | 81.02 7 | 80.52 130 | 74.27 37 | 95.73 9 | 80.98 174 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
testing_2 | | | 72.01 147 | 72.36 142 | 70.95 160 | 70.79 244 | 48.70 186 | 72.81 143 | 78.09 155 | 48.79 237 | 84.46 66 | 89.15 86 | 57.90 187 | 78.55 157 | 61.55 131 | 87.74 139 | 85.61 87 |
|
HQP_MVS | | | 78.77 57 | 78.78 59 | 78.72 54 | 85.18 55 | 65.18 90 | 82.74 38 | 85.49 21 | 65.45 62 | 78.23 133 | 89.11 87 | 60.83 142 | 86.15 22 | 71.09 56 | 90.94 90 | 84.82 94 |
|
plane_prior4 | | | | | | | | | | | | 89.11 87 | | | | | |
|
v17 | | | 75.03 96 | 75.59 91 | 73.36 118 | 73.56 205 | 47.66 208 | 75.48 119 | 81.45 86 | 60.58 115 | 85.55 49 | 89.02 89 | 58.36 171 | 81.47 93 | 69.69 73 | 86.59 159 | 90.96 24 |
|
lessismore_v0 | | | | | 72.75 137 | 79.60 119 | 56.83 144 | | 57.37 273 | | 83.80 70 | 89.01 90 | 47.45 237 | 78.74 154 | 64.39 119 | 86.49 160 | 82.69 141 |
|
XVG-OURS | | | 79.51 48 | 79.82 50 | 78.58 57 | 86.11 45 | 74.96 29 | 76.33 103 | 84.95 28 | 66.89 49 | 82.75 78 | 88.99 91 | 66.82 94 | 78.37 167 | 74.80 32 | 90.76 98 | 82.40 148 |
|
v16 | | | 74.89 101 | 75.41 95 | 73.35 119 | 73.54 206 | 47.62 209 | 75.47 120 | 81.45 86 | 60.58 115 | 85.46 51 | 88.97 92 | 58.27 172 | 81.47 93 | 69.66 74 | 85.25 181 | 90.95 25 |
|
APD-MVS | | | 81.13 34 | 81.73 38 | 79.36 48 | 84.47 69 | 70.53 55 | 83.85 31 | 83.70 52 | 69.43 42 | 83.67 71 | 88.96 93 | 75.89 26 | 86.41 14 | 72.62 45 | 92.95 59 | 81.14 170 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
Gipuma | | | 69.55 168 | 72.83 134 | 59.70 267 | 63.63 301 | 53.97 158 | 80.08 60 | 75.93 173 | 64.24 81 | 73.49 189 | 88.93 94 | 57.89 188 | 62.46 292 | 59.75 144 | 91.55 77 | 62.67 319 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
XVG-OURS-SEG-HR | | | 79.62 47 | 79.99 49 | 78.49 58 | 86.46 43 | 74.79 30 | 77.15 91 | 85.39 24 | 66.73 53 | 80.39 110 | 88.85 95 | 74.43 41 | 78.33 168 | 74.73 34 | 85.79 165 | 82.35 149 |
|
VDD-MVS | | | 70.81 156 | 71.44 156 | 68.91 189 | 79.07 132 | 46.51 228 | 67.82 215 | 70.83 218 | 61.23 107 | 74.07 184 | 88.69 96 | 59.86 149 | 75.62 195 | 51.11 200 | 90.28 104 | 84.61 100 |
|
CPTT-MVS | | | 81.51 32 | 81.76 37 | 80.76 34 | 89.20 23 | 78.75 10 | 86.48 17 | 82.03 75 | 68.80 43 | 80.92 103 | 88.52 97 | 72.00 55 | 82.39 80 | 74.80 32 | 93.04 58 | 81.14 170 |
|
v18 | | | 74.60 105 | 75.06 96 | 73.22 124 | 73.29 215 | 47.36 217 | 75.02 124 | 81.47 85 | 60.01 120 | 85.13 54 | 88.44 98 | 57.93 186 | 81.47 93 | 69.26 75 | 85.02 185 | 90.84 28 |
|
Vis-MVSNet | | | 74.85 103 | 74.56 100 | 75.72 86 | 81.63 104 | 64.64 94 | 76.35 101 | 79.06 135 | 62.85 95 | 73.33 191 | 88.41 99 | 62.54 122 | 79.59 144 | 63.94 121 | 82.92 205 | 82.94 134 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
HPM-MVS++ | | | 79.89 45 | 79.80 51 | 80.18 39 | 89.02 24 | 78.44 11 | 83.49 33 | 80.18 119 | 64.71 75 | 78.11 135 | 88.39 100 | 65.46 104 | 83.14 70 | 77.64 26 | 91.20 84 | 78.94 202 |
|
HSP-MVS | | | 79.69 46 | 79.17 56 | 81.27 28 | 89.70 12 | 77.46 19 | 87.16 8 | 80.58 110 | 64.94 72 | 81.05 100 | 88.38 101 | 57.10 197 | 87.10 7 | 79.75 7 | 83.87 197 | 79.24 199 |
|
VPA-MVSNet | | | 68.71 182 | 70.37 164 | 63.72 229 | 76.13 163 | 38.06 277 | 64.10 258 | 71.48 208 | 56.60 152 | 74.10 183 | 88.31 102 | 64.78 111 | 69.72 247 | 47.69 227 | 90.15 107 | 83.37 129 |
|
ambc | | | | | 70.10 172 | 77.74 146 | 50.21 175 | 74.28 136 | 77.93 157 | | 79.26 121 | 88.29 103 | 54.11 210 | 79.77 141 | 64.43 118 | 91.10 87 | 80.30 187 |
|
AllTest | | | 77.66 67 | 77.43 69 | 78.35 60 | 79.19 127 | 70.81 51 | 78.60 71 | 88.64 2 | 65.37 65 | 80.09 113 | 88.17 104 | 70.33 65 | 78.43 163 | 55.60 171 | 90.90 94 | 85.81 83 |
|
TestCases | | | | | 78.35 60 | 79.19 127 | 70.81 51 | | 88.64 2 | 65.37 65 | 80.09 113 | 88.17 104 | 70.33 65 | 78.43 163 | 55.60 171 | 90.90 94 | 85.81 83 |
|
LCM-MVSNet-Re | | | 69.10 174 | 71.57 154 | 61.70 249 | 70.37 250 | 34.30 305 | 61.45 281 | 79.62 125 | 56.81 148 | 89.59 10 | 88.16 106 | 68.44 80 | 72.94 214 | 42.30 260 | 87.33 148 | 77.85 216 |
|
MG-MVS | | | 70.47 160 | 71.34 157 | 67.85 198 | 79.26 124 | 40.42 261 | 74.67 134 | 75.15 183 | 58.41 130 | 68.74 238 | 88.14 107 | 56.08 204 | 83.69 60 | 59.90 141 | 81.71 221 | 79.43 198 |
|
IS-MVSNet | | | 75.10 94 | 75.42 94 | 74.15 101 | 79.23 125 | 48.05 200 | 79.43 64 | 78.04 156 | 70.09 39 | 79.17 122 | 88.02 108 | 53.04 212 | 83.60 62 | 58.05 151 | 93.76 51 | 90.79 29 |
|
tfpnnormal | | | 66.48 198 | 67.93 190 | 62.16 247 | 73.40 213 | 36.65 284 | 63.45 263 | 64.99 242 | 55.97 154 | 72.82 196 | 87.80 109 | 57.06 198 | 69.10 251 | 48.31 222 | 87.54 141 | 80.72 181 |
|
CDPH-MVS | | | 77.33 70 | 77.06 73 | 78.14 63 | 84.21 74 | 63.98 99 | 76.07 108 | 83.45 58 | 54.20 182 | 77.68 139 | 87.18 110 | 69.98 68 | 85.37 34 | 68.01 85 | 92.72 63 | 85.08 92 |
|
DU-MVS | | | 74.91 99 | 75.57 92 | 72.93 133 | 83.50 79 | 45.79 230 | 69.47 194 | 80.14 120 | 65.22 68 | 81.74 87 | 87.08 111 | 61.82 130 | 81.07 114 | 56.21 167 | 94.98 22 | 91.93 10 |
|
NR-MVSNet | | | 73.62 115 | 74.05 108 | 72.33 149 | 83.50 79 | 43.71 238 | 65.65 243 | 77.32 164 | 64.32 80 | 75.59 164 | 87.08 111 | 62.45 123 | 81.34 102 | 54.90 177 | 95.63 10 | 91.93 10 |
|
SD-MVS | | | 80.28 43 | 81.55 41 | 76.47 75 | 83.57 78 | 67.83 75 | 83.39 34 | 85.35 25 | 64.42 79 | 86.14 40 | 87.07 113 | 74.02 42 | 80.97 117 | 77.70 25 | 92.32 68 | 80.62 182 |
|
旧先验1 | | | | | | 84.55 68 | 60.36 122 | | 63.69 245 | | | 87.05 114 | 54.65 208 | | | 83.34 202 | 69.66 281 |
|
PatchT | | | 53.35 286 | 56.47 272 | 43.99 324 | 64.19 298 | 17.46 351 | 59.15 293 | 43.10 334 | 52.11 205 | 54.74 317 | 86.95 115 | 29.97 325 | 49.98 316 | 43.62 245 | 74.40 284 | 64.53 314 |
|
wuyk23d | | | 61.97 231 | 66.25 199 | 49.12 306 | 58.19 332 | 60.77 120 | 66.32 232 | 52.97 300 | 55.93 155 | 90.62 7 | 86.91 116 | 73.07 46 | 35.98 348 | 20.63 347 | 91.63 73 | 50.62 339 |
|
UniMVSNet_NR-MVSNet | | | 74.90 100 | 75.65 89 | 72.64 140 | 83.04 85 | 45.79 230 | 69.26 196 | 78.81 139 | 66.66 54 | 81.74 87 | 86.88 117 | 63.26 116 | 81.07 114 | 56.21 167 | 94.98 22 | 91.05 21 |
|
agg_prior1 | | | 75.89 80 | 76.41 80 | 74.31 98 | 84.44 71 | 66.02 84 | 76.12 107 | 78.62 144 | 54.40 180 | 76.95 145 | 86.85 118 | 66.44 98 | 80.34 132 | 72.45 51 | 91.42 79 | 76.57 224 |
|
EPP-MVSNet | | | 73.86 112 | 73.38 119 | 75.31 90 | 78.19 139 | 53.35 163 | 80.45 53 | 77.32 164 | 65.11 70 | 76.47 157 | 86.80 119 | 49.47 227 | 83.77 59 | 53.89 186 | 92.72 63 | 88.81 50 |
|
TinyColmap | | | 67.98 187 | 69.28 170 | 64.08 225 | 67.98 275 | 46.82 225 | 70.04 186 | 75.26 181 | 53.05 197 | 77.36 141 | 86.79 120 | 59.39 155 | 72.59 225 | 45.64 239 | 88.01 137 | 72.83 248 |
|
test_prior3 | | | 76.71 74 | 77.19 71 | 75.27 91 | 82.15 97 | 59.85 126 | 75.57 116 | 84.33 39 | 58.92 128 | 76.53 155 | 86.78 121 | 67.83 87 | 83.39 67 | 69.81 70 | 92.76 61 | 82.58 142 |
|
test_prior2 | | | | | | | | 75.57 116 | | 58.92 128 | 76.53 155 | 86.78 121 | 67.83 87 | | 69.81 70 | 92.76 61 | |
|
RPMNet | | | 61.25 238 | 61.55 232 | 60.36 264 | 66.37 285 | 48.24 195 | 70.93 179 | 54.45 292 | 54.66 176 | 61.35 280 | 86.77 123 | 33.29 293 | 63.22 289 | 55.93 169 | 70.17 304 | 69.62 282 |
|
TEST9 | | | | | | 85.47 53 | 69.32 65 | 76.42 99 | 78.69 141 | 53.73 191 | 76.97 143 | 86.74 124 | 66.84 93 | 81.10 112 | | | |
|
train_agg | | | 76.38 75 | 76.55 77 | 75.86 85 | 85.47 53 | 69.32 65 | 76.42 99 | 78.69 141 | 54.00 186 | 76.97 143 | 86.74 124 | 66.60 95 | 81.10 112 | 72.50 49 | 91.56 75 | 77.15 219 |
|
test_8 | | | | | | 85.09 58 | 67.89 74 | 76.26 104 | 78.66 143 | 54.00 186 | 76.89 148 | 86.72 126 | 66.60 95 | 80.89 123 | | | |
|
MVS_Test | | | 69.84 165 | 70.71 162 | 67.24 203 | 67.49 279 | 43.25 241 | 69.87 189 | 81.22 94 | 52.69 201 | 71.57 212 | 86.68 127 | 62.09 128 | 74.51 206 | 66.05 107 | 78.74 257 | 83.96 115 |
|
CR-MVSNet | | | 58.96 255 | 58.49 260 | 60.36 264 | 66.37 285 | 48.24 195 | 70.93 179 | 56.40 282 | 32.87 329 | 61.35 280 | 86.66 128 | 33.19 294 | 63.22 289 | 48.50 220 | 70.17 304 | 69.62 282 |
|
Patchmtry | | | 60.91 240 | 63.01 218 | 54.62 291 | 66.10 289 | 26.27 342 | 67.47 218 | 56.40 282 | 54.05 185 | 72.04 207 | 86.66 128 | 33.19 294 | 60.17 299 | 43.69 244 | 87.45 145 | 77.42 217 |
|
OMC-MVS | | | 79.41 50 | 78.79 58 | 81.28 27 | 80.62 111 | 70.71 54 | 80.91 50 | 84.76 33 | 62.54 98 | 81.77 85 | 86.65 130 | 71.46 58 | 83.53 64 | 67.95 89 | 92.44 65 | 89.60 35 |
|
VPNet | | | 65.58 200 | 67.56 192 | 59.65 268 | 79.72 117 | 30.17 331 | 60.27 289 | 62.14 250 | 54.19 183 | 71.24 216 | 86.63 131 | 58.80 163 | 67.62 268 | 44.17 243 | 90.87 97 | 81.18 168 |
|
IterMVS-LS | | | 73.01 126 | 73.12 126 | 72.66 139 | 73.79 204 | 49.90 177 | 71.63 165 | 78.44 147 | 58.22 131 | 80.51 107 | 86.63 131 | 58.15 175 | 79.62 142 | 62.51 127 | 88.20 133 | 88.48 59 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
testdata | | | | | 64.13 224 | 85.87 49 | 63.34 104 | | 61.80 255 | 47.83 247 | 76.42 159 | 86.60 133 | 48.83 231 | 62.31 294 | 54.46 183 | 81.26 231 | 66.74 302 |
|
LFMVS | | | 67.06 196 | 67.89 191 | 64.56 221 | 78.02 141 | 38.25 275 | 70.81 181 | 59.60 262 | 65.18 69 | 71.06 219 | 86.56 134 | 43.85 250 | 75.22 199 | 46.35 236 | 89.63 114 | 80.21 188 |
|
CNVR-MVS | | | 78.49 61 | 78.59 61 | 78.16 62 | 85.86 50 | 67.40 77 | 78.12 81 | 81.50 83 | 63.92 83 | 77.51 140 | 86.56 134 | 68.43 81 | 84.82 46 | 73.83 39 | 91.61 74 | 82.26 152 |
|
agg_prior3 | | | 76.32 76 | 76.33 82 | 76.28 79 | 85.86 50 | 70.13 59 | 76.50 97 | 78.26 151 | 53.41 195 | 75.78 161 | 86.49 136 | 66.58 97 | 81.57 91 | 72.50 49 | 91.56 75 | 77.15 219 |
|
FMVSNet2 | | | 67.48 193 | 68.21 188 | 65.29 218 | 73.14 218 | 38.94 270 | 68.81 201 | 71.21 215 | 54.81 170 | 76.73 151 | 86.48 137 | 48.63 232 | 74.60 205 | 47.98 224 | 86.11 163 | 82.35 149 |
|
WR-MVS | | | 71.20 152 | 72.48 140 | 67.36 202 | 84.98 59 | 35.70 294 | 64.43 256 | 68.66 227 | 65.05 71 | 81.49 93 | 86.43 138 | 57.57 190 | 76.48 187 | 50.36 206 | 93.32 56 | 89.90 34 |
|
UniMVSNet (Re) | | | 75.00 97 | 75.48 93 | 73.56 113 | 83.14 83 | 47.92 202 | 70.41 184 | 81.04 102 | 63.67 86 | 79.54 117 | 86.37 139 | 62.83 118 | 81.82 88 | 57.10 158 | 95.25 16 | 90.94 26 |
|
DP-MVS | | | 78.44 63 | 79.29 55 | 75.90 84 | 81.86 101 | 65.33 88 | 79.05 68 | 84.63 36 | 74.83 15 | 80.41 109 | 86.27 140 | 71.68 56 | 83.45 66 | 62.45 129 | 92.40 66 | 78.92 203 |
|
ab-mvs | | | 64.11 214 | 65.13 205 | 61.05 256 | 71.99 240 | 38.03 278 | 67.59 216 | 68.79 226 | 49.08 235 | 65.32 262 | 86.26 141 | 58.02 181 | 66.85 275 | 39.33 275 | 79.79 250 | 78.27 209 |
|
NCCC | | | 78.25 64 | 78.04 65 | 78.89 53 | 85.61 52 | 69.45 62 | 79.80 63 | 80.99 104 | 65.77 59 | 75.55 165 | 86.25 142 | 67.42 89 | 85.42 33 | 70.10 67 | 90.88 96 | 81.81 161 |
|
1121 | | | 69.23 171 | 68.26 187 | 72.12 153 | 88.36 32 | 71.40 45 | 68.59 205 | 62.06 252 | 43.80 273 | 74.75 174 | 86.18 143 | 52.92 213 | 76.85 184 | 54.47 181 | 83.27 203 | 68.12 291 |
|
ITE_SJBPF | | | | | 80.35 38 | 76.94 155 | 73.60 38 | | 80.48 111 | 66.87 50 | 83.64 72 | 86.18 143 | 70.25 67 | 79.90 140 | 61.12 135 | 88.95 124 | 87.56 67 |
|
原ACMM1 | | | | | 73.90 103 | 85.90 46 | 65.15 92 | | 81.67 81 | 50.97 224 | 74.25 181 | 86.16 145 | 61.60 132 | 83.54 63 | 56.75 159 | 91.08 88 | 73.00 246 |
|
UGNet | | | 70.20 161 | 69.05 173 | 73.65 110 | 76.24 161 | 63.64 101 | 75.87 112 | 72.53 198 | 61.48 106 | 60.93 287 | 86.14 146 | 52.37 216 | 77.12 180 | 50.67 203 | 85.21 182 | 80.17 193 |
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 |
新几何1 | | | | | 69.99 174 | 88.37 31 | 71.34 47 | | 62.08 251 | 43.85 272 | 74.99 171 | 86.11 147 | 52.85 214 | 70.57 243 | 50.99 201 | 83.23 204 | 68.05 292 |
|
mvs_anonymous | | | 65.08 203 | 65.49 202 | 63.83 228 | 63.79 299 | 37.60 281 | 66.52 231 | 69.82 223 | 43.44 278 | 73.46 190 | 86.08 148 | 58.79 166 | 71.75 237 | 51.90 195 | 75.63 276 | 82.15 153 |
|
114514_t | | | 73.40 120 | 73.33 122 | 73.64 111 | 84.15 76 | 57.11 142 | 78.20 79 | 80.02 121 | 43.76 274 | 72.55 201 | 86.07 149 | 64.00 114 | 83.35 69 | 60.14 139 | 91.03 89 | 80.45 185 |
|
NP-MVS | | | | | | 83.34 82 | 63.07 107 | | | | | 85.97 150 | | | | | |
|
HQP-MVS | | | 75.24 92 | 75.01 97 | 75.94 83 | 82.37 92 | 58.80 135 | 77.32 87 | 84.12 46 | 59.08 124 | 71.58 209 | 85.96 151 | 58.09 176 | 85.30 36 | 67.38 95 | 89.16 120 | 83.73 120 |
|
JIA-IIPM | | | 54.03 282 | 51.62 295 | 61.25 255 | 59.14 325 | 55.21 149 | 59.10 294 | 47.72 322 | 50.85 225 | 50.31 332 | 85.81 152 | 20.10 352 | 63.97 286 | 36.16 299 | 55.41 346 | 64.55 313 |
|
test222 | | | | | | 87.30 35 | 69.15 68 | 67.85 214 | 59.59 263 | 41.06 288 | 73.05 194 | 85.72 153 | 48.03 235 | | | 80.65 239 | 66.92 298 |
|
DeepC-MVS_fast | | 69.89 7 | 77.17 71 | 76.33 82 | 79.70 44 | 83.90 77 | 67.94 73 | 80.06 61 | 83.75 51 | 56.73 150 | 74.88 173 | 85.32 154 | 65.54 102 | 87.79 2 | 65.61 112 | 91.14 86 | 83.35 130 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
DeepPCF-MVS | | 71.07 5 | 78.48 62 | 77.14 72 | 82.52 16 | 84.39 73 | 77.04 21 | 76.35 101 | 84.05 48 | 56.66 151 | 80.27 111 | 85.31 155 | 68.56 78 | 87.03 9 | 67.39 94 | 91.26 82 | 83.50 122 |
|
v2v482 | | | 72.55 143 | 72.58 139 | 72.43 146 | 72.92 232 | 46.72 227 | 71.41 171 | 79.13 131 | 55.27 161 | 81.17 99 | 85.25 156 | 55.41 205 | 81.13 109 | 67.25 98 | 85.46 175 | 89.43 36 |
|
QAPM | | | 69.18 173 | 69.26 171 | 68.94 186 | 71.61 242 | 52.58 165 | 80.37 56 | 78.79 140 | 49.63 232 | 73.51 188 | 85.14 157 | 53.66 211 | 79.12 146 | 55.11 176 | 75.54 277 | 75.11 234 |
|
v1neww | | | 72.93 131 | 73.07 128 | 72.48 143 | 73.41 211 | 47.46 213 | 72.17 151 | 80.26 116 | 55.63 157 | 81.63 91 | 85.07 158 | 57.97 182 | 81.28 105 | 66.55 104 | 84.98 187 | 88.70 52 |
|
v7new | | | 72.93 131 | 73.07 128 | 72.48 143 | 73.41 211 | 47.46 213 | 72.17 151 | 80.26 116 | 55.63 157 | 81.63 91 | 85.07 158 | 57.97 182 | 81.28 105 | 66.55 104 | 84.98 187 | 88.70 52 |
|
v7 | | | 73.59 116 | 73.69 111 | 73.28 123 | 74.42 191 | 48.68 188 | 72.74 146 | 81.98 76 | 54.76 175 | 82.07 82 | 85.05 160 | 58.53 169 | 82.22 85 | 67.99 86 | 85.66 169 | 88.95 45 |
|
v6 | | | 72.93 131 | 73.08 127 | 72.48 143 | 73.42 209 | 47.47 212 | 72.17 151 | 80.25 118 | 55.63 157 | 81.65 90 | 85.04 161 | 57.95 185 | 81.28 105 | 66.56 103 | 85.01 186 | 88.70 52 |
|
divwei89l23v2f112 | | | 72.60 139 | 72.73 135 | 72.19 150 | 73.10 222 | 47.00 223 | 71.48 166 | 79.11 132 | 55.01 166 | 81.23 97 | 84.95 162 | 57.45 192 | 80.89 123 | 66.58 101 | 85.67 167 | 88.68 56 |
|
v1141 | | | 72.59 141 | 72.73 135 | 72.19 150 | 73.10 222 | 47.00 223 | 71.48 166 | 79.11 132 | 55.01 166 | 81.23 97 | 84.94 163 | 57.45 192 | 80.89 123 | 66.58 101 | 85.65 170 | 88.68 56 |
|
v1 | | | 72.60 139 | 72.73 135 | 72.19 150 | 73.12 221 | 47.01 222 | 71.48 166 | 79.10 134 | 55.01 166 | 81.24 96 | 84.92 164 | 57.46 191 | 80.90 122 | 66.59 100 | 85.67 167 | 88.68 56 |
|
v1144 | | | 73.29 123 | 73.39 118 | 73.01 129 | 74.12 198 | 48.11 198 | 72.01 155 | 81.08 100 | 53.83 190 | 81.77 85 | 84.68 165 | 58.07 179 | 81.91 87 | 68.10 82 | 86.86 156 | 88.99 44 |
|
3Dnovator | | 65.95 11 | 71.50 151 | 71.22 158 | 72.34 148 | 73.16 217 | 63.09 106 | 78.37 76 | 78.32 148 | 57.67 137 | 72.22 206 | 84.61 166 | 54.77 206 | 78.47 160 | 60.82 136 | 81.07 233 | 75.45 230 |
|
v1192 | | | 73.40 120 | 73.42 117 | 73.32 122 | 74.65 185 | 48.67 189 | 72.21 150 | 81.73 80 | 52.76 200 | 81.85 84 | 84.56 167 | 57.12 196 | 82.24 84 | 68.58 78 | 87.33 148 | 89.06 41 |
|
USDC | | | 62.80 228 | 63.10 217 | 61.89 248 | 65.19 293 | 43.30 240 | 67.42 219 | 74.20 187 | 35.80 312 | 72.25 205 | 84.48 168 | 45.67 240 | 71.95 234 | 37.95 288 | 84.97 189 | 70.42 271 |
|
PCF-MVS | | 63.80 13 | 72.70 137 | 71.69 150 | 75.72 86 | 78.10 140 | 60.01 125 | 73.04 140 | 81.50 83 | 45.34 264 | 79.66 116 | 84.35 169 | 65.15 106 | 82.65 77 | 48.70 217 | 89.38 119 | 84.50 106 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
v1240 | | | 73.06 125 | 73.14 124 | 72.84 135 | 74.74 179 | 47.27 219 | 71.88 161 | 81.11 95 | 51.80 208 | 82.28 81 | 84.21 170 | 56.22 203 | 82.34 82 | 68.82 76 | 87.17 153 | 88.91 47 |
|
v148 | | | 69.38 170 | 69.39 169 | 69.36 178 | 69.14 260 | 44.56 234 | 68.83 200 | 72.70 196 | 54.79 173 | 78.59 125 | 84.12 171 | 54.69 207 | 76.74 186 | 59.40 145 | 82.20 210 | 86.79 72 |
|
v144192 | | | 72.99 128 | 73.06 130 | 72.77 136 | 74.58 187 | 47.48 211 | 71.90 160 | 80.44 113 | 51.57 211 | 81.46 94 | 84.11 172 | 58.04 180 | 82.12 86 | 67.98 87 | 87.47 142 | 88.70 52 |
|
F-COLMAP | | | 75.29 91 | 73.99 109 | 79.18 49 | 81.73 102 | 71.90 43 | 81.86 46 | 82.98 63 | 59.86 122 | 72.27 204 | 84.00 173 | 64.56 112 | 83.07 72 | 51.48 197 | 87.19 152 | 82.56 144 |
|
v1921920 | | | 72.96 130 | 72.98 132 | 72.89 134 | 74.67 182 | 47.58 210 | 71.92 159 | 80.69 106 | 51.70 210 | 81.69 89 | 83.89 174 | 56.58 201 | 82.25 83 | 68.34 80 | 87.36 146 | 88.82 49 |
|
MIMVSNet | | | 54.39 280 | 56.12 275 | 49.20 304 | 72.57 233 | 30.91 330 | 59.98 290 | 48.43 320 | 41.66 286 | 55.94 311 | 83.86 175 | 41.19 264 | 50.42 313 | 26.05 333 | 75.38 280 | 66.27 303 |
|
MCST-MVS | | | 73.42 119 | 73.34 121 | 73.63 112 | 81.28 107 | 59.17 132 | 74.80 130 | 83.13 62 | 45.50 261 | 72.84 195 | 83.78 176 | 65.15 106 | 80.99 116 | 64.54 117 | 89.09 123 | 80.73 180 |
|
OpenMVS | | 62.51 15 | 68.76 181 | 68.75 182 | 68.78 192 | 70.56 248 | 53.91 159 | 78.29 77 | 77.35 163 | 48.85 236 | 70.22 229 | 83.52 177 | 52.65 215 | 76.93 182 | 55.31 175 | 81.99 213 | 75.49 229 |
|
TAPA-MVS | | 65.27 12 | 75.16 93 | 74.29 106 | 77.77 67 | 74.86 176 | 68.08 72 | 77.89 82 | 84.04 49 | 55.15 165 | 76.19 160 | 83.39 178 | 66.91 92 | 80.11 138 | 60.04 140 | 90.14 108 | 85.13 90 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
FMVSNet5 | | | 55.08 278 | 55.54 279 | 53.71 292 | 65.80 290 | 33.50 314 | 56.22 303 | 52.50 302 | 43.72 276 | 61.06 283 | 83.38 179 | 25.46 344 | 54.87 307 | 30.11 323 | 81.64 223 | 72.75 249 |
|
VNet | | | 64.01 216 | 65.15 204 | 60.57 260 | 73.28 216 | 35.61 295 | 57.60 301 | 67.08 232 | 54.61 177 | 66.76 255 | 83.37 180 | 56.28 202 | 66.87 273 | 42.19 261 | 85.20 183 | 79.23 200 |
|
Vis-MVSNet (Re-imp) | | | 62.74 229 | 63.21 216 | 61.34 254 | 72.19 235 | 31.56 329 | 67.31 223 | 53.87 294 | 53.60 192 | 69.88 230 | 83.37 180 | 40.52 269 | 70.98 240 | 41.40 267 | 86.78 157 | 81.48 165 |
|
diffmvs | | | 66.15 199 | 65.86 201 | 67.01 206 | 62.31 306 | 44.43 236 | 68.81 201 | 72.93 194 | 48.13 242 | 62.12 276 | 83.33 182 | 57.96 184 | 72.29 227 | 59.83 142 | 77.31 269 | 84.33 111 |
|
PAPM_NR | | | 73.91 111 | 74.16 107 | 73.16 126 | 81.90 100 | 53.50 161 | 81.28 48 | 81.40 89 | 66.17 57 | 73.30 192 | 83.31 183 | 59.96 147 | 83.10 71 | 58.45 150 | 81.66 222 | 82.87 135 |
|
test_normal | | | 68.88 178 | 68.88 178 | 68.88 190 | 69.43 258 | 47.03 221 | 69.85 190 | 74.83 184 | 46.06 257 | 78.30 131 | 83.29 184 | 58.76 167 | 78.23 170 | 57.51 153 | 81.90 215 | 81.36 166 |
|
FMVSNet3 | | | 65.00 204 | 65.16 203 | 64.52 222 | 69.47 257 | 37.56 282 | 66.63 229 | 70.38 220 | 51.55 212 | 74.72 176 | 83.27 185 | 37.89 282 | 74.44 207 | 47.12 229 | 85.37 176 | 81.57 164 |
|
V42 | | | 71.06 153 | 70.83 161 | 71.72 154 | 67.25 280 | 47.14 220 | 65.94 239 | 80.35 115 | 51.35 213 | 83.40 74 | 83.23 186 | 59.25 158 | 78.80 152 | 65.91 109 | 80.81 238 | 89.23 38 |
|
test20.03 | | | 55.74 275 | 57.51 265 | 50.42 299 | 59.89 320 | 32.09 321 | 50.63 317 | 49.01 317 | 50.11 229 | 65.07 264 | 83.23 186 | 45.61 241 | 48.11 319 | 30.22 322 | 83.82 198 | 71.07 268 |
|
CNLPA | | | 73.44 118 | 73.03 131 | 74.66 93 | 78.27 138 | 75.29 27 | 75.99 109 | 78.49 146 | 65.39 64 | 75.67 163 | 83.22 188 | 61.23 138 | 66.77 277 | 53.70 188 | 85.33 179 | 81.92 160 |
|
DI_MVS_plusplus_test | | | 69.01 177 | 69.04 174 | 68.93 187 | 69.54 255 | 46.74 226 | 70.14 185 | 75.49 177 | 46.64 254 | 78.30 131 | 83.18 189 | 58.80 163 | 78.86 150 | 57.14 156 | 82.15 211 | 81.18 168 |
|
EPNet | | | 69.10 174 | 67.32 194 | 74.46 95 | 68.33 271 | 61.27 117 | 77.56 84 | 63.57 246 | 60.95 111 | 56.62 308 | 82.75 190 | 51.53 221 | 81.24 108 | 54.36 185 | 90.20 105 | 80.88 176 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
semantic-postprocess | | | | | 72.49 142 | 73.34 214 | 58.20 140 | | 65.55 239 | 48.10 243 | 76.91 147 | 82.64 191 | 42.25 259 | 78.84 151 | 61.20 134 | 77.89 267 | 80.44 186 |
|
view600 | | | 62.88 224 | 62.90 219 | 62.82 237 | 72.97 228 | 33.66 310 | 66.10 234 | 55.01 287 | 57.05 142 | 72.66 197 | 82.56 192 | 31.60 307 | 72.78 216 | 42.64 255 | 85.55 171 | 82.02 154 |
|
view800 | | | 62.88 224 | 62.90 219 | 62.82 237 | 72.97 228 | 33.66 310 | 66.10 234 | 55.01 287 | 57.05 142 | 72.66 197 | 82.56 192 | 31.60 307 | 72.78 216 | 42.64 255 | 85.55 171 | 82.02 154 |
|
conf0.05thres1000 | | | 62.88 224 | 62.90 219 | 62.82 237 | 72.97 228 | 33.66 310 | 66.10 234 | 55.01 287 | 57.05 142 | 72.66 197 | 82.56 192 | 31.60 307 | 72.78 216 | 42.64 255 | 85.55 171 | 82.02 154 |
|
tfpn | | | 62.88 224 | 62.90 219 | 62.82 237 | 72.97 228 | 33.66 310 | 66.10 234 | 55.01 287 | 57.05 142 | 72.66 197 | 82.56 192 | 31.60 307 | 72.78 216 | 42.64 255 | 85.55 171 | 82.02 154 |
|
MVS_111021_HR | | | 72.98 129 | 72.97 133 | 72.99 130 | 80.82 110 | 65.47 87 | 68.81 201 | 72.77 195 | 57.67 137 | 75.76 162 | 82.38 196 | 71.01 62 | 77.17 179 | 61.38 132 | 86.15 161 | 76.32 225 |
|
Test4 | | | 69.04 176 | 68.95 177 | 69.32 181 | 69.52 256 | 48.10 199 | 70.69 182 | 78.25 152 | 45.90 258 | 80.99 101 | 82.24 197 | 51.91 217 | 78.11 174 | 58.46 149 | 82.58 208 | 81.74 162 |
|
pmmvs-eth3d | | | 64.41 211 | 63.27 215 | 67.82 199 | 75.81 168 | 60.18 123 | 69.49 193 | 62.05 253 | 38.81 297 | 74.13 182 | 82.23 198 | 43.76 251 | 68.65 260 | 42.53 259 | 80.63 241 | 74.63 236 |
|
MVS_0304 | | | 74.55 106 | 73.47 116 | 77.80 66 | 77.41 150 | 63.88 100 | 75.75 115 | 83.67 53 | 63.55 88 | 66.12 257 | 82.16 199 | 60.20 146 | 86.15 22 | 65.37 113 | 86.98 155 | 83.38 127 |
|
alignmvs | | | 70.54 159 | 71.00 159 | 69.15 183 | 73.50 207 | 48.04 201 | 69.85 190 | 79.62 125 | 53.94 189 | 76.54 154 | 82.00 200 | 59.00 161 | 74.68 204 | 57.32 155 | 87.21 151 | 84.72 96 |
|
MSLP-MVS++ | | | 74.48 107 | 75.78 88 | 70.59 163 | 84.66 64 | 62.40 108 | 78.65 70 | 84.24 43 | 60.55 117 | 77.71 138 | 81.98 201 | 63.12 117 | 77.64 177 | 62.95 126 | 88.14 134 | 71.73 260 |
|
DP-MVS Recon | | | 73.57 117 | 72.69 138 | 76.23 81 | 82.85 88 | 63.39 103 | 74.32 135 | 82.96 64 | 57.75 134 | 70.35 227 | 81.98 201 | 64.34 113 | 84.41 54 | 49.69 210 | 89.95 112 | 80.89 175 |
|
BH-RMVSNet | | | 68.69 183 | 68.20 189 | 70.14 171 | 76.40 159 | 53.90 160 | 64.62 253 | 73.48 190 | 58.01 133 | 73.91 186 | 81.78 203 | 59.09 160 | 78.22 171 | 48.59 218 | 77.96 266 | 78.31 208 |
|
EG-PatchMatch MVS | | | 70.70 157 | 70.88 160 | 70.16 170 | 82.64 91 | 58.80 135 | 71.48 166 | 73.64 189 | 54.98 169 | 76.55 153 | 81.77 204 | 61.10 140 | 78.94 149 | 54.87 178 | 80.84 237 | 72.74 250 |
|
MVS_111021_LR | | | 72.10 145 | 71.82 149 | 72.95 132 | 79.53 120 | 73.90 36 | 70.45 183 | 66.64 234 | 56.87 147 | 76.81 149 | 81.76 205 | 68.78 76 | 71.76 236 | 61.81 130 | 83.74 199 | 73.18 245 |
|
AdaColmap | | | 74.22 109 | 74.56 100 | 73.20 125 | 81.95 99 | 60.97 118 | 79.43 64 | 80.90 105 | 65.57 61 | 72.54 202 | 81.76 205 | 70.98 63 | 85.26 37 | 47.88 225 | 90.00 111 | 73.37 243 |
|
canonicalmvs | | | 72.29 144 | 73.38 119 | 69.04 184 | 74.23 194 | 47.37 216 | 73.93 138 | 83.18 60 | 54.36 181 | 76.61 152 | 81.64 207 | 72.03 54 | 75.34 198 | 57.12 157 | 87.28 150 | 84.40 108 |
|
MVS-HIRNet | | | 45.53 310 | 47.29 310 | 40.24 332 | 62.29 307 | 26.82 340 | 56.02 304 | 37.41 350 | 29.74 342 | 43.69 348 | 81.27 208 | 33.96 291 | 55.48 306 | 24.46 339 | 56.79 342 | 38.43 350 |
|
CMPMVS | | 48.73 20 | 61.54 236 | 60.89 237 | 63.52 231 | 61.08 313 | 51.55 168 | 68.07 213 | 68.00 230 | 33.88 321 | 65.87 259 | 81.25 209 | 37.91 281 | 67.71 266 | 49.32 213 | 82.60 207 | 71.31 263 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
testgi | | | 54.00 284 | 56.86 270 | 45.45 317 | 58.20 331 | 25.81 343 | 49.05 320 | 49.50 316 | 45.43 263 | 67.84 241 | 81.17 210 | 51.81 220 | 43.20 337 | 29.30 327 | 79.41 253 | 67.34 297 |
|
no-one | | | 56.11 272 | 55.62 278 | 57.60 278 | 62.68 303 | 49.23 183 | 39.12 343 | 58.99 265 | 33.72 323 | 60.98 286 | 80.90 211 | 36.07 286 | 60.36 298 | 30.68 319 | 97.40 1 | 63.22 316 |
|
PHI-MVS | | | 74.92 98 | 74.36 105 | 76.61 72 | 76.40 159 | 62.32 110 | 80.38 55 | 83.15 61 | 54.16 184 | 73.23 193 | 80.75 212 | 62.19 127 | 83.86 58 | 68.02 84 | 90.92 93 | 83.65 121 |
|
PLC | | 62.01 16 | 71.79 149 | 70.28 165 | 76.33 78 | 80.31 114 | 68.63 70 | 78.18 80 | 81.24 93 | 54.57 178 | 67.09 254 | 80.63 213 | 59.44 154 | 81.74 90 | 46.91 232 | 84.17 192 | 78.63 204 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
PM-MVS | | | 64.49 208 | 63.61 212 | 67.14 204 | 76.68 157 | 75.15 28 | 68.49 209 | 42.85 336 | 51.17 217 | 77.85 136 | 80.51 214 | 45.76 239 | 66.31 280 | 52.83 192 | 76.35 272 | 59.96 327 |
|
CANet | | | 73.00 127 | 71.84 148 | 76.48 74 | 75.82 167 | 61.28 116 | 74.81 128 | 80.37 114 | 63.17 93 | 62.43 275 | 80.50 215 | 61.10 140 | 85.16 42 | 64.00 120 | 84.34 191 | 83.01 133 |
|
IterMVS | | | 63.12 220 | 62.48 226 | 65.02 219 | 66.34 287 | 52.86 164 | 63.81 260 | 62.25 249 | 46.57 255 | 71.51 214 | 80.40 216 | 44.60 246 | 66.82 276 | 51.38 199 | 75.47 278 | 75.38 232 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
LF4IMVS | | | 67.50 192 | 67.31 195 | 68.08 196 | 58.86 326 | 61.93 111 | 71.43 170 | 75.90 174 | 44.67 269 | 72.42 203 | 80.20 217 | 57.16 194 | 70.44 244 | 58.99 147 | 86.12 162 | 71.88 258 |
|
CSCG | | | 74.12 110 | 74.39 103 | 73.33 121 | 79.35 122 | 61.66 115 | 77.45 86 | 81.98 76 | 62.47 100 | 79.06 123 | 80.19 218 | 61.83 129 | 78.79 153 | 59.83 142 | 87.35 147 | 79.54 196 |
|
FPMVS | | | 59.43 251 | 60.07 241 | 57.51 279 | 77.62 149 | 71.52 44 | 62.33 269 | 50.92 311 | 57.40 140 | 69.40 232 | 80.00 219 | 39.14 273 | 61.92 295 | 37.47 292 | 66.36 320 | 39.09 349 |
|
tfpn111 | | | 61.91 232 | 61.65 229 | 62.68 242 | 72.14 236 | 35.01 298 | 65.42 246 | 56.99 277 | 55.23 162 | 70.71 222 | 79.90 220 | 32.07 302 | 72.85 215 | 38.80 279 | 83.61 200 | 80.18 189 |
|
conf200view11 | | | 61.42 237 | 61.09 235 | 62.43 245 | 72.14 236 | 35.01 298 | 65.42 246 | 56.99 277 | 55.23 162 | 70.71 222 | 79.90 220 | 32.07 302 | 72.09 229 | 35.61 301 | 81.73 217 | 80.18 189 |
|
thres100view900 | | | 61.17 239 | 61.09 235 | 61.39 253 | 72.14 236 | 35.01 298 | 65.42 246 | 56.99 277 | 55.23 162 | 70.71 222 | 79.90 220 | 32.07 302 | 72.09 229 | 35.61 301 | 81.73 217 | 77.08 222 |
|
new-patchmatchnet | | | 52.89 290 | 55.76 277 | 44.26 323 | 59.94 319 | 6.31 357 | 37.36 347 | 50.76 313 | 41.10 287 | 64.28 266 | 79.82 223 | 44.77 244 | 48.43 318 | 36.24 298 | 87.61 140 | 78.03 213 |
|
testmv | | | 52.91 289 | 54.31 284 | 48.71 308 | 72.13 239 | 36.18 288 | 50.26 318 | 47.78 321 | 44.15 271 | 64.61 265 | 79.78 224 | 38.18 277 | 50.20 315 | 21.96 344 | 69.93 306 | 59.75 328 |
|
thres600view7 | | | 61.82 233 | 61.38 234 | 63.12 234 | 71.81 241 | 34.93 301 | 64.64 252 | 56.99 277 | 54.78 174 | 70.33 228 | 79.74 225 | 32.07 302 | 72.42 226 | 38.61 282 | 83.46 201 | 82.02 154 |
|
BH-untuned | | | 69.39 169 | 69.46 168 | 69.18 182 | 77.96 143 | 56.88 143 | 68.47 210 | 77.53 161 | 56.77 149 | 77.79 137 | 79.63 226 | 60.30 145 | 80.20 136 | 46.04 237 | 80.65 239 | 70.47 269 |
|
PAPM | | | 61.79 234 | 60.37 240 | 66.05 214 | 76.09 164 | 41.87 249 | 69.30 195 | 76.79 168 | 40.64 292 | 53.80 322 | 79.62 227 | 44.38 247 | 82.92 74 | 29.64 326 | 73.11 290 | 73.36 244 |
|
XXY-MVS | | | 55.19 277 | 57.40 266 | 48.56 309 | 64.45 297 | 34.84 303 | 51.54 316 | 53.59 296 | 38.99 296 | 63.79 269 | 79.43 228 | 56.59 200 | 45.57 324 | 36.92 295 | 71.29 297 | 65.25 308 |
|
MDA-MVSNet-bldmvs | | | 62.34 230 | 61.73 227 | 64.16 223 | 61.64 310 | 49.90 177 | 48.11 324 | 57.24 276 | 53.31 196 | 80.95 102 | 79.39 229 | 49.00 230 | 61.55 296 | 45.92 238 | 80.05 245 | 81.03 172 |
|
TAMVS | | | 65.31 202 | 63.75 210 | 69.97 175 | 82.23 96 | 59.76 128 | 66.78 228 | 63.37 247 | 45.20 265 | 69.79 231 | 79.37 230 | 47.42 238 | 72.17 228 | 34.48 306 | 85.15 184 | 77.99 215 |
|
PAPR | | | 69.20 172 | 68.66 184 | 70.82 161 | 75.15 172 | 47.77 205 | 75.31 122 | 81.11 95 | 49.62 233 | 66.33 256 | 79.27 231 | 61.53 133 | 82.96 73 | 48.12 223 | 81.50 224 | 81.74 162 |
|
Anonymous20231206 | | | 54.13 281 | 55.82 276 | 49.04 307 | 70.89 243 | 35.96 291 | 51.73 315 | 50.87 312 | 34.86 315 | 62.49 274 | 79.22 232 | 42.52 258 | 44.29 333 | 27.95 331 | 81.88 216 | 66.88 299 |
|
OpenMVS_ROB | | 54.93 17 | 63.23 219 | 63.28 214 | 63.07 235 | 69.81 252 | 45.34 232 | 68.52 208 | 67.14 231 | 43.74 275 | 70.61 225 | 79.22 232 | 47.90 236 | 72.66 221 | 48.75 216 | 73.84 288 | 71.21 265 |
|
PVSNet_Blended_VisFu | | | 70.04 162 | 68.88 178 | 73.53 115 | 82.71 90 | 63.62 102 | 74.81 128 | 81.95 78 | 48.53 239 | 67.16 253 | 79.18 234 | 51.42 222 | 78.38 166 | 54.39 184 | 79.72 251 | 78.60 205 |
|
MVSTER | | | 63.29 218 | 61.60 231 | 68.36 195 | 59.77 321 | 46.21 229 | 60.62 287 | 71.32 209 | 41.83 285 | 75.40 168 | 79.12 235 | 30.25 322 | 75.85 190 | 56.30 166 | 79.81 248 | 83.03 132 |
|
tpm | | | 50.60 297 | 52.42 293 | 45.14 319 | 65.18 294 | 26.29 341 | 60.30 288 | 43.50 333 | 37.41 304 | 57.01 304 | 79.09 236 | 30.20 324 | 42.32 339 | 32.77 314 | 66.36 320 | 66.81 301 |
|
PVSNet_BlendedMVS | | | 65.38 201 | 64.30 206 | 68.61 193 | 69.81 252 | 49.36 181 | 65.60 245 | 78.96 136 | 45.50 261 | 59.98 292 | 78.61 237 | 51.82 218 | 78.20 172 | 44.30 241 | 84.11 193 | 78.27 209 |
|
TSAR-MVS + GP. | | | 73.08 124 | 71.60 153 | 77.54 69 | 78.99 133 | 70.73 53 | 74.96 125 | 69.38 224 | 60.73 114 | 74.39 180 | 78.44 238 | 57.72 189 | 82.78 75 | 60.16 138 | 89.60 115 | 79.11 201 |
|
MVP-Stereo | | | 61.56 235 | 59.22 246 | 68.58 194 | 79.28 123 | 60.44 121 | 69.20 197 | 71.57 205 | 43.58 277 | 56.42 309 | 78.37 239 | 39.57 272 | 76.46 188 | 34.86 305 | 60.16 332 | 68.86 289 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
HY-MVS | | 49.31 19 | 57.96 265 | 57.59 264 | 59.10 270 | 66.85 283 | 36.17 289 | 65.13 251 | 65.39 240 | 39.24 295 | 54.69 318 | 78.14 240 | 44.28 248 | 67.18 272 | 33.75 311 | 70.79 300 | 73.95 240 |
|
Effi-MVS+-dtu | | | 75.43 87 | 72.28 144 | 84.91 2 | 77.05 151 | 83.58 2 | 78.47 75 | 77.70 158 | 57.68 135 | 74.89 172 | 78.13 241 | 64.80 109 | 84.26 56 | 56.46 164 | 85.32 180 | 86.88 71 |
|
DSMNet-mixed | | | 43.18 320 | 44.66 319 | 38.75 334 | 54.75 347 | 28.88 335 | 57.06 302 | 27.42 356 | 13.47 352 | 47.27 338 | 77.67 242 | 38.83 274 | 39.29 346 | 25.32 338 | 60.12 333 | 48.08 341 |
|
Test_1112_low_res | | | 58.78 257 | 58.69 258 | 59.04 271 | 79.41 121 | 38.13 276 | 57.62 300 | 66.98 233 | 34.74 317 | 59.62 296 | 77.56 243 | 42.92 256 | 63.65 288 | 38.66 281 | 70.73 301 | 75.35 233 |
|
API-MVS | | | 70.97 155 | 71.51 155 | 69.37 177 | 75.20 171 | 55.94 146 | 80.99 49 | 76.84 166 | 62.48 99 | 71.24 216 | 77.51 244 | 61.51 134 | 80.96 121 | 52.04 193 | 85.76 166 | 71.22 264 |
|
pmmvs4 | | | 60.78 242 | 59.04 248 | 66.00 215 | 73.06 225 | 57.67 141 | 64.53 255 | 60.22 260 | 36.91 307 | 65.96 258 | 77.27 245 | 39.66 271 | 68.54 261 | 38.87 278 | 74.89 282 | 71.80 259 |
|
tfpn200view9 | | | 60.35 245 | 59.97 242 | 61.51 251 | 70.78 245 | 35.35 296 | 63.27 266 | 57.47 271 | 53.00 198 | 68.31 239 | 77.09 246 | 32.45 299 | 72.09 229 | 35.61 301 | 81.73 217 | 77.08 222 |
|
thres400 | | | 60.77 243 | 59.97 242 | 63.15 233 | 70.78 245 | 35.35 296 | 63.27 266 | 57.47 271 | 53.00 198 | 68.31 239 | 77.09 246 | 32.45 299 | 72.09 229 | 35.61 301 | 81.73 217 | 82.02 154 |
|
Effi-MVS+ | | | 72.10 145 | 72.28 144 | 71.58 155 | 74.21 197 | 50.33 173 | 74.72 132 | 82.73 67 | 62.62 97 | 70.77 221 | 76.83 248 | 69.96 69 | 80.97 117 | 60.20 137 | 78.43 261 | 83.45 126 |
|
MVSFormer | | | 69.93 164 | 69.03 175 | 72.63 141 | 74.93 173 | 59.19 130 | 83.98 29 | 75.72 175 | 52.27 202 | 63.53 271 | 76.74 249 | 43.19 254 | 80.56 127 | 72.28 52 | 78.67 259 | 78.14 211 |
|
jason | | | 64.47 209 | 62.84 223 | 69.34 180 | 76.91 156 | 59.20 129 | 67.15 224 | 65.67 236 | 35.29 314 | 65.16 263 | 76.74 249 | 44.67 245 | 70.68 241 | 54.74 179 | 79.28 254 | 78.14 211 |
jason: jason. |
CostFormer | | | 57.35 269 | 56.14 274 | 60.97 257 | 63.76 300 | 38.43 272 | 67.50 217 | 60.22 260 | 37.14 306 | 59.12 297 | 76.34 251 | 32.78 296 | 71.99 233 | 39.12 277 | 69.27 310 | 72.47 252 |
|
tpmp4_e23 | | | 57.57 267 | 55.46 280 | 63.93 227 | 66.48 284 | 41.56 252 | 71.68 164 | 60.65 259 | 35.64 313 | 55.35 315 | 76.25 252 | 29.53 328 | 75.41 197 | 34.40 307 | 69.12 311 | 74.83 235 |
|
MDTV_nov1_ep13 | | | | 54.05 286 | | 65.54 292 | 29.30 333 | 59.00 295 | 55.22 284 | 35.96 311 | 52.44 324 | 75.98 253 | 30.77 319 | 59.62 300 | 38.21 285 | 73.33 289 | |
|
tfpn1000 | | | 58.28 263 | 58.86 256 | 56.53 286 | 68.05 274 | 32.26 320 | 62.58 268 | 51.67 310 | 51.25 216 | 67.38 251 | 75.95 254 | 27.24 339 | 68.83 258 | 43.51 247 | 82.11 212 | 68.49 290 |
|
conf0.01 | | | 59.26 252 | 58.88 250 | 60.40 262 | 68.66 261 | 31.96 323 | 62.04 271 | 51.95 303 | 50.99 218 | 67.57 245 | 75.91 255 | 28.59 332 | 69.07 252 | 42.77 249 | 81.40 225 | 80.18 189 |
|
conf0.002 | | | 59.26 252 | 58.88 250 | 60.40 262 | 68.66 261 | 31.96 323 | 62.04 271 | 51.95 303 | 50.99 218 | 67.57 245 | 75.91 255 | 28.59 332 | 69.07 252 | 42.77 249 | 81.40 225 | 80.18 189 |
|
thresconf0.02 | | | 58.38 259 | 58.88 250 | 56.91 282 | 68.66 261 | 31.96 323 | 62.04 271 | 51.95 303 | 50.99 218 | 67.57 245 | 75.91 255 | 28.59 332 | 69.07 252 | 42.77 249 | 81.40 225 | 69.70 277 |
|
tfpn_n400 | | | 58.38 259 | 58.88 250 | 56.91 282 | 68.66 261 | 31.96 323 | 62.04 271 | 51.95 303 | 50.99 218 | 67.57 245 | 75.91 255 | 28.59 332 | 69.07 252 | 42.77 249 | 81.40 225 | 69.70 277 |
|
tfpnconf | | | 58.38 259 | 58.88 250 | 56.91 282 | 68.66 261 | 31.96 323 | 62.04 271 | 51.95 303 | 50.99 218 | 67.57 245 | 75.91 255 | 28.59 332 | 69.07 252 | 42.77 249 | 81.40 225 | 69.70 277 |
|
tfpnview11 | | | 58.38 259 | 58.88 250 | 56.91 282 | 68.66 261 | 31.96 323 | 62.04 271 | 51.95 303 | 50.99 218 | 67.57 245 | 75.91 255 | 28.59 332 | 69.07 252 | 42.77 249 | 81.40 225 | 69.70 277 |
|
EU-MVSNet | | | 60.82 241 | 60.80 238 | 60.86 259 | 68.37 269 | 41.16 253 | 72.27 148 | 68.27 229 | 26.96 346 | 69.08 234 | 75.71 261 | 32.09 301 | 67.44 269 | 55.59 173 | 78.90 256 | 73.97 239 |
|
HyFIR lowres test | | | 63.01 221 | 60.47 239 | 70.61 162 | 83.04 85 | 54.10 157 | 59.93 291 | 72.24 202 | 33.67 325 | 69.00 235 | 75.63 262 | 38.69 275 | 76.93 182 | 36.60 296 | 75.45 279 | 80.81 179 |
|
Fast-Effi-MVS+ | | | 68.81 180 | 68.30 186 | 70.35 165 | 74.66 184 | 48.61 190 | 66.06 238 | 78.32 148 | 50.62 226 | 71.48 215 | 75.54 263 | 68.75 77 | 79.59 144 | 50.55 205 | 78.73 258 | 82.86 136 |
|
CDS-MVSNet | | | 64.33 212 | 62.66 225 | 69.35 179 | 80.44 113 | 58.28 139 | 65.26 249 | 65.66 237 | 44.36 270 | 67.30 252 | 75.54 263 | 43.27 253 | 71.77 235 | 37.68 289 | 84.44 190 | 78.01 214 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
tpm2 | | | 56.12 271 | 54.64 283 | 60.55 261 | 66.24 288 | 36.01 290 | 68.14 212 | 56.77 281 | 33.60 327 | 58.25 301 | 75.52 265 | 30.25 322 | 74.33 208 | 33.27 312 | 69.76 309 | 71.32 262 |
|
1111 | | | 45.08 314 | 47.96 309 | 36.43 336 | 59.56 323 | 14.82 353 | 43.56 333 | 45.65 329 | 45.60 259 | 60.04 290 | 75.47 266 | 9.31 360 | 34.46 349 | 23.66 340 | 68.76 313 | 60.02 326 |
|
.test1245 | | | 34.47 330 | 40.38 326 | 16.73 342 | 59.56 323 | 14.82 353 | 43.56 333 | 45.65 329 | 45.60 259 | 60.04 290 | 75.47 266 | 9.31 360 | 34.46 349 | 23.66 340 | 0.55 356 | 0.90 355 |
|
CANet_DTU | | | 64.04 215 | 63.83 209 | 64.66 220 | 68.39 268 | 42.97 243 | 73.45 139 | 74.50 186 | 52.05 206 | 54.78 316 | 75.44 268 | 43.99 249 | 70.42 245 | 53.49 191 | 78.41 262 | 80.59 183 |
|
DELS-MVS | | | 68.83 179 | 68.31 185 | 70.38 164 | 70.55 249 | 48.31 193 | 63.78 261 | 82.13 73 | 54.00 186 | 68.96 236 | 75.17 269 | 58.95 162 | 80.06 139 | 58.55 148 | 82.74 206 | 82.76 139 |
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 |
pmmvs5 | | | 52.49 293 | 52.58 292 | 52.21 297 | 54.99 346 | 32.38 319 | 55.45 307 | 53.84 295 | 32.15 332 | 55.49 314 | 74.81 270 | 38.08 279 | 57.37 305 | 34.02 309 | 74.40 284 | 66.88 299 |
|
MSDG | | | 67.47 194 | 67.48 193 | 67.46 201 | 70.70 247 | 54.69 154 | 66.90 227 | 78.17 153 | 60.88 112 | 70.41 226 | 74.76 271 | 61.22 139 | 73.18 212 | 47.38 228 | 76.87 270 | 74.49 237 |
|
UnsupCasMVSNet_eth | | | 52.26 294 | 53.29 289 | 49.16 305 | 55.08 345 | 33.67 309 | 50.03 319 | 58.79 266 | 37.67 303 | 63.43 273 | 74.75 272 | 41.82 261 | 45.83 323 | 38.59 283 | 59.42 335 | 67.98 293 |
|
mvs-test1 | | | 73.81 113 | 70.69 163 | 83.18 3 | 77.05 151 | 81.39 4 | 75.39 121 | 77.70 158 | 57.68 135 | 71.19 218 | 74.72 273 | 64.80 109 | 83.66 61 | 56.46 164 | 81.19 232 | 84.50 106 |
|
Fast-Effi-MVS+-dtu | | | 70.00 163 | 68.74 183 | 73.77 107 | 73.47 208 | 64.53 95 | 71.36 172 | 78.14 154 | 55.81 156 | 68.84 237 | 74.71 274 | 65.36 105 | 75.75 193 | 52.00 194 | 79.00 255 | 81.03 172 |
|
TR-MVS | | | 64.59 206 | 63.54 213 | 67.73 200 | 75.75 169 | 50.83 172 | 63.39 264 | 70.29 221 | 49.33 234 | 71.55 213 | 74.55 275 | 50.94 223 | 78.46 161 | 40.43 273 | 75.69 275 | 73.89 241 |
|
GA-MVS | | | 62.91 222 | 61.66 228 | 66.66 211 | 67.09 282 | 44.49 235 | 61.18 285 | 69.36 225 | 51.33 214 | 69.33 233 | 74.47 276 | 36.83 283 | 74.94 203 | 50.60 204 | 74.72 283 | 80.57 184 |
|
CLD-MVS | | | 72.88 134 | 72.36 142 | 74.43 96 | 77.03 153 | 54.30 156 | 68.77 204 | 83.43 59 | 52.12 204 | 76.79 150 | 74.44 277 | 69.54 71 | 83.91 57 | 55.88 170 | 93.25 57 | 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 |
CHOSEN 1792x2688 | | | 58.09 264 | 56.30 273 | 63.45 232 | 79.95 115 | 50.93 171 | 54.07 310 | 65.59 238 | 28.56 343 | 61.53 279 | 74.33 278 | 41.09 265 | 66.52 279 | 33.91 310 | 67.69 318 | 72.92 247 |
|
Patchmatch-RL test | | | 59.95 247 | 59.12 247 | 62.44 244 | 72.46 234 | 54.61 155 | 59.63 292 | 47.51 323 | 41.05 289 | 74.58 179 | 74.30 279 | 31.06 316 | 65.31 281 | 51.61 196 | 79.85 247 | 67.39 295 |
|
cdsmvs_eth3d_5k | | | 17.71 331 | 23.62 332 | 0.00 347 | 0.00 361 | 0.00 362 | 0.00 353 | 70.17 222 | 0.00 357 | 0.00 358 | 74.25 280 | 68.16 83 | 0.00 360 | 0.00 357 | 0.00 358 | 0.00 358 |
|
lupinMVS | | | 63.36 217 | 61.49 233 | 68.97 185 | 74.93 173 | 59.19 130 | 65.80 241 | 64.52 244 | 34.68 319 | 63.53 271 | 74.25 280 | 43.19 254 | 70.62 242 | 53.88 187 | 78.67 259 | 77.10 221 |
|
xiu_mvs_v1_base_debu | | | 67.87 188 | 67.07 196 | 70.26 166 | 79.13 129 | 61.90 112 | 67.34 220 | 71.25 212 | 47.98 244 | 67.70 242 | 74.19 282 | 61.31 135 | 72.62 222 | 56.51 161 | 78.26 263 | 76.27 226 |
|
xiu_mvs_v1_base | | | 67.87 188 | 67.07 196 | 70.26 166 | 79.13 129 | 61.90 112 | 67.34 220 | 71.25 212 | 47.98 244 | 67.70 242 | 74.19 282 | 61.31 135 | 72.62 222 | 56.51 161 | 78.26 263 | 76.27 226 |
|
xiu_mvs_v1_base_debi | | | 67.87 188 | 67.07 196 | 70.26 166 | 79.13 129 | 61.90 112 | 67.34 220 | 71.25 212 | 47.98 244 | 67.70 242 | 74.19 282 | 61.31 135 | 72.62 222 | 56.51 161 | 78.26 263 | 76.27 226 |
|
tpmvs | | | 55.84 273 | 55.45 281 | 57.01 281 | 60.33 317 | 33.20 316 | 65.89 240 | 59.29 264 | 47.52 251 | 56.04 310 | 73.60 285 | 31.05 317 | 68.06 264 | 40.64 271 | 64.64 323 | 69.77 276 |
|
Patchmatch-test | | | 47.93 305 | 49.96 303 | 41.84 328 | 57.42 335 | 24.26 346 | 48.75 321 | 41.49 343 | 39.30 294 | 56.79 307 | 73.48 286 | 30.48 321 | 33.87 351 | 29.29 328 | 72.61 291 | 67.39 295 |
|
MDA-MVSNet_test_wron | | | 52.57 292 | 53.49 288 | 49.81 301 | 54.24 348 | 36.47 286 | 40.48 339 | 46.58 325 | 38.13 300 | 75.47 167 | 73.32 287 | 41.05 267 | 43.85 335 | 40.98 269 | 71.20 298 | 69.10 288 |
|
YYNet1 | | | 52.58 291 | 53.50 287 | 49.85 300 | 54.15 349 | 36.45 287 | 40.53 338 | 46.55 326 | 38.09 301 | 75.52 166 | 73.31 288 | 41.08 266 | 43.88 334 | 41.10 268 | 71.14 299 | 69.21 286 |
|
LP | | | 53.02 288 | 52.27 294 | 55.27 289 | 55.76 343 | 40.55 259 | 55.64 306 | 55.07 285 | 42.46 282 | 56.95 305 | 73.21 289 | 33.67 292 | 54.18 311 | 38.41 284 | 59.29 336 | 71.08 267 |
|
PatchmatchNet | | | 54.60 279 | 54.27 285 | 55.59 288 | 65.17 295 | 39.08 267 | 66.92 226 | 51.80 309 | 39.89 293 | 58.39 299 | 73.12 290 | 31.69 306 | 58.33 303 | 43.01 248 | 58.38 341 | 69.38 285 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
EPNet_dtu | | | 58.93 256 | 58.52 259 | 60.16 266 | 67.91 276 | 47.70 207 | 69.97 187 | 58.02 267 | 49.73 231 | 47.28 337 | 73.02 291 | 38.14 278 | 62.34 293 | 36.57 297 | 85.99 164 | 70.43 270 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
ppachtmachnet_test | | | 60.26 246 | 59.61 245 | 62.20 246 | 67.70 278 | 44.33 237 | 58.18 299 | 60.96 258 | 40.75 290 | 65.80 260 | 72.57 292 | 41.23 263 | 63.92 287 | 46.87 233 | 82.42 209 | 78.33 207 |
|
N_pmnet | | | 52.06 295 | 51.11 299 | 54.92 290 | 59.64 322 | 71.03 49 | 37.42 346 | 61.62 256 | 33.68 324 | 57.12 303 | 72.10 293 | 37.94 280 | 31.03 352 | 29.13 330 | 71.35 296 | 62.70 318 |
|
Regformer-1 | | | 74.28 108 | 73.63 114 | 76.21 82 | 74.22 195 | 64.12 98 | 72.77 144 | 75.46 179 | 66.86 51 | 79.27 120 | 72.08 294 | 69.29 72 | 78.74 154 | 68.73 77 | 84.02 195 | 85.77 86 |
|
Regformer-2 | | | 75.32 90 | 74.47 102 | 77.88 65 | 74.22 195 | 66.65 79 | 72.77 144 | 77.54 160 | 68.47 46 | 80.44 108 | 72.08 294 | 70.60 64 | 80.97 117 | 70.08 68 | 84.02 195 | 86.01 80 |
|
test1235678 | | | 48.41 304 | 49.60 304 | 44.83 321 | 68.52 267 | 33.81 308 | 46.33 330 | 45.89 328 | 38.72 298 | 58.46 298 | 72.08 294 | 29.85 327 | 47.82 320 | 19.67 348 | 66.91 319 | 52.88 336 |
|
Regformer-3 | | | 72.86 135 | 72.28 144 | 74.62 94 | 74.74 179 | 60.18 123 | 72.91 141 | 71.76 203 | 64.74 74 | 78.42 129 | 72.07 297 | 67.00 91 | 76.28 189 | 67.97 88 | 80.91 234 | 87.39 68 |
|
Regformer-4 | | | 74.64 104 | 73.67 112 | 77.55 68 | 74.74 179 | 64.49 96 | 72.91 141 | 75.42 180 | 67.45 48 | 80.24 112 | 72.07 297 | 68.98 75 | 80.19 137 | 70.29 64 | 80.91 234 | 87.98 62 |
|
ADS-MVSNet2 | | | 48.76 302 | 47.25 311 | 53.29 293 | 55.90 341 | 40.54 260 | 47.34 326 | 54.99 291 | 31.41 338 | 50.48 329 | 72.06 299 | 31.23 313 | 54.26 309 | 25.93 334 | 55.93 343 | 65.07 310 |
|
ADS-MVSNet | | | 44.62 317 | 45.58 314 | 41.73 329 | 55.90 341 | 20.83 349 | 47.34 326 | 39.94 348 | 31.41 338 | 50.48 329 | 72.06 299 | 31.23 313 | 39.31 345 | 25.93 334 | 55.93 343 | 65.07 310 |
|
tfpn_ndepth | | | 56.91 270 | 57.30 267 | 55.71 287 | 67.22 281 | 33.26 315 | 61.72 279 | 53.98 293 | 48.49 240 | 64.16 267 | 71.94 301 | 27.65 338 | 68.71 259 | 40.49 272 | 80.08 244 | 65.17 309 |
|
Patchmatch-test1 | | | 57.81 266 | 58.04 262 | 57.13 280 | 70.17 251 | 41.07 255 | 65.19 250 | 53.38 298 | 43.34 281 | 61.00 285 | 71.94 301 | 45.20 242 | 62.69 291 | 41.81 265 | 70.31 303 | 67.63 294 |
|
BH-w/o | | | 64.81 205 | 64.29 207 | 66.36 212 | 76.08 165 | 54.71 153 | 65.61 244 | 75.23 182 | 50.10 230 | 71.05 220 | 71.86 303 | 54.33 209 | 79.02 147 | 38.20 286 | 76.14 273 | 65.36 307 |
|
EI-MVSNet-Vis-set | | | 72.78 136 | 71.87 147 | 75.54 88 | 74.77 178 | 59.02 134 | 72.24 149 | 71.56 206 | 63.92 83 | 78.59 125 | 71.59 304 | 66.22 99 | 78.60 156 | 67.58 91 | 80.32 242 | 89.00 43 |
|
UnsupCasMVSNet_bld | | | 50.01 300 | 51.03 300 | 46.95 310 | 58.61 328 | 32.64 318 | 48.31 322 | 53.27 299 | 34.27 320 | 60.47 288 | 71.53 305 | 41.40 262 | 47.07 321 | 30.68 319 | 60.78 331 | 61.13 323 |
|
thres200 | | | 57.55 268 | 57.02 268 | 59.17 269 | 67.89 277 | 34.93 301 | 58.91 296 | 57.25 275 | 50.24 228 | 64.01 268 | 71.46 306 | 32.49 298 | 71.39 238 | 31.31 317 | 79.57 252 | 71.19 266 |
|
EI-MVSNet-UG-set | | | 72.63 138 | 71.68 151 | 75.47 89 | 74.67 182 | 58.64 138 | 72.02 154 | 71.50 207 | 63.53 89 | 78.58 127 | 71.39 307 | 65.98 100 | 78.53 158 | 67.30 97 | 80.18 243 | 89.23 38 |
|
EI-MVSNet | | | 69.61 167 | 69.01 176 | 71.41 158 | 73.94 200 | 49.90 177 | 71.31 174 | 71.32 209 | 58.22 131 | 75.40 168 | 70.44 308 | 58.16 174 | 75.85 190 | 62.51 127 | 79.81 248 | 88.48 59 |
|
CVMVSNet | | | 59.21 254 | 58.44 261 | 61.51 251 | 73.94 200 | 47.76 206 | 71.31 174 | 64.56 243 | 26.91 347 | 60.34 289 | 70.44 308 | 36.24 285 | 67.65 267 | 53.57 190 | 68.66 314 | 69.12 287 |
|
tpm cat1 | | | 54.02 283 | 52.63 291 | 58.19 275 | 64.85 296 | 39.86 264 | 66.26 233 | 57.28 274 | 32.16 331 | 56.90 306 | 70.39 310 | 32.75 297 | 65.30 282 | 34.29 308 | 58.79 337 | 69.41 284 |
|
PMMVS2 | | | 37.74 325 | 40.87 322 | 28.36 341 | 42.41 356 | 5.35 358 | 24.61 350 | 27.75 355 | 32.15 332 | 47.85 336 | 70.27 311 | 35.85 287 | 29.51 353 | 19.08 349 | 67.85 316 | 50.22 340 |
|
EPMVS | | | 45.74 309 | 46.53 312 | 43.39 325 | 54.14 350 | 22.33 348 | 55.02 308 | 35.00 352 | 34.69 318 | 51.09 327 | 70.20 312 | 25.92 342 | 42.04 341 | 37.19 293 | 55.50 345 | 65.78 305 |
|
xiu_mvs_v2_base | | | 64.43 210 | 63.96 208 | 65.85 217 | 77.72 147 | 51.32 170 | 63.63 262 | 72.31 201 | 45.06 268 | 61.70 277 | 69.66 313 | 62.56 120 | 73.93 210 | 49.06 215 | 73.91 286 | 72.31 254 |
|
tpmrst | | | 50.15 299 | 51.38 296 | 46.45 314 | 56.05 339 | 24.77 345 | 64.40 257 | 49.98 314 | 36.14 309 | 53.32 323 | 69.59 314 | 35.16 288 | 48.69 317 | 39.24 276 | 58.51 340 | 65.89 304 |
|
WTY-MVS | | | 49.39 301 | 50.31 302 | 46.62 313 | 61.22 312 | 32.00 322 | 46.61 328 | 49.77 315 | 33.87 322 | 54.12 321 | 69.55 315 | 41.96 260 | 45.40 326 | 31.28 318 | 64.42 324 | 62.47 320 |
|
patchmatchnet-post | | | | | | | | | | | | 68.99 316 | 31.32 312 | 69.38 249 | | | |
|
PatchMatch-RL | | | 58.68 258 | 57.72 263 | 61.57 250 | 76.21 162 | 73.59 39 | 61.83 277 | 49.00 318 | 47.30 252 | 61.08 282 | 68.97 317 | 50.16 226 | 59.01 302 | 36.06 300 | 68.84 312 | 52.10 338 |
|
MS-PatchMatch | | | 55.59 276 | 54.89 282 | 57.68 277 | 69.18 259 | 49.05 184 | 61.00 286 | 62.93 248 | 35.98 310 | 58.36 300 | 68.93 318 | 36.71 284 | 66.59 278 | 37.62 291 | 63.30 326 | 57.39 331 |
|
cascas | | | 64.59 206 | 62.77 224 | 70.05 173 | 75.27 170 | 50.02 176 | 61.79 278 | 71.61 204 | 42.46 282 | 63.68 270 | 68.89 319 | 49.33 229 | 80.35 131 | 47.82 226 | 84.05 194 | 79.78 195 |
|
MVS | | | 60.62 244 | 59.97 242 | 62.58 243 | 68.13 273 | 47.28 218 | 68.59 205 | 73.96 188 | 32.19 330 | 59.94 294 | 68.86 320 | 50.48 224 | 77.64 177 | 41.85 264 | 75.74 274 | 62.83 317 |
|
PVSNet_Blended | | | 62.90 223 | 61.64 230 | 66.69 210 | 69.81 252 | 49.36 181 | 61.23 284 | 78.96 136 | 42.04 284 | 59.98 292 | 68.86 320 | 51.82 218 | 78.20 172 | 44.30 241 | 77.77 268 | 72.52 251 |
|
MAR-MVS | | | 67.72 191 | 66.16 200 | 72.40 147 | 74.45 188 | 64.99 93 | 74.87 126 | 77.50 162 | 48.67 238 | 65.78 261 | 68.58 322 | 57.01 199 | 77.79 175 | 46.68 235 | 81.92 214 | 74.42 238 |
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 |
PS-MVSNAJ | | | 64.27 213 | 63.73 211 | 65.90 216 | 77.82 145 | 51.42 169 | 63.33 265 | 72.33 200 | 45.09 267 | 61.60 278 | 68.04 323 | 62.39 124 | 73.95 209 | 49.07 214 | 73.87 287 | 72.34 253 |
|
test0.0.03 1 | | | 47.72 306 | 48.31 307 | 45.93 315 | 55.53 344 | 29.39 332 | 46.40 329 | 41.21 345 | 43.41 279 | 55.81 313 | 67.65 324 | 29.22 329 | 43.77 336 | 25.73 336 | 69.87 307 | 64.62 312 |
|
1112_ss | | | 59.48 250 | 58.99 249 | 60.96 258 | 77.84 144 | 42.39 247 | 61.42 282 | 68.45 228 | 37.96 302 | 59.93 295 | 67.46 325 | 45.11 243 | 65.07 283 | 40.89 270 | 71.81 295 | 75.41 231 |
|
ab-mvs-re | | | 5.62 333 | 7.50 334 | 0.00 347 | 0.00 361 | 0.00 362 | 0.00 353 | 0.00 363 | 0.00 357 | 0.00 358 | 67.46 325 | 0.00 365 | 0.00 360 | 0.00 357 | 0.00 358 | 0.00 358 |
|
1314 | | | 59.83 248 | 58.86 256 | 62.74 241 | 65.71 291 | 44.78 233 | 68.59 205 | 72.63 197 | 33.54 328 | 61.05 284 | 67.29 327 | 43.62 252 | 71.26 239 | 49.49 212 | 67.84 317 | 72.19 256 |
|
IB-MVS | | 49.67 18 | 59.69 249 | 56.96 269 | 67.90 197 | 68.19 272 | 50.30 174 | 61.42 282 | 65.18 241 | 47.57 250 | 55.83 312 | 67.15 328 | 23.77 347 | 79.60 143 | 43.56 246 | 79.97 246 | 73.79 242 |
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 |
sss | | | 47.59 307 | 48.32 306 | 45.40 318 | 56.73 338 | 33.96 306 | 45.17 332 | 48.51 319 | 32.11 334 | 52.37 325 | 65.79 329 | 40.39 270 | 41.91 342 | 31.85 315 | 61.97 329 | 60.35 324 |
|
dp | | | 44.09 319 | 44.88 318 | 41.72 330 | 58.53 329 | 23.18 347 | 54.70 309 | 42.38 339 | 34.80 316 | 44.25 346 | 65.61 330 | 24.48 346 | 44.80 329 | 29.77 325 | 49.42 349 | 57.18 332 |
|
PVSNet | | 43.83 21 | 51.56 296 | 51.17 297 | 52.73 294 | 68.34 270 | 38.27 274 | 48.22 323 | 53.56 297 | 36.41 308 | 54.29 320 | 64.94 331 | 34.60 289 | 54.20 310 | 30.34 321 | 69.87 307 | 65.71 306 |
|
testpf | | | 45.32 311 | 48.47 305 | 35.88 337 | 53.56 351 | 26.84 339 | 58.86 297 | 42.95 335 | 47.78 248 | 46.18 339 | 63.70 332 | 13.73 358 | 50.29 314 | 50.81 202 | 58.61 339 | 30.51 352 |
|
pmmvs3 | | | 46.71 308 | 45.09 316 | 51.55 298 | 56.76 337 | 48.25 194 | 55.78 305 | 39.53 349 | 24.13 350 | 50.35 331 | 63.40 333 | 15.90 357 | 51.08 312 | 29.29 328 | 70.69 302 | 55.33 334 |
|
testus | | | 45.03 315 | 46.49 313 | 40.65 331 | 62.53 304 | 25.24 344 | 42.54 335 | 46.23 327 | 31.16 340 | 57.69 302 | 62.90 334 | 34.60 289 | 42.33 338 | 17.72 350 | 63.01 327 | 54.37 335 |
|
gm-plane-assit | | | | | | 62.51 305 | 33.91 307 | | | 37.25 305 | | 62.71 335 | | 72.74 220 | 38.70 280 | | |
|
MVE | | 27.91 23 | 36.69 328 | 35.64 331 | 39.84 333 | 43.37 355 | 35.85 293 | 19.49 351 | 24.61 357 | 24.68 349 | 39.05 351 | 62.63 336 | 38.67 276 | 27.10 355 | 21.04 346 | 47.25 350 | 56.56 333 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
new_pmnet | | | 37.55 326 | 39.80 328 | 30.79 339 | 56.83 336 | 16.46 352 | 39.35 342 | 30.65 354 | 25.59 348 | 45.26 341 | 61.60 337 | 24.54 345 | 28.02 354 | 21.60 345 | 52.80 348 | 47.90 342 |
|
test12356 | | | 38.35 324 | 40.80 323 | 31.01 338 | 58.31 330 | 9.09 356 | 36.67 348 | 46.65 324 | 33.65 326 | 44.39 345 | 60.94 338 | 17.56 355 | 39.23 347 | 16.01 351 | 53.03 347 | 44.72 346 |
|
test-LLR | | | 50.43 298 | 50.69 301 | 49.64 302 | 60.76 314 | 41.87 249 | 53.18 312 | 45.48 331 | 43.41 279 | 49.41 333 | 60.47 339 | 29.22 329 | 44.73 330 | 42.09 262 | 72.14 293 | 62.33 321 |
|
test-mter | | | 48.56 303 | 48.20 308 | 49.64 302 | 60.76 314 | 41.87 249 | 53.18 312 | 45.48 331 | 31.91 336 | 49.41 333 | 60.47 339 | 18.34 353 | 44.73 330 | 42.09 262 | 72.14 293 | 62.33 321 |
|
DWT-MVSNet_test | | | 53.04 287 | 51.12 298 | 58.77 272 | 61.23 311 | 38.67 271 | 62.16 270 | 57.74 268 | 38.24 299 | 51.76 326 | 59.07 341 | 21.36 349 | 67.40 270 | 44.80 240 | 63.76 325 | 70.25 272 |
|
PatchFormer-LS_test | | | 53.94 285 | 52.64 290 | 57.85 276 | 61.87 308 | 39.59 265 | 61.60 280 | 57.63 269 | 40.65 291 | 54.52 319 | 58.64 342 | 29.07 331 | 64.18 285 | 46.78 234 | 62.98 328 | 69.78 275 |
|
test2356 | | | 40.85 323 | 40.47 325 | 41.98 327 | 58.78 327 | 28.65 336 | 39.45 341 | 40.98 347 | 31.95 335 | 48.47 335 | 56.63 343 | 12.54 359 | 44.41 332 | 15.84 352 | 59.58 334 | 52.88 336 |
|
TESTMET0.1,1 | | | 45.17 312 | 44.93 317 | 45.89 316 | 56.02 340 | 38.31 273 | 53.18 312 | 41.94 342 | 27.85 344 | 44.86 343 | 56.47 344 | 17.93 354 | 41.50 344 | 38.08 287 | 68.06 315 | 57.85 330 |
|
CHOSEN 280x420 | | | 41.62 321 | 39.89 327 | 46.80 312 | 61.81 309 | 51.59 167 | 33.56 349 | 35.74 351 | 27.48 345 | 37.64 353 | 53.53 345 | 23.24 348 | 42.09 340 | 27.39 332 | 58.64 338 | 46.72 343 |
|
PMMVS | | | 44.69 316 | 43.95 321 | 46.92 311 | 50.05 353 | 53.47 162 | 48.08 325 | 42.40 338 | 22.36 351 | 44.01 347 | 53.05 346 | 42.60 257 | 45.49 325 | 31.69 316 | 61.36 330 | 41.79 347 |
|
GG-mvs-BLEND | | | | | 52.24 296 | 60.64 316 | 29.21 334 | 69.73 192 | 42.41 337 | | 45.47 340 | 52.33 347 | 20.43 351 | 68.16 263 | 25.52 337 | 65.42 322 | 59.36 329 |
|
PNet_i23d | | | 36.76 327 | 36.63 330 | 37.12 335 | 58.19 332 | 33.00 317 | 39.86 340 | 32.55 353 | 48.44 241 | 39.64 349 | 51.31 348 | 6.89 362 | 41.83 343 | 22.29 343 | 30.55 352 | 36.54 351 |
|
E-PMN | | | 45.17 312 | 45.36 315 | 44.60 322 | 50.07 352 | 42.75 244 | 38.66 344 | 42.29 340 | 46.39 256 | 39.55 350 | 51.15 349 | 26.00 341 | 45.37 327 | 37.68 289 | 76.41 271 | 45.69 345 |
|
PVSNet_0 | | 36.71 22 | 41.12 322 | 40.78 324 | 42.14 326 | 59.97 318 | 40.13 262 | 40.97 337 | 42.24 341 | 30.81 341 | 44.86 343 | 49.41 350 | 40.70 268 | 45.12 328 | 23.15 342 | 34.96 351 | 41.16 348 |
|
EMVS | | | 44.61 318 | 44.45 320 | 45.10 320 | 48.91 354 | 43.00 242 | 37.92 345 | 41.10 346 | 46.75 253 | 38.00 352 | 48.43 351 | 26.42 340 | 46.27 322 | 37.11 294 | 75.38 280 | 46.03 344 |
|
DeepMVS_CX | | | | | 11.83 343 | 15.51 357 | 13.86 355 | | 11.25 360 | 5.76 353 | 20.85 355 | 26.46 352 | 17.06 356 | 9.22 356 | 9.69 354 | 13.82 354 | 12.42 353 |
|
X-MVStestdata | | | 76.81 73 | 74.79 98 | 82.85 7 | 89.43 16 | 77.61 14 | 86.80 13 | 84.66 34 | 72.71 23 | 82.87 76 | 9.95 353 | 73.86 43 | 86.31 16 | 78.84 18 | 94.03 47 | 84.64 97 |
|
tmp_tt | | | 11.98 332 | 14.73 333 | 3.72 344 | 2.28 358 | 4.62 359 | 19.44 352 | 14.50 359 | 0.47 354 | 21.55 354 | 9.58 354 | 25.78 343 | 4.57 357 | 11.61 353 | 27.37 353 | 1.96 354 |
|
test_post1 | | | | | | | | 66.63 229 | | | | 2.08 355 | 30.66 320 | 59.33 301 | 40.34 274 | | |
|
test_post | | | | | | | | | | | | 1.99 356 | 30.91 318 | 54.76 308 | | | |
|
test123 | | | 4.43 335 | 5.78 336 | 0.39 346 | 0.97 359 | 0.28 360 | 46.33 330 | 0.45 361 | 0.31 355 | 0.62 356 | 1.50 357 | 0.61 364 | 0.11 359 | 0.56 355 | 0.63 355 | 0.77 357 |
|
testmvs | | | 4.06 336 | 5.28 337 | 0.41 345 | 0.64 360 | 0.16 361 | 42.54 335 | 0.31 362 | 0.26 356 | 0.50 357 | 1.40 358 | 0.77 363 | 0.17 358 | 0.56 355 | 0.55 356 | 0.90 355 |
|
pcd_1.5k_mvsjas | | | 5.20 334 | 6.93 335 | 0.00 347 | 0.00 361 | 0.00 362 | 0.00 353 | 0.00 363 | 0.00 357 | 0.00 358 | 0.00 359 | 62.39 124 | 0.00 360 | 0.00 357 | 0.00 358 | 0.00 358 |
|
pcd1.5k->3k | | | 35.00 329 | 36.93 329 | 29.21 340 | 84.62 66 | 0.00 362 | 0.00 353 | 78.90 138 | 0.00 357 | 0.00 358 | 0.00 359 | 78.26 15 | 0.00 360 | 0.00 357 | 90.55 101 | 87.62 65 |
|
sosnet-low-res | | | 0.00 337 | 0.00 338 | 0.00 347 | 0.00 361 | 0.00 362 | 0.00 353 | 0.00 363 | 0.00 357 | 0.00 358 | 0.00 359 | 0.00 365 | 0.00 360 | 0.00 357 | 0.00 358 | 0.00 358 |
|
sosnet | | | 0.00 337 | 0.00 338 | 0.00 347 | 0.00 361 | 0.00 362 | 0.00 353 | 0.00 363 | 0.00 357 | 0.00 358 | 0.00 359 | 0.00 365 | 0.00 360 | 0.00 357 | 0.00 358 | 0.00 358 |
|
uncertanet | | | 0.00 337 | 0.00 338 | 0.00 347 | 0.00 361 | 0.00 362 | 0.00 353 | 0.00 363 | 0.00 357 | 0.00 358 | 0.00 359 | 0.00 365 | 0.00 360 | 0.00 357 | 0.00 358 | 0.00 358 |
|
Regformer | | | 0.00 337 | 0.00 338 | 0.00 347 | 0.00 361 | 0.00 362 | 0.00 353 | 0.00 363 | 0.00 357 | 0.00 358 | 0.00 359 | 0.00 365 | 0.00 360 | 0.00 357 | 0.00 358 | 0.00 358 |
|
uanet | | | 0.00 337 | 0.00 338 | 0.00 347 | 0.00 361 | 0.00 362 | 0.00 353 | 0.00 363 | 0.00 357 | 0.00 358 | 0.00 359 | 0.00 365 | 0.00 360 | 0.00 357 | 0.00 358 | 0.00 358 |
|
GSMVS | | | | | | | | | | | | | | | | | 70.05 273 |
|
test_part2 | | | | | | 85.90 46 | 66.44 80 | | | | 84.61 61 | | | | | | |
|
test_part1 | | | | | | | | | 84.94 30 | | | | 75.17 31 | | | 93.83 49 | 82.50 145 |
|
sam_mvs1 | | | | | | | | | | | | | 31.41 311 | | | | 70.05 273 |
|
sam_mvs | | | | | | | | | | | | | 31.21 315 | | | | |
|
MTGPA | | | | | | | | | 80.63 107 | | | | | | | | |
|
MTMP | | | | | | | | | 19.26 358 | | | | | | | | |
|
test9_res | | | | | | | | | | | | | | | 72.12 54 | 91.37 80 | 77.40 218 |
|
agg_prior2 | | | | | | | | | | | | | | | 70.70 62 | 90.93 92 | 78.55 206 |
|
agg_prior | | | | | | 84.44 71 | 66.02 84 | | 78.62 144 | | 76.95 145 | | | 80.34 132 | | | |
|
test_prior4 | | | | | | | 70.14 58 | 77.57 83 | | | | | | | | | |
|
test_prior | | | | | 75.27 91 | 82.15 97 | 59.85 126 | | 84.33 39 | | | | | 83.39 67 | | | 82.58 142 |
|
旧先验2 | | | | | | | | 71.17 176 | | 45.11 266 | 78.54 128 | | | 61.28 297 | 59.19 146 | | |
|
新几何2 | | | | | | | | 71.33 173 | | | | | | | | | |
|
无先验 | | | | | | | | 74.82 127 | 70.94 216 | 47.75 249 | | | | 76.85 184 | 54.47 181 | | 72.09 257 |
|
原ACMM2 | | | | | | | | 74.78 131 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 67.30 271 | 48.34 221 | | |
|
segment_acmp | | | | | | | | | | | | | 68.30 82 | | | | |
|
testdata1 | | | | | | | | 68.34 211 | | 57.24 141 | | | | | | | |
|
test12 | | | | | 76.51 73 | 82.28 95 | 60.94 119 | | 81.64 82 | | 73.60 187 | | 64.88 108 | 85.19 41 | | 90.42 103 | 83.38 127 |
|
plane_prior7 | | | | | | 85.18 55 | 66.21 83 | | | | | | | | | | |
|
plane_prior6 | | | | | | 84.18 75 | 65.31 89 | | | | | | 60.83 142 | | | | |
|
plane_prior5 | | | | | | | | | 85.49 21 | | | | | 86.15 22 | 71.09 56 | 90.94 90 | 84.82 94 |
|
plane_prior3 | | | | | | | 65.67 86 | | | 63.82 85 | 78.23 133 | | | | | | |
|
plane_prior2 | | | | | | | | 82.74 38 | | 65.45 62 | | | | | | | |
|
plane_prior1 | | | | | | 84.46 70 | | | | | | | | | | | |
|
plane_prior | | | | | | | 65.18 90 | 80.06 61 | | 61.88 103 | | | | | | 89.91 113 | |
|
n2 | | | | | | | | | 0.00 363 | | | | | | | | |
|
nn | | | | | | | | | 0.00 363 | | | | | | | | |
|
door-mid | | | | | | | | | 55.02 286 | | | | | | | | |
|
test11 | | | | | | | | | 82.71 68 | | | | | | | | |
|
door | | | | | | | | | 52.91 301 | | | | | | | | |
|
HQP5-MVS | | | | | | | 58.80 135 | | | | | | | | | | |
|
HQP-NCC | | | | | | 82.37 92 | | 77.32 87 | | 59.08 124 | 71.58 209 | | | | | | |
|
ACMP_Plane | | | | | | 82.37 92 | | 77.32 87 | | 59.08 124 | 71.58 209 | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 67.38 95 | | |
|
HQP4-MVS | | | | | | | | | | | 71.59 208 | | | 85.31 35 | | | 83.74 119 |
|
HQP3-MVS | | | | | | | | | 84.12 46 | | | | | | | 89.16 120 | |
|
HQP2-MVS | | | | | | | | | | | | | 58.09 176 | | | | |
|
MDTV_nov1_ep13_2view | | | | | | | 18.41 350 | 53.74 311 | | 31.57 337 | 44.89 342 | | 29.90 326 | | 32.93 313 | | 71.48 261 |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 89.47 118 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 91.96 70 | |
|
Test By Simon | | | | | | | | | | | | | 62.56 120 | | | | |
|