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 |
|
DTE-MVSNet | | | 80.35 42 | 82.89 29 | 72.74 138 | 89.84 8 | 37.34 285 | 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 | 39.02 271 | 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 |
|
PS-CasMVS | | | 80.41 41 | 82.86 30 | 73.07 128 | 89.93 7 | 39.21 268 | 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 |
|
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 33 | 79.70 11 | 85.76 31 | 66.91 99 | 95.46 13 | 87.89 64 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
CP-MVSNet | | | 79.48 49 | 81.65 39 | 72.98 131 | 89.66 13 | 39.06 270 | 76.76 95 | 80.46 112 | 78.91 6 | 90.32 9 | 91.70 26 | 68.49 79 | 84.89 44 | 63.40 124 | 95.12 20 | 95.01 4 |
|
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 |
|
WR-MVS_H | | | 80.22 44 | 82.17 36 | 74.39 97 | 89.46 15 | 42.69 246 | 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 |
|
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 31 | 81.18 6 | 84.31 55 | 78.12 22 | 94.47 35 | 84.15 114 |
|
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 84 | 89.95 72 | 69.14 73 | 85.26 37 | 66.15 106 | 91.24 84 | 87.61 67 |
|
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 | 80.65 107 | 90.18 69 | 59.80 152 | 87.58 4 | 73.06 42 | 91.34 82 | 89.01 43 |
|
Anonymous20231211 | | | 77.74 66 | 80.26 47 | 70.19 170 | 83.05 84 | 43.39 240 | 75.86 113 | 76.74 170 | 75.91 12 | 85.92 43 | 96.14 1 | 80.85 8 | 75.59 197 | 53.58 190 | 94.27 42 | 91.58 13 |
|
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 | 79.80 116 | 94.91 3 | 69.79 70 | 80.25 134 | 72.63 44 | 94.46 37 | 88.78 52 |
|
test_0402 | | | 78.17 65 | 79.48 54 | 74.24 99 | 83.50 79 | 59.15 133 | 72.52 148 | 74.60 186 | 75.34 13 | 88.69 15 | 91.81 23 | 75.06 34 | 82.37 81 | 65.10 114 | 88.68 128 | 81.20 168 |
|
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 | 80.41 110 | 86.27 141 | 71.68 56 | 83.45 66 | 62.45 129 | 92.40 67 | 78.92 204 |
|
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 56 | 80.02 10 | 84.48 51 | 77.73 24 | 94.34 40 | 85.93 82 |
|
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 | 76.97 182 | 64.98 115 | 93.61 52 | 72.28 256 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
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 26 | 78.23 16 | 85.20 40 | 79.45 12 | 94.91 26 | 88.15 62 |
|
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 70 | 88.91 48 |
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 |
test_part3 | | | | | | | | 83.39 34 | | 73.27 20 | | 89.25 82 | | 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 62 | 89.25 82 | 75.17 31 | 86.96 10 | 72.56 46 | 93.83 49 | 82.50 146 |
|
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 39 | 80.28 9 | 82.44 79 | 64.82 116 | 96.10 6 | 87.21 71 |
|
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 77 | 90.39 62 | 73.86 43 | 86.31 16 | 78.84 18 | 94.03 47 | 84.64 98 |
|
X-MVStestdata | | | 76.81 73 | 74.79 99 | 82.85 7 | 89.43 16 | 77.61 14 | 86.80 13 | 84.66 34 | 72.71 23 | 82.87 77 | 9.95 355 | 73.86 43 | 86.31 16 | 78.84 18 | 94.03 47 | 84.64 98 |
|
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 45 | 76.65 21 | 84.89 44 | 79.30 16 | 94.63 32 | 82.35 150 |
|
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 46 | 74.65 37 | 84.42 52 | 80.98 4 | 94.75 28 | 80.82 178 |
|
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 46 | 74.65 37 | 84.42 52 | 80.98 4 | 94.75 28 | 80.82 178 |
|
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 25 | 78.20 17 | 87.04 8 | 77.93 23 | 94.32 41 | 83.47 125 |
|
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 61 | 90.56 53 | 77.12 18 | 86.18 21 | 79.24 17 | 95.36 14 | 82.49 148 |
|
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 96 | 90.96 42 | 74.57 39 | 84.69 48 | 78.41 20 | 94.78 27 | 82.74 141 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
gg-mvs-nofinetune | | | 55.75 276 | 56.75 272 | 52.72 297 | 62.87 304 | 28.04 339 | 68.92 199 | 41.36 346 | 71.09 31 | 50.80 330 | 92.63 12 | 20.74 352 | 66.86 275 | 29.97 326 | 72.41 294 | 63.25 317 |
|
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 52 | 78.77 14 | 85.78 30 | 79.37 15 | 95.17 18 | 84.62 100 |
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 |
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 61 | 75.55 28 | 87.69 3 | 79.75 7 | 95.08 21 | 85.36 89 |
Skip Steuart: Steuart Systems R&D Blog. |
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 72 | 91.69 12 |
|
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 60 | 78.99 13 | 86.62 12 | 80.83 6 | 94.93 25 | 86.79 73 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
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 65 | 90.43 57 | 76.79 19 | 85.94 27 | 79.58 10 | 94.23 45 | 82.82 138 |
|
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 64 | 90.67 51 | 75.15 33 | 86.37 15 | 79.58 10 | 94.26 43 | 84.18 113 |
|
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 70 | 90.72 50 | 75.20 30 | 86.27 18 | 79.41 14 | 94.25 44 | 83.95 117 |
|
IS-MVSNet | | | 75.10 94 | 75.42 95 | 74.15 101 | 79.23 125 | 48.05 200 | 79.43 64 | 78.04 156 | 70.09 39 | 79.17 123 | 88.02 109 | 53.04 213 | 83.60 62 | 58.05 152 | 93.76 51 | 90.79 29 |
|
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 37 | 81.83 3 | 86.20 19 | 77.13 27 | 95.96 7 | 86.08 78 |
|
LGP-MVS_train | | | | | 80.90 32 | 87.00 37 | 70.41 56 | | 86.35 12 | 69.77 40 | 87.75 18 | 91.13 37 | 81.83 3 | 86.20 19 | 77.13 27 | 95.96 7 | 86.08 78 |
|
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 72 | 88.96 94 | 75.89 26 | 86.41 14 | 72.62 45 | 92.95 59 | 81.14 171 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
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 | 80.92 104 | 88.52 98 | 72.00 55 | 82.39 80 | 74.80 32 | 93.04 58 | 81.14 171 |
|
VDDNet | | | 71.60 151 | 73.13 126 | 67.02 206 | 86.29 44 | 41.11 255 | 69.97 188 | 66.50 236 | 68.72 44 | 74.74 176 | 91.70 26 | 59.90 149 | 75.81 193 | 48.58 220 | 91.72 72 | 84.15 114 |
|
TranMVSNet+NR-MVSNet | | | 76.13 78 | 77.66 68 | 71.56 156 | 84.61 67 | 42.57 247 | 70.98 179 | 78.29 150 | 68.67 45 | 83.04 76 | 89.26 80 | 72.99 48 | 80.75 126 | 55.58 175 | 95.47 12 | 91.35 15 |
|
Regformer-2 | | | 75.32 90 | 74.47 103 | 77.88 65 | 74.22 196 | 66.65 79 | 72.77 145 | 77.54 160 | 68.47 46 | 80.44 109 | 72.08 295 | 70.60 64 | 80.97 117 | 70.08 68 | 84.02 196 | 86.01 81 |
|
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 43 | 75.59 27 | 86.57 13 | 78.16 21 | 94.71 30 | 83.82 118 |
|
Regformer-4 | | | 74.64 105 | 73.67 113 | 77.55 68 | 74.74 180 | 64.49 96 | 72.91 142 | 75.42 181 | 67.45 48 | 80.24 113 | 72.07 298 | 68.98 75 | 80.19 137 | 70.29 64 | 80.91 235 | 87.98 63 |
|
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 79 | 88.99 92 | 66.82 94 | 78.37 168 | 74.80 32 | 90.76 99 | 82.40 149 |
|
ITE_SJBPF | | | | | 80.35 38 | 76.94 156 | 73.60 38 | | 80.48 111 | 66.87 50 | 83.64 73 | 86.18 144 | 70.25 67 | 79.90 140 | 61.12 135 | 88.95 125 | 87.56 68 |
|
Regformer-1 | | | 74.28 109 | 73.63 115 | 76.21 82 | 74.22 196 | 64.12 98 | 72.77 145 | 75.46 180 | 66.86 51 | 79.27 121 | 72.08 295 | 69.29 72 | 78.74 155 | 68.73 77 | 84.02 196 | 85.77 87 |
|
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 71 | 81.64 5 | 85.95 26 | 74.35 36 | 96.11 5 | 85.81 84 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
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 | 80.39 111 | 88.85 96 | 74.43 41 | 78.33 169 | 74.73 34 | 85.79 166 | 82.35 150 |
|
UniMVSNet_NR-MVSNet | | | 74.90 101 | 75.65 90 | 72.64 140 | 83.04 85 | 45.79 230 | 69.26 197 | 78.81 139 | 66.66 54 | 81.74 88 | 86.88 118 | 63.26 116 | 81.07 114 | 56.21 168 | 94.98 22 | 91.05 21 |
|
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 76 | 69.12 74 | 80.55 129 | 72.51 48 | 93.37 54 | 83.48 124 |
|
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 86 | 81.02 7 | 80.52 130 | 74.27 37 | 95.73 9 | 80.98 175 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
PAPM_NR | | | 73.91 112 | 74.16 108 | 73.16 126 | 81.90 100 | 53.50 161 | 81.28 48 | 81.40 89 | 66.17 57 | 73.30 193 | 83.31 184 | 59.96 148 | 83.10 71 | 58.45 151 | 81.66 223 | 82.87 136 |
|
K. test v3 | | | 73.67 115 | 73.61 116 | 73.87 104 | 79.78 116 | 55.62 148 | 74.69 134 | 62.04 255 | 66.16 58 | 84.76 60 | 93.23 6 | 49.47 228 | 80.97 117 | 65.66 110 | 86.67 159 | 85.02 94 |
|
Anonymous20240521 | | | 74.99 98 | 76.21 85 | 71.33 159 | 77.99 143 | 44.41 237 | 75.24 123 | 77.16 166 | 65.86 59 | 84.89 59 | 91.96 20 | 60.23 146 | 79.31 146 | 59.86 142 | 92.75 63 | 90.27 34 |
|
NCCC | | | 78.25 64 | 78.04 65 | 78.89 53 | 85.61 52 | 69.45 62 | 79.80 63 | 80.99 104 | 65.77 60 | 75.55 166 | 86.25 143 | 67.42 89 | 85.42 33 | 70.10 67 | 90.88 97 | 81.81 162 |
|
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 61 | 85.54 50 | 89.28 79 | 76.32 23 | 83.47 65 | 74.03 38 | 93.57 53 | 84.35 111 |
|
AdaColmap | | | 74.22 110 | 74.56 101 | 73.20 125 | 81.95 99 | 60.97 118 | 79.43 64 | 80.90 105 | 65.57 62 | 72.54 203 | 81.76 206 | 70.98 63 | 85.26 37 | 47.88 226 | 90.00 112 | 73.37 244 |
|
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 63 | 78.23 134 | 89.11 88 | 60.83 142 | 86.15 22 | 71.09 56 | 90.94 91 | 84.82 95 |
|
plane_prior2 | | | | | | | | 82.74 38 | | 65.45 63 | | | | | | | |
|
CNLPA | | | 73.44 119 | 73.03 132 | 74.66 93 | 78.27 138 | 75.29 27 | 75.99 109 | 78.49 146 | 65.39 65 | 75.67 164 | 83.22 189 | 61.23 138 | 66.77 278 | 53.70 189 | 85.33 180 | 81.92 161 |
|
AllTest | | | 77.66 67 | 77.43 69 | 78.35 60 | 79.19 127 | 70.81 51 | 78.60 71 | 88.64 2 | 65.37 66 | 80.09 114 | 88.17 105 | 70.33 65 | 78.43 164 | 55.60 172 | 90.90 95 | 85.81 84 |
|
TestCases | | | | | 78.35 60 | 79.19 127 | 70.81 51 | | 88.64 2 | 65.37 66 | 80.09 114 | 88.17 105 | 70.33 65 | 78.43 164 | 55.60 172 | 90.90 95 | 85.81 84 |
|
v748 | | | 76.93 72 | 77.95 66 | 73.87 104 | 73.94 201 | 52.44 166 | 75.90 111 | 79.98 123 | 65.34 68 | 86.97 30 | 91.77 24 | 67.40 90 | 78.40 166 | 70.23 65 | 90.01 111 | 90.76 31 |
|
DU-MVS | | | 74.91 100 | 75.57 93 | 72.93 133 | 83.50 79 | 45.79 230 | 69.47 195 | 80.14 120 | 65.22 69 | 81.74 88 | 87.08 112 | 61.82 130 | 81.07 114 | 56.21 168 | 94.98 22 | 91.93 10 |
|
LFMVS | | | 67.06 197 | 67.89 192 | 64.56 222 | 78.02 141 | 38.25 277 | 70.81 182 | 59.60 263 | 65.18 70 | 71.06 220 | 86.56 135 | 43.85 251 | 75.22 200 | 46.35 237 | 89.63 115 | 80.21 189 |
|
EPP-MVSNet | | | 73.86 113 | 73.38 120 | 75.31 90 | 78.19 139 | 53.35 163 | 80.45 53 | 77.32 164 | 65.11 71 | 76.47 158 | 86.80 120 | 49.47 228 | 83.77 59 | 53.89 187 | 92.72 64 | 88.81 51 |
|
WR-MVS | | | 71.20 153 | 72.48 141 | 67.36 203 | 84.98 59 | 35.70 296 | 64.43 257 | 68.66 228 | 65.05 72 | 81.49 94 | 86.43 139 | 57.57 191 | 76.48 188 | 50.36 207 | 93.32 56 | 89.90 35 |
|
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 73 | 81.05 101 | 88.38 102 | 57.10 198 | 87.10 7 | 79.75 7 | 83.87 198 | 79.24 200 |
|
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 74 | 83.97 69 | 91.02 40 | 75.53 29 | 85.93 29 | 82.00 2 | 94.36 39 | 83.35 131 |
|
Regformer-3 | | | 72.86 136 | 72.28 145 | 74.62 94 | 74.74 180 | 60.18 123 | 72.91 142 | 71.76 204 | 64.74 75 | 78.42 130 | 72.07 298 | 67.00 91 | 76.28 190 | 67.97 88 | 80.91 235 | 87.39 69 |
|
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 76 | 78.11 136 | 88.39 101 | 65.46 104 | 83.14 70 | 77.64 26 | 91.20 85 | 78.94 203 |
|
#test# | | | 82.40 25 | 82.71 32 | 81.48 22 | 89.25 20 | 75.69 24 | 84.47 27 | 84.27 41 | 64.45 77 | 84.47 65 | 90.43 57 | 76.79 19 | 85.94 27 | 76.01 31 | 94.23 45 | 82.82 138 |
|
v52 | | | 78.96 53 | 79.79 52 | 76.46 76 | 73.03 227 | 54.90 150 | 78.48 73 | 83.48 56 | 64.43 78 | 91.19 4 | 91.54 29 | 72.08 52 | 81.11 110 | 76.45 29 | 87.47 143 | 93.38 7 |
|
V4 | | | 78.96 53 | 79.79 52 | 76.46 76 | 73.02 228 | 54.90 150 | 78.48 73 | 83.47 57 | 64.43 78 | 91.20 3 | 91.54 29 | 72.08 52 | 81.11 110 | 76.45 29 | 87.46 145 | 93.38 7 |
|
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 80 | 86.14 40 | 87.07 114 | 74.02 42 | 80.97 117 | 77.70 25 | 92.32 69 | 80.62 183 |
|
NR-MVSNet | | | 73.62 116 | 74.05 109 | 72.33 149 | 83.50 79 | 43.71 239 | 65.65 244 | 77.32 164 | 64.32 81 | 75.59 165 | 87.08 112 | 62.45 123 | 81.34 102 | 54.90 178 | 95.63 10 | 91.93 10 |
|
Gipuma | | | 69.55 169 | 72.83 135 | 59.70 268 | 63.63 303 | 53.97 158 | 80.08 60 | 75.93 174 | 64.24 82 | 73.49 190 | 88.93 95 | 57.89 189 | 62.46 293 | 59.75 145 | 91.55 78 | 62.67 321 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
SixPastTwentyTwo | | | 75.77 82 | 76.34 81 | 74.06 102 | 81.69 103 | 54.84 152 | 76.47 98 | 75.49 178 | 64.10 83 | 87.73 20 | 92.24 17 | 50.45 226 | 81.30 104 | 67.41 93 | 91.46 79 | 86.04 80 |
|
EI-MVSNet-Vis-set | | | 72.78 137 | 71.87 148 | 75.54 88 | 74.77 179 | 59.02 134 | 72.24 150 | 71.56 207 | 63.92 84 | 78.59 126 | 71.59 305 | 66.22 99 | 78.60 157 | 67.58 91 | 80.32 243 | 89.00 44 |
|
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 84 | 77.51 141 | 86.56 135 | 68.43 81 | 84.82 46 | 73.83 39 | 91.61 75 | 82.26 153 |
|
plane_prior3 | | | | | | | 65.67 86 | | | 63.82 86 | 78.23 134 | | | | | | |
|
UniMVSNet (Re) | | | 75.00 97 | 75.48 94 | 73.56 113 | 83.14 83 | 47.92 202 | 70.41 185 | 81.04 102 | 63.67 87 | 79.54 118 | 86.37 140 | 62.83 118 | 81.82 88 | 57.10 159 | 95.25 16 | 90.94 26 |
|
ANet_high | | | 67.08 196 | 69.94 167 | 58.51 275 | 57.55 336 | 27.09 340 | 58.43 299 | 76.80 168 | 63.56 88 | 82.40 81 | 91.93 21 | 59.82 151 | 64.98 285 | 50.10 209 | 88.86 126 | 83.46 126 |
|
MVS_0304 | | | 74.55 107 | 73.47 117 | 77.80 66 | 77.41 151 | 63.88 100 | 75.75 115 | 83.67 53 | 63.55 89 | 66.12 258 | 82.16 200 | 60.20 147 | 86.15 22 | 65.37 113 | 86.98 156 | 83.38 128 |
|
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 90 | 84.31 68 | 91.47 33 | 72.87 50 | 87.16 6 | 79.74 9 | 94.47 35 | 84.61 101 |
|
EI-MVSNet-UG-set | | | 72.63 139 | 71.68 152 | 75.47 89 | 74.67 183 | 58.64 138 | 72.02 155 | 71.50 208 | 63.53 90 | 78.58 128 | 71.39 308 | 65.98 100 | 78.53 159 | 67.30 97 | 80.18 244 | 89.23 39 |
|
pmmvs6 | | | 71.82 149 | 73.66 114 | 66.31 214 | 75.94 167 | 42.01 249 | 66.99 226 | 72.53 199 | 63.45 92 | 76.43 159 | 92.78 10 | 72.95 49 | 69.69 249 | 51.41 199 | 90.46 103 | 87.22 70 |
|
ACMH | | 63.62 14 | 77.50 69 | 80.11 48 | 69.68 177 | 79.61 118 | 56.28 145 | 78.81 69 | 83.62 54 | 63.41 93 | 87.14 28 | 90.23 68 | 76.11 24 | 73.32 212 | 67.58 91 | 94.44 38 | 79.44 198 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
CANet | | | 73.00 128 | 71.84 149 | 76.48 74 | 75.82 168 | 61.28 116 | 74.81 129 | 80.37 114 | 63.17 94 | 62.43 277 | 80.50 216 | 61.10 140 | 85.16 42 | 64.00 120 | 84.34 192 | 83.01 134 |
|
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 94 | 85.38 52 | 91.26 36 | 76.33 22 | 84.67 49 | 83.30 1 | 94.96 24 | 86.17 77 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
Vis-MVSNet | | | 74.85 104 | 74.56 101 | 75.72 86 | 81.63 104 | 64.64 94 | 76.35 101 | 79.06 135 | 62.85 96 | 73.33 192 | 88.41 100 | 62.54 122 | 79.59 144 | 63.94 121 | 82.92 206 | 82.94 135 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
v13 | | | 76.23 77 | 77.02 74 | 73.86 106 | 74.61 187 | 48.80 185 | 76.91 94 | 81.10 98 | 62.66 97 | 87.02 29 | 91.01 41 | 59.76 153 | 81.41 97 | 71.29 55 | 88.78 127 | 91.38 14 |
|
Effi-MVS+ | | | 72.10 146 | 72.28 145 | 71.58 155 | 74.21 198 | 50.33 173 | 74.72 133 | 82.73 67 | 62.62 98 | 70.77 222 | 76.83 249 | 69.96 69 | 80.97 117 | 60.20 137 | 78.43 262 | 83.45 127 |
|
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 99 | 81.77 86 | 86.65 131 | 71.46 58 | 83.53 64 | 67.95 89 | 92.44 66 | 89.60 36 |
|
API-MVS | | | 70.97 156 | 71.51 156 | 69.37 178 | 75.20 172 | 55.94 146 | 80.99 49 | 76.84 167 | 62.48 100 | 71.24 217 | 77.51 245 | 61.51 134 | 80.96 121 | 52.04 194 | 85.76 167 | 71.22 265 |
|
CSCG | | | 74.12 111 | 74.39 104 | 73.33 121 | 79.35 122 | 61.66 115 | 77.45 86 | 81.98 76 | 62.47 101 | 79.06 124 | 80.19 219 | 61.83 129 | 78.79 154 | 59.83 143 | 87.35 148 | 79.54 197 |
|
v12 | | | 76.03 79 | 76.79 75 | 73.76 108 | 74.45 189 | 48.60 191 | 76.59 96 | 81.11 95 | 62.22 102 | 86.79 31 | 90.74 49 | 59.51 154 | 81.40 99 | 71.01 58 | 88.67 129 | 91.29 16 |
|
OurMVSNet-221017-0 | | | 78.57 59 | 78.53 62 | 78.67 55 | 80.48 112 | 64.16 97 | 80.24 57 | 82.06 74 | 61.89 103 | 88.77 14 | 93.32 5 | 57.15 196 | 82.60 78 | 70.08 68 | 92.80 60 | 89.25 38 |
|
plane_prior | | | | | | | 65.18 90 | 80.06 61 | | 61.88 104 | | | | | | 89.91 114 | |
|
V9 | | | 75.82 81 | 76.53 78 | 73.66 109 | 74.28 193 | 48.37 192 | 76.26 104 | 81.10 98 | 61.73 105 | 86.59 34 | 90.43 57 | 59.16 160 | 81.42 96 | 70.71 61 | 88.56 130 | 91.21 19 |
|
v11 | | | 75.76 83 | 76.51 79 | 73.48 116 | 74.28 193 | 47.81 204 | 76.16 106 | 81.28 91 | 61.56 106 | 86.39 36 | 90.38 63 | 59.32 158 | 81.41 97 | 70.85 59 | 88.41 132 | 91.23 17 |
|
UGNet | | | 70.20 162 | 69.05 174 | 73.65 110 | 76.24 162 | 63.64 101 | 75.87 112 | 72.53 199 | 61.48 107 | 60.93 289 | 86.14 147 | 52.37 217 | 77.12 181 | 50.67 204 | 85.21 183 | 80.17 194 |
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 |
VDD-MVS | | | 70.81 157 | 71.44 157 | 68.91 190 | 79.07 132 | 46.51 228 | 67.82 216 | 70.83 219 | 61.23 108 | 74.07 185 | 88.69 97 | 59.86 150 | 75.62 196 | 51.11 201 | 90.28 105 | 84.61 101 |
|
V14 | | | 75.58 86 | 76.26 84 | 73.55 114 | 74.10 200 | 48.13 197 | 75.91 110 | 81.07 101 | 61.19 109 | 86.34 37 | 90.11 70 | 58.80 164 | 81.40 99 | 70.40 63 | 88.43 131 | 91.12 20 |
|
FMVSNet1 | | | 71.06 154 | 72.48 141 | 66.81 208 | 77.65 149 | 40.68 257 | 71.96 157 | 73.03 192 | 61.14 110 | 79.45 120 | 90.36 65 | 60.44 144 | 75.20 201 | 50.20 208 | 88.05 136 | 84.54 103 |
|
TransMVSNet (Re) | | | 69.62 167 | 71.63 153 | 63.57 231 | 76.51 159 | 35.93 294 | 65.75 243 | 71.29 212 | 61.05 111 | 75.02 171 | 89.90 73 | 65.88 101 | 70.41 247 | 49.79 210 | 89.48 118 | 84.38 110 |
|
EPNet | | | 69.10 175 | 67.32 195 | 74.46 95 | 68.33 272 | 61.27 117 | 77.56 84 | 63.57 247 | 60.95 112 | 56.62 310 | 82.75 191 | 51.53 222 | 81.24 108 | 54.36 186 | 90.20 106 | 80.88 177 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
MSDG | | | 67.47 195 | 67.48 194 | 67.46 202 | 70.70 248 | 54.69 154 | 66.90 228 | 78.17 153 | 60.88 113 | 70.41 227 | 74.76 272 | 61.22 139 | 73.18 213 | 47.38 229 | 76.87 271 | 74.49 238 |
|
v15 | | | 75.37 88 | 76.01 87 | 73.44 117 | 73.91 204 | 47.87 203 | 75.55 118 | 81.04 102 | 60.76 114 | 86.11 41 | 89.76 75 | 58.53 170 | 81.40 99 | 70.11 66 | 88.32 133 | 91.04 23 |
|
TSAR-MVS + GP. | | | 73.08 125 | 71.60 154 | 77.54 69 | 78.99 133 | 70.73 53 | 74.96 126 | 69.38 225 | 60.73 115 | 74.39 181 | 78.44 239 | 57.72 190 | 82.78 75 | 60.16 138 | 89.60 116 | 79.11 202 |
|
v17 | | | 75.03 96 | 75.59 92 | 73.36 118 | 73.56 206 | 47.66 208 | 75.48 119 | 81.45 86 | 60.58 116 | 85.55 49 | 89.02 90 | 58.36 172 | 81.47 93 | 69.69 73 | 86.59 160 | 90.96 24 |
|
v16 | | | 74.89 102 | 75.41 96 | 73.35 119 | 73.54 207 | 47.62 209 | 75.47 120 | 81.45 86 | 60.58 116 | 85.46 51 | 88.97 93 | 58.27 173 | 81.47 93 | 69.66 74 | 85.25 182 | 90.95 25 |
|
MSLP-MVS++ | | | 74.48 108 | 75.78 89 | 70.59 164 | 84.66 64 | 62.40 108 | 78.65 70 | 84.24 43 | 60.55 118 | 77.71 139 | 81.98 202 | 63.12 117 | 77.64 178 | 62.95 126 | 88.14 135 | 71.73 261 |
|
Baseline_NR-MVSNet | | | 70.62 159 | 73.19 124 | 62.92 237 | 76.97 155 | 34.44 306 | 68.84 200 | 70.88 218 | 60.25 119 | 79.50 119 | 90.53 54 | 61.82 130 | 69.11 251 | 54.67 181 | 95.27 15 | 85.22 90 |
|
v8 | | | 75.07 95 | 75.64 91 | 73.35 119 | 73.42 210 | 47.46 213 | 75.20 124 | 81.45 86 | 60.05 120 | 85.64 46 | 89.26 80 | 58.08 179 | 81.80 89 | 69.71 72 | 87.97 139 | 90.79 29 |
|
v18 | | | 74.60 106 | 75.06 97 | 73.22 124 | 73.29 216 | 47.36 217 | 75.02 125 | 81.47 85 | 60.01 121 | 85.13 54 | 88.44 99 | 57.93 187 | 81.47 93 | 69.26 75 | 85.02 186 | 90.84 28 |
|
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 | 59.89 122 | 80.91 105 | 90.53 54 | 72.19 51 | 88.56 1 | 73.67 40 | 94.52 34 | 85.92 83 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
F-COLMAP | | | 75.29 91 | 73.99 110 | 79.18 49 | 81.73 102 | 71.90 43 | 81.86 46 | 82.98 63 | 59.86 123 | 72.27 205 | 84.00 174 | 64.56 112 | 83.07 72 | 51.48 198 | 87.19 153 | 82.56 145 |
|
RPSCF | | | 75.76 83 | 74.37 105 | 79.93 40 | 74.81 178 | 77.53 16 | 77.53 85 | 79.30 129 | 59.44 124 | 78.88 125 | 89.80 74 | 71.26 61 | 73.09 214 | 57.45 155 | 80.89 237 | 89.17 41 |
|
HQP-NCC | | | | | | 82.37 92 | | 77.32 87 | | 59.08 125 | 71.58 210 | | | | | | |
|
ACMP_Plane | | | | | | 82.37 92 | | 77.32 87 | | 59.08 125 | 71.58 210 | | | | | | |
|
HQP-MVS | | | 75.24 92 | 75.01 98 | 75.94 83 | 82.37 92 | 58.80 135 | 77.32 87 | 84.12 46 | 59.08 125 | 71.58 210 | 85.96 152 | 58.09 177 | 85.30 36 | 67.38 95 | 89.16 121 | 83.73 121 |
|
v10 | | | 75.69 85 | 76.20 86 | 74.16 100 | 74.44 191 | 48.69 187 | 75.84 114 | 82.93 65 | 59.02 128 | 85.92 43 | 89.17 85 | 58.56 169 | 82.74 76 | 70.73 60 | 89.14 123 | 91.05 21 |
|
test_prior3 | | | 76.71 74 | 77.19 71 | 75.27 91 | 82.15 97 | 59.85 126 | 75.57 116 | 84.33 39 | 58.92 129 | 76.53 156 | 86.78 122 | 67.83 87 | 83.39 67 | 69.81 70 | 92.76 61 | 82.58 143 |
|
test_prior2 | | | | | | | | 75.57 116 | | 58.92 129 | 76.53 156 | 86.78 122 | 67.83 87 | | 69.81 70 | 92.76 61 | |
|
MG-MVS | | | 70.47 161 | 71.34 158 | 67.85 199 | 79.26 124 | 40.42 262 | 74.67 135 | 75.15 184 | 58.41 131 | 68.74 239 | 88.14 108 | 56.08 205 | 83.69 60 | 59.90 141 | 81.71 222 | 79.43 199 |
|
EI-MVSNet | | | 69.61 168 | 69.01 177 | 71.41 158 | 73.94 201 | 49.90 177 | 71.31 175 | 71.32 210 | 58.22 132 | 75.40 169 | 70.44 309 | 58.16 175 | 75.85 191 | 62.51 127 | 79.81 249 | 88.48 60 |
|
IterMVS-LS | | | 73.01 127 | 73.12 127 | 72.66 139 | 73.79 205 | 49.90 177 | 71.63 166 | 78.44 147 | 58.22 132 | 80.51 108 | 86.63 132 | 58.15 176 | 79.62 142 | 62.51 127 | 88.20 134 | 88.48 60 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
BH-RMVSNet | | | 68.69 184 | 68.20 190 | 70.14 172 | 76.40 160 | 53.90 160 | 64.62 254 | 73.48 191 | 58.01 134 | 73.91 187 | 81.78 204 | 59.09 161 | 78.22 172 | 48.59 219 | 77.96 267 | 78.31 209 |
|
DP-MVS Recon | | | 73.57 118 | 72.69 139 | 76.23 81 | 82.85 88 | 63.39 103 | 74.32 136 | 82.96 64 | 57.75 135 | 70.35 228 | 81.98 202 | 64.34 113 | 84.41 54 | 49.69 211 | 89.95 113 | 80.89 176 |
|
Effi-MVS+-dtu | | | 75.43 87 | 72.28 145 | 84.91 2 | 77.05 152 | 83.58 2 | 78.47 75 | 77.70 158 | 57.68 136 | 74.89 173 | 78.13 242 | 64.80 109 | 84.26 56 | 56.46 165 | 85.32 181 | 86.88 72 |
|
mvs-test1 | | | 73.81 114 | 70.69 164 | 83.18 3 | 77.05 152 | 81.39 4 | 75.39 121 | 77.70 158 | 57.68 136 | 71.19 219 | 74.72 274 | 64.80 109 | 83.66 61 | 56.46 165 | 81.19 233 | 84.50 107 |
|
MVS_111021_HR | | | 72.98 130 | 72.97 134 | 72.99 130 | 80.82 110 | 65.47 87 | 68.81 202 | 72.77 196 | 57.67 138 | 75.76 163 | 82.38 197 | 71.01 62 | 77.17 180 | 61.38 132 | 86.15 162 | 76.32 226 |
|
3Dnovator | | 65.95 11 | 71.50 152 | 71.22 159 | 72.34 148 | 73.16 218 | 63.09 106 | 78.37 76 | 78.32 148 | 57.67 138 | 72.22 207 | 84.61 167 | 54.77 207 | 78.47 161 | 60.82 136 | 81.07 234 | 75.45 231 |
|
FC-MVSNet-test | | | 73.32 123 | 74.78 100 | 68.93 188 | 79.21 126 | 36.57 287 | 71.82 163 | 79.54 128 | 57.63 140 | 82.57 80 | 90.38 63 | 59.38 157 | 78.99 149 | 57.91 153 | 94.56 33 | 91.23 17 |
|
FPMVS | | | 59.43 252 | 60.07 242 | 57.51 280 | 77.62 150 | 71.52 44 | 62.33 270 | 50.92 313 | 57.40 141 | 69.40 233 | 80.00 220 | 39.14 275 | 61.92 296 | 37.47 294 | 66.36 322 | 39.09 351 |
|
testdata1 | | | | | | | | 68.34 212 | | 57.24 142 | | | | | | | |
|
view600 | | | 62.88 225 | 62.90 220 | 62.82 238 | 72.97 229 | 33.66 312 | 66.10 235 | 55.01 288 | 57.05 143 | 72.66 198 | 82.56 193 | 31.60 309 | 72.78 217 | 42.64 257 | 85.55 172 | 82.02 155 |
|
view800 | | | 62.88 225 | 62.90 220 | 62.82 238 | 72.97 229 | 33.66 312 | 66.10 235 | 55.01 288 | 57.05 143 | 72.66 198 | 82.56 193 | 31.60 309 | 72.78 217 | 42.64 257 | 85.55 172 | 82.02 155 |
|
conf0.05thres1000 | | | 62.88 225 | 62.90 220 | 62.82 238 | 72.97 229 | 33.66 312 | 66.10 235 | 55.01 288 | 57.05 143 | 72.66 198 | 82.56 193 | 31.60 309 | 72.78 217 | 42.64 257 | 85.55 172 | 82.02 155 |
|
tfpn | | | 62.88 225 | 62.90 220 | 62.82 238 | 72.97 229 | 33.66 312 | 66.10 235 | 55.01 288 | 57.05 143 | 72.66 198 | 82.56 193 | 31.60 309 | 72.78 217 | 42.64 257 | 85.55 172 | 82.02 155 |
|
MIMVSNet1 | | | 66.57 198 | 69.23 173 | 58.59 274 | 81.26 108 | 37.73 282 | 64.06 260 | 57.62 271 | 57.02 147 | 78.40 131 | 90.75 48 | 62.65 119 | 58.10 306 | 41.77 268 | 89.58 117 | 79.95 195 |
|
MVS_111021_LR | | | 72.10 146 | 71.82 150 | 72.95 132 | 79.53 120 | 73.90 36 | 70.45 184 | 66.64 235 | 56.87 148 | 76.81 150 | 81.76 206 | 68.78 76 | 71.76 237 | 61.81 130 | 83.74 200 | 73.18 246 |
|
LCM-MVSNet-Re | | | 69.10 175 | 71.57 155 | 61.70 250 | 70.37 251 | 34.30 307 | 61.45 282 | 79.62 125 | 56.81 149 | 89.59 10 | 88.16 107 | 68.44 80 | 72.94 215 | 42.30 262 | 87.33 149 | 77.85 217 |
|
BH-untuned | | | 69.39 170 | 69.46 169 | 69.18 183 | 77.96 144 | 56.88 143 | 68.47 211 | 77.53 161 | 56.77 150 | 77.79 138 | 79.63 227 | 60.30 145 | 80.20 136 | 46.04 238 | 80.65 240 | 70.47 271 |
|
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 | 56.73 151 | 74.88 174 | 85.32 155 | 65.54 102 | 87.79 2 | 65.61 112 | 91.14 87 | 83.35 131 |
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 | 56.66 152 | 80.27 112 | 85.31 156 | 68.56 78 | 87.03 9 | 67.39 94 | 91.26 83 | 83.50 123 |
|
VPA-MVSNet | | | 68.71 183 | 70.37 165 | 63.72 230 | 76.13 164 | 38.06 279 | 64.10 259 | 71.48 209 | 56.60 153 | 74.10 184 | 88.31 103 | 64.78 111 | 69.72 248 | 47.69 228 | 90.15 108 | 83.37 130 |
|
FIs | | | 72.56 143 | 73.80 111 | 68.84 192 | 78.74 135 | 37.74 281 | 71.02 178 | 79.83 124 | 56.12 154 | 80.88 106 | 89.45 77 | 58.18 174 | 78.28 170 | 56.63 161 | 93.36 55 | 90.51 32 |
|
tfpnnormal | | | 66.48 199 | 67.93 191 | 62.16 248 | 73.40 214 | 36.65 286 | 63.45 264 | 64.99 243 | 55.97 155 | 72.82 197 | 87.80 110 | 57.06 199 | 69.10 252 | 48.31 223 | 87.54 142 | 80.72 182 |
|
wuyk23d | | | 61.97 232 | 66.25 200 | 49.12 308 | 58.19 334 | 60.77 120 | 66.32 233 | 52.97 301 | 55.93 156 | 90.62 7 | 86.91 117 | 73.07 46 | 35.98 350 | 20.63 349 | 91.63 74 | 50.62 341 |
|
Fast-Effi-MVS+-dtu | | | 70.00 164 | 68.74 184 | 73.77 107 | 73.47 209 | 64.53 95 | 71.36 173 | 78.14 154 | 55.81 157 | 68.84 238 | 74.71 275 | 65.36 105 | 75.75 194 | 52.00 195 | 79.00 256 | 81.03 173 |
|
v1neww | | | 72.93 132 | 73.07 129 | 72.48 143 | 73.41 212 | 47.46 213 | 72.17 152 | 80.26 116 | 55.63 158 | 81.63 92 | 85.07 159 | 57.97 183 | 81.28 105 | 66.55 104 | 84.98 188 | 88.70 53 |
|
v7new | | | 72.93 132 | 73.07 129 | 72.48 143 | 73.41 212 | 47.46 213 | 72.17 152 | 80.26 116 | 55.63 158 | 81.63 92 | 85.07 159 | 57.97 183 | 81.28 105 | 66.55 104 | 84.98 188 | 88.70 53 |
|
v6 | | | 72.93 132 | 73.08 128 | 72.48 143 | 73.42 210 | 47.47 212 | 72.17 152 | 80.25 118 | 55.63 158 | 81.65 91 | 85.04 162 | 57.95 186 | 81.28 105 | 66.56 103 | 85.01 187 | 88.70 53 |
|
pm-mvs1 | | | 68.40 185 | 69.85 168 | 64.04 227 | 73.10 223 | 39.94 264 | 64.61 255 | 70.50 220 | 55.52 161 | 73.97 186 | 89.33 78 | 63.91 115 | 68.38 263 | 49.68 212 | 88.02 137 | 83.81 119 |
|
v2v482 | | | 72.55 144 | 72.58 140 | 72.43 146 | 72.92 233 | 46.72 227 | 71.41 172 | 79.13 131 | 55.27 162 | 81.17 100 | 85.25 157 | 55.41 206 | 81.13 109 | 67.25 98 | 85.46 176 | 89.43 37 |
|
tfpn111 | | | 61.91 233 | 61.65 230 | 62.68 243 | 72.14 237 | 35.01 300 | 65.42 247 | 56.99 278 | 55.23 163 | 70.71 223 | 79.90 221 | 32.07 304 | 72.85 216 | 38.80 281 | 83.61 201 | 80.18 190 |
|
conf200view11 | | | 61.42 238 | 61.09 236 | 62.43 246 | 72.14 237 | 35.01 300 | 65.42 247 | 56.99 278 | 55.23 163 | 70.71 223 | 79.90 221 | 32.07 304 | 72.09 230 | 35.61 303 | 81.73 218 | 80.18 190 |
|
thres100view900 | | | 61.17 240 | 61.09 236 | 61.39 254 | 72.14 237 | 35.01 300 | 65.42 247 | 56.99 278 | 55.23 163 | 70.71 223 | 79.90 221 | 32.07 304 | 72.09 230 | 35.61 303 | 81.73 218 | 77.08 223 |
|
TAPA-MVS | | 65.27 12 | 75.16 93 | 74.29 107 | 77.77 67 | 74.86 177 | 68.08 72 | 77.89 82 | 84.04 49 | 55.15 166 | 76.19 161 | 83.39 179 | 66.91 92 | 80.11 138 | 60.04 140 | 90.14 109 | 85.13 91 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
v1141 | | | 72.59 142 | 72.73 136 | 72.19 150 | 73.10 223 | 47.00 223 | 71.48 167 | 79.11 132 | 55.01 167 | 81.23 98 | 84.94 164 | 57.45 193 | 80.89 123 | 66.58 101 | 85.65 171 | 88.68 57 |
|
divwei89l23v2f112 | | | 72.60 140 | 72.73 136 | 72.19 150 | 73.10 223 | 47.00 223 | 71.48 167 | 79.11 132 | 55.01 167 | 81.23 98 | 84.95 163 | 57.45 193 | 80.89 123 | 66.58 101 | 85.67 168 | 88.68 57 |
|
v1 | | | 72.60 140 | 72.73 136 | 72.19 150 | 73.12 222 | 47.01 222 | 71.48 167 | 79.10 134 | 55.01 167 | 81.24 97 | 84.92 165 | 57.46 192 | 80.90 122 | 66.59 100 | 85.67 168 | 88.68 57 |
|
EG-PatchMatch MVS | | | 70.70 158 | 70.88 161 | 70.16 171 | 82.64 91 | 58.80 135 | 71.48 167 | 73.64 190 | 54.98 170 | 76.55 154 | 81.77 205 | 61.10 140 | 78.94 150 | 54.87 179 | 80.84 238 | 72.74 251 |
|
GBi-Net | | | 68.30 186 | 68.79 181 | 66.81 208 | 73.14 219 | 40.68 257 | 71.96 157 | 73.03 192 | 54.81 171 | 74.72 177 | 90.36 65 | 48.63 233 | 75.20 201 | 47.12 230 | 85.37 177 | 84.54 103 |
|
test1 | | | 68.30 186 | 68.79 181 | 66.81 208 | 73.14 219 | 40.68 257 | 71.96 157 | 73.03 192 | 54.81 171 | 74.72 177 | 90.36 65 | 48.63 233 | 75.20 201 | 47.12 230 | 85.37 177 | 84.54 103 |
|
FMVSNet2 | | | 67.48 194 | 68.21 189 | 65.29 219 | 73.14 219 | 38.94 272 | 68.81 202 | 71.21 216 | 54.81 171 | 76.73 152 | 86.48 138 | 48.63 233 | 74.60 206 | 47.98 225 | 86.11 164 | 82.35 150 |
|
v148 | | | 69.38 171 | 69.39 170 | 69.36 179 | 69.14 261 | 44.56 234 | 68.83 201 | 72.70 197 | 54.79 174 | 78.59 126 | 84.12 172 | 54.69 208 | 76.74 187 | 59.40 146 | 82.20 211 | 86.79 73 |
|
thres600view7 | | | 61.82 234 | 61.38 235 | 63.12 235 | 71.81 242 | 34.93 303 | 64.64 253 | 56.99 278 | 54.78 175 | 70.33 229 | 79.74 226 | 32.07 304 | 72.42 227 | 38.61 284 | 83.46 202 | 82.02 155 |
|
v7 | | | 73.59 117 | 73.69 112 | 73.28 123 | 74.42 192 | 48.68 188 | 72.74 147 | 81.98 76 | 54.76 176 | 82.07 83 | 85.05 161 | 58.53 170 | 82.22 85 | 67.99 86 | 85.66 170 | 88.95 46 |
|
RPMNet | | | 61.25 239 | 61.55 233 | 60.36 265 | 66.37 287 | 48.24 195 | 70.93 180 | 54.45 293 | 54.66 177 | 61.35 282 | 86.77 124 | 33.29 295 | 63.22 290 | 55.93 170 | 70.17 306 | 69.62 284 |
|
VNet | | | 64.01 217 | 65.15 205 | 60.57 261 | 73.28 217 | 35.61 297 | 57.60 302 | 67.08 233 | 54.61 178 | 66.76 256 | 83.37 181 | 56.28 203 | 66.87 274 | 42.19 263 | 85.20 184 | 79.23 201 |
|
PLC | | 62.01 16 | 71.79 150 | 70.28 166 | 76.33 78 | 80.31 114 | 68.63 70 | 78.18 80 | 81.24 93 | 54.57 179 | 67.09 255 | 80.63 214 | 59.44 155 | 81.74 90 | 46.91 233 | 84.17 193 | 78.63 205 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
nrg030 | | | 74.87 103 | 75.99 88 | 71.52 157 | 74.90 176 | 49.88 180 | 74.10 138 | 82.58 70 | 54.55 180 | 83.50 74 | 89.21 84 | 71.51 57 | 75.74 195 | 61.24 133 | 92.34 68 | 88.94 47 |
|
agg_prior1 | | | 75.89 80 | 76.41 80 | 74.31 98 | 84.44 71 | 66.02 84 | 76.12 107 | 78.62 144 | 54.40 181 | 76.95 146 | 86.85 119 | 66.44 98 | 80.34 132 | 72.45 51 | 91.42 80 | 76.57 225 |
|
canonicalmvs | | | 72.29 145 | 73.38 120 | 69.04 185 | 74.23 195 | 47.37 216 | 73.93 139 | 83.18 60 | 54.36 182 | 76.61 153 | 81.64 208 | 72.03 54 | 75.34 199 | 57.12 158 | 87.28 151 | 84.40 109 |
|
CDPH-MVS | | | 77.33 70 | 77.06 73 | 78.14 63 | 84.21 74 | 63.98 99 | 76.07 108 | 83.45 58 | 54.20 183 | 77.68 140 | 87.18 111 | 69.98 68 | 85.37 34 | 68.01 85 | 92.72 64 | 85.08 93 |
|
VPNet | | | 65.58 201 | 67.56 193 | 59.65 269 | 79.72 117 | 30.17 333 | 60.27 290 | 62.14 251 | 54.19 184 | 71.24 217 | 86.63 132 | 58.80 164 | 67.62 269 | 44.17 244 | 90.87 98 | 81.18 169 |
|
PHI-MVS | | | 74.92 99 | 74.36 106 | 76.61 72 | 76.40 160 | 62.32 110 | 80.38 55 | 83.15 61 | 54.16 185 | 73.23 194 | 80.75 213 | 62.19 127 | 83.86 58 | 68.02 84 | 90.92 94 | 83.65 122 |
|
Patchmtry | | | 60.91 241 | 63.01 219 | 54.62 293 | 66.10 291 | 26.27 344 | 67.47 219 | 56.40 283 | 54.05 186 | 72.04 208 | 86.66 129 | 33.19 296 | 60.17 300 | 43.69 245 | 87.45 146 | 77.42 218 |
|
train_agg | | | 76.38 75 | 76.55 77 | 75.86 85 | 85.47 53 | 69.32 65 | 76.42 99 | 78.69 141 | 54.00 187 | 76.97 144 | 86.74 125 | 66.60 95 | 81.10 112 | 72.50 49 | 91.56 76 | 77.15 220 |
|
test_8 | | | | | | 85.09 58 | 67.89 74 | 76.26 104 | 78.66 143 | 54.00 187 | 76.89 149 | 86.72 127 | 66.60 95 | 80.89 123 | | | |
|
DELS-MVS | | | 68.83 180 | 68.31 186 | 70.38 165 | 70.55 250 | 48.31 193 | 63.78 262 | 82.13 73 | 54.00 187 | 68.96 237 | 75.17 270 | 58.95 163 | 80.06 139 | 58.55 149 | 82.74 207 | 82.76 140 |
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 |
alignmvs | | | 70.54 160 | 71.00 160 | 69.15 184 | 73.50 208 | 48.04 201 | 69.85 191 | 79.62 125 | 53.94 190 | 76.54 155 | 82.00 201 | 59.00 162 | 74.68 205 | 57.32 156 | 87.21 152 | 84.72 97 |
|
v1144 | | | 73.29 124 | 73.39 119 | 73.01 129 | 74.12 199 | 48.11 198 | 72.01 156 | 81.08 100 | 53.83 191 | 81.77 86 | 84.68 166 | 58.07 180 | 81.91 87 | 68.10 82 | 86.86 157 | 88.99 45 |
|
TEST9 | | | | | | 85.47 53 | 69.32 65 | 76.42 99 | 78.69 141 | 53.73 192 | 76.97 144 | 86.74 125 | 66.84 93 | 81.10 112 | | | |
|
Vis-MVSNet (Re-imp) | | | 62.74 230 | 63.21 217 | 61.34 255 | 72.19 236 | 31.56 331 | 67.31 224 | 53.87 295 | 53.60 193 | 69.88 231 | 83.37 181 | 40.52 270 | 70.98 241 | 41.40 269 | 86.78 158 | 81.48 166 |
|
PS-MVSNAJss | | | 77.54 68 | 77.35 70 | 78.13 64 | 84.88 60 | 66.37 82 | 78.55 72 | 79.59 127 | 53.48 194 | 86.29 38 | 92.43 15 | 62.39 124 | 80.25 134 | 67.90 90 | 90.61 100 | 87.77 65 |
|
wuykxyi23d | | | 75.33 89 | 76.75 76 | 71.04 160 | 78.83 134 | 85.01 1 | 71.78 164 | 61.00 258 | 53.47 195 | 96.33 1 | 93.38 4 | 73.07 46 | 68.04 266 | 65.65 111 | 97.28 2 | 60.07 327 |
|
agg_prior3 | | | 76.32 76 | 76.33 82 | 76.28 79 | 85.86 50 | 70.13 59 | 76.50 97 | 78.26 151 | 53.41 196 | 75.78 162 | 86.49 137 | 66.58 97 | 81.57 91 | 72.50 49 | 91.56 76 | 77.15 220 |
|
MDA-MVSNet-bldmvs | | | 62.34 231 | 61.73 228 | 64.16 224 | 61.64 312 | 49.90 177 | 48.11 326 | 57.24 277 | 53.31 197 | 80.95 103 | 79.39 230 | 49.00 231 | 61.55 297 | 45.92 239 | 80.05 246 | 81.03 173 |
|
TinyColmap | | | 67.98 188 | 69.28 171 | 64.08 226 | 67.98 276 | 46.82 225 | 70.04 187 | 75.26 182 | 53.05 198 | 77.36 142 | 86.79 121 | 59.39 156 | 72.59 226 | 45.64 240 | 88.01 138 | 72.83 249 |
|
tfpn200view9 | | | 60.35 246 | 59.97 243 | 61.51 252 | 70.78 246 | 35.35 298 | 63.27 267 | 57.47 272 | 53.00 199 | 68.31 240 | 77.09 247 | 32.45 301 | 72.09 230 | 35.61 303 | 81.73 218 | 77.08 223 |
|
thres400 | | | 60.77 244 | 59.97 243 | 63.15 234 | 70.78 246 | 35.35 298 | 63.27 267 | 57.47 272 | 53.00 199 | 68.31 240 | 77.09 247 | 32.45 301 | 72.09 230 | 35.61 303 | 81.73 218 | 82.02 155 |
|
v1192 | | | 73.40 121 | 73.42 118 | 73.32 122 | 74.65 186 | 48.67 189 | 72.21 151 | 81.73 80 | 52.76 201 | 81.85 85 | 84.56 168 | 57.12 197 | 82.24 84 | 68.58 78 | 87.33 149 | 89.06 42 |
|
MVS_Test | | | 69.84 166 | 70.71 163 | 67.24 204 | 67.49 281 | 43.25 242 | 69.87 190 | 81.22 94 | 52.69 202 | 71.57 213 | 86.68 128 | 62.09 128 | 74.51 207 | 66.05 107 | 78.74 258 | 83.96 116 |
|
MVSFormer | | | 69.93 165 | 69.03 176 | 72.63 141 | 74.93 174 | 59.19 130 | 83.98 29 | 75.72 176 | 52.27 203 | 63.53 273 | 76.74 250 | 43.19 255 | 80.56 127 | 72.28 52 | 78.67 260 | 78.14 212 |
|
test_djsdf | | | 78.88 56 | 78.27 63 | 80.70 35 | 81.42 105 | 71.24 48 | 83.98 29 | 75.72 176 | 52.27 203 | 87.37 25 | 92.25 16 | 68.04 85 | 80.56 127 | 72.28 52 | 91.15 86 | 90.32 33 |
|
CLD-MVS | | | 72.88 135 | 72.36 143 | 74.43 96 | 77.03 154 | 54.30 156 | 68.77 205 | 83.43 59 | 52.12 205 | 76.79 151 | 74.44 278 | 69.54 71 | 83.91 57 | 55.88 171 | 93.25 57 | 85.09 92 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
PatchT | | | 53.35 288 | 56.47 274 | 43.99 326 | 64.19 300 | 17.46 353 | 59.15 294 | 43.10 336 | 52.11 206 | 54.74 319 | 86.95 116 | 29.97 327 | 49.98 318 | 43.62 246 | 74.40 286 | 64.53 316 |
|
CANet_DTU | | | 64.04 216 | 63.83 210 | 64.66 221 | 68.39 269 | 42.97 244 | 73.45 140 | 74.50 187 | 52.05 207 | 54.78 318 | 75.44 269 | 43.99 250 | 70.42 246 | 53.49 192 | 78.41 263 | 80.59 184 |
|
mvs_tets | | | 78.93 55 | 78.67 60 | 79.72 43 | 84.81 62 | 73.93 35 | 80.65 51 | 76.50 171 | 51.98 208 | 87.40 24 | 91.86 22 | 76.09 25 | 78.53 159 | 68.58 78 | 90.20 106 | 86.69 75 |
|
v1240 | | | 73.06 126 | 73.14 125 | 72.84 135 | 74.74 180 | 47.27 219 | 71.88 162 | 81.11 95 | 51.80 209 | 82.28 82 | 84.21 171 | 56.22 204 | 82.34 82 | 68.82 76 | 87.17 154 | 88.91 48 |
|
TSAR-MVS + MP. | | | 79.05 52 | 78.81 57 | 79.74 42 | 88.94 25 | 67.52 76 | 86.61 15 | 81.38 90 | 51.71 210 | 77.15 143 | 91.42 35 | 65.49 103 | 87.20 5 | 79.44 13 | 87.17 154 | 84.51 106 |
|
v1921920 | | | 72.96 131 | 72.98 133 | 72.89 134 | 74.67 183 | 47.58 210 | 71.92 160 | 80.69 106 | 51.70 211 | 81.69 90 | 83.89 175 | 56.58 202 | 82.25 83 | 68.34 80 | 87.36 147 | 88.82 50 |
|
v144192 | | | 72.99 129 | 73.06 131 | 72.77 136 | 74.58 188 | 47.48 211 | 71.90 161 | 80.44 113 | 51.57 212 | 81.46 95 | 84.11 173 | 58.04 181 | 82.12 86 | 67.98 87 | 87.47 143 | 88.70 53 |
|
FMVSNet3 | | | 65.00 205 | 65.16 204 | 64.52 223 | 69.47 258 | 37.56 284 | 66.63 230 | 70.38 221 | 51.55 213 | 74.72 177 | 83.27 186 | 37.89 284 | 74.44 208 | 47.12 230 | 85.37 177 | 81.57 165 |
|
V42 | | | 71.06 154 | 70.83 162 | 71.72 154 | 67.25 282 | 47.14 220 | 65.94 240 | 80.35 115 | 51.35 214 | 83.40 75 | 83.23 187 | 59.25 159 | 78.80 153 | 65.91 109 | 80.81 239 | 89.23 39 |
|
jajsoiax | | | 78.51 60 | 78.16 64 | 79.59 45 | 84.65 65 | 73.83 37 | 80.42 54 | 76.12 172 | 51.33 215 | 87.19 26 | 91.51 32 | 73.79 45 | 78.44 163 | 68.27 81 | 90.13 110 | 86.49 76 |
|
GA-MVS | | | 62.91 223 | 61.66 229 | 66.66 212 | 67.09 284 | 44.49 235 | 61.18 286 | 69.36 226 | 51.33 215 | 69.33 234 | 74.47 277 | 36.83 285 | 74.94 204 | 50.60 205 | 74.72 285 | 80.57 185 |
|
tfpn1000 | | | 58.28 264 | 58.86 257 | 56.53 287 | 68.05 275 | 32.26 322 | 62.58 269 | 51.67 312 | 51.25 217 | 67.38 252 | 75.95 255 | 27.24 341 | 68.83 259 | 43.51 248 | 82.11 213 | 68.49 292 |
|
PM-MVS | | | 64.49 209 | 63.61 213 | 67.14 205 | 76.68 158 | 75.15 28 | 68.49 210 | 42.85 338 | 51.17 218 | 77.85 137 | 80.51 215 | 45.76 240 | 66.31 281 | 52.83 193 | 76.35 273 | 59.96 329 |
|
conf0.01 | | | 59.26 253 | 58.88 251 | 60.40 263 | 68.66 262 | 31.96 325 | 62.04 272 | 51.95 305 | 50.99 219 | 67.57 246 | 75.91 256 | 28.59 334 | 69.07 253 | 42.77 251 | 81.40 226 | 80.18 190 |
|
conf0.002 | | | 59.26 253 | 58.88 251 | 60.40 263 | 68.66 262 | 31.96 325 | 62.04 272 | 51.95 305 | 50.99 219 | 67.57 246 | 75.91 256 | 28.59 334 | 69.07 253 | 42.77 251 | 81.40 226 | 80.18 190 |
|
thresconf0.02 | | | 58.38 260 | 58.88 251 | 56.91 283 | 68.66 262 | 31.96 325 | 62.04 272 | 51.95 305 | 50.99 219 | 67.57 246 | 75.91 256 | 28.59 334 | 69.07 253 | 42.77 251 | 81.40 226 | 69.70 279 |
|
tfpn_n400 | | | 58.38 260 | 58.88 251 | 56.91 283 | 68.66 262 | 31.96 325 | 62.04 272 | 51.95 305 | 50.99 219 | 67.57 246 | 75.91 256 | 28.59 334 | 69.07 253 | 42.77 251 | 81.40 226 | 69.70 279 |
|
tfpnconf | | | 58.38 260 | 58.88 251 | 56.91 283 | 68.66 262 | 31.96 325 | 62.04 272 | 51.95 305 | 50.99 219 | 67.57 246 | 75.91 256 | 28.59 334 | 69.07 253 | 42.77 251 | 81.40 226 | 69.70 279 |
|
tfpnview11 | | | 58.38 260 | 58.88 251 | 56.91 283 | 68.66 262 | 31.96 325 | 62.04 272 | 51.95 305 | 50.99 219 | 67.57 246 | 75.91 256 | 28.59 334 | 69.07 253 | 42.77 251 | 81.40 226 | 69.70 279 |
|
原ACMM1 | | | | | 73.90 103 | 85.90 46 | 65.15 92 | | 81.67 81 | 50.97 225 | 74.25 182 | 86.16 146 | 61.60 132 | 83.54 63 | 56.75 160 | 91.08 89 | 73.00 247 |
|
JIA-IIPM | | | 54.03 284 | 51.62 297 | 61.25 256 | 59.14 327 | 55.21 149 | 59.10 295 | 47.72 324 | 50.85 226 | 50.31 334 | 85.81 153 | 20.10 354 | 63.97 287 | 36.16 301 | 55.41 348 | 64.55 315 |
|
Fast-Effi-MVS+ | | | 68.81 181 | 68.30 187 | 70.35 166 | 74.66 185 | 48.61 190 | 66.06 239 | 78.32 148 | 50.62 227 | 71.48 216 | 75.54 264 | 68.75 77 | 79.59 144 | 50.55 206 | 78.73 259 | 82.86 137 |
|
anonymousdsp | | | 78.60 58 | 77.80 67 | 81.00 31 | 78.01 142 | 74.34 33 | 80.09 59 | 76.12 172 | 50.51 228 | 89.19 12 | 90.88 43 | 71.45 59 | 77.78 177 | 73.38 41 | 90.60 101 | 90.90 27 |
|
thres200 | | | 57.55 269 | 57.02 269 | 59.17 270 | 67.89 278 | 34.93 303 | 58.91 297 | 57.25 276 | 50.24 229 | 64.01 269 | 71.46 307 | 32.49 300 | 71.39 239 | 31.31 319 | 79.57 253 | 71.19 267 |
|
test20.03 | | | 55.74 277 | 57.51 266 | 50.42 301 | 59.89 322 | 32.09 323 | 50.63 319 | 49.01 319 | 50.11 230 | 65.07 265 | 83.23 187 | 45.61 242 | 48.11 321 | 30.22 324 | 83.82 199 | 71.07 269 |
|
BH-w/o | | | 64.81 206 | 64.29 208 | 66.36 213 | 76.08 166 | 54.71 153 | 65.61 245 | 75.23 183 | 50.10 231 | 71.05 221 | 71.86 304 | 54.33 210 | 79.02 148 | 38.20 288 | 76.14 274 | 65.36 309 |
|
EPNet_dtu | | | 58.93 257 | 58.52 260 | 60.16 267 | 67.91 277 | 47.70 207 | 69.97 188 | 58.02 268 | 49.73 232 | 47.28 339 | 73.02 292 | 38.14 280 | 62.34 294 | 36.57 299 | 85.99 165 | 70.43 272 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
QAPM | | | 69.18 174 | 69.26 172 | 68.94 187 | 71.61 243 | 52.58 165 | 80.37 56 | 78.79 140 | 49.63 233 | 73.51 189 | 85.14 158 | 53.66 212 | 79.12 147 | 55.11 177 | 75.54 278 | 75.11 235 |
|
PAPR | | | 69.20 173 | 68.66 185 | 70.82 162 | 75.15 173 | 47.77 205 | 75.31 122 | 81.11 95 | 49.62 234 | 66.33 257 | 79.27 232 | 61.53 133 | 82.96 73 | 48.12 224 | 81.50 225 | 81.74 163 |
|
TR-MVS | | | 64.59 207 | 63.54 214 | 67.73 201 | 75.75 170 | 50.83 172 | 63.39 265 | 70.29 222 | 49.33 235 | 71.55 214 | 74.55 276 | 50.94 224 | 78.46 162 | 40.43 275 | 75.69 276 | 73.89 242 |
|
ab-mvs | | | 64.11 215 | 65.13 206 | 61.05 257 | 71.99 241 | 38.03 280 | 67.59 217 | 68.79 227 | 49.08 236 | 65.32 263 | 86.26 142 | 58.02 182 | 66.85 276 | 39.33 277 | 79.79 251 | 78.27 210 |
|
OpenMVS | | 62.51 15 | 68.76 182 | 68.75 183 | 68.78 193 | 70.56 249 | 53.91 159 | 78.29 77 | 77.35 163 | 48.85 237 | 70.22 230 | 83.52 178 | 52.65 216 | 76.93 183 | 55.31 176 | 81.99 214 | 75.49 230 |
|
testing_2 | | | 72.01 148 | 72.36 143 | 70.95 161 | 70.79 245 | 48.70 186 | 72.81 144 | 78.09 155 | 48.79 238 | 84.46 67 | 89.15 87 | 57.90 188 | 78.55 158 | 61.55 131 | 87.74 140 | 85.61 88 |
|
MAR-MVS | | | 67.72 192 | 66.16 201 | 72.40 147 | 74.45 189 | 64.99 93 | 74.87 127 | 77.50 162 | 48.67 239 | 65.78 262 | 68.58 324 | 57.01 200 | 77.79 176 | 46.68 236 | 81.92 215 | 74.42 239 |
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 |
PVSNet_Blended_VisFu | | | 70.04 163 | 68.88 179 | 73.53 115 | 82.71 90 | 63.62 102 | 74.81 129 | 81.95 78 | 48.53 240 | 67.16 254 | 79.18 235 | 51.42 223 | 78.38 167 | 54.39 185 | 79.72 252 | 78.60 206 |
|
tfpn_ndepth | | | 56.91 271 | 57.30 268 | 55.71 289 | 67.22 283 | 33.26 317 | 61.72 280 | 53.98 294 | 48.49 241 | 64.16 268 | 71.94 302 | 27.65 340 | 68.71 260 | 40.49 274 | 80.08 245 | 65.17 311 |
|
PNet_i23d | | | 36.76 329 | 36.63 332 | 37.12 337 | 58.19 334 | 33.00 319 | 39.86 342 | 32.55 355 | 48.44 242 | 39.64 351 | 51.31 350 | 6.89 364 | 41.83 345 | 22.29 345 | 30.55 354 | 36.54 353 |
|
diffmvs | | | 66.15 200 | 65.86 202 | 67.01 207 | 62.31 308 | 44.43 236 | 68.81 202 | 72.93 195 | 48.13 243 | 62.12 278 | 83.33 183 | 57.96 185 | 72.29 228 | 59.83 143 | 77.31 270 | 84.33 112 |
|
semantic-postprocess | | | | | 72.49 142 | 73.34 215 | 58.20 140 | | 65.55 240 | 48.10 244 | 76.91 148 | 82.64 192 | 42.25 260 | 78.84 152 | 61.20 134 | 77.89 268 | 80.44 187 |
|
xiu_mvs_v1_base_debu | | | 67.87 189 | 67.07 197 | 70.26 167 | 79.13 129 | 61.90 112 | 67.34 221 | 71.25 213 | 47.98 245 | 67.70 243 | 74.19 283 | 61.31 135 | 72.62 223 | 56.51 162 | 78.26 264 | 76.27 227 |
|
xiu_mvs_v1_base | | | 67.87 189 | 67.07 197 | 70.26 167 | 79.13 129 | 61.90 112 | 67.34 221 | 71.25 213 | 47.98 245 | 67.70 243 | 74.19 283 | 61.31 135 | 72.62 223 | 56.51 162 | 78.26 264 | 76.27 227 |
|
xiu_mvs_v1_base_debi | | | 67.87 189 | 67.07 197 | 70.26 167 | 79.13 129 | 61.90 112 | 67.34 221 | 71.25 213 | 47.98 245 | 67.70 243 | 74.19 283 | 61.31 135 | 72.62 223 | 56.51 162 | 78.26 264 | 76.27 227 |
|
testdata | | | | | 64.13 225 | 85.87 49 | 63.34 104 | | 61.80 256 | 47.83 248 | 76.42 160 | 86.60 134 | 48.83 232 | 62.31 295 | 54.46 184 | 81.26 232 | 66.74 304 |
|
testpf | | | 45.32 313 | 48.47 307 | 35.88 339 | 53.56 353 | 26.84 341 | 58.86 298 | 42.95 337 | 47.78 249 | 46.18 341 | 63.70 334 | 13.73 360 | 50.29 316 | 50.81 203 | 58.61 341 | 30.51 354 |
|
无先验 | | | | | | | | 74.82 128 | 70.94 217 | 47.75 250 | | | | 76.85 185 | 54.47 182 | | 72.09 258 |
|
IB-MVS | | 49.67 18 | 59.69 250 | 56.96 270 | 67.90 198 | 68.19 273 | 50.30 174 | 61.42 283 | 65.18 242 | 47.57 251 | 55.83 314 | 67.15 330 | 23.77 349 | 79.60 143 | 43.56 247 | 79.97 247 | 73.79 243 |
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 |
tpmvs | | | 55.84 275 | 55.45 283 | 57.01 282 | 60.33 319 | 33.20 318 | 65.89 241 | 59.29 265 | 47.52 252 | 56.04 312 | 73.60 286 | 31.05 319 | 68.06 265 | 40.64 273 | 64.64 325 | 69.77 278 |
|
PatchMatch-RL | | | 58.68 259 | 57.72 264 | 61.57 251 | 76.21 163 | 73.59 39 | 61.83 278 | 49.00 320 | 47.30 253 | 61.08 284 | 68.97 319 | 50.16 227 | 59.01 303 | 36.06 302 | 68.84 314 | 52.10 340 |
|
EMVS | | | 44.61 320 | 44.45 322 | 45.10 322 | 48.91 356 | 43.00 243 | 37.92 347 | 41.10 348 | 46.75 254 | 38.00 354 | 48.43 353 | 26.42 342 | 46.27 324 | 37.11 296 | 75.38 281 | 46.03 346 |
|
DI_MVS_plusplus_test | | | 69.01 178 | 69.04 175 | 68.93 188 | 69.54 256 | 46.74 226 | 70.14 186 | 75.49 178 | 46.64 255 | 78.30 132 | 83.18 190 | 58.80 164 | 78.86 151 | 57.14 157 | 82.15 212 | 81.18 169 |
|
IterMVS | | | 63.12 221 | 62.48 227 | 65.02 220 | 66.34 289 | 52.86 164 | 63.81 261 | 62.25 250 | 46.57 256 | 71.51 215 | 80.40 217 | 44.60 247 | 66.82 277 | 51.38 200 | 75.47 279 | 75.38 233 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
E-PMN | | | 45.17 314 | 45.36 317 | 44.60 324 | 50.07 354 | 42.75 245 | 38.66 346 | 42.29 342 | 46.39 257 | 39.55 352 | 51.15 351 | 26.00 343 | 45.37 329 | 37.68 291 | 76.41 272 | 45.69 347 |
|
test_normal | | | 68.88 179 | 68.88 179 | 68.88 191 | 69.43 259 | 47.03 221 | 69.85 191 | 74.83 185 | 46.06 258 | 78.30 132 | 83.29 185 | 58.76 168 | 78.23 171 | 57.51 154 | 81.90 216 | 81.36 167 |
|
Test4 | | | 69.04 177 | 68.95 178 | 69.32 182 | 69.52 257 | 48.10 199 | 70.69 183 | 78.25 152 | 45.90 259 | 80.99 102 | 82.24 198 | 51.91 218 | 78.11 175 | 58.46 150 | 82.58 209 | 81.74 163 |
|
1111 | | | 45.08 316 | 47.96 311 | 36.43 338 | 59.56 325 | 14.82 355 | 43.56 335 | 45.65 331 | 45.60 260 | 60.04 292 | 75.47 267 | 9.31 362 | 34.46 351 | 23.66 342 | 68.76 315 | 60.02 328 |
|
.test1245 | | | 34.47 332 | 40.38 328 | 16.73 344 | 59.56 325 | 14.82 355 | 43.56 335 | 45.65 331 | 45.60 260 | 60.04 292 | 75.47 267 | 9.31 362 | 34.46 351 | 23.66 342 | 0.55 358 | 0.90 357 |
|
MCST-MVS | | | 73.42 120 | 73.34 122 | 73.63 112 | 81.28 107 | 59.17 132 | 74.80 131 | 83.13 62 | 45.50 262 | 72.84 196 | 83.78 177 | 65.15 106 | 80.99 116 | 64.54 117 | 89.09 124 | 80.73 181 |
|
PVSNet_BlendedMVS | | | 65.38 202 | 64.30 207 | 68.61 194 | 69.81 253 | 49.36 181 | 65.60 246 | 78.96 136 | 45.50 262 | 59.98 294 | 78.61 238 | 51.82 219 | 78.20 173 | 44.30 242 | 84.11 194 | 78.27 210 |
|
testgi | | | 54.00 286 | 56.86 271 | 45.45 319 | 58.20 333 | 25.81 345 | 49.05 322 | 49.50 318 | 45.43 264 | 67.84 242 | 81.17 211 | 51.81 221 | 43.20 339 | 29.30 329 | 79.41 254 | 67.34 299 |
|
PCF-MVS | | 63.80 13 | 72.70 138 | 71.69 151 | 75.72 86 | 78.10 140 | 60.01 125 | 73.04 141 | 81.50 83 | 45.34 265 | 79.66 117 | 84.35 170 | 65.15 106 | 82.65 77 | 48.70 218 | 89.38 120 | 84.50 107 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
TAMVS | | | 65.31 203 | 63.75 211 | 69.97 176 | 82.23 96 | 59.76 128 | 66.78 229 | 63.37 248 | 45.20 266 | 69.79 232 | 79.37 231 | 47.42 239 | 72.17 229 | 34.48 308 | 85.15 185 | 77.99 216 |
|
旧先验2 | | | | | | | | 71.17 177 | | 45.11 267 | 78.54 129 | | | 61.28 298 | 59.19 147 | | |
|
PS-MVSNAJ | | | 64.27 214 | 63.73 212 | 65.90 217 | 77.82 146 | 51.42 169 | 63.33 266 | 72.33 201 | 45.09 268 | 61.60 280 | 68.04 325 | 62.39 124 | 73.95 210 | 49.07 215 | 73.87 289 | 72.34 254 |
|
xiu_mvs_v2_base | | | 64.43 211 | 63.96 209 | 65.85 218 | 77.72 148 | 51.32 170 | 63.63 263 | 72.31 202 | 45.06 269 | 61.70 279 | 69.66 315 | 62.56 120 | 73.93 211 | 49.06 216 | 73.91 288 | 72.31 255 |
|
LF4IMVS | | | 67.50 193 | 67.31 196 | 68.08 197 | 58.86 328 | 61.93 111 | 71.43 171 | 75.90 175 | 44.67 270 | 72.42 204 | 80.20 218 | 57.16 195 | 70.44 245 | 58.99 148 | 86.12 163 | 71.88 259 |
|
CDS-MVSNet | | | 64.33 213 | 62.66 226 | 69.35 180 | 80.44 113 | 58.28 139 | 65.26 250 | 65.66 238 | 44.36 271 | 67.30 253 | 75.54 264 | 43.27 254 | 71.77 236 | 37.68 291 | 84.44 191 | 78.01 215 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
testmv | | | 52.91 291 | 54.31 286 | 48.71 310 | 72.13 240 | 36.18 290 | 50.26 320 | 47.78 323 | 44.15 272 | 64.61 266 | 79.78 225 | 38.18 279 | 50.20 317 | 21.96 346 | 69.93 308 | 59.75 330 |
|
新几何1 | | | | | 69.99 175 | 88.37 31 | 71.34 47 | | 62.08 252 | 43.85 273 | 74.99 172 | 86.11 148 | 52.85 215 | 70.57 244 | 50.99 202 | 83.23 205 | 68.05 294 |
|
1121 | | | 69.23 172 | 68.26 188 | 72.12 153 | 88.36 32 | 71.40 45 | 68.59 206 | 62.06 253 | 43.80 274 | 74.75 175 | 86.18 144 | 52.92 214 | 76.85 185 | 54.47 182 | 83.27 204 | 68.12 293 |
|
114514_t | | | 73.40 121 | 73.33 123 | 73.64 111 | 84.15 76 | 57.11 142 | 78.20 79 | 80.02 121 | 43.76 275 | 72.55 202 | 86.07 150 | 64.00 114 | 83.35 69 | 60.14 139 | 91.03 90 | 80.45 186 |
|
OpenMVS_ROB | | 54.93 17 | 63.23 220 | 63.28 215 | 63.07 236 | 69.81 253 | 45.34 232 | 68.52 209 | 67.14 232 | 43.74 276 | 70.61 226 | 79.22 233 | 47.90 237 | 72.66 222 | 48.75 217 | 73.84 290 | 71.21 266 |
|
FMVSNet5 | | | 55.08 280 | 55.54 281 | 53.71 294 | 65.80 292 | 33.50 316 | 56.22 304 | 52.50 303 | 43.72 277 | 61.06 285 | 83.38 180 | 25.46 346 | 54.87 309 | 30.11 325 | 81.64 224 | 72.75 250 |
|
MVP-Stereo | | | 61.56 236 | 59.22 247 | 68.58 195 | 79.28 123 | 60.44 121 | 69.20 198 | 71.57 206 | 43.58 278 | 56.42 311 | 78.37 240 | 39.57 274 | 76.46 189 | 34.86 307 | 60.16 334 | 68.86 291 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
mvs_anonymous | | | 65.08 204 | 65.49 203 | 63.83 229 | 63.79 301 | 37.60 283 | 66.52 232 | 69.82 224 | 43.44 279 | 73.46 191 | 86.08 149 | 58.79 167 | 71.75 238 | 51.90 196 | 75.63 277 | 82.15 154 |
|
test-LLR | | | 50.43 300 | 50.69 303 | 49.64 304 | 60.76 316 | 41.87 250 | 53.18 314 | 45.48 333 | 43.41 280 | 49.41 335 | 60.47 341 | 29.22 331 | 44.73 332 | 42.09 264 | 72.14 295 | 62.33 323 |
|
test0.0.03 1 | | | 47.72 308 | 48.31 309 | 45.93 317 | 55.53 346 | 29.39 334 | 46.40 331 | 41.21 347 | 43.41 280 | 55.81 315 | 67.65 326 | 29.22 331 | 43.77 338 | 25.73 338 | 69.87 309 | 64.62 314 |
|
Patchmatch-test1 | | | 57.81 267 | 58.04 263 | 57.13 281 | 70.17 252 | 41.07 256 | 65.19 251 | 53.38 299 | 43.34 282 | 61.00 287 | 71.94 302 | 45.20 243 | 62.69 292 | 41.81 267 | 70.31 305 | 67.63 296 |
|
LP | | | 53.02 290 | 52.27 296 | 55.27 291 | 55.76 345 | 40.55 260 | 55.64 307 | 55.07 286 | 42.46 283 | 56.95 307 | 73.21 290 | 33.67 294 | 54.18 313 | 38.41 286 | 59.29 338 | 71.08 268 |
|
cascas | | | 64.59 207 | 62.77 225 | 70.05 174 | 75.27 171 | 50.02 176 | 61.79 279 | 71.61 205 | 42.46 283 | 63.68 272 | 68.89 321 | 49.33 230 | 80.35 131 | 47.82 227 | 84.05 195 | 79.78 196 |
|
PVSNet_Blended | | | 62.90 224 | 61.64 231 | 66.69 211 | 69.81 253 | 49.36 181 | 61.23 285 | 78.96 136 | 42.04 285 | 59.98 294 | 68.86 322 | 51.82 219 | 78.20 173 | 44.30 242 | 77.77 269 | 72.52 252 |
|
MVSTER | | | 63.29 219 | 61.60 232 | 68.36 196 | 59.77 323 | 46.21 229 | 60.62 288 | 71.32 210 | 41.83 286 | 75.40 169 | 79.12 236 | 30.25 324 | 75.85 191 | 56.30 167 | 79.81 249 | 83.03 133 |
|
MIMVSNet | | | 54.39 282 | 56.12 277 | 49.20 306 | 72.57 234 | 30.91 332 | 59.98 291 | 48.43 322 | 41.66 287 | 55.94 313 | 83.86 176 | 41.19 265 | 50.42 315 | 26.05 335 | 75.38 281 | 66.27 305 |
|
new-patchmatchnet | | | 52.89 292 | 55.76 279 | 44.26 325 | 59.94 321 | 6.31 359 | 37.36 349 | 50.76 315 | 41.10 288 | 64.28 267 | 79.82 224 | 44.77 245 | 48.43 320 | 36.24 300 | 87.61 141 | 78.03 214 |
|
test222 | | | | | | 87.30 35 | 69.15 68 | 67.85 215 | 59.59 264 | 41.06 289 | 73.05 195 | 85.72 154 | 48.03 236 | | | 80.65 240 | 66.92 300 |
|
Patchmatch-RL test | | | 59.95 248 | 59.12 248 | 62.44 245 | 72.46 235 | 54.61 155 | 59.63 293 | 47.51 325 | 41.05 290 | 74.58 180 | 74.30 280 | 31.06 318 | 65.31 282 | 51.61 197 | 79.85 248 | 67.39 297 |
|
ppachtmachnet_test | | | 60.26 247 | 59.61 246 | 62.20 247 | 67.70 279 | 44.33 238 | 58.18 300 | 60.96 259 | 40.75 291 | 65.80 261 | 72.57 293 | 41.23 264 | 63.92 288 | 46.87 234 | 82.42 210 | 78.33 208 |
|
PatchFormer-LS_test | | | 53.94 287 | 52.64 292 | 57.85 277 | 61.87 310 | 39.59 267 | 61.60 281 | 57.63 270 | 40.65 292 | 54.52 321 | 58.64 344 | 29.07 333 | 64.18 286 | 46.78 235 | 62.98 330 | 69.78 277 |
|
PAPM | | | 61.79 235 | 60.37 241 | 66.05 215 | 76.09 165 | 41.87 250 | 69.30 196 | 76.79 169 | 40.64 293 | 53.80 324 | 79.62 228 | 44.38 248 | 82.92 74 | 29.64 328 | 73.11 292 | 73.36 245 |
|
our_test_3 | | | 56.46 272 | 56.51 273 | 56.30 288 | 67.70 279 | 39.66 266 | 55.36 309 | 52.34 304 | 40.57 294 | 63.85 270 | 69.91 314 | 40.04 272 | 58.22 305 | 43.49 249 | 75.29 283 | 71.03 270 |
|
PatchmatchNet | | | 54.60 281 | 54.27 287 | 55.59 290 | 65.17 297 | 39.08 269 | 66.92 227 | 51.80 311 | 39.89 295 | 58.39 301 | 73.12 291 | 31.69 308 | 58.33 304 | 43.01 250 | 58.38 343 | 69.38 287 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
Patchmatch-test | | | 47.93 307 | 49.96 305 | 41.84 330 | 57.42 337 | 24.26 348 | 48.75 323 | 41.49 345 | 39.30 296 | 56.79 309 | 73.48 287 | 30.48 323 | 33.87 353 | 29.29 330 | 72.61 293 | 67.39 297 |
|
HY-MVS | | 49.31 19 | 57.96 266 | 57.59 265 | 59.10 271 | 66.85 285 | 36.17 291 | 65.13 252 | 65.39 241 | 39.24 297 | 54.69 320 | 78.14 241 | 44.28 249 | 67.18 273 | 33.75 313 | 70.79 302 | 73.95 241 |
|
XXY-MVS | | | 55.19 279 | 57.40 267 | 48.56 311 | 64.45 299 | 34.84 305 | 51.54 318 | 53.59 297 | 38.99 298 | 63.79 271 | 79.43 229 | 56.59 201 | 45.57 326 | 36.92 297 | 71.29 299 | 65.25 310 |
|
pmmvs-eth3d | | | 64.41 212 | 63.27 216 | 67.82 200 | 75.81 169 | 60.18 123 | 69.49 194 | 62.05 254 | 38.81 299 | 74.13 183 | 82.23 199 | 43.76 252 | 68.65 261 | 42.53 261 | 80.63 242 | 74.63 237 |
|
test1235678 | | | 48.41 306 | 49.60 306 | 44.83 323 | 68.52 268 | 33.81 310 | 46.33 332 | 45.89 330 | 38.72 300 | 58.46 300 | 72.08 295 | 29.85 329 | 47.82 322 | 19.67 350 | 66.91 321 | 52.88 338 |
|
DWT-MVSNet_test | | | 53.04 289 | 51.12 300 | 58.77 273 | 61.23 313 | 38.67 273 | 62.16 271 | 57.74 269 | 38.24 301 | 51.76 328 | 59.07 343 | 21.36 351 | 67.40 271 | 44.80 241 | 63.76 327 | 70.25 274 |
|
MDA-MVSNet_test_wron | | | 52.57 294 | 53.49 290 | 49.81 303 | 54.24 350 | 36.47 288 | 40.48 341 | 46.58 327 | 38.13 302 | 75.47 168 | 73.32 288 | 41.05 268 | 43.85 337 | 40.98 271 | 71.20 300 | 69.10 290 |
|
YYNet1 | | | 52.58 293 | 53.50 289 | 49.85 302 | 54.15 351 | 36.45 289 | 40.53 340 | 46.55 328 | 38.09 303 | 75.52 167 | 73.31 289 | 41.08 267 | 43.88 336 | 41.10 270 | 71.14 301 | 69.21 288 |
|
1112_ss | | | 59.48 251 | 58.99 250 | 60.96 259 | 77.84 145 | 42.39 248 | 61.42 283 | 68.45 229 | 37.96 304 | 59.93 297 | 67.46 327 | 45.11 244 | 65.07 284 | 40.89 272 | 71.81 297 | 75.41 232 |
|
UnsupCasMVSNet_eth | | | 52.26 296 | 53.29 291 | 49.16 307 | 55.08 347 | 33.67 311 | 50.03 321 | 58.79 267 | 37.67 305 | 63.43 275 | 74.75 273 | 41.82 262 | 45.83 325 | 38.59 285 | 59.42 337 | 67.98 295 |
|
tpm | | | 50.60 299 | 52.42 295 | 45.14 321 | 65.18 296 | 26.29 343 | 60.30 289 | 43.50 335 | 37.41 306 | 57.01 306 | 79.09 237 | 30.20 326 | 42.32 341 | 32.77 316 | 66.36 322 | 66.81 303 |
|
gm-plane-assit | | | | | | 62.51 307 | 33.91 309 | | | 37.25 307 | | 62.71 337 | | 72.74 221 | 38.70 282 | | |
|
CostFormer | | | 57.35 270 | 56.14 276 | 60.97 258 | 63.76 302 | 38.43 274 | 67.50 218 | 60.22 261 | 37.14 308 | 59.12 299 | 76.34 252 | 32.78 298 | 71.99 234 | 39.12 279 | 69.27 312 | 72.47 253 |
|
pmmvs4 | | | 60.78 243 | 59.04 249 | 66.00 216 | 73.06 226 | 57.67 141 | 64.53 256 | 60.22 261 | 36.91 309 | 65.96 259 | 77.27 246 | 39.66 273 | 68.54 262 | 38.87 280 | 74.89 284 | 71.80 260 |
|
PVSNet | | 43.83 21 | 51.56 298 | 51.17 299 | 52.73 296 | 68.34 271 | 38.27 276 | 48.22 325 | 53.56 298 | 36.41 310 | 54.29 322 | 64.94 333 | 34.60 291 | 54.20 312 | 30.34 323 | 69.87 309 | 65.71 308 |
|
tpmrst | | | 50.15 301 | 51.38 298 | 46.45 316 | 56.05 341 | 24.77 347 | 64.40 258 | 49.98 316 | 36.14 311 | 53.32 325 | 69.59 316 | 35.16 290 | 48.69 319 | 39.24 278 | 58.51 342 | 65.89 306 |
|
MS-PatchMatch | | | 55.59 278 | 54.89 284 | 57.68 278 | 69.18 260 | 49.05 184 | 61.00 287 | 62.93 249 | 35.98 312 | 58.36 302 | 68.93 320 | 36.71 286 | 66.59 279 | 37.62 293 | 63.30 328 | 57.39 333 |
|
MDTV_nov1_ep13 | | | | 54.05 288 | | 65.54 294 | 29.30 335 | 59.00 296 | 55.22 285 | 35.96 313 | 52.44 326 | 75.98 254 | 30.77 321 | 59.62 301 | 38.21 287 | 73.33 291 | |
|
USDC | | | 62.80 229 | 63.10 218 | 61.89 249 | 65.19 295 | 43.30 241 | 67.42 220 | 74.20 188 | 35.80 314 | 72.25 206 | 84.48 169 | 45.67 241 | 71.95 235 | 37.95 290 | 84.97 190 | 70.42 273 |
|
tpmp4_e23 | | | 57.57 268 | 55.46 282 | 63.93 228 | 66.48 286 | 41.56 253 | 71.68 165 | 60.65 260 | 35.64 315 | 55.35 317 | 76.25 253 | 29.53 330 | 75.41 198 | 34.40 309 | 69.12 313 | 74.83 236 |
|
jason | | | 64.47 210 | 62.84 224 | 69.34 181 | 76.91 157 | 59.20 129 | 67.15 225 | 65.67 237 | 35.29 316 | 65.16 264 | 76.74 250 | 44.67 246 | 70.68 242 | 54.74 180 | 79.28 255 | 78.14 212 |
jason: jason. |
Anonymous20231206 | | | 54.13 283 | 55.82 278 | 49.04 309 | 70.89 244 | 35.96 293 | 51.73 317 | 50.87 314 | 34.86 317 | 62.49 276 | 79.22 233 | 42.52 259 | 44.29 335 | 27.95 333 | 81.88 217 | 66.88 301 |
|
dp | | | 44.09 321 | 44.88 320 | 41.72 332 | 58.53 331 | 23.18 349 | 54.70 311 | 42.38 341 | 34.80 318 | 44.25 348 | 65.61 332 | 24.48 348 | 44.80 331 | 29.77 327 | 49.42 351 | 57.18 334 |
|
Test_1112_low_res | | | 58.78 258 | 58.69 259 | 59.04 272 | 79.41 121 | 38.13 278 | 57.62 301 | 66.98 234 | 34.74 319 | 59.62 298 | 77.56 244 | 42.92 257 | 63.65 289 | 38.66 283 | 70.73 303 | 75.35 234 |
|
EPMVS | | | 45.74 311 | 46.53 314 | 43.39 327 | 54.14 352 | 22.33 350 | 55.02 310 | 35.00 354 | 34.69 320 | 51.09 329 | 70.20 313 | 25.92 344 | 42.04 343 | 37.19 295 | 55.50 347 | 65.78 307 |
|
lupinMVS | | | 63.36 218 | 61.49 234 | 68.97 186 | 74.93 174 | 59.19 130 | 65.80 242 | 64.52 245 | 34.68 321 | 63.53 273 | 74.25 281 | 43.19 255 | 70.62 243 | 53.88 188 | 78.67 260 | 77.10 222 |
|
UnsupCasMVSNet_bld | | | 50.01 302 | 51.03 302 | 46.95 312 | 58.61 330 | 32.64 320 | 48.31 324 | 53.27 300 | 34.27 322 | 60.47 290 | 71.53 306 | 41.40 263 | 47.07 323 | 30.68 321 | 60.78 333 | 61.13 325 |
|
CMPMVS | | 48.73 20 | 61.54 237 | 60.89 238 | 63.52 232 | 61.08 315 | 51.55 168 | 68.07 214 | 68.00 231 | 33.88 323 | 65.87 260 | 81.25 210 | 37.91 283 | 67.71 267 | 49.32 214 | 82.60 208 | 71.31 264 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
WTY-MVS | | | 49.39 303 | 50.31 304 | 46.62 315 | 61.22 314 | 32.00 324 | 46.61 330 | 49.77 317 | 33.87 324 | 54.12 323 | 69.55 317 | 41.96 261 | 45.40 328 | 31.28 320 | 64.42 326 | 62.47 322 |
|
no-one | | | 56.11 274 | 55.62 280 | 57.60 279 | 62.68 305 | 49.23 183 | 39.12 345 | 58.99 266 | 33.72 325 | 60.98 288 | 80.90 212 | 36.07 288 | 60.36 299 | 30.68 321 | 97.40 1 | 63.22 318 |
|
N_pmnet | | | 52.06 297 | 51.11 301 | 54.92 292 | 59.64 324 | 71.03 49 | 37.42 348 | 61.62 257 | 33.68 326 | 57.12 305 | 72.10 294 | 37.94 282 | 31.03 354 | 29.13 332 | 71.35 298 | 62.70 320 |
|
HyFIR lowres test | | | 63.01 222 | 60.47 240 | 70.61 163 | 83.04 85 | 54.10 157 | 59.93 292 | 72.24 203 | 33.67 327 | 69.00 236 | 75.63 263 | 38.69 277 | 76.93 183 | 36.60 298 | 75.45 280 | 80.81 180 |
|
test12356 | | | 38.35 326 | 40.80 325 | 31.01 340 | 58.31 332 | 9.09 358 | 36.67 350 | 46.65 326 | 33.65 328 | 44.39 347 | 60.94 340 | 17.56 357 | 39.23 349 | 16.01 353 | 53.03 349 | 44.72 348 |
|
tpm2 | | | 56.12 273 | 54.64 285 | 60.55 262 | 66.24 290 | 36.01 292 | 68.14 213 | 56.77 282 | 33.60 329 | 58.25 303 | 75.52 266 | 30.25 324 | 74.33 209 | 33.27 314 | 69.76 311 | 71.32 263 |
|
1314 | | | 59.83 249 | 58.86 257 | 62.74 242 | 65.71 293 | 44.78 233 | 68.59 206 | 72.63 198 | 33.54 330 | 61.05 286 | 67.29 329 | 43.62 253 | 71.26 240 | 49.49 213 | 67.84 319 | 72.19 257 |
|
CR-MVSNet | | | 58.96 256 | 58.49 261 | 60.36 265 | 66.37 287 | 48.24 195 | 70.93 180 | 56.40 283 | 32.87 331 | 61.35 282 | 86.66 129 | 33.19 296 | 63.22 290 | 48.50 221 | 70.17 306 | 69.62 284 |
|
MVS | | | 60.62 245 | 59.97 243 | 62.58 244 | 68.13 274 | 47.28 218 | 68.59 206 | 73.96 189 | 32.19 332 | 59.94 296 | 68.86 322 | 50.48 225 | 77.64 178 | 41.85 266 | 75.74 275 | 62.83 319 |
|
tpm cat1 | | | 54.02 285 | 52.63 293 | 58.19 276 | 64.85 298 | 39.86 265 | 66.26 234 | 57.28 275 | 32.16 333 | 56.90 308 | 70.39 311 | 32.75 299 | 65.30 283 | 34.29 310 | 58.79 339 | 69.41 286 |
|
pmmvs5 | | | 52.49 295 | 52.58 294 | 52.21 299 | 54.99 348 | 32.38 321 | 55.45 308 | 53.84 296 | 32.15 334 | 55.49 316 | 74.81 271 | 38.08 281 | 57.37 307 | 34.02 311 | 74.40 286 | 66.88 301 |
|
PMMVS2 | | | 37.74 327 | 40.87 324 | 28.36 343 | 42.41 358 | 5.35 360 | 24.61 352 | 27.75 357 | 32.15 334 | 47.85 338 | 70.27 312 | 35.85 289 | 29.51 355 | 19.08 351 | 67.85 318 | 50.22 342 |
|
sss | | | 47.59 309 | 48.32 308 | 45.40 320 | 56.73 340 | 33.96 308 | 45.17 334 | 48.51 321 | 32.11 336 | 52.37 327 | 65.79 331 | 40.39 271 | 41.91 344 | 31.85 317 | 61.97 331 | 60.35 326 |
|
test2356 | | | 40.85 325 | 40.47 327 | 41.98 329 | 58.78 329 | 28.65 338 | 39.45 343 | 40.98 349 | 31.95 337 | 48.47 337 | 56.63 345 | 12.54 361 | 44.41 334 | 15.84 354 | 59.58 336 | 52.88 338 |
|
test-mter | | | 48.56 305 | 48.20 310 | 49.64 304 | 60.76 316 | 41.87 250 | 53.18 314 | 45.48 333 | 31.91 338 | 49.41 335 | 60.47 341 | 18.34 355 | 44.73 332 | 42.09 264 | 72.14 295 | 62.33 323 |
|
MDTV_nov1_ep13_2view | | | | | | | 18.41 352 | 53.74 313 | | 31.57 339 | 44.89 344 | | 29.90 328 | | 32.93 315 | | 71.48 262 |
|
ADS-MVSNet2 | | | 48.76 304 | 47.25 313 | 53.29 295 | 55.90 343 | 40.54 261 | 47.34 328 | 54.99 292 | 31.41 340 | 50.48 331 | 72.06 300 | 31.23 315 | 54.26 311 | 25.93 336 | 55.93 345 | 65.07 312 |
|
ADS-MVSNet | | | 44.62 319 | 45.58 316 | 41.73 331 | 55.90 343 | 20.83 351 | 47.34 328 | 39.94 350 | 31.41 340 | 50.48 331 | 72.06 300 | 31.23 315 | 39.31 347 | 25.93 336 | 55.93 345 | 65.07 312 |
|
testus | | | 45.03 317 | 46.49 315 | 40.65 333 | 62.53 306 | 25.24 346 | 42.54 337 | 46.23 329 | 31.16 342 | 57.69 304 | 62.90 336 | 34.60 291 | 42.33 340 | 17.72 352 | 63.01 329 | 54.37 337 |
|
PVSNet_0 | | 36.71 22 | 41.12 324 | 40.78 326 | 42.14 328 | 59.97 320 | 40.13 263 | 40.97 339 | 42.24 343 | 30.81 343 | 44.86 345 | 49.41 352 | 40.70 269 | 45.12 330 | 23.15 344 | 34.96 353 | 41.16 350 |
|
MVS-HIRNet | | | 45.53 312 | 47.29 312 | 40.24 334 | 62.29 309 | 26.82 342 | 56.02 305 | 37.41 352 | 29.74 344 | 43.69 350 | 81.27 209 | 33.96 293 | 55.48 308 | 24.46 341 | 56.79 344 | 38.43 352 |
|
CHOSEN 1792x2688 | | | 58.09 265 | 56.30 275 | 63.45 233 | 79.95 115 | 50.93 171 | 54.07 312 | 65.59 239 | 28.56 345 | 61.53 281 | 74.33 279 | 41.09 266 | 66.52 280 | 33.91 312 | 67.69 320 | 72.92 248 |
|
TESTMET0.1,1 | | | 45.17 314 | 44.93 319 | 45.89 318 | 56.02 342 | 38.31 275 | 53.18 314 | 41.94 344 | 27.85 346 | 44.86 345 | 56.47 346 | 17.93 356 | 41.50 346 | 38.08 289 | 68.06 317 | 57.85 332 |
|
CHOSEN 280x420 | | | 41.62 323 | 39.89 329 | 46.80 314 | 61.81 311 | 51.59 167 | 33.56 351 | 35.74 353 | 27.48 347 | 37.64 355 | 53.53 347 | 23.24 350 | 42.09 342 | 27.39 334 | 58.64 340 | 46.72 345 |
|
EU-MVSNet | | | 60.82 242 | 60.80 239 | 60.86 260 | 68.37 270 | 41.16 254 | 72.27 149 | 68.27 230 | 26.96 348 | 69.08 235 | 75.71 262 | 32.09 303 | 67.44 270 | 55.59 174 | 78.90 257 | 73.97 240 |
|
CVMVSNet | | | 59.21 255 | 58.44 262 | 61.51 252 | 73.94 201 | 47.76 206 | 71.31 175 | 64.56 244 | 26.91 349 | 60.34 291 | 70.44 309 | 36.24 287 | 67.65 268 | 53.57 191 | 68.66 316 | 69.12 289 |
|
new_pmnet | | | 37.55 328 | 39.80 330 | 30.79 341 | 56.83 338 | 16.46 354 | 39.35 344 | 30.65 356 | 25.59 350 | 45.26 343 | 61.60 339 | 24.54 347 | 28.02 356 | 21.60 347 | 52.80 350 | 47.90 344 |
|
MVE | | 27.91 23 | 36.69 330 | 35.64 333 | 39.84 335 | 43.37 357 | 35.85 295 | 19.49 353 | 24.61 359 | 24.68 351 | 39.05 353 | 62.63 338 | 38.67 278 | 27.10 357 | 21.04 348 | 47.25 352 | 56.56 335 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
pmmvs3 | | | 46.71 310 | 45.09 318 | 51.55 300 | 56.76 339 | 48.25 194 | 55.78 306 | 39.53 351 | 24.13 352 | 50.35 333 | 63.40 335 | 15.90 359 | 51.08 314 | 29.29 330 | 70.69 304 | 55.33 336 |
|
PMMVS | | | 44.69 318 | 43.95 323 | 46.92 313 | 50.05 355 | 53.47 162 | 48.08 327 | 42.40 340 | 22.36 353 | 44.01 349 | 53.05 348 | 42.60 258 | 45.49 327 | 31.69 318 | 61.36 332 | 41.79 349 |
|
DSMNet-mixed | | | 43.18 322 | 44.66 321 | 38.75 336 | 54.75 349 | 28.88 337 | 57.06 303 | 27.42 358 | 13.47 354 | 47.27 340 | 77.67 243 | 38.83 276 | 39.29 348 | 25.32 340 | 60.12 335 | 48.08 343 |
|
DeepMVS_CX | | | | | 11.83 345 | 15.51 359 | 13.86 357 | | 11.25 362 | 5.76 355 | 20.85 357 | 26.46 354 | 17.06 358 | 9.22 358 | 9.69 356 | 13.82 356 | 12.42 355 |
|
tmp_tt | | | 11.98 334 | 14.73 335 | 3.72 346 | 2.28 360 | 4.62 361 | 19.44 354 | 14.50 361 | 0.47 356 | 21.55 356 | 9.58 356 | 25.78 345 | 4.57 359 | 11.61 355 | 27.37 355 | 1.96 356 |
|
test123 | | | 4.43 337 | 5.78 338 | 0.39 348 | 0.97 361 | 0.28 362 | 46.33 332 | 0.45 363 | 0.31 357 | 0.62 358 | 1.50 359 | 0.61 366 | 0.11 361 | 0.56 357 | 0.63 357 | 0.77 359 |
|
testmvs | | | 4.06 338 | 5.28 339 | 0.41 347 | 0.64 362 | 0.16 363 | 42.54 337 | 0.31 364 | 0.26 358 | 0.50 359 | 1.40 360 | 0.77 365 | 0.17 360 | 0.56 357 | 0.55 358 | 0.90 357 |
|
cdsmvs_eth3d_5k | | | 17.71 333 | 23.62 334 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 70.17 223 | 0.00 359 | 0.00 360 | 74.25 281 | 68.16 83 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
pcd_1.5k_mvsjas | | | 5.20 336 | 6.93 337 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 0.00 365 | 0.00 359 | 0.00 360 | 0.00 361 | 62.39 124 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
pcd1.5k->3k | | | 35.00 331 | 36.93 331 | 29.21 342 | 84.62 66 | 0.00 364 | 0.00 355 | 78.90 138 | 0.00 359 | 0.00 360 | 0.00 361 | 78.26 15 | 0.00 362 | 0.00 359 | 90.55 102 | 87.62 66 |
|
sosnet-low-res | | | 0.00 339 | 0.00 340 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 0.00 365 | 0.00 359 | 0.00 360 | 0.00 361 | 0.00 367 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
sosnet | | | 0.00 339 | 0.00 340 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 0.00 365 | 0.00 359 | 0.00 360 | 0.00 361 | 0.00 367 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
uncertanet | | | 0.00 339 | 0.00 340 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 0.00 365 | 0.00 359 | 0.00 360 | 0.00 361 | 0.00 367 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
Regformer | | | 0.00 339 | 0.00 340 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 0.00 365 | 0.00 359 | 0.00 360 | 0.00 361 | 0.00 367 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
ab-mvs-re | | | 5.62 335 | 7.50 336 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 0.00 365 | 0.00 359 | 0.00 360 | 67.46 327 | 0.00 367 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
uanet | | | 0.00 339 | 0.00 340 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 0.00 365 | 0.00 359 | 0.00 360 | 0.00 361 | 0.00 367 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
GSMVS | | | | | | | | | | | | | | | | | 70.05 275 |
|
test_part2 | | | | | | 85.90 46 | 66.44 80 | | | | 84.61 62 | | | | | | |
|
test_part1 | | | | | | | | | 84.94 30 | | | | 75.17 31 | | | 93.83 49 | 82.50 146 |
|
sam_mvs1 | | | | | | | | | | | | | 31.41 313 | | | | 70.05 275 |
|
sam_mvs | | | | | | | | | | | | | 31.21 317 | | | | |
|
ambc | | | | | 70.10 173 | 77.74 147 | 50.21 175 | 74.28 137 | 77.93 157 | | 79.26 122 | 88.29 104 | 54.11 211 | 79.77 141 | 64.43 118 | 91.10 88 | 80.30 188 |
|
MTGPA | | | | | | | | | 80.63 107 | | | | | | | | |
|
test_post1 | | | | | | | | 66.63 230 | | | | 2.08 357 | 30.66 322 | 59.33 302 | 40.34 276 | | |
|
test_post | | | | | | | | | | | | 1.99 358 | 30.91 320 | 54.76 310 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 68.99 318 | 31.32 314 | 69.38 250 | | | |
|
GG-mvs-BLEND | | | | | 52.24 298 | 60.64 318 | 29.21 336 | 69.73 193 | 42.41 339 | | 45.47 342 | 52.33 349 | 20.43 353 | 68.16 264 | 25.52 339 | 65.42 324 | 59.36 331 |
|
MTMP | | | | | | | | | 19.26 360 | | | | | | | | |
|
test9_res | | | | | | | | | | | | | | | 72.12 54 | 91.37 81 | 77.40 219 |
|
agg_prior2 | | | | | | | | | | | | | | | 70.70 62 | 90.93 93 | 78.55 207 |
|
agg_prior | | | | | | 84.44 71 | 66.02 84 | | 78.62 144 | | 76.95 146 | | | 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 | | | 82.58 143 |
|
新几何2 | | | | | | | | 71.33 174 | | | | | | | | | |
|
旧先验1 | | | | | | 84.55 68 | 60.36 122 | | 63.69 246 | | | 87.05 115 | 54.65 209 | | | 83.34 203 | 69.66 283 |
|
原ACMM2 | | | | | | | | 74.78 132 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 67.30 272 | 48.34 222 | | |
|
segment_acmp | | | | | | | | | | | | | 68.30 82 | | | | |
|
test12 | | | | | 76.51 73 | 82.28 95 | 60.94 119 | | 81.64 82 | | 73.60 188 | | 64.88 108 | 85.19 41 | | 90.42 104 | 83.38 128 |
|
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 91 | 84.82 95 |
|
plane_prior4 | | | | | | | | | | | | 89.11 88 | | | | | |
|
plane_prior1 | | | | | | 84.46 70 | | | | | | | | | | | |
|
n2 | | | | | | | | | 0.00 365 | | | | | | | | |
|
nn | | | | | | | | | 0.00 365 | | | | | | | | |
|
door-mid | | | | | | | | | 55.02 287 | | | | | | | | |
|
lessismore_v0 | | | | | 72.75 137 | 79.60 119 | 56.83 144 | | 57.37 274 | | 83.80 71 | 89.01 91 | 47.45 238 | 78.74 155 | 64.39 119 | 86.49 161 | 82.69 142 |
|
test11 | | | | | | | | | 82.71 68 | | | | | | | | |
|
door | | | | | | | | | 52.91 302 | | | | | | | | |
|
HQP5-MVS | | | | | | | 58.80 135 | | | | | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 67.38 95 | | |
|
HQP4-MVS | | | | | | | | | | | 71.59 209 | | | 85.31 35 | | | 83.74 120 |
|
HQP3-MVS | | | | | | | | | 84.12 46 | | | | | | | 89.16 121 | |
|
HQP2-MVS | | | | | | | | | | | | | 58.09 177 | | | | |
|
NP-MVS | | | | | | 83.34 82 | 63.07 107 | | | | | 85.97 151 | | | | | |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 89.47 119 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 91.96 71 | |
|
Test By Simon | | | | | | | | | | | | | 62.56 120 | | | | |
|