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