LCM-MVSNet | | | 99.86 1 | 99.86 1 | 99.87 1 | 99.99 1 | 99.77 1 | 99.77 1 | 99.80 1 | 99.97 1 | 99.97 1 | 99.95 1 | 99.74 1 | 99.98 1 | 99.56 1 | 100.00 1 | 99.85 4 |
|
Anonymous20231211 | | | 99.29 2 | 99.41 2 | 98.91 22 | 99.94 2 | 97.08 37 | 99.47 3 | 99.51 5 | 99.56 2 | 99.83 3 | 99.80 2 | 99.13 3 | 99.90 13 | 97.55 49 | 99.93 21 | 99.75 13 |
|
LTVRE_ROB | | 96.88 1 | 99.18 3 | 99.34 3 | 98.72 35 | 99.71 7 | 96.99 40 | 99.69 2 | 99.57 3 | 99.02 14 | 99.62 10 | 99.36 16 | 98.53 8 | 99.52 163 | 98.58 24 | 99.95 13 | 99.66 23 |
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 |
pmmvs6 | | | 99.07 4 | 99.24 4 | 98.56 45 | 99.81 3 | 96.38 57 | 98.87 9 | 99.30 9 | 99.01 15 | 99.63 9 | 99.66 4 | 99.27 2 | 99.68 103 | 97.75 41 | 99.89 33 | 99.62 31 |
|
v52 | | | 98.85 8 | 99.01 5 | 98.37 56 | 99.61 15 | 95.53 83 | 99.01 7 | 99.04 45 | 98.48 26 | 99.31 22 | 99.41 11 | 96.82 56 | 99.87 21 | 99.44 2 | 99.95 13 | 99.70 19 |
|
V4 | | | 98.85 8 | 99.01 5 | 98.37 56 | 99.61 15 | 95.53 83 | 99.01 7 | 99.04 45 | 98.48 26 | 99.31 22 | 99.41 11 | 96.81 57 | 99.87 21 | 99.44 2 | 99.95 13 | 99.70 19 |
|
v7n | | | 98.73 13 | 98.99 7 | 97.95 82 | 99.64 12 | 94.20 126 | 98.67 12 | 99.14 20 | 99.08 9 | 99.42 16 | 99.23 29 | 96.53 69 | 99.91 12 | 99.27 4 | 99.93 21 | 99.73 16 |
|
mvs_tets | | | 98.90 5 | 98.94 8 | 98.75 30 | 99.69 8 | 96.48 55 | 98.54 20 | 99.22 10 | 96.23 110 | 99.71 5 | 99.48 7 | 98.77 7 | 99.93 2 | 98.89 10 | 99.95 13 | 99.84 6 |
|
ANet_high | | | 98.31 31 | 98.94 8 | 96.41 177 | 99.33 47 | 89.64 214 | 97.92 55 | 99.56 4 | 99.27 5 | 99.66 8 | 99.50 6 | 97.67 25 | 99.83 30 | 97.55 49 | 99.98 3 | 99.77 9 |
|
v748 | | | 98.58 20 | 98.89 10 | 97.67 99 | 99.61 15 | 93.53 149 | 98.59 16 | 98.90 75 | 98.97 17 | 99.43 15 | 99.15 40 | 96.53 69 | 99.85 24 | 98.88 11 | 99.91 27 | 99.64 27 |
|
DTE-MVSNet | | | 98.79 10 | 98.86 11 | 98.59 43 | 99.55 21 | 96.12 64 | 98.48 24 | 99.10 25 | 99.36 3 | 99.29 25 | 99.06 47 | 97.27 37 | 99.93 2 | 97.71 43 | 99.91 27 | 99.70 19 |
|
TDRefinement | | | 98.90 5 | 98.86 11 | 99.02 8 | 99.54 23 | 98.06 6 | 99.34 5 | 99.44 7 | 98.85 19 | 99.00 40 | 99.20 31 | 97.42 31 | 99.59 144 | 97.21 62 | 99.76 50 | 99.40 91 |
|
PS-CasMVS | | | 98.73 13 | 98.85 13 | 98.39 55 | 99.55 21 | 95.47 85 | 98.49 22 | 99.13 21 | 99.22 7 | 99.22 28 | 98.96 52 | 97.35 33 | 99.92 4 | 97.79 39 | 99.93 21 | 99.79 8 |
|
PEN-MVS | | | 98.75 12 | 98.85 13 | 98.44 50 | 99.58 18 | 95.67 77 | 98.45 25 | 99.15 19 | 99.33 4 | 99.30 24 | 99.00 48 | 97.27 37 | 99.92 4 | 97.64 44 | 99.92 24 | 99.75 13 |
|
jajsoiax | | | 98.77 11 | 98.79 15 | 98.74 32 | 99.66 10 | 96.48 55 | 98.45 25 | 99.12 22 | 95.83 125 | 99.67 7 | 99.37 14 | 98.25 11 | 99.92 4 | 98.77 14 | 99.94 19 | 99.82 7 |
|
UA-Net | | | 98.88 7 | 98.76 16 | 99.22 2 | 99.11 77 | 97.89 10 | 99.47 3 | 99.32 8 | 99.08 9 | 97.87 137 | 99.67 3 | 96.47 74 | 99.92 4 | 97.88 34 | 99.98 3 | 99.85 4 |
|
ACMH | | 93.61 9 | 98.44 25 | 98.76 16 | 97.51 109 | 99.43 37 | 93.54 148 | 98.23 34 | 99.05 38 | 97.40 79 | 99.37 19 | 99.08 46 | 98.79 6 | 99.47 182 | 97.74 42 | 99.71 63 | 99.50 50 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
test_djsdf | | | 98.73 13 | 98.74 18 | 98.69 37 | 99.63 13 | 96.30 60 | 98.67 12 | 99.02 51 | 96.50 99 | 99.32 21 | 99.44 10 | 97.43 30 | 99.92 4 | 98.73 17 | 99.95 13 | 99.86 3 |
|
pm-mvs1 | | | 98.47 24 | 98.67 19 | 97.86 86 | 99.52 25 | 94.58 113 | 98.28 31 | 99.00 62 | 97.57 63 | 99.27 26 | 99.22 30 | 98.32 10 | 99.50 175 | 97.09 68 | 99.75 54 | 99.50 50 |
|
TransMVSNet (Re) | | | 98.38 28 | 98.67 19 | 97.51 109 | 99.51 26 | 93.39 153 | 98.20 39 | 98.87 81 | 98.23 35 | 99.48 12 | 99.27 25 | 98.47 9 | 99.55 156 | 96.52 78 | 99.53 105 | 99.60 34 |
|
anonymousdsp | | | 98.72 16 | 98.63 21 | 98.99 10 | 99.62 14 | 97.29 34 | 98.65 15 | 99.19 14 | 95.62 131 | 99.35 20 | 99.37 14 | 97.38 32 | 99.90 13 | 98.59 23 | 99.91 27 | 99.77 9 |
|
PS-MVSNAJss | | | 98.53 22 | 98.63 21 | 98.21 69 | 99.68 9 | 94.82 105 | 98.10 44 | 99.21 11 | 96.91 87 | 99.75 4 | 99.45 9 | 95.82 91 | 99.92 4 | 98.80 13 | 99.96 11 | 99.89 1 |
|
nrg030 | | | 98.54 21 | 98.62 23 | 98.32 61 | 99.22 56 | 95.66 78 | 97.90 56 | 99.08 30 | 98.31 32 | 99.02 37 | 98.74 65 | 97.68 24 | 99.61 134 | 97.77 40 | 99.85 39 | 99.70 19 |
|
WR-MVS_H | | | 98.65 18 | 98.62 23 | 98.75 30 | 99.51 26 | 96.61 51 | 98.55 19 | 99.17 15 | 99.05 12 | 99.17 31 | 98.79 60 | 95.47 105 | 99.89 17 | 97.95 32 | 99.91 27 | 99.75 13 |
|
OurMVSNet-221017-0 | | | 98.61 19 | 98.61 25 | 98.63 41 | 99.77 4 | 96.35 58 | 99.17 6 | 99.05 38 | 98.05 41 | 99.61 11 | 99.52 5 | 93.72 164 | 99.88 19 | 98.72 20 | 99.88 34 | 99.65 24 |
|
wuykxyi23d | | | 98.68 17 | 98.53 26 | 99.13 3 | 99.44 34 | 97.97 7 | 96.85 117 | 99.02 51 | 95.81 126 | 99.88 2 | 99.38 13 | 98.14 14 | 99.69 97 | 98.32 28 | 99.95 13 | 99.73 16 |
|
v13 | | | 98.02 44 | 98.52 27 | 96.51 170 | 99.02 88 | 90.14 205 | 98.07 46 | 99.09 29 | 98.10 40 | 99.13 32 | 99.35 18 | 94.84 122 | 99.74 59 | 99.12 5 | 99.98 3 | 99.65 24 |
|
v12 | | | 97.97 47 | 98.47 28 | 96.46 174 | 98.98 92 | 90.01 209 | 97.97 51 | 99.08 30 | 98.00 43 | 99.11 34 | 99.34 20 | 94.70 125 | 99.73 64 | 99.07 6 | 99.98 3 | 99.64 27 |
|
VPA-MVSNet | | | 98.27 32 | 98.46 29 | 97.70 95 | 99.06 82 | 93.80 138 | 97.76 64 | 99.00 62 | 98.40 29 | 99.07 35 | 98.98 50 | 96.89 50 | 99.75 54 | 97.19 65 | 99.79 47 | 99.55 44 |
|
CP-MVSNet | | | 98.42 26 | 98.46 29 | 98.30 64 | 99.46 32 | 95.22 93 | 98.27 33 | 98.84 87 | 99.05 12 | 99.01 38 | 98.65 73 | 95.37 108 | 99.90 13 | 97.57 48 | 99.91 27 | 99.77 9 |
|
MIMVSNet1 | | | 98.51 23 | 98.45 31 | 98.67 38 | 99.72 6 | 96.71 46 | 98.76 10 | 98.89 77 | 98.49 25 | 99.38 18 | 99.14 41 | 95.44 107 | 99.84 28 | 96.47 81 | 99.80 46 | 99.47 64 |
|
V9 | | | 97.90 58 | 98.40 32 | 96.40 178 | 98.93 94 | 89.86 211 | 97.86 58 | 99.07 34 | 97.88 47 | 99.05 36 | 99.30 23 | 94.53 136 | 99.72 70 | 99.01 8 | 99.98 3 | 99.63 29 |
|
FC-MVSNet-test | | | 98.16 36 | 98.37 33 | 97.56 104 | 99.49 30 | 93.10 157 | 98.35 28 | 99.21 11 | 98.43 28 | 98.89 44 | 98.83 59 | 94.30 143 | 99.81 33 | 97.87 35 | 99.91 27 | 99.77 9 |
|
v11 | | | 97.82 67 | 98.36 34 | 96.17 195 | 98.93 94 | 89.16 231 | 97.79 61 | 99.08 30 | 97.64 60 | 99.19 29 | 99.32 22 | 94.28 144 | 99.72 70 | 99.07 6 | 99.97 8 | 99.63 29 |
|
Vis-MVSNet | | | 98.27 32 | 98.34 35 | 98.07 74 | 99.33 47 | 95.21 95 | 98.04 48 | 99.46 6 | 97.32 82 | 97.82 141 | 99.11 43 | 96.75 59 | 99.86 23 | 97.84 36 | 99.36 155 | 99.15 134 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
V14 | | | 97.83 64 | 98.33 36 | 96.35 179 | 98.88 100 | 89.72 212 | 97.75 65 | 99.05 38 | 97.74 51 | 99.01 38 | 99.27 25 | 94.35 141 | 99.71 80 | 98.95 9 | 99.97 8 | 99.62 31 |
|
ACMH+ | | 93.58 10 | 98.23 35 | 98.31 37 | 97.98 81 | 99.39 42 | 95.22 93 | 97.55 81 | 99.20 13 | 98.21 36 | 99.25 27 | 98.51 82 | 98.21 12 | 99.40 210 | 94.79 145 | 99.72 59 | 99.32 107 |
|
Gipuma | | | 98.07 41 | 98.31 37 | 97.36 126 | 99.76 5 | 96.28 61 | 98.51 21 | 99.10 25 | 98.76 20 | 96.79 185 | 99.34 20 | 96.61 66 | 98.82 291 | 96.38 83 | 99.50 112 | 96.98 290 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
TranMVSNet+NR-MVSNet | | | 98.33 29 | 98.30 39 | 98.43 51 | 99.07 81 | 95.87 70 | 96.73 122 | 99.05 38 | 98.67 21 | 98.84 45 | 98.45 86 | 97.58 27 | 99.88 19 | 96.45 82 | 99.86 38 | 99.54 45 |
|
v15 | | | 97.77 70 | 98.26 40 | 96.30 184 | 98.81 102 | 89.59 219 | 97.62 74 | 99.04 45 | 97.59 62 | 98.97 42 | 99.24 27 | 94.19 148 | 99.70 88 | 98.88 11 | 99.97 8 | 99.61 33 |
|
abl_6 | | | 98.42 26 | 98.19 41 | 99.09 4 | 99.16 64 | 98.10 5 | 97.73 69 | 99.11 23 | 97.76 50 | 98.62 57 | 98.27 104 | 97.88 21 | 99.80 37 | 95.67 105 | 99.50 112 | 99.38 96 |
|
v17 | | | 97.70 75 | 98.17 42 | 96.28 187 | 98.77 106 | 89.59 219 | 97.62 74 | 99.01 60 | 97.54 65 | 98.72 54 | 99.18 35 | 94.06 152 | 99.68 103 | 98.74 16 | 99.92 24 | 99.58 36 |
|
v16 | | | 97.69 76 | 98.16 43 | 96.29 186 | 98.75 107 | 89.60 217 | 97.62 74 | 99.01 60 | 97.53 67 | 98.69 56 | 99.18 35 | 94.05 153 | 99.68 103 | 98.73 17 | 99.88 34 | 99.58 36 |
|
HPM-MVS_fast | | | 98.32 30 | 98.13 44 | 98.88 23 | 99.54 23 | 97.48 27 | 98.35 28 | 99.03 50 | 95.88 122 | 97.88 132 | 98.22 109 | 98.15 13 | 99.74 59 | 96.50 80 | 99.62 79 | 99.42 86 |
|
COLMAP_ROB | | 94.48 6 | 98.25 34 | 98.11 45 | 98.64 40 | 99.21 59 | 97.35 32 | 97.96 52 | 99.16 16 | 98.34 31 | 98.78 48 | 98.52 81 | 97.32 34 | 99.45 191 | 94.08 168 | 99.67 73 | 99.13 137 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
FMVSNet1 | | | 97.95 50 | 98.08 46 | 97.56 104 | 99.14 75 | 93.67 142 | 98.23 34 | 98.66 130 | 97.41 78 | 99.00 40 | 99.19 32 | 95.47 105 | 99.73 64 | 95.83 101 | 99.76 50 | 99.30 111 |
|
FIs | | | 97.93 54 | 98.07 47 | 97.48 116 | 99.38 43 | 92.95 159 | 98.03 50 | 99.11 23 | 98.04 42 | 98.62 57 | 98.66 71 | 93.75 163 | 99.78 39 | 97.23 61 | 99.84 40 | 99.73 16 |
|
v18 | | | 97.60 82 | 98.06 48 | 96.23 188 | 98.68 121 | 89.46 222 | 97.48 85 | 98.98 67 | 97.33 81 | 98.60 60 | 99.13 42 | 93.86 156 | 99.67 110 | 98.62 21 | 99.87 36 | 99.56 41 |
|
v8 | | | 97.60 82 | 98.06 48 | 96.23 188 | 98.71 114 | 89.44 223 | 97.43 87 | 98.82 99 | 97.29 83 | 98.74 52 | 99.10 44 | 93.86 156 | 99.68 103 | 98.61 22 | 99.94 19 | 99.56 41 |
|
APDe-MVS | | | 98.14 37 | 98.03 50 | 98.47 49 | 98.72 111 | 96.04 67 | 98.07 46 | 99.10 25 | 95.96 119 | 98.59 61 | 98.69 69 | 96.94 48 | 99.81 33 | 96.64 74 | 99.58 92 | 99.57 40 |
|
tfpnnormal | | | 97.72 73 | 97.97 51 | 96.94 147 | 99.26 51 | 92.23 170 | 97.83 60 | 98.45 152 | 98.25 34 | 99.13 32 | 98.66 71 | 96.65 63 | 99.69 97 | 93.92 175 | 99.62 79 | 98.91 173 |
|
v10 | | | 97.55 85 | 97.97 51 | 96.31 183 | 98.60 129 | 89.64 214 | 97.44 86 | 99.02 51 | 96.60 96 | 98.72 54 | 99.16 39 | 93.48 168 | 99.72 70 | 98.76 15 | 99.92 24 | 99.58 36 |
|
test_0402 | | | 97.84 63 | 97.97 51 | 97.47 117 | 99.19 62 | 94.07 129 | 96.71 123 | 98.73 113 | 98.66 22 | 98.56 63 | 98.41 88 | 96.84 55 | 99.69 97 | 94.82 142 | 99.81 43 | 98.64 201 |
|
APD-MVS_3200maxsize | | | 98.13 39 | 97.90 54 | 98.79 28 | 98.79 104 | 97.31 33 | 97.55 81 | 98.92 73 | 97.72 55 | 98.25 89 | 98.13 121 | 97.10 43 | 99.75 54 | 95.44 117 | 99.24 176 | 99.32 107 |
|
DP-MVS | | | 97.87 61 | 97.89 55 | 97.81 89 | 98.62 127 | 94.82 105 | 97.13 99 | 98.79 102 | 98.98 16 | 98.74 52 | 98.49 83 | 95.80 97 | 99.49 177 | 95.04 138 | 99.44 131 | 99.11 145 |
|
NR-MVSNet | | | 97.96 48 | 97.86 56 | 98.26 66 | 98.73 109 | 95.54 81 | 98.14 42 | 98.73 113 | 97.79 48 | 99.42 16 | 97.83 150 | 94.40 140 | 99.78 39 | 95.91 100 | 99.76 50 | 99.46 66 |
|
MTAPA | | | 98.14 37 | 97.84 57 | 99.06 5 | 99.44 34 | 97.90 8 | 97.25 92 | 98.73 113 | 97.69 57 | 97.90 128 | 97.96 138 | 95.81 95 | 99.82 31 | 96.13 88 | 99.61 84 | 99.45 71 |
|
HPM-MVS | | | 98.11 40 | 97.83 58 | 98.92 19 | 99.42 39 | 97.46 28 | 98.57 17 | 99.05 38 | 95.43 140 | 97.41 156 | 97.50 180 | 97.98 17 | 99.79 38 | 95.58 114 | 99.57 95 | 99.50 50 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
Baseline_NR-MVSNet | | | 97.72 73 | 97.79 59 | 97.50 112 | 99.56 19 | 93.29 154 | 95.44 188 | 98.86 83 | 98.20 37 | 98.37 76 | 99.24 27 | 94.69 126 | 99.55 156 | 95.98 97 | 99.79 47 | 99.65 24 |
|
EG-PatchMatch MVS | | | 97.69 76 | 97.79 59 | 97.40 124 | 99.06 82 | 93.52 150 | 95.96 161 | 98.97 69 | 94.55 177 | 98.82 46 | 98.76 63 | 97.31 35 | 99.29 238 | 97.20 64 | 99.44 131 | 99.38 96 |
|
ACMM | | 93.33 11 | 98.05 42 | 97.79 59 | 98.85 24 | 99.15 67 | 97.55 23 | 96.68 124 | 98.83 95 | 95.21 148 | 98.36 77 | 98.13 121 | 98.13 16 | 99.62 128 | 96.04 92 | 99.54 103 | 99.39 94 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
SteuartSystems-ACMMP | | | 98.02 44 | 97.76 62 | 98.79 28 | 99.43 37 | 97.21 36 | 97.15 96 | 98.90 75 | 96.58 98 | 98.08 107 | 97.87 149 | 97.02 47 | 99.76 48 | 95.25 124 | 99.59 89 | 99.40 91 |
Skip Steuart: Steuart Systems R&D Blog. |
ACMMP | | | 98.05 42 | 97.75 63 | 98.93 18 | 99.23 55 | 97.60 19 | 98.09 45 | 98.96 70 | 95.75 128 | 97.91 127 | 98.06 130 | 96.89 50 | 99.76 48 | 95.32 122 | 99.57 95 | 99.43 84 |
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 |
testing_2 | | | 97.43 92 | 97.71 64 | 96.60 163 | 98.91 97 | 90.85 195 | 96.01 154 | 98.54 144 | 94.78 169 | 98.78 48 | 98.96 52 | 96.35 78 | 99.54 158 | 97.25 60 | 99.82 42 | 99.40 91 |
|
SD-MVS | | | 97.37 97 | 97.70 65 | 96.35 179 | 98.14 192 | 95.13 96 | 96.54 125 | 98.92 73 | 95.94 120 | 99.19 29 | 98.08 126 | 97.74 22 | 95.06 351 | 95.24 125 | 99.54 103 | 98.87 183 |
|
XXY-MVS | | | 97.54 87 | 97.70 65 | 97.07 140 | 99.46 32 | 92.21 171 | 97.22 95 | 99.00 62 | 94.93 164 | 98.58 62 | 98.92 56 | 97.31 35 | 99.41 207 | 94.44 154 | 99.43 140 | 99.59 35 |
|
DeepC-MVS | | 95.41 4 | 97.82 67 | 97.70 65 | 98.16 70 | 98.78 105 | 95.72 74 | 96.23 144 | 99.02 51 | 93.92 197 | 98.62 57 | 98.99 49 | 97.69 23 | 99.62 128 | 96.18 87 | 99.87 36 | 99.15 134 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
LPG-MVS_test | | | 97.94 52 | 97.67 68 | 98.74 32 | 99.15 67 | 97.02 38 | 97.09 105 | 99.02 51 | 95.15 153 | 98.34 79 | 98.23 106 | 97.91 19 | 99.70 88 | 94.41 156 | 99.73 56 | 99.50 50 |
|
zzz-MVS | | | 98.01 46 | 97.66 69 | 99.06 5 | 99.44 34 | 97.90 8 | 95.66 177 | 98.73 113 | 97.69 57 | 97.90 128 | 97.96 138 | 95.81 95 | 99.82 31 | 96.13 88 | 99.61 84 | 99.45 71 |
|
UniMVSNet_NR-MVSNet | | | 97.83 64 | 97.65 70 | 98.37 56 | 98.72 111 | 95.78 72 | 95.66 177 | 99.02 51 | 98.11 39 | 98.31 84 | 97.69 166 | 94.65 130 | 99.85 24 | 97.02 70 | 99.71 63 | 99.48 61 |
|
UniMVSNet (Re) | | | 97.83 64 | 97.65 70 | 98.35 60 | 98.80 103 | 95.86 71 | 95.92 165 | 99.04 45 | 97.51 68 | 98.22 91 | 97.81 154 | 94.68 128 | 99.78 39 | 97.14 67 | 99.75 54 | 99.41 88 |
|
HFP-MVS | | | 97.94 52 | 97.64 72 | 98.83 25 | 99.15 67 | 97.50 25 | 97.59 78 | 98.84 87 | 96.05 113 | 97.49 149 | 97.54 175 | 97.07 45 | 99.70 88 | 95.61 111 | 99.46 126 | 99.30 111 |
|
3Dnovator | | 96.53 2 | 97.61 81 | 97.64 72 | 97.50 112 | 97.74 238 | 93.65 146 | 98.49 22 | 98.88 79 | 96.86 91 | 97.11 167 | 98.55 79 | 95.82 91 | 99.73 64 | 95.94 98 | 99.42 143 | 99.13 137 |
|
ACMMP_Plus | | | 97.89 59 | 97.63 74 | 98.67 38 | 99.35 46 | 96.84 43 | 96.36 135 | 98.79 102 | 95.07 160 | 97.88 132 | 98.35 93 | 97.24 40 | 99.72 70 | 96.05 91 | 99.58 92 | 99.45 71 |
|
XVS | | | 97.96 48 | 97.63 74 | 98.94 15 | 99.15 67 | 97.66 16 | 97.77 62 | 98.83 95 | 97.42 71 | 96.32 206 | 97.64 167 | 96.49 72 | 99.72 70 | 95.66 107 | 99.37 152 | 99.45 71 |
|
ACMMPR | | | 97.95 50 | 97.62 76 | 98.94 15 | 99.20 60 | 97.56 22 | 97.59 78 | 98.83 95 | 96.05 113 | 97.46 154 | 97.63 168 | 96.77 58 | 99.76 48 | 95.61 111 | 99.46 126 | 99.49 58 |
|
DU-MVS | | | 97.79 69 | 97.60 77 | 98.36 59 | 98.73 109 | 95.78 72 | 95.65 179 | 98.87 81 | 97.57 63 | 98.31 84 | 97.83 150 | 94.69 126 | 99.85 24 | 97.02 70 | 99.71 63 | 99.46 66 |
|
region2R | | | 97.92 55 | 97.59 78 | 98.92 19 | 99.22 56 | 97.55 23 | 97.60 77 | 98.84 87 | 96.00 117 | 97.22 161 | 97.62 169 | 96.87 53 | 99.76 48 | 95.48 115 | 99.43 140 | 99.46 66 |
|
3Dnovator+ | | 96.13 3 | 97.73 72 | 97.59 78 | 98.15 71 | 98.11 196 | 95.60 79 | 98.04 48 | 98.70 122 | 98.13 38 | 96.93 181 | 98.45 86 | 95.30 112 | 99.62 128 | 95.64 109 | 98.96 202 | 99.24 123 |
|
SixPastTwentyTwo | | | 97.49 90 | 97.57 80 | 97.26 132 | 99.56 19 | 92.33 167 | 98.28 31 | 96.97 259 | 98.30 33 | 99.45 14 | 99.35 18 | 88.43 256 | 99.89 17 | 98.01 31 | 99.76 50 | 99.54 45 |
|
CP-MVS | | | 97.92 55 | 97.56 81 | 98.99 10 | 98.99 90 | 97.82 12 | 97.93 54 | 98.96 70 | 96.11 112 | 96.89 183 | 97.45 183 | 96.85 54 | 99.78 39 | 95.19 127 | 99.63 78 | 99.38 96 |
|
mPP-MVS | | | 97.91 57 | 97.53 82 | 99.04 7 | 99.22 56 | 97.87 11 | 97.74 67 | 98.78 105 | 96.04 115 | 97.10 168 | 97.73 162 | 96.53 69 | 99.78 39 | 95.16 131 | 99.50 112 | 99.46 66 |
|
PGM-MVS | | | 97.88 60 | 97.52 83 | 98.96 13 | 99.20 60 | 97.62 18 | 97.09 105 | 99.06 36 | 95.45 138 | 97.55 145 | 97.94 142 | 97.11 42 | 99.78 39 | 94.77 147 | 99.46 126 | 99.48 61 |
|
RPSCF | | | 97.87 61 | 97.51 84 | 98.95 14 | 99.15 67 | 98.43 3 | 97.56 80 | 99.06 36 | 96.19 111 | 98.48 69 | 98.70 68 | 94.72 124 | 99.24 244 | 94.37 159 | 99.33 165 | 99.17 130 |
|
LS3D | | | 97.77 70 | 97.50 85 | 98.57 44 | 96.24 299 | 97.58 21 | 98.45 25 | 98.85 84 | 98.58 24 | 97.51 147 | 97.94 142 | 95.74 98 | 99.63 122 | 95.19 127 | 98.97 201 | 98.51 212 |
|
VPNet | | | 97.26 105 | 97.49 86 | 96.59 165 | 99.47 31 | 90.58 201 | 96.27 139 | 98.53 145 | 97.77 49 | 98.46 71 | 98.41 88 | 94.59 132 | 99.68 103 | 94.61 150 | 99.29 171 | 99.52 48 |
|
Regformer-4 | | | 97.53 89 | 97.47 87 | 97.71 94 | 97.35 266 | 93.91 134 | 95.26 207 | 98.14 198 | 97.97 44 | 98.34 79 | 97.89 147 | 95.49 103 | 99.71 80 | 97.41 57 | 99.42 143 | 99.51 49 |
|
EI-MVSNet-UG-set | | | 97.32 102 | 97.40 88 | 97.09 139 | 97.34 269 | 92.01 179 | 95.33 201 | 97.65 231 | 97.74 51 | 98.30 86 | 98.14 120 | 95.04 118 | 99.69 97 | 97.55 49 | 99.52 109 | 99.58 36 |
|
EI-MVSNet-Vis-set | | | 97.32 102 | 97.39 89 | 97.11 137 | 97.36 265 | 92.08 177 | 95.34 200 | 97.65 231 | 97.74 51 | 98.29 87 | 98.11 124 | 95.05 116 | 99.68 103 | 97.50 53 | 99.50 112 | 99.56 41 |
|
MP-MVS-pluss | | | 97.69 76 | 97.36 90 | 98.70 36 | 99.50 29 | 96.84 43 | 95.38 197 | 98.99 65 | 92.45 237 | 98.11 101 | 98.31 97 | 97.25 39 | 99.77 47 | 96.60 75 | 99.62 79 | 99.48 61 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
LCM-MVSNet-Re | | | 97.33 101 | 97.33 91 | 97.32 128 | 98.13 195 | 93.79 139 | 96.99 109 | 99.65 2 | 96.74 94 | 99.47 13 | 98.93 55 | 96.91 49 | 99.84 28 | 90.11 249 | 99.06 196 | 98.32 229 |
|
CSCG | | | 97.40 95 | 97.30 92 | 97.69 97 | 98.95 93 | 94.83 104 | 97.28 91 | 98.99 65 | 96.35 106 | 98.13 100 | 95.95 266 | 95.99 84 | 99.66 115 | 94.36 162 | 99.73 56 | 98.59 206 |
|
Regformer-3 | | | 97.25 106 | 97.29 93 | 97.11 137 | 97.35 266 | 92.32 168 | 95.26 207 | 97.62 236 | 97.67 59 | 98.17 95 | 97.89 147 | 95.05 116 | 99.56 152 | 97.16 66 | 99.42 143 | 99.46 66 |
|
IterMVS-LS | | | 96.92 123 | 97.29 93 | 95.79 218 | 98.51 143 | 88.13 254 | 95.10 213 | 98.66 130 | 96.99 84 | 98.46 71 | 98.68 70 | 92.55 194 | 99.74 59 | 96.91 72 | 99.79 47 | 99.50 50 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
XVG-ACMP-BASELINE | | | 97.58 84 | 97.28 95 | 98.49 47 | 99.16 64 | 96.90 42 | 96.39 130 | 98.98 67 | 95.05 161 | 98.06 110 | 98.02 133 | 95.86 87 | 99.56 152 | 94.37 159 | 99.64 77 | 99.00 158 |
|
OPM-MVS | | | 97.54 87 | 97.25 96 | 98.41 52 | 99.11 77 | 96.61 51 | 95.24 209 | 98.46 151 | 94.58 176 | 98.10 104 | 98.07 127 | 97.09 44 | 99.39 216 | 95.16 131 | 99.44 131 | 99.21 125 |
|
VDD-MVS | | | 97.37 97 | 97.25 96 | 97.74 93 | 98.69 120 | 94.50 116 | 97.04 107 | 95.61 285 | 98.59 23 | 98.51 66 | 98.72 66 | 92.54 196 | 99.58 146 | 96.02 94 | 99.49 119 | 99.12 142 |
|
v1neww | | | 96.97 117 | 97.24 98 | 96.15 196 | 98.70 116 | 89.44 223 | 95.97 157 | 98.33 171 | 95.25 145 | 97.88 132 | 98.15 117 | 93.83 159 | 99.61 134 | 97.50 53 | 99.50 112 | 99.41 88 |
|
Regformer-2 | | | 97.41 94 | 97.24 98 | 97.93 83 | 97.21 275 | 94.72 108 | 94.85 230 | 98.27 181 | 97.74 51 | 98.11 101 | 97.50 180 | 95.58 101 | 99.69 97 | 96.57 77 | 99.31 167 | 99.37 101 |
|
v7new | | | 96.97 117 | 97.24 98 | 96.15 196 | 98.70 116 | 89.44 223 | 95.97 157 | 98.33 171 | 95.25 145 | 97.88 132 | 98.15 117 | 93.83 159 | 99.61 134 | 97.50 53 | 99.50 112 | 99.41 88 |
|
v6 | | | 96.97 117 | 97.24 98 | 96.15 196 | 98.71 114 | 89.44 223 | 95.97 157 | 98.33 171 | 95.25 145 | 97.89 130 | 98.15 117 | 93.86 156 | 99.61 134 | 97.51 52 | 99.50 112 | 99.42 86 |
|
TSAR-MVS + MP. | | | 97.42 93 | 97.23 102 | 98.00 80 | 99.38 43 | 95.00 99 | 97.63 73 | 98.20 189 | 93.00 223 | 98.16 96 | 98.06 130 | 95.89 86 | 99.72 70 | 95.67 105 | 99.10 190 | 99.28 118 |
|
#test# | | | 97.62 80 | 97.22 103 | 98.83 25 | 99.15 67 | 97.50 25 | 96.81 119 | 98.84 87 | 94.25 188 | 97.49 149 | 97.54 175 | 97.07 45 | 99.70 88 | 94.37 159 | 99.46 126 | 99.30 111 |
|
canonicalmvs | | | 97.23 107 | 97.21 104 | 97.30 129 | 97.65 247 | 94.39 118 | 97.84 59 | 99.05 38 | 97.42 71 | 96.68 188 | 93.85 306 | 97.63 26 | 99.33 231 | 96.29 85 | 98.47 242 | 98.18 244 |
|
SMA-MVS | | | 97.55 85 | 97.19 105 | 98.61 42 | 98.83 101 | 96.71 46 | 96.74 121 | 98.81 101 | 91.81 249 | 98.78 48 | 98.36 92 | 96.63 65 | 99.68 103 | 95.17 129 | 99.59 89 | 99.45 71 |
|
MP-MVS | | | 97.64 79 | 97.18 106 | 99.00 9 | 99.32 49 | 97.77 14 | 97.49 84 | 98.73 113 | 96.27 107 | 95.59 234 | 97.75 159 | 96.30 79 | 99.78 39 | 93.70 181 | 99.48 122 | 99.45 71 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
v7 | | | 96.93 121 | 97.17 107 | 96.23 188 | 98.59 131 | 89.64 214 | 95.96 161 | 98.66 130 | 94.41 181 | 97.87 137 | 98.38 91 | 93.47 169 | 99.64 119 | 97.93 33 | 99.24 176 | 99.43 84 |
|
Regformer-1 | | | 97.27 104 | 97.16 108 | 97.61 102 | 97.21 275 | 93.86 136 | 94.85 230 | 98.04 209 | 97.62 61 | 98.03 113 | 97.50 180 | 95.34 109 | 99.63 122 | 96.52 78 | 99.31 167 | 99.35 105 |
|
V42 | | | 97.04 111 | 97.16 108 | 96.68 161 | 98.59 131 | 91.05 192 | 96.33 137 | 98.36 166 | 94.60 173 | 97.99 115 | 98.30 100 | 93.32 174 | 99.62 128 | 97.40 58 | 99.53 105 | 99.38 96 |
|
v1141 | | | 96.86 127 | 97.14 110 | 96.04 203 | 98.55 136 | 89.06 234 | 95.44 188 | 98.33 171 | 95.14 155 | 97.93 125 | 98.19 111 | 93.36 172 | 99.62 128 | 97.61 45 | 99.69 67 | 99.44 80 |
|
divwei89l23v2f112 | | | 96.86 127 | 97.14 110 | 96.04 203 | 98.54 139 | 89.06 234 | 95.44 188 | 98.33 171 | 95.14 155 | 97.93 125 | 98.19 111 | 93.36 172 | 99.61 134 | 97.61 45 | 99.68 71 | 99.44 80 |
|
v1 | | | 96.86 127 | 97.14 110 | 96.04 203 | 98.55 136 | 89.06 234 | 95.44 188 | 98.33 171 | 95.14 155 | 97.94 122 | 98.18 115 | 93.39 171 | 99.61 134 | 97.61 45 | 99.69 67 | 99.44 80 |
|
PM-MVS | | | 97.36 100 | 97.10 113 | 98.14 72 | 98.91 97 | 96.77 45 | 96.20 145 | 98.63 137 | 93.82 204 | 98.54 64 | 98.33 95 | 93.98 154 | 99.05 263 | 95.99 96 | 99.45 130 | 98.61 205 |
|
ACMP | | 92.54 13 | 97.47 91 | 97.10 113 | 98.55 46 | 99.04 85 | 96.70 48 | 96.24 143 | 98.89 77 | 93.71 207 | 97.97 119 | 97.75 159 | 97.44 29 | 99.63 122 | 93.22 189 | 99.70 66 | 99.32 107 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
v1144 | | | 96.84 130 | 97.08 115 | 96.13 200 | 98.42 153 | 89.28 229 | 95.41 195 | 98.67 128 | 94.21 190 | 97.97 119 | 98.31 97 | 93.06 179 | 99.65 116 | 98.06 30 | 99.62 79 | 99.45 71 |
|
XVG-OURS-SEG-HR | | | 97.38 96 | 97.07 116 | 98.30 64 | 99.01 89 | 97.41 31 | 94.66 235 | 99.02 51 | 95.20 149 | 98.15 98 | 97.52 178 | 98.83 5 | 98.43 319 | 94.87 140 | 96.41 313 | 99.07 152 |
|
v1192 | | | 96.83 133 | 97.06 117 | 96.15 196 | 98.28 163 | 89.29 228 | 95.36 198 | 98.77 106 | 93.73 206 | 98.11 101 | 98.34 94 | 93.02 183 | 99.67 110 | 98.35 26 | 99.58 92 | 99.50 50 |
|
v2v482 | | | 96.78 137 | 97.06 117 | 95.95 211 | 98.57 134 | 88.77 244 | 95.36 198 | 98.26 183 | 95.18 151 | 97.85 139 | 98.23 106 | 92.58 193 | 99.63 122 | 97.80 38 | 99.69 67 | 99.45 71 |
|
v1240 | | | 96.74 138 | 97.02 119 | 95.91 214 | 98.18 185 | 88.52 246 | 95.39 196 | 98.88 79 | 93.15 220 | 98.46 71 | 98.40 90 | 92.80 186 | 99.71 80 | 98.45 25 | 99.49 119 | 99.49 58 |
|
v148 | | | 96.58 149 | 96.97 120 | 95.42 229 | 98.63 126 | 87.57 269 | 95.09 215 | 97.90 212 | 95.91 121 | 98.24 90 | 97.96 138 | 93.42 170 | 99.39 216 | 96.04 92 | 99.52 109 | 99.29 117 |
|
PMVS | | 89.60 17 | 96.71 143 | 96.97 120 | 95.95 211 | 99.51 26 | 97.81 13 | 97.42 88 | 97.49 239 | 97.93 45 | 95.95 221 | 98.58 75 | 96.88 52 | 96.91 345 | 89.59 256 | 99.36 155 | 93.12 344 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
v1921920 | | | 96.72 141 | 96.96 122 | 95.99 207 | 98.21 178 | 88.79 243 | 95.42 193 | 98.79 102 | 93.22 214 | 98.19 94 | 98.26 105 | 92.68 189 | 99.70 88 | 98.34 27 | 99.55 101 | 99.49 58 |
|
EI-MVSNet | | | 96.63 147 | 96.93 123 | 95.74 219 | 97.26 273 | 88.13 254 | 95.29 205 | 97.65 231 | 96.99 84 | 97.94 122 | 98.19 111 | 92.55 194 | 99.58 146 | 96.91 72 | 99.56 97 | 99.50 50 |
|
AllTest | | | 97.20 109 | 96.92 124 | 98.06 75 | 99.08 79 | 96.16 62 | 97.14 98 | 99.16 16 | 94.35 185 | 97.78 142 | 98.07 127 | 95.84 88 | 99.12 253 | 91.41 215 | 99.42 143 | 98.91 173 |
|
v144192 | | | 96.69 144 | 96.90 125 | 96.03 206 | 98.25 174 | 88.92 237 | 95.49 186 | 98.77 106 | 93.05 222 | 98.09 105 | 98.29 101 | 92.51 198 | 99.70 88 | 98.11 29 | 99.56 97 | 99.47 64 |
|
HSP-MVS | | | 97.37 97 | 96.85 126 | 98.92 19 | 99.26 51 | 97.70 15 | 97.66 70 | 98.23 185 | 95.65 129 | 98.51 66 | 96.46 242 | 92.15 204 | 99.81 33 | 95.14 133 | 98.58 237 | 99.26 122 |
|
VDDNet | | | 96.98 116 | 96.84 127 | 97.41 123 | 99.40 41 | 93.26 155 | 97.94 53 | 95.31 287 | 99.26 6 | 98.39 75 | 99.18 35 | 87.85 263 | 99.62 128 | 95.13 134 | 99.09 191 | 99.35 105 |
|
VNet | | | 96.84 130 | 96.83 128 | 96.88 151 | 98.06 198 | 92.02 178 | 96.35 136 | 97.57 238 | 97.70 56 | 97.88 132 | 97.80 155 | 92.40 201 | 99.54 158 | 94.73 149 | 98.96 202 | 99.08 150 |
|
ESAPD | | | 97.22 108 | 96.82 129 | 98.40 54 | 99.03 86 | 96.07 65 | 95.64 181 | 98.84 87 | 94.84 165 | 98.08 107 | 97.60 171 | 96.69 61 | 99.76 48 | 91.22 221 | 99.44 131 | 99.37 101 |
|
WR-MVS | | | 96.90 125 | 96.81 130 | 97.16 134 | 98.56 135 | 92.20 173 | 94.33 243 | 98.12 200 | 97.34 80 | 98.20 93 | 97.33 193 | 92.81 185 | 99.75 54 | 94.79 145 | 99.81 43 | 99.54 45 |
|
GBi-Net | | | 96.99 113 | 96.80 131 | 97.56 104 | 97.96 209 | 93.67 142 | 98.23 34 | 98.66 130 | 95.59 133 | 97.99 115 | 99.19 32 | 89.51 247 | 99.73 64 | 94.60 151 | 99.44 131 | 99.30 111 |
|
test1 | | | 96.99 113 | 96.80 131 | 97.56 104 | 97.96 209 | 93.67 142 | 98.23 34 | 98.66 130 | 95.59 133 | 97.99 115 | 99.19 32 | 89.51 247 | 99.73 64 | 94.60 151 | 99.44 131 | 99.30 111 |
|
MVS_Test | | | 96.27 158 | 96.79 133 | 94.73 252 | 96.94 285 | 86.63 285 | 96.18 146 | 98.33 171 | 94.94 162 | 96.07 218 | 98.28 102 | 95.25 113 | 99.26 242 | 97.21 62 | 97.90 265 | 98.30 232 |
|
XVG-OURS | | | 97.12 110 | 96.74 134 | 98.26 66 | 98.99 90 | 97.45 29 | 93.82 271 | 99.05 38 | 95.19 150 | 98.32 82 | 97.70 165 | 95.22 114 | 98.41 320 | 94.27 164 | 98.13 254 | 98.93 170 |
|
MSLP-MVS++ | | | 96.42 157 | 96.71 135 | 95.57 224 | 97.82 222 | 90.56 203 | 95.71 172 | 98.84 87 | 94.72 171 | 96.71 187 | 97.39 189 | 94.91 121 | 98.10 335 | 95.28 123 | 99.02 198 | 98.05 252 |
|
IS-MVSNet | | | 96.93 121 | 96.68 136 | 97.70 95 | 99.25 54 | 94.00 132 | 98.57 17 | 96.74 267 | 98.36 30 | 98.14 99 | 97.98 137 | 88.23 257 | 99.71 80 | 93.10 192 | 99.72 59 | 99.38 96 |
|
FMVSNet2 | | | 96.72 141 | 96.67 137 | 96.87 152 | 97.96 209 | 91.88 181 | 97.15 96 | 98.06 207 | 95.59 133 | 98.50 68 | 98.62 74 | 89.51 247 | 99.65 116 | 94.99 139 | 99.60 87 | 99.07 152 |
|
test20.03 | | | 96.58 149 | 96.61 138 | 96.48 173 | 98.49 145 | 91.72 185 | 95.68 176 | 97.69 226 | 96.81 92 | 98.27 88 | 97.92 145 | 94.18 149 | 98.71 301 | 90.78 233 | 99.66 75 | 99.00 158 |
|
ab-mvs | | | 96.59 148 | 96.59 139 | 96.60 163 | 98.64 122 | 92.21 171 | 98.35 28 | 97.67 227 | 94.45 178 | 96.99 173 | 98.79 60 | 94.96 120 | 99.49 177 | 90.39 246 | 99.07 194 | 98.08 247 |
|
new-patchmatchnet | | | 95.67 176 | 96.58 140 | 92.94 302 | 97.48 256 | 80.21 329 | 92.96 295 | 98.19 193 | 94.83 167 | 98.82 46 | 98.79 60 | 93.31 175 | 99.51 173 | 95.83 101 | 99.04 197 | 99.12 142 |
|
EPP-MVSNet | | | 96.84 130 | 96.58 140 | 97.65 100 | 99.18 63 | 93.78 140 | 98.68 11 | 96.34 270 | 97.91 46 | 97.30 159 | 98.06 130 | 88.46 255 | 99.85 24 | 93.85 177 | 99.40 150 | 99.32 107 |
|
UGNet | | | 96.81 135 | 96.56 142 | 97.58 103 | 96.64 290 | 93.84 137 | 97.75 65 | 97.12 254 | 96.47 102 | 93.62 290 | 98.88 58 | 93.22 177 | 99.53 160 | 95.61 111 | 99.69 67 | 99.36 104 |
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 |
CNVR-MVS | | | 96.92 123 | 96.55 143 | 98.03 79 | 98.00 206 | 95.54 81 | 94.87 228 | 98.17 194 | 94.60 173 | 96.38 200 | 97.05 205 | 95.67 99 | 99.36 225 | 95.12 135 | 99.08 192 | 99.19 127 |
|
MVS_111021_LR | | | 96.82 134 | 96.55 143 | 97.62 101 | 98.27 165 | 95.34 88 | 93.81 272 | 98.33 171 | 94.59 175 | 96.56 192 | 96.63 233 | 96.61 66 | 98.73 299 | 94.80 144 | 99.34 160 | 98.78 192 |
|
MVS_111021_HR | | | 96.73 140 | 96.54 145 | 97.27 130 | 98.35 157 | 93.66 145 | 93.42 285 | 98.36 166 | 94.74 170 | 96.58 190 | 96.76 226 | 96.54 68 | 98.99 271 | 94.87 140 | 99.27 174 | 99.15 134 |
|
APD-MVS | | | 97.00 112 | 96.53 146 | 98.41 52 | 98.55 136 | 96.31 59 | 96.32 138 | 98.77 106 | 92.96 229 | 97.44 155 | 97.58 174 | 95.84 88 | 99.74 59 | 91.96 203 | 99.35 158 | 99.19 127 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
PHI-MVS | | | 96.96 120 | 96.53 146 | 98.25 68 | 97.48 256 | 96.50 54 | 96.76 120 | 98.85 84 | 93.52 210 | 96.19 215 | 96.85 217 | 95.94 85 | 99.42 196 | 93.79 179 | 99.43 140 | 98.83 187 |
|
DeepC-MVS_fast | | 94.34 7 | 96.74 138 | 96.51 148 | 97.44 121 | 97.69 242 | 94.15 127 | 96.02 153 | 98.43 156 | 93.17 219 | 97.30 159 | 97.38 191 | 95.48 104 | 99.28 239 | 93.74 180 | 99.34 160 | 98.88 181 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
testgi | | | 96.07 165 | 96.50 149 | 94.80 249 | 99.26 51 | 87.69 268 | 95.96 161 | 98.58 143 | 95.08 159 | 98.02 114 | 96.25 253 | 97.92 18 | 97.60 341 | 88.68 271 | 98.74 223 | 99.11 145 |
|
DeepPCF-MVS | | 94.58 5 | 96.90 125 | 96.43 150 | 98.31 63 | 97.48 256 | 97.23 35 | 92.56 304 | 98.60 140 | 92.84 231 | 98.54 64 | 97.40 186 | 96.64 64 | 98.78 295 | 94.40 158 | 99.41 149 | 98.93 170 |
|
HPM-MVS++ | | | 96.99 113 | 96.38 151 | 98.81 27 | 98.64 122 | 97.59 20 | 95.97 157 | 98.20 189 | 95.51 136 | 95.06 242 | 96.53 238 | 94.10 151 | 99.70 88 | 94.29 163 | 99.15 183 | 99.13 137 |
|
MVSFormer | | | 96.14 164 | 96.36 152 | 95.49 228 | 97.68 243 | 87.81 266 | 98.67 12 | 99.02 51 | 96.50 99 | 94.48 264 | 96.15 257 | 86.90 268 | 99.92 4 | 98.73 17 | 99.13 186 | 98.74 195 |
|
TinyColmap | | | 96.00 168 | 96.34 153 | 94.96 243 | 97.90 214 | 87.91 263 | 94.13 258 | 98.49 149 | 94.41 181 | 98.16 96 | 97.76 156 | 96.29 80 | 98.68 306 | 90.52 241 | 99.42 143 | 98.30 232 |
|
HQP_MVS | | | 96.66 146 | 96.33 154 | 97.68 98 | 98.70 116 | 94.29 121 | 96.50 127 | 98.75 110 | 96.36 104 | 96.16 216 | 96.77 224 | 91.91 216 | 99.46 187 | 92.59 197 | 99.20 179 | 99.28 118 |
|
K. test v3 | | | 96.44 155 | 96.28 155 | 96.95 146 | 99.41 40 | 91.53 187 | 97.65 71 | 90.31 334 | 98.89 18 | 98.93 43 | 99.36 16 | 84.57 281 | 99.92 4 | 97.81 37 | 99.56 97 | 99.39 94 |
|
DELS-MVS | | | 96.17 163 | 96.23 156 | 95.99 207 | 97.55 254 | 90.04 207 | 92.38 308 | 98.52 146 | 94.13 193 | 96.55 195 | 97.06 204 | 94.99 119 | 99.58 146 | 95.62 110 | 99.28 172 | 98.37 222 |
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 |
pmmvs-eth3d | | | 96.49 152 | 96.18 157 | 97.42 122 | 98.25 174 | 94.29 121 | 94.77 234 | 98.07 206 | 89.81 268 | 97.97 119 | 98.33 95 | 93.11 178 | 99.08 260 | 95.46 116 | 99.84 40 | 98.89 177 |
|
Fast-Effi-MVS+-dtu | | | 96.44 155 | 96.12 158 | 97.39 125 | 97.18 277 | 94.39 118 | 95.46 187 | 98.73 113 | 96.03 116 | 94.72 250 | 94.92 288 | 96.28 81 | 99.69 97 | 93.81 178 | 97.98 258 | 98.09 246 |
|
TSAR-MVS + GP. | | | 96.47 154 | 96.12 158 | 97.49 115 | 97.74 238 | 95.23 90 | 94.15 256 | 96.90 261 | 93.26 213 | 98.04 112 | 96.70 229 | 94.41 139 | 98.89 283 | 94.77 147 | 99.14 184 | 98.37 222 |
|
Effi-MVS+-dtu | | | 96.81 135 | 96.09 160 | 98.99 10 | 96.90 287 | 98.69 2 | 96.42 129 | 98.09 202 | 95.86 123 | 95.15 241 | 95.54 276 | 94.26 145 | 99.81 33 | 94.06 169 | 98.51 240 | 98.47 214 |
|
CPTT-MVS | | | 96.69 144 | 96.08 161 | 98.49 47 | 98.89 99 | 96.64 50 | 97.25 92 | 98.77 106 | 92.89 230 | 96.01 220 | 97.13 200 | 92.23 203 | 99.67 110 | 92.24 201 | 99.34 160 | 99.17 130 |
|
mvs_anonymous | | | 95.36 193 | 96.07 162 | 93.21 294 | 96.29 298 | 81.56 323 | 94.60 237 | 97.66 229 | 93.30 212 | 96.95 180 | 98.91 57 | 93.03 182 | 99.38 221 | 96.60 75 | 97.30 299 | 98.69 199 |
|
Effi-MVS+ | | | 96.19 162 | 96.01 163 | 96.71 158 | 97.43 262 | 92.19 174 | 96.12 149 | 99.10 25 | 95.45 138 | 93.33 303 | 94.71 290 | 97.23 41 | 99.56 152 | 93.21 190 | 97.54 288 | 98.37 222 |
|
OMC-MVS | | | 96.48 153 | 96.00 164 | 97.91 84 | 98.30 159 | 96.01 69 | 94.86 229 | 98.60 140 | 91.88 247 | 97.18 163 | 97.21 197 | 96.11 82 | 99.04 264 | 90.49 244 | 99.34 160 | 98.69 199 |
|
NCCC | | | 96.52 151 | 95.99 165 | 98.10 73 | 97.81 223 | 95.68 76 | 95.00 224 | 98.20 189 | 95.39 141 | 95.40 237 | 96.36 249 | 93.81 161 | 99.45 191 | 93.55 184 | 98.42 243 | 99.17 130 |
|
xiu_mvs_v1_base_debu | | | 95.62 177 | 95.96 166 | 94.60 257 | 98.01 203 | 88.42 247 | 93.99 263 | 98.21 186 | 92.98 224 | 95.91 222 | 94.53 292 | 96.39 75 | 99.72 70 | 95.43 119 | 98.19 251 | 95.64 323 |
|
xiu_mvs_v1_base | | | 95.62 177 | 95.96 166 | 94.60 257 | 98.01 203 | 88.42 247 | 93.99 263 | 98.21 186 | 92.98 224 | 95.91 222 | 94.53 292 | 96.39 75 | 99.72 70 | 95.43 119 | 98.19 251 | 95.64 323 |
|
xiu_mvs_v1_base_debi | | | 95.62 177 | 95.96 166 | 94.60 257 | 98.01 203 | 88.42 247 | 93.99 263 | 98.21 186 | 92.98 224 | 95.91 222 | 94.53 292 | 96.39 75 | 99.72 70 | 95.43 119 | 98.19 251 | 95.64 323 |
|
MVS_0304 | | | 96.22 160 | 95.94 169 | 97.04 142 | 97.07 281 | 92.54 163 | 94.19 252 | 99.04 45 | 95.17 152 | 93.74 285 | 96.92 214 | 91.77 218 | 99.73 64 | 95.76 103 | 99.81 43 | 98.85 186 |
|
IterMVS | | | 95.42 190 | 95.83 170 | 94.20 269 | 97.52 255 | 83.78 317 | 92.41 307 | 97.47 244 | 95.49 137 | 98.06 110 | 98.49 83 | 87.94 259 | 99.58 146 | 96.02 94 | 99.02 198 | 99.23 124 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
MCST-MVS | | | 96.24 159 | 95.80 171 | 97.56 104 | 98.75 107 | 94.13 128 | 94.66 235 | 98.17 194 | 90.17 265 | 96.21 214 | 96.10 260 | 95.14 115 | 99.43 195 | 94.13 167 | 98.85 217 | 99.13 137 |
|
PVSNet_Blended_VisFu | | | 95.95 169 | 95.80 171 | 96.42 176 | 99.28 50 | 90.62 200 | 95.31 203 | 99.08 30 | 88.40 279 | 96.97 179 | 98.17 116 | 92.11 206 | 99.78 39 | 93.64 182 | 99.21 178 | 98.86 184 |
|
UnsupCasMVSNet_eth | | | 95.91 170 | 95.73 173 | 96.44 175 | 98.48 147 | 91.52 188 | 95.31 203 | 98.45 152 | 95.76 127 | 97.48 152 | 97.54 175 | 89.53 246 | 98.69 303 | 94.43 155 | 94.61 330 | 99.13 137 |
|
MDA-MVSNet-bldmvs | | | 95.69 174 | 95.67 174 | 95.74 219 | 98.48 147 | 88.76 245 | 92.84 296 | 97.25 247 | 96.00 117 | 97.59 144 | 97.95 141 | 91.38 224 | 99.46 187 | 93.16 191 | 96.35 314 | 98.99 161 |
|
CANet | | | 95.86 173 | 95.65 175 | 96.49 172 | 96.41 297 | 90.82 197 | 94.36 242 | 98.41 161 | 94.94 162 | 92.62 315 | 96.73 227 | 92.68 189 | 99.71 80 | 95.12 135 | 99.60 87 | 98.94 166 |
|
LF4IMVS | | | 96.07 165 | 95.63 176 | 97.36 126 | 98.19 182 | 95.55 80 | 95.44 188 | 98.82 99 | 92.29 239 | 95.70 232 | 96.55 236 | 92.63 192 | 98.69 303 | 91.75 212 | 99.33 165 | 97.85 262 |
|
QAPM | | | 95.88 172 | 95.57 177 | 96.80 153 | 97.90 214 | 91.84 183 | 98.18 41 | 98.73 113 | 88.41 278 | 96.42 198 | 98.13 121 | 94.73 123 | 99.75 54 | 88.72 269 | 98.94 206 | 98.81 188 |
|
alignmvs | | | 96.01 167 | 95.52 178 | 97.50 112 | 97.77 237 | 94.71 109 | 96.07 150 | 96.84 262 | 97.48 69 | 96.78 186 | 94.28 303 | 85.50 274 | 99.40 210 | 96.22 86 | 98.73 226 | 98.40 219 |
|
mvs-test1 | | | 96.20 161 | 95.50 179 | 98.32 61 | 96.90 287 | 98.16 4 | 95.07 218 | 98.09 202 | 95.86 123 | 93.63 289 | 94.32 302 | 94.26 145 | 99.71 80 | 94.06 169 | 97.27 300 | 97.07 287 |
|
test_normal | | | 95.51 181 | 95.46 180 | 95.68 223 | 97.97 208 | 89.12 233 | 93.73 274 | 95.86 279 | 91.98 243 | 97.17 164 | 96.94 211 | 91.55 220 | 99.42 196 | 95.21 126 | 98.73 226 | 98.51 212 |
|
DI_MVS_plusplus_test | | | 95.46 187 | 95.43 181 | 95.55 225 | 98.05 199 | 88.84 241 | 94.18 253 | 95.75 281 | 91.92 246 | 97.32 158 | 96.94 211 | 91.44 222 | 99.39 216 | 94.81 143 | 98.48 241 | 98.43 218 |
|
test_prior3 | | | 95.91 170 | 95.39 182 | 97.46 118 | 97.79 232 | 94.26 124 | 93.33 289 | 98.42 159 | 94.21 190 | 94.02 276 | 96.25 253 | 93.64 165 | 99.34 228 | 91.90 204 | 98.96 202 | 98.79 190 |
|
testmv | | | 95.51 181 | 95.33 183 | 96.05 202 | 98.23 176 | 89.51 221 | 93.50 283 | 98.63 137 | 94.25 188 | 98.22 91 | 97.73 162 | 92.51 198 | 99.47 182 | 85.22 310 | 99.72 59 | 99.17 130 |
|
MVP-Stereo | | | 95.69 174 | 95.28 184 | 96.92 148 | 98.15 191 | 93.03 158 | 95.64 181 | 98.20 189 | 90.39 262 | 96.63 189 | 97.73 162 | 91.63 219 | 99.10 258 | 91.84 208 | 97.31 298 | 98.63 203 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
Test4 | | | 95.39 191 | 95.24 185 | 95.82 217 | 98.07 197 | 89.60 217 | 94.40 241 | 98.49 149 | 91.39 253 | 97.40 157 | 96.32 251 | 87.32 267 | 99.41 207 | 95.09 137 | 98.71 228 | 98.44 217 |
|
wuyk23d | | | 93.25 255 | 95.20 186 | 87.40 336 | 96.07 307 | 95.38 86 | 97.04 107 | 94.97 288 | 95.33 142 | 99.70 6 | 98.11 124 | 98.14 14 | 91.94 353 | 77.76 342 | 99.68 71 | 74.89 353 |
|
OpenMVS | | 94.22 8 | 95.48 185 | 95.20 186 | 96.32 182 | 97.16 278 | 91.96 180 | 97.74 67 | 98.84 87 | 87.26 290 | 94.36 266 | 98.01 134 | 93.95 155 | 99.67 110 | 90.70 237 | 98.75 222 | 97.35 284 |
|
DP-MVS Recon | | | 95.55 180 | 95.13 188 | 96.80 153 | 98.51 143 | 93.99 133 | 94.60 237 | 98.69 123 | 90.20 264 | 95.78 228 | 96.21 256 | 92.73 188 | 98.98 273 | 90.58 240 | 98.86 215 | 97.42 277 |
|
MSDG | | | 95.33 194 | 95.13 188 | 95.94 213 | 97.40 264 | 91.85 182 | 91.02 325 | 98.37 165 | 95.30 143 | 96.31 208 | 95.99 261 | 94.51 137 | 98.38 324 | 89.59 256 | 97.65 284 | 97.60 272 |
|
Fast-Effi-MVS+ | | | 95.49 183 | 95.07 190 | 96.75 156 | 97.67 246 | 92.82 160 | 94.22 250 | 98.60 140 | 91.61 250 | 93.42 300 | 92.90 317 | 96.73 60 | 99.70 88 | 92.60 196 | 97.89 266 | 97.74 267 |
|
CLD-MVS | | | 95.47 186 | 95.07 190 | 96.69 160 | 98.27 165 | 92.53 164 | 91.36 322 | 98.67 128 | 91.22 254 | 95.78 228 | 94.12 304 | 95.65 100 | 98.98 273 | 90.81 231 | 99.72 59 | 98.57 207 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
Anonymous20231206 | | | 95.27 198 | 95.06 192 | 95.88 215 | 98.72 111 | 89.37 227 | 95.70 173 | 97.85 215 | 88.00 286 | 96.98 174 | 97.62 169 | 91.95 212 | 99.34 228 | 89.21 261 | 99.53 105 | 98.94 166 |
|
API-MVS | | | 95.09 204 | 95.01 193 | 95.31 232 | 96.61 291 | 94.02 131 | 96.83 118 | 97.18 251 | 95.60 132 | 95.79 227 | 94.33 301 | 94.54 135 | 98.37 326 | 85.70 304 | 98.52 238 | 93.52 341 |
|
diffmvs | | | 95.00 208 | 95.00 194 | 95.01 242 | 96.53 293 | 87.96 262 | 95.73 170 | 98.32 180 | 90.67 260 | 91.89 322 | 97.43 184 | 92.07 209 | 98.90 280 | 95.44 117 | 96.88 303 | 98.16 245 |
|
FMVSNet3 | | | 95.26 199 | 94.94 195 | 96.22 192 | 96.53 293 | 90.06 206 | 95.99 155 | 97.66 229 | 94.11 194 | 97.99 115 | 97.91 146 | 80.22 292 | 99.63 122 | 94.60 151 | 99.44 131 | 98.96 163 |
|
TAMVS | | | 95.49 183 | 94.94 195 | 97.16 134 | 98.31 158 | 93.41 152 | 95.07 218 | 96.82 264 | 91.09 255 | 97.51 147 | 97.82 153 | 89.96 241 | 99.42 196 | 88.42 274 | 99.44 131 | 98.64 201 |
|
PVSNet_BlendedMVS | | | 95.02 207 | 94.93 197 | 95.27 233 | 97.79 232 | 87.40 274 | 94.14 257 | 98.68 125 | 88.94 274 | 94.51 262 | 98.01 134 | 93.04 180 | 99.30 235 | 89.77 254 | 99.49 119 | 99.11 145 |
|
MS-PatchMatch | | | 94.83 212 | 94.91 198 | 94.57 260 | 96.81 289 | 87.10 280 | 94.23 249 | 97.34 245 | 88.74 276 | 97.14 165 | 97.11 202 | 91.94 213 | 98.23 331 | 92.99 194 | 97.92 263 | 98.37 222 |
|
LFMVS | | | 95.32 195 | 94.88 199 | 96.62 162 | 98.03 200 | 91.47 189 | 97.65 71 | 90.72 329 | 99.11 8 | 97.89 130 | 98.31 97 | 79.20 294 | 99.48 180 | 93.91 176 | 99.12 189 | 98.93 170 |
|
Vis-MVSNet (Re-imp) | | | 95.11 202 | 94.85 200 | 95.87 216 | 99.12 76 | 89.17 230 | 97.54 83 | 94.92 289 | 96.50 99 | 96.58 190 | 97.27 195 | 83.64 282 | 99.48 180 | 88.42 274 | 99.67 73 | 98.97 162 |
|
ppachtmachnet_test | | | 94.49 225 | 94.84 201 | 93.46 288 | 96.16 304 | 82.10 322 | 90.59 329 | 97.48 241 | 90.53 261 | 97.01 172 | 97.59 173 | 91.01 227 | 99.36 225 | 93.97 174 | 99.18 182 | 98.94 166 |
|
YYNet1 | | | 94.73 214 | 94.84 201 | 94.41 264 | 97.47 260 | 85.09 299 | 90.29 332 | 95.85 280 | 92.52 234 | 97.53 146 | 97.76 156 | 91.97 211 | 99.18 249 | 93.31 186 | 96.86 304 | 98.95 164 |
|
MDA-MVSNet_test_wron | | | 94.73 214 | 94.83 203 | 94.42 263 | 97.48 256 | 85.15 297 | 90.28 333 | 95.87 278 | 92.52 234 | 97.48 152 | 97.76 156 | 91.92 215 | 99.17 251 | 93.32 185 | 96.80 307 | 98.94 166 |
|
no-one | | | 94.84 211 | 94.76 204 | 95.09 239 | 98.29 160 | 87.49 271 | 91.82 316 | 97.49 239 | 88.21 282 | 97.84 140 | 98.75 64 | 91.51 221 | 99.27 240 | 88.96 266 | 99.99 2 | 98.52 211 |
|
BH-untuned | | | 94.69 217 | 94.75 205 | 94.52 262 | 97.95 213 | 87.53 270 | 94.07 260 | 97.01 257 | 93.99 195 | 97.10 168 | 95.65 272 | 92.65 191 | 98.95 278 | 87.60 290 | 96.74 308 | 97.09 286 |
|
train_agg | | | 95.46 187 | 94.66 206 | 97.88 85 | 97.84 220 | 95.23 90 | 93.62 278 | 98.39 162 | 87.04 294 | 93.78 282 | 95.99 261 | 94.58 133 | 99.52 163 | 91.76 210 | 98.90 208 | 98.89 177 |
|
CDPH-MVS | | | 95.45 189 | 94.65 207 | 97.84 88 | 98.28 163 | 94.96 101 | 93.73 274 | 98.33 171 | 85.03 315 | 95.44 235 | 96.60 234 | 95.31 111 | 99.44 194 | 90.01 251 | 99.13 186 | 99.11 145 |
|
xiu_mvs_v2_base | | | 94.22 229 | 94.63 208 | 92.99 301 | 97.32 271 | 84.84 302 | 92.12 311 | 97.84 216 | 91.96 244 | 94.17 269 | 93.43 307 | 96.07 83 | 99.71 80 | 91.27 218 | 97.48 291 | 94.42 333 |
|
AdaColmap | | | 95.11 202 | 94.62 209 | 96.58 166 | 97.33 270 | 94.45 117 | 94.92 226 | 98.08 204 | 93.15 220 | 93.98 279 | 95.53 277 | 94.34 142 | 99.10 258 | 85.69 305 | 98.61 234 | 96.20 316 |
|
agg_prior1 | | | 95.39 191 | 94.60 210 | 97.75 92 | 97.80 227 | 94.96 101 | 93.39 286 | 98.36 166 | 87.20 292 | 93.49 295 | 95.97 264 | 94.65 130 | 99.53 160 | 91.69 213 | 98.86 215 | 98.77 193 |
|
Patchmtry | | | 95.03 206 | 94.59 211 | 96.33 181 | 94.83 326 | 90.82 197 | 96.38 134 | 97.20 249 | 96.59 97 | 97.49 149 | 98.57 76 | 77.67 300 | 99.38 221 | 92.95 195 | 99.62 79 | 98.80 189 |
|
our_test_3 | | | 94.20 234 | 94.58 212 | 93.07 297 | 96.16 304 | 81.20 325 | 90.42 331 | 96.84 262 | 90.72 259 | 97.14 165 | 97.13 200 | 90.47 233 | 99.11 256 | 94.04 172 | 98.25 250 | 98.91 173 |
|
HQP-MVS | | | 95.17 201 | 94.58 212 | 96.92 148 | 97.85 216 | 92.47 165 | 94.26 244 | 98.43 156 | 93.18 216 | 92.86 308 | 95.08 282 | 90.33 235 | 99.23 246 | 90.51 242 | 98.74 223 | 99.05 155 |
|
USDC | | | 94.56 223 | 94.57 214 | 94.55 261 | 97.78 236 | 86.43 287 | 92.75 299 | 98.65 136 | 85.96 303 | 96.91 182 | 97.93 144 | 90.82 230 | 98.74 298 | 90.71 236 | 99.59 89 | 98.47 214 |
|
Patchmatch-RL test | | | 94.66 218 | 94.49 215 | 95.19 235 | 98.54 139 | 88.91 238 | 92.57 303 | 98.74 112 | 91.46 252 | 98.32 82 | 97.75 159 | 77.31 305 | 98.81 293 | 96.06 90 | 99.61 84 | 97.85 262 |
|
PS-MVSNAJ | | | 94.10 236 | 94.47 216 | 93.00 300 | 97.35 266 | 84.88 301 | 91.86 315 | 97.84 216 | 91.96 244 | 94.17 269 | 92.50 324 | 95.82 91 | 99.71 80 | 91.27 218 | 97.48 291 | 94.40 334 |
|
EU-MVSNet | | | 94.25 228 | 94.47 216 | 93.60 284 | 98.14 192 | 82.60 320 | 97.24 94 | 92.72 313 | 85.08 314 | 98.48 69 | 98.94 54 | 82.59 285 | 98.76 297 | 97.47 56 | 99.53 105 | 99.44 80 |
|
CNLPA | | | 95.04 205 | 94.47 216 | 96.75 156 | 97.81 223 | 95.25 89 | 94.12 259 | 97.89 213 | 94.41 181 | 94.57 259 | 95.69 270 | 90.30 238 | 98.35 327 | 86.72 299 | 98.76 221 | 96.64 304 |
|
agg_prior3 | | | 95.30 196 | 94.46 219 | 97.80 90 | 97.80 227 | 95.00 99 | 93.63 277 | 98.34 170 | 86.33 300 | 93.40 302 | 95.84 268 | 94.15 150 | 99.50 175 | 91.76 210 | 98.90 208 | 98.89 177 |
|
BH-RMVSNet | | | 94.56 223 | 94.44 220 | 94.91 244 | 97.57 251 | 87.44 273 | 93.78 273 | 96.26 271 | 93.69 208 | 96.41 199 | 96.50 241 | 92.10 207 | 99.00 270 | 85.96 302 | 97.71 278 | 98.31 230 |
|
F-COLMAP | | | 95.30 196 | 94.38 221 | 98.05 78 | 98.64 122 | 96.04 67 | 95.61 185 | 98.66 130 | 89.00 273 | 93.22 304 | 96.40 248 | 92.90 184 | 99.35 227 | 87.45 293 | 97.53 289 | 98.77 193 |
|
pmmvs5 | | | 94.63 220 | 94.34 222 | 95.50 227 | 97.63 249 | 88.34 250 | 94.02 261 | 97.13 253 | 87.15 293 | 95.22 240 | 97.15 199 | 87.50 264 | 99.27 240 | 93.99 173 | 99.26 175 | 98.88 181 |
|
UnsupCasMVSNet_bld | | | 94.72 216 | 94.26 223 | 96.08 201 | 98.62 127 | 90.54 204 | 93.38 287 | 98.05 208 | 90.30 263 | 97.02 171 | 96.80 222 | 89.54 244 | 99.16 252 | 88.44 273 | 96.18 316 | 98.56 208 |
|
N_pmnet | | | 95.18 200 | 94.23 224 | 98.06 75 | 97.85 216 | 96.55 53 | 92.49 305 | 91.63 321 | 89.34 270 | 98.09 105 | 97.41 185 | 90.33 235 | 99.06 262 | 91.58 214 | 99.31 167 | 98.56 208 |
|
TAPA-MVS | | 93.32 12 | 94.93 209 | 94.23 224 | 97.04 142 | 98.18 185 | 94.51 114 | 95.22 210 | 98.73 113 | 81.22 332 | 96.25 212 | 95.95 266 | 93.80 162 | 98.98 273 | 89.89 252 | 98.87 213 | 97.62 270 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
CANet_DTU | | | 94.65 219 | 94.21 226 | 95.96 209 | 95.90 310 | 89.68 213 | 93.92 267 | 97.83 218 | 93.19 215 | 90.12 336 | 95.64 273 | 88.52 254 | 99.57 151 | 93.27 188 | 99.47 124 | 98.62 204 |
|
pmmvs4 | | | 94.82 213 | 94.19 227 | 96.70 159 | 97.42 263 | 92.75 162 | 92.09 313 | 96.76 265 | 86.80 297 | 95.73 231 | 97.22 196 | 89.28 250 | 98.89 283 | 93.28 187 | 99.14 184 | 98.46 216 |
|
PAPM_NR | | | 94.61 221 | 94.17 228 | 95.96 209 | 98.36 156 | 91.23 190 | 95.93 164 | 97.95 210 | 92.98 224 | 93.42 300 | 94.43 300 | 90.53 232 | 98.38 324 | 87.60 290 | 96.29 315 | 98.27 235 |
|
CDS-MVSNet | | | 94.88 210 | 94.12 229 | 97.14 136 | 97.64 248 | 93.57 147 | 93.96 266 | 97.06 256 | 90.05 266 | 96.30 209 | 96.55 236 | 86.10 271 | 99.47 182 | 90.10 250 | 99.31 167 | 98.40 219 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
PMMVS2 | | | 93.66 246 | 94.07 230 | 92.45 309 | 97.57 251 | 80.67 328 | 86.46 345 | 96.00 274 | 93.99 195 | 97.10 168 | 97.38 191 | 89.90 242 | 97.82 338 | 88.76 268 | 99.47 124 | 98.86 184 |
|
jason | | | 94.39 226 | 94.04 231 | 95.41 231 | 98.29 160 | 87.85 265 | 92.74 301 | 96.75 266 | 85.38 313 | 95.29 238 | 96.15 257 | 88.21 258 | 99.65 116 | 94.24 165 | 99.34 160 | 98.74 195 |
jason: jason. |
RPMNet | | | 94.22 229 | 94.03 232 | 94.78 250 | 95.44 319 | 88.15 252 | 96.18 146 | 93.73 297 | 97.43 70 | 94.10 272 | 98.49 83 | 79.40 293 | 99.39 216 | 95.69 104 | 95.81 318 | 96.81 298 |
|
MG-MVS | | | 94.08 238 | 94.00 233 | 94.32 266 | 97.09 280 | 85.89 288 | 93.19 293 | 95.96 276 | 92.52 234 | 94.93 247 | 97.51 179 | 89.54 244 | 98.77 296 | 87.52 292 | 97.71 278 | 98.31 230 |
|
MVSTER | | | 94.21 232 | 93.93 234 | 95.05 241 | 95.83 312 | 86.46 286 | 95.18 211 | 97.65 231 | 92.41 238 | 97.94 122 | 98.00 136 | 72.39 331 | 99.58 146 | 96.36 84 | 99.56 97 | 99.12 142 |
|
PatchMatch-RL | | | 94.61 221 | 93.81 235 | 97.02 145 | 98.19 182 | 95.72 74 | 93.66 276 | 97.23 248 | 88.17 283 | 94.94 246 | 95.62 274 | 91.43 223 | 98.57 311 | 87.36 294 | 97.68 281 | 96.76 300 |
|
sss | | | 94.22 229 | 93.72 236 | 95.74 219 | 97.71 241 | 89.95 210 | 93.84 270 | 96.98 258 | 88.38 281 | 93.75 284 | 95.74 269 | 87.94 259 | 98.89 283 | 91.02 224 | 98.10 255 | 98.37 222 |
|
PVSNet_Blended | | | 93.96 240 | 93.65 237 | 94.91 244 | 97.79 232 | 87.40 274 | 91.43 321 | 98.68 125 | 84.50 319 | 94.51 262 | 94.48 295 | 93.04 180 | 99.30 235 | 89.77 254 | 98.61 234 | 98.02 257 |
|
Patchmatch-test1 | | | 93.38 253 | 93.59 238 | 92.73 305 | 96.24 299 | 81.40 324 | 93.24 291 | 94.00 296 | 91.58 251 | 94.57 259 | 96.67 231 | 87.94 259 | 99.03 267 | 90.42 245 | 97.66 283 | 97.77 266 |
|
PatchT | | | 93.75 243 | 93.57 239 | 94.29 268 | 95.05 324 | 87.32 276 | 96.05 151 | 92.98 308 | 97.54 65 | 94.25 267 | 98.72 66 | 75.79 313 | 99.24 244 | 95.92 99 | 95.81 318 | 96.32 314 |
|
1112_ss | | | 94.12 235 | 93.42 240 | 96.23 188 | 98.59 131 | 90.85 195 | 94.24 248 | 98.85 84 | 85.49 308 | 92.97 306 | 94.94 286 | 86.01 272 | 99.64 119 | 91.78 209 | 97.92 263 | 98.20 241 |
|
CHOSEN 1792x2688 | | | 94.10 236 | 93.41 241 | 96.18 194 | 99.16 64 | 90.04 207 | 92.15 310 | 98.68 125 | 79.90 337 | 96.22 213 | 97.83 150 | 87.92 262 | 99.42 196 | 89.18 262 | 99.65 76 | 99.08 150 |
|
lupinMVS | | | 93.77 242 | 93.28 242 | 95.24 234 | 97.68 243 | 87.81 266 | 92.12 311 | 96.05 273 | 84.52 318 | 94.48 264 | 95.06 284 | 86.90 268 | 99.63 122 | 93.62 183 | 99.13 186 | 98.27 235 |
|
1121 | | | 94.26 227 | 93.26 243 | 97.27 130 | 98.26 173 | 94.73 107 | 95.86 166 | 97.71 225 | 77.96 345 | 94.53 261 | 96.71 228 | 91.93 214 | 99.40 210 | 87.71 280 | 98.64 232 | 97.69 268 |
|
Patchmatch-test | | | 93.60 248 | 93.25 244 | 94.63 255 | 96.14 306 | 87.47 272 | 96.04 152 | 94.50 293 | 93.57 209 | 96.47 196 | 96.97 209 | 76.50 308 | 98.61 309 | 90.67 238 | 98.41 244 | 97.81 265 |
|
114514_t | | | 93.96 240 | 93.22 245 | 96.19 193 | 99.06 82 | 90.97 194 | 95.99 155 | 98.94 72 | 73.88 351 | 93.43 299 | 96.93 213 | 92.38 202 | 99.37 224 | 89.09 263 | 99.28 172 | 98.25 237 |
|
OpenMVS_ROB | | 91.80 14 | 93.64 247 | 93.05 246 | 95.42 229 | 97.31 272 | 91.21 191 | 95.08 217 | 96.68 269 | 81.56 329 | 96.88 184 | 96.41 246 | 90.44 234 | 99.25 243 | 85.39 309 | 97.67 282 | 95.80 321 |
|
MAR-MVS | | | 94.21 232 | 93.03 247 | 97.76 91 | 96.94 285 | 97.44 30 | 96.97 116 | 97.15 252 | 87.89 288 | 92.00 320 | 92.73 322 | 92.14 205 | 99.12 253 | 83.92 318 | 97.51 290 | 96.73 301 |
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 |
WTY-MVS | | | 93.55 249 | 93.00 248 | 95.19 235 | 97.81 223 | 87.86 264 | 93.89 268 | 96.00 274 | 89.02 272 | 94.07 274 | 95.44 278 | 86.27 270 | 99.33 231 | 87.69 282 | 96.82 305 | 98.39 221 |
|
PLC | | 91.02 16 | 94.05 239 | 92.90 249 | 97.51 109 | 98.00 206 | 95.12 97 | 94.25 247 | 98.25 184 | 86.17 301 | 91.48 325 | 95.25 280 | 91.01 227 | 99.19 248 | 85.02 312 | 96.69 309 | 98.22 239 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
Test_1112_low_res | | | 93.53 250 | 92.86 250 | 95.54 226 | 98.60 129 | 88.86 240 | 92.75 299 | 98.69 123 | 82.66 326 | 92.65 313 | 96.92 214 | 84.75 279 | 99.56 152 | 90.94 227 | 97.76 267 | 98.19 242 |
|
MIMVSNet | | | 93.42 251 | 92.86 250 | 95.10 238 | 98.17 187 | 88.19 251 | 98.13 43 | 93.69 298 | 92.07 240 | 95.04 244 | 98.21 110 | 80.95 289 | 99.03 267 | 81.42 330 | 98.06 256 | 98.07 249 |
|
CVMVSNet | | | 92.33 270 | 92.79 252 | 90.95 322 | 97.26 273 | 75.84 344 | 95.29 205 | 92.33 316 | 81.86 327 | 96.27 210 | 98.19 111 | 81.44 287 | 98.46 318 | 94.23 166 | 98.29 245 | 98.55 210 |
|
CR-MVSNet | | | 93.29 254 | 92.79 252 | 94.78 250 | 95.44 319 | 88.15 252 | 96.18 146 | 97.20 249 | 84.94 316 | 94.10 272 | 98.57 76 | 77.67 300 | 99.39 216 | 95.17 129 | 95.81 318 | 96.81 298 |
|
LP | | | 93.12 256 | 92.78 254 | 94.14 270 | 94.50 331 | 85.48 292 | 95.73 170 | 95.68 283 | 92.97 228 | 95.05 243 | 97.17 198 | 81.93 286 | 99.40 210 | 93.06 193 | 88.96 345 | 97.55 273 |
|
HyFIR lowres test | | | 93.72 244 | 92.65 255 | 96.91 150 | 98.93 94 | 91.81 184 | 91.23 324 | 98.52 146 | 82.69 325 | 96.46 197 | 96.52 240 | 80.38 291 | 99.90 13 | 90.36 247 | 98.79 218 | 99.03 156 |
|
EPNet | | | 93.72 244 | 92.62 256 | 97.03 144 | 87.61 358 | 92.25 169 | 96.27 139 | 91.28 323 | 96.74 94 | 87.65 346 | 97.39 189 | 85.00 278 | 99.64 119 | 92.14 202 | 99.48 122 | 99.20 126 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
test1235678 | | | 92.95 257 | 92.40 257 | 94.61 256 | 96.95 284 | 86.87 282 | 90.75 327 | 97.75 221 | 91.00 257 | 96.33 202 | 95.38 279 | 85.21 276 | 98.92 279 | 79.00 336 | 99.20 179 | 98.03 255 |
|
FMVSNet5 | | | 93.39 252 | 92.35 258 | 96.50 171 | 95.83 312 | 90.81 199 | 97.31 89 | 98.27 181 | 92.74 232 | 96.27 210 | 98.28 102 | 62.23 352 | 99.67 110 | 90.86 229 | 99.36 155 | 99.03 156 |
|
1314 | | | 92.38 268 | 92.30 259 | 92.64 307 | 95.42 321 | 85.15 297 | 95.86 166 | 96.97 259 | 85.40 312 | 90.62 329 | 93.06 315 | 91.12 226 | 97.80 339 | 86.74 298 | 95.49 326 | 94.97 331 |
|
TR-MVS | | | 92.54 266 | 92.20 260 | 93.57 285 | 96.49 295 | 86.66 284 | 93.51 282 | 94.73 290 | 89.96 267 | 94.95 245 | 93.87 305 | 90.24 240 | 98.61 309 | 81.18 331 | 94.88 327 | 95.45 327 |
|
GA-MVS | | | 92.83 259 | 92.15 261 | 94.87 247 | 96.97 283 | 87.27 277 | 90.03 334 | 96.12 272 | 91.83 248 | 94.05 275 | 94.57 291 | 76.01 312 | 98.97 277 | 92.46 199 | 97.34 297 | 98.36 227 |
|
view600 | | | 92.56 262 | 92.11 262 | 93.91 275 | 98.45 149 | 84.76 304 | 97.10 101 | 90.23 335 | 97.42 71 | 96.98 174 | 94.48 295 | 73.62 321 | 99.60 140 | 82.49 325 | 98.28 246 | 97.36 278 |
|
view800 | | | 92.56 262 | 92.11 262 | 93.91 275 | 98.45 149 | 84.76 304 | 97.10 101 | 90.23 335 | 97.42 71 | 96.98 174 | 94.48 295 | 73.62 321 | 99.60 140 | 82.49 325 | 98.28 246 | 97.36 278 |
|
conf0.05thres1000 | | | 92.56 262 | 92.11 262 | 93.91 275 | 98.45 149 | 84.76 304 | 97.10 101 | 90.23 335 | 97.42 71 | 96.98 174 | 94.48 295 | 73.62 321 | 99.60 140 | 82.49 325 | 98.28 246 | 97.36 278 |
|
tfpn | | | 92.56 262 | 92.11 262 | 93.91 275 | 98.45 149 | 84.76 304 | 97.10 101 | 90.23 335 | 97.42 71 | 96.98 174 | 94.48 295 | 73.62 321 | 99.60 140 | 82.49 325 | 98.28 246 | 97.36 278 |
|
BH-w/o | | | 92.14 273 | 91.94 266 | 92.73 305 | 97.13 279 | 85.30 294 | 92.46 306 | 95.64 284 | 89.33 271 | 94.21 268 | 92.74 321 | 89.60 243 | 98.24 330 | 81.68 329 | 94.66 329 | 94.66 332 |
|
PatchmatchNet | | | 91.98 276 | 91.87 267 | 92.30 311 | 94.60 329 | 79.71 330 | 95.12 212 | 93.59 303 | 89.52 269 | 93.61 291 | 97.02 207 | 77.94 298 | 99.18 249 | 90.84 230 | 94.57 331 | 98.01 258 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
DSMNet-mixed | | | 92.19 272 | 91.83 268 | 93.25 293 | 96.18 303 | 83.68 318 | 96.27 139 | 93.68 300 | 76.97 348 | 92.54 316 | 99.18 35 | 89.20 252 | 98.55 314 | 83.88 319 | 98.60 236 | 97.51 275 |
|
HY-MVS | | 91.43 15 | 92.58 261 | 91.81 269 | 94.90 246 | 96.49 295 | 88.87 239 | 97.31 89 | 94.62 291 | 85.92 304 | 90.50 333 | 96.84 218 | 85.05 277 | 99.40 210 | 83.77 321 | 95.78 321 | 96.43 313 |
|
new_pmnet | | | 92.34 269 | 91.69 270 | 94.32 266 | 96.23 301 | 89.16 231 | 92.27 309 | 92.88 310 | 84.39 321 | 95.29 238 | 96.35 250 | 85.66 273 | 96.74 348 | 84.53 315 | 97.56 287 | 97.05 288 |
|
thres600view7 | | | 92.03 275 | 91.43 271 | 93.82 280 | 98.19 182 | 84.61 308 | 96.27 139 | 90.39 330 | 96.81 92 | 96.37 201 | 93.11 310 | 73.44 327 | 99.49 177 | 80.32 332 | 97.95 259 | 97.36 278 |
|
tfpn111 | | | 91.92 277 | 91.39 272 | 93.49 287 | 98.21 178 | 84.50 309 | 96.39 130 | 90.39 330 | 96.87 88 | 96.33 202 | 93.08 312 | 73.44 327 | 99.51 173 | 79.87 333 | 97.94 262 | 96.46 309 |
|
CMPMVS | | 73.10 23 | 92.74 260 | 91.39 272 | 96.77 155 | 93.57 343 | 94.67 111 | 94.21 251 | 97.67 227 | 80.36 336 | 93.61 291 | 96.60 234 | 82.85 284 | 97.35 342 | 84.86 313 | 98.78 219 | 98.29 234 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
cascas | | | 91.89 280 | 91.35 274 | 93.51 286 | 94.27 334 | 85.60 290 | 88.86 341 | 98.61 139 | 79.32 339 | 92.16 319 | 91.44 337 | 89.22 251 | 98.12 334 | 90.80 232 | 97.47 293 | 96.82 297 |
|
MDTV_nov1_ep13 | | | | 91.28 275 | | 94.31 333 | 73.51 348 | 94.80 232 | 93.16 307 | 86.75 298 | 93.45 298 | 97.40 186 | 76.37 309 | 98.55 314 | 88.85 267 | 96.43 312 | |
|
PAPR | | | 92.22 271 | 91.27 276 | 95.07 240 | 95.73 315 | 88.81 242 | 91.97 314 | 97.87 214 | 85.80 306 | 90.91 327 | 92.73 322 | 91.16 225 | 98.33 328 | 79.48 334 | 95.76 322 | 98.08 247 |
|
conf200view11 | | | 91.81 282 | 91.26 277 | 93.46 288 | 98.21 178 | 84.50 309 | 96.39 130 | 90.39 330 | 96.87 88 | 96.33 202 | 93.08 312 | 73.44 327 | 99.42 196 | 78.85 338 | 97.74 268 | 96.46 309 |
|
thres100view900 | | | 91.76 284 | 91.26 277 | 93.26 292 | 98.21 178 | 84.50 309 | 96.39 130 | 90.39 330 | 96.87 88 | 96.33 202 | 93.08 312 | 73.44 327 | 99.42 196 | 78.85 338 | 97.74 268 | 95.85 319 |
|
tfpn1000 | | | 91.88 281 | 91.20 279 | 93.89 279 | 97.96 209 | 87.13 279 | 97.13 99 | 88.16 350 | 94.41 181 | 94.87 248 | 92.77 319 | 68.34 347 | 99.47 182 | 89.24 260 | 97.95 259 | 95.06 329 |
|
PMMVS | | | 92.39 267 | 91.08 280 | 96.30 184 | 93.12 346 | 92.81 161 | 90.58 330 | 95.96 276 | 79.17 340 | 91.85 323 | 92.27 325 | 90.29 239 | 98.66 308 | 89.85 253 | 96.68 310 | 97.43 276 |
|
tfpn200view9 | | | 91.55 290 | 91.00 281 | 93.21 294 | 98.02 201 | 84.35 313 | 95.70 173 | 90.79 327 | 96.26 108 | 95.90 225 | 92.13 327 | 73.62 321 | 99.42 196 | 78.85 338 | 97.74 268 | 95.85 319 |
|
thres400 | | | 91.68 289 | 91.00 281 | 93.71 282 | 98.02 201 | 84.35 313 | 95.70 173 | 90.79 327 | 96.26 108 | 95.90 225 | 92.13 327 | 73.62 321 | 99.42 196 | 78.85 338 | 97.74 268 | 97.36 278 |
|
conf0.01 | | | 91.90 278 | 90.98 283 | 94.67 253 | 98.27 165 | 88.03 256 | 96.98 110 | 88.58 343 | 93.90 198 | 94.64 253 | 91.45 331 | 69.62 340 | 99.52 163 | 87.62 284 | 97.74 268 | 96.46 309 |
|
conf0.002 | | | 91.90 278 | 90.98 283 | 94.67 253 | 98.27 165 | 88.03 256 | 96.98 110 | 88.58 343 | 93.90 198 | 94.64 253 | 91.45 331 | 69.62 340 | 99.52 163 | 87.62 284 | 97.74 268 | 96.46 309 |
|
thresconf0.02 | | | 91.72 285 | 90.98 283 | 93.97 271 | 98.27 165 | 88.03 256 | 96.98 110 | 88.58 343 | 93.90 198 | 94.64 253 | 91.45 331 | 69.62 340 | 99.52 163 | 87.62 284 | 97.74 268 | 94.35 335 |
|
tfpn_n400 | | | 91.72 285 | 90.98 283 | 93.97 271 | 98.27 165 | 88.03 256 | 96.98 110 | 88.58 343 | 93.90 198 | 94.64 253 | 91.45 331 | 69.62 340 | 99.52 163 | 87.62 284 | 97.74 268 | 94.35 335 |
|
tfpnconf | | | 91.72 285 | 90.98 283 | 93.97 271 | 98.27 165 | 88.03 256 | 96.98 110 | 88.58 343 | 93.90 198 | 94.64 253 | 91.45 331 | 69.62 340 | 99.52 163 | 87.62 284 | 97.74 268 | 94.35 335 |
|
tfpnview11 | | | 91.72 285 | 90.98 283 | 93.97 271 | 98.27 165 | 88.03 256 | 96.98 110 | 88.58 343 | 93.90 198 | 94.64 253 | 91.45 331 | 69.62 340 | 99.52 163 | 87.62 284 | 97.74 268 | 94.35 335 |
|
PVSNet | | 86.72 19 | 91.10 293 | 90.97 289 | 91.49 316 | 97.56 253 | 78.04 337 | 87.17 343 | 94.60 292 | 84.65 317 | 92.34 317 | 92.20 326 | 87.37 266 | 98.47 317 | 85.17 311 | 97.69 280 | 97.96 259 |
|
tpmvs | | | 90.79 299 | 90.87 290 | 90.57 325 | 92.75 350 | 76.30 342 | 95.79 169 | 93.64 301 | 91.04 256 | 91.91 321 | 96.26 252 | 77.19 306 | 98.86 289 | 89.38 259 | 89.85 343 | 96.56 307 |
|
tpm | | | 91.08 294 | 90.85 291 | 91.75 315 | 95.33 322 | 78.09 335 | 95.03 223 | 91.27 324 | 88.75 275 | 93.53 294 | 97.40 186 | 71.24 334 | 99.30 235 | 91.25 220 | 93.87 332 | 97.87 261 |
|
X-MVStestdata | | | 92.86 258 | 90.83 292 | 98.94 15 | 99.15 67 | 97.66 16 | 97.77 62 | 98.83 95 | 97.42 71 | 96.32 206 | 36.50 355 | 96.49 72 | 99.72 70 | 95.66 107 | 99.37 152 | 99.45 71 |
|
EPNet_dtu | | | 91.39 292 | 90.75 293 | 93.31 291 | 90.48 356 | 82.61 319 | 94.80 232 | 92.88 310 | 93.39 211 | 81.74 354 | 94.90 289 | 81.36 288 | 99.11 256 | 88.28 276 | 98.87 213 | 98.21 240 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
JIA-IIPM | | | 91.79 283 | 90.69 294 | 95.11 237 | 93.80 340 | 90.98 193 | 94.16 255 | 91.78 320 | 96.38 103 | 90.30 335 | 99.30 23 | 72.02 333 | 98.90 280 | 88.28 276 | 90.17 342 | 95.45 327 |
|
PCF-MVS | | 89.43 18 | 92.12 274 | 90.64 295 | 96.57 168 | 97.80 227 | 93.48 151 | 89.88 338 | 98.45 152 | 74.46 350 | 96.04 219 | 95.68 271 | 90.71 231 | 99.31 233 | 73.73 346 | 99.01 200 | 96.91 294 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
tpmrst | | | 90.31 300 | 90.61 296 | 89.41 329 | 94.06 338 | 72.37 351 | 95.06 220 | 93.69 298 | 88.01 285 | 92.32 318 | 96.86 216 | 77.45 302 | 98.82 291 | 91.04 223 | 87.01 348 | 97.04 289 |
|
ADS-MVSNet2 | | | 91.47 291 | 90.51 297 | 94.36 265 | 95.51 317 | 85.63 289 | 95.05 221 | 95.70 282 | 83.46 323 | 92.69 311 | 96.84 218 | 79.15 295 | 99.41 207 | 85.66 306 | 90.52 340 | 98.04 253 |
|
testus | | | 90.90 298 | 90.51 297 | 92.06 313 | 96.07 307 | 79.45 331 | 88.99 339 | 98.44 155 | 85.46 310 | 94.15 271 | 90.77 341 | 89.12 253 | 98.01 337 | 73.66 347 | 97.95 259 | 98.71 198 |
|
thres200 | | | 91.00 295 | 90.42 299 | 92.77 304 | 97.47 260 | 83.98 316 | 94.01 262 | 91.18 325 | 95.12 158 | 95.44 235 | 91.21 339 | 73.93 317 | 99.31 233 | 77.76 342 | 97.63 286 | 95.01 330 |
|
ADS-MVSNet | | | 90.95 297 | 90.26 300 | 93.04 298 | 95.51 317 | 82.37 321 | 95.05 221 | 93.41 304 | 83.46 323 | 92.69 311 | 96.84 218 | 79.15 295 | 98.70 302 | 85.66 306 | 90.52 340 | 98.04 253 |
|
tfpn_ndepth | | | 90.98 296 | 90.24 301 | 93.20 296 | 97.72 240 | 87.18 278 | 96.52 126 | 88.20 349 | 92.63 233 | 93.69 288 | 90.70 344 | 68.22 348 | 99.42 196 | 86.98 296 | 97.47 293 | 93.00 345 |
|
MVS-HIRNet | | | 88.40 316 | 90.20 302 | 82.99 340 | 97.01 282 | 60.04 358 | 93.11 294 | 85.61 352 | 84.45 320 | 88.72 342 | 99.09 45 | 84.72 280 | 98.23 331 | 82.52 324 | 96.59 311 | 90.69 351 |
|
test-LLR | | | 89.97 305 | 89.90 303 | 90.16 326 | 94.24 335 | 74.98 345 | 89.89 335 | 89.06 340 | 92.02 241 | 89.97 337 | 90.77 341 | 73.92 318 | 98.57 311 | 91.88 206 | 97.36 295 | 96.92 292 |
|
E-PMN | | | 89.52 309 | 89.78 304 | 88.73 331 | 93.14 345 | 77.61 339 | 83.26 350 | 92.02 317 | 94.82 168 | 93.71 286 | 93.11 310 | 75.31 314 | 96.81 346 | 85.81 303 | 96.81 306 | 91.77 348 |
|
1111 | | | 88.78 312 | 89.39 305 | 86.96 337 | 98.53 141 | 62.84 356 | 91.49 319 | 97.48 241 | 94.45 178 | 96.56 192 | 96.45 243 | 43.83 362 | 98.87 287 | 86.33 300 | 99.40 150 | 99.18 129 |
|
CostFormer | | | 89.75 307 | 89.25 306 | 91.26 319 | 94.69 328 | 78.00 338 | 95.32 202 | 91.98 318 | 81.50 330 | 90.55 331 | 96.96 210 | 71.06 335 | 98.89 283 | 88.59 272 | 92.63 337 | 96.87 295 |
|
EMVS | | | 89.06 311 | 89.22 307 | 88.61 332 | 93.00 347 | 77.34 340 | 82.91 351 | 90.92 326 | 94.64 172 | 92.63 314 | 91.81 330 | 76.30 310 | 97.02 344 | 83.83 320 | 96.90 302 | 91.48 349 |
|
test0.0.03 1 | | | 90.11 301 | 89.21 308 | 92.83 303 | 93.89 339 | 86.87 282 | 91.74 317 | 88.74 342 | 92.02 241 | 94.71 251 | 91.14 340 | 73.92 318 | 94.48 352 | 83.75 322 | 92.94 334 | 97.16 285 |
|
MVS | | | 90.02 302 | 89.20 309 | 92.47 308 | 94.71 327 | 86.90 281 | 95.86 166 | 96.74 267 | 64.72 353 | 90.62 329 | 92.77 319 | 92.54 196 | 98.39 322 | 79.30 335 | 95.56 325 | 92.12 346 |
|
CHOSEN 280x420 | | | 89.98 304 | 89.19 310 | 92.37 310 | 95.60 316 | 81.13 326 | 86.22 346 | 97.09 255 | 81.44 331 | 87.44 347 | 93.15 309 | 73.99 316 | 99.47 182 | 88.69 270 | 99.07 194 | 96.52 308 |
|
PatchFormer-LS_test | | | 89.62 308 | 89.12 311 | 91.11 321 | 93.62 341 | 78.42 334 | 94.57 239 | 93.62 302 | 88.39 280 | 90.54 332 | 88.40 349 | 72.33 332 | 99.03 267 | 92.41 200 | 88.20 346 | 95.89 318 |
|
pmmvs3 | | | 90.00 303 | 88.90 312 | 93.32 290 | 94.20 337 | 85.34 293 | 91.25 323 | 92.56 315 | 78.59 342 | 93.82 281 | 95.17 281 | 67.36 350 | 98.69 303 | 89.08 264 | 98.03 257 | 95.92 317 |
|
FPMVS | | | 89.92 306 | 88.63 313 | 93.82 280 | 98.37 155 | 96.94 41 | 91.58 318 | 93.34 305 | 88.00 286 | 90.32 334 | 97.10 203 | 70.87 336 | 91.13 354 | 71.91 350 | 96.16 317 | 93.39 343 |
|
EPMVS | | | 89.26 310 | 88.55 314 | 91.39 317 | 92.36 351 | 79.11 332 | 95.65 179 | 79.86 355 | 88.60 277 | 93.12 305 | 96.53 238 | 70.73 337 | 98.10 335 | 90.75 234 | 89.32 344 | 96.98 290 |
|
test12356 | | | 87.98 320 | 88.41 315 | 86.69 338 | 95.84 311 | 63.49 355 | 87.15 344 | 97.32 246 | 87.21 291 | 91.78 324 | 93.36 308 | 70.66 338 | 98.39 322 | 74.70 345 | 97.64 285 | 98.19 242 |
|
dp | | | 88.08 318 | 88.05 316 | 88.16 335 | 92.85 348 | 68.81 353 | 94.17 254 | 92.88 310 | 85.47 309 | 91.38 326 | 96.14 259 | 68.87 346 | 98.81 293 | 86.88 297 | 83.80 352 | 96.87 295 |
|
tpm2 | | | 88.47 314 | 87.69 317 | 90.79 323 | 94.98 325 | 77.34 340 | 95.09 215 | 91.83 319 | 77.51 347 | 89.40 339 | 96.41 246 | 67.83 349 | 98.73 299 | 83.58 323 | 92.60 338 | 96.29 315 |
|
tpmp4_e23 | | | 88.46 315 | 87.54 318 | 91.22 320 | 94.56 330 | 78.08 336 | 95.63 184 | 93.17 306 | 79.08 341 | 85.85 349 | 96.80 222 | 65.86 351 | 98.85 290 | 84.10 317 | 92.85 335 | 96.72 302 |
|
tpm cat1 | | | 88.01 319 | 87.33 319 | 90.05 328 | 94.48 332 | 76.28 343 | 94.47 240 | 94.35 295 | 73.84 352 | 89.26 340 | 95.61 275 | 73.64 320 | 98.30 329 | 84.13 316 | 86.20 349 | 95.57 326 |
|
test-mter | | | 87.92 321 | 87.17 320 | 90.16 326 | 94.24 335 | 74.98 345 | 89.89 335 | 89.06 340 | 86.44 299 | 89.97 337 | 90.77 341 | 54.96 358 | 98.57 311 | 91.88 206 | 97.36 295 | 96.92 292 |
|
gg-mvs-nofinetune | | | 88.28 317 | 86.96 321 | 92.23 312 | 92.84 349 | 84.44 312 | 98.19 40 | 74.60 357 | 99.08 9 | 87.01 348 | 99.47 8 | 56.93 355 | 98.23 331 | 78.91 337 | 95.61 324 | 94.01 339 |
|
IB-MVS | | 85.98 20 | 88.63 313 | 86.95 322 | 93.68 283 | 95.12 323 | 84.82 303 | 90.85 326 | 90.17 339 | 87.55 289 | 88.48 343 | 91.34 338 | 58.01 354 | 99.59 144 | 87.24 295 | 93.80 333 | 96.63 306 |
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 |
DWT-MVSNet_test | | | 87.92 321 | 86.77 323 | 91.39 317 | 93.18 344 | 78.62 333 | 95.10 213 | 91.42 322 | 85.58 307 | 88.00 344 | 88.73 348 | 60.60 353 | 98.90 280 | 90.60 239 | 87.70 347 | 96.65 303 |
|
TESTMET0.1,1 | | | 87.20 324 | 86.57 324 | 89.07 330 | 93.62 341 | 72.84 350 | 89.89 335 | 87.01 351 | 85.46 310 | 89.12 341 | 90.20 346 | 56.00 357 | 97.72 340 | 90.91 228 | 96.92 301 | 96.64 304 |
|
MVE | | 73.61 22 | 86.48 325 | 85.92 325 | 88.18 334 | 96.23 301 | 85.28 295 | 81.78 353 | 75.79 356 | 86.01 302 | 82.53 353 | 91.88 329 | 92.74 187 | 87.47 356 | 71.42 351 | 94.86 328 | 91.78 347 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
PAPM | | | 87.64 323 | 85.84 326 | 93.04 298 | 96.54 292 | 84.99 300 | 88.42 342 | 95.57 286 | 79.52 338 | 83.82 351 | 93.05 316 | 80.57 290 | 98.41 320 | 62.29 354 | 92.79 336 | 95.71 322 |
|
testpf | | | 82.70 329 | 84.35 327 | 77.74 341 | 88.97 357 | 73.23 349 | 93.85 269 | 84.33 353 | 88.10 284 | 85.06 350 | 90.42 345 | 52.62 361 | 91.05 355 | 91.00 225 | 84.82 351 | 68.93 354 |
|
PNet_i23d | | | 83.82 328 | 83.39 328 | 85.10 339 | 96.07 307 | 65.16 354 | 81.87 352 | 94.37 294 | 90.87 258 | 93.92 280 | 92.89 318 | 52.80 360 | 96.44 350 | 77.52 344 | 70.22 354 | 93.70 340 |
|
test2356 | | | 85.45 326 | 83.26 329 | 92.01 314 | 91.12 353 | 80.76 327 | 85.16 347 | 92.90 309 | 83.90 322 | 90.63 328 | 87.71 351 | 53.10 359 | 97.24 343 | 69.20 352 | 95.65 323 | 98.03 255 |
|
PVSNet_0 | | 81.89 21 | 84.49 327 | 83.21 330 | 88.34 333 | 95.76 314 | 74.97 347 | 83.49 349 | 92.70 314 | 78.47 343 | 87.94 345 | 86.90 352 | 83.38 283 | 96.63 349 | 73.44 348 | 66.86 355 | 93.40 342 |
|
.test1245 | | | 73.49 330 | 79.27 331 | 56.15 343 | 98.53 141 | 62.84 356 | 91.49 319 | 97.48 241 | 94.45 178 | 96.56 192 | 96.45 243 | 43.83 362 | 98.87 287 | 86.33 300 | 8.32 357 | 6.75 357 |
|
tmp_tt | | | 57.23 331 | 62.50 332 | 41.44 344 | 34.77 359 | 49.21 360 | 83.93 348 | 60.22 361 | 15.31 355 | 71.11 356 | 79.37 354 | 70.09 339 | 44.86 358 | 64.76 353 | 82.93 353 | 30.25 355 |
|
pcd1.5k->3k | | | 41.47 332 | 44.19 333 | 33.29 345 | 99.65 11 | 0.00 363 | 0.00 354 | 99.07 34 | 0.00 358 | 0.00 359 | 0.00 360 | 99.04 4 | 0.00 361 | 0.00 358 | 99.96 11 | 99.87 2 |
|
cdsmvs_eth3d_5k | | | 24.22 333 | 32.30 334 | 0.00 348 | 0.00 362 | 0.00 363 | 0.00 354 | 98.10 201 | 0.00 358 | 0.00 359 | 95.06 284 | 97.54 28 | 0.00 361 | 0.00 358 | 0.00 359 | 0.00 359 |
|
test123 | | | 12.59 334 | 15.49 335 | 3.87 346 | 6.07 360 | 2.55 361 | 90.75 327 | 2.59 363 | 2.52 356 | 5.20 358 | 13.02 357 | 4.96 364 | 1.85 360 | 5.20 356 | 9.09 356 | 7.23 356 |
|
testmvs | | | 12.33 335 | 15.23 336 | 3.64 347 | 5.77 361 | 2.23 362 | 88.99 339 | 3.62 362 | 2.30 357 | 5.29 357 | 13.09 356 | 4.52 365 | 1.95 359 | 5.16 357 | 8.32 357 | 6.75 357 |
|
pcd_1.5k_mvsjas | | | 7.98 336 | 10.65 337 | 0.00 348 | 0.00 362 | 0.00 363 | 0.00 354 | 0.00 364 | 0.00 358 | 0.00 359 | 0.00 360 | 95.82 91 | 0.00 361 | 0.00 358 | 0.00 359 | 0.00 359 |
|
ab-mvs-re | | | 7.91 337 | 10.55 338 | 0.00 348 | 0.00 362 | 0.00 363 | 0.00 354 | 0.00 364 | 0.00 358 | 0.00 359 | 94.94 286 | 0.00 366 | 0.00 361 | 0.00 358 | 0.00 359 | 0.00 359 |
|
sosnet-low-res | | | 0.00 338 | 0.00 339 | 0.00 348 | 0.00 362 | 0.00 363 | 0.00 354 | 0.00 364 | 0.00 358 | 0.00 359 | 0.00 360 | 0.00 366 | 0.00 361 | 0.00 358 | 0.00 359 | 0.00 359 |
|
sosnet | | | 0.00 338 | 0.00 339 | 0.00 348 | 0.00 362 | 0.00 363 | 0.00 354 | 0.00 364 | 0.00 358 | 0.00 359 | 0.00 360 | 0.00 366 | 0.00 361 | 0.00 358 | 0.00 359 | 0.00 359 |
|
uncertanet | | | 0.00 338 | 0.00 339 | 0.00 348 | 0.00 362 | 0.00 363 | 0.00 354 | 0.00 364 | 0.00 358 | 0.00 359 | 0.00 360 | 0.00 366 | 0.00 361 | 0.00 358 | 0.00 359 | 0.00 359 |
|
Regformer | | | 0.00 338 | 0.00 339 | 0.00 348 | 0.00 362 | 0.00 363 | 0.00 354 | 0.00 364 | 0.00 358 | 0.00 359 | 0.00 360 | 0.00 366 | 0.00 361 | 0.00 358 | 0.00 359 | 0.00 359 |
|
uanet | | | 0.00 338 | 0.00 339 | 0.00 348 | 0.00 362 | 0.00 363 | 0.00 354 | 0.00 364 | 0.00 358 | 0.00 359 | 0.00 360 | 0.00 366 | 0.00 361 | 0.00 358 | 0.00 359 | 0.00 359 |
|
GSMVS | | | | | | | | | | | | | | | | | 98.06 250 |
|
test_part3 | | | | | | | | 95.64 181 | | 94.84 165 | | 97.60 171 | | 99.76 48 | 91.22 221 | | |
|
test_part2 | | | | | | 99.03 86 | 96.07 65 | | | | 98.08 107 | | | | | | |
|
test_part1 | | | | | | | | | 98.84 87 | | | | 96.69 61 | | | 99.44 131 | 99.37 101 |
|
sam_mvs1 | | | | | | | | | | | | | 77.80 299 | | | | 98.06 250 |
|
sam_mvs | | | | | | | | | | | | | 77.38 303 | | | | |
|
semantic-postprocess | | | | | 94.85 248 | 97.68 243 | 85.53 291 | | 97.63 235 | 96.99 84 | 98.36 77 | 98.54 80 | 87.44 265 | 99.75 54 | 97.07 69 | 99.08 192 | 99.27 121 |
|
ambc | | | | | 96.56 169 | 98.23 176 | 91.68 186 | 97.88 57 | 98.13 199 | | 98.42 74 | 98.56 78 | 94.22 147 | 99.04 264 | 94.05 171 | 99.35 158 | 98.95 164 |
|
MTGPA | | | | | | | | | 98.73 113 | | | | | | | | |
|
test_post1 | | | | | | | | 94.98 225 | | | | 10.37 359 | 76.21 311 | 99.04 264 | 89.47 258 | | |
|
test_post | | | | | | | | | | | | 10.87 358 | 76.83 307 | 99.07 261 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 96.84 218 | 77.36 304 | 99.42 196 | | | |
|
GG-mvs-BLEND | | | | | 90.60 324 | 91.00 354 | 84.21 315 | 98.23 34 | 72.63 360 | | 82.76 352 | 84.11 353 | 56.14 356 | 96.79 347 | 72.20 349 | 92.09 339 | 90.78 350 |
|
MTMP | | | | | | | | | 74.60 357 | | | | | | | | |
|
gm-plane-assit | | | | | | 91.79 352 | 71.40 352 | | | 81.67 328 | | 90.11 347 | | 98.99 271 | 84.86 313 | | |
|
test9_res | | | | | | | | | | | | | | | 91.29 217 | 98.89 212 | 99.00 158 |
|
TEST9 | | | | | | 97.84 220 | 95.23 90 | 93.62 278 | 98.39 162 | 86.81 296 | 93.78 282 | 95.99 261 | 94.68 128 | 99.52 163 | | | |
|
test_8 | | | | | | 97.81 223 | 95.07 98 | 93.54 281 | 98.38 164 | 87.04 294 | 93.71 286 | 95.96 265 | 94.58 133 | 99.52 163 | | | |
|
agg_prior2 | | | | | | | | | | | | | | | 90.34 248 | 98.90 208 | 99.10 149 |
|
agg_prior | | | | | | 97.80 227 | 94.96 101 | | 98.36 166 | | 93.49 295 | | | 99.53 160 | | | |
|
TestCases | | | | | 98.06 75 | 99.08 79 | 96.16 62 | | 99.16 16 | 94.35 185 | 97.78 142 | 98.07 127 | 95.84 88 | 99.12 253 | 91.41 215 | 99.42 143 | 98.91 173 |
|
test_prior4 | | | | | | | 95.38 86 | 93.61 280 | | | | | | | | | |
|
test_prior2 | | | | | | | | 93.33 289 | | 94.21 190 | 94.02 276 | 96.25 253 | 93.64 165 | | 91.90 204 | 98.96 202 | |
|
test_prior | | | | | 97.46 118 | 97.79 232 | 94.26 124 | | 98.42 159 | | | | | 99.34 228 | | | 98.79 190 |
|
旧先验2 | | | | | | | | 93.35 288 | | 77.95 346 | 95.77 230 | | | 98.67 307 | 90.74 235 | | |
|
新几何2 | | | | | | | | 93.43 284 | | | | | | | | | |
|
新几何1 | | | | | 97.25 133 | 98.29 160 | 94.70 110 | | 97.73 223 | 77.98 344 | 94.83 249 | 96.67 231 | 92.08 208 | 99.45 191 | 88.17 278 | 98.65 231 | 97.61 271 |
|
旧先验1 | | | | | | 97.80 227 | 93.87 135 | | 97.75 221 | | | 97.04 206 | 93.57 167 | | | 98.68 229 | 98.72 197 |
|
无先验 | | | | | | | | 93.20 292 | 97.91 211 | 80.78 333 | | | | 99.40 210 | 87.71 280 | | 97.94 260 |
|
原ACMM2 | | | | | | | | 92.82 297 | | | | | | | | | |
|
原ACMM1 | | | | | 96.58 166 | 98.16 189 | 92.12 175 | | 98.15 197 | 85.90 305 | 93.49 295 | 96.43 245 | 92.47 200 | 99.38 221 | 87.66 283 | 98.62 233 | 98.23 238 |
|
test222 | | | | | | 98.17 187 | 93.24 156 | 92.74 301 | 97.61 237 | 75.17 349 | 94.65 252 | 96.69 230 | 90.96 229 | | | 98.66 230 | 97.66 269 |
|
testdata2 | | | | | | | | | | | | | | 99.46 187 | 87.84 279 | | |
|
segment_acmp | | | | | | | | | | | | | 95.34 109 | | | | |
|
testdata | | | | | 95.70 222 | 98.16 189 | 90.58 201 | | 97.72 224 | 80.38 335 | 95.62 233 | 97.02 207 | 92.06 210 | 98.98 273 | 89.06 265 | 98.52 238 | 97.54 274 |
|
testdata1 | | | | | | | | 92.77 298 | | 93.78 205 | | | | | | | |
|
test12 | | | | | 97.46 118 | 97.61 250 | 94.07 129 | | 97.78 220 | | 93.57 293 | | 93.31 175 | 99.42 196 | | 98.78 219 | 98.89 177 |
|
plane_prior7 | | | | | | 98.70 116 | 94.67 111 | | | | | | | | | | |
|
plane_prior6 | | | | | | 98.38 154 | 94.37 120 | | | | | | 91.91 216 | | | | |
|
plane_prior5 | | | | | | | | | 98.75 110 | | | | | 99.46 187 | 92.59 197 | 99.20 179 | 99.28 118 |
|
plane_prior4 | | | | | | | | | | | | 96.77 224 | | | | | |
|
plane_prior3 | | | | | | | 94.51 114 | | | 95.29 144 | 96.16 216 | | | | | | |
|
plane_prior2 | | | | | | | | 96.50 127 | | 96.36 104 | | | | | | | |
|
plane_prior1 | | | | | | 98.49 145 | | | | | | | | | | | |
|
plane_prior | | | | | | | 94.29 121 | 95.42 193 | | 94.31 187 | | | | | | 98.93 207 | |
|
n2 | | | | | | | | | 0.00 364 | | | | | | | | |
|
nn | | | | | | | | | 0.00 364 | | | | | | | | |
|
door-mid | | | | | | | | | 98.17 194 | | | | | | | | |
|
lessismore_v0 | | | | | 97.05 141 | 99.36 45 | 92.12 175 | | 84.07 354 | | 98.77 51 | 98.98 50 | 85.36 275 | 99.74 59 | 97.34 59 | 99.37 152 | 99.30 111 |
|
LGP-MVS_train | | | | | 98.74 32 | 99.15 67 | 97.02 38 | | 99.02 51 | 95.15 153 | 98.34 79 | 98.23 106 | 97.91 19 | 99.70 88 | 94.41 156 | 99.73 56 | 99.50 50 |
|
test11 | | | | | | | | | 98.08 204 | | | | | | | | |
|
door | | | | | | | | | 97.81 219 | | | | | | | | |
|
HQP5-MVS | | | | | | | 92.47 165 | | | | | | | | | | |
|
HQP-NCC | | | | | | 97.85 216 | | 94.26 244 | | 93.18 216 | 92.86 308 | | | | | | |
|
ACMP_Plane | | | | | | 97.85 216 | | 94.26 244 | | 93.18 216 | 92.86 308 | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 90.51 242 | | |
|
HQP4-MVS | | | | | | | | | | | 92.87 307 | | | 99.23 246 | | | 99.06 154 |
|
HQP3-MVS | | | | | | | | | 98.43 156 | | | | | | | 98.74 223 | |
|
HQP2-MVS | | | | | | | | | | | | | 90.33 235 | | | | |
|
NP-MVS | | | | | | 98.14 192 | 93.72 141 | | | | | 95.08 282 | | | | | |
|
MDTV_nov1_ep13_2view | | | | | | | 57.28 359 | 94.89 227 | | 80.59 334 | 94.02 276 | | 78.66 297 | | 85.50 308 | | 97.82 264 |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 99.52 109 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 99.55 101 | |
|
Test By Simon | | | | | | | | | | | | | 94.51 137 | | | | |
|
ITE_SJBPF | | | | | 97.85 87 | 98.64 122 | 96.66 49 | | 98.51 148 | 95.63 130 | 97.22 161 | 97.30 194 | 95.52 102 | 98.55 314 | 90.97 226 | 98.90 208 | 98.34 228 |
|
DeepMVS_CX | | | | | 77.17 342 | 90.94 355 | 85.28 295 | | 74.08 359 | 52.51 354 | 80.87 355 | 88.03 350 | 75.25 315 | 70.63 357 | 59.23 355 | 84.94 350 | 75.62 352 |
|