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 |
|
wuykxyi23d | | | 96.76 16 | 96.57 26 | 97.34 21 | 97.75 86 | 96.73 3 | 94.37 106 | 96.48 164 | 91.00 122 | 99.72 2 | 98.99 6 | 96.06 15 | 98.21 200 | 94.86 22 | 99.90 2 | 97.09 191 |
|
Anonymous20231211 | | | 97.78 3 | 98.31 2 | 96.16 47 | 99.55 2 | 89.37 81 | 98.40 5 | 98.89 4 | 98.75 2 | 99.48 3 | 99.62 2 | 98.70 2 | 99.40 36 | 91.60 106 | 99.84 5 | 99.71 3 |
|
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 | 88.07 169 | 99.57 13 | 95.86 11 | 99.69 15 | 99.46 25 |
|
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 | 98.61 158 | 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 |
LCM-MVSNet-Re | | | 94.20 115 | 94.58 99 | 93.04 152 | 95.91 206 | 83.13 173 | 93.79 126 | 99.19 2 | 92.00 93 | 98.84 6 | 98.04 42 | 93.64 57 | 99.02 89 | 81.28 240 | 98.54 140 | 96.96 197 |
|
PS-MVSNAJss | | | 96.01 49 | 96.04 48 | 95.89 58 | 98.82 22 | 88.51 99 | 95.57 63 | 97.88 56 | 88.72 169 | 98.81 7 | 98.86 10 | 90.77 119 | 99.60 8 | 95.43 14 | 99.53 43 | 99.57 15 |
|
mvs_tets | | | 96.83 10 | 96.71 21 | 97.17 25 | 98.83 21 | 92.51 43 | 96.58 27 | 97.61 78 | 87.57 196 | 98.80 8 | 98.90 9 | 96.50 11 | 99.59 12 | 96.15 9 | 99.47 48 | 99.40 31 |
|
ACMH | | 88.36 12 | 96.59 26 | 97.43 5 | 94.07 122 | 98.56 35 | 85.33 150 | 96.33 39 | 98.30 16 | 94.66 35 | 98.72 9 | 98.30 36 | 97.51 5 | 98.00 211 | 94.87 21 | 99.59 34 | 98.86 86 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
jajsoiax | | | 96.59 26 | 96.42 29 | 97.12 27 | 98.76 25 | 92.49 44 | 96.44 35 | 97.42 96 | 86.96 206 | 98.71 10 | 98.72 17 | 95.36 26 | 99.56 16 | 95.92 10 | 99.45 52 | 99.32 37 |
|
wuyk23d | | | 87.83 248 | 90.79 197 | 78.96 333 | 90.46 318 | 88.63 93 | 92.72 155 | 90.67 282 | 91.65 110 | 98.68 11 | 97.64 62 | 96.06 15 | 77.53 354 | 59.84 344 | 99.41 60 | 70.73 351 |
|
DTE-MVSNet | | | 96.74 18 | 97.43 5 | 94.67 97 | 99.13 5 | 84.68 155 | 96.51 30 | 97.94 54 | 98.14 3 | 98.67 12 | 98.32 35 | 95.04 36 | 99.69 2 | 93.27 64 | 99.82 10 | 99.62 11 |
|
PS-CasMVS | | | 96.69 20 | 97.43 5 | 94.49 109 | 99.13 5 | 84.09 163 | 96.61 25 | 97.97 48 | 97.91 5 | 98.64 13 | 98.13 40 | 95.24 31 | 99.65 3 | 93.39 60 | 99.84 5 | 99.72 2 |
|
PEN-MVS | | | 96.69 20 | 97.39 8 | 94.61 99 | 99.16 3 | 84.50 156 | 96.54 29 | 98.05 37 | 98.06 4 | 98.64 13 | 98.25 38 | 95.01 39 | 99.65 3 | 92.95 73 | 99.83 8 | 99.68 5 |
|
v52 | | | 96.93 8 | 97.29 11 | 95.86 59 | 98.12 67 | 88.48 100 | 97.69 7 | 97.74 68 | 94.90 33 | 98.55 15 | 98.72 17 | 93.39 64 | 99.49 21 | 96.92 2 | 99.62 29 | 99.61 12 |
|
V4 | | | 96.93 8 | 97.29 11 | 95.86 59 | 98.11 68 | 88.47 101 | 97.69 7 | 97.74 68 | 94.91 31 | 98.55 15 | 98.72 17 | 93.37 65 | 99.49 21 | 96.92 2 | 99.62 29 | 99.61 12 |
|
SixPastTwentyTwo | | | 94.91 85 | 95.21 83 | 93.98 124 | 98.52 42 | 83.19 171 | 95.93 52 | 94.84 216 | 94.86 34 | 98.49 17 | 98.74 16 | 81.45 241 | 99.60 8 | 94.69 25 | 99.39 63 | 99.15 48 |
|
WR-MVS_H | | | 96.60 25 | 97.05 15 | 95.24 82 | 99.02 11 | 86.44 131 | 96.78 22 | 98.08 32 | 97.42 7 | 98.48 18 | 97.86 55 | 91.76 97 | 99.63 6 | 94.23 37 | 99.84 5 | 99.66 7 |
|
v748 | | | 96.51 28 | 97.05 15 | 94.89 91 | 98.35 55 | 85.82 144 | 96.58 27 | 97.47 93 | 96.25 21 | 98.46 19 | 98.35 33 | 93.27 68 | 99.33 52 | 95.13 19 | 99.59 34 | 99.52 20 |
|
v7n | | | 96.82 11 | 97.31 10 | 95.33 79 | 98.54 39 | 86.81 125 | 96.83 19 | 98.07 35 | 96.59 17 | 98.46 19 | 98.43 32 | 92.91 75 | 99.52 17 | 96.25 8 | 99.76 13 | 99.65 9 |
|
anonymousdsp | | | 96.74 18 | 96.42 29 | 97.68 7 | 98.00 76 | 94.03 21 | 96.97 16 | 97.61 78 | 87.68 195 | 98.45 21 | 98.77 15 | 94.20 53 | 99.50 18 | 96.70 5 | 99.40 61 | 99.53 17 |
|
CP-MVSNet | | | 96.19 44 | 96.80 19 | 94.38 115 | 98.99 13 | 83.82 165 | 96.31 41 | 97.53 87 | 97.60 6 | 98.34 22 | 97.52 68 | 91.98 93 | 99.63 6 | 93.08 71 | 99.81 11 | 99.70 4 |
|
test_djsdf | | | 96.62 23 | 96.49 28 | 97.01 30 | 98.55 38 | 91.77 54 | 97.15 13 | 97.37 100 | 88.98 157 | 98.26 23 | 98.86 10 | 93.35 67 | 99.60 8 | 96.41 6 | 99.45 52 | 99.66 7 |
|
ACMH+ | | 88.43 11 | 96.48 30 | 96.82 18 | 95.47 75 | 98.54 39 | 89.06 84 | 95.65 61 | 98.61 7 | 96.10 24 | 98.16 24 | 97.52 68 | 96.90 8 | 98.62 157 | 90.30 126 | 99.60 32 | 98.72 101 |
|
pmmvs6 | | | 96.80 14 | 97.36 9 | 95.15 86 | 99.12 7 | 87.82 112 | 96.68 23 | 97.86 58 | 96.10 24 | 98.14 25 | 99.28 3 | 97.94 4 | 98.21 200 | 91.38 113 | 99.69 15 | 99.42 27 |
|
ANet_high | | | 94.83 91 | 96.28 34 | 90.47 233 | 96.65 138 | 73.16 306 | 94.33 108 | 98.74 6 | 96.39 20 | 98.09 26 | 98.93 8 | 93.37 65 | 98.70 150 | 90.38 121 | 99.68 18 | 99.53 17 |
|
nrg030 | | | 96.32 40 | 96.55 27 | 95.62 70 | 97.83 83 | 88.55 97 | 95.77 58 | 98.29 18 | 92.68 70 | 98.03 27 | 97.91 52 | 95.13 33 | 98.95 100 | 93.85 43 | 99.49 47 | 99.36 35 |
|
v11 | | | 95.10 78 | 95.88 55 | 92.76 169 | 96.98 121 | 79.64 226 | 95.12 76 | 97.60 80 | 92.64 73 | 98.03 27 | 98.44 30 | 89.06 150 | 99.15 68 | 95.42 15 | 99.67 21 | 99.50 22 |
|
v13 | | | 95.39 64 | 96.12 42 | 93.18 149 | 97.22 110 | 80.81 197 | 95.55 64 | 97.57 82 | 93.42 58 | 98.02 29 | 98.49 26 | 89.62 142 | 99.18 65 | 95.54 12 | 99.68 18 | 99.54 16 |
|
v12 | | | 95.29 70 | 96.02 50 | 93.10 151 | 97.14 116 | 80.63 198 | 95.39 68 | 97.55 86 | 93.19 61 | 97.98 30 | 98.44 30 | 89.40 145 | 99.16 66 | 95.38 16 | 99.67 21 | 99.52 20 |
|
V9 | | | 95.17 76 | 95.89 54 | 93.02 154 | 97.04 119 | 80.42 200 | 95.22 74 | 97.53 87 | 92.92 68 | 97.90 31 | 98.35 33 | 89.15 149 | 99.14 70 | 95.21 18 | 99.65 25 | 99.50 22 |
|
MIMVSNet1 | | | 95.52 59 | 95.45 70 | 95.72 66 | 99.14 4 | 89.02 85 | 96.23 46 | 96.87 145 | 93.73 51 | 97.87 32 | 98.49 26 | 90.73 123 | 99.05 82 | 86.43 194 | 99.60 32 | 99.10 55 |
|
V14 | | | 95.05 79 | 95.75 61 | 92.94 160 | 96.94 123 | 80.21 203 | 95.03 81 | 97.50 91 | 92.62 74 | 97.84 33 | 98.28 37 | 88.87 152 | 99.13 72 | 95.03 20 | 99.64 26 | 99.48 24 |
|
TransMVSNet (Re) | | | 95.27 73 | 96.04 48 | 92.97 157 | 98.37 52 | 81.92 183 | 95.07 79 | 96.76 151 | 93.97 47 | 97.77 34 | 98.57 21 | 95.72 18 | 97.90 214 | 88.89 157 | 99.23 79 | 99.08 59 |
|
v15 | | | 94.93 84 | 95.62 66 | 92.86 165 | 96.83 129 | 80.01 216 | 94.84 88 | 97.48 92 | 92.36 79 | 97.76 35 | 98.20 39 | 88.61 153 | 99.11 75 | 94.86 22 | 99.62 29 | 99.46 25 |
|
test_0402 | | | 95.73 53 | 96.22 37 | 94.26 118 | 98.19 64 | 85.77 145 | 93.24 143 | 97.24 117 | 96.88 14 | 97.69 36 | 97.77 58 | 94.12 54 | 99.13 72 | 91.54 110 | 99.29 73 | 97.88 150 |
|
NR-MVSNet | | | 95.28 71 | 95.28 80 | 95.26 81 | 97.75 86 | 87.21 119 | 95.08 78 | 97.37 100 | 93.92 49 | 97.65 37 | 95.90 158 | 90.10 137 | 99.33 52 | 90.11 132 | 99.66 23 | 99.26 40 |
|
SMA-MVS | | | 95.85 52 | 95.63 65 | 96.51 43 | 98.27 58 | 91.30 58 | 95.09 77 | 97.88 56 | 86.59 211 | 97.63 38 | 97.51 70 | 94.82 43 | 99.29 54 | 93.55 52 | 99.34 66 | 98.93 79 |
|
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 39 | 97.44 73 | 96.51 10 | 99.40 36 | 94.06 41 | 99.23 79 | 98.85 89 |
|
FC-MVSNet-test | | | 95.32 67 | 95.88 55 | 93.62 135 | 98.49 46 | 81.77 184 | 95.90 54 | 98.32 13 | 93.93 48 | 97.53 40 | 97.56 65 | 88.48 155 | 99.40 36 | 92.91 74 | 99.83 8 | 99.68 5 |
|
K. test v3 | | | 93.37 137 | 93.27 144 | 93.66 134 | 98.05 72 | 82.62 177 | 94.35 107 | 86.62 306 | 96.05 26 | 97.51 41 | 98.85 12 | 76.59 275 | 99.65 3 | 93.21 66 | 98.20 176 | 98.73 100 |
|
TranMVSNet+NR-MVSNet | | | 96.07 48 | 96.26 35 | 95.50 74 | 98.26 59 | 87.69 113 | 93.75 127 | 97.86 58 | 95.96 28 | 97.48 42 | 97.14 88 | 95.33 27 | 99.44 24 | 90.79 116 | 99.76 13 | 99.38 32 |
|
v8 | | | 94.65 99 | 95.29 79 | 92.74 170 | 96.65 138 | 79.77 222 | 94.59 96 | 97.17 121 | 91.86 98 | 97.47 43 | 97.93 49 | 88.16 163 | 99.08 77 | 94.32 32 | 99.47 48 | 99.38 32 |
|
v17 | | | 94.80 92 | 95.46 69 | 92.83 166 | 96.76 134 | 80.02 214 | 94.85 86 | 97.40 98 | 92.23 86 | 97.45 44 | 98.04 42 | 88.46 157 | 99.06 80 | 94.56 27 | 99.40 61 | 99.41 28 |
|
v10 | | | 94.68 98 | 95.27 81 | 92.90 163 | 96.57 147 | 80.15 205 | 94.65 94 | 97.57 82 | 90.68 128 | 97.43 45 | 98.00 46 | 88.18 161 | 99.15 68 | 94.84 24 | 99.55 42 | 99.41 28 |
|
APDe-MVS | | | 96.46 32 | 96.64 23 | 95.93 56 | 97.68 95 | 89.38 80 | 96.90 18 | 98.41 11 | 92.52 76 | 97.43 45 | 97.92 50 | 95.11 34 | 99.50 18 | 94.45 30 | 99.30 71 | 98.92 83 |
|
v16 | | | 94.79 94 | 95.44 72 | 92.83 166 | 96.73 135 | 80.03 212 | 94.85 86 | 97.41 97 | 92.23 86 | 97.41 47 | 98.04 42 | 88.40 159 | 99.06 80 | 94.56 27 | 99.30 71 | 99.41 28 |
|
pm-mvs1 | | | 95.43 61 | 95.94 51 | 93.93 128 | 98.38 50 | 85.08 152 | 95.46 67 | 97.12 125 | 91.84 99 | 97.28 48 | 98.46 28 | 95.30 29 | 97.71 239 | 90.17 130 | 99.42 56 | 98.99 70 |
|
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 48 | 98.46 28 | 94.62 47 | 98.84 122 | 94.64 26 | 99.53 43 | 98.99 70 |
|
v18 | | | 94.63 100 | 95.26 82 | 92.74 170 | 96.60 145 | 79.81 220 | 94.64 95 | 97.37 100 | 91.87 97 | 97.26 50 | 97.91 52 | 88.13 164 | 99.04 85 | 94.30 34 | 99.24 77 | 99.38 32 |
|
SD-MVS | | | 95.19 74 | 95.73 62 | 93.55 138 | 96.62 144 | 88.88 90 | 94.67 92 | 98.05 37 | 91.26 116 | 97.25 51 | 96.40 127 | 95.42 23 | 94.36 319 | 92.72 79 | 99.19 82 | 97.40 179 |
|
ACMM | | 88.83 9 | 96.30 42 | 96.07 46 | 96.97 31 | 98.39 49 | 92.95 41 | 94.74 90 | 98.03 40 | 90.82 125 | 97.15 52 | 96.85 102 | 96.25 14 | 99.00 92 | 93.10 69 | 99.33 69 | 98.95 77 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
lessismore_v0 | | | | | 93.87 131 | 98.05 72 | 83.77 166 | | 80.32 349 | | 97.13 53 | 97.91 52 | 77.49 265 | 99.11 75 | 92.62 81 | 98.08 187 | 98.74 98 |
|
FIs | | | 94.90 86 | 95.35 74 | 93.55 138 | 98.28 57 | 81.76 185 | 95.33 70 | 98.14 28 | 93.05 63 | 97.07 54 | 97.18 86 | 87.65 175 | 99.29 54 | 91.72 102 | 99.69 15 | 99.61 12 |
|
LPG-MVS_test | | | 96.38 39 | 96.23 36 | 96.84 36 | 98.36 53 | 92.13 47 | 95.33 70 | 98.25 19 | 91.78 104 | 97.07 54 | 97.22 84 | 96.38 12 | 99.28 56 | 92.07 94 | 99.59 34 | 99.11 52 |
|
LGP-MVS_train | | | | | 96.84 36 | 98.36 53 | 92.13 47 | | 98.25 19 | 91.78 104 | 97.07 54 | 97.22 84 | 96.38 12 | 99.28 56 | 92.07 94 | 99.59 34 | 99.11 52 |
|
testing_2 | | | 94.03 118 | 94.38 104 | 93.00 155 | 96.79 133 | 81.41 190 | 92.87 153 | 96.96 133 | 85.88 219 | 97.06 57 | 97.92 50 | 91.18 115 | 98.71 149 | 91.72 102 | 99.04 99 | 98.87 85 |
|
VPA-MVSNet | | | 95.14 77 | 95.67 64 | 93.58 137 | 97.76 85 | 83.15 172 | 94.58 98 | 97.58 81 | 93.39 59 | 97.05 58 | 98.04 42 | 93.25 69 | 98.51 175 | 89.75 139 | 99.59 34 | 99.08 59 |
|
FMVSNet1 | | | 94.84 90 | 95.13 86 | 93.97 125 | 97.60 98 | 84.29 157 | 95.99 48 | 96.56 158 | 92.38 78 | 97.03 59 | 98.53 23 | 90.12 134 | 98.98 93 | 88.78 159 | 99.16 85 | 98.65 103 |
|
APD-MVS_3200maxsize | | | 96.82 11 | 96.65 22 | 97.32 22 | 97.95 80 | 93.82 29 | 96.31 41 | 98.25 19 | 95.51 30 | 96.99 60 | 97.05 94 | 95.63 20 | 99.39 41 | 93.31 63 | 98.88 109 | 98.75 97 |
|
EG-PatchMatch MVS | | | 94.54 104 | 94.67 97 | 94.14 120 | 97.87 82 | 86.50 127 | 92.00 185 | 96.74 152 | 88.16 186 | 96.93 61 | 97.61 63 | 93.04 73 | 97.90 214 | 91.60 106 | 98.12 183 | 98.03 137 |
|
MP-MVS-pluss | | | 96.08 47 | 95.92 53 | 96.57 41 | 99.06 9 | 91.21 60 | 93.25 142 | 98.32 13 | 87.89 190 | 96.86 62 | 97.38 76 | 95.55 21 | 99.39 41 | 95.47 13 | 99.47 48 | 99.11 52 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
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 62 | 97.56 65 | 95.48 22 | 98.77 138 | 90.11 132 | 99.44 54 | 98.31 120 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
UniMVSNet_NR-MVSNet | | | 95.35 66 | 95.21 83 | 95.76 64 | 97.69 94 | 88.59 95 | 92.26 177 | 97.84 61 | 94.91 31 | 96.80 64 | 95.78 166 | 90.42 129 | 99.41 32 | 91.60 106 | 99.58 39 | 99.29 39 |
|
DU-MVS | | | 95.28 71 | 95.12 87 | 95.75 65 | 97.75 86 | 88.59 95 | 92.58 159 | 97.81 63 | 93.99 45 | 96.80 64 | 95.90 158 | 90.10 137 | 99.41 32 | 91.60 106 | 99.58 39 | 99.26 40 |
|
OPM-MVS | | | 95.61 57 | 95.45 70 | 96.08 50 | 98.49 46 | 91.00 62 | 92.65 158 | 97.33 109 | 90.05 140 | 96.77 66 | 96.85 102 | 95.04 36 | 98.56 166 | 92.77 75 | 99.06 94 | 98.70 102 |
|
test_part2 | | | | | | 98.21 62 | 89.41 77 | | | | 96.72 67 | | | | | | |
|
ESAPD | | | 95.42 63 | 95.34 75 | 95.68 69 | 98.21 62 | 89.41 77 | 93.92 122 | 98.14 28 | 91.83 101 | 96.72 67 | 96.39 131 | 94.69 45 | 99.44 24 | 89.00 154 | 99.10 91 | 98.17 128 |
|
v1240 | | | 93.29 139 | 93.71 130 | 92.06 197 | 96.01 193 | 77.89 256 | 91.81 203 | 97.37 100 | 85.12 227 | 96.69 69 | 96.40 127 | 86.67 200 | 99.07 79 | 94.51 29 | 98.76 127 | 99.22 43 |
|
tfpnnormal | | | 94.27 112 | 94.87 92 | 92.48 184 | 97.71 91 | 80.88 195 | 94.55 102 | 95.41 208 | 93.70 52 | 96.67 70 | 97.72 59 | 91.40 103 | 98.18 205 | 87.45 177 | 99.18 84 | 98.36 116 |
|
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 71 | 96.57 117 | 94.99 40 | 99.36 47 | 93.48 55 | 99.34 66 | 98.82 91 |
Skip Steuart: Steuart Systems R&D Blog. |
WR-MVS | | | 93.49 131 | 93.72 129 | 92.80 168 | 97.57 100 | 80.03 212 | 90.14 249 | 95.68 197 | 93.70 52 | 96.62 72 | 95.39 183 | 87.21 186 | 99.04 85 | 87.50 176 | 99.64 26 | 99.33 36 |
|
ACMP | | 88.15 13 | 95.71 54 | 95.43 73 | 96.54 42 | 98.17 65 | 91.73 55 | 94.24 110 | 98.08 32 | 89.46 149 | 96.61 73 | 96.47 121 | 95.85 17 | 99.12 74 | 90.45 118 | 99.56 41 | 98.77 96 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
DP-MVS | | | 95.62 56 | 95.84 57 | 94.97 89 | 97.16 113 | 88.62 94 | 94.54 103 | 97.64 74 | 96.94 13 | 96.58 74 | 97.32 81 | 93.07 72 | 98.72 144 | 90.45 118 | 98.84 114 | 97.57 170 |
|
IterMVS-LS | | | 93.78 122 | 94.28 109 | 92.27 190 | 96.27 174 | 79.21 239 | 91.87 194 | 96.78 149 | 91.77 106 | 96.57 75 | 97.07 92 | 87.15 187 | 98.74 142 | 91.99 96 | 99.03 100 | 98.86 86 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
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 76 | 97.36 79 | 96.92 7 | 99.34 49 | 94.31 33 | 99.38 64 | 98.92 83 |
|
ambc | | | | | 92.98 156 | 96.88 127 | 83.01 175 | 95.92 53 | 96.38 171 | | 96.41 77 | 97.48 71 | 88.26 160 | 97.80 231 | 89.96 137 | 98.93 106 | 98.12 134 |
|
Regformer-4 | | | 94.90 86 | 94.67 97 | 95.59 71 | 92.78 290 | 89.02 85 | 92.39 171 | 95.91 190 | 94.50 38 | 96.41 77 | 95.56 174 | 92.10 89 | 99.01 91 | 94.23 37 | 98.14 180 | 98.74 98 |
|
ACMMP_Plus | | | 96.21 43 | 96.12 42 | 96.49 46 | 98.90 17 | 91.42 57 | 94.57 99 | 98.03 40 | 90.42 135 | 96.37 79 | 97.35 80 | 95.68 19 | 99.25 60 | 94.44 31 | 99.34 66 | 98.80 93 |
|
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 80 | 96.84 104 | 95.10 35 | 99.40 36 | 93.47 56 | 99.33 69 | 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 |
VDDNet | | | 94.03 118 | 94.27 111 | 93.31 146 | 98.87 19 | 82.36 179 | 95.51 66 | 91.78 275 | 97.19 10 | 96.32 81 | 98.60 20 | 84.24 221 | 98.75 139 | 87.09 182 | 98.83 117 | 98.81 92 |
|
UniMVSNet (Re) | | | 95.32 67 | 95.15 85 | 95.80 62 | 97.79 84 | 88.91 87 | 92.91 151 | 98.07 35 | 93.46 57 | 96.31 82 | 95.97 157 | 90.14 133 | 99.34 49 | 92.11 91 | 99.64 26 | 99.16 47 |
|
XVG-ACMP-BASELINE | | | 95.68 55 | 95.34 75 | 96.69 39 | 98.40 48 | 93.04 38 | 94.54 103 | 98.05 37 | 90.45 134 | 96.31 82 | 96.76 107 | 92.91 75 | 98.72 144 | 91.19 114 | 99.42 56 | 98.32 118 |
|
zzz-MVS | | | 96.47 31 | 96.14 40 | 97.47 11 | 98.95 15 | 94.05 18 | 93.69 129 | 97.62 75 | 94.46 40 | 96.29 84 | 96.94 95 | 93.56 58 | 99.37 45 | 94.29 35 | 99.42 56 | 98.99 70 |
|
MTAPA | | | 96.65 22 | 96.38 31 | 97.47 11 | 98.95 15 | 94.05 18 | 95.88 55 | 97.62 75 | 94.46 40 | 96.29 84 | 96.94 95 | 93.56 58 | 99.37 45 | 94.29 35 | 99.42 56 | 98.99 70 |
|
Baseline_NR-MVSNet | | | 94.47 106 | 95.09 88 | 92.60 178 | 98.50 45 | 80.82 196 | 92.08 182 | 96.68 154 | 93.82 50 | 96.29 84 | 98.56 22 | 90.10 137 | 97.75 237 | 90.10 134 | 99.66 23 | 99.24 42 |
|
IS-MVSNet | | | 94.49 105 | 94.35 106 | 94.92 90 | 98.25 60 | 86.46 130 | 97.13 15 | 94.31 230 | 96.24 22 | 96.28 87 | 96.36 136 | 82.88 228 | 99.35 48 | 88.19 168 | 99.52 45 | 98.96 76 |
|
VDD-MVS | | | 94.37 107 | 94.37 105 | 94.40 114 | 97.49 104 | 86.07 139 | 93.97 117 | 93.28 248 | 94.49 39 | 96.24 88 | 97.78 56 | 87.99 171 | 98.79 131 | 88.92 156 | 99.14 87 | 98.34 117 |
|
DeepPCF-MVS | | 90.46 6 | 94.20 115 | 93.56 136 | 96.14 48 | 95.96 202 | 92.96 40 | 89.48 269 | 97.46 94 | 85.14 226 | 96.23 89 | 95.42 180 | 93.19 70 | 98.08 209 | 90.37 122 | 98.76 127 | 97.38 182 |
|
PM-MVS | | | 93.33 138 | 92.67 156 | 95.33 79 | 96.58 146 | 94.06 16 | 92.26 177 | 92.18 267 | 85.92 218 | 96.22 90 | 96.61 115 | 85.64 215 | 95.99 296 | 90.35 124 | 98.23 171 | 95.93 238 |
|
DeepC-MVS | | 91.39 4 | 95.43 61 | 95.33 77 | 95.71 67 | 97.67 96 | 90.17 68 | 93.86 125 | 98.02 42 | 87.35 198 | 96.22 90 | 97.99 47 | 94.48 51 | 99.05 82 | 92.73 78 | 99.68 18 | 97.93 144 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
V42 | | | 93.43 133 | 93.58 135 | 92.97 157 | 95.34 233 | 81.22 191 | 92.67 157 | 96.49 163 | 87.25 200 | 96.20 92 | 96.37 135 | 87.32 184 | 98.85 121 | 92.39 90 | 98.21 174 | 98.85 89 |
|
CSCG | | | 94.69 97 | 94.75 93 | 94.52 107 | 97.55 101 | 87.87 110 | 95.01 82 | 97.57 82 | 92.68 70 | 96.20 92 | 93.44 246 | 91.92 94 | 98.78 134 | 89.11 153 | 99.24 77 | 96.92 199 |
|
v1921920 | | | 93.26 142 | 93.61 134 | 92.19 192 | 96.04 192 | 78.31 251 | 91.88 193 | 97.24 117 | 85.17 225 | 96.19 94 | 96.19 149 | 86.76 199 | 99.05 82 | 94.18 39 | 98.84 114 | 99.22 43 |
|
Regformer-2 | | | 94.86 89 | 94.55 100 | 95.77 63 | 92.83 288 | 89.98 70 | 91.87 194 | 96.40 168 | 94.38 42 | 96.19 94 | 95.04 194 | 92.47 86 | 99.04 85 | 93.49 54 | 98.31 161 | 98.28 122 |
|
EI-MVSNet-UG-set | | | 94.35 109 | 94.27 111 | 94.59 104 | 92.46 293 | 85.87 142 | 92.42 170 | 94.69 223 | 93.67 56 | 96.13 96 | 95.84 162 | 91.20 112 | 98.86 119 | 93.78 45 | 98.23 171 | 99.03 66 |
|
EI-MVSNet-Vis-set | | | 94.36 108 | 94.28 109 | 94.61 99 | 92.55 292 | 85.98 141 | 92.44 169 | 94.69 223 | 93.70 52 | 96.12 97 | 95.81 163 | 91.24 109 | 98.86 119 | 93.76 48 | 98.22 173 | 98.98 75 |
|
v1192 | | | 93.49 131 | 93.78 124 | 92.62 177 | 96.16 183 | 79.62 227 | 91.83 202 | 97.22 119 | 86.07 215 | 96.10 98 | 96.38 134 | 87.22 185 | 99.02 89 | 94.14 40 | 98.88 109 | 99.22 43 |
|
FMVSNet2 | | | 92.78 158 | 92.73 155 | 92.95 159 | 95.40 228 | 81.98 182 | 94.18 112 | 95.53 205 | 88.63 170 | 96.05 99 | 97.37 77 | 81.31 244 | 98.81 129 | 87.38 180 | 98.67 133 | 98.06 135 |
|
v7 | | | 93.66 124 | 93.97 116 | 92.73 172 | 96.55 148 | 80.15 205 | 92.54 160 | 96.99 131 | 87.36 197 | 95.99 100 | 96.48 120 | 88.18 161 | 98.94 103 | 93.35 62 | 98.31 161 | 99.09 56 |
|
Regformer-3 | | | 94.28 111 | 94.23 113 | 94.46 111 | 92.78 290 | 86.28 135 | 92.39 171 | 94.70 222 | 93.69 55 | 95.97 101 | 95.56 174 | 91.34 104 | 98.48 179 | 93.45 57 | 98.14 180 | 98.62 107 |
|
v144192 | | | 93.20 147 | 93.54 137 | 92.16 194 | 96.05 189 | 78.26 252 | 91.95 186 | 97.14 122 | 84.98 231 | 95.96 102 | 96.11 152 | 87.08 189 | 99.04 85 | 93.79 44 | 98.84 114 | 99.17 46 |
|
Regformer-1 | | | 94.55 103 | 94.33 107 | 95.19 84 | 92.83 288 | 88.54 98 | 91.87 194 | 95.84 194 | 93.99 45 | 95.95 103 | 95.04 194 | 92.00 91 | 98.79 131 | 93.14 68 | 98.31 161 | 98.23 124 |
|
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 | 95.95 103 | 96.41 126 | 96.71 9 | 99.42 28 | 93.99 42 | 99.36 65 | 99.13 50 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
v6 | | | 93.59 127 | 93.93 117 | 92.56 180 | 96.65 138 | 79.77 222 | 92.50 165 | 96.40 168 | 88.55 174 | 95.94 105 | 96.23 144 | 88.13 164 | 98.87 116 | 92.46 87 | 98.50 145 | 99.06 62 |
|
v148 | | | 92.87 156 | 93.29 141 | 91.62 208 | 96.25 177 | 77.72 258 | 91.28 216 | 95.05 212 | 89.69 146 | 95.93 106 | 96.04 154 | 87.34 183 | 98.38 187 | 90.05 135 | 97.99 193 | 98.78 95 |
|
v1neww | | | 93.58 128 | 93.92 119 | 92.56 180 | 96.64 142 | 79.77 222 | 92.50 165 | 96.41 166 | 88.55 174 | 95.93 106 | 96.24 142 | 88.08 166 | 98.87 116 | 92.45 88 | 98.50 145 | 99.05 63 |
|
v7new | | | 93.58 128 | 93.92 119 | 92.56 180 | 96.64 142 | 79.77 222 | 92.50 165 | 96.41 166 | 88.55 174 | 95.93 106 | 96.24 142 | 88.08 166 | 98.87 116 | 92.45 88 | 98.50 145 | 99.05 63 |
|
v1144 | | | 93.50 130 | 93.81 122 | 92.57 179 | 96.28 173 | 79.61 228 | 91.86 198 | 96.96 133 | 86.95 207 | 95.91 109 | 96.32 137 | 87.65 175 | 98.96 98 | 93.51 53 | 98.88 109 | 99.13 50 |
|
v1 | | | 93.43 133 | 93.77 125 | 92.41 186 | 96.37 160 | 79.24 234 | 91.84 199 | 96.38 171 | 88.33 181 | 95.87 110 | 96.22 147 | 87.45 179 | 98.89 106 | 92.61 82 | 98.83 117 | 99.09 56 |
|
v1141 | | | 93.42 135 | 93.76 126 | 92.40 188 | 96.37 160 | 79.24 234 | 91.84 199 | 96.38 171 | 88.33 181 | 95.86 111 | 96.23 144 | 87.41 181 | 98.89 106 | 92.61 82 | 98.82 120 | 99.08 59 |
|
divwei89l23v2f112 | | | 93.42 135 | 93.76 126 | 92.41 186 | 96.37 160 | 79.24 234 | 91.84 199 | 96.38 171 | 88.33 181 | 95.86 111 | 96.23 144 | 87.41 181 | 98.89 106 | 92.61 82 | 98.83 117 | 99.09 56 |
|
Patchmatch-RL test | | | 88.81 230 | 88.52 224 | 89.69 252 | 95.33 235 | 79.94 217 | 86.22 313 | 92.71 260 | 78.46 285 | 95.80 113 | 94.18 224 | 66.25 303 | 95.33 308 | 89.22 151 | 98.53 141 | 93.78 293 |
|
PGM-MVS | | | 96.32 40 | 95.94 51 | 97.43 15 | 98.59 34 | 93.84 28 | 95.33 70 | 98.30 16 | 91.40 114 | 95.76 114 | 96.87 101 | 95.26 30 | 99.45 23 | 92.77 75 | 99.21 81 | 99.00 68 |
|
VPNet | | | 93.08 148 | 93.76 126 | 91.03 224 | 98.60 32 | 75.83 279 | 91.51 210 | 95.62 198 | 91.84 99 | 95.74 115 | 97.10 91 | 89.31 146 | 98.32 191 | 85.07 209 | 99.06 94 | 98.93 79 |
|
EU-MVSNet | | | 87.39 258 | 86.71 260 | 89.44 259 | 93.40 280 | 76.11 274 | 94.93 85 | 90.00 285 | 57.17 350 | 95.71 116 | 97.37 77 | 64.77 310 | 97.68 241 | 92.67 80 | 94.37 291 | 94.52 274 |
|
v2v482 | | | 93.29 139 | 93.63 133 | 92.29 189 | 96.35 168 | 78.82 245 | 91.77 206 | 96.28 176 | 88.45 177 | 95.70 117 | 96.26 140 | 86.02 210 | 98.90 104 | 93.02 72 | 98.81 123 | 99.14 49 |
|
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 | 95.63 118 | 96.47 121 | 95.37 24 | 99.27 58 | 93.78 45 | 99.14 87 | 98.48 112 |
|
#test# | | | 95.89 50 | 95.51 67 | 97.04 28 | 98.51 43 | 93.37 35 | 95.14 75 | 97.98 45 | 89.34 151 | 95.63 118 | 96.47 121 | 95.37 24 | 99.27 58 | 91.99 96 | 99.14 87 | 98.48 112 |
|
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 | 95.57 120 | 96.61 115 | 94.93 42 | 99.41 32 | 93.78 45 | 99.15 86 | 99.00 68 |
|
XXY-MVS | | | 92.58 165 | 93.16 146 | 90.84 229 | 97.75 86 | 79.84 219 | 91.87 194 | 96.22 182 | 85.94 217 | 95.53 121 | 97.68 60 | 92.69 80 | 94.48 315 | 83.21 224 | 97.51 212 | 98.21 126 |
|
new-patchmatchnet | | | 88.97 226 | 90.79 197 | 83.50 322 | 94.28 266 | 55.83 352 | 85.34 318 | 93.56 244 | 86.18 213 | 95.47 122 | 95.73 167 | 83.10 226 | 96.51 281 | 85.40 202 | 98.06 188 | 98.16 130 |
|
mPP-MVS | | | 96.46 32 | 96.05 47 | 97.69 5 | 98.62 29 | 94.65 9 | 96.45 33 | 97.74 68 | 92.59 75 | 95.47 122 | 96.68 113 | 94.50 50 | 99.42 28 | 93.10 69 | 99.26 75 | 98.99 70 |
|
UA-Net | | | 97.35 5 | 97.24 13 | 97.69 5 | 98.22 61 | 93.87 26 | 98.42 4 | 98.19 24 | 96.95 12 | 95.46 124 | 99.23 4 | 93.45 60 | 99.57 13 | 95.34 17 | 99.89 4 | 99.63 10 |
|
APD-MVS | | | 95.00 81 | 94.69 95 | 95.93 56 | 97.38 106 | 90.88 65 | 94.59 96 | 97.81 63 | 89.22 155 | 95.46 124 | 96.17 151 | 93.42 63 | 99.34 49 | 89.30 145 | 98.87 112 | 97.56 172 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
semantic-postprocess | | | | | 91.94 199 | 93.89 272 | 79.22 238 | | 93.51 245 | 91.53 113 | 95.37 126 | 96.62 114 | 77.17 268 | 98.90 104 | 91.89 100 | 94.95 280 | 97.70 162 |
|
LS3D | | | 96.11 46 | 95.83 58 | 96.95 33 | 94.75 249 | 94.20 14 | 97.34 11 | 97.98 45 | 97.31 9 | 95.32 127 | 96.77 105 | 93.08 71 | 99.20 64 | 91.79 101 | 98.16 178 | 97.44 176 |
|
XVG-OURS | | | 94.72 96 | 94.12 114 | 96.50 45 | 98.00 76 | 94.23 13 | 91.48 211 | 98.17 26 | 90.72 126 | 95.30 128 | 96.47 121 | 87.94 172 | 96.98 266 | 91.41 112 | 97.61 210 | 98.30 121 |
|
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 | 95.28 129 | 96.57 117 | 95.02 38 | 99.41 32 | 93.63 49 | 99.11 90 | 98.94 78 |
|
TinyColmap | | | 92.00 177 | 92.76 153 | 89.71 249 | 95.62 221 | 77.02 266 | 90.72 230 | 96.17 184 | 87.70 194 | 95.26 130 | 96.29 138 | 92.54 83 | 96.45 284 | 81.77 235 | 98.77 126 | 95.66 246 |
|
alignmvs | | | 93.26 142 | 92.85 150 | 94.50 108 | 95.70 214 | 87.45 114 | 93.45 133 | 95.76 195 | 91.58 111 | 95.25 131 | 92.42 268 | 81.96 238 | 98.72 144 | 91.61 105 | 97.87 199 | 97.33 184 |
|
EI-MVSNet | | | 92.99 152 | 93.26 145 | 92.19 192 | 92.12 301 | 79.21 239 | 92.32 174 | 94.67 225 | 91.77 106 | 95.24 132 | 95.85 160 | 87.14 188 | 98.49 176 | 91.99 96 | 98.26 167 | 98.86 86 |
|
MVSTER | | | 89.32 219 | 88.75 222 | 91.03 224 | 90.10 322 | 76.62 269 | 90.85 226 | 94.67 225 | 82.27 256 | 95.24 132 | 95.79 164 | 61.09 330 | 98.49 176 | 90.49 117 | 98.26 167 | 97.97 143 |
|
canonicalmvs | | | 94.59 101 | 94.69 95 | 94.30 117 | 95.60 222 | 87.03 122 | 95.59 62 | 98.24 22 | 91.56 112 | 95.21 134 | 92.04 275 | 94.95 41 | 98.66 154 | 91.45 111 | 97.57 211 | 97.20 189 |
|
HSP-MVS | | | 95.18 75 | 94.49 102 | 97.23 24 | 98.67 27 | 94.05 18 | 96.41 37 | 97.00 129 | 91.26 116 | 95.12 135 | 95.15 187 | 86.60 203 | 99.50 18 | 93.43 59 | 96.81 236 | 98.13 133 |
|
Test4 | | | 91.41 189 | 91.25 187 | 91.89 200 | 95.35 232 | 80.32 201 | 90.97 223 | 96.92 138 | 81.96 258 | 95.11 136 | 93.81 236 | 81.34 243 | 98.48 179 | 88.71 161 | 97.08 228 | 96.87 203 |
|
GBi-Net | | | 93.21 145 | 92.96 147 | 93.97 125 | 95.40 228 | 84.29 157 | 95.99 48 | 96.56 158 | 88.63 170 | 95.10 137 | 98.53 23 | 81.31 244 | 98.98 93 | 86.74 185 | 98.38 152 | 98.65 103 |
|
test1 | | | 93.21 145 | 92.96 147 | 93.97 125 | 95.40 228 | 84.29 157 | 95.99 48 | 96.56 158 | 88.63 170 | 95.10 137 | 98.53 23 | 81.31 244 | 98.98 93 | 86.74 185 | 98.38 152 | 98.65 103 |
|
FMVSNet3 | | | 90.78 196 | 90.32 204 | 92.16 194 | 93.03 286 | 79.92 218 | 92.54 160 | 94.95 214 | 86.17 214 | 95.10 137 | 96.01 155 | 69.97 289 | 98.75 139 | 86.74 185 | 98.38 152 | 97.82 156 |
|
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 | 95.08 140 | 96.39 131 | 94.77 44 | 99.42 28 | 93.17 67 | 99.44 54 | 98.58 111 |
|
AllTest | | | 94.88 88 | 94.51 101 | 96.00 51 | 98.02 74 | 92.17 45 | 95.26 73 | 98.43 9 | 90.48 132 | 95.04 141 | 96.74 109 | 92.54 83 | 97.86 225 | 85.11 207 | 98.98 102 | 97.98 140 |
|
TestCases | | | | | 96.00 51 | 98.02 74 | 92.17 45 | | 98.43 9 | 90.48 132 | 95.04 141 | 96.74 109 | 92.54 83 | 97.86 225 | 85.11 207 | 98.98 102 | 97.98 140 |
|
YYNet1 | | | 88.17 242 | 88.24 229 | 87.93 289 | 92.21 298 | 73.62 298 | 80.75 338 | 88.77 288 | 82.51 254 | 94.99 143 | 95.11 190 | 82.70 231 | 93.70 324 | 83.33 222 | 93.83 299 | 96.48 219 |
|
EPP-MVSNet | | | 93.91 120 | 93.68 132 | 94.59 104 | 98.08 71 | 85.55 148 | 97.44 10 | 94.03 235 | 94.22 43 | 94.94 144 | 96.19 149 | 82.07 236 | 99.57 13 | 87.28 181 | 98.89 107 | 98.65 103 |
|
MDA-MVSNet-bldmvs | | | 91.04 192 | 90.88 193 | 91.55 211 | 94.68 254 | 80.16 204 | 85.49 317 | 92.14 270 | 90.41 136 | 94.93 145 | 95.79 164 | 85.10 216 | 96.93 268 | 85.15 205 | 94.19 296 | 97.57 170 |
|
MDA-MVSNet_test_wron | | | 88.16 243 | 88.23 230 | 87.93 289 | 92.22 297 | 73.71 297 | 80.71 339 | 88.84 287 | 82.52 253 | 94.88 146 | 95.14 188 | 82.70 231 | 93.61 325 | 83.28 223 | 93.80 300 | 96.46 220 |
|
LFMVS | | | 91.33 190 | 91.16 190 | 91.82 202 | 96.27 174 | 79.36 232 | 95.01 82 | 85.61 316 | 96.04 27 | 94.82 147 | 97.06 93 | 72.03 283 | 98.46 182 | 84.96 210 | 98.70 131 | 97.65 166 |
|
ITE_SJBPF | | | | | 95.95 53 | 97.34 108 | 93.36 37 | | 96.55 161 | 91.93 94 | 94.82 147 | 95.39 183 | 91.99 92 | 97.08 263 | 85.53 201 | 97.96 194 | 97.41 177 |
|
TSAR-MVS + MP. | | | 94.96 83 | 94.75 93 | 95.57 72 | 98.86 20 | 88.69 91 | 96.37 38 | 96.81 147 | 85.23 224 | 94.75 149 | 97.12 90 | 91.85 95 | 99.40 36 | 93.45 57 | 98.33 159 | 98.62 107 |
|
Patchmtry | | | 90.11 212 | 89.92 208 | 90.66 230 | 90.35 320 | 77.00 267 | 92.96 149 | 92.81 256 | 90.25 138 | 94.74 150 | 96.93 97 | 67.11 295 | 97.52 245 | 85.17 203 | 98.98 102 | 97.46 175 |
|
3Dnovator+ | | 92.74 2 | 95.86 51 | 95.77 60 | 96.13 49 | 96.81 131 | 90.79 67 | 96.30 43 | 97.82 62 | 96.13 23 | 94.74 150 | 97.23 83 | 91.33 105 | 99.16 66 | 93.25 65 | 98.30 164 | 98.46 114 |
|
DI_MVS_plusplus_test | | | 91.42 188 | 91.41 182 | 91.46 213 | 95.34 233 | 79.06 241 | 90.58 236 | 93.74 241 | 82.59 252 | 94.69 152 | 94.76 205 | 86.54 204 | 98.44 184 | 87.93 172 | 96.49 252 | 96.87 203 |
|
TSAR-MVS + GP. | | | 93.07 150 | 92.41 162 | 95.06 88 | 95.82 208 | 90.87 66 | 90.97 223 | 92.61 262 | 88.04 187 | 94.61 153 | 93.79 237 | 88.08 166 | 97.81 230 | 89.41 144 | 98.39 151 | 96.50 218 |
|
OMC-MVS | | | 94.22 114 | 93.69 131 | 95.81 61 | 97.25 109 | 91.27 59 | 92.27 176 | 97.40 98 | 87.10 204 | 94.56 154 | 95.42 180 | 93.74 56 | 98.11 208 | 86.62 189 | 98.85 113 | 98.06 135 |
|
testgi | | | 90.38 204 | 91.34 185 | 87.50 294 | 97.49 104 | 71.54 316 | 89.43 270 | 95.16 211 | 88.38 179 | 94.54 155 | 94.68 209 | 92.88 77 | 93.09 329 | 71.60 318 | 97.85 200 | 97.88 150 |
|
VNet | | | 92.67 162 | 92.96 147 | 91.79 203 | 96.27 174 | 80.15 205 | 91.95 186 | 94.98 213 | 92.19 89 | 94.52 156 | 96.07 153 | 87.43 180 | 97.39 253 | 84.83 211 | 98.38 152 | 97.83 154 |
|
test20.03 | | | 90.80 195 | 90.85 195 | 90.63 231 | 95.63 220 | 79.24 234 | 89.81 263 | 92.87 255 | 89.90 144 | 94.39 157 | 96.40 127 | 85.77 211 | 95.27 310 | 73.86 303 | 99.05 96 | 97.39 180 |
|
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 | 94.38 158 | 96.49 119 | 94.56 48 | 99.39 41 | 93.57 50 | 99.05 96 | 98.93 79 |
|
X-MVStestdata | | | 90.70 197 | 88.45 225 | 97.44 13 | 98.56 35 | 93.99 22 | 96.50 31 | 97.95 51 | 94.58 36 | 94.38 158 | 26.89 354 | 94.56 48 | 99.39 41 | 93.57 50 | 99.05 96 | 98.93 79 |
|
test_normal | | | 91.49 184 | 91.44 181 | 91.62 208 | 95.21 236 | 79.44 230 | 90.08 252 | 93.84 239 | 82.60 251 | 94.37 160 | 94.74 206 | 86.66 201 | 98.46 182 | 88.58 164 | 96.92 234 | 96.95 198 |
|
3Dnovator | | 92.54 3 | 94.80 92 | 94.90 90 | 94.47 110 | 95.47 226 | 87.06 121 | 96.63 24 | 97.28 115 | 91.82 103 | 94.34 161 | 97.41 74 | 90.60 127 | 98.65 156 | 92.47 86 | 98.11 184 | 97.70 162 |
|
view600 | | | 88.32 238 | 87.94 237 | 89.46 255 | 96.49 151 | 73.31 301 | 93.95 118 | 84.46 329 | 93.02 64 | 94.18 162 | 92.68 260 | 63.33 320 | 98.56 166 | 75.87 294 | 97.50 213 | 96.51 211 |
|
view800 | | | 88.32 238 | 87.94 237 | 89.46 255 | 96.49 151 | 73.31 301 | 93.95 118 | 84.46 329 | 93.02 64 | 94.18 162 | 92.68 260 | 63.33 320 | 98.56 166 | 75.87 294 | 97.50 213 | 96.51 211 |
|
conf0.05thres1000 | | | 88.32 238 | 87.94 237 | 89.46 255 | 96.49 151 | 73.31 301 | 93.95 118 | 84.46 329 | 93.02 64 | 94.18 162 | 92.68 260 | 63.33 320 | 98.56 166 | 75.87 294 | 97.50 213 | 96.51 211 |
|
tfpn | | | 88.32 238 | 87.94 237 | 89.46 255 | 96.49 151 | 73.31 301 | 93.95 118 | 84.46 329 | 93.02 64 | 94.18 162 | 92.68 260 | 63.33 320 | 98.56 166 | 75.87 294 | 97.50 213 | 96.51 211 |
|
Vis-MVSNet | | | 95.50 60 | 95.48 68 | 95.56 73 | 98.11 68 | 89.40 79 | 95.35 69 | 98.22 23 | 92.36 79 | 94.11 166 | 98.07 41 | 92.02 90 | 99.44 24 | 93.38 61 | 97.67 207 | 97.85 153 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
IterMVS | | | 90.18 210 | 90.16 205 | 90.21 243 | 93.15 284 | 75.98 276 | 87.56 298 | 92.97 254 | 86.43 212 | 94.09 167 | 96.40 127 | 78.32 260 | 97.43 249 | 87.87 173 | 94.69 286 | 97.23 187 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
MSLP-MVS++ | | | 93.25 144 | 93.88 121 | 91.37 216 | 96.34 169 | 82.81 176 | 93.11 144 | 97.74 68 | 89.37 150 | 94.08 168 | 95.29 185 | 90.40 132 | 96.35 290 | 90.35 124 | 98.25 169 | 94.96 264 |
|
BH-untuned | | | 90.68 198 | 90.90 192 | 90.05 246 | 95.98 201 | 79.57 229 | 90.04 253 | 94.94 215 | 87.91 188 | 94.07 169 | 93.00 252 | 87.76 174 | 97.78 233 | 79.19 263 | 95.17 277 | 92.80 311 |
|
pmmvs-eth3d | | | 91.54 182 | 90.73 199 | 93.99 123 | 95.76 212 | 87.86 111 | 90.83 227 | 93.98 237 | 78.23 287 | 94.02 170 | 96.22 147 | 82.62 233 | 96.83 272 | 86.57 190 | 98.33 159 | 97.29 186 |
|
UnsupCasMVSNet_eth | | | 90.33 207 | 90.34 203 | 90.28 238 | 94.64 257 | 80.24 202 | 89.69 265 | 95.88 191 | 85.77 221 | 93.94 171 | 95.69 168 | 81.99 237 | 92.98 330 | 84.21 216 | 91.30 325 | 97.62 168 |
|
CNVR-MVS | | | 94.58 102 | 94.29 108 | 95.46 76 | 96.94 123 | 89.35 82 | 91.81 203 | 96.80 148 | 89.66 147 | 93.90 172 | 95.44 179 | 92.80 79 | 98.72 144 | 92.74 77 | 98.52 142 | 98.32 118 |
|
DeepC-MVS_fast | | 89.96 7 | 93.73 123 | 93.44 139 | 94.60 103 | 96.14 184 | 87.90 109 | 93.36 135 | 97.14 122 | 85.53 223 | 93.90 172 | 95.45 178 | 91.30 107 | 98.59 162 | 89.51 142 | 98.62 134 | 97.31 185 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
XVG-OURS-SEG-HR | | | 95.38 65 | 95.00 89 | 96.51 43 | 98.10 70 | 94.07 15 | 92.46 168 | 98.13 31 | 90.69 127 | 93.75 174 | 96.25 141 | 98.03 3 | 97.02 265 | 92.08 93 | 95.55 266 | 98.45 115 |
|
QAPM | | | 92.88 155 | 92.77 152 | 93.22 148 | 95.82 208 | 83.31 169 | 96.45 33 | 97.35 108 | 83.91 239 | 93.75 174 | 96.77 105 | 89.25 147 | 98.88 110 | 84.56 214 | 97.02 231 | 97.49 174 |
|
MVS_111021_LR | | | 93.66 124 | 93.28 143 | 94.80 94 | 96.25 177 | 90.95 63 | 90.21 245 | 95.43 207 | 87.91 188 | 93.74 176 | 94.40 216 | 92.88 77 | 96.38 288 | 90.39 120 | 98.28 165 | 97.07 192 |
|
PCF-MVS | | 84.52 17 | 89.12 223 | 87.71 242 | 93.34 144 | 96.06 188 | 85.84 143 | 86.58 312 | 97.31 110 | 68.46 333 | 93.61 177 | 93.89 234 | 87.51 178 | 98.52 174 | 67.85 331 | 98.11 184 | 95.66 246 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
testmv | | | 88.46 235 | 88.11 234 | 89.48 253 | 96.00 194 | 76.14 273 | 86.20 314 | 93.75 240 | 84.48 236 | 93.57 178 | 95.52 176 | 80.91 248 | 95.09 311 | 63.97 340 | 98.61 135 | 97.22 188 |
|
MVS_111021_HR | | | 93.63 126 | 93.42 140 | 94.26 118 | 96.65 138 | 86.96 123 | 89.30 275 | 96.23 180 | 88.36 180 | 93.57 178 | 94.60 210 | 93.45 60 | 97.77 234 | 90.23 128 | 98.38 152 | 98.03 137 |
|
MP-MVS | | | 96.14 45 | 95.68 63 | 97.51 10 | 98.81 23 | 94.06 16 | 96.10 47 | 97.78 67 | 92.73 69 | 93.48 180 | 96.72 111 | 94.23 52 | 99.42 28 | 91.99 96 | 99.29 73 | 99.05 63 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
RPSCF | | | 95.58 58 | 94.89 91 | 97.62 8 | 97.58 99 | 96.30 5 | 95.97 51 | 97.53 87 | 92.42 77 | 93.41 181 | 97.78 56 | 91.21 111 | 97.77 234 | 91.06 115 | 97.06 229 | 98.80 93 |
|
OpenMVS_ROB | | 85.12 16 | 89.52 218 | 89.05 215 | 90.92 228 | 94.58 260 | 81.21 192 | 91.10 221 | 93.41 247 | 77.03 294 | 93.41 181 | 93.99 233 | 83.23 225 | 97.80 231 | 79.93 257 | 94.80 284 | 93.74 295 |
|
PMVS | | 87.21 14 | 94.97 82 | 95.33 77 | 93.91 129 | 98.97 14 | 97.16 2 | 95.54 65 | 95.85 193 | 96.47 18 | 93.40 183 | 97.46 72 | 95.31 28 | 95.47 303 | 86.18 197 | 98.78 125 | 89.11 337 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
HQP_MVS | | | 94.26 113 | 93.93 117 | 95.23 83 | 97.71 91 | 88.12 106 | 94.56 100 | 97.81 63 | 91.74 108 | 93.31 184 | 95.59 169 | 86.93 194 | 98.95 100 | 89.26 149 | 98.51 143 | 98.60 109 |
|
plane_prior3 | | | | | | | 88.43 103 | | | 90.35 137 | 93.31 184 | | | | | | |
|
thres600view7 | | | 87.66 252 | 87.10 253 | 89.36 262 | 96.05 189 | 73.17 305 | 92.72 155 | 85.31 319 | 91.89 96 | 93.29 186 | 90.97 289 | 63.42 316 | 98.39 185 | 73.23 306 | 96.99 232 | 96.51 211 |
|
CPTT-MVS | | | 94.74 95 | 94.12 114 | 96.60 40 | 98.15 66 | 93.01 39 | 95.84 56 | 97.66 73 | 89.21 156 | 93.28 187 | 95.46 177 | 88.89 151 | 98.98 93 | 89.80 138 | 98.82 120 | 97.80 157 |
|
USDC | | | 89.02 224 | 89.08 214 | 88.84 272 | 95.07 240 | 74.50 293 | 88.97 282 | 96.39 170 | 73.21 311 | 93.27 188 | 96.28 139 | 82.16 235 | 96.39 287 | 77.55 280 | 98.80 124 | 95.62 248 |
|
tfpn111 | | | 87.60 254 | 87.12 251 | 89.04 268 | 96.14 184 | 73.09 307 | 93.00 146 | 85.31 319 | 92.13 90 | 93.26 189 | 90.96 290 | 63.42 316 | 98.48 179 | 72.87 309 | 96.98 233 | 95.56 249 |
|
conf200view11 | | | 87.41 257 | 86.89 255 | 88.97 269 | 96.14 184 | 73.09 307 | 93.00 146 | 85.31 319 | 92.13 90 | 93.26 189 | 90.96 290 | 63.42 316 | 98.28 193 | 71.27 321 | 96.54 247 | 95.56 249 |
|
thres100view900 | | | 87.35 259 | 86.89 255 | 88.72 274 | 96.14 184 | 73.09 307 | 93.00 146 | 85.31 319 | 92.13 90 | 93.26 189 | 90.96 290 | 63.42 316 | 98.28 193 | 71.27 321 | 96.54 247 | 94.79 267 |
|
N_pmnet | | | 88.90 228 | 87.25 247 | 93.83 132 | 94.40 264 | 93.81 31 | 84.73 321 | 87.09 303 | 79.36 278 | 93.26 189 | 92.43 267 | 79.29 255 | 91.68 335 | 77.50 282 | 97.22 225 | 96.00 235 |
|
mvs_anonymous | | | 90.37 205 | 91.30 186 | 87.58 293 | 92.17 300 | 68.00 326 | 89.84 262 | 94.73 221 | 83.82 241 | 93.22 193 | 97.40 75 | 87.54 177 | 97.40 252 | 87.94 171 | 95.05 279 | 97.34 183 |
|
UnsupCasMVSNet_bld | | | 88.50 234 | 88.03 235 | 89.90 247 | 95.52 225 | 78.88 244 | 87.39 300 | 94.02 236 | 79.32 279 | 93.06 194 | 94.02 231 | 80.72 250 | 94.27 320 | 75.16 300 | 93.08 310 | 96.54 209 |
|
PHI-MVS | | | 94.34 110 | 93.80 123 | 95.95 53 | 95.65 218 | 91.67 56 | 94.82 89 | 97.86 58 | 87.86 191 | 93.04 195 | 94.16 225 | 91.58 99 | 98.78 134 | 90.27 127 | 98.96 105 | 97.41 177 |
|
TAMVS | | | 90.16 211 | 89.05 215 | 93.49 143 | 96.49 151 | 86.37 133 | 90.34 242 | 92.55 263 | 80.84 266 | 92.99 196 | 94.57 212 | 81.94 239 | 98.20 202 | 73.51 304 | 98.21 174 | 95.90 239 |
|
Vis-MVSNet (Re-imp) | | | 90.42 202 | 90.16 205 | 91.20 222 | 97.66 97 | 77.32 262 | 94.33 108 | 87.66 299 | 91.20 118 | 92.99 196 | 95.13 189 | 75.40 277 | 98.28 193 | 77.86 276 | 99.19 82 | 97.99 139 |
|
ab-mvs | | | 92.40 169 | 92.62 157 | 91.74 204 | 97.02 120 | 81.65 186 | 95.84 56 | 95.50 206 | 86.95 207 | 92.95 198 | 97.56 65 | 90.70 125 | 97.50 246 | 79.63 259 | 97.43 219 | 96.06 234 |
|
MCST-MVS | | | 92.91 154 | 92.51 160 | 94.10 121 | 97.52 102 | 85.72 146 | 91.36 215 | 97.13 124 | 80.33 268 | 92.91 199 | 94.24 221 | 91.23 110 | 98.72 144 | 89.99 136 | 97.93 196 | 97.86 152 |
|
TAPA-MVS | | 88.58 10 | 92.49 168 | 91.75 174 | 94.73 96 | 96.50 150 | 89.69 73 | 92.91 151 | 97.68 72 | 78.02 288 | 92.79 200 | 94.10 227 | 90.85 118 | 97.96 213 | 84.76 212 | 98.16 178 | 96.54 209 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
BH-RMVSNet | | | 90.47 200 | 90.44 202 | 90.56 232 | 95.21 236 | 78.65 249 | 89.15 279 | 93.94 238 | 88.21 184 | 92.74 201 | 94.22 222 | 86.38 205 | 97.88 222 | 78.67 272 | 95.39 272 | 95.14 260 |
|
旧先验2 | | | | | | | | 90.00 255 | | 68.65 332 | 92.71 202 | | | 96.52 280 | 85.15 205 | | |
|
tfpn200view9 | | | 87.05 268 | 86.52 264 | 88.67 275 | 95.77 210 | 72.94 310 | 91.89 191 | 86.00 311 | 90.84 123 | 92.61 203 | 89.80 304 | 63.93 313 | 98.28 193 | 71.27 321 | 96.54 247 | 94.79 267 |
|
thres400 | | | 87.20 264 | 86.52 264 | 89.24 266 | 95.77 210 | 72.94 310 | 91.89 191 | 86.00 311 | 90.84 123 | 92.61 203 | 89.80 304 | 63.93 313 | 98.28 193 | 71.27 321 | 96.54 247 | 96.51 211 |
|
MS-PatchMatch | | | 88.05 244 | 87.75 241 | 88.95 270 | 93.28 281 | 77.93 254 | 87.88 294 | 92.49 264 | 75.42 299 | 92.57 205 | 93.59 241 | 80.44 251 | 94.24 322 | 81.28 240 | 92.75 313 | 94.69 271 |
|
testdata | | | | | 91.03 224 | 96.87 128 | 82.01 181 | | 94.28 231 | 71.55 318 | 92.46 206 | 95.42 180 | 85.65 214 | 97.38 255 | 82.64 229 | 97.27 224 | 93.70 296 |
|
LF4IMVS | | | 92.72 160 | 92.02 167 | 94.84 93 | 95.65 218 | 91.99 49 | 92.92 150 | 96.60 157 | 85.08 229 | 92.44 207 | 93.62 239 | 86.80 198 | 96.35 290 | 86.81 184 | 98.25 169 | 96.18 230 |
|
HPM-MVS++ | | | 95.02 80 | 94.39 103 | 96.91 34 | 97.88 81 | 93.58 33 | 94.09 113 | 96.99 131 | 91.05 121 | 92.40 208 | 95.22 186 | 91.03 117 | 99.25 60 | 92.11 91 | 98.69 132 | 97.90 148 |
|
ppachtmachnet_test | | | 88.61 233 | 88.64 223 | 88.50 282 | 91.76 304 | 70.99 319 | 84.59 324 | 92.98 253 | 79.30 280 | 92.38 209 | 93.53 243 | 79.57 254 | 97.45 248 | 86.50 193 | 97.17 226 | 97.07 192 |
|
Anonymous20231206 | | | 88.77 231 | 88.29 227 | 90.20 244 | 96.31 171 | 78.81 246 | 89.56 268 | 93.49 246 | 74.26 305 | 92.38 209 | 95.58 172 | 82.21 234 | 95.43 305 | 72.07 313 | 98.75 129 | 96.34 224 |
|
MVS_Test | | | 92.57 166 | 93.29 141 | 90.40 235 | 93.53 279 | 75.85 277 | 92.52 162 | 96.96 133 | 88.73 168 | 92.35 211 | 96.70 112 | 90.77 119 | 98.37 190 | 92.53 85 | 95.49 268 | 96.99 196 |
|
PVSNet_Blended_VisFu | | | 91.63 180 | 91.20 188 | 92.94 160 | 97.73 90 | 83.95 164 | 92.14 181 | 97.46 94 | 78.85 283 | 92.35 211 | 94.98 197 | 84.16 222 | 99.08 77 | 86.36 195 | 96.77 238 | 95.79 241 |
|
CDPH-MVS | | | 92.67 162 | 91.83 170 | 95.18 85 | 96.94 123 | 88.46 102 | 90.70 231 | 97.07 126 | 77.38 291 | 92.34 213 | 95.08 191 | 92.67 81 | 98.88 110 | 85.74 199 | 98.57 137 | 98.20 127 |
|
NCCC | | | 94.08 117 | 93.54 137 | 95.70 68 | 96.49 151 | 89.90 72 | 92.39 171 | 96.91 141 | 90.64 129 | 92.33 214 | 94.60 210 | 90.58 128 | 98.96 98 | 90.21 129 | 97.70 205 | 98.23 124 |
|
CLD-MVS | | | 91.82 178 | 91.41 182 | 93.04 152 | 96.37 160 | 83.65 167 | 86.82 308 | 97.29 113 | 84.65 235 | 92.27 215 | 89.67 309 | 92.20 87 | 97.85 228 | 83.95 218 | 99.47 48 | 97.62 168 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
DELS-MVS | | | 92.05 176 | 92.16 164 | 91.72 205 | 94.44 262 | 80.13 208 | 87.62 295 | 97.25 116 | 87.34 199 | 92.22 216 | 93.18 251 | 89.54 144 | 98.73 143 | 89.67 141 | 98.20 176 | 96.30 226 |
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 |
API-MVS | | | 91.52 183 | 91.61 176 | 91.26 220 | 94.16 267 | 86.26 136 | 94.66 93 | 94.82 217 | 91.17 119 | 92.13 217 | 91.08 288 | 90.03 140 | 97.06 264 | 79.09 264 | 97.35 223 | 90.45 334 |
|
DP-MVS Recon | | | 92.31 171 | 91.88 169 | 93.60 136 | 97.18 112 | 86.87 124 | 91.10 221 | 97.37 100 | 84.92 232 | 92.08 218 | 94.08 228 | 88.59 154 | 98.20 202 | 83.50 221 | 98.14 180 | 95.73 243 |
|
no-one | | | 87.84 247 | 87.21 248 | 89.74 248 | 93.58 278 | 78.64 250 | 81.28 337 | 92.69 261 | 74.36 303 | 92.05 219 | 97.14 88 | 81.86 240 | 96.07 294 | 72.03 314 | 99.90 2 | 94.52 274 |
|
MSDG | | | 90.82 194 | 90.67 200 | 91.26 220 | 94.16 267 | 83.08 174 | 86.63 311 | 96.19 183 | 90.60 131 | 91.94 220 | 91.89 276 | 89.16 148 | 95.75 299 | 80.96 247 | 94.51 289 | 94.95 265 |
|
Effi-MVS+-dtu | | | 93.90 121 | 92.60 158 | 97.77 4 | 94.74 250 | 96.67 4 | 94.00 115 | 95.41 208 | 89.94 142 | 91.93 221 | 92.13 273 | 90.12 134 | 98.97 97 | 87.68 174 | 97.48 217 | 97.67 165 |
|
Gipuma | | | 95.31 69 | 95.80 59 | 93.81 133 | 97.99 79 | 90.91 64 | 96.42 36 | 97.95 51 | 96.69 15 | 91.78 222 | 98.85 12 | 91.77 96 | 95.49 302 | 91.72 102 | 99.08 93 | 95.02 263 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
HyFIR lowres test | | | 87.19 265 | 85.51 280 | 92.24 191 | 97.12 118 | 80.51 199 | 85.03 319 | 96.06 185 | 66.11 340 | 91.66 223 | 92.98 253 | 70.12 288 | 99.14 70 | 75.29 299 | 95.23 276 | 97.07 192 |
|
MVP-Stereo | | | 90.07 213 | 88.92 219 | 93.54 140 | 96.31 171 | 86.49 128 | 90.93 225 | 95.59 202 | 79.80 270 | 91.48 224 | 95.59 169 | 80.79 249 | 97.39 253 | 78.57 273 | 91.19 326 | 96.76 207 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
tfpn1000 | | | 86.83 273 | 86.23 269 | 88.64 277 | 95.53 224 | 75.25 287 | 93.57 130 | 82.28 343 | 89.27 154 | 91.46 225 | 89.24 312 | 57.22 343 | 97.86 225 | 80.63 248 | 96.88 235 | 92.81 310 |
|
thres200 | | | 85.85 282 | 85.18 282 | 87.88 291 | 94.44 262 | 72.52 312 | 89.08 280 | 86.21 308 | 88.57 173 | 91.44 226 | 88.40 317 | 64.22 311 | 98.00 211 | 68.35 330 | 95.88 262 | 93.12 306 |
|
FMVSNet5 | | | 87.82 249 | 86.56 262 | 91.62 208 | 92.31 295 | 79.81 220 | 93.49 132 | 94.81 219 | 83.26 242 | 91.36 227 | 96.93 97 | 52.77 349 | 97.49 247 | 76.07 291 | 98.03 191 | 97.55 173 |
|
新几何1 | | | | | 93.17 150 | 97.16 113 | 87.29 116 | | 94.43 227 | 67.95 334 | 91.29 228 | 94.94 198 | 86.97 193 | 98.23 199 | 81.06 245 | 97.75 201 | 93.98 288 |
|
conf0.01 | | | 86.95 270 | 86.04 270 | 89.70 250 | 95.99 195 | 75.66 280 | 93.28 136 | 82.70 336 | 88.81 162 | 91.26 229 | 88.01 321 | 58.77 335 | 97.89 216 | 78.93 265 | 96.60 241 | 95.56 249 |
|
conf0.002 | | | 86.95 270 | 86.04 270 | 89.70 250 | 95.99 195 | 75.66 280 | 93.28 136 | 82.70 336 | 88.81 162 | 91.26 229 | 88.01 321 | 58.77 335 | 97.89 216 | 78.93 265 | 96.60 241 | 95.56 249 |
|
thresconf0.02 | | | 86.69 275 | 86.04 270 | 88.64 277 | 95.99 195 | 75.66 280 | 93.28 136 | 82.70 336 | 88.81 162 | 91.26 229 | 88.01 321 | 58.77 335 | 97.89 216 | 78.93 265 | 96.60 241 | 92.36 317 |
|
tfpn_n400 | | | 86.69 275 | 86.04 270 | 88.64 277 | 95.99 195 | 75.66 280 | 93.28 136 | 82.70 336 | 88.81 162 | 91.26 229 | 88.01 321 | 58.77 335 | 97.89 216 | 78.93 265 | 96.60 241 | 92.36 317 |
|
tfpnconf | | | 86.69 275 | 86.04 270 | 88.64 277 | 95.99 195 | 75.66 280 | 93.28 136 | 82.70 336 | 88.81 162 | 91.26 229 | 88.01 321 | 58.77 335 | 97.89 216 | 78.93 265 | 96.60 241 | 92.36 317 |
|
tfpnview11 | | | 86.69 275 | 86.04 270 | 88.64 277 | 95.99 195 | 75.66 280 | 93.28 136 | 82.70 336 | 88.81 162 | 91.26 229 | 88.01 321 | 58.77 335 | 97.89 216 | 78.93 265 | 96.60 241 | 92.36 317 |
|
xiu_mvs_v1_base_debu | | | 91.47 185 | 91.52 177 | 91.33 217 | 95.69 215 | 81.56 187 | 89.92 257 | 96.05 186 | 83.22 243 | 91.26 229 | 90.74 294 | 91.55 100 | 98.82 124 | 89.29 146 | 95.91 259 | 93.62 298 |
|
xiu_mvs_v1_base | | | 91.47 185 | 91.52 177 | 91.33 217 | 95.69 215 | 81.56 187 | 89.92 257 | 96.05 186 | 83.22 243 | 91.26 229 | 90.74 294 | 91.55 100 | 98.82 124 | 89.29 146 | 95.91 259 | 93.62 298 |
|
xiu_mvs_v1_base_debi | | | 91.47 185 | 91.52 177 | 91.33 217 | 95.69 215 | 81.56 187 | 89.92 257 | 96.05 186 | 83.22 243 | 91.26 229 | 90.74 294 | 91.55 100 | 98.82 124 | 89.29 146 | 95.91 259 | 93.62 298 |
|
CDS-MVSNet | | | 89.55 216 | 88.22 231 | 93.53 141 | 95.37 231 | 86.49 128 | 89.26 276 | 93.59 243 | 79.76 271 | 91.15 238 | 92.31 270 | 77.12 269 | 98.38 187 | 77.51 281 | 97.92 197 | 95.71 244 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
OpenMVS | | 89.45 8 | 92.27 173 | 92.13 166 | 92.68 174 | 94.53 261 | 84.10 162 | 95.70 59 | 97.03 127 | 82.44 255 | 91.14 239 | 96.42 125 | 88.47 156 | 98.38 187 | 85.95 198 | 97.47 218 | 95.55 253 |
|
1121 | | | 90.26 209 | 89.23 211 | 93.34 144 | 97.15 115 | 87.40 115 | 91.94 188 | 94.39 228 | 67.88 335 | 91.02 240 | 94.91 199 | 86.91 196 | 98.59 162 | 81.17 243 | 97.71 204 | 94.02 287 |
|
CNLPA | | | 91.72 179 | 91.20 188 | 93.26 147 | 96.17 182 | 91.02 61 | 91.14 219 | 95.55 204 | 90.16 139 | 90.87 241 | 93.56 242 | 86.31 206 | 94.40 318 | 79.92 258 | 97.12 227 | 94.37 278 |
|
test_prior3 | | | 93.29 139 | 92.85 150 | 94.61 99 | 95.95 203 | 87.23 117 | 90.21 245 | 97.36 106 | 89.33 152 | 90.77 242 | 94.81 201 | 90.41 130 | 98.68 152 | 88.21 166 | 98.55 138 | 97.93 144 |
|
test_prior2 | | | | | | | | 90.21 245 | | 89.33 152 | 90.77 242 | 94.81 201 | 90.41 130 | | 88.21 166 | 98.55 138 | |
|
test222 | | | | | | 96.95 122 | 85.27 151 | 88.83 285 | 93.61 242 | 65.09 343 | 90.74 244 | 94.85 200 | 84.62 220 | | | 97.36 222 | 93.91 289 |
|
TR-MVS | | | 87.70 250 | 87.17 249 | 89.27 264 | 94.11 269 | 79.26 233 | 88.69 287 | 91.86 273 | 81.94 259 | 90.69 245 | 89.79 306 | 82.82 230 | 97.42 250 | 72.65 311 | 91.98 322 | 91.14 329 |
|
CVMVSNet | | | 85.16 286 | 84.72 284 | 86.48 301 | 92.12 301 | 70.19 320 | 92.32 174 | 88.17 295 | 56.15 351 | 90.64 246 | 95.85 160 | 67.97 293 | 96.69 276 | 88.78 159 | 90.52 329 | 92.56 314 |
|
1111 | | | 80.36 317 | 81.32 305 | 77.48 334 | 94.61 258 | 44.56 355 | 81.59 335 | 90.66 283 | 86.78 209 | 90.60 247 | 93.52 244 | 30.37 360 | 90.67 338 | 66.36 335 | 97.42 220 | 97.20 189 |
|
.test1245 | | | 64.72 329 | 70.88 330 | 46.22 342 | 94.61 258 | 44.56 355 | 81.59 335 | 90.66 283 | 86.78 209 | 90.60 247 | 93.52 244 | 30.37 360 | 90.67 338 | 66.36 335 | 3.45 356 | 3.44 356 |
|
TEST9 | | | | | | 96.45 157 | 89.46 74 | 90.60 234 | 96.92 138 | 79.09 281 | 90.49 249 | 94.39 217 | 91.31 106 | 98.88 110 | | | |
|
train_agg | | | 92.71 161 | 91.83 170 | 95.35 77 | 96.45 157 | 89.46 74 | 90.60 234 | 96.92 138 | 79.37 276 | 90.49 249 | 94.39 217 | 91.20 112 | 98.88 110 | 88.66 162 | 98.43 148 | 97.72 160 |
|
test_8 | | | | | | 96.37 160 | 89.14 83 | 90.51 238 | 96.89 142 | 79.37 276 | 90.42 251 | 94.36 219 | 91.20 112 | 98.82 124 | | | |
|
agg_prior1 | | | 92.60 164 | 91.76 173 | 95.10 87 | 96.20 179 | 88.89 88 | 90.37 240 | 96.88 143 | 79.67 273 | 90.21 252 | 94.41 214 | 91.30 107 | 98.78 134 | 88.46 165 | 98.37 157 | 97.64 167 |
|
agg_prior | | | | | | 96.20 179 | 88.89 88 | | 96.88 143 | | 90.21 252 | | | 98.78 134 | | | |
|
jason | | | 89.17 222 | 88.32 226 | 91.70 206 | 95.73 213 | 80.07 209 | 88.10 292 | 93.22 250 | 71.98 317 | 90.09 254 | 92.79 255 | 78.53 259 | 98.56 166 | 87.43 178 | 97.06 229 | 96.46 220 |
jason: jason. |
Fast-Effi-MVS+-dtu | | | 92.77 159 | 92.16 164 | 94.58 106 | 94.66 256 | 88.25 104 | 92.05 183 | 96.65 155 | 89.62 148 | 90.08 255 | 91.23 285 | 92.56 82 | 98.60 160 | 86.30 196 | 96.27 254 | 96.90 200 |
|
CHOSEN 1792x2688 | | | 87.19 265 | 85.92 278 | 91.00 227 | 97.13 117 | 79.41 231 | 84.51 325 | 95.60 199 | 64.14 344 | 90.07 256 | 94.81 201 | 78.26 261 | 97.14 261 | 73.34 305 | 95.38 273 | 96.46 220 |
|
agg_prior3 | | | 92.56 167 | 91.62 175 | 95.35 77 | 96.39 159 | 89.45 76 | 90.61 233 | 96.82 146 | 78.82 284 | 90.03 257 | 94.14 226 | 90.72 124 | 98.88 110 | 88.66 162 | 98.43 148 | 97.72 160 |
|
PatchMatch-RL | | | 89.18 221 | 88.02 236 | 92.64 175 | 95.90 207 | 92.87 42 | 88.67 288 | 91.06 279 | 80.34 267 | 90.03 257 | 91.67 280 | 83.34 224 | 94.42 317 | 76.35 290 | 94.84 283 | 90.64 333 |
|
BH-w/o | | | 87.21 263 | 87.02 254 | 87.79 292 | 94.77 248 | 77.27 263 | 87.90 293 | 93.21 252 | 81.74 260 | 89.99 259 | 88.39 318 | 83.47 223 | 96.93 268 | 71.29 320 | 92.43 316 | 89.15 336 |
|
tfpn_ndepth | | | 85.85 282 | 85.15 283 | 87.98 288 | 95.19 238 | 75.36 286 | 92.79 154 | 83.18 335 | 86.97 205 | 89.92 260 | 86.43 333 | 57.44 342 | 97.85 228 | 78.18 274 | 96.22 255 | 90.72 332 |
|
Fast-Effi-MVS+ | | | 91.28 191 | 90.86 194 | 92.53 183 | 95.45 227 | 82.53 178 | 89.25 278 | 96.52 162 | 85.00 230 | 89.91 261 | 88.55 316 | 92.94 74 | 98.84 122 | 84.72 213 | 95.44 271 | 96.22 229 |
|
AdaColmap | | | 91.63 180 | 91.36 184 | 92.47 185 | 95.56 223 | 86.36 134 | 92.24 179 | 96.27 177 | 88.88 161 | 89.90 262 | 92.69 259 | 91.65 98 | 98.32 191 | 77.38 283 | 97.64 208 | 92.72 313 |
|
mvs-test1 | | | 93.07 150 | 91.80 172 | 96.89 35 | 94.74 250 | 95.83 7 | 92.17 180 | 95.41 208 | 89.94 142 | 89.85 263 | 90.59 300 | 90.12 134 | 98.88 110 | 87.68 174 | 95.66 264 | 95.97 236 |
|
GA-MVS | | | 87.70 250 | 86.82 257 | 90.31 237 | 93.27 282 | 77.22 264 | 84.72 323 | 92.79 258 | 85.11 228 | 89.82 264 | 90.07 301 | 66.80 298 | 97.76 236 | 84.56 214 | 94.27 294 | 95.96 237 |
|
Patchmatch-test | | | 86.10 281 | 86.01 276 | 86.38 303 | 90.63 314 | 74.22 296 | 89.57 267 | 86.69 305 | 85.73 222 | 89.81 265 | 92.83 254 | 65.24 308 | 91.04 337 | 77.82 279 | 95.78 263 | 93.88 291 |
|
test12 | | | | | 94.43 113 | 95.95 203 | 86.75 126 | | 96.24 179 | | 89.76 266 | | 89.79 141 | 98.79 131 | | 97.95 195 | 97.75 159 |
|
pmmvs4 | | | 88.95 227 | 87.70 243 | 92.70 173 | 94.30 265 | 85.60 147 | 87.22 302 | 92.16 269 | 74.62 301 | 89.75 267 | 94.19 223 | 77.97 263 | 96.41 286 | 82.71 228 | 96.36 253 | 96.09 232 |
|
原ACMM1 | | | | | 92.87 164 | 96.91 126 | 84.22 160 | | 97.01 128 | 76.84 295 | 89.64 268 | 94.46 213 | 88.00 170 | 98.70 150 | 81.53 238 | 98.01 192 | 95.70 245 |
|
test1235678 | | | 84.54 289 | 83.85 291 | 86.59 300 | 93.81 276 | 73.41 300 | 82.38 332 | 91.79 274 | 79.43 274 | 89.50 269 | 91.61 282 | 70.59 286 | 92.94 331 | 58.14 346 | 97.40 221 | 93.44 302 |
|
MG-MVS | | | 89.54 217 | 89.80 209 | 88.76 273 | 94.88 242 | 72.47 313 | 89.60 266 | 92.44 265 | 85.82 220 | 89.48 270 | 95.98 156 | 82.85 229 | 97.74 238 | 81.87 234 | 95.27 275 | 96.08 233 |
|
114514_t | | | 90.51 199 | 89.80 209 | 92.63 176 | 98.00 76 | 82.24 180 | 93.40 134 | 97.29 113 | 65.84 341 | 89.40 271 | 94.80 204 | 86.99 192 | 98.75 139 | 83.88 219 | 98.61 135 | 96.89 201 |
|
Effi-MVS+ | | | 92.79 157 | 92.74 154 | 92.94 160 | 95.10 239 | 83.30 170 | 94.00 115 | 97.53 87 | 91.36 115 | 89.35 272 | 90.65 299 | 94.01 55 | 98.66 154 | 87.40 179 | 95.30 274 | 96.88 202 |
|
CR-MVSNet | | | 87.89 245 | 87.12 251 | 90.22 241 | 91.01 309 | 78.93 242 | 92.52 162 | 92.81 256 | 73.08 312 | 89.10 273 | 96.93 97 | 67.11 295 | 97.64 242 | 88.80 158 | 92.70 314 | 94.08 282 |
|
RPMNet | | | 89.30 220 | 89.00 217 | 90.22 241 | 91.01 309 | 78.93 242 | 92.52 162 | 87.85 298 | 91.91 95 | 89.10 273 | 96.89 100 | 68.84 290 | 97.64 242 | 90.17 130 | 92.70 314 | 94.08 282 |
|
PatchT | | | 87.51 255 | 88.17 232 | 85.55 307 | 90.64 313 | 66.91 330 | 92.02 184 | 86.09 309 | 92.20 88 | 89.05 275 | 97.16 87 | 64.15 312 | 96.37 289 | 89.21 152 | 92.98 312 | 93.37 304 |
|
MVSFormer | | | 92.18 174 | 92.23 163 | 92.04 198 | 94.74 250 | 80.06 210 | 97.15 13 | 97.37 100 | 88.98 157 | 88.83 276 | 92.79 255 | 77.02 270 | 99.60 8 | 96.41 6 | 96.75 239 | 96.46 220 |
|
lupinMVS | | | 88.34 237 | 87.31 245 | 91.45 214 | 94.74 250 | 80.06 210 | 87.23 301 | 92.27 266 | 71.10 321 | 88.83 276 | 91.15 286 | 77.02 270 | 98.53 173 | 86.67 188 | 96.75 239 | 95.76 242 |
|
HQP-NCC | | | | | | 96.36 165 | | 91.37 212 | | 87.16 201 | 88.81 278 | | | | | | |
|
ACMP_Plane | | | | | | 96.36 165 | | 91.37 212 | | 87.16 201 | 88.81 278 | | | | | | |
|
HQP4-MVS | | | | | | | | | | | 88.81 278 | | | 98.61 158 | | | 98.15 131 |
|
HQP-MVS | | | 92.09 175 | 91.49 180 | 93.88 130 | 96.36 165 | 84.89 153 | 91.37 212 | 97.31 110 | 87.16 201 | 88.81 278 | 93.40 247 | 84.76 218 | 98.60 160 | 86.55 191 | 97.73 202 | 98.14 132 |
|
PAPM_NR | | | 91.03 193 | 90.81 196 | 91.68 207 | 96.73 135 | 81.10 193 | 93.72 128 | 96.35 175 | 88.19 185 | 88.77 282 | 92.12 274 | 85.09 217 | 97.25 257 | 82.40 232 | 93.90 298 | 96.68 208 |
|
Patchmatch-test1 | | | 87.28 260 | 87.30 246 | 87.22 296 | 92.01 303 | 71.98 315 | 89.43 270 | 88.11 296 | 82.26 257 | 88.71 283 | 92.20 271 | 78.65 258 | 95.81 298 | 80.99 246 | 93.30 305 | 93.87 292 |
|
F-COLMAP | | | 92.28 172 | 91.06 191 | 95.95 53 | 97.52 102 | 91.90 51 | 93.53 131 | 97.18 120 | 83.98 238 | 88.70 284 | 94.04 229 | 88.41 158 | 98.55 172 | 80.17 253 | 95.99 258 | 97.39 180 |
|
PVSNet_BlendedMVS | | | 90.35 206 | 89.96 207 | 91.54 212 | 94.81 246 | 78.80 247 | 90.14 249 | 96.93 136 | 79.43 274 | 88.68 285 | 95.06 193 | 86.27 207 | 98.15 206 | 80.27 250 | 98.04 190 | 97.68 164 |
|
PVSNet_Blended | | | 88.74 232 | 88.16 233 | 90.46 234 | 94.81 246 | 78.80 247 | 86.64 310 | 96.93 136 | 74.67 300 | 88.68 285 | 89.18 313 | 86.27 207 | 98.15 206 | 80.27 250 | 96.00 257 | 94.44 277 |
|
pmmvs5 | | | 87.87 246 | 87.14 250 | 90.07 245 | 93.26 283 | 76.97 268 | 88.89 284 | 92.18 267 | 73.71 309 | 88.36 287 | 93.89 234 | 76.86 273 | 96.73 275 | 80.32 249 | 96.81 236 | 96.51 211 |
|
MVS_0304 | | | 92.99 152 | 92.54 159 | 94.35 116 | 94.67 255 | 86.06 140 | 91.16 218 | 97.92 55 | 90.01 141 | 88.33 288 | 94.41 214 | 87.02 190 | 99.22 62 | 90.36 123 | 99.00 101 | 97.76 158 |
|
WTY-MVS | | | 86.93 272 | 86.50 266 | 88.24 286 | 94.96 241 | 74.64 289 | 87.19 303 | 92.07 272 | 78.29 286 | 88.32 289 | 91.59 283 | 78.06 262 | 94.27 320 | 74.88 301 | 93.15 308 | 95.80 240 |
|
MIMVSNet | | | 87.13 267 | 86.54 263 | 88.89 271 | 96.05 189 | 76.11 274 | 94.39 105 | 88.51 290 | 81.37 262 | 88.27 290 | 96.75 108 | 72.38 281 | 95.52 301 | 65.71 338 | 95.47 270 | 95.03 262 |
|
test0.0.03 1 | | | 82.48 301 | 81.47 304 | 85.48 308 | 89.70 324 | 73.57 299 | 84.73 321 | 81.64 345 | 83.07 247 | 88.13 291 | 86.61 330 | 62.86 325 | 89.10 347 | 66.24 337 | 90.29 330 | 93.77 294 |
|
CMPMVS | | 68.83 22 | 87.28 260 | 85.67 279 | 92.09 196 | 88.77 335 | 85.42 149 | 90.31 243 | 94.38 229 | 70.02 328 | 88.00 292 | 93.30 249 | 73.78 279 | 94.03 323 | 75.96 293 | 96.54 247 | 96.83 205 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
PMMVS2 | | | 81.31 309 | 83.44 292 | 74.92 337 | 90.52 316 | 46.49 354 | 69.19 350 | 85.23 324 | 84.30 237 | 87.95 293 | 94.71 208 | 76.95 272 | 84.36 352 | 64.07 339 | 98.09 186 | 93.89 290 |
|
xiu_mvs_v2_base | | | 89.00 225 | 89.19 212 | 88.46 284 | 94.86 244 | 74.63 290 | 86.97 305 | 95.60 199 | 80.88 264 | 87.83 294 | 88.62 315 | 91.04 116 | 98.81 129 | 82.51 231 | 94.38 290 | 91.93 324 |
|
PS-MVSNAJ | | | 88.86 229 | 88.99 218 | 88.48 283 | 94.88 242 | 74.71 288 | 86.69 309 | 95.60 199 | 80.88 264 | 87.83 294 | 87.37 328 | 90.77 119 | 98.82 124 | 82.52 230 | 94.37 291 | 91.93 324 |
|
LP | | | 86.29 280 | 85.35 281 | 89.10 267 | 87.80 337 | 76.21 272 | 89.92 257 | 90.99 280 | 84.86 233 | 87.66 296 | 92.32 269 | 70.40 287 | 96.48 282 | 81.94 233 | 82.24 346 | 94.63 272 |
|
tpm | | | 84.38 291 | 84.08 288 | 85.30 312 | 90.47 317 | 63.43 345 | 89.34 273 | 85.63 315 | 77.24 293 | 87.62 297 | 95.03 196 | 61.00 331 | 97.30 256 | 79.26 262 | 91.09 328 | 95.16 258 |
|
sss | | | 87.23 262 | 86.82 257 | 88.46 284 | 93.96 270 | 77.94 253 | 86.84 307 | 92.78 259 | 77.59 289 | 87.61 298 | 91.83 277 | 78.75 257 | 91.92 334 | 77.84 277 | 94.20 295 | 95.52 254 |
|
MAR-MVS | | | 90.32 208 | 88.87 221 | 94.66 98 | 94.82 245 | 91.85 52 | 94.22 111 | 94.75 220 | 80.91 263 | 87.52 299 | 88.07 320 | 86.63 202 | 97.87 224 | 76.67 287 | 96.21 256 | 94.25 280 |
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 |
UGNet | | | 93.08 148 | 92.50 161 | 94.79 95 | 93.87 273 | 87.99 108 | 95.07 79 | 94.26 232 | 90.64 129 | 87.33 300 | 97.67 61 | 86.89 197 | 98.49 176 | 88.10 170 | 98.71 130 | 97.91 147 |
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 |
PatchmatchNet | | | 85.22 285 | 84.64 285 | 86.98 298 | 89.51 328 | 69.83 323 | 90.52 237 | 87.34 302 | 78.87 282 | 87.22 301 | 92.74 257 | 66.91 297 | 96.53 279 | 81.77 235 | 86.88 337 | 94.58 273 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
testus | | | 82.09 305 | 81.78 300 | 83.03 324 | 92.35 294 | 64.37 343 | 79.44 340 | 93.27 249 | 73.08 312 | 87.06 302 | 85.21 338 | 76.80 274 | 89.27 345 | 53.30 349 | 95.48 269 | 95.46 255 |
|
1112_ss | | | 88.42 236 | 87.41 244 | 91.45 214 | 96.69 137 | 80.99 194 | 89.72 264 | 96.72 153 | 73.37 310 | 87.00 303 | 90.69 297 | 77.38 267 | 98.20 202 | 81.38 239 | 93.72 301 | 95.15 259 |
|
cascas | | | 87.02 269 | 86.28 268 | 89.25 265 | 91.56 306 | 76.45 270 | 84.33 326 | 96.78 149 | 71.01 322 | 86.89 304 | 85.91 335 | 81.35 242 | 96.94 267 | 83.09 225 | 95.60 265 | 94.35 279 |
|
CANet | | | 92.38 170 | 91.99 168 | 93.52 142 | 93.82 275 | 83.46 168 | 91.14 219 | 97.00 129 | 89.81 145 | 86.47 305 | 94.04 229 | 87.90 173 | 99.21 63 | 89.50 143 | 98.27 166 | 97.90 148 |
|
Test_1112_low_res | | | 87.50 256 | 86.58 261 | 90.25 240 | 96.80 132 | 77.75 257 | 87.53 299 | 96.25 178 | 69.73 329 | 86.47 305 | 93.61 240 | 75.67 276 | 97.88 222 | 79.95 255 | 93.20 306 | 95.11 261 |
|
PLC | | 85.34 15 | 90.40 203 | 88.92 219 | 94.85 92 | 96.53 149 | 90.02 69 | 91.58 208 | 96.48 164 | 80.16 269 | 86.14 307 | 92.18 272 | 85.73 212 | 98.25 198 | 76.87 286 | 94.61 288 | 96.30 226 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
new_pmnet | | | 81.22 310 | 81.01 309 | 81.86 328 | 90.92 311 | 70.15 321 | 84.03 327 | 80.25 350 | 70.83 324 | 85.97 308 | 89.78 307 | 67.93 294 | 84.65 351 | 67.44 332 | 91.90 323 | 90.78 331 |
|
diffmvs | | | 90.45 201 | 90.49 201 | 90.34 236 | 92.25 296 | 77.09 265 | 91.80 205 | 95.96 189 | 82.68 250 | 85.83 309 | 95.07 192 | 87.01 191 | 97.09 262 | 89.68 140 | 94.10 297 | 96.83 205 |
|
EPMVS | | | 81.17 312 | 80.37 313 | 83.58 321 | 85.58 350 | 65.08 339 | 90.31 243 | 71.34 354 | 77.31 292 | 85.80 310 | 91.30 284 | 59.38 333 | 92.70 332 | 79.99 254 | 82.34 345 | 92.96 308 |
|
tpmvs | | | 84.22 292 | 83.97 289 | 84.94 313 | 87.09 344 | 65.18 337 | 91.21 217 | 88.35 291 | 82.87 249 | 85.21 311 | 90.96 290 | 65.24 308 | 96.75 274 | 79.60 261 | 85.25 338 | 92.90 309 |
|
FPMVS | | | 84.50 290 | 83.28 293 | 88.16 287 | 96.32 170 | 94.49 11 | 85.76 315 | 85.47 317 | 83.09 246 | 85.20 312 | 94.26 220 | 63.79 315 | 86.58 350 | 63.72 341 | 91.88 324 | 83.40 345 |
|
pmmvs3 | | | 80.83 313 | 78.96 320 | 86.45 302 | 87.23 343 | 77.48 260 | 84.87 320 | 82.31 342 | 63.83 345 | 85.03 313 | 89.50 311 | 49.66 351 | 93.10 328 | 73.12 308 | 95.10 278 | 88.78 340 |
|
PAPR | | | 87.65 253 | 86.77 259 | 90.27 239 | 92.85 287 | 77.38 261 | 88.56 289 | 96.23 180 | 76.82 296 | 84.98 314 | 89.75 308 | 86.08 209 | 97.16 260 | 72.33 312 | 93.35 304 | 96.26 228 |
|
MDTV_nov1_ep13 | | | | 83.88 290 | | 89.42 329 | 61.52 346 | 88.74 286 | 87.41 301 | 73.99 307 | 84.96 315 | 94.01 232 | 65.25 307 | 95.53 300 | 78.02 275 | 93.16 307 | |
|
1314 | | | 86.46 279 | 86.33 267 | 86.87 299 | 91.65 305 | 74.54 291 | 91.94 188 | 94.10 234 | 74.28 304 | 84.78 316 | 87.33 329 | 83.03 227 | 95.00 312 | 78.72 271 | 91.16 327 | 91.06 330 |
|
ADS-MVSNet2 | | | 84.01 293 | 82.20 299 | 89.41 260 | 89.04 332 | 76.37 271 | 87.57 296 | 90.98 281 | 72.71 315 | 84.46 317 | 92.45 264 | 68.08 291 | 96.48 282 | 70.58 326 | 83.97 339 | 95.38 256 |
|
ADS-MVSNet | | | 82.25 302 | 81.55 303 | 84.34 318 | 89.04 332 | 65.30 336 | 87.57 296 | 85.13 325 | 72.71 315 | 84.46 317 | 92.45 264 | 68.08 291 | 92.33 333 | 70.58 326 | 83.97 339 | 95.38 256 |
|
PVSNet | | 76.22 20 | 82.89 298 | 82.37 297 | 84.48 317 | 93.96 270 | 64.38 342 | 78.60 342 | 88.61 289 | 71.50 319 | 84.43 319 | 86.36 334 | 74.27 278 | 94.60 314 | 69.87 328 | 93.69 302 | 94.46 276 |
|
MVS | | | 84.98 288 | 84.30 287 | 87.01 297 | 91.03 308 | 77.69 259 | 91.94 188 | 94.16 233 | 59.36 349 | 84.23 320 | 87.50 327 | 85.66 213 | 96.80 273 | 71.79 315 | 93.05 311 | 86.54 342 |
|
tpmrst | | | 82.85 299 | 82.93 296 | 82.64 326 | 87.65 338 | 58.99 349 | 90.14 249 | 87.90 297 | 75.54 298 | 83.93 321 | 91.63 281 | 66.79 300 | 95.36 306 | 81.21 242 | 81.54 347 | 93.57 301 |
|
PatchFormer-LS_test | | | 82.62 300 | 81.71 301 | 85.32 311 | 87.92 336 | 67.31 328 | 89.03 281 | 88.20 294 | 77.58 290 | 83.79 322 | 80.50 349 | 60.96 332 | 96.42 285 | 83.86 220 | 83.59 341 | 92.23 321 |
|
HY-MVS | | 82.50 18 | 86.81 274 | 85.93 277 | 89.47 254 | 93.63 277 | 77.93 254 | 94.02 114 | 91.58 276 | 75.68 297 | 83.64 323 | 93.64 238 | 77.40 266 | 97.42 250 | 71.70 317 | 92.07 321 | 93.05 307 |
|
MDTV_nov1_ep13_2view | | | | | | | 42.48 357 | 88.45 290 | | 67.22 338 | 83.56 324 | | 66.80 298 | | 72.86 310 | | 94.06 284 |
|
CostFormer | | | 83.09 296 | 82.21 298 | 85.73 306 | 89.27 331 | 67.01 329 | 90.35 241 | 86.47 307 | 70.42 326 | 83.52 325 | 93.23 250 | 61.18 329 | 96.85 271 | 77.21 284 | 88.26 335 | 93.34 305 |
|
DSMNet-mixed | | | 82.21 303 | 81.56 302 | 84.16 319 | 89.57 327 | 70.00 322 | 90.65 232 | 77.66 352 | 54.99 352 | 83.30 326 | 97.57 64 | 77.89 264 | 90.50 341 | 66.86 334 | 95.54 267 | 91.97 323 |
|
E-PMN | | | 80.72 315 | 80.86 310 | 80.29 331 | 85.11 351 | 68.77 325 | 72.96 346 | 81.97 344 | 87.76 193 | 83.25 327 | 83.01 345 | 62.22 328 | 89.17 346 | 77.15 285 | 94.31 293 | 82.93 346 |
|
test-LLR | | | 83.58 294 | 83.17 294 | 84.79 315 | 89.68 325 | 66.86 332 | 83.08 329 | 84.52 327 | 83.07 247 | 82.85 328 | 84.78 339 | 62.86 325 | 93.49 326 | 82.85 226 | 94.86 281 | 94.03 285 |
|
test-mter | | | 81.21 311 | 80.01 317 | 84.79 315 | 89.68 325 | 66.86 332 | 83.08 329 | 84.52 327 | 73.85 308 | 82.85 328 | 84.78 339 | 43.66 357 | 93.49 326 | 82.85 226 | 94.86 281 | 94.03 285 |
|
CANet_DTU | | | 89.85 214 | 89.17 213 | 91.87 201 | 92.20 299 | 80.02 214 | 90.79 228 | 95.87 192 | 86.02 216 | 82.53 330 | 91.77 278 | 80.01 252 | 98.57 165 | 85.66 200 | 97.70 205 | 97.01 195 |
|
PNet_i23d | | | 72.03 328 | 70.91 329 | 75.38 336 | 90.46 318 | 57.84 350 | 71.73 349 | 81.53 346 | 83.86 240 | 82.21 331 | 83.49 343 | 29.97 362 | 87.80 349 | 60.78 343 | 54.12 354 | 80.51 349 |
|
JIA-IIPM | | | 85.08 287 | 83.04 295 | 91.19 223 | 87.56 339 | 86.14 138 | 89.40 272 | 84.44 333 | 88.98 157 | 82.20 332 | 97.95 48 | 56.82 345 | 96.15 292 | 76.55 289 | 83.45 342 | 91.30 328 |
|
PMMVS | | | 83.00 297 | 81.11 306 | 88.66 276 | 83.81 355 | 86.44 131 | 82.24 334 | 85.65 314 | 61.75 348 | 82.07 333 | 85.64 336 | 79.75 253 | 91.59 336 | 75.99 292 | 93.09 309 | 87.94 341 |
|
tpm2 | | | 81.46 308 | 80.35 314 | 84.80 314 | 89.90 323 | 65.14 338 | 90.44 239 | 85.36 318 | 65.82 342 | 82.05 334 | 92.44 266 | 57.94 341 | 96.69 276 | 70.71 325 | 88.49 334 | 92.56 314 |
|
IB-MVS | | 77.21 19 | 83.11 295 | 81.05 307 | 89.29 263 | 91.15 307 | 75.85 277 | 85.66 316 | 86.00 311 | 79.70 272 | 82.02 335 | 86.61 330 | 48.26 353 | 98.39 185 | 77.84 277 | 92.22 319 | 93.63 297 |
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 |
tpm cat1 | | | 80.61 316 | 79.46 318 | 84.07 320 | 88.78 334 | 65.06 340 | 89.26 276 | 88.23 293 | 62.27 347 | 81.90 336 | 89.66 310 | 62.70 327 | 95.29 309 | 71.72 316 | 80.60 348 | 91.86 326 |
|
EMVS | | | 80.35 318 | 80.28 315 | 80.54 330 | 84.73 353 | 69.07 324 | 72.54 348 | 80.73 347 | 87.80 192 | 81.66 337 | 81.73 346 | 62.89 324 | 89.84 343 | 75.79 298 | 94.65 287 | 82.71 347 |
|
dp | | | 79.28 320 | 78.62 321 | 81.24 329 | 85.97 349 | 56.45 351 | 86.91 306 | 85.26 323 | 72.97 314 | 81.45 338 | 89.17 314 | 56.01 348 | 95.45 304 | 73.19 307 | 76.68 350 | 91.82 327 |
|
DWT-MVSNet_test | | | 80.74 314 | 79.18 319 | 85.43 309 | 87.51 341 | 66.87 331 | 89.87 261 | 86.01 310 | 74.20 306 | 80.86 339 | 80.62 348 | 48.84 352 | 96.68 278 | 81.54 237 | 83.14 344 | 92.75 312 |
|
test2356 | | | 75.58 324 | 73.13 326 | 82.95 325 | 86.10 348 | 66.42 334 | 75.07 343 | 84.87 326 | 70.91 323 | 80.85 340 | 80.66 347 | 38.02 359 | 88.98 348 | 49.32 352 | 92.35 317 | 93.44 302 |
|
EPNet | | | 89.80 215 | 88.25 228 | 94.45 112 | 83.91 354 | 86.18 137 | 93.87 124 | 87.07 304 | 91.16 120 | 80.64 341 | 94.72 207 | 78.83 256 | 98.89 106 | 85.17 203 | 98.89 107 | 98.28 122 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
test12356 | | | 76.35 323 | 77.41 324 | 73.19 339 | 90.70 312 | 38.86 358 | 74.56 344 | 91.14 278 | 74.55 302 | 80.54 342 | 88.18 319 | 52.36 350 | 90.49 342 | 52.38 351 | 92.26 318 | 90.21 335 |
|
TESTMET0.1,1 | | | 79.09 321 | 78.04 322 | 82.25 327 | 87.52 340 | 64.03 344 | 83.08 329 | 80.62 348 | 70.28 327 | 80.16 343 | 83.22 344 | 44.13 356 | 90.56 340 | 79.95 255 | 93.36 303 | 92.15 322 |
|
gg-mvs-nofinetune | | | 82.10 304 | 81.02 308 | 85.34 310 | 87.46 342 | 71.04 317 | 94.74 90 | 67.56 355 | 96.44 19 | 79.43 344 | 98.99 6 | 45.24 354 | 96.15 292 | 67.18 333 | 92.17 320 | 88.85 338 |
|
tpmp4_e23 | | | 81.87 307 | 80.41 312 | 86.27 304 | 89.29 330 | 67.84 327 | 91.58 208 | 87.61 300 | 67.42 336 | 78.60 345 | 92.71 258 | 56.42 346 | 96.87 270 | 71.44 319 | 88.63 333 | 94.10 281 |
|
PVSNet_0 | | 70.34 21 | 74.58 325 | 72.96 327 | 79.47 332 | 90.63 314 | 66.24 335 | 73.26 345 | 83.40 334 | 63.67 346 | 78.02 346 | 78.35 350 | 72.53 280 | 89.59 344 | 56.68 347 | 60.05 353 | 82.57 348 |
|
MVS-HIRNet | | | 78.83 322 | 80.60 311 | 73.51 338 | 93.07 285 | 47.37 353 | 87.10 304 | 78.00 351 | 68.94 331 | 77.53 347 | 97.26 82 | 71.45 284 | 94.62 313 | 63.28 342 | 88.74 332 | 78.55 350 |
|
CHOSEN 280x420 | | | 80.04 319 | 77.97 323 | 86.23 305 | 90.13 321 | 74.53 292 | 72.87 347 | 89.59 286 | 66.38 339 | 76.29 348 | 85.32 337 | 56.96 344 | 95.36 306 | 69.49 329 | 94.72 285 | 88.79 339 |
|
PAPM | | | 81.91 306 | 80.11 316 | 87.31 295 | 93.87 273 | 72.32 314 | 84.02 328 | 93.22 250 | 69.47 330 | 76.13 349 | 89.84 303 | 72.15 282 | 97.23 258 | 53.27 350 | 89.02 331 | 92.37 316 |
|
testpf | | | 74.01 326 | 76.37 325 | 66.95 340 | 80.56 356 | 60.00 347 | 88.43 291 | 75.07 353 | 81.54 261 | 75.75 350 | 83.73 341 | 38.93 358 | 83.09 353 | 84.01 217 | 79.32 349 | 57.75 352 |
|
GG-mvs-BLEND | | | | | 83.24 323 | 85.06 352 | 71.03 318 | 94.99 84 | 65.55 356 | | 74.09 351 | 75.51 351 | 44.57 355 | 94.46 316 | 59.57 345 | 87.54 336 | 84.24 344 |
|
EPNet_dtu | | | 85.63 284 | 84.37 286 | 89.40 261 | 86.30 347 | 74.33 295 | 91.64 207 | 88.26 292 | 84.84 234 | 72.96 352 | 89.85 302 | 71.27 285 | 97.69 240 | 76.60 288 | 97.62 209 | 96.18 230 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
MVE | | 59.87 23 | 73.86 327 | 72.65 328 | 77.47 335 | 87.00 346 | 74.35 294 | 61.37 352 | 60.93 357 | 67.27 337 | 69.69 353 | 86.49 332 | 81.24 247 | 72.33 355 | 56.45 348 | 83.45 342 | 85.74 343 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
DeepMVS_CX | | | | | 53.83 341 | 70.38 357 | 64.56 341 | | 48.52 359 | 33.01 353 | 65.50 354 | 74.21 352 | 56.19 347 | 46.64 356 | 38.45 354 | 70.07 351 | 50.30 353 |
|
tmp_tt | | | 37.97 331 | 44.33 331 | 18.88 344 | 11.80 358 | 21.54 359 | 63.51 351 | 45.66 360 | 4.23 354 | 51.34 355 | 50.48 353 | 59.08 334 | 22.11 357 | 44.50 353 | 68.35 352 | 13.00 354 |
|
test123 | | | 9.49 333 | 12.01 334 | 1.91 345 | 2.87 359 | 1.30 360 | 82.38 332 | 1.34 362 | 1.36 355 | 2.84 356 | 6.56 356 | 2.45 363 | 0.97 358 | 2.73 355 | 5.56 355 | 3.47 355 |
|
testmvs | | | 9.02 334 | 11.42 335 | 1.81 346 | 2.77 360 | 1.13 361 | 79.44 340 | 1.90 361 | 1.18 356 | 2.65 357 | 6.80 355 | 1.95 364 | 0.87 359 | 2.62 356 | 3.45 356 | 3.44 356 |
|
cdsmvs_eth3d_5k | | | 23.35 332 | 31.13 333 | 0.00 347 | 0.00 361 | 0.00 362 | 0.00 353 | 95.58 203 | 0.00 357 | 0.00 358 | 91.15 286 | 93.43 62 | 0.00 360 | 0.00 357 | 0.00 358 | 0.00 358 |
|
pcd_1.5k_mvsjas | | | 7.56 335 | 10.09 336 | 0.00 347 | 0.00 361 | 0.00 362 | 0.00 353 | 0.00 363 | 0.00 357 | 0.00 358 | 0.00 359 | 90.77 119 | 0.00 360 | 0.00 357 | 0.00 358 | 0.00 358 |
|
pcd1.5k->3k | | | 41.03 330 | 43.65 332 | 33.18 343 | 98.74 26 | 0.00 362 | 0.00 353 | 97.57 82 | 0.00 357 | 0.00 358 | 0.00 359 | 97.01 6 | 0.00 360 | 0.00 357 | 99.52 45 | 99.53 17 |
|
sosnet-low-res | | | 0.00 337 | 0.00 338 | 0.00 347 | 0.00 361 | 0.00 362 | 0.00 353 | 0.00 363 | 0.00 357 | 0.00 358 | 0.00 359 | 0.00 365 | 0.00 360 | 0.00 357 | 0.00 358 | 0.00 358 |
|
sosnet | | | 0.00 337 | 0.00 338 | 0.00 347 | 0.00 361 | 0.00 362 | 0.00 353 | 0.00 363 | 0.00 357 | 0.00 358 | 0.00 359 | 0.00 365 | 0.00 360 | 0.00 357 | 0.00 358 | 0.00 358 |
|
uncertanet | | | 0.00 337 | 0.00 338 | 0.00 347 | 0.00 361 | 0.00 362 | 0.00 353 | 0.00 363 | 0.00 357 | 0.00 358 | 0.00 359 | 0.00 365 | 0.00 360 | 0.00 357 | 0.00 358 | 0.00 358 |
|
Regformer | | | 0.00 337 | 0.00 338 | 0.00 347 | 0.00 361 | 0.00 362 | 0.00 353 | 0.00 363 | 0.00 357 | 0.00 358 | 0.00 359 | 0.00 365 | 0.00 360 | 0.00 357 | 0.00 358 | 0.00 358 |
|
ab-mvs-re | | | 7.56 335 | 10.08 337 | 0.00 347 | 0.00 361 | 0.00 362 | 0.00 353 | 0.00 363 | 0.00 357 | 0.00 358 | 90.69 297 | 0.00 365 | 0.00 360 | 0.00 357 | 0.00 358 | 0.00 358 |
|
uanet | | | 0.00 337 | 0.00 338 | 0.00 347 | 0.00 361 | 0.00 362 | 0.00 353 | 0.00 363 | 0.00 357 | 0.00 358 | 0.00 359 | 0.00 365 | 0.00 360 | 0.00 357 | 0.00 358 | 0.00 358 |
|
GSMVS | | | | | | | | | | | | | | | | | 94.75 269 |
|
test_part3 | | | | | | | | 93.92 122 | | 91.83 101 | | 96.39 131 | | 99.44 24 | 89.00 154 | | |
|
test_part1 | | | | | | | | | 98.14 28 | | | | 94.69 45 | | | 99.10 91 | 98.17 128 |
|
sam_mvs1 | | | | | | | | | | | | | 66.64 301 | | | | 94.75 269 |
|
sam_mvs | | | | | | | | | | | | | 66.41 302 | | | | |
|
MTGPA | | | | | | | | | 97.62 75 | | | | | | | | |
|
test_post1 | | | | | | | | 90.21 245 | | | | 5.85 358 | 65.36 306 | 96.00 295 | 79.61 260 | | |
|
test_post | | | | | | | | | | | | 6.07 357 | 65.74 305 | 95.84 297 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 91.71 279 | 66.22 304 | 97.59 244 | | | |
|
MTMP | | | | | | | | | 54.62 358 | | | | | | | | |
|
gm-plane-assit | | | | | | 87.08 345 | 59.33 348 | | | 71.22 320 | | 83.58 342 | | 97.20 259 | 73.95 302 | | |
|
test9_res | | | | | | | | | | | | | | | 88.16 169 | 98.40 150 | 97.83 154 |
|
agg_prior2 | | | | | | | | | | | | | | | 87.06 183 | 98.36 158 | 97.98 140 |
|
test_prior4 | | | | | | | 89.91 71 | 90.74 229 | | | | | | | | | |
|
test_prior | | | | | 94.61 99 | 95.95 203 | 87.23 117 | | 97.36 106 | | | | | 98.68 152 | | | 97.93 144 |
|
新几何2 | | | | | | | | 90.02 254 | | | | | | | | | |
|
旧先验1 | | | | | | 96.20 179 | 84.17 161 | | 94.82 217 | | | 95.57 173 | 89.57 143 | | | 97.89 198 | 96.32 225 |
|
无先验 | | | | | | | | 89.94 256 | 95.75 196 | 70.81 325 | | | | 98.59 162 | 81.17 243 | | 94.81 266 |
|
原ACMM2 | | | | | | | | 89.34 273 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 98.03 210 | 80.24 252 | | |
|
segment_acmp | | | | | | | | | | | | | 92.14 88 | | | | |
|
testdata1 | | | | | | | | 88.96 283 | | 88.44 178 | | | | | | | |
|
plane_prior7 | | | | | | 97.71 91 | 88.68 92 | | | | | | | | | | |
|
plane_prior6 | | | | | | 97.21 111 | 88.23 105 | | | | | | 86.93 194 | | | | |
|
plane_prior5 | | | | | | | | | 97.81 63 | | | | | 98.95 100 | 89.26 149 | 98.51 143 | 98.60 109 |
|
plane_prior4 | | | | | | | | | | | | 95.59 169 | | | | | |
|
plane_prior2 | | | | | | | | 94.56 100 | | 91.74 108 | | | | | | | |
|
plane_prior1 | | | | | | 97.38 106 | | | | | | | | | | | |
|
plane_prior | | | | | | | 88.12 106 | 93.01 145 | | 88.98 157 | | | | | | 98.06 188 | |
|
n2 | | | | | | | | | 0.00 363 | | | | | | | | |
|
nn | | | | | | | | | 0.00 363 | | | | | | | | |
|
door-mid | | | | | | | | | 92.13 271 | | | | | | | | |
|
test11 | | | | | | | | | 96.65 155 | | | | | | | | |
|
door | | | | | | | | | 91.26 277 | | | | | | | | |
|
HQP5-MVS | | | | | | | 84.89 153 | | | | | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 86.55 191 | | |
|
HQP3-MVS | | | | | | | | | 97.31 110 | | | | | | | 97.73 202 | |
|
HQP2-MVS | | | | | | | | | | | | | 84.76 218 | | | | |
|
NP-MVS | | | | | | 96.82 130 | 87.10 120 | | | | | 93.40 247 | | | | | |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 98.82 120 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 99.25 76 | |
|
Test By Simon | | | | | | | | | | | | | 90.61 126 | | | | |
|