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 |
|
PEN-MVS | | | 96.69 19 | 97.39 7 | 94.61 100 | 99.16 2 | 84.50 158 | 96.54 30 | 98.05 37 | 98.06 3 | 98.64 13 | 98.25 39 | 95.01 38 | 99.65 3 | 92.95 76 | 99.83 7 | 99.68 4 |
|
MIMVSNet1 | | | 95.52 60 | 95.45 72 | 95.72 65 | 99.14 3 | 89.02 84 | 96.23 47 | 96.87 147 | 93.73 54 | 97.87 33 | 98.49 26 | 90.73 125 | 99.05 83 | 86.43 197 | 99.60 33 | 99.10 56 |
|
PS-CasMVS | | | 96.69 19 | 97.43 4 | 94.49 111 | 99.13 4 | 84.09 165 | 96.61 26 | 97.97 48 | 97.91 4 | 98.64 13 | 98.13 41 | 95.24 30 | 99.65 3 | 93.39 61 | 99.84 5 | 99.72 2 |
|
DTE-MVSNet | | | 96.74 17 | 97.43 4 | 94.67 98 | 99.13 4 | 84.68 157 | 96.51 31 | 97.94 54 | 98.14 2 | 98.67 12 | 98.32 36 | 95.04 35 | 99.69 2 | 93.27 65 | 99.82 9 | 99.62 10 |
|
pmmvs6 | | | 96.80 13 | 97.36 8 | 95.15 86 | 99.12 6 | 87.82 112 | 96.68 24 | 97.86 57 | 96.10 25 | 98.14 26 | 99.28 2 | 97.94 3 | 98.21 203 | 91.38 116 | 99.69 16 | 99.42 27 |
|
HPM-MVS_fast | | | 97.01 6 | 96.89 17 | 97.39 18 | 99.12 6 | 93.92 24 | 97.16 12 | 98.17 26 | 93.11 65 | 96.48 78 | 97.36 81 | 96.92 6 | 99.34 48 | 94.31 33 | 99.38 65 | 98.92 83 |
|
MP-MVS-pluss | | | 96.08 48 | 95.92 54 | 96.57 41 | 99.06 8 | 91.21 59 | 93.25 146 | 98.32 12 | 87.89 194 | 96.86 64 | 97.38 78 | 95.55 20 | 99.39 40 | 95.47 13 | 99.47 49 | 99.11 53 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
OurMVSNet-221017-0 | | | 96.80 13 | 96.75 20 | 96.96 32 | 99.03 9 | 91.85 52 | 97.98 5 | 98.01 43 | 94.15 46 | 98.93 3 | 99.07 4 | 88.07 173 | 99.57 13 | 95.86 11 | 99.69 16 | 99.46 25 |
|
WR-MVS_H | | | 96.60 24 | 97.05 15 | 95.24 82 | 99.02 10 | 86.44 131 | 96.78 23 | 98.08 32 | 97.42 7 | 98.48 18 | 97.86 57 | 91.76 98 | 99.63 6 | 94.23 37 | 99.84 5 | 99.66 6 |
|
TDRefinement | | | 97.68 3 | 97.60 3 | 97.93 2 | 99.02 10 | 95.95 6 | 98.61 3 | 98.81 4 | 97.41 8 | 97.28 50 | 98.46 29 | 94.62 45 | 98.84 123 | 94.64 26 | 99.53 44 | 98.99 71 |
|
CP-MVSNet | | | 96.19 45 | 96.80 19 | 94.38 117 | 98.99 12 | 83.82 167 | 96.31 42 | 97.53 88 | 97.60 5 | 98.34 22 | 97.52 70 | 91.98 94 | 99.63 6 | 93.08 74 | 99.81 10 | 99.70 3 |
|
PMVS | | 87.21 14 | 94.97 84 | 95.33 79 | 93.91 131 | 98.97 13 | 97.16 2 | 95.54 66 | 95.85 195 | 96.47 19 | 93.40 186 | 97.46 73 | 95.31 27 | 95.47 308 | 86.18 200 | 98.78 127 | 89.11 342 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
zzz-MVS | | | 96.47 31 | 96.14 41 | 97.47 11 | 98.95 14 | 94.05 18 | 93.69 133 | 97.62 76 | 94.46 42 | 96.29 86 | 96.94 98 | 93.56 57 | 99.37 44 | 94.29 35 | 99.42 57 | 98.99 71 |
|
MTAPA | | | 96.65 21 | 96.38 32 | 97.47 11 | 98.95 14 | 94.05 18 | 95.88 56 | 97.62 76 | 94.46 42 | 96.29 86 | 96.94 98 | 93.56 57 | 99.37 44 | 94.29 35 | 99.42 57 | 98.99 71 |
|
ACMMP_Plus | | | 96.21 44 | 96.12 43 | 96.49 45 | 98.90 16 | 91.42 57 | 94.57 102 | 98.03 40 | 90.42 139 | 96.37 81 | 97.35 82 | 95.68 18 | 99.25 60 | 94.44 31 | 99.34 67 | 98.80 93 |
|
HPM-MVS | | | 96.81 12 | 96.62 25 | 97.36 20 | 98.89 17 | 93.53 34 | 97.51 8 | 98.44 7 | 92.35 84 | 95.95 105 | 96.41 131 | 96.71 8 | 99.42 28 | 93.99 43 | 99.36 66 | 99.13 51 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
VDDNet | | | 94.03 120 | 94.27 113 | 93.31 148 | 98.87 18 | 82.36 183 | 95.51 67 | 91.78 278 | 97.19 10 | 96.32 83 | 98.60 20 | 84.24 224 | 98.75 142 | 87.09 185 | 98.83 119 | 98.81 92 |
|
TSAR-MVS + MP. | | | 94.96 85 | 94.75 95 | 95.57 71 | 98.86 19 | 88.69 90 | 96.37 39 | 96.81 149 | 85.23 228 | 94.75 152 | 97.12 92 | 91.85 96 | 99.40 36 | 93.45 57 | 98.33 162 | 98.62 109 |
|
mvs_tets | | | 96.83 9 | 96.71 21 | 97.17 25 | 98.83 20 | 92.51 43 | 96.58 28 | 97.61 79 | 87.57 200 | 98.80 7 | 98.90 9 | 96.50 10 | 99.59 12 | 96.15 9 | 99.47 49 | 99.40 31 |
|
PS-MVSNAJss | | | 96.01 50 | 96.04 49 | 95.89 57 | 98.82 21 | 88.51 98 | 95.57 64 | 97.88 56 | 88.72 173 | 98.81 6 | 98.86 10 | 90.77 121 | 99.60 8 | 95.43 14 | 99.53 44 | 99.57 14 |
|
MP-MVS | | | 96.14 46 | 95.68 65 | 97.51 10 | 98.81 22 | 94.06 16 | 96.10 48 | 97.78 67 | 92.73 72 | 93.48 183 | 96.72 114 | 94.23 51 | 99.42 28 | 91.99 100 | 99.29 73 | 99.05 64 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
LTVRE_ROB | | 93.87 1 | 97.93 2 | 98.16 2 | 97.26 23 | 98.81 22 | 93.86 27 | 99.07 2 | 98.98 3 | 97.01 11 | 98.92 4 | 98.78 14 | 95.22 31 | 98.61 161 | 96.85 4 | 99.77 11 | 99.31 39 |
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 |
jajsoiax | | | 96.59 26 | 96.42 30 | 97.12 27 | 98.76 24 | 92.49 44 | 96.44 36 | 97.42 97 | 86.96 210 | 98.71 10 | 98.72 17 | 95.36 25 | 99.56 16 | 95.92 10 | 99.45 53 | 99.32 38 |
|
pcd1.5k->3k | | | 41.03 335 | 43.65 337 | 33.18 348 | 98.74 25 | 0.00 367 | 0.00 359 | 97.57 83 | 0.00 362 | 0.00 363 | 0.00 364 | 97.01 5 | 0.00 365 | 0.00 362 | 99.52 46 | 99.53 16 |
|
HSP-MVS | | | 95.18 77 | 94.49 104 | 97.23 24 | 98.67 26 | 94.05 18 | 96.41 38 | 97.00 131 | 91.26 119 | 95.12 138 | 95.15 192 | 86.60 206 | 99.50 18 | 93.43 59 | 96.81 239 | 98.13 135 |
|
SteuartSystems-ACMMP | | | 96.40 37 | 96.30 34 | 96.71 38 | 98.63 27 | 91.96 50 | 95.70 60 | 98.01 43 | 93.34 63 | 96.64 73 | 96.57 121 | 94.99 39 | 99.36 46 | 93.48 55 | 99.34 67 | 98.82 91 |
Skip Steuart: Steuart Systems R&D Blog. |
region2R | | | 96.41 36 | 96.09 45 | 97.38 19 | 98.62 28 | 93.81 31 | 96.32 41 | 97.96 49 | 92.26 87 | 95.28 132 | 96.57 121 | 95.02 37 | 99.41 32 | 93.63 50 | 99.11 91 | 98.94 79 |
|
mPP-MVS | | | 96.46 32 | 96.05 48 | 97.69 5 | 98.62 28 | 94.65 9 | 96.45 34 | 97.74 69 | 92.59 78 | 95.47 125 | 96.68 116 | 94.50 48 | 99.42 28 | 93.10 72 | 99.26 76 | 98.99 71 |
|
ACMMP | | | 96.61 23 | 96.34 33 | 97.43 15 | 98.61 30 | 93.88 25 | 96.95 18 | 98.18 25 | 92.26 87 | 96.33 82 | 96.84 107 | 95.10 34 | 99.40 36 | 93.47 56 | 99.33 69 | 99.02 68 |
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 |
VPNet | | | 93.08 150 | 93.76 128 | 91.03 228 | 98.60 31 | 75.83 283 | 91.51 215 | 95.62 201 | 91.84 102 | 95.74 117 | 97.10 93 | 89.31 149 | 98.32 194 | 85.07 213 | 99.06 95 | 98.93 80 |
|
ACMMPR | | | 96.46 32 | 96.14 41 | 97.41 17 | 98.60 31 | 93.82 29 | 96.30 44 | 97.96 49 | 92.35 84 | 95.57 123 | 96.61 119 | 94.93 41 | 99.41 32 | 93.78 46 | 99.15 87 | 99.00 69 |
|
PGM-MVS | | | 96.32 41 | 95.94 52 | 97.43 15 | 98.59 33 | 93.84 28 | 95.33 71 | 98.30 15 | 91.40 117 | 95.76 116 | 96.87 104 | 95.26 29 | 99.45 23 | 92.77 78 | 99.21 82 | 99.00 69 |
|
XVS | | | 96.49 29 | 96.18 39 | 97.44 13 | 98.56 34 | 93.99 22 | 96.50 32 | 97.95 51 | 94.58 38 | 94.38 161 | 96.49 123 | 94.56 46 | 99.39 40 | 93.57 51 | 99.05 97 | 98.93 80 |
|
X-MVStestdata | | | 90.70 202 | 88.45 229 | 97.44 13 | 98.56 34 | 93.99 22 | 96.50 32 | 97.95 51 | 94.58 38 | 94.38 161 | 26.89 359 | 94.56 46 | 99.39 40 | 93.57 51 | 99.05 97 | 98.93 80 |
|
ACMH | | 88.36 12 | 96.59 26 | 97.43 4 | 94.07 124 | 98.56 34 | 85.33 152 | 96.33 40 | 98.30 15 | 94.66 37 | 98.72 8 | 98.30 37 | 97.51 4 | 98.00 214 | 94.87 21 | 99.59 35 | 98.86 86 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
test_djsdf | | | 96.62 22 | 96.49 29 | 97.01 30 | 98.55 37 | 91.77 54 | 97.15 13 | 97.37 101 | 88.98 161 | 98.26 23 | 98.86 10 | 93.35 66 | 99.60 8 | 96.41 6 | 99.45 53 | 99.66 6 |
|
v7n | | | 96.82 10 | 97.31 9 | 95.33 79 | 98.54 38 | 86.81 125 | 96.83 20 | 98.07 35 | 96.59 18 | 98.46 19 | 98.43 33 | 92.91 76 | 99.52 17 | 96.25 8 | 99.76 12 | 99.65 8 |
|
abl_6 | | | 97.31 5 | 97.12 14 | 97.86 3 | 98.54 38 | 95.32 8 | 96.61 26 | 98.35 11 | 95.81 30 | 97.55 40 | 97.44 74 | 96.51 9 | 99.40 36 | 94.06 42 | 99.23 80 | 98.85 89 |
|
ACMH+ | | 88.43 11 | 96.48 30 | 96.82 18 | 95.47 75 | 98.54 38 | 89.06 83 | 95.65 62 | 98.61 6 | 96.10 25 | 98.16 25 | 97.52 70 | 96.90 7 | 98.62 160 | 90.30 129 | 99.60 33 | 98.72 103 |
|
SixPastTwentyTwo | | | 94.91 87 | 95.21 85 | 93.98 126 | 98.52 41 | 83.19 175 | 95.93 53 | 94.84 219 | 94.86 35 | 98.49 17 | 98.74 16 | 81.45 245 | 99.60 8 | 94.69 25 | 99.39 64 | 99.15 49 |
|
HFP-MVS | | | 96.39 38 | 96.17 40 | 97.04 28 | 98.51 42 | 93.37 35 | 96.30 44 | 97.98 45 | 92.35 84 | 95.63 120 | 96.47 126 | 95.37 23 | 99.27 57 | 93.78 46 | 99.14 88 | 98.48 114 |
|
#test# | | | 95.89 51 | 95.51 68 | 97.04 28 | 98.51 42 | 93.37 35 | 95.14 77 | 97.98 45 | 89.34 155 | 95.63 120 | 96.47 126 | 95.37 23 | 99.27 57 | 91.99 100 | 99.14 88 | 98.48 114 |
|
Baseline_NR-MVSNet | | | 94.47 108 | 95.09 90 | 92.60 182 | 98.50 44 | 80.82 200 | 92.08 188 | 96.68 157 | 93.82 53 | 96.29 86 | 98.56 22 | 90.10 139 | 97.75 240 | 90.10 137 | 99.66 24 | 99.24 43 |
|
Anonymous20240521 | | | 96.37 40 | 96.66 22 | 95.50 73 | 98.49 45 | 87.84 111 | 97.47 9 | 97.77 68 | 94.75 36 | 98.22 24 | 98.49 26 | 90.93 119 | 99.28 54 | 94.12 41 | 99.74 14 | 99.38 32 |
|
OPM-MVS | | | 95.61 58 | 95.45 72 | 96.08 49 | 98.49 45 | 91.00 62 | 92.65 162 | 97.33 110 | 90.05 144 | 96.77 68 | 96.85 105 | 95.04 35 | 98.56 169 | 92.77 78 | 99.06 95 | 98.70 104 |
|
FC-MVSNet-test | | | 95.32 69 | 95.88 56 | 93.62 137 | 98.49 45 | 81.77 188 | 95.90 55 | 98.32 12 | 93.93 51 | 97.53 41 | 97.56 67 | 88.48 158 | 99.40 36 | 92.91 77 | 99.83 7 | 99.68 4 |
|
XVG-ACMP-BASELINE | | | 95.68 56 | 95.34 77 | 96.69 39 | 98.40 48 | 93.04 38 | 94.54 106 | 98.05 37 | 90.45 137 | 96.31 84 | 96.76 110 | 92.91 76 | 98.72 147 | 91.19 117 | 99.42 57 | 98.32 120 |
|
ACMM | | 88.83 9 | 96.30 43 | 96.07 47 | 96.97 31 | 98.39 49 | 92.95 41 | 94.74 93 | 98.03 40 | 90.82 128 | 97.15 54 | 96.85 105 | 96.25 13 | 99.00 93 | 93.10 72 | 99.33 69 | 98.95 78 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
pm-mvs1 | | | 95.43 63 | 95.94 52 | 93.93 130 | 98.38 50 | 85.08 154 | 95.46 68 | 97.12 126 | 91.84 102 | 97.28 50 | 98.46 29 | 95.30 28 | 97.71 242 | 90.17 133 | 99.42 57 | 98.99 71 |
|
COLMAP_ROB | | 91.06 5 | 96.75 16 | 96.62 25 | 97.13 26 | 98.38 50 | 94.31 12 | 96.79 22 | 98.32 12 | 96.69 15 | 96.86 64 | 97.56 67 | 95.48 21 | 98.77 140 | 90.11 135 | 99.44 55 | 98.31 122 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
TransMVSNet (Re) | | | 95.27 75 | 96.04 49 | 92.97 160 | 98.37 52 | 81.92 187 | 95.07 81 | 96.76 154 | 93.97 50 | 97.77 35 | 98.57 21 | 95.72 17 | 97.90 217 | 88.89 159 | 99.23 80 | 99.08 60 |
|
LPG-MVS_test | | | 96.38 39 | 96.23 37 | 96.84 36 | 98.36 53 | 92.13 47 | 95.33 71 | 98.25 18 | 91.78 107 | 97.07 56 | 97.22 86 | 96.38 11 | 99.28 54 | 92.07 98 | 99.59 35 | 99.11 53 |
|
LGP-MVS_train | | | | | 96.84 36 | 98.36 53 | 92.13 47 | | 98.25 18 | 91.78 107 | 97.07 56 | 97.22 86 | 96.38 11 | 99.28 54 | 92.07 98 | 99.59 35 | 99.11 53 |
|
v748 | | | 96.51 28 | 97.05 15 | 94.89 92 | 98.35 55 | 85.82 146 | 96.58 28 | 97.47 94 | 96.25 22 | 98.46 19 | 98.35 34 | 93.27 67 | 99.33 51 | 95.13 19 | 99.59 35 | 99.52 19 |
|
CP-MVS | | | 96.44 35 | 96.08 46 | 97.54 9 | 98.29 56 | 94.62 10 | 96.80 21 | 98.08 32 | 92.67 75 | 95.08 143 | 96.39 136 | 94.77 42 | 99.42 28 | 93.17 70 | 99.44 55 | 98.58 113 |
|
FIs | | | 94.90 88 | 95.35 76 | 93.55 140 | 98.28 57 | 81.76 189 | 95.33 71 | 98.14 28 | 93.05 66 | 97.07 56 | 97.18 88 | 87.65 179 | 99.29 53 | 91.72 106 | 99.69 16 | 99.61 11 |
|
TranMVSNet+NR-MVSNet | | | 96.07 49 | 96.26 36 | 95.50 73 | 98.26 58 | 87.69 113 | 93.75 131 | 97.86 57 | 95.96 29 | 97.48 43 | 97.14 90 | 95.33 26 | 99.44 24 | 90.79 119 | 99.76 12 | 99.38 32 |
|
IS-MVSNet | | | 94.49 107 | 94.35 108 | 94.92 91 | 98.25 59 | 86.46 130 | 97.13 15 | 94.31 233 | 96.24 23 | 96.28 89 | 96.36 141 | 82.88 231 | 99.35 47 | 88.19 171 | 99.52 46 | 98.96 77 |
|
UA-Net | | | 97.35 4 | 97.24 12 | 97.69 5 | 98.22 60 | 93.87 26 | 98.42 4 | 98.19 24 | 96.95 12 | 95.46 127 | 99.23 3 | 93.45 59 | 99.57 13 | 95.34 17 | 99.89 4 | 99.63 9 |
|
test_part2 | | | | | | 98.21 61 | 89.41 77 | | | | 96.72 69 | | | | | | |
|
ESAPD | | | 95.42 65 | 95.34 77 | 95.68 68 | 98.21 61 | 89.41 77 | 93.92 126 | 98.14 28 | 91.83 104 | 96.72 69 | 96.39 136 | 94.69 43 | 99.44 24 | 89.00 156 | 99.10 92 | 98.17 130 |
|
test_0402 | | | 95.73 53 | 96.22 38 | 94.26 120 | 98.19 63 | 85.77 147 | 93.24 147 | 97.24 118 | 96.88 14 | 97.69 37 | 97.77 60 | 94.12 53 | 99.13 73 | 91.54 113 | 99.29 73 | 97.88 152 |
|
SMA-MVS | | | 95.73 53 | 95.51 68 | 96.41 46 | 98.17 64 | 91.19 60 | 95.09 79 | 97.79 66 | 86.48 215 | 97.42 48 | 97.42 75 | 94.47 50 | 99.26 59 | 93.42 60 | 99.29 73 | 98.79 95 |
|
ACMP | | 88.15 13 | 95.71 55 | 95.43 75 | 96.54 42 | 98.17 64 | 91.73 55 | 94.24 114 | 98.08 32 | 89.46 153 | 96.61 75 | 96.47 126 | 95.85 16 | 99.12 75 | 90.45 121 | 99.56 42 | 98.77 98 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
CPTT-MVS | | | 94.74 97 | 94.12 116 | 96.60 40 | 98.15 66 | 93.01 39 | 95.84 57 | 97.66 74 | 89.21 160 | 93.28 190 | 95.46 182 | 88.89 154 | 98.98 94 | 89.80 141 | 98.82 122 | 97.80 159 |
|
v52 | | | 96.93 7 | 97.29 10 | 95.86 58 | 98.12 67 | 88.48 99 | 97.69 6 | 97.74 69 | 94.90 34 | 98.55 15 | 98.72 17 | 93.39 63 | 99.49 21 | 96.92 2 | 99.62 30 | 99.61 11 |
|
V4 | | | 96.93 7 | 97.29 10 | 95.86 58 | 98.11 68 | 88.47 100 | 97.69 6 | 97.74 69 | 94.91 32 | 98.55 15 | 98.72 17 | 93.37 64 | 99.49 21 | 96.92 2 | 99.62 30 | 99.61 11 |
|
Vis-MVSNet | | | 95.50 61 | 95.48 70 | 95.56 72 | 98.11 68 | 89.40 79 | 95.35 70 | 98.22 23 | 92.36 82 | 94.11 169 | 98.07 42 | 92.02 91 | 99.44 24 | 93.38 62 | 97.67 210 | 97.85 155 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
XVG-OURS-SEG-HR | | | 95.38 67 | 95.00 91 | 96.51 43 | 98.10 70 | 94.07 15 | 92.46 173 | 98.13 31 | 90.69 130 | 93.75 177 | 96.25 146 | 98.03 2 | 97.02 269 | 92.08 97 | 95.55 270 | 98.45 117 |
|
EPP-MVSNet | | | 93.91 122 | 93.68 134 | 94.59 105 | 98.08 71 | 85.55 150 | 97.44 10 | 94.03 238 | 94.22 45 | 94.94 147 | 96.19 154 | 82.07 239 | 99.57 13 | 87.28 184 | 98.89 109 | 98.65 105 |
|
K. test v3 | | | 93.37 139 | 93.27 146 | 93.66 136 | 98.05 72 | 82.62 181 | 94.35 111 | 86.62 311 | 96.05 27 | 97.51 42 | 98.85 12 | 76.59 280 | 99.65 3 | 93.21 68 | 98.20 179 | 98.73 102 |
|
lessismore_v0 | | | | | 93.87 133 | 98.05 72 | 83.77 168 | | 80.32 354 | | 97.13 55 | 97.91 54 | 77.49 270 | 99.11 76 | 92.62 85 | 98.08 190 | 98.74 100 |
|
AllTest | | | 94.88 90 | 94.51 103 | 96.00 50 | 98.02 74 | 92.17 45 | 95.26 74 | 98.43 8 | 90.48 135 | 95.04 144 | 96.74 112 | 92.54 84 | 97.86 228 | 85.11 211 | 98.98 104 | 97.98 142 |
|
TestCases | | | | | 96.00 50 | 98.02 74 | 92.17 45 | | 98.43 8 | 90.48 135 | 95.04 144 | 96.74 112 | 92.54 84 | 97.86 228 | 85.11 211 | 98.98 104 | 97.98 142 |
|
anonymousdsp | | | 96.74 17 | 96.42 30 | 97.68 7 | 98.00 76 | 94.03 21 | 96.97 17 | 97.61 79 | 87.68 199 | 98.45 21 | 98.77 15 | 94.20 52 | 99.50 18 | 96.70 5 | 99.40 62 | 99.53 16 |
|
XVG-OURS | | | 94.72 98 | 94.12 116 | 96.50 44 | 98.00 76 | 94.23 13 | 91.48 216 | 98.17 26 | 90.72 129 | 95.30 131 | 96.47 126 | 87.94 176 | 96.98 270 | 91.41 115 | 97.61 213 | 98.30 123 |
|
114514_t | | | 90.51 204 | 89.80 213 | 92.63 180 | 98.00 76 | 82.24 184 | 93.40 138 | 97.29 114 | 65.84 346 | 89.40 276 | 94.80 208 | 86.99 195 | 98.75 142 | 83.88 224 | 98.61 137 | 96.89 205 |
|
Gipuma | | | 95.31 71 | 95.80 61 | 93.81 135 | 97.99 79 | 90.91 64 | 96.42 37 | 97.95 51 | 96.69 15 | 91.78 226 | 98.85 12 | 91.77 97 | 95.49 307 | 91.72 106 | 99.08 94 | 95.02 268 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
APD-MVS_3200maxsize | | | 96.82 10 | 96.65 23 | 97.32 22 | 97.95 80 | 93.82 29 | 96.31 42 | 98.25 18 | 95.51 31 | 96.99 62 | 97.05 96 | 95.63 19 | 99.39 40 | 93.31 64 | 98.88 111 | 98.75 99 |
|
HPM-MVS++ | | | 95.02 82 | 94.39 105 | 96.91 34 | 97.88 81 | 93.58 33 | 94.09 117 | 96.99 133 | 91.05 124 | 92.40 211 | 95.22 191 | 91.03 118 | 99.25 60 | 92.11 95 | 98.69 134 | 97.90 150 |
|
EG-PatchMatch MVS | | | 94.54 106 | 94.67 99 | 94.14 122 | 97.87 82 | 86.50 127 | 92.00 191 | 96.74 155 | 88.16 190 | 96.93 63 | 97.61 65 | 93.04 74 | 97.90 217 | 91.60 110 | 98.12 186 | 98.03 139 |
|
nrg030 | | | 96.32 41 | 96.55 28 | 95.62 69 | 97.83 83 | 88.55 96 | 95.77 59 | 98.29 17 | 92.68 73 | 98.03 28 | 97.91 54 | 95.13 32 | 98.95 101 | 93.85 44 | 99.49 48 | 99.36 36 |
|
UniMVSNet (Re) | | | 95.32 69 | 95.15 87 | 95.80 61 | 97.79 84 | 88.91 86 | 92.91 155 | 98.07 35 | 93.46 60 | 96.31 84 | 95.97 162 | 90.14 135 | 99.34 48 | 92.11 95 | 99.64 27 | 99.16 48 |
|
VPA-MVSNet | | | 95.14 79 | 95.67 66 | 93.58 139 | 97.76 85 | 83.15 176 | 94.58 101 | 97.58 82 | 93.39 62 | 97.05 60 | 98.04 44 | 93.25 68 | 98.51 178 | 89.75 142 | 99.59 35 | 99.08 60 |
|
DU-MVS | | | 95.28 73 | 95.12 89 | 95.75 64 | 97.75 86 | 88.59 94 | 92.58 163 | 97.81 62 | 93.99 48 | 96.80 66 | 95.90 163 | 90.10 139 | 99.41 32 | 91.60 110 | 99.58 40 | 99.26 41 |
|
NR-MVSNet | | | 95.28 73 | 95.28 82 | 95.26 81 | 97.75 86 | 87.21 119 | 95.08 80 | 97.37 101 | 93.92 52 | 97.65 38 | 95.90 163 | 90.10 139 | 99.33 51 | 90.11 135 | 99.66 24 | 99.26 41 |
|
XXY-MVS | | | 92.58 167 | 93.16 148 | 90.84 233 | 97.75 86 | 79.84 223 | 91.87 200 | 96.22 185 | 85.94 221 | 95.53 124 | 97.68 62 | 92.69 81 | 94.48 320 | 83.21 229 | 97.51 215 | 98.21 128 |
|
wuykxyi23d | | | 96.76 15 | 96.57 27 | 97.34 21 | 97.75 86 | 96.73 3 | 94.37 110 | 96.48 167 | 91.00 125 | 99.72 2 | 98.99 5 | 96.06 14 | 98.21 203 | 94.86 22 | 99.90 2 | 97.09 194 |
|
PVSNet_Blended_VisFu | | | 91.63 185 | 91.20 193 | 92.94 163 | 97.73 90 | 83.95 166 | 92.14 187 | 97.46 95 | 78.85 288 | 92.35 214 | 94.98 201 | 84.16 225 | 99.08 78 | 86.36 198 | 96.77 241 | 95.79 246 |
|
tfpnnormal | | | 94.27 114 | 94.87 94 | 92.48 188 | 97.71 91 | 80.88 199 | 94.55 105 | 95.41 211 | 93.70 55 | 96.67 72 | 97.72 61 | 91.40 104 | 98.18 208 | 87.45 180 | 99.18 85 | 98.36 118 |
|
HQP_MVS | | | 94.26 115 | 93.93 119 | 95.23 83 | 97.71 91 | 88.12 105 | 94.56 103 | 97.81 62 | 91.74 111 | 93.31 187 | 95.59 174 | 86.93 197 | 98.95 101 | 89.26 151 | 98.51 146 | 98.60 111 |
|
plane_prior7 | | | | | | 97.71 91 | 88.68 91 | | | | | | | | | | |
|
UniMVSNet_NR-MVSNet | | | 95.35 68 | 95.21 85 | 95.76 63 | 97.69 94 | 88.59 94 | 92.26 183 | 97.84 60 | 94.91 32 | 96.80 66 | 95.78 171 | 90.42 131 | 99.41 32 | 91.60 110 | 99.58 40 | 99.29 40 |
|
APDe-MVS | | | 96.46 32 | 96.64 24 | 95.93 55 | 97.68 95 | 89.38 80 | 96.90 19 | 98.41 10 | 92.52 79 | 97.43 46 | 97.92 52 | 95.11 33 | 99.50 18 | 94.45 30 | 99.30 71 | 98.92 83 |
|
DeepC-MVS | | 91.39 4 | 95.43 63 | 95.33 79 | 95.71 66 | 97.67 96 | 90.17 68 | 93.86 129 | 98.02 42 | 87.35 202 | 96.22 92 | 97.99 49 | 94.48 49 | 99.05 83 | 92.73 81 | 99.68 19 | 97.93 146 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
Vis-MVSNet (Re-imp) | | | 90.42 206 | 90.16 209 | 91.20 226 | 97.66 97 | 77.32 267 | 94.33 112 | 87.66 304 | 91.20 121 | 92.99 199 | 95.13 194 | 75.40 282 | 98.28 196 | 77.86 281 | 99.19 83 | 97.99 141 |
|
FMVSNet1 | | | 94.84 92 | 95.13 88 | 93.97 127 | 97.60 98 | 84.29 159 | 95.99 49 | 96.56 161 | 92.38 81 | 97.03 61 | 98.53 23 | 90.12 136 | 98.98 94 | 88.78 161 | 99.16 86 | 98.65 105 |
|
RPSCF | | | 95.58 59 | 94.89 93 | 97.62 8 | 97.58 99 | 96.30 5 | 95.97 52 | 97.53 88 | 92.42 80 | 93.41 184 | 97.78 58 | 91.21 112 | 97.77 237 | 91.06 118 | 97.06 232 | 98.80 93 |
|
WR-MVS | | | 93.49 133 | 93.72 131 | 92.80 172 | 97.57 100 | 80.03 216 | 90.14 254 | 95.68 200 | 93.70 55 | 96.62 74 | 95.39 188 | 87.21 190 | 99.04 86 | 87.50 179 | 99.64 27 | 99.33 37 |
|
CSCG | | | 94.69 99 | 94.75 95 | 94.52 108 | 97.55 101 | 87.87 109 | 95.01 84 | 97.57 83 | 92.68 73 | 96.20 94 | 93.44 250 | 91.92 95 | 98.78 136 | 89.11 155 | 99.24 78 | 96.92 202 |
|
MCST-MVS | | | 92.91 156 | 92.51 163 | 94.10 123 | 97.52 102 | 85.72 148 | 91.36 220 | 97.13 125 | 80.33 272 | 92.91 202 | 94.24 225 | 91.23 111 | 98.72 147 | 89.99 139 | 97.93 199 | 97.86 154 |
|
F-COLMAP | | | 92.28 176 | 91.06 196 | 95.95 52 | 97.52 102 | 91.90 51 | 93.53 135 | 97.18 121 | 83.98 242 | 88.70 289 | 94.04 233 | 88.41 161 | 98.55 175 | 80.17 258 | 95.99 262 | 97.39 183 |
|
VDD-MVS | | | 94.37 109 | 94.37 107 | 94.40 116 | 97.49 104 | 86.07 140 | 93.97 121 | 93.28 251 | 94.49 41 | 96.24 90 | 97.78 58 | 87.99 175 | 98.79 132 | 88.92 158 | 99.14 88 | 98.34 119 |
|
testgi | | | 90.38 208 | 91.34 190 | 87.50 298 | 97.49 104 | 71.54 320 | 89.43 275 | 95.16 214 | 88.38 183 | 94.54 158 | 94.68 213 | 92.88 78 | 93.09 334 | 71.60 323 | 97.85 203 | 97.88 152 |
|
Anonymous20231211 | | | 96.60 24 | 97.13 13 | 95.00 89 | 97.46 106 | 86.35 135 | 97.11 16 | 98.24 21 | 97.58 6 | 98.72 8 | 98.97 7 | 93.15 71 | 99.15 68 | 93.18 69 | 99.74 14 | 99.50 21 |
|
plane_prior1 | | | | | | 97.38 107 | | | | | | | | | | | |
|
APD-MVS | | | 95.00 83 | 94.69 97 | 95.93 55 | 97.38 107 | 90.88 65 | 94.59 99 | 97.81 62 | 89.22 159 | 95.46 127 | 96.17 156 | 93.42 62 | 99.34 48 | 89.30 147 | 98.87 114 | 97.56 175 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
ITE_SJBPF | | | | | 95.95 52 | 97.34 109 | 93.36 37 | | 96.55 164 | 91.93 97 | 94.82 150 | 95.39 188 | 91.99 93 | 97.08 267 | 85.53 204 | 97.96 197 | 97.41 180 |
|
Anonymous20240529 | | | 95.50 61 | 95.83 59 | 94.50 109 | 97.33 110 | 85.93 143 | 95.19 76 | 96.77 153 | 96.64 17 | 97.61 39 | 98.05 43 | 93.23 69 | 98.79 132 | 88.60 166 | 99.04 100 | 98.78 96 |
|
OMC-MVS | | | 94.22 116 | 93.69 133 | 95.81 60 | 97.25 111 | 91.27 58 | 92.27 182 | 97.40 99 | 87.10 208 | 94.56 157 | 95.42 185 | 93.74 55 | 98.11 211 | 86.62 192 | 98.85 115 | 98.06 137 |
|
v13 | | | 95.39 66 | 96.12 43 | 93.18 151 | 97.22 112 | 80.81 201 | 95.55 65 | 97.57 83 | 93.42 61 | 98.02 30 | 98.49 26 | 89.62 145 | 99.18 65 | 95.54 12 | 99.68 19 | 99.54 15 |
|
plane_prior6 | | | | | | 97.21 113 | 88.23 104 | | | | | | 86.93 197 | | | | |
|
DP-MVS Recon | | | 92.31 175 | 91.88 174 | 93.60 138 | 97.18 114 | 86.87 124 | 91.10 226 | 97.37 101 | 84.92 236 | 92.08 221 | 94.08 232 | 88.59 157 | 98.20 205 | 83.50 226 | 98.14 183 | 95.73 248 |
|
新几何1 | | | | | 93.17 152 | 97.16 115 | 87.29 116 | | 94.43 230 | 67.95 339 | 91.29 232 | 94.94 202 | 86.97 196 | 98.23 202 | 81.06 250 | 97.75 204 | 93.98 293 |
|
DP-MVS | | | 95.62 57 | 95.84 58 | 94.97 90 | 97.16 115 | 88.62 93 | 94.54 106 | 97.64 75 | 96.94 13 | 96.58 76 | 97.32 83 | 93.07 73 | 98.72 147 | 90.45 121 | 98.84 116 | 97.57 173 |
|
1121 | | | 90.26 213 | 89.23 215 | 93.34 146 | 97.15 117 | 87.40 115 | 91.94 194 | 94.39 231 | 67.88 340 | 91.02 244 | 94.91 203 | 86.91 199 | 98.59 165 | 81.17 248 | 97.71 207 | 94.02 292 |
|
v12 | | | 95.29 72 | 96.02 51 | 93.10 153 | 97.14 118 | 80.63 202 | 95.39 69 | 97.55 87 | 93.19 64 | 97.98 31 | 98.44 31 | 89.40 148 | 99.16 66 | 95.38 16 | 99.67 22 | 99.52 19 |
|
CHOSEN 1792x2688 | | | 87.19 270 | 85.92 283 | 91.00 231 | 97.13 119 | 79.41 235 | 84.51 330 | 95.60 202 | 64.14 349 | 90.07 260 | 94.81 205 | 78.26 266 | 97.14 266 | 73.34 310 | 95.38 277 | 96.46 224 |
|
HyFIR lowres test | | | 87.19 270 | 85.51 285 | 92.24 195 | 97.12 120 | 80.51 203 | 85.03 324 | 96.06 188 | 66.11 345 | 91.66 227 | 92.98 257 | 70.12 293 | 99.14 71 | 75.29 304 | 95.23 280 | 97.07 195 |
|
V9 | | | 95.17 78 | 95.89 55 | 93.02 156 | 97.04 121 | 80.42 204 | 95.22 75 | 97.53 88 | 92.92 71 | 97.90 32 | 98.35 34 | 89.15 152 | 99.14 71 | 95.21 18 | 99.65 26 | 99.50 21 |
|
ab-mvs | | | 92.40 173 | 92.62 160 | 91.74 208 | 97.02 122 | 81.65 190 | 95.84 57 | 95.50 209 | 86.95 211 | 92.95 201 | 97.56 67 | 90.70 127 | 97.50 249 | 79.63 264 | 97.43 222 | 96.06 239 |
|
v11 | | | 95.10 80 | 95.88 56 | 92.76 173 | 96.98 123 | 79.64 230 | 95.12 78 | 97.60 81 | 92.64 76 | 98.03 28 | 98.44 31 | 89.06 153 | 99.15 68 | 95.42 15 | 99.67 22 | 99.50 21 |
|
test222 | | | | | | 96.95 124 | 85.27 153 | 88.83 290 | 93.61 245 | 65.09 348 | 90.74 248 | 94.85 204 | 84.62 223 | | | 97.36 225 | 93.91 294 |
|
V14 | | | 95.05 81 | 95.75 63 | 92.94 163 | 96.94 125 | 80.21 207 | 95.03 83 | 97.50 92 | 92.62 77 | 97.84 34 | 98.28 38 | 88.87 155 | 99.13 73 | 95.03 20 | 99.64 27 | 99.48 24 |
|
CDPH-MVS | | | 92.67 164 | 91.83 175 | 95.18 85 | 96.94 125 | 88.46 101 | 90.70 236 | 97.07 127 | 77.38 296 | 92.34 216 | 95.08 196 | 92.67 82 | 98.88 111 | 85.74 202 | 98.57 139 | 98.20 129 |
|
CNVR-MVS | | | 94.58 104 | 94.29 110 | 95.46 76 | 96.94 125 | 89.35 81 | 91.81 209 | 96.80 150 | 89.66 151 | 93.90 175 | 95.44 184 | 92.80 80 | 98.72 147 | 92.74 80 | 98.52 144 | 98.32 120 |
|
原ACMM1 | | | | | 92.87 167 | 96.91 128 | 84.22 162 | | 97.01 130 | 76.84 300 | 89.64 273 | 94.46 217 | 88.00 174 | 98.70 153 | 81.53 243 | 98.01 195 | 95.70 250 |
|
ambc | | | | | 92.98 159 | 96.88 129 | 83.01 179 | 95.92 54 | 96.38 174 | | 96.41 79 | 97.48 72 | 88.26 163 | 97.80 234 | 89.96 140 | 98.93 108 | 98.12 136 |
|
testdata | | | | | 91.03 228 | 96.87 130 | 82.01 185 | | 94.28 234 | 71.55 323 | 92.46 209 | 95.42 185 | 85.65 217 | 97.38 259 | 82.64 234 | 97.27 227 | 93.70 301 |
|
v15 | | | 94.93 86 | 95.62 67 | 92.86 168 | 96.83 131 | 80.01 220 | 94.84 90 | 97.48 93 | 92.36 82 | 97.76 36 | 98.20 40 | 88.61 156 | 99.11 76 | 94.86 22 | 99.62 30 | 99.46 25 |
|
NP-MVS | | | | | | 96.82 132 | 87.10 120 | | | | | 93.40 251 | | | | | |
|
3Dnovator+ | | 92.74 2 | 95.86 52 | 95.77 62 | 96.13 48 | 96.81 133 | 90.79 67 | 96.30 44 | 97.82 61 | 96.13 24 | 94.74 153 | 97.23 85 | 91.33 106 | 99.16 66 | 93.25 66 | 98.30 167 | 98.46 116 |
|
Test_1112_low_res | | | 87.50 261 | 86.58 266 | 90.25 244 | 96.80 134 | 77.75 261 | 87.53 304 | 96.25 181 | 69.73 334 | 86.47 311 | 93.61 244 | 75.67 281 | 97.88 225 | 79.95 260 | 93.20 311 | 95.11 266 |
|
testing_2 | | | 94.03 120 | 94.38 106 | 93.00 158 | 96.79 135 | 81.41 194 | 92.87 157 | 96.96 135 | 85.88 223 | 97.06 59 | 97.92 52 | 91.18 116 | 98.71 152 | 91.72 106 | 99.04 100 | 98.87 85 |
|
v17 | | | 94.80 94 | 95.46 71 | 92.83 169 | 96.76 136 | 80.02 218 | 94.85 88 | 97.40 99 | 92.23 89 | 97.45 45 | 98.04 44 | 88.46 160 | 99.06 81 | 94.56 27 | 99.40 62 | 99.41 28 |
|
v16 | | | 94.79 96 | 95.44 74 | 92.83 169 | 96.73 137 | 80.03 216 | 94.85 88 | 97.41 98 | 92.23 89 | 97.41 49 | 98.04 44 | 88.40 162 | 99.06 81 | 94.56 27 | 99.30 71 | 99.41 28 |
|
PAPM_NR | | | 91.03 198 | 90.81 201 | 91.68 211 | 96.73 137 | 81.10 197 | 93.72 132 | 96.35 178 | 88.19 189 | 88.77 287 | 92.12 279 | 85.09 220 | 97.25 262 | 82.40 237 | 93.90 303 | 96.68 211 |
|
1112_ss | | | 88.42 240 | 87.41 249 | 91.45 218 | 96.69 139 | 80.99 198 | 89.72 269 | 96.72 156 | 73.37 315 | 87.00 309 | 90.69 302 | 77.38 272 | 98.20 205 | 81.38 244 | 93.72 306 | 95.15 264 |
|
v8 | | | 94.65 101 | 95.29 81 | 92.74 174 | 96.65 140 | 79.77 226 | 94.59 99 | 97.17 122 | 91.86 101 | 97.47 44 | 97.93 51 | 88.16 167 | 99.08 78 | 94.32 32 | 99.47 49 | 99.38 32 |
|
v6 | | | 93.59 129 | 93.93 119 | 92.56 184 | 96.65 140 | 79.77 226 | 92.50 170 | 96.40 171 | 88.55 178 | 95.94 107 | 96.23 149 | 88.13 168 | 98.87 117 | 92.46 91 | 98.50 148 | 99.06 63 |
|
MVS_111021_HR | | | 93.63 128 | 93.42 142 | 94.26 120 | 96.65 140 | 86.96 123 | 89.30 280 | 96.23 183 | 88.36 184 | 93.57 181 | 94.60 214 | 93.45 59 | 97.77 237 | 90.23 131 | 98.38 155 | 98.03 139 |
|
ANet_high | | | 94.83 93 | 96.28 35 | 90.47 238 | 96.65 140 | 73.16 310 | 94.33 112 | 98.74 5 | 96.39 21 | 98.09 27 | 98.93 8 | 93.37 64 | 98.70 153 | 90.38 124 | 99.68 19 | 99.53 16 |
|
v1neww | | | 93.58 130 | 93.92 121 | 92.56 184 | 96.64 144 | 79.77 226 | 92.50 170 | 96.41 169 | 88.55 178 | 95.93 108 | 96.24 147 | 88.08 170 | 98.87 117 | 92.45 92 | 98.50 148 | 99.05 64 |
|
v7new | | | 93.58 130 | 93.92 121 | 92.56 184 | 96.64 144 | 79.77 226 | 92.50 170 | 96.41 169 | 88.55 178 | 95.93 108 | 96.24 147 | 88.08 170 | 98.87 117 | 92.45 92 | 98.50 148 | 99.05 64 |
|
SD-MVS | | | 95.19 76 | 95.73 64 | 93.55 140 | 96.62 146 | 88.88 89 | 94.67 95 | 98.05 37 | 91.26 119 | 97.25 53 | 96.40 132 | 95.42 22 | 94.36 324 | 92.72 82 | 99.19 83 | 97.40 182 |
|
v18 | | | 94.63 102 | 95.26 84 | 92.74 174 | 96.60 147 | 79.81 224 | 94.64 98 | 97.37 101 | 91.87 100 | 97.26 52 | 97.91 54 | 88.13 168 | 99.04 86 | 94.30 34 | 99.24 78 | 99.38 32 |
|
PM-MVS | | | 93.33 140 | 92.67 159 | 95.33 79 | 96.58 148 | 94.06 16 | 92.26 183 | 92.18 270 | 85.92 222 | 96.22 92 | 96.61 119 | 85.64 218 | 95.99 300 | 90.35 127 | 98.23 174 | 95.93 243 |
|
v10 | | | 94.68 100 | 95.27 83 | 92.90 166 | 96.57 149 | 80.15 209 | 94.65 97 | 97.57 83 | 90.68 131 | 97.43 46 | 98.00 48 | 88.18 165 | 99.15 68 | 94.84 24 | 99.55 43 | 99.41 28 |
|
Anonymous202405211 | | | 92.58 167 | 92.50 164 | 92.83 169 | 96.55 150 | 83.22 174 | 92.43 175 | 91.64 279 | 94.10 47 | 95.59 122 | 96.64 117 | 81.88 243 | 97.50 249 | 85.12 210 | 98.52 144 | 97.77 160 |
|
v7 | | | 93.66 126 | 93.97 118 | 92.73 176 | 96.55 150 | 80.15 209 | 92.54 164 | 96.99 133 | 87.36 201 | 95.99 102 | 96.48 124 | 88.18 165 | 98.94 104 | 93.35 63 | 98.31 164 | 99.09 57 |
|
PLC | | 85.34 15 | 90.40 207 | 88.92 223 | 94.85 93 | 96.53 152 | 90.02 69 | 91.58 213 | 96.48 167 | 80.16 273 | 86.14 313 | 92.18 276 | 85.73 215 | 98.25 201 | 76.87 291 | 94.61 293 | 96.30 230 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
TAPA-MVS | | 88.58 10 | 92.49 172 | 91.75 179 | 94.73 97 | 96.50 153 | 89.69 73 | 92.91 155 | 97.68 73 | 78.02 293 | 92.79 203 | 94.10 231 | 90.85 120 | 97.96 216 | 84.76 217 | 98.16 181 | 96.54 213 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
view600 | | | 88.32 242 | 87.94 241 | 89.46 259 | 96.49 154 | 73.31 305 | 93.95 122 | 84.46 334 | 93.02 67 | 94.18 165 | 92.68 264 | 63.33 325 | 98.56 169 | 75.87 299 | 97.50 216 | 96.51 215 |
|
view800 | | | 88.32 242 | 87.94 241 | 89.46 259 | 96.49 154 | 73.31 305 | 93.95 122 | 84.46 334 | 93.02 67 | 94.18 165 | 92.68 264 | 63.33 325 | 98.56 169 | 75.87 299 | 97.50 216 | 96.51 215 |
|
conf0.05thres1000 | | | 88.32 242 | 87.94 241 | 89.46 259 | 96.49 154 | 73.31 305 | 93.95 122 | 84.46 334 | 93.02 67 | 94.18 165 | 92.68 264 | 63.33 325 | 98.56 169 | 75.87 299 | 97.50 216 | 96.51 215 |
|
tfpn | | | 88.32 242 | 87.94 241 | 89.46 259 | 96.49 154 | 73.31 305 | 93.95 122 | 84.46 334 | 93.02 67 | 94.18 165 | 92.68 264 | 63.33 325 | 98.56 169 | 75.87 299 | 97.50 216 | 96.51 215 |
|
NCCC | | | 94.08 119 | 93.54 139 | 95.70 67 | 96.49 154 | 89.90 72 | 92.39 177 | 96.91 143 | 90.64 132 | 92.33 217 | 94.60 214 | 90.58 130 | 98.96 99 | 90.21 132 | 97.70 208 | 98.23 126 |
|
TAMVS | | | 90.16 215 | 89.05 219 | 93.49 145 | 96.49 154 | 86.37 133 | 90.34 247 | 92.55 266 | 80.84 270 | 92.99 199 | 94.57 216 | 81.94 242 | 98.20 205 | 73.51 309 | 98.21 177 | 95.90 244 |
|
TEST9 | | | | | | 96.45 160 | 89.46 74 | 90.60 239 | 96.92 140 | 79.09 286 | 90.49 253 | 94.39 221 | 91.31 107 | 98.88 111 | | | |
|
train_agg | | | 92.71 163 | 91.83 175 | 95.35 77 | 96.45 160 | 89.46 74 | 90.60 239 | 96.92 140 | 79.37 281 | 90.49 253 | 94.39 221 | 91.20 113 | 98.88 111 | 88.66 164 | 98.43 151 | 97.72 163 |
|
agg_prior3 | | | 92.56 170 | 91.62 180 | 95.35 77 | 96.39 162 | 89.45 76 | 90.61 238 | 96.82 148 | 78.82 289 | 90.03 261 | 94.14 230 | 90.72 126 | 98.88 111 | 88.66 164 | 98.43 151 | 97.72 163 |
|
test_8 | | | | | | 96.37 163 | 89.14 82 | 90.51 243 | 96.89 144 | 79.37 281 | 90.42 255 | 94.36 223 | 91.20 113 | 98.82 125 | | | |
|
v1141 | | | 93.42 137 | 93.76 128 | 92.40 192 | 96.37 163 | 79.24 238 | 91.84 205 | 96.38 174 | 88.33 185 | 95.86 113 | 96.23 149 | 87.41 185 | 98.89 107 | 92.61 86 | 98.82 122 | 99.08 60 |
|
divwei89l23v2f112 | | | 93.42 137 | 93.76 128 | 92.41 190 | 96.37 163 | 79.24 238 | 91.84 205 | 96.38 174 | 88.33 185 | 95.86 113 | 96.23 149 | 87.41 185 | 98.89 107 | 92.61 86 | 98.83 119 | 99.09 57 |
|
v1 | | | 93.43 135 | 93.77 127 | 92.41 190 | 96.37 163 | 79.24 238 | 91.84 205 | 96.38 174 | 88.33 185 | 95.87 112 | 96.22 152 | 87.45 183 | 98.89 107 | 92.61 86 | 98.83 119 | 99.09 57 |
|
CLD-MVS | | | 91.82 183 | 91.41 187 | 93.04 154 | 96.37 163 | 83.65 169 | 86.82 313 | 97.29 114 | 84.65 239 | 92.27 218 | 89.67 314 | 92.20 88 | 97.85 231 | 83.95 223 | 99.47 49 | 97.62 171 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
HQP-NCC | | | | | | 96.36 168 | | 91.37 217 | | 87.16 205 | 88.81 283 | | | | | | |
|
ACMP_Plane | | | | | | 96.36 168 | | 91.37 217 | | 87.16 205 | 88.81 283 | | | | | | |
|
HQP-MVS | | | 92.09 180 | 91.49 185 | 93.88 132 | 96.36 168 | 84.89 155 | 91.37 217 | 97.31 111 | 87.16 205 | 88.81 283 | 93.40 251 | 84.76 221 | 98.60 163 | 86.55 194 | 97.73 205 | 98.14 134 |
|
v2v482 | | | 93.29 141 | 93.63 135 | 92.29 193 | 96.35 171 | 78.82 249 | 91.77 211 | 96.28 179 | 88.45 181 | 95.70 119 | 96.26 145 | 86.02 213 | 98.90 105 | 93.02 75 | 98.81 125 | 99.14 50 |
|
MSLP-MVS++ | | | 93.25 146 | 93.88 123 | 91.37 220 | 96.34 172 | 82.81 180 | 93.11 148 | 97.74 69 | 89.37 154 | 94.08 171 | 95.29 190 | 90.40 134 | 96.35 294 | 90.35 127 | 98.25 172 | 94.96 269 |
|
FPMVS | | | 84.50 295 | 83.28 298 | 88.16 291 | 96.32 173 | 94.49 11 | 85.76 320 | 85.47 322 | 83.09 250 | 85.20 317 | 94.26 224 | 63.79 320 | 86.58 355 | 63.72 346 | 91.88 329 | 83.40 350 |
|
Anonymous20231206 | | | 88.77 235 | 88.29 231 | 90.20 248 | 96.31 174 | 78.81 250 | 89.56 273 | 93.49 249 | 74.26 310 | 92.38 212 | 95.58 177 | 82.21 237 | 95.43 310 | 72.07 318 | 98.75 131 | 96.34 228 |
|
MVP-Stereo | | | 90.07 217 | 88.92 223 | 93.54 142 | 96.31 174 | 86.49 128 | 90.93 230 | 95.59 205 | 79.80 274 | 91.48 228 | 95.59 174 | 80.79 253 | 97.39 257 | 78.57 278 | 91.19 331 | 96.76 210 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
v1144 | | | 93.50 132 | 93.81 124 | 92.57 183 | 96.28 176 | 79.61 232 | 91.86 204 | 96.96 135 | 86.95 211 | 95.91 111 | 96.32 142 | 87.65 179 | 98.96 99 | 93.51 53 | 98.88 111 | 99.13 51 |
|
LFMVS | | | 91.33 195 | 91.16 195 | 91.82 206 | 96.27 177 | 79.36 236 | 95.01 84 | 85.61 321 | 96.04 28 | 94.82 150 | 97.06 95 | 72.03 288 | 98.46 185 | 84.96 214 | 98.70 133 | 97.65 169 |
|
VNet | | | 92.67 164 | 92.96 149 | 91.79 207 | 96.27 177 | 80.15 209 | 91.95 192 | 94.98 216 | 92.19 92 | 94.52 159 | 96.07 158 | 87.43 184 | 97.39 257 | 84.83 215 | 98.38 155 | 97.83 156 |
|
IterMVS-LS | | | 93.78 124 | 94.28 111 | 92.27 194 | 96.27 177 | 79.21 243 | 91.87 200 | 96.78 151 | 91.77 109 | 96.57 77 | 97.07 94 | 87.15 191 | 98.74 145 | 91.99 100 | 99.03 102 | 98.86 86 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
v148 | | | 92.87 158 | 93.29 143 | 91.62 212 | 96.25 180 | 77.72 262 | 91.28 221 | 95.05 215 | 89.69 150 | 95.93 108 | 96.04 159 | 87.34 187 | 98.38 190 | 90.05 138 | 97.99 196 | 98.78 96 |
|
MVS_111021_LR | | | 93.66 126 | 93.28 145 | 94.80 95 | 96.25 180 | 90.95 63 | 90.21 250 | 95.43 210 | 87.91 192 | 93.74 179 | 94.40 220 | 92.88 78 | 96.38 292 | 90.39 123 | 98.28 168 | 97.07 195 |
|
agg_prior1 | | | 92.60 166 | 91.76 178 | 95.10 87 | 96.20 182 | 88.89 87 | 90.37 245 | 96.88 145 | 79.67 278 | 90.21 256 | 94.41 218 | 91.30 108 | 98.78 136 | 88.46 168 | 98.37 160 | 97.64 170 |
|
agg_prior | | | | | | 96.20 182 | 88.89 87 | | 96.88 145 | | 90.21 256 | | | 98.78 136 | | | |
|
旧先验1 | | | | | | 96.20 182 | 84.17 163 | | 94.82 220 | | | 95.57 178 | 89.57 146 | | | 97.89 201 | 96.32 229 |
|
CNLPA | | | 91.72 184 | 91.20 193 | 93.26 149 | 96.17 185 | 91.02 61 | 91.14 224 | 95.55 207 | 90.16 143 | 90.87 245 | 93.56 246 | 86.31 209 | 94.40 323 | 79.92 263 | 97.12 230 | 94.37 283 |
|
v1192 | | | 93.49 133 | 93.78 126 | 92.62 181 | 96.16 186 | 79.62 231 | 91.83 208 | 97.22 120 | 86.07 219 | 96.10 100 | 96.38 139 | 87.22 189 | 99.02 90 | 94.14 40 | 98.88 111 | 99.22 44 |
|
tfpn111 | | | 87.60 258 | 87.12 256 | 89.04 272 | 96.14 187 | 73.09 311 | 93.00 150 | 85.31 324 | 92.13 93 | 93.26 192 | 90.96 295 | 63.42 321 | 98.48 182 | 72.87 314 | 96.98 236 | 95.56 254 |
|
conf200view11 | | | 87.41 262 | 86.89 260 | 88.97 273 | 96.14 187 | 73.09 311 | 93.00 150 | 85.31 324 | 92.13 93 | 93.26 192 | 90.96 295 | 63.42 321 | 98.28 196 | 71.27 326 | 96.54 250 | 95.56 254 |
|
thres100view900 | | | 87.35 264 | 86.89 260 | 88.72 278 | 96.14 187 | 73.09 311 | 93.00 150 | 85.31 324 | 92.13 93 | 93.26 192 | 90.96 295 | 63.42 321 | 98.28 196 | 71.27 326 | 96.54 250 | 94.79 272 |
|
DeepC-MVS_fast | | 89.96 7 | 93.73 125 | 93.44 141 | 94.60 104 | 96.14 187 | 87.90 108 | 93.36 139 | 97.14 123 | 85.53 227 | 93.90 175 | 95.45 183 | 91.30 108 | 98.59 165 | 89.51 144 | 98.62 136 | 97.31 188 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
PCF-MVS | | 84.52 17 | 89.12 227 | 87.71 246 | 93.34 146 | 96.06 191 | 85.84 145 | 86.58 317 | 97.31 111 | 68.46 338 | 93.61 180 | 93.89 238 | 87.51 182 | 98.52 177 | 67.85 336 | 98.11 187 | 95.66 251 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
v144192 | | | 93.20 149 | 93.54 139 | 92.16 198 | 96.05 192 | 78.26 256 | 91.95 192 | 97.14 123 | 84.98 235 | 95.96 104 | 96.11 157 | 87.08 193 | 99.04 86 | 93.79 45 | 98.84 116 | 99.17 47 |
|
thres600view7 | | | 87.66 256 | 87.10 258 | 89.36 266 | 96.05 192 | 73.17 309 | 92.72 159 | 85.31 324 | 91.89 99 | 93.29 189 | 90.97 294 | 63.42 321 | 98.39 188 | 73.23 311 | 96.99 235 | 96.51 215 |
|
MIMVSNet | | | 87.13 272 | 86.54 268 | 88.89 275 | 96.05 192 | 76.11 278 | 94.39 109 | 88.51 295 | 81.37 266 | 88.27 296 | 96.75 111 | 72.38 286 | 95.52 306 | 65.71 343 | 95.47 274 | 95.03 267 |
|
v1921920 | | | 93.26 144 | 93.61 136 | 92.19 196 | 96.04 195 | 78.31 255 | 91.88 199 | 97.24 118 | 85.17 229 | 96.19 96 | 96.19 154 | 86.76 202 | 99.05 83 | 94.18 39 | 98.84 116 | 99.22 44 |
|
v1240 | | | 93.29 141 | 93.71 132 | 92.06 201 | 96.01 196 | 77.89 260 | 91.81 209 | 97.37 101 | 85.12 231 | 96.69 71 | 96.40 132 | 86.67 203 | 99.07 80 | 94.51 29 | 98.76 129 | 99.22 44 |
|
testmv | | | 88.46 239 | 88.11 238 | 89.48 257 | 96.00 197 | 76.14 277 | 86.20 319 | 93.75 243 | 84.48 240 | 93.57 181 | 95.52 181 | 80.91 252 | 95.09 316 | 63.97 345 | 98.61 137 | 97.22 191 |
|
conf0.01 | | | 86.95 275 | 86.04 275 | 89.70 254 | 95.99 198 | 75.66 284 | 93.28 140 | 82.70 341 | 88.81 166 | 91.26 233 | 88.01 326 | 58.77 340 | 97.89 219 | 78.93 270 | 96.60 244 | 95.56 254 |
|
conf0.002 | | | 86.95 275 | 86.04 275 | 89.70 254 | 95.99 198 | 75.66 284 | 93.28 140 | 82.70 341 | 88.81 166 | 91.26 233 | 88.01 326 | 58.77 340 | 97.89 219 | 78.93 270 | 96.60 244 | 95.56 254 |
|
thresconf0.02 | | | 86.69 280 | 86.04 275 | 88.64 281 | 95.99 198 | 75.66 284 | 93.28 140 | 82.70 341 | 88.81 166 | 91.26 233 | 88.01 326 | 58.77 340 | 97.89 219 | 78.93 270 | 96.60 244 | 92.36 322 |
|
tfpn_n400 | | | 86.69 280 | 86.04 275 | 88.64 281 | 95.99 198 | 75.66 284 | 93.28 140 | 82.70 341 | 88.81 166 | 91.26 233 | 88.01 326 | 58.77 340 | 97.89 219 | 78.93 270 | 96.60 244 | 92.36 322 |
|
tfpnconf | | | 86.69 280 | 86.04 275 | 88.64 281 | 95.99 198 | 75.66 284 | 93.28 140 | 82.70 341 | 88.81 166 | 91.26 233 | 88.01 326 | 58.77 340 | 97.89 219 | 78.93 270 | 96.60 244 | 92.36 322 |
|
tfpnview11 | | | 86.69 280 | 86.04 275 | 88.64 281 | 95.99 198 | 75.66 284 | 93.28 140 | 82.70 341 | 88.81 166 | 91.26 233 | 88.01 326 | 58.77 340 | 97.89 219 | 78.93 270 | 96.60 244 | 92.36 322 |
|
BH-untuned | | | 90.68 203 | 90.90 197 | 90.05 250 | 95.98 204 | 79.57 233 | 90.04 258 | 94.94 218 | 87.91 192 | 94.07 172 | 93.00 256 | 87.76 178 | 97.78 236 | 79.19 268 | 95.17 281 | 92.80 316 |
|
DeepPCF-MVS | | 90.46 6 | 94.20 117 | 93.56 138 | 96.14 47 | 95.96 205 | 92.96 40 | 89.48 274 | 97.46 95 | 85.14 230 | 96.23 91 | 95.42 185 | 93.19 70 | 98.08 212 | 90.37 125 | 98.76 129 | 97.38 185 |
|
test_prior3 | | | 93.29 141 | 92.85 152 | 94.61 100 | 95.95 206 | 87.23 117 | 90.21 250 | 97.36 107 | 89.33 156 | 90.77 246 | 94.81 205 | 90.41 132 | 98.68 155 | 88.21 169 | 98.55 140 | 97.93 146 |
|
test_prior | | | | | 94.61 100 | 95.95 206 | 87.23 117 | | 97.36 107 | | | | | 98.68 155 | | | 97.93 146 |
|
test12 | | | | | 94.43 115 | 95.95 206 | 86.75 126 | | 96.24 182 | | 89.76 271 | | 89.79 143 | 98.79 132 | | 97.95 198 | 97.75 162 |
|
LCM-MVSNet-Re | | | 94.20 117 | 94.58 101 | 93.04 154 | 95.91 209 | 83.13 177 | 93.79 130 | 99.19 2 | 92.00 96 | 98.84 5 | 98.04 44 | 93.64 56 | 99.02 90 | 81.28 245 | 98.54 142 | 96.96 200 |
|
PatchMatch-RL | | | 89.18 225 | 88.02 240 | 92.64 179 | 95.90 210 | 92.87 42 | 88.67 293 | 91.06 283 | 80.34 271 | 90.03 261 | 91.67 285 | 83.34 227 | 94.42 322 | 76.35 295 | 94.84 287 | 90.64 338 |
|
TSAR-MVS + GP. | | | 93.07 152 | 92.41 166 | 95.06 88 | 95.82 211 | 90.87 66 | 90.97 228 | 92.61 265 | 88.04 191 | 94.61 156 | 93.79 241 | 88.08 170 | 97.81 233 | 89.41 146 | 98.39 154 | 96.50 222 |
|
QAPM | | | 92.88 157 | 92.77 154 | 93.22 150 | 95.82 211 | 83.31 172 | 96.45 34 | 97.35 109 | 83.91 243 | 93.75 177 | 96.77 108 | 89.25 150 | 98.88 111 | 84.56 219 | 97.02 234 | 97.49 177 |
|
tfpn200view9 | | | 87.05 273 | 86.52 269 | 88.67 279 | 95.77 213 | 72.94 314 | 91.89 197 | 86.00 316 | 90.84 126 | 92.61 206 | 89.80 309 | 63.93 318 | 98.28 196 | 71.27 326 | 96.54 250 | 94.79 272 |
|
thres400 | | | 87.20 269 | 86.52 269 | 89.24 270 | 95.77 213 | 72.94 314 | 91.89 197 | 86.00 316 | 90.84 126 | 92.61 206 | 89.80 309 | 63.93 318 | 98.28 196 | 71.27 326 | 96.54 250 | 96.51 215 |
|
pmmvs-eth3d | | | 91.54 187 | 90.73 204 | 93.99 125 | 95.76 215 | 87.86 110 | 90.83 232 | 93.98 240 | 78.23 292 | 94.02 173 | 96.22 152 | 82.62 236 | 96.83 276 | 86.57 193 | 98.33 162 | 97.29 189 |
|
jason | | | 89.17 226 | 88.32 230 | 91.70 210 | 95.73 216 | 80.07 213 | 88.10 297 | 93.22 253 | 71.98 322 | 90.09 258 | 92.79 259 | 78.53 264 | 98.56 169 | 87.43 181 | 97.06 232 | 96.46 224 |
jason: jason. |
alignmvs | | | 93.26 144 | 92.85 152 | 94.50 109 | 95.70 217 | 87.45 114 | 93.45 137 | 95.76 197 | 91.58 114 | 95.25 134 | 92.42 272 | 81.96 241 | 98.72 147 | 91.61 109 | 97.87 202 | 97.33 187 |
|
xiu_mvs_v1_base_debu | | | 91.47 190 | 91.52 182 | 91.33 221 | 95.69 218 | 81.56 191 | 89.92 262 | 96.05 189 | 83.22 247 | 91.26 233 | 90.74 299 | 91.55 101 | 98.82 125 | 89.29 148 | 95.91 263 | 93.62 303 |
|
xiu_mvs_v1_base | | | 91.47 190 | 91.52 182 | 91.33 221 | 95.69 218 | 81.56 191 | 89.92 262 | 96.05 189 | 83.22 247 | 91.26 233 | 90.74 299 | 91.55 101 | 98.82 125 | 89.29 148 | 95.91 263 | 93.62 303 |
|
xiu_mvs_v1_base_debi | | | 91.47 190 | 91.52 182 | 91.33 221 | 95.69 218 | 81.56 191 | 89.92 262 | 96.05 189 | 83.22 247 | 91.26 233 | 90.74 299 | 91.55 101 | 98.82 125 | 89.29 148 | 95.91 263 | 93.62 303 |
|
PHI-MVS | | | 94.34 112 | 93.80 125 | 95.95 52 | 95.65 221 | 91.67 56 | 94.82 91 | 97.86 57 | 87.86 195 | 93.04 198 | 94.16 229 | 91.58 100 | 98.78 136 | 90.27 130 | 98.96 107 | 97.41 180 |
|
LF4IMVS | | | 92.72 162 | 92.02 172 | 94.84 94 | 95.65 221 | 91.99 49 | 92.92 154 | 96.60 160 | 85.08 233 | 92.44 210 | 93.62 243 | 86.80 201 | 96.35 294 | 86.81 187 | 98.25 172 | 96.18 235 |
|
test20.03 | | | 90.80 200 | 90.85 200 | 90.63 235 | 95.63 223 | 79.24 238 | 89.81 268 | 92.87 258 | 89.90 148 | 94.39 160 | 96.40 132 | 85.77 214 | 95.27 315 | 73.86 308 | 99.05 97 | 97.39 183 |
|
TinyColmap | | | 92.00 182 | 92.76 155 | 89.71 253 | 95.62 224 | 77.02 270 | 90.72 235 | 96.17 187 | 87.70 198 | 95.26 133 | 96.29 143 | 92.54 84 | 96.45 288 | 81.77 240 | 98.77 128 | 95.66 251 |
|
canonicalmvs | | | 94.59 103 | 94.69 97 | 94.30 119 | 95.60 225 | 87.03 122 | 95.59 63 | 98.24 21 | 91.56 115 | 95.21 137 | 92.04 280 | 94.95 40 | 98.66 157 | 91.45 114 | 97.57 214 | 97.20 192 |
|
AdaColmap | | | 91.63 185 | 91.36 189 | 92.47 189 | 95.56 226 | 86.36 134 | 92.24 185 | 96.27 180 | 88.88 165 | 89.90 267 | 92.69 263 | 91.65 99 | 98.32 194 | 77.38 288 | 97.64 211 | 92.72 318 |
|
tfpn1000 | | | 86.83 278 | 86.23 274 | 88.64 281 | 95.53 227 | 75.25 291 | 93.57 134 | 82.28 348 | 89.27 158 | 91.46 229 | 89.24 317 | 57.22 348 | 97.86 228 | 80.63 253 | 96.88 238 | 92.81 315 |
|
UnsupCasMVSNet_bld | | | 88.50 238 | 88.03 239 | 89.90 251 | 95.52 228 | 78.88 248 | 87.39 305 | 94.02 239 | 79.32 284 | 93.06 197 | 94.02 235 | 80.72 254 | 94.27 325 | 75.16 305 | 93.08 315 | 96.54 213 |
|
3Dnovator | | 92.54 3 | 94.80 94 | 94.90 92 | 94.47 112 | 95.47 229 | 87.06 121 | 96.63 25 | 97.28 116 | 91.82 106 | 94.34 164 | 97.41 76 | 90.60 129 | 98.65 159 | 92.47 90 | 98.11 187 | 97.70 165 |
|
Fast-Effi-MVS+ | | | 91.28 196 | 90.86 199 | 92.53 187 | 95.45 230 | 82.53 182 | 89.25 283 | 96.52 165 | 85.00 234 | 89.91 266 | 88.55 321 | 92.94 75 | 98.84 123 | 84.72 218 | 95.44 275 | 96.22 233 |
|
GBi-Net | | | 93.21 147 | 92.96 149 | 93.97 127 | 95.40 231 | 84.29 159 | 95.99 49 | 96.56 161 | 88.63 174 | 95.10 140 | 98.53 23 | 81.31 248 | 98.98 94 | 86.74 188 | 98.38 155 | 98.65 105 |
|
test1 | | | 93.21 147 | 92.96 149 | 93.97 127 | 95.40 231 | 84.29 159 | 95.99 49 | 96.56 161 | 88.63 174 | 95.10 140 | 98.53 23 | 81.31 248 | 98.98 94 | 86.74 188 | 98.38 155 | 98.65 105 |
|
FMVSNet2 | | | 92.78 160 | 92.73 157 | 92.95 162 | 95.40 231 | 81.98 186 | 94.18 116 | 95.53 208 | 88.63 174 | 96.05 101 | 97.37 79 | 81.31 248 | 98.81 130 | 87.38 183 | 98.67 135 | 98.06 137 |
|
CDS-MVSNet | | | 89.55 220 | 88.22 235 | 93.53 143 | 95.37 234 | 86.49 128 | 89.26 281 | 93.59 246 | 79.76 276 | 91.15 242 | 92.31 274 | 77.12 274 | 98.38 190 | 77.51 286 | 97.92 200 | 95.71 249 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
Test4 | | | 91.41 194 | 91.25 192 | 91.89 204 | 95.35 235 | 80.32 205 | 90.97 228 | 96.92 140 | 81.96 262 | 95.11 139 | 93.81 240 | 81.34 247 | 98.48 182 | 88.71 163 | 97.08 231 | 96.87 207 |
|
V42 | | | 93.43 135 | 93.58 137 | 92.97 160 | 95.34 236 | 81.22 195 | 92.67 161 | 96.49 166 | 87.25 204 | 96.20 94 | 96.37 140 | 87.32 188 | 98.85 122 | 92.39 94 | 98.21 177 | 98.85 89 |
|
DI_MVS_plusplus_test | | | 91.42 193 | 91.41 187 | 91.46 217 | 95.34 236 | 79.06 245 | 90.58 241 | 93.74 244 | 82.59 256 | 94.69 155 | 94.76 209 | 86.54 207 | 98.44 187 | 87.93 175 | 96.49 256 | 96.87 207 |
|
Patchmatch-RL test | | | 88.81 234 | 88.52 228 | 89.69 256 | 95.33 238 | 79.94 221 | 86.22 318 | 92.71 263 | 78.46 290 | 95.80 115 | 94.18 228 | 66.25 308 | 95.33 313 | 89.22 153 | 98.53 143 | 93.78 298 |
|
test_normal | | | 91.49 189 | 91.44 186 | 91.62 212 | 95.21 239 | 79.44 234 | 90.08 257 | 93.84 242 | 82.60 255 | 94.37 163 | 94.74 210 | 86.66 204 | 98.46 185 | 88.58 167 | 96.92 237 | 96.95 201 |
|
BH-RMVSNet | | | 90.47 205 | 90.44 206 | 90.56 236 | 95.21 239 | 78.65 253 | 89.15 284 | 93.94 241 | 88.21 188 | 92.74 204 | 94.22 226 | 86.38 208 | 97.88 225 | 78.67 277 | 95.39 276 | 95.14 265 |
|
tfpn_ndepth | | | 85.85 287 | 85.15 288 | 87.98 292 | 95.19 241 | 75.36 290 | 92.79 158 | 83.18 340 | 86.97 209 | 89.92 265 | 86.43 338 | 57.44 347 | 97.85 231 | 78.18 279 | 96.22 259 | 90.72 337 |
|
Effi-MVS+ | | | 92.79 159 | 92.74 156 | 92.94 163 | 95.10 242 | 83.30 173 | 94.00 119 | 97.53 88 | 91.36 118 | 89.35 277 | 90.65 304 | 94.01 54 | 98.66 157 | 87.40 182 | 95.30 278 | 96.88 206 |
|
USDC | | | 89.02 228 | 89.08 218 | 88.84 276 | 95.07 243 | 74.50 297 | 88.97 287 | 96.39 173 | 73.21 316 | 93.27 191 | 96.28 144 | 82.16 238 | 96.39 291 | 77.55 285 | 98.80 126 | 95.62 253 |
|
WTY-MVS | | | 86.93 277 | 86.50 271 | 88.24 290 | 94.96 244 | 74.64 293 | 87.19 308 | 92.07 275 | 78.29 291 | 88.32 294 | 91.59 288 | 78.06 267 | 94.27 325 | 74.88 306 | 93.15 313 | 95.80 245 |
|
PS-MVSNAJ | | | 88.86 233 | 88.99 222 | 88.48 287 | 94.88 245 | 74.71 292 | 86.69 314 | 95.60 202 | 80.88 268 | 87.83 300 | 87.37 333 | 90.77 121 | 98.82 125 | 82.52 235 | 94.37 296 | 91.93 329 |
|
MG-MVS | | | 89.54 221 | 89.80 213 | 88.76 277 | 94.88 245 | 72.47 317 | 89.60 271 | 92.44 268 | 85.82 224 | 89.48 275 | 95.98 161 | 82.85 232 | 97.74 241 | 81.87 239 | 95.27 279 | 96.08 238 |
|
xiu_mvs_v2_base | | | 89.00 229 | 89.19 216 | 88.46 288 | 94.86 247 | 74.63 294 | 86.97 310 | 95.60 202 | 80.88 268 | 87.83 300 | 88.62 320 | 91.04 117 | 98.81 130 | 82.51 236 | 94.38 295 | 91.93 329 |
|
MAR-MVS | | | 90.32 212 | 88.87 225 | 94.66 99 | 94.82 248 | 91.85 52 | 94.22 115 | 94.75 223 | 80.91 267 | 87.52 305 | 88.07 325 | 86.63 205 | 97.87 227 | 76.67 292 | 96.21 260 | 94.25 285 |
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020 |
PVSNet_BlendedMVS | | | 90.35 210 | 89.96 211 | 91.54 216 | 94.81 249 | 78.80 251 | 90.14 254 | 96.93 138 | 79.43 279 | 88.68 290 | 95.06 197 | 86.27 210 | 98.15 209 | 80.27 255 | 98.04 193 | 97.68 167 |
|
PVSNet_Blended | | | 88.74 236 | 88.16 237 | 90.46 239 | 94.81 249 | 78.80 251 | 86.64 315 | 96.93 138 | 74.67 305 | 88.68 290 | 89.18 318 | 86.27 210 | 98.15 209 | 80.27 255 | 96.00 261 | 94.44 282 |
|
BH-w/o | | | 87.21 268 | 87.02 259 | 87.79 296 | 94.77 251 | 77.27 268 | 87.90 298 | 93.21 255 | 81.74 264 | 89.99 263 | 88.39 323 | 83.47 226 | 96.93 272 | 71.29 325 | 92.43 321 | 89.15 341 |
|
LS3D | | | 96.11 47 | 95.83 59 | 96.95 33 | 94.75 252 | 94.20 14 | 97.34 11 | 97.98 45 | 97.31 9 | 95.32 130 | 96.77 108 | 93.08 72 | 99.20 64 | 91.79 105 | 98.16 181 | 97.44 179 |
|
Effi-MVS+-dtu | | | 93.90 123 | 92.60 161 | 97.77 4 | 94.74 253 | 96.67 4 | 94.00 119 | 95.41 211 | 89.94 146 | 91.93 225 | 92.13 278 | 90.12 136 | 98.97 98 | 87.68 177 | 97.48 220 | 97.67 168 |
|
mvs-test1 | | | 93.07 152 | 91.80 177 | 96.89 35 | 94.74 253 | 95.83 7 | 92.17 186 | 95.41 211 | 89.94 146 | 89.85 268 | 90.59 305 | 90.12 136 | 98.88 111 | 87.68 177 | 95.66 268 | 95.97 241 |
|
MVSFormer | | | 92.18 178 | 92.23 168 | 92.04 202 | 94.74 253 | 80.06 214 | 97.15 13 | 97.37 101 | 88.98 161 | 88.83 281 | 92.79 259 | 77.02 275 | 99.60 8 | 96.41 6 | 96.75 242 | 96.46 224 |
|
lupinMVS | | | 88.34 241 | 87.31 250 | 91.45 218 | 94.74 253 | 80.06 214 | 87.23 306 | 92.27 269 | 71.10 326 | 88.83 281 | 91.15 291 | 77.02 275 | 98.53 176 | 86.67 191 | 96.75 242 | 95.76 247 |
|
casdiffmvs | | | 92.55 171 | 92.40 167 | 93.01 157 | 94.72 257 | 83.36 171 | 94.54 106 | 97.04 128 | 83.00 253 | 89.97 264 | 96.95 97 | 88.23 164 | 98.76 141 | 93.22 67 | 93.95 302 | 96.92 202 |
|
MDA-MVSNet-bldmvs | | | 91.04 197 | 90.88 198 | 91.55 215 | 94.68 258 | 80.16 208 | 85.49 322 | 92.14 273 | 90.41 140 | 94.93 148 | 95.79 169 | 85.10 219 | 96.93 272 | 85.15 208 | 94.19 301 | 97.57 173 |
|
MVS_0304 | | | 92.99 154 | 92.54 162 | 94.35 118 | 94.67 259 | 86.06 141 | 91.16 223 | 97.92 55 | 90.01 145 | 88.33 293 | 94.41 218 | 87.02 194 | 99.22 62 | 90.36 126 | 99.00 103 | 97.76 161 |
|
Fast-Effi-MVS+-dtu | | | 92.77 161 | 92.16 169 | 94.58 107 | 94.66 260 | 88.25 103 | 92.05 189 | 96.65 158 | 89.62 152 | 90.08 259 | 91.23 290 | 92.56 83 | 98.60 163 | 86.30 199 | 96.27 258 | 96.90 204 |
|
UnsupCasMVSNet_eth | | | 90.33 211 | 90.34 207 | 90.28 242 | 94.64 261 | 80.24 206 | 89.69 270 | 95.88 193 | 85.77 225 | 93.94 174 | 95.69 173 | 81.99 240 | 92.98 335 | 84.21 221 | 91.30 330 | 97.62 171 |
|
1111 | | | 80.36 322 | 81.32 310 | 77.48 339 | 94.61 262 | 44.56 360 | 81.59 341 | 90.66 287 | 86.78 213 | 90.60 251 | 93.52 248 | 30.37 365 | 90.67 343 | 66.36 340 | 97.42 223 | 97.20 192 |
|
.test1245 | | | 64.72 334 | 70.88 335 | 46.22 347 | 94.61 262 | 44.56 360 | 81.59 341 | 90.66 287 | 86.78 213 | 90.60 251 | 93.52 248 | 30.37 365 | 90.67 343 | 66.36 340 | 3.45 361 | 3.44 361 |
|
OpenMVS_ROB | | 85.12 16 | 89.52 222 | 89.05 219 | 90.92 232 | 94.58 264 | 81.21 196 | 91.10 226 | 93.41 250 | 77.03 299 | 93.41 184 | 93.99 237 | 83.23 228 | 97.80 234 | 79.93 262 | 94.80 288 | 93.74 300 |
|
OpenMVS | | 89.45 8 | 92.27 177 | 92.13 171 | 92.68 178 | 94.53 265 | 84.10 164 | 95.70 60 | 97.03 129 | 82.44 259 | 91.14 243 | 96.42 130 | 88.47 159 | 98.38 190 | 85.95 201 | 97.47 221 | 95.55 258 |
|
thres200 | | | 85.85 287 | 85.18 287 | 87.88 295 | 94.44 266 | 72.52 316 | 89.08 285 | 86.21 313 | 88.57 177 | 91.44 230 | 88.40 322 | 64.22 316 | 98.00 214 | 68.35 335 | 95.88 266 | 93.12 311 |
|
DELS-MVS | | | 92.05 181 | 92.16 169 | 91.72 209 | 94.44 266 | 80.13 212 | 87.62 300 | 97.25 117 | 87.34 203 | 92.22 219 | 93.18 255 | 89.54 147 | 98.73 146 | 89.67 143 | 98.20 179 | 96.30 230 |
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 |
N_pmnet | | | 88.90 232 | 87.25 252 | 93.83 134 | 94.40 268 | 93.81 31 | 84.73 326 | 87.09 308 | 79.36 283 | 93.26 192 | 92.43 271 | 79.29 259 | 91.68 340 | 77.50 287 | 97.22 228 | 96.00 240 |
|
pmmvs4 | | | 88.95 231 | 87.70 247 | 92.70 177 | 94.30 269 | 85.60 149 | 87.22 307 | 92.16 272 | 74.62 306 | 89.75 272 | 94.19 227 | 77.97 268 | 96.41 290 | 82.71 233 | 96.36 257 | 96.09 237 |
|
new-patchmatchnet | | | 88.97 230 | 90.79 202 | 83.50 327 | 94.28 270 | 55.83 357 | 85.34 323 | 93.56 247 | 86.18 217 | 95.47 125 | 95.73 172 | 83.10 229 | 96.51 285 | 85.40 205 | 98.06 191 | 98.16 132 |
|
API-MVS | | | 91.52 188 | 91.61 181 | 91.26 224 | 94.16 271 | 86.26 137 | 94.66 96 | 94.82 220 | 91.17 122 | 92.13 220 | 91.08 293 | 90.03 142 | 97.06 268 | 79.09 269 | 97.35 226 | 90.45 339 |
|
MSDG | | | 90.82 199 | 90.67 205 | 91.26 224 | 94.16 271 | 83.08 178 | 86.63 316 | 96.19 186 | 90.60 134 | 91.94 224 | 91.89 281 | 89.16 151 | 95.75 303 | 80.96 252 | 94.51 294 | 94.95 270 |
|
TR-MVS | | | 87.70 254 | 87.17 254 | 89.27 268 | 94.11 273 | 79.26 237 | 88.69 292 | 91.86 276 | 81.94 263 | 90.69 249 | 89.79 311 | 82.82 233 | 97.42 254 | 72.65 316 | 91.98 327 | 91.14 334 |
|
sss | | | 87.23 267 | 86.82 262 | 88.46 288 | 93.96 274 | 77.94 257 | 86.84 312 | 92.78 262 | 77.59 294 | 87.61 304 | 91.83 282 | 78.75 261 | 91.92 339 | 77.84 282 | 94.20 300 | 95.52 259 |
|
PVSNet | | 76.22 20 | 82.89 303 | 82.37 302 | 84.48 322 | 93.96 274 | 64.38 347 | 78.60 348 | 88.61 294 | 71.50 324 | 84.43 324 | 86.36 339 | 74.27 283 | 94.60 319 | 69.87 333 | 93.69 307 | 94.46 281 |
|
semantic-postprocess | | | | | 91.94 203 | 93.89 276 | 79.22 242 | | 93.51 248 | 91.53 116 | 95.37 129 | 96.62 118 | 77.17 273 | 98.90 105 | 91.89 104 | 94.95 284 | 97.70 165 |
|
UGNet | | | 93.08 150 | 92.50 164 | 94.79 96 | 93.87 277 | 87.99 107 | 95.07 81 | 94.26 235 | 90.64 132 | 87.33 306 | 97.67 63 | 86.89 200 | 98.49 179 | 88.10 173 | 98.71 132 | 97.91 149 |
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 |
PAPM | | | 81.91 311 | 80.11 321 | 87.31 300 | 93.87 277 | 72.32 318 | 84.02 333 | 93.22 253 | 69.47 335 | 76.13 354 | 89.84 308 | 72.15 287 | 97.23 263 | 53.27 355 | 89.02 336 | 92.37 321 |
|
CANet | | | 92.38 174 | 91.99 173 | 93.52 144 | 93.82 279 | 83.46 170 | 91.14 224 | 97.00 131 | 89.81 149 | 86.47 311 | 94.04 233 | 87.90 177 | 99.21 63 | 89.50 145 | 98.27 169 | 97.90 150 |
|
test1235678 | | | 84.54 294 | 83.85 296 | 86.59 305 | 93.81 280 | 73.41 304 | 82.38 338 | 91.79 277 | 79.43 279 | 89.50 274 | 91.61 287 | 70.59 291 | 92.94 336 | 58.14 351 | 97.40 224 | 93.44 307 |
|
HY-MVS | | 82.50 18 | 86.81 279 | 85.93 282 | 89.47 258 | 93.63 281 | 77.93 258 | 94.02 118 | 91.58 280 | 75.68 302 | 83.64 328 | 93.64 242 | 77.40 271 | 97.42 254 | 71.70 322 | 92.07 326 | 93.05 312 |
|
no-one | | | 87.84 251 | 87.21 253 | 89.74 252 | 93.58 282 | 78.64 254 | 81.28 343 | 92.69 264 | 74.36 308 | 92.05 223 | 97.14 90 | 81.86 244 | 96.07 298 | 72.03 319 | 99.90 2 | 94.52 279 |
|
MVS_Test | | | 92.57 169 | 93.29 143 | 90.40 240 | 93.53 283 | 75.85 281 | 92.52 167 | 96.96 135 | 88.73 172 | 92.35 214 | 96.70 115 | 90.77 121 | 98.37 193 | 92.53 89 | 95.49 272 | 96.99 199 |
|
EU-MVSNet | | | 87.39 263 | 86.71 265 | 89.44 263 | 93.40 284 | 76.11 278 | 94.93 87 | 90.00 290 | 57.17 355 | 95.71 118 | 97.37 79 | 64.77 315 | 97.68 244 | 92.67 83 | 94.37 296 | 94.52 279 |
|
MS-PatchMatch | | | 88.05 248 | 87.75 245 | 88.95 274 | 93.28 285 | 77.93 258 | 87.88 299 | 92.49 267 | 75.42 304 | 92.57 208 | 93.59 245 | 80.44 255 | 94.24 327 | 81.28 245 | 92.75 318 | 94.69 276 |
|
GA-MVS | | | 87.70 254 | 86.82 262 | 90.31 241 | 93.27 286 | 77.22 269 | 84.72 328 | 92.79 261 | 85.11 232 | 89.82 269 | 90.07 306 | 66.80 303 | 97.76 239 | 84.56 219 | 94.27 299 | 95.96 242 |
|
pmmvs5 | | | 87.87 250 | 87.14 255 | 90.07 249 | 93.26 287 | 76.97 272 | 88.89 289 | 92.18 270 | 73.71 314 | 88.36 292 | 93.89 238 | 76.86 278 | 96.73 279 | 80.32 254 | 96.81 239 | 96.51 215 |
|
IterMVS | | | 90.18 214 | 90.16 209 | 90.21 247 | 93.15 288 | 75.98 280 | 87.56 303 | 92.97 257 | 86.43 216 | 94.09 170 | 96.40 132 | 78.32 265 | 97.43 253 | 87.87 176 | 94.69 291 | 97.23 190 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
MVS-HIRNet | | | 78.83 327 | 80.60 316 | 73.51 343 | 93.07 289 | 47.37 358 | 87.10 309 | 78.00 356 | 68.94 336 | 77.53 352 | 97.26 84 | 71.45 289 | 94.62 318 | 63.28 347 | 88.74 337 | 78.55 355 |
|
FMVSNet3 | | | 90.78 201 | 90.32 208 | 92.16 198 | 93.03 290 | 79.92 222 | 92.54 164 | 94.95 217 | 86.17 218 | 95.10 140 | 96.01 160 | 69.97 294 | 98.75 142 | 86.74 188 | 98.38 155 | 97.82 158 |
|
PAPR | | | 87.65 257 | 86.77 264 | 90.27 243 | 92.85 291 | 77.38 266 | 88.56 294 | 96.23 183 | 76.82 301 | 84.98 319 | 89.75 313 | 86.08 212 | 97.16 265 | 72.33 317 | 93.35 309 | 96.26 232 |
|
Regformer-1 | | | 94.55 105 | 94.33 109 | 95.19 84 | 92.83 292 | 88.54 97 | 91.87 200 | 95.84 196 | 93.99 48 | 95.95 105 | 95.04 198 | 92.00 92 | 98.79 132 | 93.14 71 | 98.31 164 | 98.23 126 |
|
Regformer-2 | | | 94.86 91 | 94.55 102 | 95.77 62 | 92.83 292 | 89.98 70 | 91.87 200 | 96.40 171 | 94.38 44 | 96.19 96 | 95.04 198 | 92.47 87 | 99.04 86 | 93.49 54 | 98.31 164 | 98.28 124 |
|
Regformer-3 | | | 94.28 113 | 94.23 115 | 94.46 113 | 92.78 294 | 86.28 136 | 92.39 177 | 94.70 225 | 93.69 58 | 95.97 103 | 95.56 179 | 91.34 105 | 98.48 182 | 93.45 57 | 98.14 183 | 98.62 109 |
|
Regformer-4 | | | 94.90 88 | 94.67 99 | 95.59 70 | 92.78 294 | 89.02 84 | 92.39 177 | 95.91 192 | 94.50 40 | 96.41 79 | 95.56 179 | 92.10 90 | 99.01 92 | 94.23 37 | 98.14 183 | 98.74 100 |
|
EI-MVSNet-Vis-set | | | 94.36 110 | 94.28 111 | 94.61 100 | 92.55 296 | 85.98 142 | 92.44 174 | 94.69 226 | 93.70 55 | 96.12 99 | 95.81 168 | 91.24 110 | 98.86 120 | 93.76 49 | 98.22 176 | 98.98 76 |
|
EI-MVSNet-UG-set | | | 94.35 111 | 94.27 113 | 94.59 105 | 92.46 297 | 85.87 144 | 92.42 176 | 94.69 226 | 93.67 59 | 96.13 98 | 95.84 167 | 91.20 113 | 98.86 120 | 93.78 46 | 98.23 174 | 99.03 67 |
|
testus | | | 82.09 310 | 81.78 305 | 83.03 329 | 92.35 298 | 64.37 348 | 79.44 346 | 93.27 252 | 73.08 317 | 87.06 308 | 85.21 343 | 76.80 279 | 89.27 350 | 53.30 354 | 95.48 273 | 95.46 260 |
|
FMVSNet5 | | | 87.82 253 | 86.56 267 | 91.62 212 | 92.31 299 | 79.81 224 | 93.49 136 | 94.81 222 | 83.26 246 | 91.36 231 | 96.93 100 | 52.77 354 | 97.49 251 | 76.07 296 | 98.03 194 | 97.55 176 |
|
diffmvs | | | 92.17 179 | 92.73 157 | 90.49 237 | 92.22 300 | 77.47 265 | 92.53 166 | 95.74 199 | 90.43 138 | 88.32 294 | 96.48 124 | 89.76 144 | 97.38 259 | 92.63 84 | 96.50 255 | 96.63 212 |
|
MDA-MVSNet_test_wron | | | 88.16 247 | 88.23 234 | 87.93 293 | 92.22 300 | 73.71 301 | 80.71 345 | 88.84 292 | 82.52 257 | 94.88 149 | 95.14 193 | 82.70 234 | 93.61 330 | 83.28 228 | 93.80 305 | 96.46 224 |
|
YYNet1 | | | 88.17 246 | 88.24 233 | 87.93 293 | 92.21 302 | 73.62 302 | 80.75 344 | 88.77 293 | 82.51 258 | 94.99 146 | 95.11 195 | 82.70 234 | 93.70 329 | 83.33 227 | 93.83 304 | 96.48 223 |
|
CANet_DTU | | | 89.85 218 | 89.17 217 | 91.87 205 | 92.20 303 | 80.02 218 | 90.79 233 | 95.87 194 | 86.02 220 | 82.53 335 | 91.77 283 | 80.01 256 | 98.57 168 | 85.66 203 | 97.70 208 | 97.01 198 |
|
mvs_anonymous | | | 90.37 209 | 91.30 191 | 87.58 297 | 92.17 304 | 68.00 331 | 89.84 267 | 94.73 224 | 83.82 245 | 93.22 196 | 97.40 77 | 87.54 181 | 97.40 256 | 87.94 174 | 95.05 283 | 97.34 186 |
|
EI-MVSNet | | | 92.99 154 | 93.26 147 | 92.19 196 | 92.12 305 | 79.21 243 | 92.32 180 | 94.67 228 | 91.77 109 | 95.24 135 | 95.85 165 | 87.14 192 | 98.49 179 | 91.99 100 | 98.26 170 | 98.86 86 |
|
CVMVSNet | | | 85.16 291 | 84.72 289 | 86.48 306 | 92.12 305 | 70.19 325 | 92.32 180 | 88.17 300 | 56.15 356 | 90.64 250 | 95.85 165 | 67.97 298 | 96.69 280 | 88.78 161 | 90.52 334 | 92.56 319 |
|
Patchmatch-test1 | | | 87.28 265 | 87.30 251 | 87.22 301 | 92.01 307 | 71.98 319 | 89.43 275 | 88.11 301 | 82.26 261 | 88.71 288 | 92.20 275 | 78.65 262 | 95.81 302 | 80.99 251 | 93.30 310 | 93.87 297 |
|
our_test_3 | | | 87.55 259 | 87.59 248 | 87.44 299 | 91.76 308 | 70.48 324 | 83.83 334 | 90.55 289 | 79.79 275 | 92.06 222 | 92.17 277 | 78.63 263 | 95.63 304 | 84.77 216 | 94.73 289 | 96.22 233 |
|
ppachtmachnet_test | | | 88.61 237 | 88.64 227 | 88.50 286 | 91.76 308 | 70.99 323 | 84.59 329 | 92.98 256 | 79.30 285 | 92.38 212 | 93.53 247 | 79.57 258 | 97.45 252 | 86.50 196 | 97.17 229 | 97.07 195 |
|
1314 | | | 86.46 284 | 86.33 272 | 86.87 304 | 91.65 310 | 74.54 295 | 91.94 194 | 94.10 237 | 74.28 309 | 84.78 321 | 87.33 334 | 83.03 230 | 95.00 317 | 78.72 276 | 91.16 332 | 91.06 335 |
|
cascas | | | 87.02 274 | 86.28 273 | 89.25 269 | 91.56 311 | 76.45 274 | 84.33 331 | 96.78 151 | 71.01 327 | 86.89 310 | 85.91 340 | 81.35 246 | 96.94 271 | 83.09 230 | 95.60 269 | 94.35 284 |
|
IB-MVS | | 77.21 19 | 83.11 300 | 81.05 312 | 89.29 267 | 91.15 312 | 75.85 281 | 85.66 321 | 86.00 316 | 79.70 277 | 82.02 340 | 86.61 335 | 48.26 358 | 98.39 188 | 77.84 282 | 92.22 324 | 93.63 302 |
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 |
MVS | | | 84.98 293 | 84.30 292 | 87.01 302 | 91.03 313 | 77.69 263 | 91.94 194 | 94.16 236 | 59.36 354 | 84.23 325 | 87.50 332 | 85.66 216 | 96.80 277 | 71.79 320 | 93.05 316 | 86.54 347 |
|
CR-MVSNet | | | 87.89 249 | 87.12 256 | 90.22 245 | 91.01 314 | 78.93 246 | 92.52 167 | 92.81 259 | 73.08 317 | 89.10 278 | 96.93 100 | 67.11 300 | 97.64 245 | 88.80 160 | 92.70 319 | 94.08 287 |
|
RPMNet | | | 89.30 224 | 89.00 221 | 90.22 245 | 91.01 314 | 78.93 246 | 92.52 167 | 87.85 303 | 91.91 98 | 89.10 278 | 96.89 103 | 68.84 295 | 97.64 245 | 90.17 133 | 92.70 319 | 94.08 287 |
|
new_pmnet | | | 81.22 315 | 81.01 314 | 81.86 333 | 90.92 316 | 70.15 326 | 84.03 332 | 80.25 355 | 70.83 329 | 85.97 314 | 89.78 312 | 67.93 299 | 84.65 356 | 67.44 337 | 91.90 328 | 90.78 336 |
|
test12356 | | | 76.35 328 | 77.41 329 | 73.19 344 | 90.70 317 | 38.86 363 | 74.56 350 | 91.14 282 | 74.55 307 | 80.54 347 | 88.18 324 | 52.36 355 | 90.49 347 | 52.38 356 | 92.26 323 | 90.21 340 |
|
PatchT | | | 87.51 260 | 88.17 236 | 85.55 312 | 90.64 318 | 66.91 335 | 92.02 190 | 86.09 314 | 92.20 91 | 89.05 280 | 97.16 89 | 64.15 317 | 96.37 293 | 89.21 154 | 92.98 317 | 93.37 309 |
|
Patchmatch-test | | | 86.10 286 | 86.01 281 | 86.38 308 | 90.63 319 | 74.22 300 | 89.57 272 | 86.69 310 | 85.73 226 | 89.81 270 | 92.83 258 | 65.24 313 | 91.04 342 | 77.82 284 | 95.78 267 | 93.88 296 |
|
PVSNet_0 | | 70.34 21 | 74.58 330 | 72.96 332 | 79.47 337 | 90.63 319 | 66.24 340 | 73.26 351 | 83.40 339 | 63.67 351 | 78.02 351 | 78.35 355 | 72.53 285 | 89.59 349 | 56.68 352 | 60.05 358 | 82.57 353 |
|
PMMVS2 | | | 81.31 314 | 83.44 297 | 74.92 342 | 90.52 321 | 46.49 359 | 69.19 356 | 85.23 329 | 84.30 241 | 87.95 299 | 94.71 212 | 76.95 277 | 84.36 357 | 64.07 344 | 98.09 189 | 93.89 295 |
|
tpm | | | 84.38 296 | 84.08 293 | 85.30 317 | 90.47 322 | 63.43 350 | 89.34 278 | 85.63 320 | 77.24 298 | 87.62 303 | 95.03 200 | 61.00 336 | 97.30 261 | 79.26 267 | 91.09 333 | 95.16 263 |
|
PNet_i23d | | | 72.03 333 | 70.91 334 | 75.38 341 | 90.46 323 | 57.84 355 | 71.73 355 | 81.53 351 | 83.86 244 | 82.21 336 | 83.49 348 | 29.97 367 | 87.80 354 | 60.78 348 | 54.12 359 | 80.51 354 |
|
wuyk23d | | | 87.83 252 | 90.79 202 | 78.96 338 | 90.46 323 | 88.63 92 | 92.72 159 | 90.67 286 | 91.65 113 | 98.68 11 | 97.64 64 | 96.06 14 | 77.53 359 | 59.84 349 | 99.41 61 | 70.73 356 |
|
Patchmtry | | | 90.11 216 | 89.92 212 | 90.66 234 | 90.35 325 | 77.00 271 | 92.96 153 | 92.81 259 | 90.25 142 | 94.74 153 | 96.93 100 | 67.11 300 | 97.52 248 | 85.17 206 | 98.98 104 | 97.46 178 |
|
CHOSEN 280x420 | | | 80.04 324 | 77.97 328 | 86.23 310 | 90.13 326 | 74.53 296 | 72.87 353 | 89.59 291 | 66.38 344 | 76.29 353 | 85.32 342 | 56.96 349 | 95.36 311 | 69.49 334 | 94.72 290 | 88.79 344 |
|
MVSTER | | | 89.32 223 | 88.75 226 | 91.03 228 | 90.10 327 | 76.62 273 | 90.85 231 | 94.67 228 | 82.27 260 | 95.24 135 | 95.79 169 | 61.09 335 | 98.49 179 | 90.49 120 | 98.26 170 | 97.97 145 |
|
tpm2 | | | 81.46 313 | 80.35 319 | 84.80 319 | 89.90 328 | 65.14 343 | 90.44 244 | 85.36 323 | 65.82 347 | 82.05 339 | 92.44 270 | 57.94 346 | 96.69 280 | 70.71 330 | 88.49 339 | 92.56 319 |
|
test0.0.03 1 | | | 82.48 306 | 81.47 309 | 85.48 313 | 89.70 329 | 73.57 303 | 84.73 326 | 81.64 350 | 83.07 251 | 88.13 297 | 86.61 335 | 62.86 330 | 89.10 352 | 66.24 342 | 90.29 335 | 93.77 299 |
|
test-LLR | | | 83.58 299 | 83.17 299 | 84.79 320 | 89.68 330 | 66.86 337 | 83.08 335 | 84.52 332 | 83.07 251 | 82.85 333 | 84.78 344 | 62.86 330 | 93.49 331 | 82.85 231 | 94.86 285 | 94.03 290 |
|
test-mter | | | 81.21 316 | 80.01 322 | 84.79 320 | 89.68 330 | 66.86 337 | 83.08 335 | 84.52 332 | 73.85 313 | 82.85 333 | 84.78 344 | 43.66 362 | 93.49 331 | 82.85 231 | 94.86 285 | 94.03 290 |
|
DSMNet-mixed | | | 82.21 308 | 81.56 307 | 84.16 324 | 89.57 332 | 70.00 327 | 90.65 237 | 77.66 357 | 54.99 357 | 83.30 331 | 97.57 66 | 77.89 269 | 90.50 346 | 66.86 339 | 95.54 271 | 91.97 328 |
|
PatchmatchNet | | | 85.22 290 | 84.64 290 | 86.98 303 | 89.51 333 | 69.83 328 | 90.52 242 | 87.34 307 | 78.87 287 | 87.22 307 | 92.74 261 | 66.91 302 | 96.53 283 | 81.77 240 | 86.88 342 | 94.58 278 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
MDTV_nov1_ep13 | | | | 83.88 295 | | 89.42 334 | 61.52 351 | 88.74 291 | 87.41 306 | 73.99 312 | 84.96 320 | 94.01 236 | 65.25 312 | 95.53 305 | 78.02 280 | 93.16 312 | |
|
tpmp4_e23 | | | 81.87 312 | 80.41 317 | 86.27 309 | 89.29 335 | 67.84 332 | 91.58 213 | 87.61 305 | 67.42 341 | 78.60 350 | 92.71 262 | 56.42 351 | 96.87 274 | 71.44 324 | 88.63 338 | 94.10 286 |
|
CostFormer | | | 83.09 301 | 82.21 303 | 85.73 311 | 89.27 336 | 67.01 334 | 90.35 246 | 86.47 312 | 70.42 331 | 83.52 330 | 93.23 254 | 61.18 334 | 96.85 275 | 77.21 289 | 88.26 340 | 93.34 310 |
|
ADS-MVSNet2 | | | 84.01 298 | 82.20 304 | 89.41 264 | 89.04 337 | 76.37 275 | 87.57 301 | 90.98 285 | 72.71 320 | 84.46 322 | 92.45 268 | 68.08 296 | 96.48 286 | 70.58 331 | 83.97 344 | 95.38 261 |
|
ADS-MVSNet | | | 82.25 307 | 81.55 308 | 84.34 323 | 89.04 337 | 65.30 341 | 87.57 301 | 85.13 330 | 72.71 320 | 84.46 322 | 92.45 268 | 68.08 296 | 92.33 338 | 70.58 331 | 83.97 344 | 95.38 261 |
|
tpm cat1 | | | 80.61 321 | 79.46 323 | 84.07 325 | 88.78 339 | 65.06 345 | 89.26 281 | 88.23 298 | 62.27 352 | 81.90 341 | 89.66 315 | 62.70 332 | 95.29 314 | 71.72 321 | 80.60 353 | 91.86 331 |
|
CMPMVS | | 68.83 22 | 87.28 265 | 85.67 284 | 92.09 200 | 88.77 340 | 85.42 151 | 90.31 248 | 94.38 232 | 70.02 333 | 88.00 298 | 93.30 253 | 73.78 284 | 94.03 328 | 75.96 298 | 96.54 250 | 96.83 209 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
PatchFormer-LS_test | | | 82.62 305 | 81.71 306 | 85.32 316 | 87.92 341 | 67.31 333 | 89.03 286 | 88.20 299 | 77.58 295 | 83.79 327 | 80.50 354 | 60.96 337 | 96.42 289 | 83.86 225 | 83.59 346 | 92.23 326 |
|
LP | | | 86.29 285 | 85.35 286 | 89.10 271 | 87.80 342 | 76.21 276 | 89.92 262 | 90.99 284 | 84.86 237 | 87.66 302 | 92.32 273 | 70.40 292 | 96.48 286 | 81.94 238 | 82.24 351 | 94.63 277 |
|
tpmrst | | | 82.85 304 | 82.93 301 | 82.64 331 | 87.65 343 | 58.99 354 | 90.14 254 | 87.90 302 | 75.54 303 | 83.93 326 | 91.63 286 | 66.79 305 | 95.36 311 | 81.21 247 | 81.54 352 | 93.57 306 |
|
JIA-IIPM | | | 85.08 292 | 83.04 300 | 91.19 227 | 87.56 344 | 86.14 139 | 89.40 277 | 84.44 338 | 88.98 161 | 82.20 337 | 97.95 50 | 56.82 350 | 96.15 296 | 76.55 294 | 83.45 347 | 91.30 333 |
|
TESTMET0.1,1 | | | 79.09 326 | 78.04 327 | 82.25 332 | 87.52 345 | 64.03 349 | 83.08 335 | 80.62 353 | 70.28 332 | 80.16 348 | 83.22 349 | 44.13 361 | 90.56 345 | 79.95 260 | 93.36 308 | 92.15 327 |
|
DWT-MVSNet_test | | | 80.74 319 | 79.18 324 | 85.43 314 | 87.51 346 | 66.87 336 | 89.87 266 | 86.01 315 | 74.20 311 | 80.86 344 | 80.62 353 | 48.84 357 | 96.68 282 | 81.54 242 | 83.14 349 | 92.75 317 |
|
gg-mvs-nofinetune | | | 82.10 309 | 81.02 313 | 85.34 315 | 87.46 347 | 71.04 321 | 94.74 93 | 67.56 360 | 96.44 20 | 79.43 349 | 98.99 5 | 45.24 359 | 96.15 296 | 67.18 338 | 92.17 325 | 88.85 343 |
|
pmmvs3 | | | 80.83 318 | 78.96 325 | 86.45 307 | 87.23 348 | 77.48 264 | 84.87 325 | 82.31 347 | 63.83 350 | 85.03 318 | 89.50 316 | 49.66 356 | 93.10 333 | 73.12 313 | 95.10 282 | 88.78 345 |
|
tpmvs | | | 84.22 297 | 83.97 294 | 84.94 318 | 87.09 349 | 65.18 342 | 91.21 222 | 88.35 296 | 82.87 254 | 85.21 316 | 90.96 295 | 65.24 313 | 96.75 278 | 79.60 266 | 85.25 343 | 92.90 314 |
|
gm-plane-assit | | | | | | 87.08 350 | 59.33 353 | | | 71.22 325 | | 83.58 347 | | 97.20 264 | 73.95 307 | | |
|
MVE | | 59.87 23 | 73.86 332 | 72.65 333 | 77.47 340 | 87.00 351 | 74.35 298 | 61.37 358 | 60.93 362 | 67.27 342 | 69.69 358 | 86.49 337 | 81.24 251 | 72.33 360 | 56.45 353 | 83.45 347 | 85.74 348 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
EPNet_dtu | | | 85.63 289 | 84.37 291 | 89.40 265 | 86.30 352 | 74.33 299 | 91.64 212 | 88.26 297 | 84.84 238 | 72.96 357 | 89.85 307 | 71.27 290 | 97.69 243 | 76.60 293 | 97.62 212 | 96.18 235 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
test2356 | | | 75.58 329 | 73.13 331 | 82.95 330 | 86.10 353 | 66.42 339 | 75.07 349 | 84.87 331 | 70.91 328 | 80.85 345 | 80.66 352 | 38.02 364 | 88.98 353 | 49.32 357 | 92.35 322 | 93.44 307 |
|
dp | | | 79.28 325 | 78.62 326 | 81.24 334 | 85.97 354 | 56.45 356 | 86.91 311 | 85.26 328 | 72.97 319 | 81.45 343 | 89.17 319 | 56.01 353 | 95.45 309 | 73.19 312 | 76.68 355 | 91.82 332 |
|
EPMVS | | | 81.17 317 | 80.37 318 | 83.58 326 | 85.58 355 | 65.08 344 | 90.31 248 | 71.34 359 | 77.31 297 | 85.80 315 | 91.30 289 | 59.38 338 | 92.70 337 | 79.99 259 | 82.34 350 | 92.96 313 |
|
E-PMN | | | 80.72 320 | 80.86 315 | 80.29 336 | 85.11 356 | 68.77 330 | 72.96 352 | 81.97 349 | 87.76 197 | 83.25 332 | 83.01 350 | 62.22 333 | 89.17 351 | 77.15 290 | 94.31 298 | 82.93 351 |
|
GG-mvs-BLEND | | | | | 83.24 328 | 85.06 357 | 71.03 322 | 94.99 86 | 65.55 361 | | 74.09 356 | 75.51 356 | 44.57 360 | 94.46 321 | 59.57 350 | 87.54 341 | 84.24 349 |
|
EMVS | | | 80.35 323 | 80.28 320 | 80.54 335 | 84.73 358 | 69.07 329 | 72.54 354 | 80.73 352 | 87.80 196 | 81.66 342 | 81.73 351 | 62.89 329 | 89.84 348 | 75.79 303 | 94.65 292 | 82.71 352 |
|
EPNet | | | 89.80 219 | 88.25 232 | 94.45 114 | 83.91 359 | 86.18 138 | 93.87 128 | 87.07 309 | 91.16 123 | 80.64 346 | 94.72 211 | 78.83 260 | 98.89 107 | 85.17 206 | 98.89 109 | 98.28 124 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
PMMVS | | | 83.00 302 | 81.11 311 | 88.66 280 | 83.81 360 | 86.44 131 | 82.24 340 | 85.65 319 | 61.75 353 | 82.07 338 | 85.64 341 | 79.75 257 | 91.59 341 | 75.99 297 | 93.09 314 | 87.94 346 |
|
testpf | | | 74.01 331 | 76.37 330 | 66.95 345 | 80.56 361 | 60.00 352 | 88.43 296 | 75.07 358 | 81.54 265 | 75.75 355 | 83.73 346 | 38.93 363 | 83.09 358 | 84.01 222 | 79.32 354 | 57.75 357 |
|
DeepMVS_CX | | | | | 53.83 346 | 70.38 362 | 64.56 346 | | 48.52 364 | 33.01 358 | 65.50 359 | 74.21 357 | 56.19 352 | 46.64 361 | 38.45 359 | 70.07 356 | 50.30 358 |
|
tmp_tt | | | 37.97 336 | 44.33 336 | 18.88 349 | 11.80 363 | 21.54 364 | 63.51 357 | 45.66 365 | 4.23 359 | 51.34 360 | 50.48 358 | 59.08 339 | 22.11 362 | 44.50 358 | 68.35 357 | 13.00 359 |
|
test123 | | | 9.49 338 | 12.01 339 | 1.91 350 | 2.87 364 | 1.30 365 | 82.38 338 | 1.34 367 | 1.36 360 | 2.84 361 | 6.56 361 | 2.45 368 | 0.97 363 | 2.73 360 | 5.56 360 | 3.47 360 |
|
testmvs | | | 9.02 339 | 11.42 340 | 1.81 351 | 2.77 365 | 1.13 366 | 79.44 346 | 1.90 366 | 1.18 361 | 2.65 362 | 6.80 360 | 1.95 369 | 0.87 364 | 2.62 361 | 3.45 361 | 3.44 361 |
|
cdsmvs_eth3d_5k | | | 23.35 337 | 31.13 338 | 0.00 352 | 0.00 366 | 0.00 367 | 0.00 359 | 95.58 206 | 0.00 362 | 0.00 363 | 91.15 291 | 93.43 61 | 0.00 365 | 0.00 362 | 0.00 363 | 0.00 363 |
|
pcd_1.5k_mvsjas | | | 7.56 340 | 10.09 341 | 0.00 352 | 0.00 366 | 0.00 367 | 0.00 359 | 0.00 368 | 0.00 362 | 0.00 363 | 0.00 364 | 90.77 121 | 0.00 365 | 0.00 362 | 0.00 363 | 0.00 363 |
|
sosnet-low-res | | | 0.00 342 | 0.00 343 | 0.00 352 | 0.00 366 | 0.00 367 | 0.00 359 | 0.00 368 | 0.00 362 | 0.00 363 | 0.00 364 | 0.00 370 | 0.00 365 | 0.00 362 | 0.00 363 | 0.00 363 |
|
sosnet | | | 0.00 342 | 0.00 343 | 0.00 352 | 0.00 366 | 0.00 367 | 0.00 359 | 0.00 368 | 0.00 362 | 0.00 363 | 0.00 364 | 0.00 370 | 0.00 365 | 0.00 362 | 0.00 363 | 0.00 363 |
|
uncertanet | | | 0.00 342 | 0.00 343 | 0.00 352 | 0.00 366 | 0.00 367 | 0.00 359 | 0.00 368 | 0.00 362 | 0.00 363 | 0.00 364 | 0.00 370 | 0.00 365 | 0.00 362 | 0.00 363 | 0.00 363 |
|
Regformer | | | 0.00 342 | 0.00 343 | 0.00 352 | 0.00 366 | 0.00 367 | 0.00 359 | 0.00 368 | 0.00 362 | 0.00 363 | 0.00 364 | 0.00 370 | 0.00 365 | 0.00 362 | 0.00 363 | 0.00 363 |
|
ab-mvs-re | | | 7.56 340 | 10.08 342 | 0.00 352 | 0.00 366 | 0.00 367 | 0.00 359 | 0.00 368 | 0.00 362 | 0.00 363 | 90.69 302 | 0.00 370 | 0.00 365 | 0.00 362 | 0.00 363 | 0.00 363 |
|
uanet | | | 0.00 342 | 0.00 343 | 0.00 352 | 0.00 366 | 0.00 367 | 0.00 359 | 0.00 368 | 0.00 362 | 0.00 363 | 0.00 364 | 0.00 370 | 0.00 365 | 0.00 362 | 0.00 363 | 0.00 363 |
|
GSMVS | | | | | | | | | | | | | | | | | 94.75 274 |
|
test_part3 | | | | | | | | 93.92 126 | | 91.83 104 | | 96.39 136 | | 99.44 24 | 89.00 156 | | |
|
test_part1 | | | | | | | | | 98.14 28 | | | | 94.69 43 | | | 99.10 92 | 98.17 130 |
|
sam_mvs1 | | | | | | | | | | | | | 66.64 306 | | | | 94.75 274 |
|
sam_mvs | | | | | | | | | | | | | 66.41 307 | | | | |
|
MTGPA | | | | | | | | | 97.62 76 | | | | | | | | |
|
test_post1 | | | | | | | | 90.21 250 | | | | 5.85 363 | 65.36 311 | 96.00 299 | 79.61 265 | | |
|
test_post | | | | | | | | | | | | 6.07 362 | 65.74 310 | 95.84 301 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 91.71 284 | 66.22 309 | 97.59 247 | | | |
|
MTMP | | | | | | | | 94.82 91 | 54.62 363 | | | | | | | | |
|
test9_res | | | | | | | | | | | | | | | 88.16 172 | 98.40 153 | 97.83 156 |
|
agg_prior2 | | | | | | | | | | | | | | | 87.06 186 | 98.36 161 | 97.98 142 |
|
test_prior4 | | | | | | | 89.91 71 | 90.74 234 | | | | | | | | | |
|
test_prior2 | | | | | | | | 90.21 250 | | 89.33 156 | 90.77 246 | 94.81 205 | 90.41 132 | | 88.21 169 | 98.55 140 | |
|
旧先验2 | | | | | | | | 90.00 260 | | 68.65 337 | 92.71 205 | | | 96.52 284 | 85.15 208 | | |
|
新几何2 | | | | | | | | 90.02 259 | | | | | | | | | |
|
无先验 | | | | | | | | 89.94 261 | 95.75 198 | 70.81 330 | | | | 98.59 165 | 81.17 248 | | 94.81 271 |
|
原ACMM2 | | | | | | | | 89.34 278 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 98.03 213 | 80.24 257 | | |
|
segment_acmp | | | | | | | | | | | | | 92.14 89 | | | | |
|
testdata1 | | | | | | | | 88.96 288 | | 88.44 182 | | | | | | | |
|
plane_prior5 | | | | | | | | | 97.81 62 | | | | | 98.95 101 | 89.26 151 | 98.51 146 | 98.60 111 |
|
plane_prior4 | | | | | | | | | | | | 95.59 174 | | | | | |
|
plane_prior3 | | | | | | | 88.43 102 | | | 90.35 141 | 93.31 187 | | | | | | |
|
plane_prior2 | | | | | | | | 94.56 103 | | 91.74 111 | | | | | | | |
|
plane_prior | | | | | | | 88.12 105 | 93.01 149 | | 88.98 161 | | | | | | 98.06 191 | |
|
n2 | | | | | | | | | 0.00 368 | | | | | | | | |
|
nn | | | | | | | | | 0.00 368 | | | | | | | | |
|
door-mid | | | | | | | | | 92.13 274 | | | | | | | | |
|
test11 | | | | | | | | | 96.65 158 | | | | | | | | |
|
door | | | | | | | | | 91.26 281 | | | | | | | | |
|
HQP5-MVS | | | | | | | 84.89 155 | | | | | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 86.55 194 | | |
|
HQP4-MVS | | | | | | | | | | | 88.81 283 | | | 98.61 161 | | | 98.15 133 |
|
HQP3-MVS | | | | | | | | | 97.31 111 | | | | | | | 97.73 205 | |
|
HQP2-MVS | | | | | | | | | | | | | 84.76 221 | | | | |
|
MDTV_nov1_ep13_2view | | | | | | | 42.48 362 | 88.45 295 | | 67.22 343 | 83.56 329 | | 66.80 303 | | 72.86 315 | | 94.06 289 |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 98.82 122 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 99.25 77 | |
|
Test By Simon | | | | | | | | | | | | | 90.61 128 | | | | |
|