region2R | | | 97.07 18 | 96.84 19 | 97.77 23 | 99.46 1 | 93.79 38 | 98.52 10 | 98.24 28 | 93.19 63 | 97.14 24 | 98.34 28 | 91.59 40 | 99.87 5 | 95.46 45 | 99.59 10 | 99.64 4 |
|
ACMMPR | | | 97.07 18 | 96.84 19 | 97.79 20 | 99.44 2 | 93.88 34 | 98.52 10 | 98.31 21 | 93.21 60 | 97.15 23 | 98.33 31 | 91.35 42 | 99.86 8 | 95.63 40 | 99.59 10 | 99.62 8 |
|
HFP-MVS | | | 97.14 15 | 96.92 16 | 97.83 16 | 99.42 3 | 94.12 28 | 98.52 10 | 98.32 19 | 93.21 60 | 97.18 21 | 98.29 37 | 92.08 29 | 99.83 15 | 95.63 40 | 99.59 10 | 99.54 20 |
|
#test# | | | 97.02 21 | 96.75 26 | 97.83 16 | 99.42 3 | 94.12 28 | 98.15 29 | 98.32 19 | 92.57 83 | 97.18 21 | 98.29 37 | 92.08 29 | 99.83 15 | 95.12 52 | 99.59 10 | 99.54 20 |
|
HSP-MVS | | | 97.53 5 | 97.49 4 | 97.63 35 | 99.40 5 | 93.77 41 | 98.53 9 | 97.85 89 | 95.55 5 | 98.56 4 | 97.81 62 | 93.90 6 | 99.65 42 | 96.62 14 | 99.21 51 | 99.48 29 |
|
mPP-MVS | | | 96.86 27 | 96.60 29 | 97.64 33 | 99.40 5 | 93.44 48 | 98.50 13 | 98.09 50 | 93.27 59 | 95.95 61 | 98.33 31 | 91.04 46 | 99.88 3 | 95.20 47 | 99.57 14 | 99.60 11 |
|
MP-MVS | | | 96.77 31 | 96.45 36 | 97.72 26 | 99.39 7 | 93.80 37 | 98.41 17 | 98.06 58 | 93.37 55 | 95.54 76 | 98.34 28 | 90.59 53 | 99.88 3 | 94.83 62 | 99.54 16 | 99.49 27 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
XVS | | | 97.18 12 | 96.96 14 | 97.81 18 | 99.38 8 | 94.03 32 | 98.59 7 | 98.20 31 | 94.85 17 | 96.59 38 | 98.29 37 | 91.70 37 | 99.80 21 | 95.66 38 | 99.40 33 | 99.62 8 |
|
X-MVStestdata | | | 91.71 177 | 89.67 235 | 97.81 18 | 99.38 8 | 94.03 32 | 98.59 7 | 98.20 31 | 94.85 17 | 96.59 38 | 32.69 355 | 91.70 37 | 99.80 21 | 95.66 38 | 99.40 33 | 99.62 8 |
|
zzz-MVS | | | 97.07 18 | 96.77 25 | 97.97 12 | 99.37 10 | 94.42 19 | 97.15 128 | 98.08 51 | 95.07 14 | 96.11 52 | 98.59 6 | 90.88 50 | 99.90 1 | 96.18 28 | 99.50 22 | 99.58 12 |
|
MTAPA | | | 97.08 17 | 96.78 24 | 97.97 12 | 99.37 10 | 94.42 19 | 97.24 116 | 98.08 51 | 95.07 14 | 96.11 52 | 98.59 6 | 90.88 50 | 99.90 1 | 96.18 28 | 99.50 22 | 99.58 12 |
|
HPM-MVS | | | 96.69 34 | 96.45 36 | 97.40 41 | 99.36 12 | 93.11 56 | 98.87 1 | 98.06 58 | 91.17 124 | 96.40 46 | 97.99 51 | 90.99 47 | 99.58 56 | 95.61 42 | 99.61 9 | 99.49 27 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
PGM-MVS | | | 96.81 29 | 96.53 32 | 97.65 31 | 99.35 13 | 93.53 46 | 97.65 71 | 98.98 1 | 92.22 88 | 97.14 24 | 98.44 17 | 91.17 44 | 99.85 11 | 94.35 69 | 99.46 26 | 99.57 14 |
|
CP-MVS | | | 97.02 21 | 96.81 22 | 97.64 33 | 99.33 14 | 93.54 45 | 98.80 3 | 98.28 23 | 92.99 69 | 96.45 45 | 98.30 36 | 91.90 34 | 99.85 11 | 95.61 42 | 99.68 2 | 99.54 20 |
|
HPM-MVS_fast | | | 96.51 39 | 96.27 40 | 97.22 52 | 99.32 15 | 92.74 64 | 98.74 4 | 98.06 58 | 90.57 145 | 96.77 31 | 98.35 25 | 90.21 57 | 99.53 71 | 94.80 64 | 99.63 5 | 99.38 40 |
|
MCST-MVS | | | 97.18 12 | 96.84 19 | 98.20 6 | 99.30 16 | 95.35 5 | 97.12 130 | 98.07 56 | 93.54 53 | 96.08 54 | 97.69 69 | 93.86 7 | 99.71 30 | 96.50 18 | 99.39 35 | 99.55 18 |
|
test_part2 | | | | | | 99.28 17 | 95.74 3 | | | | 98.10 7 | | | | | | |
|
ESAPD | | | 97.57 4 | 97.29 7 | 98.41 2 | 99.28 17 | 95.74 3 | 97.50 92 | 98.26 25 | 93.81 45 | 98.10 7 | 98.53 12 | 95.31 1 | 99.87 5 | 95.19 48 | 99.63 5 | 99.63 5 |
|
CPTT-MVS | | | 95.57 60 | 95.19 61 | 96.70 63 | 99.27 19 | 91.48 98 | 98.33 20 | 98.11 46 | 87.79 226 | 95.17 80 | 98.03 47 | 87.09 93 | 99.61 48 | 93.51 84 | 99.42 31 | 99.02 65 |
|
TSAR-MVS + MP. | | | 97.42 6 | 97.33 6 | 97.69 29 | 99.25 20 | 94.24 24 | 98.07 34 | 97.85 89 | 93.72 47 | 98.57 3 | 98.35 25 | 93.69 9 | 99.40 88 | 97.06 3 | 99.46 26 | 99.44 33 |
|
CSCG | | | 96.05 51 | 95.91 47 | 96.46 79 | 99.24 21 | 90.47 131 | 98.30 21 | 98.57 11 | 89.01 180 | 93.97 98 | 97.57 82 | 92.62 19 | 99.76 24 | 94.66 67 | 99.27 46 | 99.15 56 |
|
ACMMP | | | 96.27 46 | 95.93 46 | 97.28 47 | 99.24 21 | 92.62 68 | 98.25 25 | 98.81 3 | 92.99 69 | 94.56 87 | 98.39 23 | 88.96 66 | 99.85 11 | 94.57 68 | 97.63 97 | 99.36 42 |
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 |
MP-MVS-pluss | | | 96.70 33 | 96.27 40 | 97.98 11 | 99.23 23 | 94.71 14 | 96.96 140 | 98.06 58 | 90.67 135 | 95.55 75 | 98.78 3 | 91.07 45 | 99.86 8 | 96.58 16 | 99.55 15 | 99.38 40 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
DP-MVS Recon | | | 95.68 58 | 95.12 63 | 97.37 42 | 99.19 24 | 94.19 25 | 97.03 133 | 98.08 51 | 88.35 212 | 95.09 81 | 97.65 73 | 89.97 60 | 99.48 78 | 92.08 107 | 98.59 76 | 98.44 112 |
|
APDe-MVS | | | 97.82 1 | 97.73 1 | 98.08 9 | 99.15 25 | 94.82 13 | 98.81 2 | 98.30 22 | 94.76 24 | 98.30 5 | 98.90 2 | 93.77 8 | 99.68 38 | 97.93 1 | 99.69 1 | 99.75 1 |
|
ACMMP_Plus | | | 97.20 11 | 96.86 18 | 98.23 5 | 99.09 26 | 95.16 9 | 97.60 84 | 98.19 33 | 92.82 78 | 97.93 11 | 98.74 4 | 91.60 39 | 99.86 8 | 96.26 21 | 99.52 18 | 99.67 2 |
|
HPM-MVS++ | | | 97.34 9 | 96.97 13 | 98.47 1 | 99.08 27 | 96.16 1 | 97.55 89 | 97.97 79 | 95.59 4 | 96.61 36 | 97.89 53 | 92.57 20 | 99.84 14 | 95.95 33 | 99.51 20 | 99.40 36 |
|
114514_t | | | 93.95 99 | 93.06 108 | 96.63 66 | 99.07 28 | 91.61 94 | 97.46 99 | 97.96 80 | 77.99 327 | 93.00 121 | 97.57 82 | 86.14 104 | 99.33 93 | 89.22 151 | 99.15 55 | 98.94 75 |
|
SMA-MVS | | | 97.36 8 | 97.06 9 | 98.25 4 | 99.06 29 | 95.30 7 | 97.94 42 | 98.19 33 | 90.66 137 | 99.06 1 | 98.94 1 | 93.33 11 | 99.83 15 | 96.72 13 | 99.68 2 | 99.63 5 |
|
APD-MVS | | | 96.95 24 | 96.60 29 | 98.01 10 | 99.03 30 | 94.93 12 | 97.72 62 | 98.10 48 | 91.50 113 | 98.01 9 | 98.32 33 | 92.33 24 | 99.58 56 | 94.85 61 | 99.51 20 | 99.53 23 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
APD-MVS_3200maxsize | | | 96.81 29 | 96.71 27 | 97.12 56 | 99.01 31 | 92.31 74 | 97.98 41 | 98.06 58 | 93.11 66 | 97.44 16 | 98.55 10 | 90.93 48 | 99.55 66 | 96.06 30 | 99.25 47 | 99.51 24 |
|
CDPH-MVS | | | 95.97 54 | 95.38 56 | 97.77 23 | 98.93 32 | 94.44 18 | 96.35 203 | 97.88 84 | 86.98 247 | 96.65 35 | 97.89 53 | 91.99 33 | 99.47 79 | 92.26 98 | 99.46 26 | 99.39 37 |
|
CNVR-MVS | | | 97.68 2 | 97.44 5 | 98.37 3 | 98.90 33 | 95.86 2 | 97.27 114 | 98.08 51 | 95.81 3 | 97.87 12 | 98.31 34 | 94.26 4 | 99.68 38 | 97.02 4 | 99.49 24 | 99.57 14 |
|
abl_6 | | | 96.40 42 | 96.21 42 | 96.98 60 | 98.89 34 | 92.20 79 | 97.89 46 | 98.03 67 | 93.34 58 | 97.22 20 | 98.42 19 | 87.93 80 | 99.72 29 | 95.10 53 | 99.07 62 | 99.02 65 |
|
PAPM_NR | | | 95.01 71 | 94.59 72 | 96.26 91 | 98.89 34 | 90.68 126 | 97.24 116 | 97.73 95 | 91.80 107 | 92.93 126 | 96.62 125 | 89.13 65 | 99.14 107 | 89.21 152 | 97.78 94 | 98.97 71 |
|
NCCC | | | 97.30 10 | 97.03 11 | 98.11 8 | 98.77 36 | 95.06 11 | 97.34 108 | 98.04 65 | 95.96 2 | 97.09 28 | 97.88 55 | 93.18 12 | 99.71 30 | 95.84 36 | 99.17 54 | 99.56 16 |
|
DP-MVS | | | 92.76 139 | 91.51 162 | 96.52 71 | 98.77 36 | 90.99 115 | 97.38 106 | 96.08 226 | 82.38 302 | 89.29 222 | 97.87 56 | 83.77 127 | 99.69 36 | 81.37 282 | 96.69 123 | 98.89 81 |
|
MSLP-MVS++ | | | 96.94 25 | 97.06 9 | 96.59 69 | 98.72 38 | 91.86 89 | 97.67 68 | 98.49 12 | 94.66 27 | 97.24 19 | 98.41 22 | 92.31 27 | 98.94 128 | 96.61 15 | 99.46 26 | 98.96 72 |
|
TEST9 | | | | | | 98.70 39 | 94.19 25 | 96.41 195 | 98.02 68 | 88.17 219 | 96.03 55 | 97.56 84 | 92.74 15 | 99.59 53 | | | |
|
train_agg | | | 96.30 45 | 95.83 48 | 97.72 26 | 98.70 39 | 94.19 25 | 96.41 195 | 98.02 68 | 88.58 198 | 96.03 55 | 97.56 84 | 92.73 16 | 99.59 53 | 95.04 54 | 99.37 40 | 99.39 37 |
|
test_8 | | | | | | 98.67 41 | 94.06 31 | 96.37 202 | 98.01 70 | 88.58 198 | 95.98 60 | 97.55 86 | 92.73 16 | 99.58 56 | | | |
|
agg_prior3 | | | 96.16 49 | 95.67 50 | 97.62 36 | 98.67 41 | 93.88 34 | 96.41 195 | 98.00 72 | 87.93 223 | 95.81 65 | 97.47 88 | 92.33 24 | 99.59 53 | 95.04 54 | 99.37 40 | 99.39 37 |
|
agg_prior1 | | | 96.22 48 | 95.77 49 | 97.56 37 | 98.67 41 | 93.79 38 | 96.28 211 | 98.00 72 | 88.76 195 | 95.68 69 | 97.55 86 | 92.70 18 | 99.57 64 | 95.01 56 | 99.32 42 | 99.32 44 |
|
agg_prior | | | | | | 98.67 41 | 93.79 38 | | 98.00 72 | | 95.68 69 | | | 99.57 64 | | | |
|
test_prior3 | | | 96.46 41 | 96.20 43 | 97.23 50 | 98.67 41 | 92.99 58 | 96.35 203 | 98.00 72 | 92.80 79 | 96.03 55 | 97.59 80 | 92.01 31 | 99.41 86 | 95.01 56 | 99.38 36 | 99.29 46 |
|
test_prior | | | | | 97.23 50 | 98.67 41 | 92.99 58 | | 98.00 72 | | | | | 99.41 86 | | | 99.29 46 |
|
DeepC-MVS_fast | | 93.89 2 | 96.93 26 | 96.64 28 | 97.78 21 | 98.64 47 | 94.30 21 | 97.41 100 | 98.04 65 | 94.81 22 | 96.59 38 | 98.37 24 | 91.24 43 | 99.64 47 | 95.16 50 | 99.52 18 | 99.42 35 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
新几何1 | | | | | 97.32 44 | 98.60 48 | 93.59 44 | | 97.75 93 | 81.58 309 | 95.75 68 | 97.85 59 | 90.04 59 | 99.67 40 | 86.50 205 | 99.13 57 | 98.69 92 |
|
原ACMM1 | | | | | 96.38 82 | 98.59 49 | 91.09 114 | | 97.89 83 | 87.41 235 | 95.22 79 | 97.68 70 | 90.25 55 | 99.54 68 | 87.95 175 | 99.12 60 | 98.49 105 |
|
AdaColmap | | | 94.34 86 | 93.68 90 | 96.31 86 | 98.59 49 | 91.68 93 | 96.59 186 | 97.81 91 | 89.87 153 | 92.15 138 | 97.06 102 | 83.62 129 | 99.54 68 | 89.34 147 | 98.07 87 | 97.70 143 |
|
PLC | | 91.00 6 | 94.11 93 | 93.43 100 | 96.13 95 | 98.58 51 | 91.15 113 | 96.69 175 | 97.39 138 | 87.29 238 | 91.37 152 | 96.71 111 | 88.39 75 | 99.52 74 | 87.33 193 | 97.13 112 | 97.73 141 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
1121 | | | 94.71 82 | 93.83 85 | 97.34 43 | 98.57 52 | 93.64 43 | 96.04 225 | 97.73 95 | 81.56 311 | 95.68 69 | 97.85 59 | 90.23 56 | 99.65 42 | 87.68 182 | 99.12 60 | 98.73 88 |
|
SD-MVS | | | 97.41 7 | 97.53 2 | 97.06 57 | 98.57 52 | 94.46 17 | 97.92 44 | 98.14 41 | 94.82 21 | 99.01 2 | 98.55 10 | 94.18 5 | 97.41 279 | 96.94 5 | 99.64 4 | 99.32 44 |
|
test12 | | | | | 97.65 31 | 98.46 54 | 94.26 22 | | 97.66 104 | | 95.52 77 | | 90.89 49 | 99.46 80 | | 99.25 47 | 99.22 51 |
|
MVS_111021_HR | | | 96.68 36 | 96.58 31 | 96.99 59 | 98.46 54 | 92.31 74 | 96.20 218 | 98.90 2 | 94.30 35 | 95.86 63 | 97.74 67 | 92.33 24 | 99.38 91 | 96.04 31 | 99.42 31 | 99.28 49 |
|
OMC-MVS | | | 95.09 70 | 94.70 70 | 96.25 92 | 98.46 54 | 91.28 104 | 96.43 193 | 97.57 112 | 92.04 102 | 94.77 85 | 97.96 52 | 87.01 94 | 99.09 118 | 91.31 126 | 96.77 119 | 98.36 119 |
|
MG-MVS | | | 95.61 59 | 95.38 56 | 96.31 86 | 98.42 57 | 90.53 129 | 96.04 225 | 97.48 121 | 93.47 54 | 95.67 72 | 98.10 43 | 89.17 64 | 99.25 97 | 91.27 127 | 98.77 71 | 99.13 58 |
|
PHI-MVS | | | 96.77 31 | 96.46 35 | 97.71 28 | 98.40 58 | 94.07 30 | 98.21 28 | 98.45 15 | 89.86 154 | 97.11 27 | 98.01 49 | 92.52 22 | 99.69 36 | 96.03 32 | 99.53 17 | 99.36 42 |
|
F-COLMAP | | | 93.58 111 | 92.98 109 | 95.37 128 | 98.40 58 | 88.98 190 | 97.18 125 | 97.29 148 | 87.75 228 | 90.49 175 | 97.10 101 | 85.21 112 | 99.50 77 | 86.70 202 | 96.72 122 | 97.63 144 |
|
SteuartSystems-ACMMP | | | 97.62 3 | 97.53 2 | 97.87 14 | 98.39 60 | 94.25 23 | 98.43 16 | 98.27 24 | 95.34 9 | 98.11 6 | 98.56 8 | 94.53 3 | 99.71 30 | 96.57 17 | 99.62 8 | 99.65 3 |
Skip Steuart: Steuart Systems R&D Blog. |
旧先验1 | | | | | | 98.38 61 | 93.38 50 | | 97.75 93 | | | 98.09 44 | 92.30 28 | | | 99.01 65 | 99.16 54 |
|
CNLPA | | | 94.28 87 | 93.53 95 | 96.52 71 | 98.38 61 | 92.55 70 | 96.59 186 | 96.88 192 | 90.13 150 | 91.91 142 | 97.24 94 | 85.21 112 | 99.09 118 | 87.64 185 | 97.83 92 | 97.92 132 |
|
Regformer-3 | | | 96.85 28 | 96.80 23 | 97.01 58 | 98.34 63 | 92.02 85 | 96.96 140 | 97.76 92 | 95.01 16 | 97.08 29 | 98.42 19 | 91.71 36 | 99.54 68 | 96.80 9 | 99.13 57 | 99.48 29 |
|
Regformer-4 | | | 96.97 23 | 96.87 17 | 97.25 49 | 98.34 63 | 92.66 67 | 96.96 140 | 98.01 70 | 95.12 13 | 97.14 24 | 98.42 19 | 91.82 35 | 99.61 48 | 96.90 6 | 99.13 57 | 99.50 25 |
|
TAPA-MVS | | 90.10 7 | 92.30 158 | 91.22 172 | 95.56 116 | 98.33 65 | 89.60 159 | 96.79 160 | 97.65 106 | 81.83 306 | 91.52 149 | 97.23 95 | 87.94 79 | 98.91 130 | 71.31 327 | 98.37 80 | 98.17 123 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
Regformer-1 | | | 97.10 16 | 96.96 14 | 97.54 38 | 98.32 66 | 93.48 47 | 96.83 153 | 97.99 77 | 95.20 12 | 97.46 15 | 98.25 40 | 92.48 23 | 99.58 56 | 96.79 11 | 99.29 44 | 99.55 18 |
|
Regformer-2 | | | 97.16 14 | 96.99 12 | 97.67 30 | 98.32 66 | 93.84 36 | 96.83 153 | 98.10 48 | 95.24 10 | 97.49 14 | 98.25 40 | 92.57 20 | 99.61 48 | 96.80 9 | 99.29 44 | 99.56 16 |
|
TSAR-MVS + GP. | | | 96.69 34 | 96.49 33 | 97.27 48 | 98.31 68 | 93.39 49 | 96.79 160 | 96.72 198 | 94.17 36 | 97.44 16 | 97.66 72 | 92.76 14 | 99.33 93 | 96.86 8 | 97.76 96 | 99.08 63 |
|
CHOSEN 1792x2688 | | | 94.15 90 | 93.51 96 | 96.06 96 | 98.27 69 | 89.38 174 | 95.18 265 | 98.48 14 | 85.60 268 | 93.76 100 | 97.11 100 | 83.15 135 | 99.61 48 | 91.33 125 | 98.72 73 | 99.19 52 |
|
PVSNet_BlendedMVS | | | 94.06 95 | 93.92 83 | 94.47 173 | 98.27 69 | 89.46 168 | 96.73 165 | 98.36 16 | 90.17 149 | 94.36 90 | 95.24 190 | 88.02 77 | 99.58 56 | 93.44 87 | 90.72 220 | 94.36 287 |
|
PVSNet_Blended | | | 94.87 79 | 94.56 73 | 95.81 105 | 98.27 69 | 89.46 168 | 95.47 253 | 98.36 16 | 88.84 189 | 94.36 90 | 96.09 147 | 88.02 77 | 99.58 56 | 93.44 87 | 98.18 84 | 98.40 115 |
|
EI-MVSNet-Vis-set | | | 96.51 39 | 96.47 34 | 96.63 66 | 98.24 72 | 91.20 108 | 96.89 149 | 97.73 95 | 94.74 25 | 96.49 42 | 98.49 14 | 90.88 50 | 99.58 56 | 96.44 19 | 98.32 81 | 99.13 58 |
|
test222 | | | | | | 98.24 72 | 92.21 77 | 95.33 257 | 97.60 109 | 79.22 322 | 95.25 78 | 97.84 61 | 88.80 69 | | | 99.15 55 | 98.72 89 |
|
HyFIR lowres test | | | 93.66 108 | 92.92 111 | 95.87 103 | 98.24 72 | 89.88 145 | 94.58 272 | 98.49 12 | 85.06 275 | 93.78 99 | 95.78 162 | 82.86 156 | 98.67 149 | 91.77 114 | 95.71 140 | 99.07 64 |
|
MVS_111021_LR | | | 96.24 47 | 96.19 44 | 96.39 81 | 98.23 75 | 91.35 103 | 96.24 216 | 98.79 4 | 93.99 39 | 95.80 66 | 97.65 73 | 89.92 61 | 99.24 98 | 95.87 34 | 99.20 52 | 98.58 95 |
|
EI-MVSNet-UG-set | | | 96.34 44 | 96.30 39 | 96.47 77 | 98.20 76 | 90.93 119 | 96.86 151 | 97.72 98 | 94.67 26 | 96.16 51 | 98.46 15 | 90.43 54 | 99.58 56 | 96.23 22 | 97.96 90 | 98.90 79 |
|
PVSNet_Blended_VisFu | | | 95.27 65 | 94.91 65 | 96.38 82 | 98.20 76 | 90.86 121 | 97.27 114 | 98.25 27 | 90.21 148 | 94.18 94 | 97.27 92 | 87.48 88 | 99.73 26 | 93.53 83 | 97.77 95 | 98.55 96 |
|
PatchMatch-RL | | | 92.90 133 | 92.02 138 | 95.56 116 | 98.19 78 | 90.80 123 | 95.27 262 | 97.18 152 | 87.96 222 | 91.86 144 | 95.68 169 | 80.44 206 | 98.99 126 | 84.01 247 | 97.54 99 | 96.89 169 |
|
testdata | | | | | 95.46 125 | 98.18 79 | 88.90 192 | | 97.66 104 | 82.73 300 | 97.03 30 | 98.07 45 | 90.06 58 | 98.85 136 | 89.67 141 | 98.98 66 | 98.64 94 |
|
LFMVS | | | 93.60 110 | 92.63 121 | 96.52 71 | 98.13 80 | 91.27 105 | 97.94 42 | 93.39 319 | 90.57 145 | 96.29 47 | 98.31 34 | 69.00 309 | 99.16 104 | 94.18 70 | 95.87 136 | 99.12 60 |
|
DeepPCF-MVS | | 93.97 1 | 96.61 37 | 97.09 8 | 95.15 136 | 98.09 81 | 86.63 256 | 96.00 229 | 98.15 39 | 95.43 7 | 97.95 10 | 98.56 8 | 93.40 10 | 99.36 92 | 96.77 12 | 99.48 25 | 99.45 31 |
|
VNet | | | 95.89 56 | 95.45 53 | 97.21 53 | 98.07 82 | 92.94 61 | 97.50 92 | 98.15 39 | 93.87 41 | 97.52 13 | 97.61 79 | 85.29 111 | 99.53 71 | 95.81 37 | 95.27 144 | 99.16 54 |
|
MAR-MVS | | | 94.22 88 | 93.46 98 | 96.51 74 | 98.00 83 | 92.19 80 | 97.67 68 | 97.47 124 | 88.13 221 | 93.00 121 | 95.84 155 | 84.86 118 | 99.51 75 | 87.99 174 | 98.17 85 | 97.83 138 |
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 |
view600 | | | 92.55 142 | 91.68 148 | 95.18 131 | 97.98 84 | 89.44 170 | 98.00 37 | 94.57 293 | 92.09 96 | 93.17 115 | 95.52 177 | 78.14 253 | 99.11 109 | 81.61 271 | 94.04 164 | 96.98 160 |
|
view800 | | | 92.55 142 | 91.68 148 | 95.18 131 | 97.98 84 | 89.44 170 | 98.00 37 | 94.57 293 | 92.09 96 | 93.17 115 | 95.52 177 | 78.14 253 | 99.11 109 | 81.61 271 | 94.04 164 | 96.98 160 |
|
conf0.05thres1000 | | | 92.55 142 | 91.68 148 | 95.18 131 | 97.98 84 | 89.44 170 | 98.00 37 | 94.57 293 | 92.09 96 | 93.17 115 | 95.52 177 | 78.14 253 | 99.11 109 | 81.61 271 | 94.04 164 | 96.98 160 |
|
tfpn | | | 92.55 142 | 91.68 148 | 95.18 131 | 97.98 84 | 89.44 170 | 98.00 37 | 94.57 293 | 92.09 96 | 93.17 115 | 95.52 177 | 78.14 253 | 99.11 109 | 81.61 271 | 94.04 164 | 96.98 160 |
|
DeepC-MVS | | 93.07 3 | 96.06 50 | 95.66 51 | 97.29 46 | 97.96 88 | 93.17 55 | 97.30 113 | 98.06 58 | 93.92 40 | 93.38 106 | 98.66 5 | 86.83 95 | 99.73 26 | 95.60 44 | 99.22 50 | 98.96 72 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
COLMAP_ROB | | 87.81 15 | 90.40 235 | 89.28 242 | 93.79 204 | 97.95 89 | 87.13 245 | 96.92 147 | 95.89 239 | 82.83 299 | 86.88 268 | 97.18 96 | 73.77 289 | 99.29 95 | 78.44 305 | 93.62 174 | 94.95 257 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
AllTest | | | 90.23 239 | 88.98 246 | 93.98 191 | 97.94 90 | 86.64 253 | 96.51 190 | 95.54 252 | 85.38 269 | 85.49 278 | 96.77 109 | 70.28 305 | 99.15 105 | 80.02 295 | 92.87 185 | 96.15 192 |
|
TestCases | | | | | 93.98 191 | 97.94 90 | 86.64 253 | | 95.54 252 | 85.38 269 | 85.49 278 | 96.77 109 | 70.28 305 | 99.15 105 | 80.02 295 | 92.87 185 | 96.15 192 |
|
tfpn111 | | | 92.45 149 | 91.58 155 | 95.06 140 | 97.92 92 | 89.37 175 | 97.71 64 | 94.66 288 | 92.20 90 | 93.31 108 | 94.90 199 | 78.06 257 | 99.11 109 | 81.37 282 | 94.06 162 | 96.70 175 |
|
conf200view11 | | | 92.45 149 | 91.58 155 | 95.05 141 | 97.92 92 | 89.37 175 | 97.71 64 | 94.66 288 | 92.20 90 | 93.31 108 | 94.90 199 | 78.06 257 | 99.08 120 | 81.40 278 | 94.08 158 | 96.70 175 |
|
thres100view900 | | | 92.43 151 | 91.58 155 | 94.98 146 | 97.92 92 | 89.37 175 | 97.71 64 | 94.66 288 | 92.20 90 | 93.31 108 | 94.90 199 | 78.06 257 | 99.08 120 | 81.40 278 | 94.08 158 | 96.48 183 |
|
thres600view7 | | | 92.49 148 | 91.60 154 | 95.18 131 | 97.91 95 | 89.47 166 | 97.65 71 | 94.66 288 | 92.18 95 | 93.33 107 | 94.91 198 | 78.06 257 | 99.10 115 | 81.61 271 | 94.06 162 | 96.98 160 |
|
API-MVS | | | 94.84 80 | 94.49 77 | 95.90 102 | 97.90 96 | 92.00 86 | 97.80 53 | 97.48 121 | 89.19 170 | 94.81 84 | 96.71 111 | 88.84 68 | 99.17 103 | 88.91 160 | 98.76 72 | 96.53 180 |
|
VDD-MVS | | | 93.82 103 | 93.08 107 | 96.02 98 | 97.88 97 | 89.96 143 | 97.72 62 | 95.85 240 | 92.43 85 | 95.86 63 | 98.44 17 | 68.42 313 | 99.39 89 | 96.31 20 | 94.85 148 | 98.71 91 |
|
tfpn200view9 | | | 92.38 154 | 91.52 160 | 94.95 149 | 97.85 98 | 89.29 181 | 97.41 100 | 94.88 283 | 92.19 93 | 93.27 112 | 94.46 223 | 78.17 250 | 99.08 120 | 81.40 278 | 94.08 158 | 96.48 183 |
|
thres400 | | | 92.42 152 | 91.52 160 | 95.12 139 | 97.85 98 | 89.29 181 | 97.41 100 | 94.88 283 | 92.19 93 | 93.27 112 | 94.46 223 | 78.17 250 | 99.08 120 | 81.40 278 | 94.08 158 | 96.98 160 |
|
DELS-MVS | | | 96.61 37 | 96.38 38 | 97.30 45 | 97.79 100 | 93.19 54 | 95.96 230 | 98.18 36 | 95.23 11 | 95.87 62 | 97.65 73 | 91.45 41 | 99.70 35 | 95.87 34 | 99.44 30 | 99.00 70 |
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 |
tfpn1000 | | | 91.99 170 | 91.05 175 | 94.80 157 | 97.78 101 | 89.66 156 | 97.91 45 | 92.90 330 | 88.99 181 | 91.73 145 | 94.84 204 | 78.99 234 | 98.33 182 | 82.41 267 | 93.91 170 | 96.40 185 |
|
PVSNet | | 86.66 18 | 92.24 161 | 91.74 147 | 93.73 211 | 97.77 102 | 83.69 287 | 92.88 308 | 96.72 198 | 87.91 224 | 93.00 121 | 94.86 203 | 78.51 245 | 99.05 124 | 86.53 203 | 97.45 104 | 98.47 108 |
|
MVS_0304 | | | 96.05 51 | 95.45 53 | 97.85 15 | 97.75 103 | 94.50 16 | 96.87 150 | 97.95 82 | 95.46 6 | 95.60 73 | 98.01 49 | 80.96 193 | 99.83 15 | 97.23 2 | 99.25 47 | 99.23 50 |
|
tfpn_ndepth | | | 91.88 173 | 90.96 179 | 94.62 166 | 97.73 104 | 89.93 144 | 97.75 56 | 92.92 329 | 88.93 186 | 91.73 145 | 93.80 260 | 78.91 235 | 98.49 166 | 83.02 259 | 93.86 171 | 95.45 226 |
|
WTY-MVS | | | 94.71 82 | 94.02 82 | 96.79 62 | 97.71 105 | 92.05 83 | 96.59 186 | 97.35 144 | 90.61 142 | 94.64 86 | 96.93 104 | 86.41 99 | 99.39 89 | 91.20 129 | 94.71 154 | 98.94 75 |
|
UA-Net | | | 95.95 55 | 95.53 52 | 97.20 54 | 97.67 106 | 92.98 60 | 97.65 71 | 98.13 42 | 94.81 22 | 96.61 36 | 98.35 25 | 88.87 67 | 99.51 75 | 90.36 134 | 97.35 107 | 99.11 61 |
|
IS-MVSNet | | | 94.90 77 | 94.52 76 | 96.05 97 | 97.67 106 | 90.56 128 | 98.44 15 | 96.22 221 | 93.21 60 | 93.99 96 | 97.74 67 | 85.55 109 | 98.45 167 | 89.98 135 | 97.86 91 | 99.14 57 |
|
PAPR | | | 94.18 89 | 93.42 102 | 96.48 76 | 97.64 108 | 91.42 102 | 95.55 248 | 97.71 101 | 88.99 181 | 92.34 134 | 95.82 157 | 89.19 63 | 99.11 109 | 86.14 210 | 97.38 105 | 98.90 79 |
|
CANet | | | 96.39 43 | 96.02 45 | 97.50 39 | 97.62 109 | 93.38 50 | 97.02 135 | 97.96 80 | 95.42 8 | 94.86 83 | 97.81 62 | 87.38 90 | 99.82 19 | 96.88 7 | 99.20 52 | 99.29 46 |
|
thres200 | | | 92.23 162 | 91.39 163 | 94.75 161 | 97.61 110 | 89.03 189 | 96.60 185 | 95.09 273 | 92.08 101 | 93.28 111 | 94.00 253 | 78.39 248 | 99.04 125 | 81.26 290 | 94.18 157 | 96.19 189 |
|
Vis-MVSNet (Re-imp) | | | 94.15 90 | 93.88 84 | 94.95 149 | 97.61 110 | 87.92 228 | 98.10 31 | 95.80 243 | 92.22 88 | 93.02 120 | 97.45 89 | 84.53 122 | 97.91 243 | 88.24 169 | 97.97 89 | 99.02 65 |
|
canonicalmvs | | | 96.02 53 | 95.45 53 | 97.75 25 | 97.59 112 | 95.15 10 | 98.28 22 | 97.60 109 | 94.52 29 | 96.27 48 | 96.12 144 | 87.65 84 | 99.18 102 | 96.20 27 | 94.82 150 | 98.91 78 |
|
LS3D | | | 93.57 112 | 92.61 123 | 96.47 77 | 97.59 112 | 91.61 94 | 97.67 68 | 97.72 98 | 85.17 273 | 90.29 180 | 98.34 28 | 84.60 120 | 99.73 26 | 83.85 251 | 98.27 82 | 98.06 129 |
|
alignmvs | | | 95.87 57 | 95.23 60 | 97.78 21 | 97.56 114 | 95.19 8 | 97.86 48 | 97.17 154 | 94.39 32 | 96.47 43 | 96.40 134 | 85.89 105 | 99.20 99 | 96.21 26 | 95.11 146 | 98.95 74 |
|
conf0.01 | | | 91.74 175 | 90.67 194 | 94.94 152 | 97.55 115 | 89.68 150 | 97.64 75 | 93.14 321 | 88.43 203 | 91.24 162 | 94.30 235 | 78.91 235 | 98.45 167 | 81.28 284 | 93.57 178 | 96.70 175 |
|
conf0.002 | | | 91.74 175 | 90.67 194 | 94.94 152 | 97.55 115 | 89.68 150 | 97.64 75 | 93.14 321 | 88.43 203 | 91.24 162 | 94.30 235 | 78.91 235 | 98.45 167 | 81.28 284 | 93.57 178 | 96.70 175 |
|
thresconf0.02 | | | 91.69 182 | 90.67 194 | 94.75 161 | 97.55 115 | 89.68 150 | 97.64 75 | 93.14 321 | 88.43 203 | 91.24 162 | 94.30 235 | 78.91 235 | 98.45 167 | 81.28 284 | 93.57 178 | 96.11 195 |
|
tfpn_n400 | | | 91.69 182 | 90.67 194 | 94.75 161 | 97.55 115 | 89.68 150 | 97.64 75 | 93.14 321 | 88.43 203 | 91.24 162 | 94.30 235 | 78.91 235 | 98.45 167 | 81.28 284 | 93.57 178 | 96.11 195 |
|
tfpnconf | | | 91.69 182 | 90.67 194 | 94.75 161 | 97.55 115 | 89.68 150 | 97.64 75 | 93.14 321 | 88.43 203 | 91.24 162 | 94.30 235 | 78.91 235 | 98.45 167 | 81.28 284 | 93.57 178 | 96.11 195 |
|
tfpnview11 | | | 91.69 182 | 90.67 194 | 94.75 161 | 97.55 115 | 89.68 150 | 97.64 75 | 93.14 321 | 88.43 203 | 91.24 162 | 94.30 235 | 78.91 235 | 98.45 167 | 81.28 284 | 93.57 178 | 96.11 195 |
|
EPP-MVSNet | | | 95.22 67 | 95.04 64 | 95.76 107 | 97.49 121 | 89.56 161 | 98.67 5 | 97.00 177 | 90.69 134 | 94.24 93 | 97.62 78 | 89.79 62 | 98.81 139 | 93.39 90 | 96.49 127 | 98.92 77 |
|
PS-MVSNAJ | | | 95.37 62 | 95.33 58 | 95.49 121 | 97.35 122 | 90.66 127 | 95.31 259 | 97.48 121 | 93.85 42 | 96.51 41 | 95.70 168 | 88.65 71 | 99.65 42 | 94.80 64 | 98.27 82 | 96.17 190 |
|
ab-mvs | | | 93.57 112 | 92.55 125 | 96.64 64 | 97.28 123 | 91.96 88 | 95.40 255 | 97.45 130 | 89.81 158 | 93.22 114 | 96.28 138 | 79.62 220 | 99.46 80 | 90.74 131 | 93.11 184 | 98.50 103 |
|
xiu_mvs_v2_base | | | 95.32 64 | 95.29 59 | 95.40 127 | 97.22 124 | 90.50 130 | 95.44 254 | 97.44 133 | 93.70 49 | 96.46 44 | 96.18 141 | 88.59 74 | 99.53 71 | 94.79 66 | 97.81 93 | 96.17 190 |
|
BH-untuned | | | 92.94 131 | 92.62 122 | 93.92 200 | 97.22 124 | 86.16 260 | 96.40 199 | 96.25 219 | 90.06 151 | 89.79 202 | 96.17 143 | 83.19 133 | 98.35 179 | 87.19 196 | 97.27 109 | 97.24 157 |
|
Vis-MVSNet | | | 95.23 66 | 94.81 66 | 96.51 74 | 97.18 126 | 91.58 97 | 98.26 24 | 98.12 43 | 94.38 33 | 94.90 82 | 98.15 42 | 82.28 171 | 98.92 129 | 91.45 124 | 98.58 77 | 99.01 69 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
BH-RMVSNet | | | 92.72 140 | 91.97 140 | 94.97 147 | 97.16 127 | 87.99 223 | 96.15 219 | 95.60 249 | 90.62 140 | 91.87 143 | 97.15 99 | 78.41 247 | 98.57 157 | 83.16 256 | 97.60 98 | 98.36 119 |
|
MSDG | | | 91.42 200 | 90.24 213 | 94.96 148 | 97.15 128 | 88.91 191 | 93.69 292 | 96.32 215 | 85.72 267 | 86.93 266 | 96.47 131 | 80.24 210 | 98.98 127 | 80.57 292 | 95.05 147 | 96.98 160 |
|
HY-MVS | | 89.66 9 | 93.87 101 | 92.95 110 | 96.63 66 | 97.10 129 | 92.49 72 | 95.64 245 | 96.64 206 | 89.05 179 | 93.00 121 | 95.79 161 | 85.77 108 | 99.45 82 | 89.16 154 | 94.35 155 | 97.96 130 |
|
XVG-OURS | | | 93.72 107 | 93.35 103 | 94.80 157 | 97.07 130 | 88.61 195 | 94.79 269 | 97.46 126 | 91.97 105 | 93.99 96 | 97.86 58 | 81.74 183 | 98.88 135 | 92.64 97 | 92.67 189 | 96.92 168 |
|
sss | | | 94.51 84 | 93.80 86 | 96.64 64 | 97.07 130 | 91.97 87 | 96.32 207 | 98.06 58 | 88.94 185 | 94.50 88 | 96.78 108 | 84.60 120 | 99.27 96 | 91.90 110 | 96.02 132 | 98.68 93 |
|
XVG-OURS-SEG-HR | | | 93.86 102 | 93.55 93 | 94.81 156 | 97.06 132 | 88.53 197 | 95.28 260 | 97.45 130 | 91.68 110 | 94.08 95 | 97.68 70 | 82.41 169 | 98.90 131 | 93.84 79 | 92.47 190 | 96.98 160 |
|
1112_ss | | | 93.37 117 | 92.42 131 | 96.21 93 | 97.05 133 | 90.99 115 | 96.31 208 | 96.72 198 | 86.87 253 | 89.83 200 | 96.69 115 | 86.51 98 | 99.14 107 | 88.12 171 | 93.67 172 | 98.50 103 |
|
Test_1112_low_res | | | 92.84 137 | 91.84 143 | 95.85 104 | 97.04 134 | 89.97 141 | 95.53 250 | 96.64 206 | 85.38 269 | 89.65 210 | 95.18 191 | 85.86 106 | 99.10 115 | 87.70 180 | 93.58 177 | 98.49 105 |
|
BH-w/o | | | 92.14 166 | 91.75 145 | 93.31 234 | 96.99 135 | 85.73 263 | 95.67 242 | 95.69 245 | 88.73 196 | 89.26 224 | 94.82 207 | 82.97 151 | 98.07 205 | 85.26 227 | 96.32 130 | 96.13 194 |
|
3Dnovator+ | | 91.43 4 | 95.40 61 | 94.48 78 | 98.16 7 | 96.90 136 | 95.34 6 | 98.48 14 | 97.87 86 | 94.65 28 | 88.53 235 | 98.02 48 | 83.69 128 | 99.71 30 | 93.18 92 | 98.96 67 | 99.44 33 |
|
UGNet | | | 94.04 97 | 93.28 105 | 96.31 86 | 96.85 137 | 91.19 109 | 97.88 47 | 97.68 103 | 94.40 31 | 93.00 121 | 96.18 141 | 73.39 292 | 99.61 48 | 91.72 115 | 98.46 78 | 98.13 124 |
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 |
VDDNet | | | 93.05 127 | 92.07 135 | 96.02 98 | 96.84 138 | 90.39 133 | 98.08 33 | 95.85 240 | 86.22 261 | 95.79 67 | 98.46 15 | 67.59 316 | 99.19 100 | 94.92 60 | 94.85 148 | 98.47 108 |
|
RPSCF | | | 90.75 225 | 90.86 184 | 90.42 304 | 96.84 138 | 76.29 328 | 95.61 247 | 96.34 214 | 83.89 289 | 91.38 151 | 97.87 56 | 76.45 268 | 98.78 141 | 87.16 198 | 92.23 193 | 96.20 188 |
|
MVS_Test | | | 94.89 78 | 94.62 71 | 95.68 112 | 96.83 140 | 89.55 162 | 96.70 173 | 97.17 154 | 91.17 124 | 95.60 73 | 96.11 146 | 87.87 81 | 98.76 144 | 93.01 95 | 97.17 111 | 98.72 89 |
|
LCM-MVSNet-Re | | | 92.50 146 | 92.52 128 | 92.44 257 | 96.82 141 | 81.89 300 | 96.92 147 | 93.71 314 | 92.41 86 | 84.30 287 | 94.60 217 | 85.08 114 | 97.03 292 | 91.51 121 | 97.36 106 | 98.40 115 |
|
Fast-Effi-MVS+ | | | 93.46 114 | 92.75 116 | 95.59 115 | 96.77 142 | 90.03 135 | 96.81 157 | 97.13 160 | 88.19 217 | 91.30 157 | 94.27 245 | 86.21 101 | 98.63 151 | 87.66 184 | 96.46 129 | 98.12 125 |
|
QAPM | | | 93.45 115 | 92.27 133 | 96.98 60 | 96.77 142 | 92.62 68 | 98.39 18 | 98.12 43 | 84.50 283 | 88.27 241 | 97.77 65 | 82.39 170 | 99.81 20 | 85.40 224 | 98.81 70 | 98.51 101 |
|
CHOSEN 280x420 | | | 93.12 124 | 92.72 119 | 94.34 179 | 96.71 144 | 87.27 239 | 90.29 329 | 97.72 98 | 86.61 257 | 91.34 154 | 95.29 187 | 84.29 124 | 98.41 174 | 93.25 91 | 98.94 68 | 97.35 156 |
|
Effi-MVS+ | | | 94.93 76 | 94.45 79 | 96.36 84 | 96.61 145 | 91.47 99 | 96.41 195 | 97.41 137 | 91.02 129 | 94.50 88 | 95.92 151 | 87.53 87 | 98.78 141 | 93.89 77 | 96.81 118 | 98.84 85 |
|
PCF-MVS | | 89.48 11 | 91.56 193 | 89.95 224 | 96.36 84 | 96.60 146 | 92.52 71 | 92.51 313 | 97.26 149 | 79.41 320 | 88.90 227 | 96.56 127 | 84.04 125 | 99.55 66 | 77.01 311 | 97.30 108 | 97.01 159 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
xiu_mvs_v1_base_debu | | | 95.01 71 | 94.76 67 | 95.75 108 | 96.58 147 | 91.71 90 | 96.25 213 | 97.35 144 | 92.99 69 | 96.70 32 | 96.63 122 | 82.67 160 | 99.44 83 | 96.22 23 | 97.46 100 | 96.11 195 |
|
xiu_mvs_v1_base | | | 95.01 71 | 94.76 67 | 95.75 108 | 96.58 147 | 91.71 90 | 96.25 213 | 97.35 144 | 92.99 69 | 96.70 32 | 96.63 122 | 82.67 160 | 99.44 83 | 96.22 23 | 97.46 100 | 96.11 195 |
|
xiu_mvs_v1_base_debi | | | 95.01 71 | 94.76 67 | 95.75 108 | 96.58 147 | 91.71 90 | 96.25 213 | 97.35 144 | 92.99 69 | 96.70 32 | 96.63 122 | 82.67 160 | 99.44 83 | 96.22 23 | 97.46 100 | 96.11 195 |
|
MVSTER | | | 93.20 122 | 92.81 113 | 94.37 177 | 96.56 150 | 89.59 160 | 97.06 132 | 97.12 161 | 91.24 123 | 91.30 157 | 95.96 149 | 82.02 177 | 98.05 213 | 93.48 86 | 90.55 222 | 95.47 224 |
|
3Dnovator | | 91.36 5 | 95.19 69 | 94.44 80 | 97.44 40 | 96.56 150 | 93.36 52 | 98.65 6 | 98.36 16 | 94.12 37 | 89.25 225 | 98.06 46 | 82.20 174 | 99.77 23 | 93.41 89 | 99.32 42 | 99.18 53 |
|
FMVSNet3 | | | 91.78 174 | 90.69 193 | 95.03 143 | 96.53 152 | 92.27 76 | 97.02 135 | 96.93 187 | 89.79 159 | 89.35 219 | 94.65 215 | 77.01 266 | 97.47 274 | 86.12 211 | 88.82 237 | 95.35 236 |
|
GBi-Net | | | 91.35 204 | 90.27 211 | 94.59 167 | 96.51 153 | 91.18 110 | 97.50 92 | 96.93 187 | 88.82 191 | 89.35 219 | 94.51 219 | 73.87 286 | 97.29 286 | 86.12 211 | 88.82 237 | 95.31 238 |
|
test1 | | | 91.35 204 | 90.27 211 | 94.59 167 | 96.51 153 | 91.18 110 | 97.50 92 | 96.93 187 | 88.82 191 | 89.35 219 | 94.51 219 | 73.87 286 | 97.29 286 | 86.12 211 | 88.82 237 | 95.31 238 |
|
FMVSNet2 | | | 91.31 207 | 90.08 218 | 94.99 144 | 96.51 153 | 92.21 77 | 97.41 100 | 96.95 185 | 88.82 191 | 88.62 232 | 94.75 211 | 73.87 286 | 97.42 278 | 85.20 228 | 88.55 243 | 95.35 236 |
|
ACMH+ | | 87.92 14 | 90.20 240 | 89.18 244 | 93.25 236 | 96.48 156 | 86.45 257 | 96.99 138 | 96.68 203 | 88.83 190 | 84.79 284 | 96.22 140 | 70.16 307 | 98.53 160 | 84.42 240 | 88.04 245 | 94.77 275 |
|
diffmvs | | | 93.43 116 | 92.75 116 | 95.48 123 | 96.47 157 | 89.61 158 | 96.09 222 | 97.14 158 | 85.97 264 | 93.09 119 | 95.35 185 | 84.87 117 | 98.55 159 | 89.51 145 | 96.26 131 | 98.28 121 |
|
CANet_DTU | | | 94.37 85 | 93.65 91 | 96.55 70 | 96.46 158 | 92.13 81 | 96.21 217 | 96.67 205 | 94.38 33 | 93.53 103 | 97.03 103 | 79.34 223 | 99.71 30 | 90.76 130 | 98.45 79 | 97.82 139 |
|
mvs_anonymous | | | 93.82 103 | 93.74 87 | 94.06 187 | 96.44 159 | 85.41 268 | 95.81 237 | 97.05 170 | 89.85 156 | 90.09 191 | 96.36 136 | 87.44 89 | 97.75 256 | 93.97 73 | 96.69 123 | 99.02 65 |
|
TR-MVS | | | 91.48 197 | 90.59 202 | 94.16 184 | 96.40 160 | 87.33 237 | 95.67 242 | 95.34 262 | 87.68 230 | 91.46 150 | 95.52 177 | 76.77 267 | 98.35 179 | 82.85 261 | 93.61 175 | 96.79 172 |
|
ACMP | | 89.59 10 | 92.62 141 | 92.14 134 | 94.05 188 | 96.40 160 | 88.20 209 | 97.36 107 | 97.25 151 | 91.52 112 | 88.30 239 | 96.64 118 | 78.46 246 | 98.72 148 | 91.86 113 | 91.48 208 | 95.23 245 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
MVSFormer | | | 95.37 62 | 95.16 62 | 95.99 100 | 96.34 162 | 91.21 106 | 98.22 26 | 97.57 112 | 91.42 117 | 96.22 49 | 97.32 90 | 86.20 102 | 97.92 240 | 94.07 71 | 99.05 63 | 98.85 83 |
|
lupinMVS | | | 94.99 75 | 94.56 73 | 96.29 89 | 96.34 162 | 91.21 106 | 95.83 236 | 96.27 217 | 88.93 186 | 96.22 49 | 96.88 106 | 86.20 102 | 98.85 136 | 95.27 46 | 99.05 63 | 98.82 86 |
|
ACMM | | 89.79 8 | 92.96 130 | 92.50 129 | 94.35 178 | 96.30 164 | 88.71 193 | 97.58 87 | 97.36 143 | 91.40 119 | 90.53 174 | 96.65 117 | 79.77 217 | 98.75 145 | 91.24 128 | 91.64 204 | 95.59 221 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
IterMVS-LS | | | 92.29 159 | 91.94 141 | 93.34 233 | 96.25 165 | 86.97 249 | 96.57 189 | 97.05 170 | 90.67 135 | 89.50 216 | 94.80 209 | 86.59 96 | 97.64 264 | 89.91 136 | 86.11 260 | 95.40 232 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
HQP_MVS | | | 93.78 105 | 93.43 100 | 94.82 154 | 96.21 166 | 89.99 138 | 97.74 58 | 97.51 119 | 94.85 17 | 91.34 154 | 96.64 118 | 81.32 189 | 98.60 154 | 93.02 93 | 92.23 193 | 95.86 205 |
|
plane_prior7 | | | | | | 96.21 166 | 89.98 140 | | | | | | | | | | |
|
ACMH | | 87.59 16 | 90.53 233 | 89.42 240 | 93.87 201 | 96.21 166 | 87.92 228 | 97.24 116 | 96.94 186 | 88.45 202 | 83.91 293 | 96.27 139 | 71.92 294 | 98.62 153 | 84.43 239 | 89.43 233 | 95.05 256 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
CDS-MVSNet | | | 94.14 92 | 93.54 94 | 95.93 101 | 96.18 169 | 91.46 100 | 96.33 206 | 97.04 173 | 88.97 184 | 93.56 101 | 96.51 129 | 87.55 86 | 97.89 244 | 89.80 138 | 95.95 134 | 98.44 112 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
LTVRE_ROB | | 88.41 13 | 90.99 217 | 89.92 225 | 94.19 182 | 96.18 169 | 89.55 162 | 96.31 208 | 97.09 164 | 87.88 225 | 85.67 276 | 95.91 152 | 78.79 243 | 98.57 157 | 81.50 276 | 89.98 228 | 94.44 285 |
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 |
LPG-MVS_test | | | 92.94 131 | 92.56 124 | 94.10 185 | 96.16 171 | 88.26 203 | 97.65 71 | 97.46 126 | 91.29 120 | 90.12 188 | 97.16 97 | 79.05 227 | 98.73 146 | 92.25 100 | 91.89 201 | 95.31 238 |
|
LGP-MVS_train | | | | | 94.10 185 | 96.16 171 | 88.26 203 | | 97.46 126 | 91.29 120 | 90.12 188 | 97.16 97 | 79.05 227 | 98.73 146 | 92.25 100 | 91.89 201 | 95.31 238 |
|
TAMVS | | | 94.01 98 | 93.46 98 | 95.64 113 | 96.16 171 | 90.45 132 | 96.71 170 | 96.89 191 | 89.27 168 | 93.46 105 | 96.92 105 | 87.29 91 | 97.94 236 | 88.70 166 | 95.74 138 | 98.53 98 |
|
plane_prior1 | | | | | | 96.14 174 | | | | | | | | | | | |
|
CLD-MVS | | | 92.98 129 | 92.53 127 | 94.32 180 | 96.12 175 | 89.20 186 | 95.28 260 | 97.47 124 | 92.66 81 | 89.90 195 | 95.62 171 | 80.58 203 | 98.40 175 | 92.73 96 | 92.40 191 | 95.38 234 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
plane_prior6 | | | | | | 96.10 176 | 90.00 136 | | | | | | 81.32 189 | | | | |
|
Effi-MVS+-dtu | | | 93.08 125 | 93.21 106 | 92.68 254 | 96.02 177 | 83.25 291 | 97.14 129 | 96.72 198 | 93.85 42 | 91.20 169 | 93.44 275 | 83.08 141 | 98.30 184 | 91.69 118 | 95.73 139 | 96.50 182 |
|
mvs-test1 | | | 93.63 109 | 93.69 89 | 93.46 228 | 96.02 177 | 84.61 278 | 97.24 116 | 96.72 198 | 93.85 42 | 92.30 135 | 95.76 163 | 83.08 141 | 98.89 133 | 91.69 118 | 96.54 126 | 96.87 170 |
|
NP-MVS | | | | | | 95.99 179 | 89.81 147 | | | | | 95.87 153 | | | | | |
|
ADS-MVSNet2 | | | 89.45 253 | 88.59 251 | 92.03 272 | 95.86 180 | 82.26 298 | 90.93 325 | 94.32 303 | 83.23 297 | 91.28 160 | 91.81 300 | 79.01 231 | 95.99 314 | 79.52 297 | 91.39 210 | 97.84 136 |
|
ADS-MVSNet | | | 89.89 246 | 88.68 250 | 93.53 224 | 95.86 180 | 84.89 275 | 90.93 325 | 95.07 275 | 83.23 297 | 91.28 160 | 91.81 300 | 79.01 231 | 97.85 246 | 79.52 297 | 91.39 210 | 97.84 136 |
|
HQP-NCC | | | | | | 95.86 180 | | 96.65 178 | | 93.55 50 | 90.14 182 | | | | | | |
|
ACMP_Plane | | | | | | 95.86 180 | | 96.65 178 | | 93.55 50 | 90.14 182 | | | | | | |
|
HQP-MVS | | | 93.19 123 | 92.74 118 | 94.54 172 | 95.86 180 | 89.33 178 | 96.65 178 | 97.39 138 | 93.55 50 | 90.14 182 | 95.87 153 | 80.95 194 | 98.50 163 | 92.13 104 | 92.10 198 | 95.78 212 |
|
EI-MVSNet | | | 93.03 128 | 92.88 112 | 93.48 226 | 95.77 185 | 86.98 248 | 96.44 191 | 97.12 161 | 90.66 137 | 91.30 157 | 97.64 76 | 86.56 97 | 98.05 213 | 89.91 136 | 90.55 222 | 95.41 228 |
|
CVMVSNet | | | 91.23 209 | 91.75 145 | 89.67 310 | 95.77 185 | 74.69 330 | 96.44 191 | 94.88 283 | 85.81 265 | 92.18 137 | 97.64 76 | 79.07 226 | 95.58 322 | 88.06 172 | 95.86 137 | 98.74 87 |
|
FIs | | | 94.09 94 | 93.70 88 | 95.27 129 | 95.70 187 | 92.03 84 | 98.10 31 | 98.68 7 | 93.36 57 | 90.39 178 | 96.70 113 | 87.63 85 | 97.94 236 | 92.25 100 | 90.50 224 | 95.84 208 |
|
VPA-MVSNet | | | 93.24 121 | 92.48 130 | 95.51 119 | 95.70 187 | 92.39 73 | 97.86 48 | 98.66 9 | 92.30 87 | 92.09 140 | 95.37 184 | 80.49 205 | 98.40 175 | 93.95 74 | 85.86 261 | 95.75 216 |
|
Patchmatch-test1 | | | 91.54 195 | 90.85 185 | 93.59 220 | 95.59 189 | 84.95 274 | 94.72 270 | 95.58 251 | 90.82 130 | 92.25 136 | 93.58 268 | 75.80 272 | 97.41 279 | 83.35 253 | 95.98 133 | 98.40 115 |
|
VPNet | | | 92.23 162 | 91.31 167 | 94.99 144 | 95.56 190 | 90.96 117 | 97.22 121 | 97.86 88 | 92.96 75 | 90.96 170 | 96.62 125 | 75.06 278 | 98.20 188 | 91.90 110 | 83.65 297 | 95.80 211 |
|
semantic-postprocess | | | | | 91.82 277 | 95.52 191 | 84.20 281 | | 96.15 224 | 90.61 142 | 87.39 256 | 94.27 245 | 75.63 274 | 96.44 301 | 87.34 192 | 86.88 256 | 94.82 269 |
|
jason | | | 94.84 80 | 94.39 81 | 96.18 94 | 95.52 191 | 90.93 119 | 96.09 222 | 96.52 210 | 89.28 167 | 96.01 59 | 97.32 90 | 84.70 119 | 98.77 143 | 95.15 51 | 98.91 69 | 98.85 83 |
jason: jason. |
FC-MVSNet-test | | | 93.94 100 | 93.57 92 | 95.04 142 | 95.48 193 | 91.45 101 | 98.12 30 | 98.71 5 | 93.37 55 | 90.23 181 | 96.70 113 | 87.66 83 | 97.85 246 | 91.49 122 | 90.39 225 | 95.83 209 |
|
IterMVS | | | 90.15 242 | 89.67 235 | 91.61 284 | 95.48 193 | 83.72 284 | 94.33 278 | 96.12 225 | 89.99 152 | 87.31 259 | 94.15 250 | 75.78 273 | 96.27 304 | 86.97 200 | 86.89 255 | 94.83 267 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
FMVSNet1 | | | 89.88 247 | 88.31 255 | 94.59 167 | 95.41 195 | 91.18 110 | 97.50 92 | 96.93 187 | 86.62 256 | 87.41 255 | 94.51 219 | 65.94 323 | 97.29 286 | 83.04 258 | 87.43 251 | 95.31 238 |
|
UniMVSNet (Re) | | | 93.31 119 | 92.55 125 | 95.61 114 | 95.39 196 | 93.34 53 | 97.39 104 | 98.71 5 | 93.14 65 | 90.10 190 | 94.83 206 | 87.71 82 | 98.03 219 | 91.67 120 | 83.99 290 | 95.46 225 |
|
MVS-HIRNet | | | 82.47 308 | 81.21 309 | 86.26 321 | 95.38 197 | 69.21 341 | 88.96 337 | 89.49 345 | 66.28 343 | 80.79 308 | 74.08 346 | 68.48 312 | 97.39 281 | 71.93 325 | 95.47 141 | 92.18 327 |
|
PatchmatchNet | | | 91.91 171 | 91.35 164 | 93.59 220 | 95.38 197 | 84.11 282 | 93.15 304 | 95.39 256 | 89.54 160 | 92.10 139 | 93.68 264 | 82.82 158 | 98.13 194 | 84.81 231 | 95.32 143 | 98.52 99 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
UniMVSNet_NR-MVSNet | | | 93.37 117 | 92.67 120 | 95.47 124 | 95.34 199 | 92.83 62 | 97.17 126 | 98.58 10 | 92.98 74 | 90.13 186 | 95.80 158 | 88.37 76 | 97.85 246 | 91.71 116 | 83.93 291 | 95.73 218 |
|
ITE_SJBPF | | | | | 92.43 258 | 95.34 199 | 85.37 269 | | 95.92 232 | 91.47 114 | 87.75 248 | 96.39 135 | 71.00 301 | 97.96 234 | 82.36 268 | 89.86 231 | 93.97 296 |
|
OpenMVS | | 89.19 12 | 92.86 135 | 91.68 148 | 96.40 80 | 95.34 199 | 92.73 65 | 98.27 23 | 98.12 43 | 84.86 278 | 85.78 275 | 97.75 66 | 78.89 242 | 99.74 25 | 87.50 189 | 98.65 74 | 96.73 173 |
|
1314 | | | 92.81 138 | 92.03 137 | 95.14 137 | 95.33 202 | 89.52 165 | 96.04 225 | 97.44 133 | 87.72 229 | 86.25 272 | 95.33 186 | 83.84 126 | 98.79 140 | 89.26 149 | 97.05 113 | 97.11 158 |
|
PAPM | | | 91.52 196 | 90.30 209 | 95.20 130 | 95.30 203 | 89.83 146 | 93.38 299 | 96.85 194 | 86.26 260 | 88.59 234 | 95.80 158 | 84.88 116 | 98.15 193 | 75.67 315 | 95.93 135 | 97.63 144 |
|
Fast-Effi-MVS+-dtu | | | 92.29 159 | 91.99 139 | 93.21 239 | 95.27 204 | 85.52 267 | 97.03 133 | 96.63 208 | 92.09 96 | 89.11 226 | 95.14 193 | 80.33 209 | 98.08 201 | 87.54 188 | 94.74 153 | 96.03 202 |
|
Patchmatch-test | | | 89.42 254 | 87.99 258 | 93.70 214 | 95.27 204 | 85.11 270 | 88.98 336 | 94.37 301 | 81.11 312 | 87.10 263 | 93.69 263 | 82.28 171 | 97.50 272 | 74.37 318 | 94.76 151 | 98.48 107 |
|
PVSNet_0 | | 82.17 19 | 85.46 298 | 83.64 299 | 90.92 295 | 95.27 204 | 79.49 319 | 90.55 328 | 95.60 249 | 83.76 292 | 83.00 296 | 89.95 307 | 71.09 300 | 97.97 230 | 82.75 263 | 60.79 346 | 95.31 238 |
|
IB-MVS | | 87.33 17 | 89.91 245 | 88.28 256 | 94.79 159 | 95.26 207 | 87.70 234 | 95.12 266 | 93.95 312 | 89.35 166 | 87.03 264 | 92.49 288 | 70.74 303 | 99.19 100 | 89.18 153 | 81.37 310 | 97.49 153 |
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 |
PatchFormer-LS_test | | | 91.68 187 | 91.18 174 | 93.19 240 | 95.24 208 | 83.63 288 | 95.53 250 | 95.44 255 | 89.82 157 | 91.37 152 | 92.58 287 | 80.85 201 | 98.52 161 | 89.65 143 | 90.16 227 | 97.42 155 |
|
nrg030 | | | 94.05 96 | 93.31 104 | 96.27 90 | 95.22 209 | 94.59 15 | 98.34 19 | 97.46 126 | 92.93 76 | 91.21 168 | 96.64 118 | 87.23 92 | 98.22 187 | 94.99 59 | 85.80 262 | 95.98 203 |
|
MDTV_nov1_ep13 | | | | 90.76 189 | | 95.22 209 | 80.33 312 | 93.03 307 | 95.28 263 | 88.14 220 | 92.84 127 | 93.83 258 | 81.34 188 | 98.08 201 | 82.86 260 | 94.34 156 | |
|
MVS | | | 91.71 177 | 90.44 204 | 95.51 119 | 95.20 211 | 91.59 96 | 96.04 225 | 97.45 130 | 73.44 339 | 87.36 257 | 95.60 172 | 85.42 110 | 99.10 115 | 85.97 215 | 97.46 100 | 95.83 209 |
|
tpmp4_e23 | | | 89.58 251 | 88.59 251 | 92.54 256 | 95.16 212 | 81.53 302 | 94.11 284 | 95.09 273 | 81.66 307 | 88.60 233 | 93.44 275 | 75.11 277 | 98.33 182 | 82.45 266 | 91.72 203 | 97.75 140 |
|
tfpnnormal | | | 89.70 250 | 88.40 254 | 93.60 219 | 95.15 213 | 90.10 134 | 97.56 88 | 98.16 38 | 87.28 239 | 86.16 273 | 94.63 216 | 77.57 264 | 98.05 213 | 74.48 316 | 84.59 285 | 92.65 311 |
|
tpmrst | | | 91.44 199 | 91.32 166 | 91.79 279 | 95.15 213 | 79.20 322 | 93.42 298 | 95.37 258 | 88.55 200 | 93.49 104 | 93.67 265 | 82.49 166 | 98.27 185 | 90.41 133 | 89.34 234 | 97.90 133 |
|
WR-MVS | | | 92.34 155 | 91.53 159 | 94.77 160 | 95.13 215 | 90.83 122 | 96.40 199 | 97.98 78 | 91.88 106 | 89.29 222 | 95.54 176 | 82.50 165 | 97.80 251 | 89.79 139 | 85.27 269 | 95.69 219 |
|
tpm cat1 | | | 88.36 275 | 87.21 275 | 91.81 278 | 95.13 215 | 80.55 310 | 92.58 312 | 95.70 244 | 74.97 335 | 87.45 253 | 91.96 298 | 78.01 261 | 98.17 192 | 80.39 294 | 88.74 240 | 96.72 174 |
|
WR-MVS_H | | | 92.00 169 | 91.35 164 | 93.95 196 | 95.09 217 | 89.47 166 | 98.04 35 | 98.68 7 | 91.46 115 | 88.34 237 | 94.68 213 | 85.86 106 | 97.56 268 | 85.77 218 | 84.24 288 | 94.82 269 |
|
CP-MVSNet | | | 91.89 172 | 91.24 170 | 93.82 202 | 95.05 218 | 88.57 196 | 97.82 52 | 98.19 33 | 91.70 109 | 88.21 242 | 95.76 163 | 81.96 178 | 97.52 271 | 87.86 176 | 84.65 284 | 95.37 235 |
|
DWT-MVSNet_test | | | 90.76 223 | 89.89 226 | 93.38 231 | 95.04 219 | 83.70 286 | 95.85 235 | 94.30 304 | 88.19 217 | 90.46 176 | 92.80 282 | 73.61 290 | 98.50 163 | 88.16 170 | 90.58 221 | 97.95 131 |
|
test_0402 | | | 86.46 290 | 84.79 293 | 91.45 287 | 95.02 220 | 85.55 266 | 96.29 210 | 94.89 282 | 80.90 313 | 82.21 297 | 93.97 254 | 68.21 314 | 97.29 286 | 62.98 337 | 88.68 242 | 91.51 332 |
|
cascas | | | 91.20 210 | 90.08 218 | 94.58 171 | 94.97 221 | 89.16 188 | 93.65 294 | 97.59 111 | 79.90 319 | 89.40 217 | 92.92 281 | 75.36 276 | 98.36 178 | 92.14 103 | 94.75 152 | 96.23 187 |
|
PS-CasMVS | | | 91.55 194 | 90.84 187 | 93.69 215 | 94.96 222 | 88.28 202 | 97.84 51 | 98.24 28 | 91.46 115 | 88.04 244 | 95.80 158 | 79.67 219 | 97.48 273 | 87.02 199 | 84.54 286 | 95.31 238 |
|
DU-MVS | | | 92.90 133 | 92.04 136 | 95.49 121 | 94.95 223 | 92.83 62 | 97.16 127 | 98.24 28 | 93.02 68 | 90.13 186 | 95.71 166 | 83.47 130 | 97.85 246 | 91.71 116 | 83.93 291 | 95.78 212 |
|
NR-MVSNet | | | 92.34 155 | 91.27 169 | 95.53 118 | 94.95 223 | 93.05 57 | 97.39 104 | 98.07 56 | 92.65 82 | 84.46 285 | 95.71 166 | 85.00 115 | 97.77 255 | 89.71 140 | 83.52 298 | 95.78 212 |
|
Anonymous20240521 | | | 91.32 206 | 90.43 206 | 93.98 191 | 94.93 225 | 89.28 183 | 98.04 35 | 97.53 116 | 89.49 163 | 86.68 269 | 94.82 207 | 81.72 184 | 98.05 213 | 85.31 225 | 85.39 266 | 94.61 280 |
|
tpmvs | | | 89.83 249 | 89.15 245 | 91.89 275 | 94.92 226 | 80.30 313 | 93.11 305 | 95.46 254 | 86.28 259 | 88.08 243 | 92.65 284 | 80.44 206 | 98.52 161 | 81.47 277 | 89.92 230 | 96.84 171 |
|
PMMVS | | | 92.86 135 | 92.34 132 | 94.42 176 | 94.92 226 | 86.73 252 | 94.53 274 | 96.38 213 | 84.78 280 | 94.27 92 | 95.12 195 | 83.13 137 | 98.40 175 | 91.47 123 | 96.49 127 | 98.12 125 |
|
tpm2 | | | 89.96 244 | 89.21 243 | 92.23 263 | 94.91 228 | 81.25 304 | 93.78 289 | 94.42 299 | 80.62 317 | 91.56 148 | 93.44 275 | 76.44 269 | 97.94 236 | 85.60 221 | 92.08 200 | 97.49 153 |
|
TinyColmap | | | 86.82 288 | 85.35 290 | 91.21 290 | 94.91 228 | 82.99 292 | 93.94 287 | 94.02 311 | 83.58 293 | 81.56 304 | 94.68 213 | 62.34 330 | 98.13 194 | 75.78 313 | 87.35 254 | 92.52 314 |
|
CostFormer | | | 91.18 213 | 90.70 192 | 92.62 255 | 94.84 230 | 81.76 301 | 94.09 285 | 94.43 298 | 84.15 286 | 92.72 128 | 93.77 261 | 79.43 222 | 98.20 188 | 90.70 132 | 92.18 196 | 97.90 133 |
|
MIMVSNet | | | 88.50 270 | 86.76 279 | 93.72 213 | 94.84 230 | 87.77 232 | 91.39 320 | 94.05 309 | 86.41 258 | 87.99 245 | 92.59 286 | 63.27 327 | 95.82 318 | 77.44 307 | 92.84 187 | 97.57 151 |
|
FMVSNet5 | | | 87.29 285 | 85.79 286 | 91.78 280 | 94.80 232 | 87.28 238 | 95.49 252 | 95.28 263 | 84.09 287 | 83.85 294 | 91.82 299 | 62.95 328 | 94.17 330 | 78.48 304 | 85.34 268 | 93.91 297 |
|
TranMVSNet+NR-MVSNet | | | 92.50 146 | 91.63 153 | 95.14 137 | 94.76 233 | 92.07 82 | 97.53 90 | 98.11 46 | 92.90 77 | 89.56 213 | 96.12 144 | 83.16 134 | 97.60 267 | 89.30 148 | 83.20 301 | 95.75 216 |
|
XXY-MVS | | | 92.16 164 | 91.23 171 | 94.95 149 | 94.75 234 | 90.94 118 | 97.47 98 | 97.43 135 | 89.14 177 | 88.90 227 | 96.43 133 | 79.71 218 | 98.24 186 | 89.56 144 | 87.68 248 | 95.67 220 |
|
EPMVS | | | 90.70 229 | 89.81 230 | 93.37 232 | 94.73 235 | 84.21 280 | 93.67 293 | 88.02 346 | 89.50 162 | 92.38 132 | 93.49 272 | 77.82 263 | 97.78 253 | 86.03 214 | 92.68 188 | 98.11 128 |
|
USDC | | | 88.94 257 | 87.83 260 | 92.27 259 | 94.66 236 | 84.96 273 | 93.86 288 | 95.90 234 | 87.34 237 | 83.40 295 | 95.56 174 | 67.43 317 | 98.19 190 | 82.64 265 | 89.67 232 | 93.66 299 |
|
GA-MVS | | | 91.38 202 | 90.31 208 | 94.59 167 | 94.65 237 | 87.62 235 | 94.34 277 | 96.19 222 | 90.73 133 | 90.35 179 | 93.83 258 | 71.84 295 | 97.96 234 | 87.22 195 | 93.61 175 | 98.21 122 |
|
OPM-MVS | | | 93.28 120 | 92.76 114 | 94.82 154 | 94.63 238 | 90.77 125 | 96.65 178 | 97.18 152 | 93.72 47 | 91.68 147 | 97.26 93 | 79.33 224 | 98.63 151 | 92.13 104 | 92.28 192 | 95.07 251 |
|
test-LLR | | | 91.42 200 | 91.19 173 | 92.12 269 | 94.59 239 | 80.66 307 | 94.29 279 | 92.98 327 | 91.11 126 | 90.76 172 | 92.37 290 | 79.02 229 | 98.07 205 | 88.81 164 | 96.74 120 | 97.63 144 |
|
test-mter | | | 90.19 241 | 89.54 238 | 92.12 269 | 94.59 239 | 80.66 307 | 94.29 279 | 92.98 327 | 87.68 230 | 90.76 172 | 92.37 290 | 67.67 315 | 98.07 205 | 88.81 164 | 96.74 120 | 97.63 144 |
|
dp | | | 88.90 259 | 88.26 257 | 90.81 297 | 94.58 241 | 76.62 327 | 92.85 309 | 94.93 281 | 85.12 274 | 90.07 193 | 93.07 279 | 75.81 271 | 98.12 196 | 80.53 293 | 87.42 252 | 97.71 142 |
|
PEN-MVS | | | 91.20 210 | 90.44 204 | 93.48 226 | 94.49 242 | 87.91 230 | 97.76 55 | 98.18 36 | 91.29 120 | 87.78 247 | 95.74 165 | 80.35 208 | 97.33 284 | 85.46 223 | 82.96 302 | 95.19 247 |
|
gg-mvs-nofinetune | | | 87.82 280 | 85.61 287 | 94.44 174 | 94.46 243 | 89.27 185 | 91.21 324 | 84.61 352 | 80.88 314 | 89.89 197 | 74.98 344 | 71.50 297 | 97.53 270 | 85.75 219 | 97.21 110 | 96.51 181 |
|
CR-MVSNet | | | 90.82 222 | 89.77 231 | 93.95 196 | 94.45 244 | 87.19 243 | 90.23 330 | 95.68 247 | 86.89 252 | 92.40 130 | 92.36 293 | 80.91 197 | 97.05 290 | 81.09 291 | 93.95 168 | 97.60 149 |
|
RPMNet | | | 88.52 268 | 86.72 281 | 93.95 196 | 94.45 244 | 87.19 243 | 90.23 330 | 94.99 278 | 77.87 329 | 92.40 130 | 87.55 334 | 80.17 212 | 97.05 290 | 68.84 331 | 93.95 168 | 97.60 149 |
|
TESTMET0.1,1 | | | 90.06 243 | 89.42 240 | 91.97 273 | 94.41 246 | 80.62 309 | 94.29 279 | 91.97 336 | 87.28 239 | 90.44 177 | 92.47 289 | 68.79 310 | 97.67 261 | 88.50 168 | 96.60 125 | 97.61 148 |
|
TransMVSNet (Re) | | | 88.94 257 | 87.56 261 | 93.08 242 | 94.35 247 | 88.45 200 | 97.73 60 | 95.23 267 | 87.47 233 | 84.26 288 | 95.29 187 | 79.86 216 | 97.33 284 | 79.44 301 | 74.44 336 | 93.45 302 |
|
MS-PatchMatch | | | 90.27 237 | 89.77 231 | 91.78 280 | 94.33 248 | 84.72 277 | 95.55 248 | 96.73 197 | 86.17 262 | 86.36 271 | 95.28 189 | 71.28 299 | 97.80 251 | 84.09 244 | 98.14 86 | 92.81 310 |
|
XVG-ACMP-BASELINE | | | 90.93 219 | 90.21 216 | 93.09 241 | 94.31 249 | 85.89 261 | 95.33 257 | 97.26 149 | 91.06 128 | 89.38 218 | 95.44 183 | 68.61 311 | 98.60 154 | 89.46 146 | 91.05 215 | 94.79 273 |
|
pcd1.5k->3k | | | 38.37 332 | 40.51 333 | 31.96 345 | 94.29 250 | 0.00 364 | 0.00 355 | 97.69 102 | 0.00 359 | 0.00 360 | 0.00 361 | 81.45 187 | 0.00 362 | 0.00 359 | 91.11 214 | 95.89 204 |
|
pm-mvs1 | | | 90.72 227 | 89.65 237 | 93.96 195 | 94.29 250 | 89.63 157 | 97.79 54 | 96.82 195 | 89.07 178 | 86.12 274 | 95.48 182 | 78.61 244 | 97.78 253 | 86.97 200 | 81.67 308 | 94.46 284 |
|
v1neww | | | 91.70 180 | 91.01 176 | 93.75 208 | 94.19 252 | 88.14 214 | 97.20 122 | 96.98 178 | 89.18 172 | 89.87 198 | 94.44 225 | 83.10 139 | 98.06 210 | 89.06 156 | 85.09 273 | 95.06 254 |
|
v7new | | | 91.70 180 | 91.01 176 | 93.75 208 | 94.19 252 | 88.14 214 | 97.20 122 | 96.98 178 | 89.18 172 | 89.87 198 | 94.44 225 | 83.10 139 | 98.06 210 | 89.06 156 | 85.09 273 | 95.06 254 |
|
v16 | | | 88.69 264 | 87.50 263 | 92.26 261 | 94.19 252 | 88.11 218 | 96.81 157 | 95.95 230 | 87.01 245 | 80.71 311 | 89.80 311 | 83.08 141 | 96.20 306 | 84.61 236 | 75.34 326 | 92.48 317 |
|
v18 | | | 88.71 263 | 87.52 262 | 92.27 259 | 94.16 255 | 88.11 218 | 96.82 156 | 95.96 229 | 87.03 243 | 80.76 309 | 89.81 310 | 83.15 135 | 96.22 305 | 84.69 233 | 75.31 327 | 92.49 315 |
|
v8 | | | 91.29 208 | 90.53 203 | 93.57 223 | 94.15 256 | 88.12 216 | 97.34 108 | 97.06 169 | 88.99 181 | 88.32 238 | 94.26 247 | 83.08 141 | 98.01 223 | 87.62 186 | 83.92 293 | 94.57 281 |
|
v6 | | | 91.69 182 | 91.00 178 | 93.75 208 | 94.14 257 | 88.12 216 | 97.20 122 | 96.98 178 | 89.19 170 | 89.90 195 | 94.42 227 | 83.04 145 | 98.07 205 | 89.07 155 | 85.10 272 | 95.07 251 |
|
v17 | | | 88.67 265 | 87.47 265 | 92.26 261 | 94.13 258 | 88.09 220 | 96.81 157 | 95.95 230 | 87.02 244 | 80.72 310 | 89.75 312 | 83.11 138 | 96.20 306 | 84.61 236 | 75.15 329 | 92.49 315 |
|
v7 | | | 91.47 198 | 90.73 191 | 93.68 216 | 94.13 258 | 88.16 212 | 97.09 131 | 97.05 170 | 88.38 210 | 89.80 201 | 94.52 218 | 82.21 173 | 98.01 223 | 88.00 173 | 85.42 265 | 94.87 263 |
|
V14 | | | 88.52 268 | 87.30 268 | 92.17 266 | 94.12 260 | 87.99 223 | 96.72 168 | 95.91 233 | 86.98 247 | 80.50 315 | 89.63 313 | 83.03 146 | 96.12 310 | 84.23 242 | 74.60 332 | 92.40 322 |
|
v10 | | | 91.04 216 | 90.23 214 | 93.49 225 | 94.12 260 | 88.16 212 | 97.32 111 | 97.08 166 | 88.26 214 | 88.29 240 | 94.22 248 | 82.17 175 | 97.97 230 | 86.45 206 | 84.12 289 | 94.33 288 |
|
V9 | | | 88.49 271 | 87.26 270 | 92.18 265 | 94.12 260 | 87.97 226 | 96.73 165 | 95.90 234 | 86.95 249 | 80.40 317 | 89.61 314 | 82.98 150 | 96.13 308 | 84.14 243 | 74.55 333 | 92.44 319 |
|
v12 | | | 88.46 272 | 87.23 273 | 92.17 266 | 94.10 263 | 87.99 223 | 96.71 170 | 95.90 234 | 86.91 250 | 80.34 319 | 89.58 317 | 82.92 154 | 96.11 312 | 84.09 244 | 74.50 335 | 92.42 320 |
|
v15 | | | 88.53 267 | 87.31 267 | 92.20 264 | 94.09 264 | 88.05 221 | 96.72 168 | 95.90 234 | 87.01 245 | 80.53 314 | 89.60 316 | 83.02 147 | 96.13 308 | 84.29 241 | 74.64 330 | 92.41 321 |
|
Patchmtry | | | 88.64 266 | 87.25 271 | 92.78 250 | 94.09 264 | 86.64 253 | 89.82 333 | 95.68 247 | 80.81 316 | 87.63 252 | 92.36 293 | 80.91 197 | 97.03 292 | 78.86 303 | 85.12 271 | 94.67 277 |
|
v13 | | | 88.45 273 | 87.22 274 | 92.16 268 | 94.08 266 | 87.95 227 | 96.71 170 | 95.90 234 | 86.86 254 | 80.27 321 | 89.55 318 | 82.92 154 | 96.12 310 | 84.02 246 | 74.63 331 | 92.40 322 |
|
v11 | | | 88.41 274 | 87.19 277 | 92.08 271 | 94.08 266 | 87.77 232 | 96.75 163 | 95.85 240 | 86.74 255 | 80.50 315 | 89.50 319 | 82.49 166 | 96.08 313 | 83.55 252 | 75.20 328 | 92.38 324 |
|
PatchT | | | 88.87 260 | 87.42 266 | 93.22 238 | 94.08 266 | 85.10 271 | 89.51 334 | 94.64 292 | 81.92 305 | 92.36 133 | 88.15 329 | 80.05 213 | 97.01 294 | 72.43 323 | 93.65 173 | 97.54 152 |
|
V42 | | | 91.58 192 | 90.87 183 | 93.73 211 | 94.05 269 | 88.50 198 | 97.32 111 | 96.97 181 | 88.80 194 | 89.71 206 | 94.33 232 | 82.54 164 | 98.05 213 | 89.01 158 | 85.07 275 | 94.64 279 |
|
v1141 | | | 91.61 188 | 90.89 180 | 93.78 205 | 94.01 270 | 88.24 205 | 96.96 140 | 96.96 182 | 89.17 174 | 89.75 204 | 94.29 241 | 82.99 149 | 98.03 219 | 88.85 162 | 85.00 278 | 95.07 251 |
|
divwei89l23v2f112 | | | 91.61 188 | 90.89 180 | 93.78 205 | 94.01 270 | 88.22 207 | 96.96 140 | 96.96 182 | 89.17 174 | 89.75 204 | 94.28 243 | 83.02 147 | 98.03 219 | 88.86 161 | 84.98 281 | 95.08 249 |
|
v1 | | | 91.61 188 | 90.89 180 | 93.78 205 | 94.01 270 | 88.21 208 | 96.96 140 | 96.96 182 | 89.17 174 | 89.78 203 | 94.29 241 | 82.97 151 | 98.05 213 | 88.85 162 | 84.99 279 | 95.08 249 |
|
DTE-MVSNet | | | 90.56 232 | 89.75 233 | 93.01 243 | 93.95 273 | 87.25 240 | 97.64 75 | 97.65 106 | 90.74 132 | 87.12 261 | 95.68 169 | 79.97 215 | 97.00 295 | 83.33 255 | 81.66 309 | 94.78 274 |
|
tpm | | | 90.25 238 | 89.74 234 | 91.76 282 | 93.92 274 | 79.73 318 | 93.98 286 | 93.54 318 | 88.28 213 | 91.99 141 | 93.25 278 | 77.51 265 | 97.44 276 | 87.30 194 | 87.94 246 | 98.12 125 |
|
PS-MVSNAJss | | | 93.74 106 | 93.51 96 | 94.44 174 | 93.91 275 | 89.28 183 | 97.75 56 | 97.56 115 | 92.50 84 | 89.94 194 | 96.54 128 | 88.65 71 | 98.18 191 | 93.83 80 | 90.90 217 | 95.86 205 |
|
v1144 | | | 91.37 203 | 90.60 201 | 93.68 216 | 93.89 276 | 88.23 206 | 96.84 152 | 97.03 175 | 88.37 211 | 89.69 208 | 94.39 228 | 82.04 176 | 97.98 227 | 87.80 178 | 85.37 267 | 94.84 265 |
|
v2v482 | | | 91.59 191 | 90.85 185 | 93.80 203 | 93.87 277 | 88.17 211 | 96.94 146 | 96.88 192 | 89.54 160 | 89.53 214 | 94.90 199 | 81.70 185 | 98.02 222 | 89.25 150 | 85.04 277 | 95.20 246 |
|
v148 | | | 90.99 217 | 90.38 207 | 92.81 249 | 93.83 278 | 85.80 262 | 96.78 162 | 96.68 203 | 89.45 164 | 88.75 231 | 93.93 256 | 82.96 153 | 97.82 250 | 87.83 177 | 83.25 299 | 94.80 271 |
|
Baseline_NR-MVSNet | | | 91.20 210 | 90.62 200 | 92.95 245 | 93.83 278 | 88.03 222 | 97.01 137 | 95.12 272 | 88.42 209 | 89.70 207 | 95.13 194 | 83.47 130 | 97.44 276 | 89.66 142 | 83.24 300 | 93.37 304 |
|
EPNet_dtu | | | 91.71 177 | 91.28 168 | 92.99 244 | 93.76 280 | 83.71 285 | 96.69 175 | 95.28 263 | 93.15 64 | 87.02 265 | 95.95 150 | 83.37 132 | 97.38 282 | 79.46 300 | 96.84 116 | 97.88 135 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
v1192 | | | 91.07 214 | 90.23 214 | 93.58 222 | 93.70 281 | 87.82 231 | 96.73 165 | 97.07 167 | 87.77 227 | 89.58 211 | 94.32 233 | 80.90 200 | 97.97 230 | 86.52 204 | 85.48 263 | 94.95 257 |
|
GG-mvs-BLEND | | | | | 93.62 218 | 93.69 282 | 89.20 186 | 92.39 316 | 83.33 353 | | 87.98 246 | 89.84 309 | 71.00 301 | 96.87 297 | 82.08 270 | 95.40 142 | 94.80 271 |
|
v144192 | | | 91.06 215 | 90.28 210 | 93.39 230 | 93.66 283 | 87.23 242 | 96.83 153 | 97.07 167 | 87.43 234 | 89.69 208 | 94.28 243 | 81.48 186 | 98.00 226 | 87.18 197 | 84.92 282 | 94.93 261 |
|
v1921920 | | | 90.85 221 | 90.03 221 | 93.29 235 | 93.55 284 | 86.96 250 | 96.74 164 | 97.04 173 | 87.36 236 | 89.52 215 | 94.34 231 | 80.23 211 | 97.97 230 | 86.27 207 | 85.21 270 | 94.94 259 |
|
v7n | | | 90.76 223 | 89.86 227 | 93.45 229 | 93.54 285 | 87.60 236 | 97.70 67 | 97.37 141 | 88.85 188 | 87.65 251 | 94.08 252 | 81.08 191 | 98.10 198 | 84.68 234 | 83.79 296 | 94.66 278 |
|
JIA-IIPM | | | 88.26 277 | 87.04 278 | 91.91 274 | 93.52 286 | 81.42 303 | 89.38 335 | 94.38 300 | 80.84 315 | 90.93 171 | 80.74 341 | 79.22 225 | 97.92 240 | 82.76 262 | 91.62 205 | 96.38 186 |
|
v1240 | | | 90.70 229 | 89.85 228 | 93.23 237 | 93.51 287 | 86.80 251 | 96.61 183 | 97.02 176 | 87.16 241 | 89.58 211 | 94.31 234 | 79.55 221 | 97.98 227 | 85.52 222 | 85.44 264 | 94.90 262 |
|
test_djsdf | | | 93.07 126 | 92.76 114 | 94.00 190 | 93.49 288 | 88.70 194 | 98.22 26 | 97.57 112 | 91.42 117 | 90.08 192 | 95.55 175 | 82.85 157 | 97.92 240 | 94.07 71 | 91.58 206 | 95.40 232 |
|
SixPastTwentyTwo | | | 89.15 256 | 88.54 253 | 90.98 293 | 93.49 288 | 80.28 314 | 96.70 173 | 94.70 287 | 90.78 131 | 84.15 290 | 95.57 173 | 71.78 296 | 97.71 259 | 84.63 235 | 85.07 275 | 94.94 259 |
|
mvs_tets | | | 92.31 157 | 91.76 144 | 93.94 199 | 93.41 290 | 88.29 201 | 97.63 82 | 97.53 116 | 92.04 102 | 88.76 230 | 96.45 132 | 74.62 282 | 98.09 200 | 93.91 76 | 91.48 208 | 95.45 226 |
|
OurMVSNet-221017-0 | | | 90.51 234 | 90.19 217 | 91.44 288 | 93.41 290 | 81.25 304 | 96.98 139 | 96.28 216 | 91.68 110 | 86.55 270 | 96.30 137 | 74.20 285 | 97.98 227 | 88.96 159 | 87.40 253 | 95.09 248 |
|
pmmvs4 | | | 90.93 219 | 89.85 228 | 94.17 183 | 93.34 292 | 90.79 124 | 94.60 271 | 96.02 227 | 84.62 281 | 87.45 253 | 95.15 192 | 81.88 181 | 97.45 275 | 87.70 180 | 87.87 247 | 94.27 292 |
|
DI_MVS_plusplus_test | | | 92.01 167 | 90.77 188 | 95.73 111 | 93.34 292 | 89.78 148 | 96.14 220 | 96.18 223 | 90.58 144 | 81.80 302 | 93.50 271 | 74.95 280 | 98.90 131 | 93.51 84 | 96.94 115 | 98.51 101 |
|
jajsoiax | | | 92.42 152 | 91.89 142 | 94.03 189 | 93.33 294 | 88.50 198 | 97.73 60 | 97.53 116 | 92.00 104 | 88.85 229 | 96.50 130 | 75.62 275 | 98.11 197 | 93.88 78 | 91.56 207 | 95.48 222 |
|
v748 | | | 90.34 236 | 89.54 238 | 92.75 251 | 93.25 295 | 85.71 264 | 97.61 83 | 97.17 154 | 88.54 201 | 87.20 260 | 93.54 269 | 81.02 192 | 98.01 223 | 85.73 220 | 81.80 306 | 94.52 282 |
|
test_normal | | | 92.01 167 | 90.75 190 | 95.80 106 | 93.24 296 | 89.97 141 | 95.93 232 | 96.24 220 | 90.62 140 | 81.63 303 | 93.45 274 | 74.98 279 | 98.89 133 | 93.61 82 | 97.04 114 | 98.55 96 |
|
v52 | | | 90.70 229 | 90.00 222 | 92.82 246 | 93.24 296 | 87.03 246 | 97.60 84 | 97.14 158 | 88.21 215 | 87.69 249 | 93.94 255 | 80.91 197 | 98.07 205 | 87.39 190 | 83.87 295 | 93.36 305 |
|
gm-plane-assit | | | | | | 93.22 298 | 78.89 324 | | | 84.82 279 | | 93.52 270 | | 98.64 150 | 87.72 179 | | |
|
V4 | | | 90.71 228 | 90.00 222 | 92.82 246 | 93.21 299 | 87.03 246 | 97.59 86 | 97.16 157 | 88.21 215 | 87.69 249 | 93.92 257 | 80.93 196 | 98.06 210 | 87.39 190 | 83.90 294 | 93.39 303 |
|
LP | | | 84.13 302 | 81.85 307 | 90.97 294 | 93.20 300 | 82.12 299 | 87.68 340 | 94.27 306 | 76.80 330 | 81.93 300 | 88.52 324 | 72.97 293 | 95.95 315 | 59.53 342 | 81.73 307 | 94.84 265 |
|
MVP-Stereo | | | 90.74 226 | 90.08 218 | 92.71 252 | 93.19 301 | 88.20 209 | 95.86 234 | 96.27 217 | 86.07 263 | 84.86 283 | 94.76 210 | 77.84 262 | 97.75 256 | 83.88 250 | 98.01 88 | 92.17 328 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
EU-MVSNet | | | 88.72 262 | 88.90 247 | 88.20 313 | 93.15 302 | 74.21 331 | 96.63 182 | 94.22 307 | 85.18 272 | 87.32 258 | 95.97 148 | 76.16 270 | 94.98 327 | 85.27 226 | 86.17 258 | 95.41 228 |
|
MDA-MVSNet-bldmvs | | | 85.00 299 | 82.95 301 | 91.17 292 | 93.13 303 | 83.33 290 | 94.56 273 | 95.00 277 | 84.57 282 | 65.13 343 | 92.65 284 | 70.45 304 | 95.85 316 | 73.57 321 | 77.49 319 | 94.33 288 |
|
K. test v3 | | | 87.64 282 | 86.75 280 | 90.32 305 | 93.02 304 | 79.48 320 | 96.61 183 | 92.08 335 | 90.66 137 | 80.25 322 | 94.09 251 | 67.21 319 | 96.65 300 | 85.96 216 | 80.83 313 | 94.83 267 |
|
pmmvs5 | | | 89.86 248 | 88.87 248 | 92.82 246 | 92.86 305 | 86.23 259 | 96.26 212 | 95.39 256 | 84.24 285 | 87.12 261 | 94.51 219 | 74.27 284 | 97.36 283 | 87.61 187 | 87.57 249 | 94.86 264 |
|
testgi | | | 87.97 278 | 87.21 275 | 90.24 306 | 92.86 305 | 80.76 306 | 96.67 177 | 94.97 279 | 91.74 108 | 85.52 277 | 95.83 156 | 62.66 329 | 94.47 329 | 76.25 312 | 88.36 244 | 95.48 222 |
|
EPNet | | | 95.20 68 | 94.56 73 | 97.14 55 | 92.80 307 | 92.68 66 | 97.85 50 | 94.87 286 | 96.64 1 | 92.46 129 | 97.80 64 | 86.23 100 | 99.65 42 | 93.72 81 | 98.62 75 | 99.10 62 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
N_pmnet | | | 78.73 313 | 78.71 312 | 78.79 331 | 92.80 307 | 46.50 358 | 94.14 283 | 43.71 361 | 78.61 325 | 80.83 306 | 91.66 303 | 74.94 281 | 96.36 302 | 67.24 332 | 84.45 287 | 93.50 300 |
|
EG-PatchMatch MVS | | | 87.02 287 | 85.44 288 | 91.76 282 | 92.67 309 | 85.00 272 | 96.08 224 | 96.45 211 | 83.41 296 | 79.52 324 | 93.49 272 | 57.10 337 | 97.72 258 | 79.34 302 | 90.87 218 | 92.56 313 |
|
Gipuma | | | 67.86 322 | 65.41 323 | 75.18 335 | 92.66 310 | 73.45 333 | 66.50 353 | 94.52 297 | 53.33 348 | 57.80 347 | 66.07 350 | 30.81 352 | 89.20 346 | 48.15 351 | 78.88 317 | 62.90 352 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
anonymousdsp | | | 92.16 164 | 91.55 158 | 93.97 194 | 92.58 311 | 89.55 162 | 97.51 91 | 97.42 136 | 89.42 165 | 88.40 236 | 94.84 204 | 80.66 202 | 97.88 245 | 91.87 112 | 91.28 212 | 94.48 283 |
|
test0.0.03 1 | | | 89.37 255 | 88.70 249 | 91.41 289 | 92.47 312 | 85.63 265 | 95.22 264 | 92.70 332 | 91.11 126 | 86.91 267 | 93.65 266 | 79.02 229 | 93.19 335 | 78.00 306 | 89.18 235 | 95.41 228 |
|
our_test_3 | | | 88.78 261 | 87.98 259 | 91.20 291 | 92.45 313 | 82.53 294 | 93.61 296 | 95.69 245 | 85.77 266 | 84.88 282 | 93.71 262 | 79.99 214 | 96.78 299 | 79.47 299 | 86.24 257 | 94.28 291 |
|
ppachtmachnet_test | | | 88.35 276 | 87.29 269 | 91.53 285 | 92.45 313 | 83.57 289 | 93.75 290 | 95.97 228 | 84.28 284 | 85.32 281 | 94.18 249 | 79.00 233 | 96.93 296 | 75.71 314 | 84.99 279 | 94.10 293 |
|
YYNet1 | | | 85.87 295 | 84.23 297 | 90.78 300 | 92.38 315 | 82.46 296 | 93.17 302 | 95.14 271 | 82.12 304 | 67.69 338 | 92.36 293 | 78.16 252 | 95.50 324 | 77.31 309 | 79.73 315 | 94.39 286 |
|
MDA-MVSNet_test_wron | | | 85.87 295 | 84.23 297 | 90.80 299 | 92.38 315 | 82.57 293 | 93.17 302 | 95.15 270 | 82.15 303 | 67.65 339 | 92.33 296 | 78.20 249 | 95.51 323 | 77.33 308 | 79.74 314 | 94.31 290 |
|
LF4IMVS | | | 87.94 279 | 87.25 271 | 89.98 308 | 92.38 315 | 80.05 317 | 94.38 276 | 95.25 266 | 87.59 232 | 84.34 286 | 94.74 212 | 64.31 326 | 97.66 263 | 84.83 230 | 87.45 250 | 92.23 326 |
|
lessismore_v0 | | | | | 90.45 303 | 91.96 318 | 79.09 323 | | 87.19 349 | | 80.32 320 | 94.39 228 | 66.31 321 | 97.55 269 | 84.00 248 | 76.84 321 | 94.70 276 |
|
testpf | | | 80.97 310 | 81.40 308 | 79.65 329 | 91.53 319 | 72.43 335 | 73.47 351 | 89.55 344 | 78.63 324 | 80.81 307 | 89.06 321 | 61.36 331 | 91.36 341 | 83.34 254 | 84.89 283 | 75.15 348 |
|
pmmvs6 | | | 87.81 281 | 86.19 283 | 92.69 253 | 91.32 320 | 86.30 258 | 97.34 108 | 96.41 212 | 80.59 318 | 84.05 292 | 94.37 230 | 67.37 318 | 97.67 261 | 84.75 232 | 79.51 316 | 94.09 295 |
|
Anonymous20231206 | | | 87.09 286 | 86.14 284 | 89.93 309 | 91.22 321 | 80.35 311 | 96.11 221 | 95.35 259 | 83.57 294 | 84.16 289 | 93.02 280 | 73.54 291 | 95.61 320 | 72.16 324 | 86.14 259 | 93.84 298 |
|
DeepMVS_CX | | | | | 74.68 336 | 90.84 322 | 64.34 347 | | 81.61 356 | 65.34 344 | 67.47 341 | 88.01 330 | 48.60 346 | 80.13 353 | 62.33 339 | 73.68 338 | 79.58 346 |
|
Test4 | | | 89.48 252 | 87.50 263 | 95.44 126 | 90.76 323 | 89.72 149 | 95.78 240 | 97.09 164 | 90.28 147 | 77.67 328 | 91.74 302 | 55.42 341 | 98.08 201 | 91.92 109 | 96.83 117 | 98.52 99 |
|
test20.03 | | | 86.14 293 | 85.40 289 | 88.35 311 | 90.12 324 | 80.06 316 | 95.90 233 | 95.20 268 | 88.59 197 | 81.29 305 | 93.62 267 | 71.43 298 | 92.65 336 | 71.26 328 | 81.17 311 | 92.34 325 |
|
OpenMVS_ROB | | 81.14 20 | 84.42 301 | 82.28 302 | 90.83 296 | 90.06 325 | 84.05 283 | 95.73 241 | 94.04 310 | 73.89 338 | 80.17 323 | 91.53 304 | 59.15 334 | 97.64 264 | 66.92 333 | 89.05 236 | 90.80 335 |
|
UnsupCasMVSNet_eth | | | 85.99 294 | 84.45 295 | 90.62 301 | 89.97 326 | 82.40 297 | 93.62 295 | 97.37 141 | 89.86 154 | 78.59 327 | 92.37 290 | 65.25 325 | 95.35 325 | 82.27 269 | 70.75 339 | 94.10 293 |
|
DSMNet-mixed | | | 86.34 291 | 86.12 285 | 87.00 318 | 89.88 327 | 70.43 336 | 94.93 268 | 90.08 343 | 77.97 328 | 85.42 280 | 92.78 283 | 74.44 283 | 93.96 331 | 74.43 317 | 95.14 145 | 96.62 179 |
|
new_pmnet | | | 82.89 305 | 81.12 310 | 88.18 314 | 89.63 328 | 80.18 315 | 91.77 319 | 92.57 333 | 76.79 331 | 75.56 331 | 88.23 328 | 61.22 332 | 94.48 328 | 71.43 326 | 82.92 303 | 89.87 337 |
|
MIMVSNet1 | | | 84.93 300 | 83.05 300 | 90.56 302 | 89.56 329 | 84.84 276 | 95.40 255 | 95.35 259 | 83.91 288 | 80.38 318 | 92.21 297 | 57.23 336 | 93.34 334 | 70.69 330 | 82.75 305 | 93.50 300 |
|
CMPMVS | | 62.92 21 | 85.62 297 | 84.92 292 | 87.74 315 | 89.14 330 | 73.12 334 | 94.17 282 | 96.80 196 | 73.98 337 | 73.65 333 | 94.93 197 | 66.36 320 | 97.61 266 | 83.95 249 | 91.28 212 | 92.48 317 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
Patchmatch-RL test | | | 87.38 283 | 86.24 282 | 90.81 297 | 88.74 331 | 78.40 325 | 88.12 339 | 93.17 320 | 87.11 242 | 82.17 298 | 89.29 320 | 81.95 179 | 95.60 321 | 88.64 167 | 77.02 320 | 98.41 114 |
|
pmmvs-eth3d | | | 86.22 292 | 84.45 295 | 91.53 285 | 88.34 332 | 87.25 240 | 94.47 275 | 95.01 276 | 83.47 295 | 79.51 325 | 89.61 314 | 69.75 308 | 95.71 319 | 83.13 257 | 76.73 322 | 91.64 330 |
|
UnsupCasMVSNet_bld | | | 82.13 309 | 79.46 311 | 90.14 307 | 88.00 333 | 82.47 295 | 90.89 327 | 96.62 209 | 78.94 323 | 75.61 330 | 84.40 339 | 56.63 338 | 96.31 303 | 77.30 310 | 66.77 345 | 91.63 331 |
|
PM-MVS | | | 83.48 303 | 81.86 306 | 88.31 312 | 87.83 334 | 77.59 326 | 93.43 297 | 91.75 337 | 86.91 250 | 80.63 312 | 89.91 308 | 44.42 348 | 95.84 317 | 85.17 229 | 76.73 322 | 91.50 333 |
|
testing_2 | | | 87.33 284 | 85.03 291 | 94.22 181 | 87.77 335 | 89.32 180 | 94.97 267 | 97.11 163 | 89.22 169 | 71.64 337 | 88.73 323 | 55.16 342 | 97.94 236 | 91.95 108 | 88.73 241 | 95.41 228 |
|
testus | | | 82.63 307 | 82.15 303 | 84.07 323 | 87.31 336 | 67.67 342 | 93.18 300 | 94.29 305 | 82.47 301 | 82.14 299 | 90.69 305 | 53.01 343 | 91.94 339 | 66.30 334 | 89.96 229 | 92.62 312 |
|
new-patchmatchnet | | | 83.18 304 | 81.87 305 | 87.11 317 | 86.88 337 | 75.99 329 | 93.70 291 | 95.18 269 | 85.02 276 | 77.30 329 | 88.40 326 | 65.99 322 | 93.88 332 | 74.19 320 | 70.18 340 | 91.47 334 |
|
Anonymous20231211 | | | 78.22 315 | 75.30 316 | 86.99 319 | 86.14 338 | 74.16 332 | 95.62 246 | 93.88 313 | 66.43 342 | 74.44 332 | 87.86 331 | 41.39 349 | 95.11 326 | 62.49 338 | 69.46 342 | 91.71 329 |
|
test2356 | | | 82.77 306 | 82.14 304 | 84.65 322 | 85.77 339 | 70.36 337 | 91.22 323 | 93.69 317 | 81.58 309 | 81.82 301 | 89.00 322 | 60.63 333 | 90.77 342 | 64.74 335 | 90.80 219 | 92.82 308 |
|
1111 | | | 78.29 314 | 77.55 314 | 80.50 327 | 83.89 340 | 59.98 350 | 91.89 317 | 93.71 314 | 75.06 333 | 73.60 334 | 87.67 332 | 55.66 339 | 92.60 337 | 58.54 344 | 77.92 318 | 88.93 339 |
|
.test1245 | | | 65.38 323 | 69.22 321 | 53.86 343 | 83.89 340 | 59.98 350 | 91.89 317 | 93.71 314 | 75.06 333 | 73.60 334 | 87.67 332 | 55.66 339 | 92.60 337 | 58.54 344 | 2.96 357 | 9.00 357 |
|
ambc | | | | | 86.56 320 | 83.60 342 | 70.00 340 | 85.69 343 | 94.97 279 | | 80.60 313 | 88.45 325 | 37.42 350 | 96.84 298 | 82.69 264 | 75.44 325 | 92.86 307 |
|
pmmvs3 | | | 79.97 311 | 77.50 315 | 87.39 316 | 82.80 343 | 79.38 321 | 92.70 311 | 90.75 341 | 70.69 341 | 78.66 326 | 87.47 335 | 51.34 345 | 93.40 333 | 73.39 322 | 69.65 341 | 89.38 338 |
|
test1235678 | | | 79.82 312 | 78.53 313 | 83.69 324 | 82.55 344 | 67.55 343 | 92.50 314 | 94.13 308 | 79.28 321 | 72.10 336 | 86.45 337 | 57.27 335 | 90.68 343 | 61.60 340 | 80.90 312 | 92.82 308 |
|
TDRefinement | | | 86.53 289 | 84.76 294 | 91.85 276 | 82.23 345 | 84.25 279 | 96.38 201 | 95.35 259 | 84.97 277 | 84.09 291 | 94.94 196 | 65.76 324 | 98.34 181 | 84.60 238 | 74.52 334 | 92.97 306 |
|
test12356 | | | 74.97 316 | 74.13 317 | 77.49 332 | 78.81 346 | 56.23 354 | 88.53 338 | 92.75 331 | 75.14 332 | 67.50 340 | 85.07 338 | 44.88 347 | 89.96 344 | 58.71 343 | 75.75 324 | 86.26 340 |
|
PMMVS2 | | | 70.19 320 | 66.92 322 | 80.01 328 | 76.35 347 | 65.67 345 | 86.22 342 | 87.58 348 | 64.83 345 | 62.38 344 | 80.29 343 | 26.78 357 | 88.49 348 | 63.79 336 | 54.07 347 | 85.88 342 |
|
FPMVS | | | 71.27 319 | 69.85 319 | 75.50 334 | 74.64 348 | 59.03 352 | 91.30 321 | 91.50 338 | 58.80 346 | 57.92 346 | 88.28 327 | 29.98 355 | 85.53 350 | 53.43 348 | 82.84 304 | 81.95 344 |
|
E-PMN | | | 53.28 328 | 52.56 329 | 55.43 341 | 74.43 349 | 47.13 357 | 83.63 346 | 76.30 357 | 42.23 352 | 42.59 351 | 62.22 352 | 28.57 356 | 74.40 355 | 31.53 354 | 31.51 352 | 44.78 353 |
|
no-one | | | 68.12 321 | 63.78 324 | 81.13 326 | 74.01 350 | 70.22 339 | 87.61 341 | 90.71 342 | 72.63 340 | 53.13 348 | 71.89 347 | 30.29 353 | 91.45 340 | 61.53 341 | 32.21 351 | 81.72 345 |
|
PNet_i23d | | | 59.01 325 | 55.87 326 | 68.44 338 | 73.98 351 | 51.37 355 | 81.36 347 | 82.41 354 | 52.37 349 | 42.49 352 | 70.39 349 | 11.39 360 | 79.99 354 | 49.77 350 | 38.71 349 | 73.97 349 |
|
wuyk23d | | | 25.11 333 | 24.57 335 | 26.74 346 | 73.98 351 | 39.89 361 | 57.88 354 | 9.80 362 | 12.27 356 | 10.39 357 | 6.97 360 | 7.03 362 | 36.44 359 | 25.43 356 | 17.39 356 | 3.89 359 |
|
testmv | | | 72.22 318 | 70.02 318 | 78.82 330 | 73.06 353 | 61.75 348 | 91.24 322 | 92.31 334 | 74.45 336 | 61.06 345 | 80.51 342 | 34.21 351 | 88.63 347 | 55.31 347 | 68.07 344 | 86.06 341 |
|
EMVS | | | 52.08 330 | 51.31 330 | 54.39 342 | 72.62 354 | 45.39 359 | 83.84 345 | 75.51 358 | 41.13 353 | 40.77 353 | 59.65 353 | 30.08 354 | 73.60 356 | 28.31 355 | 29.90 354 | 44.18 354 |
|
LCM-MVSNet | | | 72.55 317 | 69.39 320 | 82.03 325 | 70.81 355 | 65.42 346 | 90.12 332 | 94.36 302 | 55.02 347 | 65.88 342 | 81.72 340 | 24.16 359 | 89.96 344 | 74.32 319 | 68.10 343 | 90.71 336 |
|
MVE | | 50.73 23 | 53.25 329 | 48.81 332 | 66.58 340 | 65.34 356 | 57.50 353 | 72.49 352 | 70.94 359 | 40.15 354 | 39.28 354 | 63.51 351 | 6.89 364 | 73.48 357 | 38.29 353 | 42.38 348 | 68.76 351 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
wuykxyi23d | | | 56.92 327 | 51.11 331 | 74.38 337 | 62.30 357 | 61.47 349 | 80.09 348 | 84.87 351 | 49.62 350 | 30.80 356 | 57.20 354 | 7.03 362 | 82.94 351 | 55.69 346 | 32.36 350 | 78.72 347 |
|
ANet_high | | | 63.94 324 | 59.58 325 | 77.02 333 | 61.24 358 | 66.06 344 | 85.66 344 | 87.93 347 | 78.53 326 | 42.94 350 | 71.04 348 | 25.42 358 | 80.71 352 | 52.60 349 | 30.83 353 | 84.28 343 |
|
PMVS | | 53.92 22 | 58.58 326 | 55.40 327 | 68.12 339 | 51.00 359 | 48.64 356 | 78.86 349 | 87.10 350 | 46.77 351 | 35.84 355 | 74.28 345 | 8.76 361 | 86.34 349 | 42.07 352 | 73.91 337 | 69.38 350 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
tmp_tt | | | 51.94 331 | 53.82 328 | 46.29 344 | 33.73 360 | 45.30 360 | 78.32 350 | 67.24 360 | 18.02 355 | 50.93 349 | 87.05 336 | 52.99 344 | 53.11 358 | 70.76 329 | 25.29 355 | 40.46 355 |
|
testmvs | | | 13.36 335 | 16.33 336 | 4.48 348 | 5.04 361 | 2.26 363 | 93.18 300 | 3.28 363 | 2.70 357 | 8.24 358 | 21.66 356 | 2.29 366 | 2.19 360 | 7.58 357 | 2.96 357 | 9.00 357 |
|
test123 | | | 13.04 336 | 15.66 337 | 5.18 347 | 4.51 362 | 3.45 362 | 92.50 314 | 1.81 364 | 2.50 358 | 7.58 359 | 20.15 357 | 3.67 365 | 2.18 361 | 7.13 358 | 1.07 359 | 9.90 356 |
|
cdsmvs_eth3d_5k | | | 23.24 334 | 30.99 334 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 97.63 108 | 0.00 359 | 0.00 360 | 96.88 106 | 84.38 123 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
pcd_1.5k_mvsjas | | | 7.39 338 | 9.85 339 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 0.00 365 | 0.00 359 | 0.00 360 | 0.00 361 | 88.65 71 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
sosnet-low-res | | | 0.00 339 | 0.00 340 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 0.00 365 | 0.00 359 | 0.00 360 | 0.00 361 | 0.00 367 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
sosnet | | | 0.00 339 | 0.00 340 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 0.00 365 | 0.00 359 | 0.00 360 | 0.00 361 | 0.00 367 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
uncertanet | | | 0.00 339 | 0.00 340 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 0.00 365 | 0.00 359 | 0.00 360 | 0.00 361 | 0.00 367 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
Regformer | | | 0.00 339 | 0.00 340 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 0.00 365 | 0.00 359 | 0.00 360 | 0.00 361 | 0.00 367 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
ab-mvs-re | | | 8.06 337 | 10.74 338 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 0.00 365 | 0.00 359 | 0.00 360 | 96.69 115 | 0.00 367 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
uanet | | | 0.00 339 | 0.00 340 | 0.00 349 | 0.00 363 | 0.00 364 | 0.00 355 | 0.00 365 | 0.00 359 | 0.00 360 | 0.00 361 | 0.00 367 | 0.00 362 | 0.00 359 | 0.00 360 | 0.00 360 |
|
GSMVS | | | | | | | | | | | | | | | | | 98.45 110 |
|
test_part3 | | | | | | | | 97.50 92 | | 93.81 45 | | 98.53 12 | | 99.87 5 | 95.19 48 | | |
|
test_part1 | | | | | | | | | 98.26 25 | | | | 95.31 1 | | | 99.63 5 | 99.63 5 |
|
sam_mvs1 | | | | | | | | | | | | | 82.76 159 | | | | 98.45 110 |
|
sam_mvs | | | | | | | | | | | | | 81.94 180 | | | | |
|
MTGPA | | | | | | | | | 98.08 51 | | | | | | | | |
|
test_post1 | | | | | | | | 92.81 310 | | | | 16.58 359 | 80.53 204 | 97.68 260 | 86.20 209 | | |
|
test_post | | | | | | | | | | | | 17.58 358 | 81.76 182 | 98.08 201 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 90.45 306 | 82.65 163 | 98.10 198 | | | |
|
MTMP | | | | | | | | | 82.03 355 | | | | | | | | |
|
test9_res | | | | | | | | | | | | | | | 94.81 63 | 99.38 36 | 99.45 31 |
|
agg_prior2 | | | | | | | | | | | | | | | 93.94 75 | 99.38 36 | 99.50 25 |
|
test_prior4 | | | | | | | 93.66 42 | 96.42 194 | | | | | | | | | |
|
test_prior2 | | | | | | | | 96.35 203 | | 92.80 79 | 96.03 55 | 97.59 80 | 92.01 31 | | 95.01 56 | 99.38 36 | |
|
旧先验2 | | | | | | | | 95.94 231 | | 81.66 307 | 97.34 18 | | | 98.82 138 | 92.26 98 | | |
|
新几何2 | | | | | | | | 95.79 238 | | | | | | | | | |
|
无先验 | | | | | | | | 95.79 238 | 97.87 86 | 83.87 291 | | | | 99.65 42 | 87.68 182 | | 98.89 81 |
|
原ACMM2 | | | | | | | | 95.67 242 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 99.67 40 | 85.96 216 | | |
|
segment_acmp | | | | | | | | | | | | | 92.89 13 | | | | |
|
testdata1 | | | | | | | | 95.26 263 | | 93.10 67 | | | | | | | |
|
plane_prior5 | | | | | | | | | 97.51 119 | | | | | 98.60 154 | 93.02 93 | 92.23 193 | 95.86 205 |
|
plane_prior4 | | | | | | | | | | | | 96.64 118 | | | | | |
|
plane_prior3 | | | | | | | 90.00 136 | | | 94.46 30 | 91.34 154 | | | | | | |
|
plane_prior2 | | | | | | | | 97.74 58 | | 94.85 17 | | | | | | | |
|
plane_prior | | | | | | | 89.99 138 | 97.24 116 | | 94.06 38 | | | | | | 92.16 197 | |
|
n2 | | | | | | | | | 0.00 365 | | | | | | | | |
|
nn | | | | | | | | | 0.00 365 | | | | | | | | |
|
door-mid | | | | | | | | | 91.06 340 | | | | | | | | |
|
test11 | | | | | | | | | 97.88 84 | | | | | | | | |
|
door | | | | | | | | | 91.13 339 | | | | | | | | |
|
HQP5-MVS | | | | | | | 89.33 178 | | | | | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 92.13 104 | | |
|
HQP4-MVS | | | | | | | | | | | 90.14 182 | | | 98.50 163 | | | 95.78 212 |
|
HQP3-MVS | | | | | | | | | 97.39 138 | | | | | | | 92.10 198 | |
|
HQP2-MVS | | | | | | | | | | | | | 80.95 194 | | | | |
|
MDTV_nov1_ep13_2view | | | | | | | 70.35 338 | 93.10 306 | | 83.88 290 | 93.55 102 | | 82.47 168 | | 86.25 208 | | 98.38 118 |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 90.30 226 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 91.02 216 | |
|
Test By Simon | | | | | | | | | | | | | 88.73 70 | | | | |
|