MG-MVS | | | 87.11 24 | 86.27 30 | 89.62 5 | 97.79 1 | 76.27 3 | 94.96 31 | 94.49 34 | 78.74 51 | 83.87 45 | 92.94 86 | 64.34 64 | 96.94 77 | 75.19 102 | 94.09 24 | 95.66 29 |
|
MCST-MVS | | | 91.08 1 | 91.46 2 | 89.94 2 | 97.66 2 | 73.37 7 | 97.13 1 | 95.58 13 | 89.33 1 | 85.77 25 | 96.26 9 | 72.84 11 | 99.38 1 | 92.64 4 | 95.93 4 | 97.08 4 |
|
DP-MVS Recon | | | 82.73 76 | 81.65 81 | 85.98 65 | 97.31 3 | 67.06 85 | 95.15 25 | 91.99 126 | 69.08 203 | 76.50 103 | 93.89 70 | 54.48 169 | 98.20 24 | 70.76 138 | 85.66 103 | 92.69 125 |
|
CNVR-MVS | | | 90.32 3 | 90.89 4 | 88.61 11 | 96.76 4 | 70.65 18 | 96.47 6 | 94.83 24 | 84.83 9 | 89.07 9 | 96.80 4 | 70.86 16 | 99.06 3 | 92.64 4 | 95.71 5 | 96.12 18 |
|
NCCC | | | 89.07 9 | 89.46 9 | 87.91 16 | 96.60 5 | 69.05 40 | 96.38 7 | 94.64 31 | 84.42 10 | 86.74 20 | 96.20 10 | 66.56 39 | 98.76 13 | 89.03 18 | 94.56 20 | 95.92 27 |
|
AdaColmap | | | 78.94 134 | 77.00 147 | 84.76 101 | 96.34 6 | 65.86 131 | 92.66 92 | 87.97 252 | 62.18 268 | 70.56 153 | 92.37 98 | 43.53 256 | 97.35 51 | 64.50 190 | 82.86 122 | 91.05 152 |
|
test_part2 | | | | | | 96.29 7 | 68.16 60 | | | | 90.78 3 | | | | | | |
|
ESAPD | | | 89.08 8 | 89.53 8 | 87.72 20 | 96.29 7 | 68.16 60 | 94.96 31 | 94.26 41 | 68.52 212 | 90.78 3 | 97.23 2 | 77.03 4 | 98.90 7 | 91.52 6 | 95.18 8 | 96.11 19 |
|
MAR-MVS | | | 84.18 57 | 83.43 58 | 86.44 53 | 96.25 9 | 65.93 130 | 94.28 37 | 94.27 40 | 74.41 100 | 79.16 76 | 95.61 23 | 53.99 175 | 98.88 11 | 69.62 146 | 93.26 38 | 94.50 73 |
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 |
API-MVS | | | 82.28 83 | 80.53 94 | 87.54 24 | 96.13 10 | 70.59 19 | 93.63 61 | 91.04 162 | 65.72 238 | 75.45 110 | 92.83 90 | 56.11 143 | 98.89 10 | 64.10 193 | 89.75 76 | 93.15 114 |
|
APDe-MVS | | | 87.54 18 | 87.84 15 | 86.65 43 | 96.07 11 | 66.30 123 | 94.84 34 | 93.78 47 | 69.35 200 | 88.39 12 | 96.34 8 | 67.74 30 | 97.66 38 | 90.62 11 | 93.44 36 | 96.01 24 |
|
PAPR | | | 85.15 46 | 84.47 48 | 87.18 31 | 96.02 12 | 68.29 56 | 91.85 126 | 93.00 91 | 76.59 76 | 79.03 77 | 95.00 41 | 61.59 87 | 97.61 42 | 78.16 86 | 89.00 78 | 95.63 30 |
|
APD-MVS | | | 85.93 39 | 85.99 34 | 85.76 74 | 95.98 13 | 65.21 142 | 93.59 63 | 92.58 106 | 66.54 230 | 86.17 21 | 95.88 16 | 63.83 68 | 97.00 69 | 86.39 34 | 92.94 40 | 95.06 51 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
DeepC-MVS_fast | | 79.48 2 | 87.95 14 | 88.00 14 | 87.79 19 | 95.86 14 | 68.32 55 | 95.74 12 | 94.11 43 | 83.82 12 | 83.49 46 | 96.19 11 | 64.53 63 | 98.44 19 | 83.42 53 | 94.88 14 | 96.61 9 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
DP-MVS | | | 69.90 251 | 66.48 257 | 80.14 205 | 95.36 15 | 62.93 192 | 89.56 193 | 76.11 319 | 50.27 314 | 57.69 271 | 85.23 188 | 39.68 270 | 95.73 112 | 33.35 324 | 71.05 205 | 81.78 282 |
|
114514_t | | | 79.17 130 | 77.67 132 | 83.68 125 | 95.32 16 | 65.53 138 | 92.85 84 | 91.60 141 | 63.49 258 | 67.92 199 | 90.63 118 | 46.65 239 | 95.72 116 | 67.01 166 | 83.54 119 | 89.79 163 |
|
Regformer-1 | | | 87.24 22 | 87.60 19 | 86.15 63 | 95.14 17 | 65.83 133 | 93.95 51 | 95.12 18 | 82.11 21 | 84.25 39 | 95.73 19 | 67.88 29 | 98.35 21 | 85.60 38 | 88.64 80 | 94.26 77 |
|
Regformer-2 | | | 87.00 26 | 87.43 21 | 85.71 77 | 95.14 17 | 64.73 152 | 93.95 51 | 94.95 21 | 81.69 26 | 84.03 43 | 95.73 19 | 67.35 33 | 98.19 25 | 85.40 40 | 88.64 80 | 94.20 79 |
|
HPM-MVS++ | | | 89.37 7 | 89.95 7 | 87.64 21 | 95.10 19 | 68.23 59 | 95.24 22 | 94.49 34 | 82.43 17 | 88.90 10 | 96.35 7 | 71.89 15 | 98.63 15 | 88.76 20 | 96.40 2 | 96.06 21 |
|
CSCG | | | 86.87 28 | 86.26 31 | 88.72 9 | 95.05 20 | 70.79 17 | 93.83 58 | 95.33 15 | 68.48 215 | 77.63 90 | 94.35 61 | 73.04 10 | 98.45 18 | 84.92 43 | 93.71 32 | 96.92 6 |
|
LFMVS | | | 84.34 55 | 82.73 70 | 89.18 8 | 94.76 21 | 73.25 8 | 94.99 30 | 91.89 130 | 71.90 156 | 82.16 52 | 93.49 76 | 47.98 229 | 97.05 64 | 82.55 57 | 84.82 107 | 97.25 2 |
|
CDPH-MVS | | | 85.71 42 | 85.46 42 | 86.46 52 | 94.75 22 | 67.19 81 | 93.89 56 | 92.83 96 | 70.90 179 | 83.09 48 | 95.28 31 | 63.62 71 | 97.36 50 | 80.63 70 | 94.18 23 | 94.84 60 |
|
test_prior3 | | | 87.38 20 | 87.70 17 | 86.42 54 | 94.71 23 | 67.35 77 | 95.10 27 | 93.10 87 | 75.40 89 | 85.25 32 | 95.61 23 | 67.94 26 | 96.84 81 | 87.47 25 | 94.77 15 | 95.05 52 |
|
test_prior | | | | | 86.42 54 | 94.71 23 | 67.35 77 | | 93.10 87 | | | | | 96.84 81 | | | 95.05 52 |
|
test12 | | | | | 87.09 35 | 94.60 25 | 68.86 44 | | 92.91 93 | | 82.67 50 | | 65.44 49 | 97.55 43 | | 93.69 33 | 94.84 60 |
|
CANet | | | 89.61 6 | 89.99 6 | 88.46 13 | 94.39 26 | 69.71 32 | 96.53 5 | 93.78 47 | 86.89 4 | 89.68 6 | 95.78 17 | 65.94 44 | 99.10 2 | 92.99 1 | 93.91 27 | 96.58 11 |
|
agg_prior3 | | | 86.93 27 | 87.08 25 | 86.48 51 | 94.21 27 | 66.95 90 | 94.14 41 | 93.40 71 | 71.80 163 | 84.86 34 | 95.13 38 | 66.16 41 | 97.25 58 | 89.09 15 | 94.90 12 | 95.25 45 |
|
test_8 | | | | | | 94.19 28 | 67.19 81 | 94.15 40 | 93.42 70 | 71.87 158 | 85.38 30 | 95.35 27 | 68.19 23 | 96.95 76 | | | |
|
TEST9 | | | | | | 94.18 29 | 67.28 79 | 94.16 38 | 93.51 59 | 71.75 166 | 85.52 28 | 95.33 28 | 68.01 25 | 97.27 56 | | | |
|
train_agg | | | 87.21 23 | 87.42 22 | 86.60 45 | 94.18 29 | 67.28 79 | 94.16 38 | 93.51 59 | 71.87 158 | 85.52 28 | 95.33 28 | 68.19 23 | 97.27 56 | 89.09 15 | 94.90 12 | 95.25 45 |
|
Regformer-3 | | | 85.80 41 | 85.92 35 | 85.46 81 | 94.17 31 | 65.09 148 | 92.95 80 | 95.11 19 | 81.13 27 | 81.68 55 | 95.04 39 | 65.82 46 | 98.32 22 | 83.02 54 | 84.36 111 | 92.97 120 |
|
Regformer-4 | | | 85.45 44 | 85.69 40 | 84.73 102 | 94.17 31 | 63.23 185 | 92.95 80 | 94.83 24 | 80.66 29 | 81.29 57 | 95.04 39 | 65.12 51 | 98.08 28 | 82.74 55 | 84.36 111 | 92.88 124 |
|
agg_prior1 | | | 87.02 25 | 87.26 24 | 86.28 61 | 94.16 33 | 66.97 88 | 94.08 44 | 93.31 75 | 71.85 160 | 84.49 37 | 95.39 26 | 68.91 19 | 96.75 85 | 88.84 19 | 94.32 22 | 95.13 49 |
|
agg_prior | | | | | | 94.16 33 | 66.97 88 | | 93.31 75 | | 84.49 37 | | | 96.75 85 | | | |
|
PAPM_NR | | | 82.97 74 | 81.84 78 | 86.37 57 | 94.10 35 | 66.76 102 | 87.66 234 | 92.84 95 | 69.96 195 | 74.07 121 | 93.57 74 | 63.10 79 | 97.50 45 | 70.66 139 | 90.58 70 | 94.85 59 |
|
VNet | | | 86.20 36 | 85.65 41 | 87.84 18 | 93.92 36 | 69.99 26 | 95.73 14 | 95.94 12 | 78.43 53 | 86.00 23 | 93.07 83 | 58.22 116 | 97.00 69 | 85.22 41 | 84.33 114 | 96.52 13 |
|
PVSNet_BlendedMVS | | | 83.38 68 | 83.43 58 | 83.22 132 | 93.76 37 | 67.53 74 | 94.06 45 | 93.61 55 | 79.13 43 | 81.00 59 | 85.14 189 | 63.19 77 | 97.29 54 | 87.08 29 | 73.91 185 | 84.83 252 |
|
PVSNet_Blended | | | 86.73 32 | 86.86 28 | 86.31 60 | 93.76 37 | 67.53 74 | 96.33 8 | 93.61 55 | 82.34 18 | 81.00 59 | 93.08 81 | 63.19 77 | 97.29 54 | 87.08 29 | 91.38 61 | 94.13 85 |
|
HFP-MVS | | | 84.73 50 | 84.40 50 | 85.72 75 | 93.75 39 | 65.01 149 | 93.50 66 | 93.19 82 | 72.19 150 | 79.22 74 | 94.93 44 | 59.04 111 | 97.67 35 | 81.55 64 | 92.21 48 | 94.49 74 |
|
#test# | | | 84.98 48 | 84.74 47 | 85.72 75 | 93.75 39 | 65.01 149 | 94.09 43 | 93.19 82 | 73.55 124 | 79.22 74 | 94.93 44 | 59.04 111 | 97.67 35 | 82.66 56 | 92.21 48 | 94.49 74 |
|
SD-MVS | | | 87.49 19 | 87.49 20 | 87.50 25 | 93.60 41 | 68.82 46 | 93.90 55 | 92.63 104 | 76.86 71 | 87.90 14 | 95.76 18 | 66.17 40 | 97.63 40 | 89.06 17 | 91.48 60 | 96.05 22 |
|
ACMMPR | | | 84.37 53 | 84.06 51 | 85.28 89 | 93.56 42 | 64.37 162 | 93.50 66 | 93.15 85 | 72.19 150 | 78.85 80 | 94.86 48 | 56.69 136 | 97.45 46 | 81.55 64 | 92.20 50 | 94.02 93 |
|
region2R | | | 84.36 54 | 84.03 52 | 85.36 87 | 93.54 43 | 64.31 164 | 93.43 69 | 92.95 92 | 72.16 153 | 78.86 79 | 94.84 49 | 56.97 129 | 97.53 44 | 81.38 67 | 92.11 52 | 94.24 78 |
|
TSAR-MVS + GP. | | | 87.96 13 | 88.37 12 | 86.70 42 | 93.51 44 | 65.32 140 | 95.15 25 | 93.84 46 | 78.17 55 | 85.93 24 | 94.80 50 | 75.80 6 | 98.21 23 | 89.38 13 | 88.78 79 | 96.59 10 |
|
PHI-MVS | | | 86.83 30 | 86.85 29 | 86.78 41 | 93.47 45 | 65.55 137 | 95.39 20 | 95.10 20 | 71.77 165 | 85.69 27 | 96.52 5 | 62.07 84 | 98.77 12 | 86.06 36 | 95.60 6 | 96.03 23 |
|
EPNet | | | 87.84 16 | 88.38 11 | 86.23 62 | 93.30 46 | 66.05 127 | 95.26 21 | 94.84 23 | 87.09 3 | 88.06 13 | 94.53 54 | 66.79 36 | 97.34 52 | 83.89 50 | 91.68 56 | 95.29 40 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
XVS | | | 83.87 63 | 83.47 56 | 85.05 95 | 93.22 47 | 63.78 173 | 92.92 82 | 92.66 102 | 73.99 111 | 78.18 84 | 94.31 64 | 55.25 149 | 97.41 47 | 79.16 78 | 91.58 58 | 93.95 95 |
|
X-MVStestdata | | | 76.86 175 | 74.13 189 | 85.05 95 | 93.22 47 | 63.78 173 | 92.92 82 | 92.66 102 | 73.99 111 | 78.18 84 | 10.19 353 | 55.25 149 | 97.41 47 | 79.16 78 | 91.58 58 | 93.95 95 |
|
原ACMM1 | | | | | 84.42 111 | 93.21 49 | 64.27 167 | | 93.40 71 | 65.39 239 | 79.51 72 | 92.50 93 | 58.11 118 | 96.69 87 | 65.27 184 | 93.96 25 | 92.32 134 |
|
MVS_111021_HR | | | 86.19 37 | 85.80 38 | 87.37 27 | 93.17 50 | 69.79 30 | 93.99 49 | 93.76 50 | 79.08 45 | 78.88 78 | 93.99 68 | 62.25 83 | 98.15 26 | 85.93 37 | 91.15 64 | 94.15 84 |
|
CP-MVS | | | 83.71 67 | 83.40 60 | 84.65 105 | 93.14 51 | 63.84 171 | 94.59 35 | 92.28 112 | 71.03 177 | 77.41 93 | 94.92 46 | 55.21 152 | 96.19 95 | 81.32 68 | 90.70 68 | 93.91 97 |
|
DELS-MVS | | | 90.05 4 | 90.09 5 | 89.94 2 | 93.14 51 | 73.88 6 | 97.01 2 | 94.40 38 | 88.32 2 | 85.71 26 | 94.91 47 | 74.11 9 | 98.91 6 | 87.26 28 | 95.94 3 | 97.03 5 |
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 |
MVS_0304 | | | 88.39 10 | 88.35 13 | 88.50 12 | 93.01 53 | 70.11 23 | 95.90 10 | 92.20 119 | 86.27 6 | 88.70 11 | 95.92 15 | 56.76 132 | 99.02 4 | 92.68 3 | 93.76 30 | 96.37 15 |
|
DeepPCF-MVS | | 81.17 1 | 89.72 5 | 91.38 3 | 84.72 104 | 93.00 54 | 58.16 259 | 96.72 3 | 94.41 37 | 86.50 5 | 90.25 5 | 97.83 1 | 75.46 7 | 98.67 14 | 92.78 2 | 95.49 7 | 97.32 1 |
|
PLC | | 68.80 14 | 75.23 199 | 73.68 196 | 79.86 214 | 92.93 55 | 58.68 257 | 90.64 170 | 88.30 245 | 60.90 276 | 64.43 232 | 90.53 119 | 42.38 260 | 94.57 147 | 56.52 235 | 76.54 168 | 86.33 222 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
DWT-MVSNet_test | | | 83.95 61 | 82.80 68 | 87.41 26 | 92.90 56 | 70.07 25 | 89.12 203 | 94.42 36 | 82.15 20 | 77.64 89 | 91.77 105 | 70.81 17 | 96.22 94 | 65.03 185 | 81.36 130 | 95.94 25 |
|
HSP-MVS | | | 90.38 2 | 91.89 1 | 85.84 69 | 92.83 57 | 64.03 170 | 93.06 76 | 94.52 32 | 82.19 19 | 93.65 1 | 96.15 12 | 85.89 1 | 97.19 59 | 91.02 10 | 97.75 1 | 96.29 16 |
|
mPP-MVS | | | 82.96 75 | 82.44 72 | 84.52 109 | 92.83 57 | 62.92 194 | 92.76 85 | 91.85 132 | 71.52 172 | 75.61 108 | 94.24 65 | 53.48 184 | 96.99 72 | 78.97 81 | 90.73 67 | 93.64 102 |
|
WTY-MVS | | | 86.32 34 | 85.81 37 | 87.85 17 | 92.82 59 | 69.37 37 | 95.20 23 | 95.25 16 | 82.71 15 | 81.91 53 | 94.73 51 | 67.93 28 | 97.63 40 | 79.55 75 | 82.25 126 | 96.54 12 |
|
PGM-MVS | | | 83.25 69 | 82.70 71 | 84.92 97 | 92.81 60 | 64.07 169 | 90.44 173 | 92.20 119 | 71.28 175 | 77.23 96 | 94.43 55 | 55.17 153 | 97.31 53 | 79.33 77 | 91.38 61 | 93.37 106 |
|
EI-MVSNet-Vis-set | | | 83.77 65 | 83.67 53 | 84.06 116 | 92.79 61 | 63.56 182 | 91.76 131 | 94.81 26 | 79.65 36 | 77.87 86 | 94.09 66 | 63.35 75 | 97.90 31 | 79.35 76 | 79.36 140 | 90.74 154 |
|
MVSTER | | | 82.47 80 | 82.05 75 | 83.74 121 | 92.68 62 | 69.01 41 | 91.90 123 | 93.21 79 | 79.83 32 | 72.14 142 | 85.71 185 | 74.72 8 | 94.72 144 | 75.72 98 | 72.49 195 | 87.50 197 |
|
MP-MVS | | | 85.02 47 | 84.97 46 | 85.17 93 | 92.60 63 | 64.27 167 | 93.24 71 | 92.27 113 | 73.13 130 | 79.63 71 | 94.43 55 | 61.90 85 | 97.17 60 | 85.00 42 | 92.56 45 | 94.06 91 |
|
thres200 | | | 79.66 122 | 78.33 122 | 83.66 127 | 92.54 64 | 65.82 134 | 93.06 76 | 96.31 9 | 74.90 97 | 73.30 126 | 88.66 143 | 59.67 105 | 95.61 118 | 47.84 266 | 78.67 147 | 89.56 167 |
|
APD-MVS_3200maxsize | | | 81.64 92 | 81.32 84 | 82.59 145 | 92.36 65 | 58.74 256 | 91.39 143 | 91.01 163 | 63.35 259 | 79.72 70 | 94.62 53 | 51.82 196 | 96.14 97 | 79.71 73 | 87.93 85 | 92.89 123 |
|
PatchFormer-LS_test | | | 83.14 71 | 81.81 79 | 87.12 33 | 92.34 66 | 69.92 28 | 88.64 210 | 93.32 74 | 82.07 23 | 74.87 114 | 91.62 109 | 68.91 19 | 96.08 101 | 66.07 176 | 78.45 150 | 95.37 35 |
|
新几何1 | | | | | 84.73 102 | 92.32 67 | 64.28 166 | | 91.46 147 | 59.56 284 | 79.77 69 | 92.90 88 | 56.95 130 | 96.57 90 | 63.40 197 | 92.91 41 | 93.34 107 |
|
1121 | | | 81.25 96 | 80.05 96 | 84.87 99 | 92.30 68 | 64.31 164 | 87.91 223 | 91.39 149 | 59.44 285 | 79.94 67 | 92.91 87 | 57.09 125 | 97.01 67 | 66.63 168 | 92.81 43 | 93.29 110 |
|
EI-MVSNet-UG-set | | | 83.14 71 | 82.96 64 | 83.67 126 | 92.28 69 | 63.19 189 | 91.38 145 | 94.68 29 | 79.22 40 | 76.60 101 | 93.75 71 | 62.64 81 | 97.76 33 | 78.07 87 | 78.01 151 | 90.05 161 |
|
HPM-MVS | | | 83.25 69 | 82.95 65 | 84.17 114 | 92.25 70 | 62.88 196 | 90.91 162 | 91.86 131 | 70.30 192 | 77.12 97 | 93.96 69 | 56.75 134 | 96.28 93 | 82.04 60 | 91.34 63 | 93.34 107 |
|
HY-MVS | | 76.49 5 | 84.28 56 | 83.36 62 | 87.02 37 | 92.22 71 | 67.74 68 | 84.65 259 | 94.50 33 | 79.15 42 | 82.23 51 | 87.93 156 | 66.88 35 | 96.94 77 | 80.53 71 | 82.20 127 | 96.39 14 |
|
tfpn200view9 | | | 78.79 138 | 77.43 138 | 82.88 136 | 92.21 72 | 64.49 154 | 92.05 110 | 96.28 10 | 73.48 125 | 71.75 147 | 88.26 151 | 60.07 102 | 95.32 127 | 45.16 275 | 77.58 156 | 88.83 171 |
|
thres400 | | | 78.68 141 | 77.43 138 | 82.43 150 | 92.21 72 | 64.49 154 | 92.05 110 | 96.28 10 | 73.48 125 | 71.75 147 | 88.26 151 | 60.07 102 | 95.32 127 | 45.16 275 | 77.58 156 | 87.48 198 |
|
PS-MVSNAJ | | | 88.14 11 | 87.61 18 | 89.71 4 | 92.06 74 | 76.72 1 | 95.75 11 | 93.26 77 | 83.86 11 | 89.55 7 | 96.06 13 | 53.55 180 | 97.89 32 | 91.10 8 | 93.31 37 | 94.54 70 |
|
MSLP-MVS++ | | | 86.27 35 | 85.91 36 | 87.35 28 | 92.01 75 | 68.97 43 | 95.04 29 | 92.70 99 | 79.04 46 | 81.50 56 | 96.50 6 | 58.98 113 | 96.78 83 | 83.49 52 | 93.93 26 | 96.29 16 |
|
旧先验1 | | | | | | 91.94 76 | 60.74 225 | | 91.50 145 | | | 94.36 57 | 65.23 50 | | | 91.84 53 | 94.55 68 |
|
thres600view7 | | | 78.00 152 | 76.66 150 | 82.03 173 | 91.93 77 | 63.69 178 | 91.30 150 | 96.33 5 | 72.43 141 | 70.46 155 | 87.89 157 | 60.31 97 | 94.92 137 | 42.64 287 | 76.64 166 | 87.48 198 |
|
LS3D | | | 69.17 258 | 66.40 258 | 77.50 259 | 91.92 78 | 56.12 281 | 85.12 256 | 80.37 312 | 46.96 321 | 56.50 276 | 87.51 164 | 37.25 286 | 93.71 193 | 32.52 329 | 79.40 139 | 82.68 277 |
|
GG-mvs-BLEND | | | | | 86.53 50 | 91.91 79 | 69.67 34 | 75.02 314 | 94.75 27 | | 78.67 83 | 90.85 114 | 77.91 2 | 94.56 148 | 72.25 121 | 93.74 31 | 95.36 36 |
|
tfpn111 | | | 78.00 152 | 76.62 151 | 82.13 167 | 91.89 80 | 63.21 186 | 91.19 156 | 96.33 5 | 72.28 145 | 70.45 156 | 87.89 157 | 60.31 97 | 94.91 138 | 42.61 288 | 76.64 166 | 88.27 183 |
|
conf200view11 | | | 78.32 149 | 77.01 145 | 82.27 160 | 91.89 80 | 63.21 186 | 91.19 156 | 96.33 5 | 72.28 145 | 70.45 156 | 87.89 157 | 60.31 97 | 95.32 127 | 45.16 275 | 77.58 156 | 88.27 183 |
|
thres100view900 | | | 78.37 147 | 77.01 145 | 82.46 146 | 91.89 80 | 63.21 186 | 91.19 156 | 96.33 5 | 72.28 145 | 70.45 156 | 87.89 157 | 60.31 97 | 95.32 127 | 45.16 275 | 77.58 156 | 88.83 171 |
|
MPTG | | | 84.73 50 | 84.47 48 | 85.50 79 | 91.89 80 | 65.16 143 | 91.55 139 | 92.23 114 | 75.32 91 | 80.53 62 | 95.21 36 | 56.06 144 | 97.16 61 | 84.86 44 | 92.55 46 | 94.18 80 |
|
MTAPA | | | 83.91 62 | 83.38 61 | 85.50 79 | 91.89 80 | 65.16 143 | 81.75 280 | 92.23 114 | 75.32 91 | 80.53 62 | 95.21 36 | 56.06 144 | 97.16 61 | 84.86 44 | 92.55 46 | 94.18 80 |
|
canonicalmvs | | | 86.85 29 | 86.25 32 | 88.66 10 | 91.80 85 | 71.92 10 | 93.54 65 | 91.71 137 | 80.26 31 | 87.55 15 | 95.25 34 | 63.59 73 | 96.93 79 | 88.18 21 | 84.34 113 | 97.11 3 |
|
TSAR-MVS + MP. | | | 88.11 12 | 88.64 10 | 86.54 48 | 91.73 86 | 68.04 63 | 90.36 176 | 93.55 58 | 82.89 14 | 91.29 2 | 92.89 89 | 72.27 12 | 96.03 102 | 87.99 22 | 94.77 15 | 95.54 33 |
|
ACMMP | | | 81.49 93 | 80.67 91 | 83.93 118 | 91.71 87 | 62.90 195 | 92.13 104 | 92.22 118 | 71.79 164 | 71.68 149 | 93.49 76 | 50.32 206 | 96.96 75 | 78.47 83 | 84.22 118 | 91.93 140 |
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 |
BH-RMVSNet | | | 79.46 127 | 77.65 133 | 84.89 98 | 91.68 88 | 65.66 135 | 93.55 64 | 88.09 249 | 72.93 134 | 73.37 125 | 91.12 112 | 46.20 244 | 96.12 98 | 56.28 237 | 85.61 104 | 92.91 122 |
|
ACMMP_Plus | | | 86.05 38 | 85.80 38 | 86.80 40 | 91.58 89 | 67.53 74 | 91.79 128 | 93.49 61 | 74.93 96 | 84.61 35 | 95.30 30 | 59.42 107 | 97.92 30 | 86.13 35 | 94.92 11 | 94.94 58 |
|
MVS_Test | | | 84.16 58 | 83.20 63 | 87.05 36 | 91.56 90 | 69.82 29 | 89.99 185 | 92.05 124 | 77.77 59 | 82.84 49 | 86.57 175 | 63.93 67 | 96.09 99 | 74.91 108 | 89.18 77 | 95.25 45 |
|
HPM-MVS_fast | | | 80.25 109 | 79.55 107 | 82.33 157 | 91.55 91 | 59.95 240 | 91.32 149 | 89.16 224 | 65.23 242 | 74.71 115 | 93.07 83 | 47.81 231 | 95.74 111 | 74.87 110 | 88.23 82 | 91.31 150 |
|
CPTT-MVS | | | 79.59 124 | 79.16 115 | 80.89 198 | 91.54 92 | 59.80 242 | 92.10 106 | 88.54 242 | 60.42 279 | 72.96 127 | 93.28 78 | 48.27 225 | 92.80 214 | 78.89 82 | 86.50 98 | 90.06 160 |
|
CNLPA | | | 74.31 214 | 72.30 217 | 80.32 202 | 91.49 93 | 61.66 213 | 90.85 163 | 80.72 311 | 56.67 299 | 63.85 236 | 90.64 116 | 46.75 237 | 90.84 260 | 53.79 245 | 75.99 171 | 88.47 179 |
|
view600 | | | 76.93 171 | 75.50 171 | 81.23 185 | 91.44 94 | 62.00 207 | 89.94 186 | 96.56 1 | 70.68 183 | 68.54 190 | 87.31 166 | 60.79 91 | 94.19 169 | 38.90 301 | 75.31 174 | 87.48 198 |
|
view800 | | | 76.93 171 | 75.50 171 | 81.23 185 | 91.44 94 | 62.00 207 | 89.94 186 | 96.56 1 | 70.68 183 | 68.54 190 | 87.31 166 | 60.79 91 | 94.19 169 | 38.90 301 | 75.31 174 | 87.48 198 |
|
conf0.05thres1000 | | | 76.93 171 | 75.50 171 | 81.23 185 | 91.44 94 | 62.00 207 | 89.94 186 | 96.56 1 | 70.68 183 | 68.54 190 | 87.31 166 | 60.79 91 | 94.19 169 | 38.90 301 | 75.31 174 | 87.48 198 |
|
tfpn | | | 76.93 171 | 75.50 171 | 81.23 185 | 91.44 94 | 62.00 207 | 89.94 186 | 96.56 1 | 70.68 183 | 68.54 190 | 87.31 166 | 60.79 91 | 94.19 169 | 38.90 301 | 75.31 174 | 87.48 198 |
|
MP-MVS-pluss | | | 85.24 45 | 85.13 45 | 85.56 78 | 91.42 98 | 65.59 136 | 91.54 140 | 92.51 108 | 74.56 99 | 80.62 61 | 95.64 22 | 59.15 110 | 97.00 69 | 86.94 31 | 93.80 28 | 94.07 90 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
gg-mvs-nofinetune | | | 77.18 169 | 74.31 186 | 85.80 71 | 91.42 98 | 68.36 54 | 71.78 317 | 94.72 28 | 49.61 315 | 77.12 97 | 45.92 338 | 77.41 3 | 93.98 184 | 67.62 161 | 93.16 39 | 95.05 52 |
|
xiu_mvs_v2_base | | | 87.92 15 | 87.38 23 | 89.55 7 | 91.41 100 | 76.43 2 | 95.74 12 | 93.12 86 | 83.53 13 | 89.55 7 | 95.95 14 | 53.45 185 | 97.68 34 | 91.07 9 | 92.62 44 | 94.54 70 |
|
alignmvs | | | 87.28 21 | 86.97 26 | 88.24 15 | 91.30 101 | 71.14 16 | 95.61 16 | 93.56 57 | 79.30 38 | 87.07 19 | 95.25 34 | 68.43 21 | 96.93 79 | 87.87 23 | 84.33 114 | 96.65 8 |
|
EPMVS | | | 78.49 146 | 75.98 159 | 86.02 64 | 91.21 102 | 69.68 33 | 80.23 294 | 91.20 155 | 75.25 93 | 72.48 137 | 78.11 267 | 54.65 166 | 93.69 194 | 57.66 234 | 83.04 121 | 94.69 63 |
|
FMVSNet3 | | | 77.73 157 | 76.04 158 | 82.80 137 | 91.20 103 | 68.99 42 | 91.87 124 | 91.99 126 | 73.35 128 | 67.04 210 | 83.19 207 | 56.62 137 | 92.14 233 | 59.80 224 | 69.34 217 | 87.28 209 |
|
tpmvs | | | 72.88 225 | 69.76 233 | 82.22 164 | 90.98 104 | 67.05 86 | 78.22 308 | 88.30 245 | 63.10 263 | 64.35 233 | 74.98 287 | 55.09 156 | 94.27 166 | 43.25 281 | 69.57 216 | 85.34 248 |
|
tfpn_ndepth | | | 76.45 182 | 75.22 176 | 80.14 205 | 90.97 105 | 58.92 253 | 90.11 181 | 93.24 78 | 65.96 235 | 67.37 208 | 90.52 120 | 66.67 37 | 92.29 231 | 37.71 307 | 74.44 180 | 89.21 169 |
|
MVS | | | 84.66 52 | 82.86 67 | 90.06 1 | 90.93 106 | 74.56 5 | 87.91 223 | 95.54 14 | 68.55 211 | 72.35 141 | 94.71 52 | 59.78 104 | 98.90 7 | 81.29 69 | 94.69 19 | 96.74 7 |
|
PVSNet | | 73.49 8 | 80.05 114 | 78.63 119 | 84.31 112 | 90.92 107 | 64.97 151 | 92.47 98 | 91.05 161 | 79.18 41 | 72.43 139 | 90.51 121 | 37.05 291 | 94.06 178 | 68.06 156 | 86.00 101 | 93.90 98 |
|
3Dnovator+ | | 73.60 7 | 82.10 88 | 80.60 93 | 86.60 45 | 90.89 108 | 66.80 101 | 95.20 23 | 93.44 69 | 74.05 110 | 67.42 205 | 92.49 94 | 49.46 215 | 97.65 39 | 70.80 137 | 91.68 56 | 95.33 37 |
|
VDD-MVS | | | 83.06 73 | 81.81 79 | 86.81 39 | 90.86 109 | 67.70 69 | 95.40 19 | 91.50 145 | 75.46 86 | 81.78 54 | 92.34 99 | 40.09 269 | 97.13 63 | 86.85 32 | 82.04 128 | 95.60 31 |
|
BH-w/o | | | 80.49 106 | 79.30 112 | 84.05 117 | 90.83 110 | 64.36 163 | 93.60 62 | 89.42 215 | 74.35 105 | 69.09 180 | 90.15 125 | 55.23 151 | 95.61 118 | 64.61 188 | 86.43 99 | 92.17 138 |
|
TR-MVS | | | 78.77 139 | 77.37 141 | 82.95 135 | 90.49 111 | 60.88 220 | 93.67 60 | 90.07 194 | 70.08 194 | 74.51 116 | 91.37 110 | 45.69 245 | 95.70 117 | 60.12 222 | 80.32 135 | 92.29 135 |
|
SteuartSystems-ACMMP | | | 86.82 31 | 86.90 27 | 86.58 47 | 90.42 112 | 66.38 120 | 96.09 9 | 93.87 45 | 77.73 60 | 84.01 44 | 95.66 21 | 63.39 74 | 97.94 29 | 87.40 27 | 93.55 35 | 95.42 34 |
Skip Steuart: Steuart Systems R&D Blog. |
TAPA-MVS | | 70.22 12 | 74.94 206 | 73.53 203 | 79.17 233 | 90.40 113 | 52.07 298 | 89.19 201 | 89.61 210 | 62.69 265 | 70.07 163 | 92.67 92 | 48.89 223 | 94.32 163 | 38.26 306 | 79.97 136 | 91.12 151 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
mvs_anonymous | | | 81.36 95 | 79.99 98 | 85.46 81 | 90.39 114 | 68.40 52 | 86.88 247 | 90.61 173 | 74.41 100 | 70.31 161 | 84.67 194 | 63.79 69 | 92.32 230 | 73.13 112 | 85.70 102 | 95.67 28 |
|
tfpn1000 | | | 75.25 198 | 74.00 192 | 79.03 236 | 90.30 115 | 57.56 267 | 88.55 211 | 93.36 73 | 64.14 255 | 65.17 224 | 89.76 138 | 67.06 34 | 91.46 257 | 34.54 322 | 73.09 190 | 88.06 189 |
|
CANet_DTU | | | 84.09 59 | 83.52 54 | 85.81 70 | 90.30 115 | 66.82 95 | 91.87 124 | 89.01 231 | 85.27 7 | 86.09 22 | 93.74 72 | 47.71 232 | 96.98 73 | 77.90 89 | 89.78 75 | 93.65 101 |
|
Fast-Effi-MVS+ | | | 81.14 97 | 80.01 97 | 84.51 110 | 90.24 117 | 65.86 131 | 94.12 42 | 89.15 225 | 73.81 118 | 75.37 111 | 88.26 151 | 57.26 124 | 94.53 151 | 66.97 167 | 84.92 106 | 93.15 114 |
|
tpmrst | | | 80.57 103 | 79.14 116 | 84.84 100 | 90.10 118 | 68.28 57 | 81.70 281 | 89.72 208 | 77.63 62 | 75.96 104 | 79.54 260 | 64.94 61 | 92.71 217 | 75.43 100 | 77.28 164 | 93.55 103 |
|
PVSNet_Blended_VisFu | | | 83.97 60 | 83.50 55 | 85.39 86 | 90.02 119 | 66.59 113 | 93.77 59 | 91.73 135 | 77.43 66 | 77.08 99 | 89.81 136 | 63.77 70 | 96.97 74 | 79.67 74 | 88.21 83 | 92.60 128 |
|
UGNet | | | 79.87 118 | 78.68 118 | 83.45 131 | 89.96 120 | 61.51 215 | 92.13 104 | 90.79 166 | 76.83 72 | 78.85 80 | 86.33 178 | 38.16 277 | 96.17 96 | 67.93 158 | 87.17 89 | 92.67 126 |
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 |
CHOSEN 1792x2688 | | | 84.98 48 | 83.45 57 | 89.57 6 | 89.94 121 | 75.14 4 | 92.07 109 | 92.32 111 | 81.87 25 | 75.68 105 | 88.27 150 | 60.18 101 | 98.60 16 | 80.46 72 | 90.27 73 | 94.96 57 |
|
abl_6 | | | 79.82 119 | 79.20 114 | 81.70 179 | 89.85 122 | 58.34 258 | 88.47 213 | 90.07 194 | 62.56 266 | 77.71 88 | 93.08 81 | 47.65 233 | 96.78 83 | 77.94 88 | 85.45 105 | 89.99 162 |
|
BH-untuned | | | 78.68 141 | 77.08 143 | 83.48 130 | 89.84 123 | 63.74 175 | 92.70 88 | 88.59 240 | 71.57 170 | 66.83 213 | 88.65 144 | 51.75 198 | 95.39 125 | 59.03 227 | 84.77 108 | 91.32 149 |
|
test222 | | | | | | 89.77 124 | 61.60 214 | 89.55 194 | 89.42 215 | 56.83 298 | 77.28 95 | 92.43 96 | 52.76 189 | | | 91.14 65 | 93.09 116 |
|
PMMVS | | | 81.98 90 | 82.04 76 | 81.78 175 | 89.76 125 | 56.17 280 | 91.13 159 | 90.69 169 | 77.96 57 | 80.09 66 | 93.57 74 | 46.33 242 | 94.99 133 | 81.41 66 | 87.46 88 | 94.17 82 |
|
conf0.01 | | | 74.95 204 | 73.61 197 | 78.96 237 | 89.65 126 | 56.94 273 | 87.72 227 | 93.45 62 | 65.14 243 | 65.68 216 | 89.99 129 | 65.09 52 | 91.67 243 | 35.16 314 | 70.61 206 | 88.27 183 |
|
conf0.002 | | | 74.95 204 | 73.61 197 | 78.96 237 | 89.65 126 | 56.94 273 | 87.72 227 | 93.45 62 | 65.14 243 | 65.68 216 | 89.99 129 | 65.09 52 | 91.67 243 | 35.16 314 | 70.61 206 | 88.27 183 |
|
thresconf0.02 | | | 74.92 207 | 73.61 197 | 78.85 240 | 89.65 126 | 56.94 273 | 87.72 227 | 93.45 62 | 65.14 243 | 65.68 216 | 89.99 129 | 65.09 52 | 91.67 243 | 35.16 314 | 70.61 206 | 87.94 190 |
|
tfpn_n400 | | | 74.92 207 | 73.61 197 | 78.85 240 | 89.65 126 | 56.94 273 | 87.72 227 | 93.45 62 | 65.14 243 | 65.68 216 | 89.99 129 | 65.09 52 | 91.67 243 | 35.16 314 | 70.61 206 | 87.94 190 |
|
tfpnconf | | | 74.92 207 | 73.61 197 | 78.85 240 | 89.65 126 | 56.94 273 | 87.72 227 | 93.45 62 | 65.14 243 | 65.68 216 | 89.99 129 | 65.09 52 | 91.67 243 | 35.16 314 | 70.61 206 | 87.94 190 |
|
tfpnview11 | | | 74.92 207 | 73.61 197 | 78.85 240 | 89.65 126 | 56.94 273 | 87.72 227 | 93.45 62 | 65.14 243 | 65.68 216 | 89.99 129 | 65.09 52 | 91.67 243 | 35.16 314 | 70.61 206 | 87.94 190 |
|
QAPM | | | 79.95 117 | 77.39 140 | 87.64 21 | 89.63 132 | 71.41 12 | 93.30 70 | 93.70 52 | 65.34 241 | 67.39 207 | 91.75 107 | 47.83 230 | 98.96 5 | 57.71 233 | 89.81 74 | 92.54 130 |
|
3Dnovator | | 73.91 6 | 82.69 79 | 80.82 89 | 88.31 14 | 89.57 133 | 71.26 13 | 92.60 93 | 94.39 39 | 78.84 48 | 67.89 200 | 92.48 95 | 48.42 224 | 98.52 17 | 68.80 154 | 94.40 21 | 95.15 48 |
|
Effi-MVS+ | | | 83.82 64 | 82.76 69 | 86.99 38 | 89.56 134 | 69.40 36 | 91.35 147 | 86.12 278 | 72.59 138 | 83.22 47 | 92.81 91 | 59.60 106 | 96.01 104 | 81.76 62 | 87.80 86 | 95.56 32 |
|
PatchmatchNet | | | 77.46 159 | 74.63 180 | 85.96 66 | 89.55 135 | 70.35 21 | 79.97 298 | 89.55 211 | 72.23 148 | 70.94 152 | 76.91 279 | 57.03 127 | 92.79 215 | 54.27 243 | 81.17 131 | 94.74 62 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
PatchMatch-RL | | | 72.06 230 | 69.98 229 | 78.28 248 | 89.51 136 | 55.70 283 | 83.49 266 | 83.39 300 | 61.24 275 | 63.72 237 | 82.76 209 | 34.77 298 | 93.03 205 | 53.37 249 | 77.59 155 | 86.12 227 |
|
UA-Net | | | 80.02 115 | 79.65 103 | 81.11 191 | 89.33 137 | 57.72 263 | 86.33 252 | 89.00 232 | 77.44 65 | 81.01 58 | 89.15 140 | 59.33 108 | 95.90 105 | 61.01 217 | 84.28 116 | 89.73 165 |
|
dp | | | 75.01 202 | 72.09 219 | 83.76 120 | 89.28 138 | 66.22 126 | 79.96 299 | 89.75 203 | 71.16 176 | 67.80 202 | 77.19 275 | 51.81 197 | 92.54 222 | 50.39 256 | 71.44 203 | 92.51 131 |
|
tpmp4_e23 | | | 78.85 135 | 76.55 152 | 85.77 73 | 89.25 139 | 68.39 53 | 81.63 284 | 91.38 150 | 70.40 190 | 75.21 112 | 79.22 262 | 67.37 32 | 94.79 139 | 58.98 229 | 75.51 173 | 94.13 85 |
|
sss | | | 82.71 78 | 82.38 73 | 83.73 123 | 89.25 139 | 59.58 245 | 92.24 102 | 94.89 22 | 77.96 57 | 79.86 68 | 92.38 97 | 56.70 135 | 97.05 64 | 77.26 92 | 80.86 134 | 94.55 68 |
|
MVSFormer | | | 83.75 66 | 82.88 66 | 86.37 57 | 89.24 141 | 71.18 14 | 89.07 204 | 90.69 169 | 65.80 236 | 87.13 17 | 94.34 62 | 64.99 59 | 92.67 219 | 72.83 115 | 91.80 54 | 95.27 42 |
|
lupinMVS | | | 87.74 17 | 87.77 16 | 87.63 23 | 89.24 141 | 71.18 14 | 96.57 4 | 92.90 94 | 82.70 16 | 87.13 17 | 95.27 32 | 64.99 59 | 95.80 108 | 89.34 14 | 91.80 54 | 95.93 26 |
|
IB-MVS | | 77.80 4 | 82.18 84 | 80.46 95 | 87.35 28 | 89.14 143 | 70.28 22 | 95.59 17 | 95.17 17 | 78.85 47 | 70.19 162 | 85.82 183 | 70.66 18 | 97.67 35 | 72.19 124 | 66.52 237 | 94.09 88 |
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 |
MDTV_nov1_ep13 | | | | 72.61 213 | | 89.06 144 | 68.48 50 | 80.33 292 | 90.11 193 | 71.84 161 | 71.81 146 | 75.92 284 | 53.01 187 | 93.92 187 | 48.04 265 | 73.38 186 | |
|
testdata | | | | | 81.34 183 | 89.02 145 | 57.72 263 | | 89.84 201 | 58.65 289 | 85.32 31 | 94.09 66 | 57.03 127 | 93.28 201 | 69.34 149 | 90.56 71 | 93.03 118 |
|
CostFormer | | | 82.33 82 | 81.15 85 | 85.86 68 | 89.01 146 | 68.46 51 | 82.39 277 | 93.01 89 | 75.59 84 | 80.25 65 | 81.57 230 | 72.03 14 | 94.96 134 | 79.06 80 | 77.48 161 | 94.16 83 |
|
GBi-Net | | | 75.65 192 | 73.83 194 | 81.10 192 | 88.85 147 | 65.11 145 | 90.01 182 | 90.32 180 | 70.84 180 | 67.04 210 | 80.25 251 | 48.03 226 | 91.54 252 | 59.80 224 | 69.34 217 | 86.64 218 |
|
test1 | | | 75.65 192 | 73.83 194 | 81.10 192 | 88.85 147 | 65.11 145 | 90.01 182 | 90.32 180 | 70.84 180 | 67.04 210 | 80.25 251 | 48.03 226 | 91.54 252 | 59.80 224 | 69.34 217 | 86.64 218 |
|
FMVSNet2 | | | 76.07 186 | 74.01 191 | 82.26 163 | 88.85 147 | 67.66 70 | 91.33 148 | 91.61 140 | 70.84 180 | 65.98 215 | 82.25 217 | 48.03 226 | 92.00 238 | 58.46 230 | 68.73 223 | 87.10 211 |
|
DeepC-MVS | | 77.85 3 | 85.52 43 | 85.24 44 | 86.37 57 | 88.80 150 | 66.64 110 | 92.15 103 | 93.68 53 | 81.07 28 | 76.91 100 | 93.64 73 | 62.59 82 | 98.44 19 | 85.50 39 | 92.84 42 | 94.03 92 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
EPP-MVSNet | | | 81.79 91 | 81.52 82 | 82.61 144 | 88.77 151 | 60.21 235 | 93.02 78 | 93.66 54 | 68.52 212 | 72.90 129 | 90.39 122 | 72.19 13 | 94.96 134 | 74.93 107 | 79.29 142 | 92.67 126 |
|
1112_ss | | | 80.56 104 | 79.83 101 | 82.77 138 | 88.65 152 | 60.78 222 | 92.29 100 | 88.36 244 | 72.58 139 | 72.46 138 | 94.95 42 | 65.09 52 | 93.42 200 | 66.38 172 | 77.71 153 | 94.10 87 |
|
diffmvs | | | 80.18 110 | 78.55 121 | 85.07 94 | 88.56 153 | 66.93 91 | 86.70 250 | 88.62 239 | 70.42 189 | 78.69 82 | 85.26 187 | 56.93 131 | 94.77 140 | 68.68 155 | 83.09 120 | 93.51 104 |
|
tpm cat1 | | | 75.30 197 | 72.21 218 | 84.58 107 | 88.52 154 | 67.77 67 | 78.16 309 | 88.02 250 | 61.88 272 | 68.45 195 | 76.37 280 | 60.65 95 | 94.03 182 | 53.77 246 | 74.11 182 | 91.93 140 |
|
LCM-MVSNet-Re | | | 72.93 223 | 71.84 220 | 76.18 272 | 88.49 155 | 48.02 314 | 80.07 297 | 70.17 336 | 73.96 114 | 52.25 297 | 80.09 254 | 49.98 210 | 88.24 292 | 67.35 162 | 84.23 117 | 92.28 136 |
|
Vis-MVSNet | | | 80.92 101 | 79.98 99 | 83.74 121 | 88.48 156 | 61.80 212 | 93.44 68 | 88.26 248 | 73.96 114 | 77.73 87 | 91.76 106 | 49.94 211 | 94.76 141 | 65.84 179 | 90.37 72 | 94.65 66 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
Vis-MVSNet (Re-imp) | | | 79.24 129 | 79.57 104 | 78.24 251 | 88.46 157 | 52.29 297 | 90.41 175 | 89.12 226 | 74.24 106 | 69.13 179 | 91.91 103 | 65.77 47 | 90.09 273 | 59.00 228 | 88.09 84 | 92.33 133 |
|
ab-mvs | | | 80.18 110 | 78.31 123 | 85.80 71 | 88.44 158 | 65.49 139 | 83.00 274 | 92.67 101 | 71.82 162 | 77.36 94 | 85.01 190 | 54.50 167 | 96.59 88 | 76.35 97 | 75.63 172 | 95.32 39 |
|
gm-plane-assit | | | | | | 88.42 159 | 67.04 87 | | | 78.62 52 | | 91.83 104 | | 97.37 49 | 76.57 95 | | |
|
MVS_111021_LR | | | 82.02 89 | 81.52 82 | 83.51 129 | 88.42 159 | 62.88 196 | 89.77 192 | 88.93 233 | 76.78 73 | 75.55 109 | 93.10 79 | 50.31 207 | 95.38 126 | 83.82 51 | 87.02 90 | 92.26 137 |
|
tpm2 | | | 79.80 120 | 77.95 129 | 85.34 88 | 88.28 161 | 68.26 58 | 81.56 285 | 91.42 148 | 70.11 193 | 77.59 92 | 80.50 246 | 67.40 31 | 94.26 168 | 67.34 163 | 77.35 162 | 93.51 104 |
|
Test_1112_low_res | | | 79.56 125 | 78.60 120 | 82.43 150 | 88.24 162 | 60.39 231 | 92.09 107 | 87.99 251 | 72.10 154 | 71.84 145 | 87.42 165 | 64.62 62 | 93.04 204 | 65.80 180 | 77.30 163 | 93.85 99 |
|
PAPM | | | 85.89 40 | 85.46 42 | 87.18 31 | 88.20 163 | 72.42 9 | 92.41 99 | 92.77 97 | 82.11 21 | 80.34 64 | 93.07 83 | 68.27 22 | 95.02 132 | 78.39 85 | 93.59 34 | 94.09 88 |
|
TESTMET0.1,1 | | | 82.41 81 | 81.98 77 | 83.72 124 | 88.08 164 | 63.74 175 | 92.70 88 | 93.77 49 | 79.30 38 | 77.61 91 | 87.57 163 | 58.19 117 | 94.08 177 | 73.91 111 | 86.68 94 | 93.33 109 |
|
ADS-MVSNet2 | | | 66.90 272 | 63.44 275 | 77.26 265 | 88.06 165 | 60.70 226 | 68.01 327 | 75.56 324 | 57.57 291 | 64.48 230 | 69.87 314 | 38.68 271 | 84.10 308 | 40.87 293 | 67.89 229 | 86.97 212 |
|
ADS-MVSNet | | | 68.54 262 | 64.38 272 | 81.03 195 | 88.06 165 | 66.90 92 | 68.01 327 | 84.02 293 | 57.57 291 | 64.48 230 | 69.87 314 | 38.68 271 | 89.21 285 | 40.87 293 | 67.89 229 | 86.97 212 |
|
EPNet_dtu | | | 78.80 137 | 79.26 113 | 77.43 261 | 88.06 165 | 49.71 310 | 91.96 116 | 91.95 129 | 77.67 61 | 76.56 102 | 91.28 111 | 58.51 115 | 90.20 268 | 56.37 236 | 80.95 133 | 92.39 132 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
IS-MVSNet | | | 80.14 112 | 79.41 109 | 82.33 157 | 87.91 168 | 60.08 239 | 91.97 115 | 88.27 247 | 72.90 135 | 71.44 151 | 91.73 108 | 61.44 88 | 93.66 195 | 62.47 210 | 86.53 97 | 93.24 111 |
|
CLD-MVS | | | 82.73 76 | 82.35 74 | 83.86 119 | 87.90 169 | 67.65 71 | 95.45 18 | 92.18 122 | 85.06 8 | 72.58 134 | 92.27 100 | 52.46 193 | 95.78 109 | 84.18 46 | 79.06 143 | 88.16 187 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
HyFIR lowres test | | | 81.03 99 | 79.56 105 | 85.43 84 | 87.81 170 | 68.11 62 | 90.18 180 | 90.01 197 | 70.65 187 | 72.95 128 | 86.06 181 | 63.61 72 | 94.50 152 | 75.01 106 | 79.75 138 | 93.67 100 |
|
1314 | | | 80.70 102 | 78.95 117 | 85.94 67 | 87.77 171 | 67.56 73 | 87.91 223 | 92.55 107 | 72.17 152 | 67.44 204 | 93.09 80 | 50.27 208 | 97.04 66 | 71.68 127 | 87.64 87 | 93.23 112 |
|
HQP-NCC | | | | | | 87.54 172 | | 94.06 45 | | 79.80 33 | 74.18 117 | | | | | | |
|
ACMP_Plane | | | | | | 87.54 172 | | 94.06 45 | | 79.80 33 | 74.18 117 | | | | | | |
|
HQP-MVS | | | 81.14 97 | 80.64 92 | 82.64 143 | 87.54 172 | 63.66 180 | 94.06 45 | 91.70 138 | 79.80 33 | 74.18 117 | 90.30 123 | 51.63 200 | 95.61 118 | 77.63 90 | 78.90 144 | 88.63 175 |
|
NP-MVS | | | | | | 87.41 175 | 63.04 190 | | | | | 90.30 123 | | | | | |
|
plane_prior6 | | | | | | 87.23 176 | 62.32 203 | | | | | | 50.66 204 | | | | |
|
plane_prior1 | | | | | | 87.15 177 | | | | | | | | | | | |
|
cascas | | | 78.18 150 | 75.77 162 | 85.41 85 | 87.14 178 | 69.11 39 | 92.96 79 | 91.15 157 | 66.71 229 | 70.47 154 | 86.07 180 | 37.49 285 | 96.48 92 | 70.15 142 | 79.80 137 | 90.65 155 |
|
CHOSEN 280x420 | | | 77.35 164 | 76.95 148 | 78.55 245 | 87.07 179 | 62.68 200 | 69.71 323 | 82.95 304 | 68.80 206 | 71.48 150 | 87.27 171 | 66.03 43 | 84.00 312 | 76.47 96 | 82.81 124 | 88.95 170 |
|
HQP_MVS | | | 80.34 108 | 79.75 102 | 82.12 168 | 86.94 180 | 62.42 201 | 93.13 74 | 91.31 152 | 78.81 49 | 72.53 135 | 89.14 141 | 50.66 204 | 95.55 122 | 76.74 93 | 78.53 148 | 88.39 180 |
|
plane_prior7 | | | | | | 86.94 180 | 61.51 215 | | | | | | | | | | |
|
test-LLR | | | 80.10 113 | 79.56 105 | 81.72 177 | 86.93 182 | 61.17 217 | 92.70 88 | 91.54 142 | 71.51 173 | 75.62 106 | 86.94 172 | 53.83 176 | 92.38 227 | 72.21 122 | 84.76 109 | 91.60 143 |
|
test-mter | | | 79.96 116 | 79.38 111 | 81.72 177 | 86.93 182 | 61.17 217 | 92.70 88 | 91.54 142 | 73.85 116 | 75.62 106 | 86.94 172 | 49.84 213 | 92.38 227 | 72.21 122 | 84.76 109 | 91.60 143 |
|
Patchmatch-test1 | | | 75.00 203 | 71.80 222 | 84.58 107 | 86.63 184 | 70.08 24 | 81.06 287 | 89.19 222 | 71.60 169 | 70.01 164 | 77.16 277 | 45.53 246 | 88.63 286 | 51.79 252 | 73.27 187 | 95.02 56 |
|
xiu_mvs_v1_base_debu | | | 82.16 85 | 81.12 86 | 85.26 90 | 86.42 185 | 68.72 47 | 92.59 95 | 90.44 174 | 73.12 131 | 84.20 40 | 94.36 57 | 38.04 279 | 95.73 112 | 84.12 47 | 86.81 91 | 91.33 146 |
|
xiu_mvs_v1_base | | | 82.16 85 | 81.12 86 | 85.26 90 | 86.42 185 | 68.72 47 | 92.59 95 | 90.44 174 | 73.12 131 | 84.20 40 | 94.36 57 | 38.04 279 | 95.73 112 | 84.12 47 | 86.81 91 | 91.33 146 |
|
xiu_mvs_v1_base_debi | | | 82.16 85 | 81.12 86 | 85.26 90 | 86.42 185 | 68.72 47 | 92.59 95 | 90.44 174 | 73.12 131 | 84.20 40 | 94.36 57 | 38.04 279 | 95.73 112 | 84.12 47 | 86.81 91 | 91.33 146 |
|
F-COLMAP | | | 70.66 245 | 68.44 244 | 77.32 263 | 86.37 188 | 55.91 282 | 88.00 219 | 86.32 273 | 56.94 297 | 57.28 273 | 88.07 155 | 33.58 300 | 92.49 224 | 51.02 254 | 68.37 225 | 83.55 261 |
|
CDS-MVSNet | | | 81.43 94 | 80.74 90 | 83.52 128 | 86.26 189 | 64.45 157 | 92.09 107 | 90.65 172 | 75.83 83 | 73.95 123 | 89.81 136 | 63.97 66 | 92.91 211 | 71.27 131 | 82.82 123 | 93.20 113 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
VDDNet | | | 80.50 105 | 78.26 124 | 87.21 30 | 86.19 190 | 69.79 30 | 94.48 36 | 91.31 152 | 60.42 279 | 79.34 73 | 90.91 113 | 38.48 275 | 96.56 91 | 82.16 58 | 81.05 132 | 95.27 42 |
|
jason | | | 86.40 33 | 86.17 33 | 87.11 34 | 86.16 191 | 70.54 20 | 95.71 15 | 92.19 121 | 82.00 24 | 84.58 36 | 94.34 62 | 61.86 86 | 95.53 124 | 87.76 24 | 90.89 66 | 95.27 42 |
jason: jason. |
PCF-MVS | | 73.15 9 | 79.29 128 | 77.63 134 | 84.29 113 | 86.06 192 | 65.96 129 | 87.03 242 | 91.10 159 | 69.86 196 | 69.79 169 | 90.64 116 | 57.54 123 | 96.59 88 | 64.37 192 | 82.29 125 | 90.32 158 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
MS-PatchMatch | | | 77.90 156 | 76.50 153 | 82.12 168 | 85.99 193 | 69.95 27 | 91.75 133 | 92.70 99 | 73.97 113 | 62.58 247 | 84.44 197 | 41.11 266 | 95.78 109 | 63.76 194 | 92.17 51 | 80.62 301 |
|
FIs | | | 79.47 126 | 79.41 109 | 79.67 217 | 85.95 194 | 59.40 247 | 91.68 135 | 93.94 44 | 78.06 56 | 68.96 183 | 88.28 149 | 66.61 38 | 91.77 241 | 66.20 175 | 74.99 178 | 87.82 194 |
|
VPA-MVSNet | | | 79.03 131 | 78.00 128 | 82.11 171 | 85.95 194 | 64.48 156 | 93.22 73 | 94.66 30 | 75.05 95 | 74.04 122 | 84.95 191 | 52.17 195 | 93.52 197 | 74.90 109 | 67.04 233 | 88.32 182 |
|
tpm | | | 78.58 144 | 77.03 144 | 83.22 132 | 85.94 196 | 64.56 153 | 83.21 272 | 91.14 158 | 78.31 54 | 73.67 124 | 79.68 257 | 64.01 65 | 92.09 236 | 66.07 176 | 71.26 204 | 93.03 118 |
|
OpenMVS | | 70.45 11 | 78.54 145 | 75.92 160 | 86.41 56 | 85.93 197 | 71.68 11 | 92.74 86 | 92.51 108 | 66.49 231 | 64.56 229 | 91.96 102 | 43.88 255 | 98.10 27 | 54.61 241 | 90.65 69 | 89.44 168 |
|
OMC-MVS | | | 78.67 143 | 77.91 131 | 80.95 197 | 85.76 198 | 57.40 268 | 88.49 212 | 88.67 237 | 73.85 116 | 72.43 139 | 92.10 101 | 49.29 217 | 94.55 149 | 72.73 117 | 77.89 152 | 90.91 153 |
|
EI-MVSNet | | | 78.97 133 | 78.22 125 | 81.25 184 | 85.33 199 | 62.73 199 | 89.53 196 | 93.21 79 | 72.39 143 | 72.14 142 | 90.13 126 | 60.99 89 | 94.72 144 | 67.73 160 | 72.49 195 | 86.29 223 |
|
CVMVSNet | | | 74.04 216 | 74.27 187 | 73.33 287 | 85.33 199 | 43.94 324 | 89.53 196 | 88.39 243 | 54.33 305 | 70.37 159 | 90.13 126 | 49.17 219 | 84.05 309 | 61.83 214 | 79.36 140 | 91.99 139 |
|
ACMH | | 63.93 17 | 68.62 260 | 64.81 266 | 80.03 209 | 85.22 201 | 63.25 184 | 87.72 227 | 84.66 288 | 60.83 277 | 51.57 300 | 79.43 261 | 27.29 321 | 94.96 134 | 41.76 289 | 64.84 252 | 81.88 281 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
TAMVS | | | 80.37 107 | 79.45 108 | 83.13 134 | 85.14 202 | 63.37 183 | 91.23 152 | 90.76 168 | 74.81 98 | 72.65 132 | 88.49 145 | 60.63 96 | 92.95 207 | 69.41 148 | 81.95 129 | 93.08 117 |
|
MSDG | | | 69.54 256 | 65.73 260 | 80.96 196 | 85.11 203 | 63.71 177 | 84.19 261 | 83.28 301 | 56.95 296 | 54.50 282 | 84.03 198 | 31.50 309 | 96.03 102 | 42.87 285 | 69.13 220 | 83.14 271 |
|
ACMP | | 71.68 10 | 75.58 195 | 74.23 188 | 79.62 219 | 84.97 204 | 59.64 243 | 90.80 165 | 89.07 229 | 70.39 191 | 62.95 243 | 87.30 170 | 38.28 276 | 93.87 189 | 72.89 114 | 71.45 202 | 85.36 247 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
FC-MVSNet-test | | | 77.99 154 | 78.08 127 | 77.70 256 | 84.89 205 | 55.51 284 | 90.27 178 | 93.75 51 | 76.87 70 | 66.80 214 | 87.59 162 | 65.71 48 | 90.23 267 | 62.89 205 | 73.94 184 | 87.37 205 |
|
PVSNet_0 | | 68.08 15 | 71.81 232 | 68.32 246 | 82.27 160 | 84.68 206 | 62.31 204 | 88.68 209 | 90.31 183 | 75.84 82 | 57.93 268 | 80.65 245 | 37.85 282 | 94.19 169 | 69.94 143 | 29.05 341 | 90.31 159 |
|
WR-MVS | | | 76.76 178 | 75.74 163 | 79.82 215 | 84.60 207 | 62.27 205 | 92.60 93 | 92.51 108 | 76.06 80 | 67.87 201 | 85.34 186 | 56.76 132 | 90.24 266 | 62.20 211 | 63.69 262 | 86.94 215 |
|
ACMH+ | | 65.35 16 | 67.65 267 | 64.55 268 | 76.96 266 | 84.59 208 | 57.10 271 | 88.08 218 | 80.79 310 | 58.59 290 | 53.00 295 | 81.09 241 | 26.63 323 | 92.95 207 | 46.51 270 | 61.69 274 | 80.82 298 |
|
VPNet | | | 78.82 136 | 77.53 136 | 82.70 140 | 84.52 209 | 66.44 119 | 93.93 53 | 92.23 114 | 80.46 30 | 72.60 133 | 88.38 148 | 49.18 218 | 93.13 203 | 72.47 120 | 63.97 260 | 88.55 177 |
|
IterMVS-LS | | | 76.49 180 | 75.18 177 | 80.43 201 | 84.49 210 | 62.74 198 | 90.64 170 | 88.80 235 | 72.40 142 | 65.16 225 | 81.72 229 | 60.98 90 | 92.27 232 | 67.74 159 | 64.65 255 | 86.29 223 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
UniMVSNet_NR-MVSNet | | | 78.15 151 | 77.55 135 | 79.98 210 | 84.46 211 | 60.26 233 | 92.25 101 | 93.20 81 | 77.50 64 | 68.88 184 | 86.61 174 | 66.10 42 | 92.13 234 | 66.38 172 | 62.55 263 | 87.54 196 |
|
FMVSNet5 | | | 68.04 265 | 65.66 261 | 75.18 276 | 84.43 212 | 57.89 260 | 83.54 265 | 86.26 275 | 61.83 273 | 53.64 292 | 73.30 291 | 37.15 289 | 85.08 304 | 48.99 261 | 61.77 270 | 82.56 278 |
|
MVS-HIRNet | | | 60.25 296 | 55.55 302 | 74.35 281 | 84.37 213 | 56.57 279 | 71.64 318 | 74.11 328 | 34.44 339 | 45.54 319 | 42.24 341 | 31.11 312 | 89.81 280 | 40.36 296 | 76.10 170 | 76.67 323 |
|
LPG-MVS_test | | | 75.82 191 | 74.58 182 | 79.56 221 | 84.31 214 | 59.37 248 | 90.44 173 | 89.73 206 | 69.49 198 | 64.86 226 | 88.42 146 | 38.65 273 | 94.30 164 | 72.56 118 | 72.76 192 | 85.01 250 |
|
LGP-MVS_train | | | | | 79.56 221 | 84.31 214 | 59.37 248 | | 89.73 206 | 69.49 198 | 64.86 226 | 88.42 146 | 38.65 273 | 94.30 164 | 72.56 118 | 72.76 192 | 85.01 250 |
|
ACMM | | 69.62 13 | 74.34 213 | 72.73 211 | 79.17 233 | 84.25 216 | 57.87 261 | 90.36 176 | 89.93 199 | 63.17 262 | 65.64 222 | 86.04 182 | 37.79 283 | 94.10 175 | 65.89 178 | 71.52 201 | 85.55 244 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
UniMVSNet (Re) | | | 77.58 158 | 76.78 149 | 79.98 210 | 84.11 217 | 60.80 221 | 91.76 131 | 93.17 84 | 76.56 77 | 69.93 168 | 84.78 193 | 63.32 76 | 92.36 229 | 64.89 186 | 62.51 265 | 86.78 217 |
|
test_0402 | | | 64.54 282 | 61.09 287 | 74.92 277 | 84.10 218 | 60.75 224 | 87.95 220 | 79.71 314 | 52.03 309 | 52.41 296 | 77.20 274 | 32.21 306 | 91.64 249 | 23.14 340 | 61.03 277 | 72.36 329 |
|
LTVRE_ROB | | 59.60 19 | 66.27 275 | 63.54 274 | 74.45 280 | 84.00 219 | 51.55 300 | 67.08 330 | 83.53 297 | 58.78 288 | 54.94 280 | 80.31 249 | 34.54 299 | 93.23 202 | 40.64 295 | 68.03 227 | 78.58 317 |
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 |
Patchmatch-test | | | 65.86 277 | 60.94 288 | 80.62 199 | 83.75 220 | 58.83 254 | 58.91 340 | 75.26 326 | 44.50 330 | 50.95 304 | 77.09 278 | 58.81 114 | 87.90 294 | 35.13 320 | 64.03 258 | 95.12 50 |
|
nrg030 | | | 80.93 100 | 79.86 100 | 84.13 115 | 83.69 221 | 68.83 45 | 93.23 72 | 91.20 155 | 75.55 85 | 75.06 113 | 88.22 154 | 63.04 80 | 94.74 143 | 81.88 61 | 66.88 234 | 88.82 173 |
|
GA-MVS | | | 78.33 148 | 76.23 156 | 84.65 105 | 83.65 222 | 66.30 123 | 91.44 141 | 90.14 192 | 76.01 81 | 70.32 160 | 84.02 199 | 42.50 259 | 94.72 144 | 70.98 135 | 77.00 165 | 92.94 121 |
|
FMVSNet1 | | | 72.71 227 | 69.91 231 | 81.10 192 | 83.60 223 | 65.11 145 | 90.01 182 | 90.32 180 | 63.92 256 | 63.56 238 | 80.25 251 | 36.35 293 | 91.54 252 | 54.46 242 | 66.75 235 | 86.64 218 |
|
OPM-MVS | | | 79.00 132 | 78.09 126 | 81.73 176 | 83.52 224 | 63.83 172 | 91.64 138 | 90.30 184 | 76.36 79 | 71.97 144 | 89.93 135 | 46.30 243 | 95.17 131 | 75.10 103 | 77.70 154 | 86.19 225 |
|
tfpnnormal | | | 70.10 249 | 67.36 252 | 78.32 247 | 83.45 225 | 60.97 219 | 88.85 206 | 92.77 97 | 64.85 250 | 60.83 252 | 78.53 265 | 43.52 257 | 93.48 198 | 31.73 330 | 61.70 273 | 80.52 302 |
|
Effi-MVS+-dtu | | | 76.14 185 | 75.28 175 | 78.72 244 | 83.22 226 | 55.17 286 | 89.87 190 | 87.78 253 | 75.42 87 | 67.98 198 | 81.43 231 | 45.08 249 | 92.52 223 | 75.08 104 | 71.63 199 | 88.48 178 |
|
mvs-test1 | | | 78.74 140 | 77.95 129 | 81.14 190 | 83.22 226 | 57.13 270 | 93.96 50 | 87.78 253 | 75.42 87 | 72.68 131 | 90.80 115 | 45.08 249 | 94.54 150 | 75.08 104 | 77.49 160 | 91.74 142 |
|
CR-MVSNet | | | 73.79 219 | 70.82 228 | 82.70 140 | 83.15 228 | 67.96 64 | 70.25 320 | 84.00 294 | 73.67 122 | 69.97 166 | 72.41 299 | 57.82 120 | 89.48 282 | 52.99 250 | 73.13 188 | 90.64 156 |
|
RPMNet | | | 69.58 255 | 65.21 265 | 82.70 140 | 83.15 228 | 67.96 64 | 70.25 320 | 86.15 277 | 46.83 323 | 69.97 166 | 65.10 324 | 56.48 140 | 89.48 282 | 35.79 313 | 73.13 188 | 90.64 156 |
|
DU-MVS | | | 76.86 175 | 75.84 161 | 79.91 212 | 82.96 230 | 60.26 233 | 91.26 151 | 91.54 142 | 76.46 78 | 68.88 184 | 86.35 176 | 56.16 141 | 92.13 234 | 66.38 172 | 62.55 263 | 87.35 207 |
|
NR-MVSNet | | | 76.05 187 | 74.59 181 | 80.44 200 | 82.96 230 | 62.18 206 | 90.83 164 | 91.73 135 | 77.12 67 | 60.96 251 | 86.35 176 | 59.28 109 | 91.80 240 | 60.74 218 | 61.34 276 | 87.35 207 |
|
XXY-MVS | | | 77.94 155 | 76.44 154 | 82.43 150 | 82.60 232 | 64.44 158 | 92.01 112 | 91.83 133 | 73.59 123 | 70.00 165 | 85.82 183 | 54.43 170 | 94.76 141 | 69.63 145 | 68.02 228 | 88.10 188 |
|
TranMVSNet+NR-MVSNet | | | 75.86 190 | 74.52 184 | 79.89 213 | 82.44 233 | 60.64 228 | 91.37 146 | 91.37 151 | 76.63 75 | 67.65 203 | 86.21 179 | 52.37 194 | 91.55 251 | 61.84 213 | 60.81 279 | 87.48 198 |
|
IterMVS | | | 72.65 229 | 70.83 227 | 78.09 254 | 82.17 234 | 62.96 191 | 87.64 235 | 86.28 274 | 71.56 171 | 60.44 253 | 78.85 264 | 45.42 248 | 86.66 300 | 63.30 198 | 61.83 269 | 84.65 254 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
Patchmtry | | | 67.53 269 | 63.93 273 | 78.34 246 | 82.12 235 | 64.38 161 | 68.72 324 | 84.00 294 | 48.23 320 | 59.24 258 | 72.41 299 | 57.82 120 | 89.27 284 | 46.10 272 | 56.68 295 | 81.36 289 |
|
PatchT | | | 69.11 259 | 65.37 264 | 80.32 202 | 82.07 236 | 63.68 179 | 67.96 329 | 87.62 255 | 50.86 313 | 69.37 176 | 65.18 323 | 57.09 125 | 88.53 290 | 41.59 291 | 66.60 236 | 88.74 174 |
|
MIMVSNet | | | 71.64 235 | 68.44 244 | 81.23 185 | 81.97 237 | 64.44 158 | 73.05 316 | 88.80 235 | 69.67 197 | 64.59 228 | 74.79 288 | 32.79 302 | 87.82 295 | 53.99 244 | 76.35 169 | 91.42 145 |
|
MVP-Stereo | | | 77.12 170 | 76.23 156 | 79.79 216 | 81.72 238 | 66.34 122 | 89.29 198 | 90.88 164 | 70.56 188 | 62.01 250 | 82.88 208 | 49.34 216 | 94.13 174 | 65.55 182 | 93.80 28 | 78.88 314 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
semantic-postprocess | | | | | 76.32 270 | 81.48 239 | 60.67 227 | | 85.99 280 | 66.17 233 | 59.50 257 | 78.88 263 | 45.51 247 | 83.65 314 | 62.58 209 | 61.93 268 | 84.63 255 |
|
pcd1.5k->3k | | | 31.17 323 | 31.85 321 | 29.12 338 | 81.48 239 | 0.00 360 | 0.00 351 | 91.79 134 | 0.00 355 | 0.00 356 | 0.00 357 | 41.05 267 | 0.00 358 | 0.00 355 | 72.34 197 | 87.36 206 |
|
COLMAP_ROB | | 57.96 20 | 62.98 291 | 59.65 291 | 72.98 290 | 81.44 241 | 53.00 295 | 83.75 263 | 75.53 325 | 48.34 319 | 48.81 309 | 81.40 233 | 24.14 325 | 90.30 263 | 32.95 326 | 60.52 281 | 75.65 325 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
JIA-IIPM | | | 66.06 276 | 62.45 282 | 76.88 267 | 81.42 242 | 54.45 290 | 57.49 341 | 88.67 237 | 49.36 316 | 63.86 235 | 46.86 337 | 56.06 144 | 90.25 264 | 49.53 260 | 68.83 221 | 85.95 234 |
|
WR-MVS_H | | | 70.59 246 | 69.94 230 | 72.53 293 | 81.03 243 | 51.43 301 | 87.35 239 | 92.03 125 | 67.38 226 | 60.23 254 | 80.70 243 | 55.84 148 | 83.45 316 | 46.33 271 | 58.58 289 | 82.72 276 |
|
DI_MVS_plusplus_test | | | 79.78 121 | 77.50 137 | 86.62 44 | 80.90 244 | 69.46 35 | 90.69 168 | 91.97 128 | 77.00 68 | 59.07 261 | 82.34 215 | 46.82 236 | 95.88 106 | 82.14 59 | 86.59 96 | 94.53 72 |
|
Fast-Effi-MVS+-dtu | | | 75.04 201 | 73.37 206 | 80.07 208 | 80.86 245 | 59.52 246 | 91.20 155 | 85.38 285 | 71.90 156 | 65.20 223 | 84.84 192 | 41.46 265 | 92.97 206 | 66.50 171 | 72.96 191 | 87.73 195 |
|
Baseline_NR-MVSNet | | | 73.99 217 | 72.83 210 | 77.48 260 | 80.78 246 | 59.29 250 | 91.79 128 | 84.55 289 | 68.85 205 | 68.99 182 | 80.70 243 | 56.16 141 | 92.04 237 | 62.67 208 | 60.98 278 | 81.11 295 |
|
v18 | | | 71.94 231 | 69.43 234 | 79.50 223 | 80.74 247 | 66.82 95 | 88.16 217 | 86.66 264 | 68.95 204 | 55.55 277 | 72.66 294 | 55.03 158 | 90.15 269 | 64.78 187 | 52.30 305 | 81.54 283 |
|
test_normal | | | 79.66 122 | 77.36 142 | 86.54 48 | 80.72 248 | 69.21 38 | 90.68 169 | 92.16 123 | 76.99 69 | 58.63 265 | 82.03 224 | 46.70 238 | 95.86 107 | 81.74 63 | 86.63 95 | 94.56 67 |
|
CP-MVSNet | | | 70.50 248 | 69.91 231 | 72.26 296 | 80.71 249 | 51.00 304 | 87.23 240 | 90.30 184 | 67.84 220 | 59.64 256 | 82.69 210 | 50.23 209 | 82.30 323 | 51.28 253 | 59.28 283 | 83.46 265 |
|
v1neww | | | 77.39 161 | 75.71 164 | 82.44 147 | 80.69 250 | 66.83 93 | 91.94 120 | 90.18 189 | 74.19 107 | 69.60 170 | 82.51 211 | 54.99 160 | 94.44 153 | 71.68 127 | 65.60 240 | 86.05 230 |
|
v7new | | | 77.39 161 | 75.71 164 | 82.44 147 | 80.69 250 | 66.83 93 | 91.94 120 | 90.18 189 | 74.19 107 | 69.60 170 | 82.51 211 | 54.99 160 | 94.44 153 | 71.68 127 | 65.60 240 | 86.05 230 |
|
v8 | | | 75.35 196 | 73.26 207 | 81.61 180 | 80.67 252 | 66.82 95 | 89.54 195 | 89.27 219 | 71.65 167 | 63.30 241 | 80.30 250 | 54.99 160 | 94.06 178 | 67.33 164 | 62.33 266 | 83.94 258 |
|
v6 | | | 77.39 161 | 75.71 164 | 82.44 147 | 80.67 252 | 66.82 95 | 91.94 120 | 90.18 189 | 74.19 107 | 69.60 170 | 82.50 214 | 55.00 159 | 94.44 153 | 71.68 127 | 65.60 240 | 86.05 230 |
|
v16 | | | 71.81 232 | 69.26 236 | 79.47 224 | 80.66 254 | 66.81 99 | 87.93 221 | 86.63 266 | 68.70 209 | 55.35 278 | 72.51 295 | 54.75 164 | 90.12 271 | 64.51 189 | 52.28 306 | 81.47 284 |
|
PS-MVSNAJss | | | 77.26 168 | 76.31 155 | 80.13 207 | 80.64 255 | 59.16 251 | 90.63 172 | 91.06 160 | 72.80 136 | 68.58 189 | 84.57 196 | 53.55 180 | 93.96 185 | 72.97 113 | 71.96 198 | 87.27 210 |
|
v1141 | | | 77.28 166 | 75.57 168 | 82.42 153 | 80.63 256 | 66.73 103 | 91.96 116 | 90.42 177 | 74.41 100 | 69.46 173 | 82.12 221 | 55.09 156 | 94.40 158 | 70.99 134 | 65.05 247 | 86.12 227 |
|
v17 | | | 71.77 234 | 69.20 237 | 79.46 225 | 80.62 257 | 66.81 99 | 87.93 221 | 86.63 266 | 68.71 208 | 55.25 279 | 72.49 296 | 54.72 165 | 90.11 272 | 64.50 190 | 51.97 307 | 81.47 284 |
|
divwei89l23v2f112 | | | 77.28 166 | 75.57 168 | 82.42 153 | 80.62 257 | 66.72 105 | 91.96 116 | 90.42 177 | 74.41 100 | 69.46 173 | 82.12 221 | 55.11 155 | 94.40 158 | 71.00 132 | 65.04 248 | 86.12 227 |
|
TransMVSNet (Re) | | | 70.07 250 | 67.66 251 | 77.31 264 | 80.62 257 | 59.13 252 | 91.78 130 | 84.94 287 | 65.97 234 | 60.08 255 | 80.44 247 | 50.78 203 | 91.87 239 | 48.84 262 | 45.46 325 | 80.94 297 |
|
v1 | | | 77.29 165 | 75.57 168 | 82.42 153 | 80.61 260 | 66.73 103 | 91.96 116 | 90.42 177 | 74.41 100 | 69.46 173 | 82.12 221 | 55.14 154 | 94.40 158 | 71.00 132 | 65.04 248 | 86.13 226 |
|
v15 | | | 71.40 236 | 68.75 239 | 79.35 226 | 80.39 261 | 66.70 107 | 87.57 236 | 86.64 265 | 68.66 210 | 54.68 281 | 72.00 303 | 54.50 167 | 89.98 274 | 63.69 195 | 50.66 312 | 81.38 288 |
|
v2v482 | | | 77.42 160 | 75.65 167 | 82.73 139 | 80.38 262 | 67.13 84 | 91.85 126 | 90.23 186 | 75.09 94 | 69.37 176 | 83.39 205 | 53.79 178 | 94.44 153 | 71.77 125 | 65.00 251 | 86.63 221 |
|
v7 | | | 76.83 177 | 75.01 178 | 82.29 159 | 80.35 263 | 66.70 107 | 91.68 135 | 89.97 198 | 73.47 127 | 69.22 178 | 82.22 218 | 52.52 191 | 94.43 157 | 69.73 144 | 65.96 239 | 85.74 241 |
|
PS-CasMVS | | | 69.86 252 | 69.13 238 | 72.07 299 | 80.35 263 | 50.57 306 | 87.02 243 | 89.75 203 | 67.27 227 | 59.19 259 | 82.28 216 | 46.58 240 | 82.24 324 | 50.69 255 | 59.02 286 | 83.39 267 |
|
v11 | | | 71.05 242 | 68.32 246 | 79.23 230 | 80.34 265 | 66.57 114 | 87.01 244 | 86.55 270 | 68.11 218 | 54.40 284 | 71.66 308 | 52.94 188 | 89.91 277 | 62.71 207 | 51.12 310 | 81.21 292 |
|
V14 | | | 71.29 238 | 68.61 241 | 79.31 227 | 80.34 265 | 66.65 109 | 87.39 238 | 86.61 268 | 68.41 216 | 54.49 283 | 71.91 304 | 54.25 172 | 89.96 275 | 63.50 196 | 50.62 313 | 81.33 290 |
|
v10 | | | 74.77 211 | 72.54 215 | 81.46 181 | 80.33 267 | 66.71 106 | 89.15 202 | 89.08 228 | 70.94 178 | 63.08 242 | 79.86 255 | 52.52 191 | 94.04 181 | 65.70 181 | 62.17 267 | 83.64 260 |
|
V9 | | | 71.16 239 | 68.46 243 | 79.27 229 | 80.26 268 | 66.60 111 | 87.21 241 | 86.56 269 | 68.17 217 | 54.26 286 | 71.81 306 | 54.00 174 | 89.93 276 | 63.28 199 | 50.57 314 | 81.27 291 |
|
test0.0.03 1 | | | 72.76 226 | 72.71 212 | 72.88 291 | 80.25 269 | 47.99 315 | 91.22 153 | 89.45 213 | 71.51 173 | 62.51 248 | 87.66 161 | 53.83 176 | 85.06 305 | 50.16 257 | 67.84 231 | 85.58 242 |
|
v1144 | | | 76.73 179 | 74.88 179 | 82.27 160 | 80.23 270 | 66.60 111 | 91.68 135 | 90.21 188 | 73.69 120 | 69.06 181 | 81.89 226 | 52.73 190 | 94.40 158 | 69.21 150 | 65.23 244 | 85.80 237 |
|
v12 | | | 71.02 243 | 68.29 248 | 79.22 231 | 80.18 271 | 66.53 116 | 87.01 244 | 86.54 271 | 67.90 219 | 54.00 289 | 71.70 307 | 53.66 179 | 89.91 277 | 63.09 201 | 50.51 315 | 81.21 292 |
|
v13 | | | 70.90 244 | 68.15 249 | 79.15 235 | 80.08 272 | 66.45 118 | 86.83 248 | 86.50 272 | 67.62 225 | 53.78 291 | 71.61 309 | 53.51 183 | 89.87 279 | 62.89 205 | 50.50 316 | 81.14 294 |
|
LP | | | 56.71 303 | 51.64 307 | 71.91 301 | 80.08 272 | 60.33 232 | 61.72 335 | 75.61 323 | 43.87 332 | 43.76 325 | 60.30 331 | 30.46 314 | 84.05 309 | 22.94 341 | 46.06 324 | 71.34 331 |
|
v148 | | | 76.19 184 | 74.47 185 | 81.36 182 | 80.05 274 | 64.44 158 | 91.75 133 | 90.23 186 | 73.68 121 | 67.13 209 | 80.84 242 | 55.92 147 | 93.86 191 | 68.95 152 | 61.73 272 | 85.76 240 |
|
v1192 | | | 75.98 189 | 73.92 193 | 82.15 166 | 79.73 275 | 66.24 125 | 91.22 153 | 89.75 203 | 72.67 137 | 68.49 194 | 81.42 232 | 49.86 212 | 94.27 166 | 67.08 165 | 65.02 250 | 85.95 234 |
|
AllTest | | | 61.66 292 | 58.06 293 | 72.46 294 | 79.57 276 | 51.42 302 | 80.17 295 | 68.61 338 | 51.25 311 | 45.88 315 | 81.23 235 | 19.86 333 | 86.58 301 | 38.98 299 | 57.01 293 | 79.39 311 |
|
TestCases | | | | | 72.46 294 | 79.57 276 | 51.42 302 | | 68.61 338 | 51.25 311 | 45.88 315 | 81.23 235 | 19.86 333 | 86.58 301 | 38.98 299 | 57.01 293 | 79.39 311 |
|
MDA-MVSNet-bldmvs | | | 61.54 294 | 57.70 295 | 73.05 289 | 79.53 278 | 57.00 272 | 83.08 273 | 81.23 307 | 57.57 291 | 34.91 336 | 72.45 298 | 32.79 302 | 86.26 303 | 35.81 312 | 41.95 329 | 75.89 324 |
|
v144192 | | | 76.05 187 | 74.03 190 | 82.12 168 | 79.50 279 | 66.55 115 | 91.39 143 | 89.71 209 | 72.30 144 | 68.17 196 | 81.33 234 | 51.75 198 | 94.03 182 | 67.94 157 | 64.19 257 | 85.77 238 |
|
v1921920 | | | 75.63 194 | 73.49 204 | 82.06 172 | 79.38 280 | 66.35 121 | 91.07 161 | 89.48 212 | 71.98 155 | 67.99 197 | 81.22 237 | 49.16 220 | 93.90 188 | 66.56 170 | 64.56 256 | 85.92 236 |
|
PEN-MVS | | | 69.46 257 | 68.56 242 | 72.17 298 | 79.27 281 | 49.71 310 | 86.90 246 | 89.24 221 | 67.24 228 | 59.08 260 | 82.51 211 | 47.23 235 | 83.54 315 | 48.42 264 | 57.12 291 | 83.25 268 |
|
v1240 | | | 75.21 200 | 72.98 209 | 81.88 174 | 79.20 282 | 66.00 128 | 90.75 167 | 89.11 227 | 71.63 168 | 67.41 206 | 81.22 237 | 47.36 234 | 93.87 189 | 65.46 183 | 64.72 254 | 85.77 238 |
|
pmmvs4 | | | 73.92 218 | 71.81 221 | 80.25 204 | 79.17 283 | 65.24 141 | 87.43 237 | 87.26 261 | 67.64 224 | 63.46 239 | 83.91 200 | 48.96 222 | 91.53 255 | 62.94 204 | 65.49 243 | 83.96 257 |
|
V42 | | | 76.46 181 | 74.55 183 | 82.19 165 | 79.14 284 | 67.82 66 | 90.26 179 | 89.42 215 | 73.75 119 | 68.63 188 | 81.89 226 | 51.31 202 | 94.09 176 | 71.69 126 | 64.84 252 | 84.66 253 |
|
testpf | | | 57.17 301 | 56.93 298 | 57.88 323 | 79.13 285 | 42.40 325 | 34.23 347 | 85.97 281 | 52.64 307 | 47.66 313 | 66.50 318 | 36.33 294 | 79.65 331 | 53.60 247 | 56.31 296 | 51.60 342 |
|
pm-mvs1 | | | 72.89 224 | 71.09 226 | 78.26 250 | 79.10 286 | 57.62 265 | 90.80 165 | 89.30 218 | 67.66 222 | 62.91 244 | 81.78 228 | 49.11 221 | 92.95 207 | 60.29 221 | 58.89 288 | 84.22 256 |
|
TinyColmap | | | 60.32 295 | 56.42 301 | 72.00 300 | 78.78 287 | 53.18 294 | 78.36 307 | 75.64 322 | 52.30 308 | 41.59 331 | 75.82 285 | 14.76 340 | 88.35 291 | 35.84 311 | 54.71 301 | 74.46 326 |
|
SixPastTwentyTwo | | | 64.92 280 | 61.78 286 | 74.34 282 | 78.74 288 | 49.76 309 | 83.42 269 | 79.51 315 | 62.86 264 | 50.27 305 | 77.35 271 | 30.92 313 | 90.49 262 | 45.89 273 | 47.06 322 | 82.78 273 |
|
EG-PatchMatch MVS | | | 68.55 261 | 65.41 263 | 77.96 255 | 78.69 289 | 62.93 192 | 89.86 191 | 89.17 223 | 60.55 278 | 50.27 305 | 77.73 270 | 22.60 329 | 94.06 178 | 47.18 269 | 72.65 194 | 76.88 322 |
|
pmmvs5 | | | 73.35 221 | 71.52 223 | 78.86 239 | 78.64 290 | 60.61 229 | 91.08 160 | 86.90 262 | 67.69 221 | 63.32 240 | 83.64 201 | 44.33 254 | 90.53 261 | 62.04 212 | 66.02 238 | 85.46 245 |
|
XVG-OURS | | | 74.25 215 | 72.46 216 | 79.63 218 | 78.45 291 | 57.59 266 | 80.33 292 | 87.39 256 | 63.86 257 | 68.76 186 | 89.62 139 | 40.50 268 | 91.72 242 | 69.00 151 | 74.25 181 | 89.58 166 |
|
XVG-OURS-SEG-HR | | | 74.70 212 | 73.08 208 | 79.57 220 | 78.25 292 | 57.33 269 | 80.49 290 | 87.32 259 | 63.22 261 | 68.76 186 | 90.12 128 | 44.89 252 | 91.59 250 | 70.55 140 | 74.09 183 | 89.79 163 |
|
MDA-MVSNet_test_wron | | | 63.78 288 | 60.16 289 | 74.64 278 | 78.15 293 | 60.41 230 | 83.49 266 | 84.03 292 | 56.17 302 | 39.17 333 | 71.59 311 | 37.22 287 | 83.24 319 | 42.87 285 | 48.73 319 | 80.26 305 |
|
YYNet1 | | | 63.76 289 | 60.14 290 | 74.62 279 | 78.06 294 | 60.19 236 | 83.46 268 | 83.99 296 | 56.18 301 | 39.25 332 | 71.56 312 | 37.18 288 | 83.34 317 | 42.90 284 | 48.70 320 | 80.32 304 |
|
DTE-MVSNet | | | 68.46 263 | 67.33 253 | 71.87 302 | 77.94 295 | 49.00 313 | 86.16 254 | 88.58 241 | 66.36 232 | 58.19 266 | 82.21 219 | 46.36 241 | 83.87 313 | 44.97 279 | 55.17 298 | 82.73 275 |
|
USDC | | | 67.43 271 | 64.51 269 | 76.19 271 | 77.94 295 | 55.29 285 | 78.38 306 | 85.00 286 | 73.17 129 | 48.36 310 | 80.37 248 | 21.23 331 | 92.48 225 | 52.15 251 | 64.02 259 | 80.81 299 |
|
jajsoiax | | | 73.05 222 | 71.51 224 | 77.67 257 | 77.46 297 | 54.83 287 | 88.81 207 | 90.04 196 | 69.13 202 | 62.85 245 | 83.51 203 | 31.16 311 | 92.75 216 | 70.83 136 | 69.80 213 | 85.43 246 |
|
mvs_tets | | | 72.71 227 | 71.11 225 | 77.52 258 | 77.41 298 | 54.52 289 | 88.45 214 | 89.76 202 | 68.76 207 | 62.70 246 | 83.26 206 | 29.49 315 | 92.71 217 | 70.51 141 | 69.62 215 | 85.34 248 |
|
N_pmnet | | | 50.55 310 | 49.11 312 | 54.88 327 | 77.17 299 | 4.02 357 | 84.36 260 | 2.00 357 | 48.59 317 | 45.86 317 | 68.82 316 | 32.22 305 | 82.80 320 | 31.58 331 | 51.38 309 | 77.81 320 |
|
test_djsdf | | | 73.76 220 | 72.56 214 | 77.39 262 | 77.00 300 | 53.93 291 | 89.07 204 | 90.69 169 | 65.80 236 | 63.92 234 | 82.03 224 | 43.14 258 | 92.67 219 | 72.83 115 | 68.53 224 | 85.57 243 |
|
OpenMVS_ROB | | 61.12 18 | 66.39 274 | 62.92 279 | 76.80 268 | 76.51 301 | 57.77 262 | 89.22 199 | 83.41 299 | 55.48 303 | 53.86 290 | 77.84 269 | 26.28 324 | 93.95 186 | 34.90 321 | 68.76 222 | 78.68 316 |
|
v7n | | | 71.31 237 | 68.65 240 | 79.28 228 | 76.40 302 | 60.77 223 | 86.71 249 | 89.45 213 | 64.17 254 | 58.77 264 | 78.24 266 | 44.59 253 | 93.54 196 | 57.76 232 | 61.75 271 | 83.52 263 |
|
K. test v3 | | | 63.09 290 | 59.61 292 | 73.53 286 | 76.26 303 | 49.38 312 | 83.27 270 | 77.15 318 | 64.35 253 | 47.77 311 | 72.32 301 | 28.73 316 | 87.79 296 | 49.93 259 | 36.69 336 | 83.41 266 |
|
RPSCF | | | 64.24 284 | 61.98 285 | 71.01 303 | 76.10 304 | 45.00 321 | 75.83 313 | 75.94 321 | 46.94 322 | 58.96 262 | 84.59 195 | 31.40 310 | 82.00 325 | 47.76 267 | 60.33 282 | 86.04 233 |
|
OurMVSNet-221017-0 | | | 64.68 281 | 62.17 284 | 72.21 297 | 76.08 305 | 47.35 318 | 80.67 289 | 81.02 309 | 56.19 300 | 51.60 299 | 79.66 258 | 27.05 322 | 88.56 289 | 53.60 247 | 53.63 303 | 80.71 300 |
|
Test4 | | | 76.45 182 | 73.45 205 | 85.45 83 | 76.07 306 | 67.61 72 | 88.38 215 | 90.83 165 | 76.71 74 | 53.06 294 | 79.65 259 | 31.61 308 | 94.35 162 | 78.47 83 | 86.22 100 | 94.40 76 |
|
v748 | | | 70.55 247 | 67.97 250 | 78.27 249 | 75.75 307 | 58.78 255 | 86.29 253 | 89.25 220 | 65.12 249 | 56.66 275 | 77.17 276 | 45.05 251 | 92.95 207 | 58.13 231 | 58.33 290 | 83.10 272 |
|
Anonymous20231206 | | | 67.53 269 | 65.78 259 | 72.79 292 | 74.95 308 | 47.59 317 | 88.23 216 | 87.32 259 | 61.75 274 | 58.07 267 | 77.29 273 | 37.79 283 | 87.29 298 | 42.91 283 | 63.71 261 | 83.48 264 |
|
ITE_SJBPF | | | | | 70.43 304 | 74.44 309 | 47.06 319 | | 77.32 317 | 60.16 281 | 54.04 288 | 83.53 202 | 23.30 328 | 84.01 311 | 43.07 282 | 61.58 275 | 80.21 306 |
|
EU-MVSNet | | | 64.01 286 | 63.01 278 | 67.02 312 | 74.40 310 | 38.86 336 | 83.27 270 | 86.19 276 | 45.11 327 | 54.27 285 | 81.15 240 | 36.91 292 | 80.01 329 | 48.79 263 | 57.02 292 | 82.19 280 |
|
XVG-ACMP-BASELINE | | | 68.04 265 | 65.53 262 | 75.56 274 | 74.06 311 | 52.37 296 | 78.43 305 | 85.88 282 | 62.03 269 | 58.91 263 | 81.21 239 | 20.38 332 | 91.15 259 | 60.69 219 | 68.18 226 | 83.16 270 |
|
anonymousdsp | | | 71.14 240 | 69.37 235 | 76.45 269 | 72.95 312 | 54.71 288 | 84.19 261 | 88.88 234 | 61.92 271 | 62.15 249 | 79.77 256 | 38.14 278 | 91.44 258 | 68.90 153 | 67.45 232 | 83.21 269 |
|
lessismore_v0 | | | | | 73.72 285 | 72.93 313 | 47.83 316 | | 61.72 346 | | 45.86 317 | 73.76 290 | 28.63 318 | 89.81 280 | 47.75 268 | 31.37 340 | 83.53 262 |
|
pmmvs6 | | | 67.57 268 | 64.76 267 | 76.00 273 | 72.82 314 | 53.37 293 | 88.71 208 | 86.78 263 | 53.19 306 | 57.58 272 | 78.03 268 | 35.33 296 | 92.41 226 | 55.56 239 | 54.88 300 | 82.21 279 |
|
V4 | | | 69.80 253 | 67.02 255 | 78.15 252 | 71.86 315 | 60.10 237 | 82.02 278 | 87.39 256 | 64.48 251 | 57.78 270 | 75.98 282 | 41.49 263 | 92.90 212 | 63.00 202 | 59.16 284 | 81.44 287 |
|
v52 | | | 69.80 253 | 67.01 256 | 78.15 252 | 71.84 316 | 60.10 237 | 82.02 278 | 87.39 256 | 64.48 251 | 57.80 269 | 75.97 283 | 41.47 264 | 92.90 212 | 63.00 202 | 59.13 285 | 81.45 286 |
|
testgi | | | 64.48 283 | 62.87 280 | 69.31 306 | 71.24 317 | 40.62 331 | 85.49 255 | 79.92 313 | 65.36 240 | 54.18 287 | 83.49 204 | 23.74 327 | 84.55 306 | 41.60 290 | 60.79 280 | 82.77 274 |
|
Patchmatch-RL test | | | 68.17 264 | 64.49 270 | 79.19 232 | 71.22 318 | 53.93 291 | 70.07 322 | 71.54 335 | 69.22 201 | 56.79 274 | 62.89 327 | 56.58 138 | 88.61 287 | 69.53 147 | 52.61 304 | 95.03 55 |
|
Gipuma | | | 34.91 320 | 31.44 322 | 45.30 332 | 70.99 319 | 39.64 335 | 19.85 350 | 72.56 332 | 20.10 346 | 16.16 346 | 21.47 349 | 5.08 353 | 71.16 341 | 13.07 347 | 43.70 328 | 25.08 348 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
UnsupCasMVSNet_eth | | | 65.79 278 | 63.10 277 | 73.88 283 | 70.71 320 | 50.29 308 | 81.09 286 | 89.88 200 | 72.58 139 | 49.25 308 | 74.77 289 | 32.57 304 | 87.43 297 | 55.96 238 | 41.04 331 | 83.90 259 |
|
CMPMVS | | 48.56 21 | 66.77 273 | 64.41 271 | 73.84 284 | 70.65 321 | 50.31 307 | 77.79 310 | 85.73 284 | 45.54 326 | 44.76 321 | 82.14 220 | 35.40 295 | 90.14 270 | 63.18 200 | 74.54 179 | 81.07 296 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
test20.03 | | | 63.83 287 | 62.65 281 | 67.38 311 | 70.58 322 | 39.94 332 | 86.57 251 | 84.17 291 | 63.29 260 | 51.86 298 | 77.30 272 | 37.09 290 | 82.47 321 | 38.87 305 | 54.13 302 | 79.73 309 |
|
testing_2 | | | 71.09 241 | 67.32 254 | 82.40 156 | 69.82 323 | 66.52 117 | 83.64 264 | 90.77 167 | 72.21 149 | 45.12 320 | 71.07 313 | 27.60 320 | 93.74 192 | 75.71 99 | 69.96 212 | 86.95 214 |
|
MIMVSNet1 | | | 60.16 297 | 57.33 297 | 68.67 307 | 69.71 324 | 44.13 323 | 78.92 303 | 84.21 290 | 55.05 304 | 44.63 322 | 71.85 305 | 23.91 326 | 81.54 327 | 32.63 328 | 55.03 299 | 80.35 303 |
|
pmmvs-eth3d | | | 65.53 279 | 62.32 283 | 75.19 275 | 69.39 325 | 59.59 244 | 82.80 275 | 83.43 298 | 62.52 267 | 51.30 302 | 72.49 296 | 32.86 301 | 87.16 299 | 55.32 240 | 50.73 311 | 78.83 315 |
|
test2356 | | | 64.16 285 | 63.28 276 | 66.81 313 | 69.37 326 | 39.86 334 | 87.76 226 | 86.02 279 | 59.83 283 | 53.54 293 | 73.23 292 | 34.94 297 | 80.67 328 | 39.66 297 | 65.20 245 | 79.89 307 |
|
UnsupCasMVSNet_bld | | | 61.60 293 | 57.71 294 | 73.29 288 | 68.73 327 | 51.64 299 | 78.61 304 | 89.05 230 | 57.20 295 | 46.11 314 | 61.96 329 | 28.70 317 | 88.60 288 | 50.08 258 | 38.90 334 | 79.63 310 |
|
testus | | | 59.36 299 | 57.51 296 | 64.90 316 | 66.72 328 | 37.56 337 | 84.98 257 | 81.09 308 | 57.46 294 | 47.72 312 | 72.76 293 | 11.43 344 | 78.78 335 | 36.56 308 | 58.91 287 | 78.36 319 |
|
new-patchmatchnet | | | 59.30 300 | 56.48 300 | 67.79 309 | 65.86 329 | 44.19 322 | 82.47 276 | 81.77 305 | 59.94 282 | 43.65 326 | 66.20 320 | 27.67 319 | 81.68 326 | 39.34 298 | 41.40 330 | 77.50 321 |
|
Anonymous20231211 | | | 53.57 309 | 49.43 311 | 66.00 315 | 65.01 330 | 42.08 326 | 80.95 288 | 72.60 331 | 38.46 337 | 41.65 330 | 64.48 326 | 15.72 337 | 84.23 307 | 25.78 337 | 40.24 333 | 71.68 330 |
|
PM-MVS | | | 59.40 298 | 56.59 299 | 67.84 308 | 63.63 331 | 41.86 328 | 76.76 311 | 63.22 344 | 59.01 287 | 51.07 303 | 72.27 302 | 11.72 342 | 83.25 318 | 61.34 215 | 50.28 317 | 78.39 318 |
|
DSMNet-mixed | | | 56.78 302 | 54.44 304 | 63.79 318 | 63.21 332 | 29.44 345 | 64.43 333 | 64.10 343 | 42.12 335 | 51.32 301 | 71.60 310 | 31.76 307 | 75.04 338 | 36.23 310 | 65.20 245 | 86.87 216 |
|
new_pmnet | | | 49.31 311 | 46.44 313 | 57.93 322 | 62.84 333 | 40.74 330 | 68.47 326 | 62.96 345 | 36.48 338 | 35.09 335 | 57.81 333 | 14.97 339 | 72.18 339 | 32.86 327 | 46.44 323 | 60.88 340 |
|
LF4IMVS | | | 54.01 308 | 52.12 306 | 59.69 321 | 62.41 334 | 39.91 333 | 68.59 325 | 68.28 340 | 42.96 333 | 44.55 323 | 75.18 286 | 14.09 341 | 68.39 342 | 41.36 292 | 51.68 308 | 70.78 332 |
|
1111 | | | 56.66 304 | 54.98 303 | 61.69 319 | 61.99 335 | 31.38 341 | 79.81 300 | 83.17 302 | 45.66 324 | 41.94 328 | 65.44 321 | 41.50 261 | 79.56 332 | 27.64 334 | 47.68 321 | 74.14 327 |
|
.test1245 | | | 46.52 314 | 49.68 310 | 37.02 336 | 61.99 335 | 31.38 341 | 79.81 300 | 83.17 302 | 45.66 324 | 41.94 328 | 65.44 321 | 41.50 261 | 79.56 332 | 27.64 334 | 0.01 353 | 0.13 354 |
|
ambc | | | | | 69.61 305 | 61.38 337 | 41.35 329 | 49.07 344 | 85.86 283 | | 50.18 307 | 66.40 319 | 10.16 345 | 88.14 293 | 45.73 274 | 44.20 326 | 79.32 313 |
|
test1235678 | | | 55.73 305 | 52.74 305 | 64.68 317 | 60.16 338 | 35.56 339 | 81.65 282 | 81.46 306 | 51.27 310 | 38.93 334 | 62.82 328 | 17.44 335 | 78.58 336 | 30.87 332 | 50.09 318 | 79.89 307 |
|
TDRefinement | | | 55.28 307 | 51.58 308 | 66.39 314 | 59.53 339 | 46.15 320 | 76.23 312 | 72.80 330 | 44.60 329 | 42.49 327 | 76.28 281 | 15.29 338 | 82.39 322 | 33.20 325 | 43.75 327 | 70.62 333 |
|
pmmvs3 | | | 55.51 306 | 51.50 309 | 67.53 310 | 57.90 340 | 50.93 305 | 80.37 291 | 73.66 329 | 40.63 336 | 44.15 324 | 64.75 325 | 16.30 336 | 78.97 334 | 44.77 280 | 40.98 332 | 72.69 328 |
|
test12356 | | | 47.51 312 | 44.82 314 | 55.56 325 | 52.53 341 | 21.09 352 | 71.45 319 | 76.03 320 | 44.14 331 | 30.69 337 | 58.18 332 | 9.01 348 | 76.14 337 | 26.95 336 | 34.43 339 | 69.46 335 |
|
DeepMVS_CX | | | | | 34.71 337 | 51.45 342 | 24.73 351 | | 28.48 356 | 31.46 340 | 17.49 345 | 52.75 335 | 5.80 352 | 42.60 353 | 18.18 345 | 19.42 342 | 36.81 346 |
|
FPMVS | | | 45.64 315 | 43.10 316 | 53.23 329 | 51.42 343 | 36.46 338 | 64.97 332 | 71.91 333 | 29.13 341 | 27.53 339 | 61.55 330 | 9.83 346 | 65.01 346 | 16.00 346 | 55.58 297 | 58.22 341 |
|
PNet_i23d | | | 32.77 321 | 29.98 323 | 41.11 334 | 48.05 344 | 29.17 346 | 65.82 331 | 50.02 349 | 21.42 344 | 14.74 347 | 37.19 345 | 1.11 358 | 55.11 349 | 19.75 344 | 11.77 346 | 39.06 344 |
|
wuyk23d | | | 11.30 330 | 10.95 331 | 12.33 342 | 48.05 344 | 19.89 353 | 25.89 349 | 1.92 358 | 3.58 351 | 3.12 353 | 1.37 354 | 0.64 359 | 15.77 355 | 6.23 352 | 7.77 352 | 1.35 352 |
|
testmv | | | 46.98 313 | 43.53 315 | 57.35 324 | 47.75 346 | 30.41 344 | 74.99 315 | 77.69 316 | 42.84 334 | 28.03 338 | 53.36 334 | 8.18 349 | 71.18 340 | 24.36 339 | 34.55 337 | 70.46 334 |
|
PMMVS2 | | | 37.93 319 | 33.61 320 | 50.92 330 | 46.31 347 | 24.76 350 | 60.55 339 | 50.05 348 | 28.94 342 | 20.93 341 | 47.59 336 | 4.41 355 | 65.13 345 | 25.14 338 | 18.55 343 | 62.87 338 |
|
no-one | | | 44.13 316 | 38.39 317 | 61.34 320 | 45.91 348 | 41.94 327 | 61.67 336 | 75.07 327 | 45.05 328 | 20.07 342 | 40.68 344 | 11.58 343 | 79.82 330 | 30.18 333 | 15.30 344 | 62.26 339 |
|
E-PMN | | | 24.61 325 | 24.00 326 | 26.45 339 | 43.74 349 | 18.44 354 | 60.86 337 | 39.66 351 | 15.11 347 | 9.53 350 | 22.10 348 | 6.52 351 | 46.94 351 | 8.31 350 | 10.14 347 | 13.98 350 |
|
EMVS | | | 23.76 327 | 23.20 328 | 25.46 340 | 41.52 350 | 16.90 355 | 60.56 338 | 38.79 354 | 14.62 348 | 8.99 351 | 20.24 352 | 7.35 350 | 45.82 352 | 7.25 351 | 9.46 349 | 13.64 351 |
|
LCM-MVSNet | | | 40.54 317 | 35.79 318 | 54.76 328 | 36.92 351 | 30.81 343 | 51.41 342 | 69.02 337 | 22.07 343 | 24.63 340 | 45.37 339 | 4.56 354 | 65.81 344 | 33.67 323 | 34.50 338 | 67.67 336 |
|
ANet_high | | | 40.27 318 | 35.20 319 | 55.47 326 | 34.74 352 | 34.47 340 | 63.84 334 | 71.56 334 | 48.42 318 | 18.80 344 | 41.08 342 | 9.52 347 | 64.45 347 | 20.18 343 | 8.66 351 | 67.49 337 |
|
MVE | | 24.84 23 | 24.35 326 | 19.77 330 | 38.09 335 | 34.56 353 | 26.92 349 | 26.57 348 | 38.87 353 | 11.73 350 | 11.37 349 | 27.44 346 | 1.37 357 | 50.42 350 | 11.41 348 | 14.60 345 | 36.93 345 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
wuykxyi23d | | | 29.03 324 | 23.09 329 | 46.84 331 | 31.67 354 | 28.82 347 | 43.46 345 | 57.72 347 | 14.39 349 | 7.52 352 | 20.84 350 | 0.64 359 | 60.29 348 | 21.57 342 | 10.04 348 | 51.40 343 |
|
PMVS | | 26.43 22 | 31.84 322 | 28.16 324 | 42.89 333 | 25.87 355 | 27.58 348 | 50.92 343 | 49.78 350 | 21.37 345 | 14.17 348 | 40.81 343 | 2.01 356 | 66.62 343 | 9.61 349 | 38.88 335 | 34.49 347 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
tmp_tt | | | 22.26 328 | 23.75 327 | 17.80 341 | 5.23 356 | 12.06 356 | 35.26 346 | 39.48 352 | 2.82 352 | 18.94 343 | 44.20 340 | 22.23 330 | 24.64 354 | 36.30 309 | 9.31 350 | 16.69 349 |
|
testmvs | | | 7.23 332 | 9.62 333 | 0.06 344 | 0.04 357 | 0.02 359 | 84.98 257 | 0.02 359 | 0.03 353 | 0.18 354 | 1.21 355 | 0.01 362 | 0.02 356 | 0.14 353 | 0.01 353 | 0.13 354 |
|
test123 | | | 6.92 333 | 9.21 334 | 0.08 343 | 0.03 358 | 0.05 358 | 81.65 282 | 0.01 360 | 0.02 354 | 0.14 355 | 0.85 356 | 0.03 361 | 0.02 356 | 0.12 354 | 0.00 355 | 0.16 353 |
|
cdsmvs_eth3d_5k | | | 19.86 329 | 26.47 325 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 93.45 62 | 0.00 355 | 0.00 356 | 95.27 32 | 49.56 214 | 0.00 358 | 0.00 355 | 0.00 355 | 0.00 356 |
|
pcd_1.5k_mvsjas | | | 4.46 334 | 5.95 335 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 0.00 357 | 53.55 180 | 0.00 358 | 0.00 355 | 0.00 355 | 0.00 356 |
|
sosnet-low-res | | | 0.00 335 | 0.00 336 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 0.00 357 | 0.00 363 | 0.00 358 | 0.00 355 | 0.00 355 | 0.00 356 |
|
sosnet | | | 0.00 335 | 0.00 336 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 0.00 357 | 0.00 363 | 0.00 358 | 0.00 355 | 0.00 355 | 0.00 356 |
|
uncertanet | | | 0.00 335 | 0.00 336 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 0.00 357 | 0.00 363 | 0.00 358 | 0.00 355 | 0.00 355 | 0.00 356 |
|
Regformer | | | 0.00 335 | 0.00 336 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 0.00 357 | 0.00 363 | 0.00 358 | 0.00 355 | 0.00 355 | 0.00 356 |
|
ab-mvs-re | | | 7.91 331 | 10.55 332 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 94.95 42 | 0.00 363 | 0.00 358 | 0.00 355 | 0.00 355 | 0.00 356 |
|
uanet | | | 0.00 335 | 0.00 336 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 0.00 357 | 0.00 363 | 0.00 358 | 0.00 355 | 0.00 355 | 0.00 356 |
|
GSMVS | | | | | | | | | | | | | | | | | 94.68 64 |
|
test_part3 | | | | | | | | 94.96 31 | | 68.52 212 | | 97.23 2 | | 98.90 7 | 91.52 6 | | |
|
test_part1 | | | | | | | | | 94.26 41 | | | | 77.03 4 | | | 95.18 8 | 96.11 19 |
|
sam_mvs1 | | | | | | | | | | | | | 57.85 119 | | | | 94.68 64 |
|
sam_mvs | | | | | | | | | | | | | 54.91 163 | | | | |
|
MTGPA | | | | | | | | | 92.23 114 | | | | | | | | |
|
test_post1 | | | | | | | | 78.95 302 | | | | 20.70 351 | 53.05 186 | 91.50 256 | 60.43 220 | | |
|
test_post | | | | | | | | | | | | 23.01 347 | 56.49 139 | 92.67 219 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 67.62 317 | 57.62 122 | 90.25 264 | | | |
|
MTMP | | | | | | | | | 32.52 355 | | | | | | | | |
|
test9_res | | | | | | | | | | | | | | | 89.41 12 | 94.96 10 | 95.29 40 |
|
agg_prior2 | | | | | | | | | | | | | | | 86.41 33 | 94.75 18 | 95.33 37 |
|
test_prior4 | | | | | | | 67.18 83 | 93.92 54 | | | | | | | | | |
|
test_prior2 | | | | | | | | 95.10 27 | | 75.40 89 | 85.25 32 | 95.61 23 | 67.94 26 | | 87.47 25 | 94.77 15 | |
|
旧先验2 | | | | | | | | 92.00 114 | | 59.37 286 | 87.54 16 | | | 93.47 199 | 75.39 101 | | |
|
新几何2 | | | | | | | | 91.41 142 | | | | | | | | | |
|
无先验 | | | | | | | | 92.71 87 | 92.61 105 | 62.03 269 | | | | 97.01 67 | 66.63 168 | | 93.97 94 |
|
原ACMM2 | | | | | | | | 92.01 112 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 96.09 99 | 61.26 216 | | |
|
segment_acmp | | | | | | | | | | | | | 65.94 44 | | | | |
|
testdata1 | | | | | | | | 89.21 200 | | 77.55 63 | | | | | | | |
|
plane_prior5 | | | | | | | | | 91.31 152 | | | | | 95.55 122 | 76.74 93 | 78.53 148 | 88.39 180 |
|
plane_prior4 | | | | | | | | | | | | 89.14 141 | | | | | |
|
plane_prior3 | | | | | | | 61.95 211 | | | 79.09 44 | 72.53 135 | | | | | | |
|
plane_prior2 | | | | | | | | 93.13 74 | | 78.81 49 | | | | | | | |
|
plane_prior | | | | | | | 62.42 201 | 93.85 57 | | 79.38 37 | | | | | | 78.80 146 | |
|
n2 | | | | | | | | | 0.00 361 | | | | | | | | |
|
nn | | | | | | | | | 0.00 361 | | | | | | | | |
|
door-mid | | | | | | | | | 66.01 342 | | | | | | | | |
|
test11 | | | | | | | | | 93.01 89 | | | | | | | | |
|
door | | | | | | | | | 66.57 341 | | | | | | | | |
|
HQP5-MVS | | | | | | | 63.66 180 | | | | | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 77.63 90 | | |
|
HQP4-MVS | | | | | | | | | | | 74.18 117 | | | 95.61 118 | | | 88.63 175 |
|
HQP3-MVS | | | | | | | | | 91.70 138 | | | | | | | 78.90 144 | |
|
HQP2-MVS | | | | | | | | | | | | | 51.63 200 | | | | |
|
MDTV_nov1_ep13_2view | | | | | | | 59.90 241 | 80.13 296 | | 67.65 223 | 72.79 130 | | 54.33 171 | | 59.83 223 | | 92.58 129 |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 71.63 199 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 69.72 214 | |
|
Test By Simon | | | | | | | | | | | | | 54.21 173 | | | | |
|