DeepPCF-MVS | | 81.17 1 | 89.72 5 | 91.38 3 | 84.72 104 | 93.00 54 | 58.16 258 | 96.72 3 | 94.41 36 | 86.50 5 | 90.25 5 | 97.83 1 | 75.46 7 | 98.67 14 | 92.78 2 | 95.49 7 | 97.32 1 |
|
test_part3 | | | | | | | | 94.96 31 | | 68.52 211 | | 97.23 2 | | 98.90 7 | 91.52 6 | | |
|
ESAPD | | | 89.08 8 | 89.53 8 | 87.72 20 | 96.29 7 | 68.16 60 | 94.96 31 | 94.26 40 | 68.52 211 | 90.78 3 | 97.23 2 | 77.03 4 | 98.90 7 | 91.52 6 | 95.18 8 | 96.11 19 |
|
CNVR-MVS | | | 90.32 3 | 90.89 4 | 88.61 11 | 96.76 4 | 70.65 18 | 96.47 6 | 94.83 23 | 84.83 9 | 89.07 9 | 96.80 4 | 70.86 16 | 99.06 3 | 92.64 4 | 95.71 5 | 96.12 18 |
|
PHI-MVS | | | 86.83 30 | 86.85 29 | 86.78 41 | 93.47 45 | 65.55 137 | 95.39 20 | 95.10 19 | 71.77 164 | 85.69 27 | 96.52 5 | 62.07 84 | 98.77 12 | 86.06 36 | 95.60 6 | 96.03 23 |
|
MSLP-MVS++ | | | 86.27 35 | 85.91 36 | 87.35 28 | 92.01 75 | 68.97 43 | 95.04 29 | 92.70 98 | 79.04 46 | 81.50 56 | 96.50 6 | 58.98 112 | 96.78 83 | 83.49 52 | 93.93 26 | 96.29 16 |
|
HPM-MVS++ | | | 89.37 7 | 89.95 7 | 87.64 21 | 95.10 19 | 68.23 59 | 95.24 22 | 94.49 33 | 82.43 17 | 88.90 10 | 96.35 7 | 71.89 15 | 98.63 15 | 88.76 20 | 96.40 2 | 96.06 21 |
|
APDe-MVS | | | 87.54 18 | 87.84 15 | 86.65 43 | 96.07 11 | 66.30 123 | 94.84 34 | 93.78 46 | 69.35 199 | 88.39 12 | 96.34 8 | 67.74 30 | 97.66 38 | 90.62 11 | 93.44 36 | 96.01 24 |
|
MCST-MVS | | | 91.08 1 | 91.46 2 | 89.94 2 | 97.66 2 | 73.37 7 | 97.13 1 | 95.58 12 | 89.33 1 | 85.77 25 | 96.26 9 | 72.84 11 | 99.38 1 | 92.64 4 | 95.93 4 | 97.08 4 |
|
NCCC | | | 89.07 9 | 89.46 9 | 87.91 16 | 96.60 5 | 69.05 40 | 96.38 7 | 94.64 30 | 84.42 10 | 86.74 20 | 96.20 10 | 66.56 39 | 98.76 13 | 89.03 18 | 94.56 20 | 95.92 27 |
|
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 42 | 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 |
HSP-MVS | | | 90.38 2 | 91.89 1 | 85.84 69 | 92.83 57 | 64.03 170 | 93.06 76 | 94.52 31 | 82.19 19 | 93.65 1 | 96.15 12 | 85.89 1 | 97.19 59 | 91.02 10 | 97.75 1 | 96.29 16 |
|
PS-MVSNAJ | | | 88.14 11 | 87.61 18 | 89.71 4 | 92.06 74 | 76.72 1 | 95.75 11 | 93.26 76 | 83.86 11 | 89.55 7 | 96.06 13 | 53.55 179 | 97.89 32 | 91.10 8 | 93.31 37 | 94.54 70 |
|
xiu_mvs_v2_base | | | 87.92 15 | 87.38 23 | 89.55 7 | 91.41 99 | 76.43 2 | 95.74 12 | 93.12 85 | 83.53 13 | 89.55 7 | 95.95 14 | 53.45 184 | 97.68 34 | 91.07 9 | 92.62 44 | 94.54 70 |
|
MVS_0304 | | | 88.39 10 | 88.35 13 | 88.50 12 | 93.01 53 | 70.11 23 | 95.90 10 | 92.20 118 | 86.27 6 | 88.70 11 | 95.92 15 | 56.76 131 | 99.02 4 | 92.68 3 | 93.76 30 | 96.37 15 |
|
APD-MVS | | | 85.93 39 | 85.99 34 | 85.76 74 | 95.98 13 | 65.21 142 | 93.59 63 | 92.58 105 | 66.54 229 | 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 |
CANet | | | 89.61 6 | 89.99 6 | 88.46 13 | 94.39 26 | 69.71 32 | 96.53 5 | 93.78 46 | 86.89 4 | 89.68 6 | 95.78 17 | 65.94 44 | 99.10 2 | 92.99 1 | 93.91 27 | 96.58 11 |
|
SD-MVS | | | 87.49 19 | 87.49 20 | 87.50 25 | 93.60 41 | 68.82 46 | 93.90 55 | 92.63 103 | 76.86 71 | 87.90 14 | 95.76 18 | 66.17 40 | 97.63 40 | 89.06 17 | 91.48 60 | 96.05 22 |
|
Regformer-1 | | | 87.24 22 | 87.60 19 | 86.15 63 | 95.14 17 | 65.83 133 | 93.95 51 | 95.12 17 | 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 20 | 81.69 26 | 84.03 43 | 95.73 19 | 67.35 33 | 98.19 25 | 85.40 40 | 88.64 80 | 94.20 79 |
|
SteuartSystems-ACMMP | | | 86.82 31 | 86.90 27 | 86.58 47 | 90.42 111 | 66.38 120 | 96.09 9 | 93.87 44 | 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. |
MP-MVS-pluss | | | 85.24 45 | 85.13 45 | 85.56 78 | 91.42 97 | 65.59 136 | 91.54 140 | 92.51 107 | 74.56 99 | 80.62 61 | 95.64 22 | 59.15 109 | 97.00 69 | 86.94 31 | 93.80 28 | 94.07 90 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
test_prior3 | | | 87.38 20 | 87.70 17 | 86.42 54 | 94.71 23 | 67.35 77 | 95.10 27 | 93.10 86 | 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_prior2 | | | | | | | | 95.10 27 | | 75.40 89 | 85.25 32 | 95.61 23 | 67.94 26 | | 87.47 25 | 94.77 15 | |
|
MAR-MVS | | | 84.18 57 | 83.43 58 | 86.44 53 | 96.25 9 | 65.93 130 | 94.28 37 | 94.27 39 | 74.41 100 | 79.16 76 | 95.61 23 | 53.99 174 | 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 |
agg_prior1 | | | 87.02 25 | 87.26 24 | 86.28 61 | 94.16 33 | 66.97 88 | 94.08 44 | 93.31 74 | 71.85 159 | 84.49 37 | 95.39 26 | 68.91 19 | 96.75 85 | 88.84 19 | 94.32 22 | 95.13 49 |
|
test_8 | | | | | | 94.19 28 | 67.19 81 | 94.15 40 | 93.42 69 | 71.87 157 | 85.38 30 | 95.35 27 | 68.19 23 | 96.95 76 | | | |
|
TEST9 | | | | | | 94.18 29 | 67.28 79 | 94.16 38 | 93.51 58 | 71.75 165 | 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 58 | 71.87 157 | 85.52 28 | 95.33 28 | 68.19 23 | 97.27 56 | 89.09 15 | 94.90 12 | 95.25 45 |
|
ACMMP_Plus | | | 86.05 38 | 85.80 38 | 86.80 40 | 91.58 88 | 67.53 74 | 91.79 128 | 93.49 60 | 74.93 96 | 84.61 35 | 95.30 30 | 59.42 106 | 97.92 30 | 86.13 35 | 94.92 11 | 94.94 58 |
|
CDPH-MVS | | | 85.71 42 | 85.46 42 | 86.46 52 | 94.75 22 | 67.19 81 | 93.89 56 | 92.83 95 | 70.90 178 | 83.09 48 | 95.28 31 | 63.62 71 | 97.36 50 | 80.63 70 | 94.18 23 | 94.84 60 |
|
cdsmvs_eth3d_5k | | | 19.86 328 | 26.47 324 | 0.00 344 | 0.00 358 | 0.00 359 | 0.00 350 | 93.45 61 | 0.00 354 | 0.00 355 | 95.27 32 | 49.56 213 | 0.00 357 | 0.00 354 | 0.00 354 | 0.00 355 |
|
lupinMVS | | | 87.74 17 | 87.77 16 | 87.63 23 | 89.24 140 | 71.18 14 | 96.57 4 | 92.90 93 | 82.70 16 | 87.13 17 | 95.27 32 | 64.99 59 | 95.80 108 | 89.34 14 | 91.80 54 | 95.93 26 |
|
canonicalmvs | | | 86.85 29 | 86.25 32 | 88.66 10 | 91.80 84 | 71.92 10 | 93.54 65 | 91.71 136 | 80.26 31 | 87.55 15 | 95.25 34 | 63.59 73 | 96.93 79 | 88.18 21 | 84.34 113 | 97.11 3 |
|
alignmvs | | | 87.28 21 | 86.97 26 | 88.24 15 | 91.30 100 | 71.14 16 | 95.61 16 | 93.56 56 | 79.30 38 | 87.07 19 | 95.25 34 | 68.43 21 | 96.93 79 | 87.87 23 | 84.33 114 | 96.65 8 |
|
MPTG | | | 84.73 50 | 84.47 48 | 85.50 79 | 91.89 80 | 65.16 143 | 91.55 139 | 92.23 113 | 75.32 91 | 80.53 62 | 95.21 36 | 56.06 143 | 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 | 81.75 279 | 92.23 113 | 75.32 91 | 80.53 62 | 95.21 36 | 56.06 143 | 97.16 61 | 84.86 44 | 92.55 46 | 94.18 80 |
|
agg_prior3 | | | 86.93 27 | 87.08 25 | 86.48 51 | 94.21 27 | 66.95 90 | 94.14 41 | 93.40 70 | 71.80 162 | 84.86 34 | 95.13 38 | 66.16 41 | 97.25 58 | 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 18 | 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 23 | 80.66 29 | 81.29 57 | 95.04 39 | 65.12 51 | 98.08 28 | 82.74 55 | 84.36 111 | 92.88 124 |
|
PAPR | | | 85.15 46 | 84.47 48 | 87.18 31 | 96.02 12 | 68.29 56 | 91.85 126 | 93.00 90 | 76.59 76 | 79.03 77 | 95.00 41 | 61.59 87 | 97.61 42 | 78.16 86 | 89.00 78 | 95.63 30 |
|
1112_ss | | | 80.56 104 | 79.83 101 | 82.77 138 | 88.65 151 | 60.78 221 | 92.29 100 | 88.36 243 | 72.58 139 | 72.46 138 | 94.95 42 | 65.09 52 | 93.42 199 | 66.38 172 | 77.71 153 | 94.10 87 |
|
ab-mvs-re | | | 7.91 330 | 10.55 331 | 0.00 344 | 0.00 358 | 0.00 359 | 0.00 350 | 0.00 360 | 0.00 354 | 0.00 355 | 94.95 42 | 0.00 362 | 0.00 357 | 0.00 354 | 0.00 354 | 0.00 355 |
|
HFP-MVS | | | 84.73 50 | 84.40 50 | 85.72 75 | 93.75 39 | 65.01 149 | 93.50 66 | 93.19 81 | 72.19 149 | 79.22 74 | 94.93 44 | 59.04 110 | 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 81 | 73.55 124 | 79.22 74 | 94.93 44 | 59.04 110 | 97.67 35 | 82.66 56 | 92.21 48 | 94.49 74 |
|
CP-MVS | | | 83.71 67 | 83.40 60 | 84.65 105 | 93.14 51 | 63.84 171 | 94.59 35 | 92.28 111 | 71.03 176 | 77.41 93 | 94.92 46 | 55.21 151 | 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 37 | 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 |
ACMMPR | | | 84.37 53 | 84.06 51 | 85.28 89 | 93.56 42 | 64.37 162 | 93.50 66 | 93.15 84 | 72.19 149 | 78.85 80 | 94.86 48 | 56.69 135 | 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 91 | 72.16 152 | 78.86 79 | 94.84 49 | 56.97 128 | 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 45 | 78.17 55 | 85.93 24 | 94.80 50 | 75.80 6 | 98.21 23 | 89.38 13 | 88.78 79 | 96.59 10 |
|
WTY-MVS | | | 86.32 34 | 85.81 37 | 87.85 17 | 92.82 59 | 69.37 37 | 95.20 23 | 95.25 15 | 82.71 15 | 81.91 53 | 94.73 51 | 67.93 28 | 97.63 40 | 79.55 75 | 82.25 126 | 96.54 12 |
|
MVS | | | 84.66 52 | 82.86 67 | 90.06 1 | 90.93 105 | 74.56 5 | 87.91 222 | 95.54 13 | 68.55 210 | 72.35 141 | 94.71 52 | 59.78 103 | 98.90 7 | 81.29 69 | 94.69 19 | 96.74 7 |
|
APD-MVS_3200maxsize | | | 81.64 92 | 81.32 84 | 82.59 145 | 92.36 65 | 58.74 255 | 91.39 143 | 91.01 162 | 63.35 258 | 79.72 70 | 94.62 53 | 51.82 195 | 96.14 97 | 79.71 73 | 87.93 85 | 92.89 123 |
|
EPNet | | | 87.84 16 | 88.38 11 | 86.23 62 | 93.30 46 | 66.05 127 | 95.26 21 | 94.84 22 | 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 |
MP-MVS | | | 85.02 47 | 84.97 46 | 85.17 93 | 92.60 63 | 64.27 167 | 93.24 71 | 92.27 112 | 73.13 130 | 79.63 71 | 94.43 55 | 61.90 85 | 97.17 60 | 85.00 42 | 92.56 45 | 94.06 91 |
|
PGM-MVS | | | 83.25 69 | 82.70 71 | 84.92 97 | 92.81 60 | 64.07 169 | 90.44 172 | 92.20 118 | 71.28 174 | 77.23 96 | 94.43 55 | 55.17 152 | 97.31 53 | 79.33 77 | 91.38 61 | 93.37 106 |
|
xiu_mvs_v1_base_debu | | | 82.16 85 | 81.12 86 | 85.26 90 | 86.42 184 | 68.72 47 | 92.59 95 | 90.44 173 | 73.12 131 | 84.20 40 | 94.36 57 | 38.04 278 | 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 | 86.42 184 | 68.72 47 | 92.59 95 | 90.44 173 | 73.12 131 | 84.20 40 | 94.36 57 | 38.04 278 | 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 | 86.42 184 | 68.72 47 | 92.59 95 | 90.44 173 | 73.12 131 | 84.20 40 | 94.36 57 | 38.04 278 | 95.73 112 | 84.12 47 | 86.81 91 | 91.33 146 |
|
旧先验1 | | | | | | 91.94 76 | 60.74 224 | | 91.50 144 | | | 94.36 57 | 65.23 50 | | | 91.84 53 | 94.55 68 |
|
CSCG | | | 86.87 28 | 86.26 31 | 88.72 9 | 95.05 20 | 70.79 17 | 93.83 58 | 95.33 14 | 68.48 214 | 77.63 90 | 94.35 61 | 73.04 10 | 98.45 18 | 84.92 43 | 93.71 32 | 96.92 6 |
|
MVSFormer | | | 83.75 66 | 82.88 66 | 86.37 57 | 89.24 140 | 71.18 14 | 89.07 203 | 90.69 168 | 65.80 235 | 87.13 17 | 94.34 62 | 64.99 59 | 92.67 218 | 72.83 115 | 91.80 54 | 95.27 42 |
|
jason | | | 86.40 33 | 86.17 33 | 87.11 34 | 86.16 190 | 70.54 20 | 95.71 15 | 92.19 120 | 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. |
XVS | | | 83.87 63 | 83.47 56 | 85.05 95 | 93.22 47 | 63.78 173 | 92.92 82 | 92.66 101 | 73.99 111 | 78.18 84 | 94.31 64 | 55.25 148 | 97.41 47 | 79.16 78 | 91.58 58 | 93.95 95 |
|
mPP-MVS | | | 82.96 75 | 82.44 72 | 84.52 109 | 92.83 57 | 62.92 193 | 92.76 85 | 91.85 131 | 71.52 171 | 75.61 108 | 94.24 65 | 53.48 183 | 96.99 72 | 78.97 81 | 90.73 67 | 93.64 102 |
|
EI-MVSNet-Vis-set | | | 83.77 65 | 83.67 53 | 84.06 116 | 92.79 61 | 63.56 182 | 91.76 131 | 94.81 25 | 79.65 36 | 77.87 86 | 94.09 66 | 63.35 75 | 97.90 31 | 79.35 76 | 79.36 140 | 90.74 154 |
|
testdata | | | | | 81.34 182 | 89.02 144 | 57.72 262 | | 89.84 200 | 58.65 288 | 85.32 31 | 94.09 66 | 57.03 126 | 93.28 200 | 69.34 149 | 90.56 71 | 93.03 118 |
|
MVS_111021_HR | | | 86.19 37 | 85.80 38 | 87.37 27 | 93.17 50 | 69.79 30 | 93.99 49 | 93.76 49 | 79.08 45 | 78.88 78 | 93.99 68 | 62.25 83 | 98.15 26 | 85.93 37 | 91.15 64 | 94.15 84 |
|
HPM-MVS | | | 83.25 69 | 82.95 65 | 84.17 114 | 92.25 70 | 62.88 195 | 90.91 161 | 91.86 130 | 70.30 191 | 77.12 97 | 93.96 69 | 56.75 133 | 96.28 93 | 82.04 60 | 91.34 63 | 93.34 107 |
|
DP-MVS Recon | | | 82.73 76 | 81.65 81 | 85.98 65 | 97.31 3 | 67.06 85 | 95.15 25 | 91.99 125 | 69.08 202 | 76.50 103 | 93.89 70 | 54.48 168 | 98.20 24 | 70.76 138 | 85.66 103 | 92.69 125 |
|
EI-MVSNet-UG-set | | | 83.14 71 | 82.96 64 | 83.67 126 | 92.28 69 | 63.19 188 | 91.38 145 | 94.68 28 | 79.22 40 | 76.60 101 | 93.75 71 | 62.64 81 | 97.76 33 | 78.07 87 | 78.01 151 | 90.05 161 |
|
CANet_DTU | | | 84.09 59 | 83.52 54 | 85.81 70 | 90.30 114 | 66.82 95 | 91.87 124 | 89.01 230 | 85.27 7 | 86.09 22 | 93.74 72 | 47.71 231 | 96.98 73 | 77.90 89 | 89.78 75 | 93.65 101 |
|
DeepC-MVS | | 77.85 3 | 85.52 43 | 85.24 44 | 86.37 57 | 88.80 149 | 66.64 110 | 92.15 103 | 93.68 52 | 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 |
PAPM_NR | | | 82.97 74 | 81.84 78 | 86.37 57 | 94.10 35 | 66.76 102 | 87.66 233 | 92.84 94 | 69.96 194 | 74.07 121 | 93.57 74 | 63.10 79 | 97.50 45 | 70.66 139 | 90.58 70 | 94.85 59 |
|
PMMVS | | | 81.98 90 | 82.04 76 | 81.78 174 | 89.76 124 | 56.17 279 | 91.13 158 | 90.69 168 | 77.96 57 | 80.09 66 | 93.57 74 | 46.33 241 | 94.99 133 | 81.41 66 | 87.46 88 | 94.17 82 |
|
LFMVS | | | 84.34 55 | 82.73 70 | 89.18 8 | 94.76 21 | 73.25 8 | 94.99 30 | 91.89 129 | 71.90 155 | 82.16 52 | 93.49 76 | 47.98 228 | 97.05 64 | 82.55 57 | 84.82 107 | 97.25 2 |
|
ACMMP | | | 81.49 93 | 80.67 91 | 83.93 118 | 91.71 86 | 62.90 194 | 92.13 104 | 92.22 117 | 71.79 163 | 71.68 149 | 93.49 76 | 50.32 205 | 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 |
CPTT-MVS | | | 79.59 124 | 79.16 115 | 80.89 197 | 91.54 91 | 59.80 241 | 92.10 106 | 88.54 241 | 60.42 278 | 72.96 127 | 93.28 78 | 48.27 224 | 92.80 213 | 78.89 82 | 86.50 98 | 90.06 160 |
|
MVS_111021_LR | | | 82.02 89 | 81.52 82 | 83.51 129 | 88.42 158 | 62.88 195 | 89.77 191 | 88.93 232 | 76.78 73 | 75.55 109 | 93.10 79 | 50.31 206 | 95.38 126 | 83.82 51 | 87.02 90 | 92.26 137 |
|
1314 | | | 80.70 102 | 78.95 117 | 85.94 67 | 87.77 170 | 67.56 73 | 87.91 222 | 92.55 106 | 72.17 151 | 67.44 203 | 93.09 80 | 50.27 207 | 97.04 66 | 71.68 127 | 87.64 87 | 93.23 112 |
|
abl_6 | | | 79.82 119 | 79.20 114 | 81.70 178 | 89.85 121 | 58.34 257 | 88.47 212 | 90.07 193 | 62.56 265 | 77.71 88 | 93.08 81 | 47.65 232 | 96.78 83 | 77.94 88 | 85.45 105 | 89.99 162 |
|
PVSNet_Blended | | | 86.73 32 | 86.86 28 | 86.31 60 | 93.76 37 | 67.53 74 | 96.33 8 | 93.61 54 | 82.34 18 | 81.00 59 | 93.08 81 | 63.19 77 | 97.29 54 | 87.08 29 | 91.38 61 | 94.13 85 |
|
VNet | | | 86.20 36 | 85.65 41 | 87.84 18 | 93.92 36 | 69.99 26 | 95.73 14 | 95.94 11 | 78.43 53 | 86.00 23 | 93.07 83 | 58.22 115 | 97.00 69 | 85.22 41 | 84.33 114 | 96.52 13 |
|
HPM-MVS_fast | | | 80.25 109 | 79.55 107 | 82.33 157 | 91.55 90 | 59.95 239 | 91.32 149 | 89.16 223 | 65.23 241 | 74.71 115 | 93.07 83 | 47.81 230 | 95.74 111 | 74.87 110 | 88.23 82 | 91.31 150 |
|
PAPM | | | 85.89 40 | 85.46 42 | 87.18 31 | 88.20 162 | 72.42 9 | 92.41 99 | 92.77 96 | 82.11 21 | 80.34 64 | 93.07 83 | 68.27 22 | 95.02 132 | 78.39 85 | 93.59 34 | 94.09 88 |
|
MG-MVS | | | 87.11 24 | 86.27 30 | 89.62 5 | 97.79 1 | 76.27 3 | 94.96 31 | 94.49 33 | 78.74 51 | 83.87 45 | 92.94 86 | 64.34 64 | 96.94 77 | 75.19 102 | 94.09 24 | 95.66 29 |
|
1121 | | | 81.25 96 | 80.05 96 | 84.87 99 | 92.30 68 | 64.31 164 | 87.91 222 | 91.39 148 | 59.44 284 | 79.94 67 | 92.91 87 | 57.09 124 | 97.01 67 | 66.63 168 | 92.81 43 | 93.29 110 |
|
æ–°å‡ ä½•1 | | | | | 84.73 102 | 92.32 67 | 64.28 166 | | 91.46 146 | 59.56 283 | 79.77 69 | 92.90 88 | 56.95 129 | 96.57 90 | 63.40 197 | 92.91 41 | 93.34 107 |
|
TSAR-MVS + MP. | | | 88.11 12 | 88.64 10 | 86.54 48 | 91.73 85 | 68.04 63 | 90.36 175 | 93.55 57 | 82.89 14 | 91.29 2 | 92.89 89 | 72.27 12 | 96.03 102 | 87.99 22 | 94.77 15 | 95.54 33 |
|
API-MVS | | | 82.28 83 | 80.53 94 | 87.54 24 | 96.13 10 | 70.59 19 | 93.63 61 | 91.04 161 | 65.72 237 | 75.45 110 | 92.83 90 | 56.11 142 | 98.89 10 | 64.10 193 | 89.75 76 | 93.15 114 |
|
Effi-MVS+ | | | 83.82 64 | 82.76 69 | 86.99 38 | 89.56 133 | 69.40 36 | 91.35 147 | 86.12 277 | 72.59 138 | 83.22 47 | 92.81 91 | 59.60 105 | 96.01 104 | 81.76 62 | 87.80 86 | 95.56 32 |
|
TAPA-MVS | | 70.22 12 | 74.94 205 | 73.53 202 | 79.17 232 | 90.40 112 | 52.07 297 | 89.19 200 | 89.61 209 | 62.69 264 | 70.07 162 | 92.67 92 | 48.89 222 | 94.32 162 | 38.26 305 | 79.97 136 | 91.12 151 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
原ACMM1 | | | | | 84.42 111 | 93.21 49 | 64.27 167 | | 93.40 70 | 65.39 238 | 79.51 72 | 92.50 93 | 58.11 117 | 96.69 87 | 65.27 184 | 93.96 25 | 92.32 134 |
|
3Dnovator+ | | 73.60 7 | 82.10 88 | 80.60 93 | 86.60 45 | 90.89 107 | 66.80 101 | 95.20 23 | 93.44 68 | 74.05 110 | 67.42 204 | 92.49 94 | 49.46 214 | 97.65 39 | 70.80 137 | 91.68 56 | 95.33 37 |
|
3Dnovator | | 73.91 6 | 82.69 79 | 80.82 89 | 88.31 14 | 89.57 132 | 71.26 13 | 92.60 93 | 94.39 38 | 78.84 48 | 67.89 199 | 92.48 95 | 48.42 223 | 98.52 17 | 68.80 154 | 94.40 21 | 95.15 48 |
|
test222 | | | | | | 89.77 123 | 61.60 213 | 89.55 193 | 89.42 214 | 56.83 297 | 77.28 95 | 92.43 96 | 52.76 188 | | | 91.14 65 | 93.09 116 |
|
sss | | | 82.71 78 | 82.38 73 | 83.73 123 | 89.25 138 | 59.58 244 | 92.24 102 | 94.89 21 | 77.96 57 | 79.86 68 | 92.38 97 | 56.70 134 | 97.05 64 | 77.26 92 | 80.86 134 | 94.55 68 |
|
AdaColmap | | | 78.94 134 | 77.00 147 | 84.76 101 | 96.34 6 | 65.86 131 | 92.66 92 | 87.97 251 | 62.18 267 | 70.56 153 | 92.37 98 | 43.53 255 | 97.35 51 | 64.50 190 | 82.86 122 | 91.05 152 |
|
VDD-MVS | | | 83.06 73 | 81.81 79 | 86.81 39 | 90.86 108 | 67.70 69 | 95.40 19 | 91.50 144 | 75.46 86 | 81.78 54 | 92.34 99 | 40.09 268 | 97.13 63 | 86.85 32 | 82.04 128 | 95.60 31 |
|
CLD-MVS | | | 82.73 76 | 82.35 74 | 83.86 119 | 87.90 168 | 67.65 71 | 95.45 18 | 92.18 121 | 85.06 8 | 72.58 134 | 92.27 100 | 52.46 192 | 95.78 109 | 84.18 46 | 79.06 143 | 88.16 186 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
OMC-MVS | | | 78.67 143 | 77.91 131 | 80.95 196 | 85.76 197 | 57.40 267 | 88.49 211 | 88.67 236 | 73.85 116 | 72.43 139 | 92.10 101 | 49.29 216 | 94.55 148 | 72.73 117 | 77.89 152 | 90.91 153 |
|
OpenMVS | | 70.45 11 | 78.54 145 | 75.92 159 | 86.41 56 | 85.93 196 | 71.68 11 | 92.74 86 | 92.51 107 | 66.49 230 | 64.56 228 | 91.96 102 | 43.88 254 | 98.10 27 | 54.61 241 | 90.65 69 | 89.44 168 |
|
Vis-MVSNet (Re-imp) | | | 79.24 129 | 79.57 104 | 78.24 250 | 88.46 156 | 52.29 296 | 90.41 174 | 89.12 225 | 74.24 106 | 69.13 178 | 91.91 103 | 65.77 47 | 90.09 272 | 59.00 228 | 88.09 84 | 92.33 133 |
|
gm-plane-assit | | | | | | 88.42 158 | 67.04 87 | | | 78.62 52 | | 91.83 104 | | 97.37 49 | 76.57 95 | | |
|
DWT-MVSNet_test | | | 83.95 61 | 82.80 68 | 87.41 26 | 92.90 56 | 70.07 25 | 89.12 202 | 94.42 35 | 82.15 20 | 77.64 89 | 91.77 105 | 70.81 17 | 96.22 94 | 65.03 185 | 81.36 130 | 95.94 25 |
|
Vis-MVSNet | | | 80.92 101 | 79.98 99 | 83.74 121 | 88.48 155 | 61.80 211 | 93.44 68 | 88.26 247 | 73.96 114 | 77.73 87 | 91.76 106 | 49.94 210 | 94.76 140 | 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 |
QAPM | | | 79.95 117 | 77.39 140 | 87.64 21 | 89.63 131 | 71.41 12 | 93.30 70 | 93.70 51 | 65.34 240 | 67.39 206 | 91.75 107 | 47.83 229 | 98.96 5 | 57.71 233 | 89.81 74 | 92.54 130 |
|
IS-MVSNet | | | 80.14 112 | 79.41 109 | 82.33 157 | 87.91 167 | 60.08 238 | 91.97 115 | 88.27 246 | 72.90 135 | 71.44 151 | 91.73 108 | 61.44 88 | 93.66 194 | 62.47 210 | 86.53 97 | 93.24 111 |
|
PatchFormer-LS_test | | | 83.14 71 | 81.81 79 | 87.12 33 | 92.34 66 | 69.92 28 | 88.64 209 | 93.32 73 | 82.07 23 | 74.87 114 | 91.62 109 | 68.91 19 | 96.08 101 | 66.07 176 | 78.45 150 | 95.37 35 |
|
TR-MVS | | | 78.77 139 | 77.37 141 | 82.95 135 | 90.49 110 | 60.88 219 | 93.67 60 | 90.07 193 | 70.08 193 | 74.51 116 | 91.37 110 | 45.69 244 | 95.70 117 | 60.12 222 | 80.32 135 | 92.29 135 |
|
EPNet_dtu | | | 78.80 137 | 79.26 113 | 77.43 260 | 88.06 164 | 49.71 309 | 91.96 116 | 91.95 128 | 77.67 61 | 76.56 102 | 91.28 111 | 58.51 114 | 90.20 267 | 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 |
BH-RMVSNet | | | 79.46 127 | 77.65 133 | 84.89 98 | 91.68 87 | 65.66 135 | 93.55 64 | 88.09 248 | 72.93 134 | 73.37 125 | 91.12 112 | 46.20 243 | 96.12 98 | 56.28 237 | 85.61 104 | 92.91 122 |
|
VDDNet | | | 80.50 105 | 78.26 124 | 87.21 30 | 86.19 189 | 69.79 30 | 94.48 36 | 91.31 151 | 60.42 278 | 79.34 73 | 90.91 113 | 38.48 274 | 96.56 91 | 82.16 58 | 81.05 132 | 95.27 42 |
|
GG-mvs-BLEND | | | | | 86.53 50 | 91.91 79 | 69.67 34 | 75.02 313 | 94.75 26 | | 78.67 83 | 90.85 114 | 77.91 2 | 94.56 147 | 72.25 121 | 93.74 31 | 95.36 36 |
|
mvs-test1 | | | 78.74 140 | 77.95 129 | 81.14 189 | 83.22 225 | 57.13 269 | 93.96 50 | 87.78 252 | 75.42 87 | 72.68 131 | 90.80 115 | 45.08 248 | 94.54 149 | 75.08 104 | 77.49 160 | 91.74 142 |
|
CNLPA | | | 74.31 213 | 72.30 216 | 80.32 201 | 91.49 92 | 61.66 212 | 90.85 162 | 80.72 310 | 56.67 298 | 63.85 235 | 90.64 116 | 46.75 236 | 90.84 259 | 53.79 245 | 75.99 170 | 88.47 179 |
|
PCF-MVS | | 73.15 9 | 79.29 128 | 77.63 134 | 84.29 113 | 86.06 191 | 65.96 129 | 87.03 241 | 91.10 158 | 69.86 195 | 69.79 168 | 90.64 116 | 57.54 122 | 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 |
114514_t | | | 79.17 130 | 77.67 132 | 83.68 125 | 95.32 16 | 65.53 138 | 92.85 84 | 91.60 140 | 63.49 257 | 67.92 198 | 90.63 118 | 46.65 238 | 95.72 116 | 67.01 166 | 83.54 119 | 89.79 163 |
|
PLC | | 68.80 14 | 75.23 198 | 73.68 195 | 79.86 213 | 92.93 55 | 58.68 256 | 90.64 169 | 88.30 244 | 60.90 275 | 64.43 231 | 90.53 119 | 42.38 259 | 94.57 146 | 56.52 235 | 76.54 167 | 86.33 221 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
tfpn_ndepth | | | 76.45 181 | 75.22 175 | 80.14 204 | 90.97 104 | 58.92 252 | 90.11 180 | 93.24 77 | 65.96 234 | 67.37 207 | 90.52 120 | 66.67 37 | 92.29 230 | 37.71 306 | 74.44 179 | 89.21 169 |
|
PVSNet | | 73.49 8 | 80.05 114 | 78.63 119 | 84.31 112 | 90.92 106 | 64.97 151 | 92.47 98 | 91.05 160 | 79.18 41 | 72.43 139 | 90.51 121 | 37.05 290 | 94.06 177 | 68.06 156 | 86.00 101 | 93.90 98 |
|
EPP-MVSNet | | | 81.79 91 | 81.52 82 | 82.61 144 | 88.77 150 | 60.21 234 | 93.02 78 | 93.66 53 | 68.52 211 | 72.90 129 | 90.39 122 | 72.19 13 | 94.96 134 | 74.93 107 | 79.29 142 | 92.67 126 |
|
NP-MVS | | | | | | 87.41 174 | 63.04 189 | | | | | 90.30 123 | | | | | |
|
HQP-MVS | | | 81.14 97 | 80.64 92 | 82.64 143 | 87.54 171 | 63.66 180 | 94.06 45 | 91.70 137 | 79.80 33 | 74.18 117 | 90.30 123 | 51.63 199 | 95.61 118 | 77.63 90 | 78.90 144 | 88.63 175 |
|
BH-w/o | | | 80.49 106 | 79.30 112 | 84.05 117 | 90.83 109 | 64.36 163 | 93.60 62 | 89.42 214 | 74.35 105 | 69.09 179 | 90.15 125 | 55.23 150 | 95.61 118 | 64.61 188 | 86.43 99 | 92.17 138 |
|
EI-MVSNet | | | 78.97 133 | 78.22 125 | 81.25 183 | 85.33 198 | 62.73 198 | 89.53 195 | 93.21 78 | 72.39 143 | 72.14 142 | 90.13 126 | 60.99 89 | 94.72 143 | 67.73 160 | 72.49 194 | 86.29 222 |
|
CVMVSNet | | | 74.04 215 | 74.27 186 | 73.33 286 | 85.33 198 | 43.94 323 | 89.53 195 | 88.39 242 | 54.33 304 | 70.37 158 | 90.13 126 | 49.17 218 | 84.05 308 | 61.83 214 | 79.36 140 | 91.99 139 |
|
XVG-OURS-SEG-HR | | | 74.70 211 | 73.08 207 | 79.57 219 | 78.25 291 | 57.33 268 | 80.49 289 | 87.32 258 | 63.22 260 | 68.76 185 | 90.12 128 | 44.89 251 | 91.59 249 | 70.55 140 | 74.09 182 | 89.79 163 |
|
conf0.01 | | | 74.95 203 | 73.61 196 | 78.96 236 | 89.65 125 | 56.94 272 | 87.72 226 | 93.45 61 | 65.14 242 | 65.68 215 | 89.99 129 | 65.09 52 | 91.67 242 | 35.16 313 | 70.61 205 | 88.27 183 |
|
conf0.002 | | | 74.95 203 | 73.61 196 | 78.96 236 | 89.65 125 | 56.94 272 | 87.72 226 | 93.45 61 | 65.14 242 | 65.68 215 | 89.99 129 | 65.09 52 | 91.67 242 | 35.16 313 | 70.61 205 | 88.27 183 |
|
thresconf0.02 | | | 74.92 206 | 73.61 196 | 78.85 239 | 89.65 125 | 56.94 272 | 87.72 226 | 93.45 61 | 65.14 242 | 65.68 215 | 89.99 129 | 65.09 52 | 91.67 242 | 35.16 313 | 70.61 205 | 87.94 189 |
|
tfpn_n400 | | | 74.92 206 | 73.61 196 | 78.85 239 | 89.65 125 | 56.94 272 | 87.72 226 | 93.45 61 | 65.14 242 | 65.68 215 | 89.99 129 | 65.09 52 | 91.67 242 | 35.16 313 | 70.61 205 | 87.94 189 |
|
tfpnconf | | | 74.92 206 | 73.61 196 | 78.85 239 | 89.65 125 | 56.94 272 | 87.72 226 | 93.45 61 | 65.14 242 | 65.68 215 | 89.99 129 | 65.09 52 | 91.67 242 | 35.16 313 | 70.61 205 | 87.94 189 |
|
tfpnview11 | | | 74.92 206 | 73.61 196 | 78.85 239 | 89.65 125 | 56.94 272 | 87.72 226 | 93.45 61 | 65.14 242 | 65.68 215 | 89.99 129 | 65.09 52 | 91.67 242 | 35.16 313 | 70.61 205 | 87.94 189 |
|
OPM-MVS | | | 79.00 132 | 78.09 126 | 81.73 175 | 83.52 223 | 63.83 172 | 91.64 138 | 90.30 183 | 76.36 79 | 71.97 144 | 89.93 135 | 46.30 242 | 95.17 131 | 75.10 103 | 77.70 154 | 86.19 224 |
|
PVSNet_Blended_VisFu | | | 83.97 60 | 83.50 55 | 85.39 86 | 90.02 118 | 66.59 113 | 93.77 59 | 91.73 134 | 77.43 66 | 77.08 99 | 89.81 136 | 63.77 70 | 96.97 74 | 79.67 74 | 88.21 83 | 92.60 128 |
|
CDS-MVSNet | | | 81.43 94 | 80.74 90 | 83.52 128 | 86.26 188 | 64.45 157 | 92.09 107 | 90.65 171 | 75.83 83 | 73.95 123 | 89.81 136 | 63.97 66 | 92.91 210 | 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 |
tfpn1000 | | | 75.25 197 | 74.00 191 | 79.03 235 | 90.30 114 | 57.56 266 | 88.55 210 | 93.36 72 | 64.14 254 | 65.17 223 | 89.76 138 | 67.06 34 | 91.46 256 | 34.54 321 | 73.09 189 | 88.06 188 |
|
XVG-OURS | | | 74.25 214 | 72.46 215 | 79.63 217 | 78.45 290 | 57.59 265 | 80.33 291 | 87.39 255 | 63.86 256 | 68.76 185 | 89.62 139 | 40.50 267 | 91.72 241 | 69.00 151 | 74.25 180 | 89.58 166 |
|
UA-Net | | | 80.02 115 | 79.65 103 | 81.11 190 | 89.33 136 | 57.72 262 | 86.33 251 | 89.00 231 | 77.44 65 | 81.01 58 | 89.15 140 | 59.33 107 | 95.90 105 | 61.01 217 | 84.28 116 | 89.73 165 |
|
HQP_MVS | | | 80.34 108 | 79.75 102 | 82.12 167 | 86.94 179 | 62.42 200 | 93.13 74 | 91.31 151 | 78.81 49 | 72.53 135 | 89.14 141 | 50.66 203 | 95.55 122 | 76.74 93 | 78.53 148 | 88.39 180 |
|
plane_prior4 | | | | | | | | | | | | 89.14 141 | | | | | |
|
thres200 | | | 79.66 122 | 78.33 122 | 83.66 127 | 92.54 64 | 65.82 134 | 93.06 76 | 96.31 8 | 74.90 97 | 73.30 126 | 88.66 143 | 59.67 104 | 95.61 118 | 47.84 266 | 78.67 147 | 89.56 167 |
|
BH-untuned | | | 78.68 141 | 77.08 143 | 83.48 130 | 89.84 122 | 63.74 175 | 92.70 88 | 88.59 239 | 71.57 169 | 66.83 212 | 88.65 144 | 51.75 197 | 95.39 125 | 59.03 227 | 84.77 108 | 91.32 149 |
|
TAMVS | | | 80.37 107 | 79.45 108 | 83.13 134 | 85.14 201 | 63.37 183 | 91.23 152 | 90.76 167 | 74.81 98 | 72.65 132 | 88.49 145 | 60.63 96 | 92.95 206 | 69.41 148 | 81.95 129 | 93.08 117 |
|
LPG-MVS_test | | | 75.82 190 | 74.58 181 | 79.56 220 | 84.31 213 | 59.37 247 | 90.44 172 | 89.73 205 | 69.49 197 | 64.86 225 | 88.42 146 | 38.65 272 | 94.30 163 | 72.56 118 | 72.76 191 | 85.01 249 |
|
LGP-MVS_train | | | | | 79.56 220 | 84.31 213 | 59.37 247 | | 89.73 205 | 69.49 197 | 64.86 225 | 88.42 146 | 38.65 272 | 94.30 163 | 72.56 118 | 72.76 191 | 85.01 249 |
|
VPNet | | | 78.82 136 | 77.53 136 | 82.70 140 | 84.52 208 | 66.44 119 | 93.93 53 | 92.23 113 | 80.46 30 | 72.60 133 | 88.38 148 | 49.18 217 | 93.13 202 | 72.47 120 | 63.97 259 | 88.55 177 |
|
FIs | | | 79.47 126 | 79.41 109 | 79.67 216 | 85.95 193 | 59.40 246 | 91.68 135 | 93.94 43 | 78.06 56 | 68.96 182 | 88.28 149 | 66.61 38 | 91.77 240 | 66.20 175 | 74.99 177 | 87.82 193 |
|
CHOSEN 1792x2688 | | | 84.98 48 | 83.45 57 | 89.57 6 | 89.94 120 | 75.14 4 | 92.07 109 | 92.32 110 | 81.87 25 | 75.68 105 | 88.27 150 | 60.18 100 | 98.60 16 | 80.46 72 | 90.27 73 | 94.96 57 |
|
tfpn200view9 | | | 78.79 138 | 77.43 138 | 82.88 136 | 92.21 72 | 64.49 154 | 92.05 110 | 96.28 9 | 73.48 125 | 71.75 147 | 88.26 151 | 60.07 101 | 95.32 127 | 45.16 275 | 77.58 156 | 88.83 171 |
|
Fast-Effi-MVS+ | | | 81.14 97 | 80.01 97 | 84.51 110 | 90.24 116 | 65.86 131 | 94.12 42 | 89.15 224 | 73.81 118 | 75.37 111 | 88.26 151 | 57.26 123 | 94.53 150 | 66.97 167 | 84.92 106 | 93.15 114 |
|
thres400 | | | 78.68 141 | 77.43 138 | 82.43 150 | 92.21 72 | 64.49 154 | 92.05 110 | 96.28 9 | 73.48 125 | 71.75 147 | 88.26 151 | 60.07 101 | 95.32 127 | 45.16 275 | 77.58 156 | 87.48 197 |
|
nrg030 | | | 80.93 100 | 79.86 100 | 84.13 115 | 83.69 220 | 68.83 45 | 93.23 72 | 91.20 154 | 75.55 85 | 75.06 113 | 88.22 154 | 63.04 80 | 94.74 142 | 81.88 61 | 66.88 233 | 88.82 173 |
|
F-COLMAP | | | 70.66 244 | 68.44 243 | 77.32 262 | 86.37 187 | 55.91 281 | 88.00 218 | 86.32 272 | 56.94 296 | 57.28 272 | 88.07 155 | 33.58 299 | 92.49 223 | 51.02 254 | 68.37 224 | 83.55 260 |
|
HY-MVS | | 76.49 5 | 84.28 56 | 83.36 62 | 87.02 37 | 92.22 71 | 67.74 68 | 84.65 258 | 94.50 32 | 79.15 42 | 82.23 51 | 87.93 156 | 66.88 35 | 96.94 77 | 80.53 71 | 82.20 127 | 96.39 14 |
|
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 |
|
thres600view7 | | | 78.00 152 | 76.66 150 | 82.03 172 | 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 | 87.48 197 |
|
test0.0.03 1 | | | 72.76 225 | 72.71 211 | 72.88 290 | 80.25 268 | 47.99 314 | 91.22 153 | 89.45 212 | 71.51 172 | 62.51 247 | 87.66 160 | 53.83 175 | 85.06 304 | 50.16 257 | 67.84 230 | 85.58 241 |
|
FC-MVSNet-test | | | 77.99 153 | 78.08 127 | 77.70 255 | 84.89 204 | 55.51 283 | 90.27 177 | 93.75 50 | 76.87 70 | 66.80 213 | 87.59 161 | 65.71 48 | 90.23 266 | 62.89 205 | 73.94 183 | 87.37 204 |
|
TESTMET0.1,1 | | | 82.41 81 | 81.98 77 | 83.72 124 | 88.08 163 | 63.74 175 | 92.70 88 | 93.77 48 | 79.30 38 | 77.61 91 | 87.57 162 | 58.19 116 | 94.08 176 | 73.91 111 | 86.68 94 | 93.33 109 |
|
LS3D | | | 69.17 257 | 66.40 257 | 77.50 258 | 91.92 78 | 56.12 280 | 85.12 255 | 80.37 311 | 46.96 320 | 56.50 275 | 87.51 163 | 37.25 285 | 93.71 192 | 32.52 328 | 79.40 139 | 82.68 276 |
|
Test_1112_low_res | | | 79.56 125 | 78.60 120 | 82.43 150 | 88.24 161 | 60.39 230 | 92.09 107 | 87.99 250 | 72.10 153 | 71.84 145 | 87.42 164 | 64.62 62 | 93.04 203 | 65.80 180 | 77.30 163 | 93.85 99 |
|
view600 | | | 76.93 170 | 75.50 170 | 81.23 184 | 91.44 93 | 62.00 206 | 89.94 185 | 96.56 1 | 70.68 182 | 68.54 189 | 87.31 165 | 60.79 91 | 94.19 168 | 38.90 300 | 75.31 173 | 87.48 197 |
|
view800 | | | 76.93 170 | 75.50 170 | 81.23 184 | 91.44 93 | 62.00 206 | 89.94 185 | 96.56 1 | 70.68 182 | 68.54 189 | 87.31 165 | 60.79 91 | 94.19 168 | 38.90 300 | 75.31 173 | 87.48 197 |
|
conf0.05thres1000 | | | 76.93 170 | 75.50 170 | 81.23 184 | 91.44 93 | 62.00 206 | 89.94 185 | 96.56 1 | 70.68 182 | 68.54 189 | 87.31 165 | 60.79 91 | 94.19 168 | 38.90 300 | 75.31 173 | 87.48 197 |
|
tfpn | | | 76.93 170 | 75.50 170 | 81.23 184 | 91.44 93 | 62.00 206 | 89.94 185 | 96.56 1 | 70.68 182 | 68.54 189 | 87.31 165 | 60.79 91 | 94.19 168 | 38.90 300 | 75.31 173 | 87.48 197 |
|
ACMP | | 71.68 10 | 75.58 194 | 74.23 187 | 79.62 218 | 84.97 203 | 59.64 242 | 90.80 164 | 89.07 228 | 70.39 190 | 62.95 242 | 87.30 169 | 38.28 275 | 93.87 188 | 72.89 114 | 71.45 201 | 85.36 246 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
CHOSEN 280x420 | | | 77.35 163 | 76.95 148 | 78.55 244 | 87.07 178 | 62.68 199 | 69.71 322 | 82.95 303 | 68.80 205 | 71.48 150 | 87.27 170 | 66.03 43 | 84.00 311 | 76.47 96 | 82.81 124 | 88.95 170 |
|
test-LLR | | | 80.10 113 | 79.56 105 | 81.72 176 | 86.93 181 | 61.17 216 | 92.70 88 | 91.54 141 | 71.51 172 | 75.62 106 | 86.94 171 | 53.83 175 | 92.38 226 | 72.21 122 | 84.76 109 | 91.60 143 |
|
test-mter | | | 79.96 116 | 79.38 111 | 81.72 176 | 86.93 181 | 61.17 216 | 92.70 88 | 91.54 141 | 73.85 116 | 75.62 106 | 86.94 171 | 49.84 212 | 92.38 226 | 72.21 122 | 84.76 109 | 91.60 143 |
|
UniMVSNet_NR-MVSNet | | | 78.15 151 | 77.55 135 | 79.98 209 | 84.46 210 | 60.26 232 | 92.25 101 | 93.20 80 | 77.50 64 | 68.88 183 | 86.61 173 | 66.10 42 | 92.13 233 | 66.38 172 | 62.55 262 | 87.54 195 |
|
MVS_Test | | | 84.16 58 | 83.20 63 | 87.05 36 | 91.56 89 | 69.82 29 | 89.99 184 | 92.05 123 | 77.77 59 | 82.84 49 | 86.57 174 | 63.93 67 | 96.09 99 | 74.91 108 | 89.18 77 | 95.25 45 |
|
DU-MVS | | | 76.86 174 | 75.84 160 | 79.91 211 | 82.96 229 | 60.26 232 | 91.26 151 | 91.54 141 | 76.46 78 | 68.88 183 | 86.35 175 | 56.16 140 | 92.13 233 | 66.38 172 | 62.55 262 | 87.35 206 |
|
NR-MVSNet | | | 76.05 186 | 74.59 180 | 80.44 199 | 82.96 229 | 62.18 205 | 90.83 163 | 91.73 134 | 77.12 67 | 60.96 250 | 86.35 175 | 59.28 108 | 91.80 239 | 60.74 218 | 61.34 275 | 87.35 206 |
|
UGNet | | | 79.87 118 | 78.68 118 | 83.45 131 | 89.96 119 | 61.51 214 | 92.13 104 | 90.79 165 | 76.83 72 | 78.85 80 | 86.33 177 | 38.16 276 | 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 |
TranMVSNet+NR-MVSNet | | | 75.86 189 | 74.52 183 | 79.89 212 | 82.44 232 | 60.64 227 | 91.37 146 | 91.37 150 | 76.63 75 | 67.65 202 | 86.21 178 | 52.37 193 | 91.55 250 | 61.84 213 | 60.81 278 | 87.48 197 |
|
cascas | | | 78.18 150 | 75.77 161 | 85.41 85 | 87.14 177 | 69.11 39 | 92.96 79 | 91.15 156 | 66.71 228 | 70.47 154 | 86.07 179 | 37.49 284 | 96.48 92 | 70.15 142 | 79.80 137 | 90.65 155 |
|
HyFIR lowres test | | | 81.03 99 | 79.56 105 | 85.43 84 | 87.81 169 | 68.11 62 | 90.18 179 | 90.01 196 | 70.65 186 | 72.95 128 | 86.06 180 | 63.61 72 | 94.50 151 | 75.01 106 | 79.75 138 | 93.67 100 |
|
ACMM | | 69.62 13 | 74.34 212 | 72.73 210 | 79.17 232 | 84.25 215 | 57.87 260 | 90.36 175 | 89.93 198 | 63.17 261 | 65.64 221 | 86.04 181 | 37.79 282 | 94.10 174 | 65.89 178 | 71.52 200 | 85.55 243 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
XXY-MVS | | | 77.94 154 | 76.44 153 | 82.43 150 | 82.60 231 | 64.44 158 | 92.01 112 | 91.83 132 | 73.59 123 | 70.00 164 | 85.82 182 | 54.43 169 | 94.76 140 | 69.63 145 | 68.02 227 | 88.10 187 |
|
IB-MVS | | 77.80 4 | 82.18 84 | 80.46 95 | 87.35 28 | 89.14 142 | 70.28 22 | 95.59 17 | 95.17 16 | 78.85 47 | 70.19 161 | 85.82 182 | 70.66 18 | 97.67 35 | 72.19 124 | 66.52 236 | 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 |
MVSTER | | | 82.47 80 | 82.05 75 | 83.74 121 | 92.68 62 | 69.01 41 | 91.90 123 | 93.21 78 | 79.83 32 | 72.14 142 | 85.71 184 | 74.72 8 | 94.72 143 | 75.72 98 | 72.49 194 | 87.50 196 |
|
WR-MVS | | | 76.76 177 | 75.74 162 | 79.82 214 | 84.60 206 | 62.27 204 | 92.60 93 | 92.51 107 | 76.06 80 | 67.87 200 | 85.34 185 | 56.76 131 | 90.24 265 | 62.20 211 | 63.69 261 | 86.94 214 |
|
diffmvs | | | 80.18 110 | 78.55 121 | 85.07 94 | 88.56 152 | 66.93 91 | 86.70 249 | 88.62 238 | 70.42 188 | 78.69 82 | 85.26 186 | 56.93 130 | 94.77 139 | 68.68 155 | 83.09 120 | 93.51 104 |
|
DP-MVS | | | 69.90 250 | 66.48 256 | 80.14 204 | 95.36 15 | 62.93 191 | 89.56 192 | 76.11 318 | 50.27 313 | 57.69 270 | 85.23 187 | 39.68 269 | 95.73 112 | 33.35 323 | 71.05 204 | 81.78 281 |
|
PVSNet_BlendedMVS | | | 83.38 68 | 83.43 58 | 83.22 132 | 93.76 37 | 67.53 74 | 94.06 45 | 93.61 54 | 79.13 43 | 81.00 59 | 85.14 188 | 63.19 77 | 97.29 54 | 87.08 29 | 73.91 184 | 84.83 251 |
|
ab-mvs | | | 80.18 110 | 78.31 123 | 85.80 71 | 88.44 157 | 65.49 139 | 83.00 273 | 92.67 100 | 71.82 161 | 77.36 94 | 85.01 189 | 54.50 166 | 96.59 88 | 76.35 97 | 75.63 171 | 95.32 39 |
|
VPA-MVSNet | | | 79.03 131 | 78.00 128 | 82.11 170 | 85.95 193 | 64.48 156 | 93.22 73 | 94.66 29 | 75.05 95 | 74.04 122 | 84.95 190 | 52.17 194 | 93.52 196 | 74.90 109 | 67.04 232 | 88.32 182 |
|
Fast-Effi-MVS+-dtu | | | 75.04 200 | 73.37 205 | 80.07 207 | 80.86 244 | 59.52 245 | 91.20 155 | 85.38 284 | 71.90 155 | 65.20 222 | 84.84 191 | 41.46 264 | 92.97 205 | 66.50 171 | 72.96 190 | 87.73 194 |
|
UniMVSNet (Re) | | | 77.58 157 | 76.78 149 | 79.98 209 | 84.11 216 | 60.80 220 | 91.76 131 | 93.17 83 | 76.56 77 | 69.93 167 | 84.78 192 | 63.32 76 | 92.36 228 | 64.89 186 | 62.51 264 | 86.78 216 |
|
mvs_anonymous | | | 81.36 95 | 79.99 98 | 85.46 81 | 90.39 113 | 68.40 52 | 86.88 246 | 90.61 172 | 74.41 100 | 70.31 160 | 84.67 193 | 63.79 69 | 92.32 229 | 73.13 112 | 85.70 102 | 95.67 28 |
|
RPSCF | | | 64.24 283 | 61.98 284 | 71.01 302 | 76.10 303 | 45.00 320 | 75.83 312 | 75.94 320 | 46.94 321 | 58.96 261 | 84.59 194 | 31.40 309 | 82.00 324 | 47.76 267 | 60.33 281 | 86.04 232 |
|
PS-MVSNAJss | | | 77.26 167 | 76.31 154 | 80.13 206 | 80.64 254 | 59.16 250 | 90.63 171 | 91.06 159 | 72.80 136 | 68.58 188 | 84.57 195 | 53.55 179 | 93.96 184 | 72.97 113 | 71.96 197 | 87.27 209 |
|
MS-PatchMatch | | | 77.90 155 | 76.50 152 | 82.12 167 | 85.99 192 | 69.95 27 | 91.75 133 | 92.70 98 | 73.97 113 | 62.58 246 | 84.44 196 | 41.11 265 | 95.78 109 | 63.76 194 | 92.17 51 | 80.62 300 |
|
MSDG | | | 69.54 255 | 65.73 259 | 80.96 195 | 85.11 202 | 63.71 177 | 84.19 260 | 83.28 300 | 56.95 295 | 54.50 281 | 84.03 197 | 31.50 308 | 96.03 102 | 42.87 285 | 69.13 219 | 83.14 270 |
|
GA-MVS | | | 78.33 148 | 76.23 155 | 84.65 105 | 83.65 221 | 66.30 123 | 91.44 141 | 90.14 191 | 76.01 81 | 70.32 159 | 84.02 198 | 42.50 258 | 94.72 143 | 70.98 135 | 77.00 165 | 92.94 121 |
|
pmmvs4 | | | 73.92 217 | 71.81 220 | 80.25 203 | 79.17 282 | 65.24 141 | 87.43 236 | 87.26 260 | 67.64 223 | 63.46 238 | 83.91 199 | 48.96 221 | 91.53 254 | 62.94 204 | 65.49 242 | 83.96 256 |
|
pmmvs5 | | | 73.35 220 | 71.52 222 | 78.86 238 | 78.64 289 | 60.61 228 | 91.08 159 | 86.90 261 | 67.69 220 | 63.32 239 | 83.64 200 | 44.33 253 | 90.53 260 | 62.04 212 | 66.02 237 | 85.46 244 |
|
ITE_SJBPF | | | | | 70.43 303 | 74.44 308 | 47.06 318 | | 77.32 316 | 60.16 280 | 54.04 287 | 83.53 201 | 23.30 327 | 84.01 310 | 43.07 282 | 61.58 274 | 80.21 305 |
|
jajsoiax | | | 73.05 221 | 71.51 223 | 77.67 256 | 77.46 296 | 54.83 286 | 88.81 206 | 90.04 195 | 69.13 201 | 62.85 244 | 83.51 202 | 31.16 310 | 92.75 215 | 70.83 136 | 69.80 212 | 85.43 245 |
|
testgi | | | 64.48 282 | 62.87 279 | 69.31 305 | 71.24 316 | 40.62 330 | 85.49 254 | 79.92 312 | 65.36 239 | 54.18 286 | 83.49 203 | 23.74 326 | 84.55 305 | 41.60 289 | 60.79 279 | 82.77 273 |
|
v2v482 | | | 77.42 159 | 75.65 166 | 82.73 139 | 80.38 261 | 67.13 84 | 91.85 126 | 90.23 185 | 75.09 94 | 69.37 175 | 83.39 204 | 53.79 177 | 94.44 152 | 71.77 125 | 65.00 250 | 86.63 220 |
|
mvs_tets | | | 72.71 226 | 71.11 224 | 77.52 257 | 77.41 297 | 54.52 288 | 88.45 213 | 89.76 201 | 68.76 206 | 62.70 245 | 83.26 205 | 29.49 314 | 92.71 216 | 70.51 141 | 69.62 214 | 85.34 247 |
|
FMVSNet3 | | | 77.73 156 | 76.04 157 | 82.80 137 | 91.20 102 | 68.99 42 | 91.87 124 | 91.99 125 | 73.35 128 | 67.04 209 | 83.19 206 | 56.62 136 | 92.14 232 | 59.80 224 | 69.34 216 | 87.28 208 |
|
MVP-Stereo | | | 77.12 169 | 76.23 155 | 79.79 215 | 81.72 237 | 66.34 122 | 89.29 197 | 90.88 163 | 70.56 187 | 62.01 249 | 82.88 207 | 49.34 215 | 94.13 173 | 65.55 182 | 93.80 28 | 78.88 313 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
PatchMatch-RL | | | 72.06 229 | 69.98 228 | 78.28 247 | 89.51 135 | 55.70 282 | 83.49 265 | 83.39 299 | 61.24 274 | 63.72 236 | 82.76 208 | 34.77 297 | 93.03 204 | 53.37 249 | 77.59 155 | 86.12 226 |
|
CP-MVSNet | | | 70.50 247 | 69.91 230 | 72.26 295 | 80.71 248 | 51.00 303 | 87.23 239 | 90.30 183 | 67.84 219 | 59.64 255 | 82.69 209 | 50.23 208 | 82.30 322 | 51.28 253 | 59.28 282 | 83.46 264 |
|
v1neww | | | 77.39 160 | 75.71 163 | 82.44 147 | 80.69 249 | 66.83 93 | 91.94 120 | 90.18 188 | 74.19 107 | 69.60 169 | 82.51 210 | 54.99 159 | 94.44 152 | 71.68 127 | 65.60 239 | 86.05 229 |
|
v7new | | | 77.39 160 | 75.71 163 | 82.44 147 | 80.69 249 | 66.83 93 | 91.94 120 | 90.18 188 | 74.19 107 | 69.60 169 | 82.51 210 | 54.99 159 | 94.44 152 | 71.68 127 | 65.60 239 | 86.05 229 |
|
PEN-MVS | | | 69.46 256 | 68.56 241 | 72.17 297 | 79.27 280 | 49.71 309 | 86.90 245 | 89.24 220 | 67.24 227 | 59.08 259 | 82.51 210 | 47.23 234 | 83.54 314 | 48.42 264 | 57.12 290 | 83.25 267 |
|
v6 | | | 77.39 160 | 75.71 163 | 82.44 147 | 80.67 251 | 66.82 95 | 91.94 120 | 90.18 188 | 74.19 107 | 69.60 169 | 82.50 213 | 55.00 158 | 94.44 152 | 71.68 127 | 65.60 239 | 86.05 229 |
|
DI_MVS_plusplus_test | | | 79.78 121 | 77.50 137 | 86.62 44 | 80.90 243 | 69.46 35 | 90.69 167 | 91.97 127 | 77.00 68 | 59.07 260 | 82.34 214 | 46.82 235 | 95.88 106 | 82.14 59 | 86.59 96 | 94.53 72 |
|
PS-CasMVS | | | 69.86 251 | 69.13 237 | 72.07 298 | 80.35 262 | 50.57 305 | 87.02 242 | 89.75 202 | 67.27 226 | 59.19 258 | 82.28 215 | 46.58 239 | 82.24 323 | 50.69 255 | 59.02 285 | 83.39 266 |
|
FMVSNet2 | | | 76.07 185 | 74.01 190 | 82.26 163 | 88.85 146 | 67.66 70 | 91.33 148 | 91.61 139 | 70.84 179 | 65.98 214 | 82.25 216 | 48.03 225 | 92.00 237 | 58.46 230 | 68.73 222 | 87.10 210 |
|
v7 | | | 76.83 176 | 75.01 177 | 82.29 159 | 80.35 262 | 66.70 107 | 91.68 135 | 89.97 197 | 73.47 127 | 69.22 177 | 82.22 217 | 52.52 190 | 94.43 156 | 69.73 144 | 65.96 238 | 85.74 240 |
|
DTE-MVSNet | | | 68.46 262 | 67.33 252 | 71.87 301 | 77.94 294 | 49.00 312 | 86.16 253 | 88.58 240 | 66.36 231 | 58.19 265 | 82.21 218 | 46.36 240 | 83.87 312 | 44.97 279 | 55.17 297 | 82.73 274 |
|
CMPMVS | | 48.56 21 | 66.77 272 | 64.41 270 | 73.84 283 | 70.65 320 | 50.31 306 | 77.79 309 | 85.73 283 | 45.54 325 | 44.76 320 | 82.14 219 | 35.40 294 | 90.14 269 | 63.18 200 | 74.54 178 | 81.07 295 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
v1141 | | | 77.28 165 | 75.57 167 | 82.42 153 | 80.63 255 | 66.73 103 | 91.96 116 | 90.42 176 | 74.41 100 | 69.46 172 | 82.12 220 | 55.09 155 | 94.40 157 | 70.99 134 | 65.05 246 | 86.12 226 |
|
divwei89l23v2f112 | | | 77.28 165 | 75.57 167 | 82.42 153 | 80.62 256 | 66.72 105 | 91.96 116 | 90.42 176 | 74.41 100 | 69.46 172 | 82.12 220 | 55.11 154 | 94.40 157 | 71.00 132 | 65.04 247 | 86.12 226 |
|
v1 | | | 77.29 164 | 75.57 167 | 82.42 153 | 80.61 259 | 66.73 103 | 91.96 116 | 90.42 176 | 74.41 100 | 69.46 172 | 82.12 220 | 55.14 153 | 94.40 157 | 71.00 132 | 65.04 247 | 86.13 225 |
|
test_normal | | | 79.66 122 | 77.36 142 | 86.54 48 | 80.72 247 | 69.21 38 | 90.68 168 | 92.16 122 | 76.99 69 | 58.63 264 | 82.03 223 | 46.70 237 | 95.86 107 | 81.74 63 | 86.63 95 | 94.56 67 |
|
test_djsdf | | | 73.76 219 | 72.56 213 | 77.39 261 | 77.00 299 | 53.93 290 | 89.07 203 | 90.69 168 | 65.80 235 | 63.92 233 | 82.03 223 | 43.14 257 | 92.67 218 | 72.83 115 | 68.53 223 | 85.57 242 |
|
v1144 | | | 76.73 178 | 74.88 178 | 82.27 160 | 80.23 269 | 66.60 111 | 91.68 135 | 90.21 187 | 73.69 120 | 69.06 180 | 81.89 225 | 52.73 189 | 94.40 157 | 69.21 150 | 65.23 243 | 85.80 236 |
|
V42 | | | 76.46 180 | 74.55 182 | 82.19 165 | 79.14 283 | 67.82 66 | 90.26 178 | 89.42 214 | 73.75 119 | 68.63 187 | 81.89 225 | 51.31 201 | 94.09 175 | 71.69 126 | 64.84 251 | 84.66 252 |
|
pm-mvs1 | | | 72.89 223 | 71.09 225 | 78.26 249 | 79.10 285 | 57.62 264 | 90.80 164 | 89.30 217 | 67.66 221 | 62.91 243 | 81.78 227 | 49.11 220 | 92.95 206 | 60.29 221 | 58.89 287 | 84.22 255 |
|
IterMVS-LS | | | 76.49 179 | 75.18 176 | 80.43 200 | 84.49 209 | 62.74 197 | 90.64 169 | 88.80 234 | 72.40 142 | 65.16 224 | 81.72 228 | 60.98 90 | 92.27 231 | 67.74 159 | 64.65 254 | 86.29 222 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
CostFormer | | | 82.33 82 | 81.15 85 | 85.86 68 | 89.01 145 | 68.46 51 | 82.39 276 | 93.01 88 | 75.59 84 | 80.25 65 | 81.57 229 | 72.03 14 | 94.96 134 | 79.06 80 | 77.48 161 | 94.16 83 |
|
Effi-MVS+-dtu | | | 76.14 184 | 75.28 174 | 78.72 243 | 83.22 225 | 55.17 285 | 89.87 189 | 87.78 252 | 75.42 87 | 67.98 197 | 81.43 230 | 45.08 248 | 92.52 222 | 75.08 104 | 71.63 198 | 88.48 178 |
|
v1192 | | | 75.98 188 | 73.92 192 | 82.15 166 | 79.73 274 | 66.24 125 | 91.22 153 | 89.75 202 | 72.67 137 | 68.49 193 | 81.42 231 | 49.86 211 | 94.27 165 | 67.08 165 | 65.02 249 | 85.95 233 |
|
COLMAP_ROB | | 57.96 20 | 62.98 290 | 59.65 290 | 72.98 289 | 81.44 240 | 53.00 294 | 83.75 262 | 75.53 324 | 48.34 318 | 48.81 308 | 81.40 232 | 24.14 324 | 90.30 262 | 32.95 325 | 60.52 280 | 75.65 324 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
v144192 | | | 76.05 186 | 74.03 189 | 82.12 167 | 79.50 278 | 66.55 115 | 91.39 143 | 89.71 208 | 72.30 144 | 68.17 195 | 81.33 233 | 51.75 197 | 94.03 181 | 67.94 157 | 64.19 256 | 85.77 237 |
|
AllTest | | | 61.66 291 | 58.06 292 | 72.46 293 | 79.57 275 | 51.42 301 | 80.17 294 | 68.61 337 | 51.25 310 | 45.88 314 | 81.23 234 | 19.86 332 | 86.58 300 | 38.98 298 | 57.01 292 | 79.39 310 |
|
TestCases | | | | | 72.46 293 | 79.57 275 | 51.42 301 | | 68.61 337 | 51.25 310 | 45.88 314 | 81.23 234 | 19.86 332 | 86.58 300 | 38.98 298 | 57.01 292 | 79.39 310 |
|
v1921920 | | | 75.63 193 | 73.49 203 | 82.06 171 | 79.38 279 | 66.35 121 | 91.07 160 | 89.48 211 | 71.98 154 | 67.99 196 | 81.22 236 | 49.16 219 | 93.90 187 | 66.56 170 | 64.56 255 | 85.92 235 |
|
v1240 | | | 75.21 199 | 72.98 208 | 81.88 173 | 79.20 281 | 66.00 128 | 90.75 166 | 89.11 226 | 71.63 167 | 67.41 205 | 81.22 236 | 47.36 233 | 93.87 188 | 65.46 183 | 64.72 253 | 85.77 237 |
|
XVG-ACMP-BASELINE | | | 68.04 264 | 65.53 261 | 75.56 273 | 74.06 310 | 52.37 295 | 78.43 304 | 85.88 281 | 62.03 268 | 58.91 262 | 81.21 238 | 20.38 331 | 91.15 258 | 60.69 219 | 68.18 225 | 83.16 269 |
|
EU-MVSNet | | | 64.01 285 | 63.01 277 | 67.02 311 | 74.40 309 | 38.86 335 | 83.27 269 | 86.19 275 | 45.11 326 | 54.27 284 | 81.15 239 | 36.91 291 | 80.01 328 | 48.79 263 | 57.02 291 | 82.19 279 |
|
ACMH+ | | 65.35 16 | 67.65 266 | 64.55 267 | 76.96 265 | 84.59 207 | 57.10 270 | 88.08 217 | 80.79 309 | 58.59 289 | 53.00 294 | 81.09 240 | 26.63 322 | 92.95 206 | 46.51 270 | 61.69 273 | 80.82 297 |
|
v148 | | | 76.19 183 | 74.47 184 | 81.36 181 | 80.05 273 | 64.44 158 | 91.75 133 | 90.23 185 | 73.68 121 | 67.13 208 | 80.84 241 | 55.92 146 | 93.86 190 | 68.95 152 | 61.73 271 | 85.76 239 |
|
WR-MVS_H | | | 70.59 245 | 69.94 229 | 72.53 292 | 81.03 242 | 51.43 300 | 87.35 238 | 92.03 124 | 67.38 225 | 60.23 253 | 80.70 242 | 55.84 147 | 83.45 315 | 46.33 271 | 58.58 288 | 82.72 275 |
|
Baseline_NR-MVSNet | | | 73.99 216 | 72.83 209 | 77.48 259 | 80.78 245 | 59.29 249 | 91.79 128 | 84.55 288 | 68.85 204 | 68.99 181 | 80.70 242 | 56.16 140 | 92.04 236 | 62.67 208 | 60.98 277 | 81.11 294 |
|
PVSNet_0 | | 68.08 15 | 71.81 231 | 68.32 245 | 82.27 160 | 84.68 205 | 62.31 203 | 88.68 208 | 90.31 182 | 75.84 82 | 57.93 267 | 80.65 244 | 37.85 281 | 94.19 168 | 69.94 143 | 29.05 340 | 90.31 159 |
|
tpm2 | | | 79.80 120 | 77.95 129 | 85.34 88 | 88.28 160 | 68.26 58 | 81.56 284 | 91.42 147 | 70.11 192 | 77.59 92 | 80.50 245 | 67.40 31 | 94.26 167 | 67.34 163 | 77.35 162 | 93.51 104 |
|
TransMVSNet (Re) | | | 70.07 249 | 67.66 250 | 77.31 263 | 80.62 256 | 59.13 251 | 91.78 130 | 84.94 286 | 65.97 233 | 60.08 254 | 80.44 246 | 50.78 202 | 91.87 238 | 48.84 262 | 45.46 324 | 80.94 296 |
|
USDC | | | 67.43 270 | 64.51 268 | 76.19 270 | 77.94 294 | 55.29 284 | 78.38 305 | 85.00 285 | 73.17 129 | 48.36 309 | 80.37 247 | 21.23 330 | 92.48 224 | 52.15 251 | 64.02 258 | 80.81 298 |
|
LTVRE_ROB | | 59.60 19 | 66.27 274 | 63.54 273 | 74.45 279 | 84.00 218 | 51.55 299 | 67.08 329 | 83.53 296 | 58.78 287 | 54.94 279 | 80.31 248 | 34.54 298 | 93.23 201 | 40.64 294 | 68.03 226 | 78.58 316 |
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 |
v8 | | | 75.35 195 | 73.26 206 | 81.61 179 | 80.67 251 | 66.82 95 | 89.54 194 | 89.27 218 | 71.65 166 | 63.30 240 | 80.30 249 | 54.99 159 | 94.06 177 | 67.33 164 | 62.33 265 | 83.94 257 |
|
GBi-Net | | | 75.65 191 | 73.83 193 | 81.10 191 | 88.85 146 | 65.11 145 | 90.01 181 | 90.32 179 | 70.84 179 | 67.04 209 | 80.25 250 | 48.03 225 | 91.54 251 | 59.80 224 | 69.34 216 | 86.64 217 |
|
test1 | | | 75.65 191 | 73.83 193 | 81.10 191 | 88.85 146 | 65.11 145 | 90.01 181 | 90.32 179 | 70.84 179 | 67.04 209 | 80.25 250 | 48.03 225 | 91.54 251 | 59.80 224 | 69.34 216 | 86.64 217 |
|
FMVSNet1 | | | 72.71 226 | 69.91 230 | 81.10 191 | 83.60 222 | 65.11 145 | 90.01 181 | 90.32 179 | 63.92 255 | 63.56 237 | 80.25 250 | 36.35 292 | 91.54 251 | 54.46 242 | 66.75 234 | 86.64 217 |
|
LCM-MVSNet-Re | | | 72.93 222 | 71.84 219 | 76.18 271 | 88.49 154 | 48.02 313 | 80.07 296 | 70.17 335 | 73.96 114 | 52.25 296 | 80.09 253 | 49.98 209 | 88.24 291 | 67.35 162 | 84.23 117 | 92.28 136 |
|
v10 | | | 74.77 210 | 72.54 214 | 81.46 180 | 80.33 266 | 66.71 106 | 89.15 201 | 89.08 227 | 70.94 177 | 63.08 241 | 79.86 254 | 52.52 190 | 94.04 180 | 65.70 181 | 62.17 266 | 83.64 259 |
|
anonymousdsp | | | 71.14 239 | 69.37 234 | 76.45 268 | 72.95 311 | 54.71 287 | 84.19 260 | 88.88 233 | 61.92 270 | 62.15 248 | 79.77 255 | 38.14 277 | 91.44 257 | 68.90 153 | 67.45 231 | 83.21 268 |
|
tpm | | | 78.58 144 | 77.03 144 | 83.22 132 | 85.94 195 | 64.56 153 | 83.21 271 | 91.14 157 | 78.31 54 | 73.67 124 | 79.68 256 | 64.01 65 | 92.09 235 | 66.07 176 | 71.26 203 | 93.03 118 |
|
OurMVSNet-221017-0 | | | 64.68 280 | 62.17 283 | 72.21 296 | 76.08 304 | 47.35 317 | 80.67 288 | 81.02 308 | 56.19 299 | 51.60 298 | 79.66 257 | 27.05 321 | 88.56 288 | 53.60 247 | 53.63 302 | 80.71 299 |
|
Test4 | | | 76.45 181 | 73.45 204 | 85.45 83 | 76.07 305 | 67.61 72 | 88.38 214 | 90.83 164 | 76.71 74 | 53.06 293 | 79.65 258 | 31.61 307 | 94.35 161 | 78.47 83 | 86.22 100 | 94.40 76 |
|
tpmrst | | | 80.57 103 | 79.14 116 | 84.84 100 | 90.10 117 | 68.28 57 | 81.70 280 | 89.72 207 | 77.63 62 | 75.96 104 | 79.54 259 | 64.94 61 | 92.71 216 | 75.43 100 | 77.28 164 | 93.55 103 |
|
ACMH | | 63.93 17 | 68.62 259 | 64.81 265 | 80.03 208 | 85.22 200 | 63.25 184 | 87.72 226 | 84.66 287 | 60.83 276 | 51.57 299 | 79.43 260 | 27.29 320 | 94.96 134 | 41.76 288 | 64.84 251 | 81.88 280 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
tpmp4_e23 | | | 78.85 135 | 76.55 151 | 85.77 73 | 89.25 138 | 68.39 53 | 81.63 283 | 91.38 149 | 70.40 189 | 75.21 112 | 79.22 261 | 67.37 32 | 94.79 138 | 58.98 229 | 75.51 172 | 94.13 85 |
|
semantic-postprocess | | | | | 76.32 269 | 81.48 238 | 60.67 226 | | 85.99 279 | 66.17 232 | 59.50 256 | 78.88 262 | 45.51 246 | 83.65 313 | 62.58 209 | 61.93 267 | 84.63 254 |
|
IterMVS | | | 72.65 228 | 70.83 226 | 78.09 253 | 82.17 233 | 62.96 190 | 87.64 234 | 86.28 273 | 71.56 170 | 60.44 252 | 78.85 263 | 45.42 247 | 86.66 299 | 63.30 198 | 61.83 268 | 84.65 253 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
tfpnnormal | | | 70.10 248 | 67.36 251 | 78.32 246 | 83.45 224 | 60.97 218 | 88.85 205 | 92.77 96 | 64.85 249 | 60.83 251 | 78.53 264 | 43.52 256 | 93.48 197 | 31.73 329 | 61.70 272 | 80.52 301 |
|
v7n | | | 71.31 236 | 68.65 239 | 79.28 227 | 76.40 301 | 60.77 222 | 86.71 248 | 89.45 212 | 64.17 253 | 58.77 263 | 78.24 265 | 44.59 252 | 93.54 195 | 57.76 232 | 61.75 270 | 83.52 262 |
|
EPMVS | | | 78.49 146 | 75.98 158 | 86.02 64 | 91.21 101 | 69.68 33 | 80.23 293 | 91.20 154 | 75.25 93 | 72.48 137 | 78.11 266 | 54.65 165 | 93.69 193 | 57.66 234 | 83.04 121 | 94.69 63 |
|
pmmvs6 | | | 67.57 267 | 64.76 266 | 76.00 272 | 72.82 313 | 53.37 292 | 88.71 207 | 86.78 262 | 53.19 305 | 57.58 271 | 78.03 267 | 35.33 295 | 92.41 225 | 55.56 239 | 54.88 299 | 82.21 278 |
|
OpenMVS_ROB | | 61.12 18 | 66.39 273 | 62.92 278 | 76.80 267 | 76.51 300 | 57.77 261 | 89.22 198 | 83.41 298 | 55.48 302 | 53.86 289 | 77.84 268 | 26.28 323 | 93.95 185 | 34.90 320 | 68.76 221 | 78.68 315 |
|
EG-PatchMatch MVS | | | 68.55 260 | 65.41 262 | 77.96 254 | 78.69 288 | 62.93 191 | 89.86 190 | 89.17 222 | 60.55 277 | 50.27 304 | 77.73 269 | 22.60 328 | 94.06 177 | 47.18 269 | 72.65 193 | 76.88 321 |
|
SixPastTwentyTwo | | | 64.92 279 | 61.78 285 | 74.34 281 | 78.74 287 | 49.76 308 | 83.42 268 | 79.51 314 | 62.86 263 | 50.27 304 | 77.35 270 | 30.92 312 | 90.49 261 | 45.89 273 | 47.06 321 | 82.78 272 |
|
test20.03 | | | 63.83 286 | 62.65 280 | 67.38 310 | 70.58 321 | 39.94 331 | 86.57 250 | 84.17 290 | 63.29 259 | 51.86 297 | 77.30 271 | 37.09 289 | 82.47 320 | 38.87 304 | 54.13 301 | 79.73 308 |
|
Anonymous20231206 | | | 67.53 268 | 65.78 258 | 72.79 291 | 74.95 307 | 47.59 316 | 88.23 215 | 87.32 258 | 61.75 273 | 58.07 266 | 77.29 272 | 37.79 282 | 87.29 297 | 42.91 283 | 63.71 260 | 83.48 263 |
|
test_0402 | | | 64.54 281 | 61.09 286 | 74.92 276 | 84.10 217 | 60.75 223 | 87.95 219 | 79.71 313 | 52.03 308 | 52.41 295 | 77.20 273 | 32.21 305 | 91.64 248 | 23.14 339 | 61.03 276 | 72.36 328 |
|
dp | | | 75.01 201 | 72.09 218 | 83.76 120 | 89.28 137 | 66.22 126 | 79.96 298 | 89.75 202 | 71.16 175 | 67.80 201 | 77.19 274 | 51.81 196 | 92.54 221 | 50.39 256 | 71.44 202 | 92.51 131 |
|
v748 | | | 70.55 246 | 67.97 249 | 78.27 248 | 75.75 306 | 58.78 254 | 86.29 252 | 89.25 219 | 65.12 248 | 56.66 274 | 77.17 275 | 45.05 250 | 92.95 206 | 58.13 231 | 58.33 289 | 83.10 271 |
|
Patchmatch-test1 | | | 75.00 202 | 71.80 221 | 84.58 107 | 86.63 183 | 70.08 24 | 81.06 286 | 89.19 221 | 71.60 168 | 70.01 163 | 77.16 276 | 45.53 245 | 88.63 285 | 51.79 252 | 73.27 186 | 95.02 56 |
|
Patchmatch-test | | | 65.86 276 | 60.94 287 | 80.62 198 | 83.75 219 | 58.83 253 | 58.91 339 | 75.26 325 | 44.50 329 | 50.95 303 | 77.09 277 | 58.81 113 | 87.90 293 | 35.13 319 | 64.03 257 | 95.12 50 |
|
PatchmatchNet | | | 77.46 158 | 74.63 179 | 85.96 66 | 89.55 134 | 70.35 21 | 79.97 297 | 89.55 210 | 72.23 147 | 70.94 152 | 76.91 278 | 57.03 126 | 92.79 214 | 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. |
tpm cat1 | | | 75.30 196 | 72.21 217 | 84.58 107 | 88.52 153 | 67.77 67 | 78.16 308 | 88.02 249 | 61.88 271 | 68.45 194 | 76.37 279 | 60.65 95 | 94.03 181 | 53.77 246 | 74.11 181 | 91.93 140 |
|
TDRefinement | | | 55.28 306 | 51.58 307 | 66.39 313 | 59.53 338 | 46.15 319 | 76.23 311 | 72.80 329 | 44.60 328 | 42.49 326 | 76.28 280 | 15.29 337 | 82.39 321 | 33.20 324 | 43.75 326 | 70.62 332 |
|
V4 | | | 69.80 252 | 67.02 254 | 78.15 251 | 71.86 314 | 60.10 236 | 82.02 277 | 87.39 255 | 64.48 250 | 57.78 269 | 75.98 281 | 41.49 262 | 92.90 211 | 63.00 202 | 59.16 283 | 81.44 286 |
|
v52 | | | 69.80 252 | 67.01 255 | 78.15 251 | 71.84 315 | 60.10 236 | 82.02 277 | 87.39 255 | 64.48 250 | 57.80 268 | 75.97 282 | 41.47 263 | 92.90 211 | 63.00 202 | 59.13 284 | 81.45 285 |
|
MDTV_nov1_ep13 | | | | 72.61 212 | | 89.06 143 | 68.48 50 | 80.33 291 | 90.11 192 | 71.84 160 | 71.81 146 | 75.92 283 | 53.01 186 | 93.92 186 | 48.04 265 | 73.38 185 | |
|
TinyColmap | | | 60.32 294 | 56.42 300 | 72.00 299 | 78.78 286 | 53.18 293 | 78.36 306 | 75.64 321 | 52.30 307 | 41.59 330 | 75.82 284 | 14.76 339 | 88.35 290 | 35.84 310 | 54.71 300 | 74.46 325 |
|
LF4IMVS | | | 54.01 307 | 52.12 305 | 59.69 320 | 62.41 333 | 39.91 332 | 68.59 324 | 68.28 339 | 42.96 332 | 44.55 322 | 75.18 285 | 14.09 340 | 68.39 341 | 41.36 291 | 51.68 307 | 70.78 331 |
|
tpmvs | | | 72.88 224 | 69.76 232 | 82.22 164 | 90.98 103 | 67.05 86 | 78.22 307 | 88.30 244 | 63.10 262 | 64.35 232 | 74.98 286 | 55.09 155 | 94.27 165 | 43.25 281 | 69.57 215 | 85.34 247 |
|
MIMVSNet | | | 71.64 234 | 68.44 243 | 81.23 184 | 81.97 236 | 64.44 158 | 73.05 315 | 88.80 234 | 69.67 196 | 64.59 227 | 74.79 287 | 32.79 301 | 87.82 294 | 53.99 244 | 76.35 168 | 91.42 145 |
|
UnsupCasMVSNet_eth | | | 65.79 277 | 63.10 276 | 73.88 282 | 70.71 319 | 50.29 307 | 81.09 285 | 89.88 199 | 72.58 139 | 49.25 307 | 74.77 288 | 32.57 303 | 87.43 296 | 55.96 238 | 41.04 330 | 83.90 258 |
|
lessismore_v0 | | | | | 73.72 284 | 72.93 312 | 47.83 315 | | 61.72 345 | | 45.86 316 | 73.76 289 | 28.63 317 | 89.81 279 | 47.75 268 | 31.37 339 | 83.53 261 |
|
FMVSNet5 | | | 68.04 264 | 65.66 260 | 75.18 275 | 84.43 211 | 57.89 259 | 83.54 264 | 86.26 274 | 61.83 272 | 53.64 291 | 73.30 290 | 37.15 288 | 85.08 303 | 48.99 261 | 61.77 269 | 82.56 277 |
|
test2356 | | | 64.16 284 | 63.28 275 | 66.81 312 | 69.37 325 | 39.86 333 | 87.76 225 | 86.02 278 | 59.83 282 | 53.54 292 | 73.23 291 | 34.94 296 | 80.67 327 | 39.66 296 | 65.20 244 | 79.89 306 |
|
testus | | | 59.36 298 | 57.51 295 | 64.90 315 | 66.72 327 | 37.56 336 | 84.98 256 | 81.09 307 | 57.46 293 | 47.72 311 | 72.76 292 | 11.43 343 | 78.78 334 | 36.56 307 | 58.91 286 | 78.36 318 |
|
v18 | | | 71.94 230 | 69.43 233 | 79.50 222 | 80.74 246 | 66.82 95 | 88.16 216 | 86.66 263 | 68.95 203 | 55.55 276 | 72.66 293 | 55.03 157 | 90.15 268 | 64.78 187 | 52.30 304 | 81.54 282 |
|
v16 | | | 71.81 231 | 69.26 235 | 79.47 223 | 80.66 253 | 66.81 99 | 87.93 220 | 86.63 265 | 68.70 208 | 55.35 277 | 72.51 294 | 54.75 163 | 90.12 270 | 64.51 189 | 52.28 305 | 81.47 283 |
|
pmmvs-eth3d | | | 65.53 278 | 62.32 282 | 75.19 274 | 69.39 324 | 59.59 243 | 82.80 274 | 83.43 297 | 62.52 266 | 51.30 301 | 72.49 295 | 32.86 300 | 87.16 298 | 55.32 240 | 50.73 310 | 78.83 314 |
|
v17 | | | 71.77 233 | 69.20 236 | 79.46 224 | 80.62 256 | 66.81 99 | 87.93 220 | 86.63 265 | 68.71 207 | 55.25 278 | 72.49 295 | 54.72 164 | 90.11 271 | 64.50 190 | 51.97 306 | 81.47 283 |
|
MDA-MVSNet-bldmvs | | | 61.54 293 | 57.70 294 | 73.05 288 | 79.53 277 | 57.00 271 | 83.08 272 | 81.23 306 | 57.57 290 | 34.91 335 | 72.45 297 | 32.79 301 | 86.26 302 | 35.81 311 | 41.95 328 | 75.89 323 |
|
CR-MVSNet | | | 73.79 218 | 70.82 227 | 82.70 140 | 83.15 227 | 67.96 64 | 70.25 319 | 84.00 293 | 73.67 122 | 69.97 165 | 72.41 298 | 57.82 119 | 89.48 281 | 52.99 250 | 73.13 187 | 90.64 156 |
|
Patchmtry | | | 67.53 268 | 63.93 272 | 78.34 245 | 82.12 234 | 64.38 161 | 68.72 323 | 84.00 293 | 48.23 319 | 59.24 257 | 72.41 298 | 57.82 119 | 89.27 283 | 46.10 272 | 56.68 294 | 81.36 288 |
|
K. test v3 | | | 63.09 289 | 59.61 291 | 73.53 285 | 76.26 302 | 49.38 311 | 83.27 269 | 77.15 317 | 64.35 252 | 47.77 310 | 72.32 300 | 28.73 315 | 87.79 295 | 49.93 259 | 36.69 335 | 83.41 265 |
|
PM-MVS | | | 59.40 297 | 56.59 298 | 67.84 307 | 63.63 330 | 41.86 327 | 76.76 310 | 63.22 343 | 59.01 286 | 51.07 302 | 72.27 301 | 11.72 341 | 83.25 317 | 61.34 215 | 50.28 316 | 78.39 317 |
|
v15 | | | 71.40 235 | 68.75 238 | 79.35 225 | 80.39 260 | 66.70 107 | 87.57 235 | 86.64 264 | 68.66 209 | 54.68 280 | 72.00 302 | 54.50 166 | 89.98 273 | 63.69 195 | 50.66 311 | 81.38 287 |
|
V14 | | | 71.29 237 | 68.61 240 | 79.31 226 | 80.34 264 | 66.65 109 | 87.39 237 | 86.61 267 | 68.41 215 | 54.49 282 | 71.91 303 | 54.25 171 | 89.96 274 | 63.50 196 | 50.62 312 | 81.33 289 |
|
MIMVSNet1 | | | 60.16 296 | 57.33 296 | 68.67 306 | 69.71 323 | 44.13 322 | 78.92 302 | 84.21 289 | 55.05 303 | 44.63 321 | 71.85 304 | 23.91 325 | 81.54 326 | 32.63 327 | 55.03 298 | 80.35 302 |
|
V9 | | | 71.16 238 | 68.46 242 | 79.27 228 | 80.26 267 | 66.60 111 | 87.21 240 | 86.56 268 | 68.17 216 | 54.26 285 | 71.81 305 | 54.00 173 | 89.93 275 | 63.28 199 | 50.57 313 | 81.27 290 |
|
v12 | | | 71.02 242 | 68.29 247 | 79.22 230 | 80.18 270 | 66.53 116 | 87.01 243 | 86.54 270 | 67.90 218 | 54.00 288 | 71.70 306 | 53.66 178 | 89.91 276 | 63.09 201 | 50.51 314 | 81.21 291 |
|
v11 | | | 71.05 241 | 68.32 245 | 79.23 229 | 80.34 264 | 66.57 114 | 87.01 243 | 86.55 269 | 68.11 217 | 54.40 283 | 71.66 307 | 52.94 187 | 89.91 276 | 62.71 207 | 51.12 309 | 81.21 291 |
|
v13 | | | 70.90 243 | 68.15 248 | 79.15 234 | 80.08 271 | 66.45 118 | 86.83 247 | 86.50 271 | 67.62 224 | 53.78 290 | 71.61 308 | 53.51 182 | 89.87 278 | 62.89 205 | 50.50 315 | 81.14 293 |
|
DSMNet-mixed | | | 56.78 301 | 54.44 303 | 63.79 317 | 63.21 331 | 29.44 344 | 64.43 332 | 64.10 342 | 42.12 334 | 51.32 300 | 71.60 309 | 31.76 306 | 75.04 337 | 36.23 309 | 65.20 244 | 86.87 215 |
|
MDA-MVSNet_test_wron | | | 63.78 287 | 60.16 288 | 74.64 277 | 78.15 292 | 60.41 229 | 83.49 265 | 84.03 291 | 56.17 301 | 39.17 332 | 71.59 310 | 37.22 286 | 83.24 318 | 42.87 285 | 48.73 318 | 80.26 304 |
|
YYNet1 | | | 63.76 288 | 60.14 289 | 74.62 278 | 78.06 293 | 60.19 235 | 83.46 267 | 83.99 295 | 56.18 300 | 39.25 331 | 71.56 311 | 37.18 287 | 83.34 316 | 42.90 284 | 48.70 319 | 80.32 303 |
|
testing_2 | | | 71.09 240 | 67.32 253 | 82.40 156 | 69.82 322 | 66.52 117 | 83.64 263 | 90.77 166 | 72.21 148 | 45.12 319 | 71.07 312 | 27.60 319 | 93.74 191 | 75.71 99 | 69.96 211 | 86.95 213 |
|
ADS-MVSNet2 | | | 66.90 271 | 63.44 274 | 77.26 264 | 88.06 164 | 60.70 225 | 68.01 326 | 75.56 323 | 57.57 290 | 64.48 229 | 69.87 313 | 38.68 270 | 84.10 307 | 40.87 292 | 67.89 228 | 86.97 211 |
|
ADS-MVSNet | | | 68.54 261 | 64.38 271 | 81.03 194 | 88.06 164 | 66.90 92 | 68.01 326 | 84.02 292 | 57.57 290 | 64.48 229 | 69.87 313 | 38.68 270 | 89.21 284 | 40.87 292 | 67.89 228 | 86.97 211 |
|
N_pmnet | | | 50.55 309 | 49.11 311 | 54.88 326 | 77.17 298 | 4.02 356 | 84.36 259 | 2.00 356 | 48.59 316 | 45.86 316 | 68.82 315 | 32.22 304 | 82.80 319 | 31.58 330 | 51.38 308 | 77.81 319 |
|
patchmatchnet-post | | | | | | | | | | | | 67.62 316 | 57.62 121 | 90.25 263 | | | |
|
testpf | | | 57.17 300 | 56.93 297 | 57.88 322 | 79.13 284 | 42.40 324 | 34.23 346 | 85.97 280 | 52.64 306 | 47.66 312 | 66.50 317 | 36.33 293 | 79.65 330 | 53.60 247 | 56.31 295 | 51.60 341 |
|
ambc | | | | | 69.61 304 | 61.38 336 | 41.35 328 | 49.07 343 | 85.86 282 | | 50.18 306 | 66.40 318 | 10.16 344 | 88.14 292 | 45.73 274 | 44.20 325 | 79.32 312 |
|
new-patchmatchnet | | | 59.30 299 | 56.48 299 | 67.79 308 | 65.86 328 | 44.19 321 | 82.47 275 | 81.77 304 | 59.94 281 | 43.65 325 | 66.20 319 | 27.67 318 | 81.68 325 | 39.34 297 | 41.40 329 | 77.50 320 |
|
1111 | | | 56.66 303 | 54.98 302 | 61.69 318 | 61.99 334 | 31.38 340 | 79.81 299 | 83.17 301 | 45.66 323 | 41.94 327 | 65.44 320 | 41.50 260 | 79.56 331 | 27.64 333 | 47.68 320 | 74.14 326 |
|
.test1245 | | | 46.52 313 | 49.68 309 | 37.02 335 | 61.99 334 | 31.38 340 | 79.81 299 | 83.17 301 | 45.66 323 | 41.94 327 | 65.44 320 | 41.50 260 | 79.56 331 | 27.64 333 | 0.01 352 | 0.13 353 |
|
PatchT | | | 69.11 258 | 65.37 263 | 80.32 201 | 82.07 235 | 63.68 179 | 67.96 328 | 87.62 254 | 50.86 312 | 69.37 175 | 65.18 322 | 57.09 124 | 88.53 289 | 41.59 290 | 66.60 235 | 88.74 174 |
|
RPMNet | | | 69.58 254 | 65.21 264 | 82.70 140 | 83.15 227 | 67.96 64 | 70.25 319 | 86.15 276 | 46.83 322 | 69.97 165 | 65.10 323 | 56.48 139 | 89.48 281 | 35.79 312 | 73.13 187 | 90.64 156 |
|
pmmvs3 | | | 55.51 305 | 51.50 308 | 67.53 309 | 57.90 339 | 50.93 304 | 80.37 290 | 73.66 328 | 40.63 335 | 44.15 323 | 64.75 324 | 16.30 335 | 78.97 333 | 44.77 280 | 40.98 331 | 72.69 327 |
|
Anonymous20231211 | | | 53.57 308 | 49.43 310 | 66.00 314 | 65.01 329 | 42.08 325 | 80.95 287 | 72.60 330 | 38.46 336 | 41.65 329 | 64.48 325 | 15.72 336 | 84.23 306 | 25.78 336 | 40.24 332 | 71.68 329 |
|
Patchmatch-RL test | | | 68.17 263 | 64.49 269 | 79.19 231 | 71.22 317 | 53.93 290 | 70.07 321 | 71.54 334 | 69.22 200 | 56.79 273 | 62.89 326 | 56.58 137 | 88.61 286 | 69.53 147 | 52.61 303 | 95.03 55 |
|
test1235678 | | | 55.73 304 | 52.74 304 | 64.68 316 | 60.16 337 | 35.56 338 | 81.65 281 | 81.46 305 | 51.27 309 | 38.93 333 | 62.82 327 | 17.44 334 | 78.58 335 | 30.87 331 | 50.09 317 | 79.89 306 |
|
UnsupCasMVSNet_bld | | | 61.60 292 | 57.71 293 | 73.29 287 | 68.73 326 | 51.64 298 | 78.61 303 | 89.05 229 | 57.20 294 | 46.11 313 | 61.96 328 | 28.70 316 | 88.60 287 | 50.08 258 | 38.90 333 | 79.63 309 |
|
FPMVS | | | 45.64 314 | 43.10 315 | 53.23 328 | 51.42 342 | 36.46 337 | 64.97 331 | 71.91 332 | 29.13 340 | 27.53 338 | 61.55 329 | 9.83 345 | 65.01 345 | 16.00 345 | 55.58 296 | 58.22 340 |
|
LP | | | 56.71 302 | 51.64 306 | 71.91 300 | 80.08 271 | 60.33 231 | 61.72 334 | 75.61 322 | 43.87 331 | 43.76 324 | 60.30 330 | 30.46 313 | 84.05 308 | 22.94 340 | 46.06 323 | 71.34 330 |
|
test12356 | | | 47.51 311 | 44.82 313 | 55.56 324 | 52.53 340 | 21.09 351 | 71.45 318 | 76.03 319 | 44.14 330 | 30.69 336 | 58.18 331 | 9.01 347 | 76.14 336 | 26.95 335 | 34.43 338 | 69.46 334 |
|
new_pmnet | | | 49.31 310 | 46.44 312 | 57.93 321 | 62.84 332 | 40.74 329 | 68.47 325 | 62.96 344 | 36.48 337 | 35.09 334 | 57.81 332 | 14.97 338 | 72.18 338 | 32.86 326 | 46.44 322 | 60.88 339 |
|
testmv | | | 46.98 312 | 43.53 314 | 57.35 323 | 47.75 345 | 30.41 343 | 74.99 314 | 77.69 315 | 42.84 333 | 28.03 337 | 53.36 333 | 8.18 348 | 71.18 339 | 24.36 338 | 34.55 336 | 70.46 333 |
|
DeepMVS_CX | | | | | 34.71 336 | 51.45 341 | 24.73 350 | | 28.48 355 | 31.46 339 | 17.49 344 | 52.75 334 | 5.80 351 | 42.60 352 | 18.18 344 | 19.42 341 | 36.81 345 |
|
PMMVS2 | | | 37.93 318 | 33.61 319 | 50.92 329 | 46.31 346 | 24.76 349 | 60.55 338 | 50.05 347 | 28.94 341 | 20.93 340 | 47.59 335 | 4.41 354 | 65.13 344 | 25.14 337 | 18.55 342 | 62.87 337 |
|
JIA-IIPM | | | 66.06 275 | 62.45 281 | 76.88 266 | 81.42 241 | 54.45 289 | 57.49 340 | 88.67 236 | 49.36 315 | 63.86 234 | 46.86 336 | 56.06 143 | 90.25 263 | 49.53 260 | 68.83 220 | 85.95 233 |
|
gg-mvs-nofinetune | | | 77.18 168 | 74.31 185 | 85.80 71 | 91.42 97 | 68.36 54 | 71.78 316 | 94.72 27 | 49.61 314 | 77.12 97 | 45.92 337 | 77.41 3 | 93.98 183 | 67.62 161 | 93.16 39 | 95.05 52 |
|
LCM-MVSNet | | | 40.54 316 | 35.79 317 | 54.76 327 | 36.92 350 | 30.81 342 | 51.41 341 | 69.02 336 | 22.07 342 | 24.63 339 | 45.37 338 | 4.56 353 | 65.81 343 | 33.67 322 | 34.50 337 | 67.67 335 |
|
tmp_tt | | | 22.26 327 | 23.75 326 | 17.80 340 | 5.23 355 | 12.06 355 | 35.26 345 | 39.48 351 | 2.82 351 | 18.94 342 | 44.20 339 | 22.23 329 | 24.64 353 | 36.30 308 | 9.31 349 | 16.69 348 |
|
MVS-HIRNet | | | 60.25 295 | 55.55 301 | 74.35 280 | 84.37 212 | 56.57 278 | 71.64 317 | 74.11 327 | 34.44 338 | 45.54 318 | 42.24 340 | 31.11 311 | 89.81 279 | 40.36 295 | 76.10 169 | 76.67 322 |
|
ANet_high | | | 40.27 317 | 35.20 318 | 55.47 325 | 34.74 351 | 34.47 339 | 63.84 333 | 71.56 333 | 48.42 317 | 18.80 343 | 41.08 341 | 9.52 346 | 64.45 346 | 20.18 342 | 8.66 350 | 67.49 336 |
|
PMVS | | 26.43 22 | 31.84 321 | 28.16 323 | 42.89 332 | 25.87 354 | 27.58 347 | 50.92 342 | 49.78 349 | 21.37 344 | 14.17 347 | 40.81 342 | 2.01 355 | 66.62 342 | 9.61 348 | 38.88 334 | 34.49 346 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
no-one | | | 44.13 315 | 38.39 316 | 61.34 319 | 45.91 347 | 41.94 326 | 61.67 335 | 75.07 326 | 45.05 327 | 20.07 341 | 40.68 343 | 11.58 342 | 79.82 329 | 30.18 332 | 15.30 343 | 62.26 338 |
|
PNet_i23d | | | 32.77 320 | 29.98 322 | 41.11 333 | 48.05 343 | 29.17 345 | 65.82 330 | 50.02 348 | 21.42 343 | 14.74 346 | 37.19 344 | 1.11 357 | 55.11 348 | 19.75 343 | 11.77 345 | 39.06 343 |
|
MVE | | 24.84 23 | 24.35 325 | 19.77 329 | 38.09 334 | 34.56 352 | 26.92 348 | 26.57 347 | 38.87 352 | 11.73 349 | 11.37 348 | 27.44 345 | 1.37 356 | 50.42 349 | 11.41 347 | 14.60 344 | 36.93 344 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
test_post | | | | | | | | | | | | 23.01 346 | 56.49 138 | 92.67 218 | | | |
|
E-PMN | | | 24.61 324 | 24.00 325 | 26.45 338 | 43.74 348 | 18.44 353 | 60.86 336 | 39.66 350 | 15.11 346 | 9.53 349 | 22.10 347 | 6.52 350 | 46.94 350 | 8.31 349 | 10.14 346 | 13.98 349 |
|
Gipuma | | | 34.91 319 | 31.44 321 | 45.30 331 | 70.99 318 | 39.64 334 | 19.85 349 | 72.56 331 | 20.10 345 | 16.16 345 | 21.47 348 | 5.08 352 | 71.16 340 | 13.07 346 | 43.70 327 | 25.08 347 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
wuykxyi23d | | | 29.03 323 | 23.09 328 | 46.84 330 | 31.67 353 | 28.82 346 | 43.46 344 | 57.72 346 | 14.39 348 | 7.52 351 | 20.84 349 | 0.64 358 | 60.29 347 | 21.57 341 | 10.04 347 | 51.40 342 |
|
test_post1 | | | | | | | | 78.95 301 | | | | 20.70 350 | 53.05 185 | 91.50 255 | 60.43 220 | | |
|
EMVS | | | 23.76 326 | 23.20 327 | 25.46 339 | 41.52 349 | 16.90 354 | 60.56 337 | 38.79 353 | 14.62 347 | 8.99 350 | 20.24 351 | 7.35 349 | 45.82 351 | 7.25 350 | 9.46 348 | 13.64 350 |
|
X-MVStestdata | | | 76.86 174 | 74.13 188 | 85.05 95 | 93.22 47 | 63.78 173 | 92.92 82 | 92.66 101 | 73.99 111 | 78.18 84 | 10.19 352 | 55.25 148 | 97.41 47 | 79.16 78 | 91.58 58 | 93.95 95 |
|
wuyk23d | | | 11.30 329 | 10.95 330 | 12.33 341 | 48.05 343 | 19.89 352 | 25.89 348 | 1.92 357 | 3.58 350 | 3.12 352 | 1.37 353 | 0.64 358 | 15.77 354 | 6.23 351 | 7.77 351 | 1.35 351 |
|
testmvs | | | 7.23 331 | 9.62 332 | 0.06 343 | 0.04 356 | 0.02 358 | 84.98 256 | 0.02 358 | 0.03 352 | 0.18 353 | 1.21 354 | 0.01 361 | 0.02 355 | 0.14 352 | 0.01 352 | 0.13 353 |
|
test123 | | | 6.92 332 | 9.21 333 | 0.08 342 | 0.03 357 | 0.05 357 | 81.65 281 | 0.01 359 | 0.02 353 | 0.14 354 | 0.85 355 | 0.03 360 | 0.02 355 | 0.12 353 | 0.00 354 | 0.16 352 |
|
pcd_1.5k_mvsjas | | | 4.46 333 | 5.95 334 | 0.00 344 | 0.00 358 | 0.00 359 | 0.00 350 | 0.00 360 | 0.00 354 | 0.00 355 | 0.00 356 | 53.55 179 | 0.00 357 | 0.00 354 | 0.00 354 | 0.00 355 |
|
pcd1.5k->3k | | | 31.17 322 | 31.85 320 | 29.12 337 | 81.48 238 | 0.00 359 | 0.00 350 | 91.79 133 | 0.00 354 | 0.00 355 | 0.00 356 | 41.05 266 | 0.00 357 | 0.00 354 | 72.34 196 | 87.36 205 |
|
sosnet-low-res | | | 0.00 334 | 0.00 335 | 0.00 344 | 0.00 358 | 0.00 359 | 0.00 350 | 0.00 360 | 0.00 354 | 0.00 355 | 0.00 356 | 0.00 362 | 0.00 357 | 0.00 354 | 0.00 354 | 0.00 355 |
|
sosnet | | | 0.00 334 | 0.00 335 | 0.00 344 | 0.00 358 | 0.00 359 | 0.00 350 | 0.00 360 | 0.00 354 | 0.00 355 | 0.00 356 | 0.00 362 | 0.00 357 | 0.00 354 | 0.00 354 | 0.00 355 |
|
uncertanet | | | 0.00 334 | 0.00 335 | 0.00 344 | 0.00 358 | 0.00 359 | 0.00 350 | 0.00 360 | 0.00 354 | 0.00 355 | 0.00 356 | 0.00 362 | 0.00 357 | 0.00 354 | 0.00 354 | 0.00 355 |
|
Regformer | | | 0.00 334 | 0.00 335 | 0.00 344 | 0.00 358 | 0.00 359 | 0.00 350 | 0.00 360 | 0.00 354 | 0.00 355 | 0.00 356 | 0.00 362 | 0.00 357 | 0.00 354 | 0.00 354 | 0.00 355 |
|
uanet | | | 0.00 334 | 0.00 335 | 0.00 344 | 0.00 358 | 0.00 359 | 0.00 350 | 0.00 360 | 0.00 354 | 0.00 355 | 0.00 356 | 0.00 362 | 0.00 357 | 0.00 354 | 0.00 354 | 0.00 355 |
|
GSMVS | | | | | | | | | | | | | | | | | 94.68 64 |
|
test_part2 | | | | | | 96.29 7 | 68.16 60 | | | | 90.78 3 | | | | | | |
|
test_part1 | | | | | | | | | 94.26 40 | | | | 77.03 4 | | | 95.18 8 | 96.11 19 |
|
sam_mvs1 | | | | | | | | | | | | | 57.85 118 | | | | 94.68 64 |
|
sam_mvs | | | | | | | | | | | | | 54.91 162 | | | | |
|
MTGPA | | | | | | | | | 92.23 113 | | | | | | | | |
|
MTMP | | | | | | | | | 32.52 354 | | | | | | | | |
|
test9_res | | | | | | | | | | | | | | | 89.41 12 | 94.96 10 | 95.29 40 |
|
agg_prior2 | | | | | | | | | | | | | | | 86.41 33 | 94.75 18 | 95.33 37 |
|
agg_prior | | | | | | 94.16 33 | 66.97 88 | | 93.31 74 | | 84.49 37 | | | 96.75 85 | | | |
|
test_prior4 | | | | | | | 67.18 83 | 93.92 54 | | | | | | | | | |
|
test_prior | | | | | 86.42 54 | 94.71 23 | 67.35 77 | | 93.10 86 | | | | | 96.84 81 | | | 95.05 52 |
|
旧先验2 | | | | | | | | 92.00 114 | | 59.37 285 | 87.54 16 | | | 93.47 198 | 75.39 101 | | |
|
æ–°å‡ ä½•2 | | | | | | | | 91.41 142 | | | | | | | | | |
|
æ— å…ˆéªŒ | | | | | | | | 92.71 87 | 92.61 104 | 62.03 268 | | | | 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 | | | | | | | | 89.21 199 | | 77.55 63 | | | | | | | |
|
test12 | | | | | 87.09 35 | 94.60 25 | 68.86 44 | | 92.91 92 | | 82.67 50 | | 65.44 49 | 97.55 43 | | 93.69 33 | 94.84 60 |
|
plane_prior7 | | | | | | 86.94 179 | 61.51 214 | | | | | | | | | | |
|
plane_prior6 | | | | | | 87.23 175 | 62.32 202 | | | | | | 50.66 203 | | | | |
|
plane_prior5 | | | | | | | | | 91.31 151 | | | | | 95.55 122 | 76.74 93 | 78.53 148 | 88.39 180 |
|
plane_prior3 | | | | | | | 61.95 210 | | | 79.09 44 | 72.53 135 | | | | | | |
|
plane_prior2 | | | | | | | | 93.13 74 | | 78.81 49 | | | | | | | |
|
plane_prior1 | | | | | | 87.15 176 | | | | | | | | | | | |
|
plane_prior | | | | | | | 62.42 200 | 93.85 57 | | 79.38 37 | | | | | | 78.80 146 | |
|
n2 | | | | | | | | | 0.00 360 | | | | | | | | |
|
nn | | | | | | | | | 0.00 360 | | | | | | | | |
|
door-mid | | | | | | | | | 66.01 341 | | | | | | | | |
|
test11 | | | | | | | | | 93.01 88 | | | | | | | | |
|
door | | | | | | | | | 66.57 340 | | | | | | | | |
|
HQP5-MVS | | | | | | | 63.66 180 | | | | | | | | | | |
|
HQP-NCC | | | | | | 87.54 171 | | 94.06 45 | | 79.80 33 | 74.18 117 | | | | | | |
|
ACMP_Plane | | | | | | 87.54 171 | | 94.06 45 | | 79.80 33 | 74.18 117 | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 77.63 90 | | |
|
HQP4-MVS | | | | | | | | | | | 74.18 117 | | | 95.61 118 | | | 88.63 175 |
|
HQP3-MVS | | | | | | | | | 91.70 137 | | | | | | | 78.90 144 | |
|
HQP2-MVS | | | | | | | | | | | | | 51.63 199 | | | | |
|
MDTV_nov1_ep13_2view | | | | | | | 59.90 240 | 80.13 295 | | 67.65 222 | 72.79 130 | | 54.33 170 | | 59.83 223 | | 92.58 129 |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 71.63 198 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 69.72 213 | |
|
Test By Simon | | | | | | | | | | | | | 54.21 172 | | | | |
|