CP-MVS | | | 87.11 22 | 86.92 22 | 87.68 24 | 94.20 18 | 73.86 5 | 93.98 1 | 92.82 35 | 76.62 56 | 83.68 49 | 94.46 11 | 67.93 58 | 95.95 34 | 84.20 27 | 94.39 36 | 93.23 53 |
|
APDe-MVS | | | 89.15 2 | 89.63 2 | 87.73 19 | 94.49 8 | 71.69 40 | 93.83 2 | 93.96 2 | 75.70 70 | 91.06 2 | 96.03 1 | 76.84 3 | 97.03 4 | 89.09 2 | 95.65 13 | 94.47 11 |
|
SteuartSystems-ACMMP | | | 88.72 5 | 88.86 5 | 88.32 4 | 92.14 52 | 72.96 19 | 93.73 3 | 93.67 6 | 80.19 14 | 88.10 8 | 94.80 4 | 73.76 20 | 97.11 2 | 87.51 8 | 95.82 8 | 94.90 4 |
Skip Steuart: Steuart Systems R&D Blog. |
3Dnovator+ | | 77.84 4 | 85.48 43 | 84.47 51 | 88.51 2 | 91.08 63 | 73.49 13 | 93.18 4 | 93.78 5 | 80.79 10 | 76.66 141 | 93.37 34 | 60.40 159 | 96.75 10 | 77.20 77 | 93.73 44 | 95.29 1 |
|
HFP-MVS | | | 87.58 13 | 87.47 14 | 87.94 10 | 94.58 5 | 73.54 11 | 93.04 5 | 93.24 15 | 76.78 51 | 84.91 29 | 94.44 12 | 70.78 38 | 96.61 15 | 84.53 22 | 94.89 26 | 93.66 35 |
|
ACMMPR | | | 87.44 15 | 87.23 17 | 88.08 7 | 94.64 3 | 73.59 8 | 93.04 5 | 93.20 17 | 76.78 51 | 84.66 35 | 94.52 7 | 68.81 54 | 96.65 13 | 84.53 22 | 94.90 25 | 94.00 27 |
|
region2R | | | 87.42 17 | 87.20 18 | 88.09 6 | 94.63 4 | 73.55 9 | 93.03 7 | 93.12 20 | 76.73 54 | 84.45 38 | 94.52 7 | 69.09 52 | 96.70 11 | 84.37 25 | 94.83 28 | 94.03 24 |
|
HSP-MVS | | | 89.28 1 | 89.76 1 | 87.85 17 | 94.28 15 | 73.46 14 | 92.90 8 | 92.73 36 | 80.27 12 | 91.35 1 | 94.16 20 | 78.35 2 | 96.77 8 | 89.59 1 | 94.22 41 | 93.33 51 |
|
XVS | | | 87.18 21 | 86.91 23 | 88.00 8 | 94.42 10 | 73.33 16 | 92.78 9 | 92.99 26 | 79.14 20 | 83.67 50 | 94.17 19 | 67.45 63 | 96.60 17 | 83.06 35 | 94.50 33 | 94.07 22 |
|
X-MVStestdata | | | 80.37 115 | 77.83 151 | 88.00 8 | 94.42 10 | 73.33 16 | 92.78 9 | 92.99 26 | 79.14 20 | 83.67 50 | 12.47 329 | 67.45 63 | 96.60 17 | 83.06 35 | 94.50 33 | 94.07 22 |
|
mPP-MVS | | | 86.67 28 | 86.32 29 | 87.72 21 | 94.41 12 | 73.55 9 | 92.74 11 | 92.22 50 | 76.87 49 | 82.81 60 | 94.25 17 | 66.44 71 | 96.24 25 | 82.88 39 | 94.28 39 | 93.38 48 |
|
ACMMP | | | 85.89 39 | 85.39 41 | 87.38 27 | 93.59 27 | 72.63 26 | 92.74 11 | 93.18 19 | 76.78 51 | 80.73 81 | 93.82 28 | 64.33 86 | 96.29 23 | 82.67 41 | 90.69 66 | 93.23 53 |
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence |
MP-MVS | | | 87.71 11 | 87.64 12 | 87.93 13 | 94.36 14 | 73.88 4 | 92.71 13 | 92.65 39 | 77.57 34 | 83.84 47 | 94.40 15 | 72.24 30 | 96.28 24 | 85.65 13 | 95.30 21 | 93.62 42 |
|
HPM-MVS++ | | | 89.02 3 | 89.15 3 | 88.63 1 | 95.01 1 | 76.03 1 | 92.38 14 | 92.85 32 | 80.26 13 | 87.78 11 | 94.27 16 | 75.89 7 | 96.81 7 | 87.45 9 | 96.44 1 | 93.05 61 |
|
#test# | | | 87.33 19 | 87.13 19 | 87.94 10 | 94.58 5 | 73.54 11 | 92.34 15 | 93.24 15 | 75.23 78 | 84.91 29 | 94.44 12 | 70.78 38 | 96.61 15 | 83.75 30 | 94.89 26 | 93.66 35 |
|
EPP-MVSNet | | | 83.40 62 | 83.02 60 | 84.57 76 | 90.13 75 | 64.47 177 | 92.32 16 | 90.73 99 | 74.45 89 | 79.35 89 | 91.10 72 | 69.05 53 | 95.12 56 | 72.78 120 | 87.22 107 | 94.13 19 |
|
PHI-MVS | | | 86.43 30 | 86.17 33 | 87.24 29 | 90.88 67 | 70.96 46 | 92.27 17 | 94.07 1 | 72.45 127 | 85.22 25 | 91.90 57 | 69.47 49 | 96.42 21 | 83.28 33 | 95.94 5 | 94.35 13 |
|
HPM-MVS | | | 87.11 22 | 86.98 21 | 87.50 26 | 93.88 22 | 72.16 35 | 92.19 18 | 93.33 14 | 76.07 67 | 83.81 48 | 93.95 26 | 69.77 47 | 96.01 31 | 85.15 14 | 94.66 30 | 94.32 15 |
|
HPM-MVS_fast | | | 85.35 47 | 84.95 48 | 86.57 43 | 93.69 24 | 70.58 55 | 92.15 19 | 91.62 77 | 73.89 96 | 82.67 62 | 94.09 23 | 62.60 119 | 95.54 41 | 80.93 48 | 92.93 47 | 93.57 43 |
|
CPTT-MVS | | | 83.73 55 | 83.33 56 | 84.92 71 | 93.28 32 | 70.86 50 | 92.09 20 | 90.38 110 | 68.75 184 | 79.57 86 | 92.83 47 | 60.60 155 | 93.04 150 | 80.92 49 | 91.56 58 | 90.86 117 |
|
APD-MVS_3200maxsize | | | 85.97 37 | 85.88 35 | 86.22 48 | 92.69 44 | 69.53 72 | 91.93 21 | 92.99 26 | 73.54 103 | 85.94 17 | 94.51 10 | 65.80 78 | 95.61 38 | 83.04 37 | 92.51 52 | 93.53 46 |
|
APD-MVS | | | 87.44 15 | 87.52 13 | 87.19 30 | 94.24 16 | 72.39 32 | 91.86 22 | 92.83 33 | 73.01 116 | 88.58 6 | 94.52 7 | 73.36 21 | 96.49 20 | 84.26 26 | 95.01 23 | 92.70 68 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
SD-MVS | | | 88.06 7 | 88.50 7 | 86.71 39 | 92.60 48 | 72.71 24 | 91.81 23 | 93.19 18 | 77.87 31 | 90.32 3 | 94.00 25 | 74.83 10 | 93.78 113 | 87.63 7 | 94.27 40 | 93.65 40 |
|
QAPM | | | 80.88 94 | 79.50 110 | 85.03 66 | 88.01 134 | 68.97 82 | 91.59 24 | 92.00 60 | 66.63 204 | 75.15 168 | 92.16 52 | 57.70 171 | 95.45 44 | 63.52 191 | 88.76 85 | 90.66 126 |
|
IS-MVSNet | | | 83.15 64 | 82.81 63 | 84.18 89 | 89.94 80 | 63.30 200 | 91.59 24 | 88.46 181 | 79.04 24 | 79.49 87 | 92.16 52 | 65.10 82 | 94.28 85 | 67.71 160 | 91.86 55 | 94.95 3 |
|
TSAR-MVS + MP. | | | 88.02 10 | 88.11 8 | 87.72 21 | 93.68 25 | 72.13 36 | 91.41 26 | 92.35 47 | 74.62 87 | 88.90 5 | 93.85 27 | 75.75 8 | 96.00 32 | 87.80 5 | 94.63 31 | 95.04 2 |
|
DeepC-MVS_fast | | 79.65 3 | 86.91 25 | 86.62 26 | 87.76 18 | 93.52 28 | 72.37 33 | 91.26 27 | 93.04 21 | 76.62 56 | 84.22 43 | 93.36 35 | 71.44 34 | 96.76 9 | 80.82 50 | 95.33 19 | 94.16 18 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
HQP_MVS | | | 83.64 57 | 83.14 57 | 85.14 63 | 90.08 76 | 68.71 90 | 91.25 28 | 92.44 42 | 79.12 22 | 78.92 93 | 91.00 79 | 60.42 157 | 95.38 49 | 78.71 62 | 86.32 119 | 91.33 104 |
|
plane_prior2 | | | | | | | | 91.25 28 | | 79.12 22 | | | | | | | |
|
NCCC | | | 88.06 7 | 88.01 10 | 88.24 5 | 94.41 12 | 73.62 7 | 91.22 30 | 92.83 33 | 81.50 6 | 85.79 21 | 93.47 33 | 73.02 24 | 97.00 5 | 84.90 17 | 94.94 24 | 94.10 20 |
|
API-MVS | | | 81.99 79 | 81.23 81 | 84.26 87 | 90.94 65 | 70.18 63 | 91.10 31 | 89.32 148 | 71.51 142 | 78.66 97 | 88.28 133 | 65.26 80 | 95.10 60 | 64.74 187 | 91.23 62 | 87.51 224 |
|
EPNet | | | 83.72 56 | 82.92 62 | 86.14 50 | 84.22 194 | 69.48 73 | 91.05 32 | 85.27 221 | 81.30 7 | 76.83 138 | 91.65 61 | 66.09 74 | 95.56 40 | 76.00 88 | 93.85 43 | 93.38 48 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
ACMMP_Plus | | | 88.05 9 | 88.08 9 | 87.94 10 | 93.70 23 | 73.05 18 | 90.86 33 | 93.59 7 | 76.27 64 | 88.14 7 | 95.09 3 | 71.06 36 | 96.67 12 | 87.67 6 | 96.37 2 | 94.09 21 |
|
CSCG | | | 86.41 32 | 86.19 32 | 87.07 34 | 92.91 40 | 72.48 30 | 90.81 34 | 93.56 8 | 73.95 95 | 83.16 55 | 91.07 75 | 75.94 6 | 95.19 54 | 79.94 57 | 94.38 37 | 93.55 44 |
|
abl_6 | | | 85.23 48 | 84.95 48 | 86.07 51 | 92.23 51 | 70.48 56 | 90.80 35 | 92.08 55 | 73.51 105 | 85.26 24 | 94.16 20 | 62.75 112 | 95.92 35 | 82.46 43 | 91.30 61 | 91.81 95 |
|
MSLP-MVS++ | | | 85.43 45 | 85.76 38 | 84.45 80 | 91.93 55 | 70.24 57 | 90.71 36 | 92.86 31 | 77.46 40 | 84.22 43 | 92.81 50 | 67.16 67 | 92.94 152 | 80.36 53 | 94.35 38 | 90.16 147 |
|
3Dnovator | | 76.31 5 | 83.38 63 | 82.31 69 | 86.59 42 | 87.94 135 | 72.94 22 | 90.64 37 | 92.14 54 | 77.21 41 | 75.47 155 | 92.83 47 | 58.56 166 | 94.72 74 | 73.24 117 | 92.71 50 | 92.13 86 |
|
OpenMVS | | 72.83 10 | 79.77 131 | 78.33 143 | 84.09 91 | 85.17 178 | 69.91 64 | 90.57 38 | 90.97 95 | 66.70 200 | 72.17 198 | 91.91 56 | 54.70 194 | 93.96 99 | 61.81 208 | 90.95 64 | 88.41 208 |
|
CNVR-MVS | | | 88.93 4 | 89.13 4 | 88.33 3 | 94.77 2 | 73.82 6 | 90.51 39 | 93.00 24 | 80.90 9 | 88.06 9 | 94.06 24 | 76.43 4 | 96.84 6 | 88.48 4 | 95.99 4 | 94.34 14 |
|
MVSFormer | | | 82.85 69 | 82.05 72 | 85.24 61 | 87.35 149 | 70.21 58 | 90.50 40 | 90.38 110 | 68.55 187 | 81.32 72 | 89.47 105 | 61.68 133 | 93.46 129 | 78.98 60 | 90.26 70 | 92.05 89 |
|
test_djsdf | | | 80.30 116 | 79.32 115 | 83.27 118 | 83.98 211 | 65.37 148 | 90.50 40 | 90.38 110 | 68.55 187 | 76.19 148 | 88.70 120 | 56.44 182 | 93.46 129 | 78.98 60 | 80.14 179 | 90.97 114 |
|
nrg030 | | | 83.88 53 | 83.53 53 | 84.96 68 | 86.77 162 | 69.28 77 | 90.46 42 | 92.67 37 | 74.79 85 | 82.95 56 | 91.33 70 | 72.70 26 | 93.09 147 | 80.79 51 | 79.28 186 | 92.50 74 |
|
canonicalmvs | | | 85.91 38 | 85.87 36 | 86.04 52 | 89.84 82 | 69.44 76 | 90.45 43 | 93.00 24 | 76.70 55 | 88.01 10 | 91.23 71 | 73.28 22 | 93.91 104 | 81.50 46 | 88.80 84 | 94.77 5 |
|
plane_prior | | | | | | | 68.71 90 | 90.38 44 | | 77.62 33 | | | | | | 86.16 121 | |
|
DeepC-MVS | | 79.81 2 | 87.08 24 | 86.88 24 | 87.69 23 | 91.16 62 | 72.32 34 | 90.31 45 | 93.94 3 | 77.12 43 | 82.82 59 | 94.23 18 | 72.13 31 | 97.09 3 | 84.83 20 | 95.37 16 | 93.65 40 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
Vis-MVSNet | | | 83.46 60 | 82.80 64 | 85.43 57 | 90.25 74 | 68.74 88 | 90.30 46 | 90.13 124 | 76.33 63 | 80.87 79 | 92.89 45 | 61.00 148 | 94.20 90 | 72.45 126 | 90.97 63 | 93.35 50 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
MPTG | | | 87.53 14 | 87.41 15 | 87.90 14 | 94.18 19 | 74.25 2 | 90.23 47 | 92.02 57 | 79.45 18 | 85.88 18 | 94.80 4 | 68.07 56 | 96.21 26 | 86.69 10 | 95.34 17 | 93.23 53 |
|
PGM-MVS | | | 86.68 27 | 86.27 30 | 87.90 14 | 94.22 17 | 73.38 15 | 90.22 48 | 93.04 21 | 75.53 72 | 83.86 46 | 94.42 14 | 67.87 60 | 96.64 14 | 82.70 40 | 94.57 32 | 93.66 35 |
|
LPG-MVS_test | | | 82.08 76 | 81.27 80 | 84.50 78 | 89.23 99 | 68.76 86 | 90.22 48 | 91.94 64 | 75.37 76 | 76.64 142 | 91.51 66 | 54.29 197 | 94.91 66 | 78.44 64 | 83.78 139 | 89.83 163 |
|
ACMM | | 73.20 8 | 80.78 104 | 79.84 99 | 83.58 108 | 89.31 96 | 68.37 97 | 89.99 50 | 91.60 78 | 70.28 157 | 77.25 131 | 89.66 100 | 53.37 204 | 93.53 127 | 74.24 107 | 82.85 148 | 88.85 186 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
ACMP | | 74.13 6 | 81.51 89 | 80.57 89 | 84.36 83 | 89.42 89 | 68.69 93 | 89.97 51 | 91.50 84 | 74.46 88 | 75.04 171 | 90.41 87 | 53.82 202 | 94.54 76 | 77.56 73 | 82.91 147 | 89.86 162 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
LFMVS | | | 81.82 82 | 81.23 81 | 83.57 109 | 91.89 56 | 63.43 198 | 89.84 52 | 81.85 253 | 77.04 46 | 83.21 53 | 93.10 39 | 52.26 213 | 93.43 133 | 71.98 132 | 89.95 75 | 93.85 32 |
|
MCST-MVS | | | 87.37 18 | 87.25 16 | 87.73 19 | 94.53 7 | 72.46 31 | 89.82 53 | 93.82 4 | 73.07 115 | 84.86 34 | 92.89 45 | 76.22 5 | 96.33 22 | 84.89 19 | 95.13 22 | 94.40 12 |
|
MAR-MVS | | | 81.84 81 | 80.70 87 | 85.27 60 | 91.32 61 | 71.53 42 | 89.82 53 | 90.92 96 | 69.77 163 | 78.50 99 | 86.21 192 | 62.36 126 | 94.52 79 | 65.36 181 | 92.05 53 | 89.77 166 |
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 |
MP-MVS-pluss | | | 87.67 12 | 87.72 11 | 87.54 25 | 93.64 26 | 72.04 37 | 89.80 55 | 93.50 9 | 75.17 81 | 86.34 16 | 95.29 2 | 70.86 37 | 96.00 32 | 88.78 3 | 96.04 3 | 94.58 7 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
UA-Net | | | 85.08 51 | 84.96 47 | 85.45 56 | 92.07 53 | 68.07 104 | 89.78 56 | 90.86 98 | 82.48 2 | 84.60 37 | 93.20 37 | 69.35 50 | 95.22 53 | 71.39 139 | 90.88 65 | 93.07 60 |
|
alignmvs | | | 85.48 43 | 85.32 43 | 85.96 53 | 89.51 87 | 69.47 74 | 89.74 57 | 92.47 41 | 76.17 65 | 87.73 12 | 91.46 69 | 70.32 41 | 93.78 113 | 81.51 45 | 88.95 81 | 94.63 6 |
|
VDDNet | | | 81.52 87 | 80.67 88 | 84.05 93 | 90.44 71 | 64.13 183 | 89.73 58 | 85.91 217 | 71.11 145 | 83.18 54 | 93.48 31 | 50.54 233 | 93.49 128 | 73.40 116 | 88.25 95 | 94.54 10 |
|
114514_t | | | 80.68 105 | 79.51 109 | 84.20 88 | 94.09 21 | 67.27 117 | 89.64 59 | 91.11 93 | 58.75 271 | 74.08 179 | 90.72 83 | 58.10 169 | 95.04 62 | 69.70 149 | 89.42 79 | 90.30 144 |
|
DeepPCF-MVS | | 80.84 1 | 88.10 6 | 88.56 6 | 86.73 38 | 92.24 50 | 69.03 78 | 89.57 60 | 93.39 13 | 77.53 38 | 89.79 4 | 94.12 22 | 78.98 1 | 96.58 19 | 85.66 12 | 95.72 9 | 94.58 7 |
|
MVS_0305 | | | 86.01 36 | 85.56 39 | 87.36 28 | 90.03 78 | 70.65 54 | 89.55 61 | 92.48 40 | 81.57 5 | 80.80 80 | 91.10 72 | 67.18 66 | 97.13 1 | 84.06 28 | 93.27 45 | 94.30 16 |
|
UGNet | | | 80.83 98 | 79.59 105 | 84.54 77 | 88.04 132 | 68.09 103 | 89.42 62 | 88.16 183 | 76.95 47 | 76.22 147 | 89.46 107 | 49.30 241 | 93.94 101 | 68.48 157 | 90.31 69 | 91.60 97 |
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 |
AdaColmap | | | 80.58 108 | 79.42 111 | 84.06 92 | 93.09 38 | 68.91 83 | 89.36 63 | 88.97 165 | 69.27 172 | 75.70 154 | 89.69 99 | 57.20 178 | 95.77 36 | 63.06 195 | 88.41 94 | 87.50 225 |
|
mvs-test1 | | | 80.88 94 | 79.40 112 | 85.29 59 | 85.13 181 | 69.75 68 | 89.28 64 | 88.10 186 | 74.99 82 | 76.44 145 | 86.72 168 | 57.27 175 | 94.26 89 | 73.53 114 | 83.18 145 | 91.87 92 |
|
PS-MVSNAJss | | | 82.07 77 | 81.31 79 | 84.34 85 | 86.51 165 | 67.27 117 | 89.27 65 | 91.51 82 | 71.75 136 | 79.37 88 | 90.22 91 | 63.15 100 | 94.27 86 | 77.69 72 | 82.36 155 | 91.49 102 |
|
jajsoiax | | | 79.29 142 | 77.96 148 | 83.27 118 | 84.68 187 | 66.57 127 | 89.25 66 | 90.16 123 | 69.20 174 | 75.46 156 | 89.49 104 | 45.75 261 | 93.13 145 | 76.84 83 | 80.80 168 | 90.11 150 |
|
mvs_tets | | | 79.13 145 | 77.77 154 | 83.22 121 | 84.70 186 | 66.37 129 | 89.17 67 | 90.19 122 | 69.38 170 | 75.40 159 | 89.46 107 | 44.17 267 | 93.15 143 | 76.78 84 | 80.70 170 | 90.14 148 |
|
HQP-NCC | | | | | | 89.33 91 | | 89.17 67 | | 76.41 58 | 77.23 133 | | | | | | |
|
ACMP_Plane | | | | | | 89.33 91 | | 89.17 67 | | 76.41 58 | 77.23 133 | | | | | | |
|
HQP-MVS | | | 82.61 72 | 82.02 73 | 84.37 82 | 89.33 91 | 66.98 121 | 89.17 67 | 92.19 52 | 76.41 58 | 77.23 133 | 90.23 90 | 60.17 160 | 95.11 57 | 77.47 74 | 85.99 123 | 91.03 111 |
|
LS3D | | | 76.95 192 | 74.82 198 | 83.37 115 | 90.45 70 | 67.36 116 | 89.15 71 | 86.94 204 | 61.87 249 | 69.52 231 | 90.61 85 | 51.71 220 | 94.53 77 | 46.38 289 | 86.71 114 | 88.21 210 |
|
OPM-MVS | | | 83.50 59 | 82.95 61 | 85.14 63 | 88.79 112 | 70.95 47 | 89.13 72 | 91.52 81 | 77.55 37 | 80.96 78 | 91.75 59 | 60.71 151 | 94.50 80 | 79.67 58 | 86.51 117 | 89.97 159 |
|
TSAR-MVS + GP. | | | 85.71 41 | 85.33 42 | 86.84 36 | 91.34 60 | 72.50 29 | 89.07 73 | 87.28 201 | 76.41 58 | 85.80 20 | 90.22 91 | 74.15 19 | 95.37 51 | 81.82 44 | 91.88 54 | 92.65 71 |
|
test_prior4 | | | | | | | 72.60 27 | 89.01 74 | | | | | | | | | |
|
VDD-MVS | | | 83.01 68 | 82.36 68 | 84.96 68 | 91.02 64 | 66.40 128 | 88.91 75 | 88.11 184 | 77.57 34 | 84.39 41 | 93.29 36 | 52.19 214 | 93.91 104 | 77.05 80 | 88.70 86 | 94.57 9 |
|
Effi-MVS+ | | | 83.62 58 | 83.08 58 | 85.24 61 | 88.38 125 | 67.45 112 | 88.89 76 | 89.15 155 | 75.50 73 | 82.27 63 | 88.28 133 | 69.61 48 | 94.45 81 | 77.81 71 | 87.84 98 | 93.84 33 |
|
ACMH+ | | 68.96 14 | 76.01 202 | 74.01 206 | 82.03 163 | 88.60 117 | 65.31 150 | 88.86 77 | 87.55 197 | 70.25 158 | 67.75 250 | 87.47 151 | 41.27 282 | 93.19 141 | 58.37 236 | 75.94 224 | 87.60 222 |
|
test_prior3 | | | 86.73 26 | 86.86 25 | 86.33 45 | 92.61 46 | 69.59 70 | 88.85 78 | 92.97 29 | 75.41 74 | 84.91 29 | 93.54 29 | 74.28 17 | 95.48 42 | 83.31 31 | 95.86 6 | 93.91 29 |
|
test_prior2 | | | | | | | | 88.85 78 | | 75.41 74 | 84.91 29 | 93.54 29 | 74.28 17 | | 83.31 31 | 95.86 6 | |
|
DP-MVS Recon | | | 83.11 66 | 82.09 71 | 86.15 49 | 94.44 9 | 70.92 49 | 88.79 80 | 92.20 51 | 70.53 154 | 79.17 90 | 91.03 78 | 64.12 88 | 96.03 30 | 68.39 159 | 90.14 72 | 91.50 101 |
|
Effi-MVS+-dtu | | | 80.03 124 | 78.57 134 | 84.42 81 | 85.13 181 | 68.74 88 | 88.77 81 | 88.10 186 | 74.99 82 | 74.97 172 | 83.49 232 | 57.27 175 | 93.36 134 | 73.53 114 | 80.88 166 | 91.18 109 |
|
agg_prior3 | | | 86.16 35 | 85.85 37 | 87.10 33 | 93.31 30 | 72.86 23 | 88.77 81 | 91.68 76 | 68.29 191 | 84.26 42 | 92.83 47 | 72.83 25 | 95.42 46 | 84.97 15 | 95.71 10 | 93.02 62 |
|
TEST9 | | | | | | 93.26 33 | 72.96 19 | 88.75 83 | 91.89 66 | 68.44 189 | 85.00 27 | 93.10 39 | 74.36 16 | 95.41 47 | | | |
|
train_agg | | | 86.43 30 | 86.20 31 | 87.13 32 | 93.26 33 | 72.96 19 | 88.75 83 | 91.89 66 | 68.69 185 | 85.00 27 | 93.10 39 | 74.43 13 | 95.41 47 | 84.97 15 | 95.71 10 | 93.02 62 |
|
PVSNet_Blended_VisFu | | | 82.62 71 | 81.83 76 | 84.96 68 | 90.80 69 | 69.76 67 | 88.74 85 | 91.70 75 | 69.39 168 | 78.96 92 | 88.46 129 | 65.47 79 | 94.87 70 | 74.42 104 | 88.57 89 | 90.24 145 |
|
test_8 | | | | | | 93.13 35 | 72.57 28 | 88.68 86 | 91.84 69 | 68.69 185 | 84.87 33 | 93.10 39 | 74.43 13 | 95.16 55 | | | |
|
ACMH | | 67.68 16 | 75.89 203 | 73.93 207 | 81.77 168 | 88.71 115 | 66.61 126 | 88.62 87 | 89.01 160 | 69.81 162 | 66.78 260 | 86.70 173 | 41.95 281 | 91.51 191 | 55.64 253 | 78.14 194 | 87.17 233 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
agg_prior1 | | | 86.22 34 | 86.09 34 | 86.62 41 | 92.85 41 | 71.94 38 | 88.59 88 | 91.78 72 | 68.96 182 | 84.41 39 | 93.18 38 | 74.94 9 | 94.93 64 | 84.75 21 | 95.33 19 | 93.01 64 |
|
CDPH-MVS | | | 85.76 40 | 85.29 45 | 87.17 31 | 93.49 29 | 71.08 44 | 88.58 89 | 92.42 45 | 68.32 190 | 84.61 36 | 93.48 31 | 72.32 29 | 96.15 29 | 79.00 59 | 95.43 15 | 94.28 17 |
|
DP-MVS | | | 76.78 194 | 74.57 200 | 83.42 112 | 93.29 31 | 69.46 75 | 88.55 90 | 83.70 234 | 63.98 230 | 70.20 218 | 88.89 117 | 54.01 201 | 94.80 72 | 46.66 286 | 81.88 159 | 86.01 253 |
|
Regformer-1 | | | 86.41 32 | 86.33 28 | 86.64 40 | 89.33 91 | 70.93 48 | 88.43 91 | 91.39 86 | 82.14 3 | 86.65 15 | 90.09 93 | 74.39 15 | 95.01 63 | 83.97 29 | 90.63 67 | 93.97 28 |
|
Regformer-2 | | | 86.63 29 | 86.53 27 | 86.95 35 | 89.33 91 | 71.24 43 | 88.43 91 | 92.05 56 | 82.50 1 | 86.88 14 | 90.09 93 | 74.45 12 | 95.61 38 | 84.38 24 | 90.63 67 | 94.01 26 |
|
WR-MVS_H | | | 78.51 154 | 78.49 136 | 78.56 224 | 88.02 133 | 56.38 267 | 88.43 91 | 92.67 37 | 77.14 42 | 73.89 180 | 87.55 148 | 66.25 72 | 89.24 229 | 58.92 230 | 73.55 251 | 90.06 156 |
|
F-COLMAP | | | 76.38 198 | 74.33 204 | 82.50 155 | 89.28 97 | 66.95 124 | 88.41 94 | 89.03 157 | 64.05 228 | 66.83 259 | 88.61 124 | 46.78 253 | 92.89 153 | 57.48 243 | 78.55 188 | 87.67 220 |
|
GBi-Net | | | 78.40 155 | 77.40 159 | 81.40 183 | 87.60 144 | 63.01 206 | 88.39 95 | 89.28 149 | 71.63 138 | 75.34 161 | 87.28 154 | 54.80 190 | 91.11 199 | 62.72 196 | 79.57 181 | 90.09 152 |
|
test1 | | | 78.40 155 | 77.40 159 | 81.40 183 | 87.60 144 | 63.01 206 | 88.39 95 | 89.28 149 | 71.63 138 | 75.34 161 | 87.28 154 | 54.80 190 | 91.11 199 | 62.72 196 | 79.57 181 | 90.09 152 |
|
FMVSNet1 | | | 77.44 186 | 76.12 182 | 81.40 183 | 86.81 161 | 63.01 206 | 88.39 95 | 89.28 149 | 70.49 155 | 74.39 177 | 87.28 154 | 49.06 244 | 91.11 199 | 60.91 215 | 78.52 189 | 90.09 152 |
|
v7n | | | 78.97 149 | 77.58 157 | 83.14 124 | 83.45 223 | 65.51 143 | 88.32 98 | 91.21 90 | 73.69 98 | 72.41 195 | 86.32 191 | 57.93 170 | 93.81 111 | 69.18 153 | 75.65 228 | 90.11 150 |
|
COLMAP_ROB | | 66.92 17 | 73.01 228 | 70.41 235 | 80.81 193 | 87.13 156 | 65.63 140 | 88.30 99 | 84.19 231 | 62.96 237 | 63.80 279 | 87.69 144 | 38.04 294 | 92.56 163 | 46.66 286 | 74.91 238 | 84.24 269 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
Regformer-3 | | | 85.23 48 | 85.07 46 | 85.70 55 | 88.95 104 | 69.01 80 | 88.29 100 | 89.91 134 | 80.95 8 | 85.01 26 | 90.01 95 | 72.45 28 | 94.19 91 | 82.50 42 | 87.57 100 | 93.90 31 |
|
Regformer-4 | | | 85.68 42 | 85.45 40 | 86.35 44 | 88.95 104 | 69.67 69 | 88.29 100 | 91.29 88 | 81.73 4 | 85.36 23 | 90.01 95 | 72.62 27 | 95.35 52 | 83.28 33 | 87.57 100 | 94.03 24 |
|
FIs | | | 82.07 77 | 82.42 65 | 81.04 190 | 88.80 111 | 58.34 242 | 88.26 102 | 93.49 10 | 76.93 48 | 78.47 101 | 91.04 76 | 69.92 45 | 92.34 170 | 69.87 148 | 84.97 128 | 92.44 76 |
|
PLC | | 70.83 11 | 78.05 164 | 76.37 175 | 83.08 127 | 91.88 57 | 67.80 108 | 88.19 103 | 89.46 145 | 64.33 226 | 69.87 228 | 88.38 130 | 53.66 203 | 93.58 124 | 58.86 231 | 82.73 150 | 87.86 217 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
MG-MVS | | | 83.41 61 | 83.45 54 | 83.28 117 | 92.74 43 | 62.28 216 | 88.17 104 | 89.50 143 | 75.22 79 | 81.49 71 | 92.74 51 | 66.75 68 | 95.11 57 | 72.85 119 | 91.58 57 | 92.45 75 |
|
TAPA-MVS | | 73.13 9 | 79.15 144 | 77.94 149 | 82.79 148 | 89.59 85 | 62.99 209 | 88.16 105 | 91.51 82 | 65.77 212 | 77.14 136 | 91.09 74 | 60.91 149 | 93.21 138 | 50.26 275 | 87.05 110 | 92.17 85 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
PS-CasMVS | | | 78.01 166 | 78.09 146 | 77.77 235 | 87.71 142 | 54.39 280 | 88.02 106 | 91.22 89 | 77.50 39 | 73.26 185 | 88.64 123 | 60.73 150 | 88.41 243 | 61.88 206 | 73.88 248 | 90.53 136 |
|
OMC-MVS | | | 82.69 70 | 81.97 75 | 84.85 72 | 88.75 114 | 67.42 113 | 87.98 107 | 90.87 97 | 74.92 84 | 79.72 85 | 91.65 61 | 62.19 130 | 93.96 99 | 75.26 100 | 86.42 118 | 93.16 58 |
|
v8 | | | 79.97 126 | 79.02 125 | 82.80 146 | 84.09 204 | 64.50 175 | 87.96 108 | 90.29 118 | 74.13 94 | 75.24 166 | 86.81 165 | 62.88 105 | 93.89 106 | 74.39 105 | 75.40 233 | 90.00 158 |
|
FC-MVSNet-test | | | 81.52 87 | 82.02 73 | 80.03 205 | 88.42 124 | 55.97 272 | 87.95 109 | 93.42 12 | 77.10 44 | 77.38 128 | 90.98 81 | 69.96 44 | 91.79 178 | 68.46 158 | 84.50 133 | 92.33 78 |
|
CP-MVSNet | | | 78.22 158 | 78.34 142 | 77.84 233 | 87.83 138 | 54.54 278 | 87.94 110 | 91.17 92 | 77.65 32 | 73.48 183 | 88.49 128 | 62.24 129 | 88.43 242 | 62.19 202 | 74.07 244 | 90.55 135 |
|
PAPM_NR | | | 83.02 67 | 82.41 66 | 84.82 73 | 92.47 49 | 66.37 129 | 87.93 111 | 91.80 70 | 73.82 97 | 77.32 130 | 90.66 84 | 67.90 59 | 94.90 68 | 70.37 144 | 89.48 78 | 93.19 57 |
|
PEN-MVS | | | 77.73 173 | 77.69 156 | 77.84 233 | 87.07 157 | 53.91 282 | 87.91 112 | 91.18 91 | 77.56 36 | 73.14 187 | 88.82 119 | 61.23 143 | 89.17 230 | 59.95 221 | 72.37 257 | 90.43 140 |
|
v10 | | | 79.74 132 | 78.67 129 | 82.97 137 | 84.06 209 | 64.95 158 | 87.88 113 | 90.62 103 | 73.11 114 | 75.11 169 | 86.56 182 | 61.46 137 | 94.05 97 | 73.68 110 | 75.55 230 | 89.90 160 |
|
TranMVSNet+NR-MVSNet | | | 80.84 96 | 80.31 93 | 82.42 156 | 87.85 137 | 62.33 214 | 87.74 114 | 91.33 87 | 80.55 11 | 77.99 119 | 89.86 97 | 65.23 81 | 92.62 160 | 67.05 169 | 75.24 237 | 92.30 80 |
|
EI-MVSNet-Vis-set | | | 84.19 52 | 83.81 52 | 85.31 58 | 88.18 129 | 67.85 107 | 87.66 115 | 89.73 138 | 80.05 16 | 82.95 56 | 89.59 102 | 70.74 40 | 94.82 71 | 80.66 52 | 84.72 132 | 93.28 52 |
|
UniMVSNet (Re) | | | 81.60 86 | 81.11 83 | 83.09 126 | 88.38 125 | 64.41 179 | 87.60 116 | 93.02 23 | 78.42 30 | 78.56 98 | 88.16 135 | 69.78 46 | 93.26 137 | 69.58 150 | 76.49 218 | 91.60 97 |
|
CNLPA | | | 78.08 163 | 76.79 169 | 81.97 164 | 90.40 72 | 71.07 45 | 87.59 117 | 84.55 227 | 66.03 211 | 72.38 196 | 89.64 101 | 57.56 173 | 86.04 260 | 59.61 224 | 83.35 142 | 88.79 189 |
|
v1neww | | | 80.40 111 | 79.54 106 | 82.98 133 | 84.10 202 | 64.51 171 | 87.57 118 | 90.22 119 | 73.25 108 | 78.47 101 | 86.65 176 | 62.83 108 | 93.86 107 | 75.72 91 | 77.02 205 | 90.58 132 |
|
v7new | | | 80.40 111 | 79.54 106 | 82.98 133 | 84.10 202 | 64.51 171 | 87.57 118 | 90.22 119 | 73.25 108 | 78.47 101 | 86.65 176 | 62.83 108 | 93.86 107 | 75.72 91 | 77.02 205 | 90.58 132 |
|
v6 | | | 80.40 111 | 79.54 106 | 82.98 133 | 84.09 204 | 64.50 175 | 87.57 118 | 90.22 119 | 73.25 108 | 78.47 101 | 86.63 178 | 62.84 107 | 93.86 107 | 75.73 90 | 77.02 205 | 90.58 132 |
|
v7 | | | 80.24 117 | 79.26 120 | 83.15 123 | 84.07 208 | 64.94 159 | 87.56 121 | 90.67 100 | 72.26 131 | 78.28 108 | 86.51 186 | 61.45 138 | 94.03 98 | 75.14 101 | 77.41 199 | 90.49 137 |
|
v748 | | | 77.97 167 | 76.65 171 | 81.92 166 | 82.29 249 | 63.28 201 | 87.53 122 | 90.35 114 | 73.50 106 | 70.76 212 | 85.55 205 | 58.28 168 | 92.81 158 | 68.81 156 | 72.76 256 | 89.67 168 |
|
DTE-MVSNet | | | 76.99 191 | 76.80 168 | 77.54 239 | 86.24 167 | 53.06 286 | 87.52 123 | 90.66 102 | 77.08 45 | 72.50 193 | 88.67 122 | 60.48 156 | 89.52 224 | 57.33 246 | 70.74 268 | 90.05 157 |
|
æ— å…ˆéªŒ | | | | | | | | 87.48 124 | 88.98 164 | 60.00 261 | | | | 94.12 94 | 67.28 165 | | 88.97 185 |
|
FMVSNet2 | | | 78.20 160 | 77.21 162 | 81.20 186 | 87.60 144 | 62.89 210 | 87.47 125 | 89.02 158 | 71.63 138 | 75.29 165 | 87.28 154 | 54.80 190 | 91.10 202 | 62.38 200 | 79.38 184 | 89.61 169 |
|
EI-MVSNet-UG-set | | | 83.81 54 | 83.38 55 | 85.09 65 | 87.87 136 | 67.53 111 | 87.44 126 | 89.66 139 | 79.74 17 | 82.23 64 | 89.41 111 | 70.24 42 | 94.74 73 | 79.95 56 | 83.92 138 | 92.99 65 |
|
v13 | | | 77.50 184 | 76.07 187 | 81.77 168 | 84.23 193 | 65.07 156 | 87.34 127 | 88.91 172 | 72.92 117 | 68.35 247 | 81.97 250 | 62.53 123 | 91.69 187 | 72.20 131 | 66.22 289 | 88.56 203 |
|
v17 | | | 77.68 175 | 76.35 179 | 81.69 173 | 84.15 199 | 64.65 166 | 87.33 128 | 88.99 162 | 72.70 124 | 69.25 237 | 82.07 246 | 62.82 110 | 91.79 178 | 72.69 123 | 67.15 281 | 88.63 194 |
|
v16 | | | 77.69 174 | 76.36 178 | 81.68 174 | 84.15 199 | 64.63 168 | 87.33 128 | 88.99 162 | 72.69 125 | 69.31 236 | 82.08 245 | 62.80 111 | 91.79 178 | 72.70 122 | 67.23 279 | 88.63 194 |
|
v12 | | | 77.51 182 | 76.09 186 | 81.76 170 | 84.22 194 | 64.99 157 | 87.30 130 | 88.93 171 | 72.92 117 | 68.48 246 | 81.97 250 | 62.54 122 | 91.70 186 | 72.24 130 | 66.21 290 | 88.58 201 |
|
v18 | | | 77.67 177 | 76.35 179 | 81.64 176 | 84.09 204 | 64.47 177 | 87.27 131 | 89.01 160 | 72.59 126 | 69.39 233 | 82.04 247 | 62.85 106 | 91.80 177 | 72.72 121 | 67.20 280 | 88.63 194 |
|
V9 | | | 77.52 180 | 76.11 185 | 81.73 171 | 84.19 198 | 64.89 161 | 87.26 132 | 88.94 170 | 72.87 120 | 68.65 242 | 81.96 252 | 62.65 118 | 91.72 183 | 72.27 129 | 66.24 288 | 88.60 198 |
|
anonymousdsp | | | 78.60 153 | 77.15 163 | 82.98 133 | 80.51 271 | 67.08 119 | 87.24 133 | 89.53 142 | 65.66 214 | 75.16 167 | 87.19 160 | 52.52 207 | 92.25 172 | 77.17 78 | 79.34 185 | 89.61 169 |
|
V14 | | | 77.52 180 | 76.12 182 | 81.70 172 | 84.15 199 | 64.77 164 | 87.21 134 | 88.95 167 | 72.80 121 | 68.79 239 | 81.94 253 | 62.69 115 | 91.72 183 | 72.31 128 | 66.27 287 | 88.60 198 |
|
v1141 | | | 80.19 120 | 79.31 116 | 82.85 142 | 83.84 214 | 64.12 184 | 87.14 135 | 90.08 126 | 73.13 111 | 78.27 109 | 86.39 188 | 62.67 117 | 93.75 117 | 75.40 98 | 76.83 213 | 90.68 123 |
|
v15 | | | 77.51 182 | 76.12 182 | 81.66 175 | 84.09 204 | 64.65 166 | 87.14 135 | 88.96 166 | 72.76 122 | 68.90 238 | 81.91 254 | 62.74 113 | 91.73 181 | 72.32 127 | 66.29 286 | 88.61 197 |
|
v1 | | | 80.19 120 | 79.31 116 | 82.85 142 | 83.83 216 | 64.12 184 | 87.14 135 | 90.07 128 | 73.13 111 | 78.27 109 | 86.38 190 | 62.72 114 | 93.75 117 | 75.41 97 | 76.82 214 | 90.68 123 |
|
divwei89l23v2f112 | | | 80.19 120 | 79.31 116 | 82.85 142 | 83.84 214 | 64.11 186 | 87.13 138 | 90.08 126 | 73.13 111 | 78.27 109 | 86.39 188 | 62.69 115 | 93.75 117 | 75.40 98 | 76.82 214 | 90.68 123 |
|
UniMVSNet_NR-MVSNet | | | 81.88 80 | 81.54 78 | 82.92 138 | 88.46 122 | 63.46 196 | 87.13 138 | 92.37 46 | 80.19 14 | 78.38 105 | 89.14 113 | 71.66 33 | 93.05 148 | 70.05 145 | 76.46 219 | 92.25 82 |
|
v1144 | | | 80.03 124 | 79.03 124 | 83.01 131 | 83.78 217 | 64.51 171 | 87.11 140 | 90.57 105 | 71.96 135 | 78.08 118 | 86.20 193 | 61.41 139 | 93.94 101 | 74.93 102 | 77.23 201 | 90.60 129 |
|
v11 | | | 77.45 185 | 76.06 188 | 81.59 179 | 84.22 194 | 64.52 169 | 87.11 140 | 89.02 158 | 72.76 122 | 68.76 240 | 81.90 255 | 62.09 131 | 91.71 185 | 71.98 132 | 66.73 282 | 88.56 203 |
|
v2v482 | | | 80.23 118 | 79.29 119 | 83.05 129 | 83.62 219 | 64.14 182 | 87.04 142 | 89.97 130 | 73.61 100 | 78.18 115 | 87.22 158 | 61.10 146 | 93.82 110 | 76.11 86 | 76.78 216 | 91.18 109 |
|
DU-MVS | | | 81.12 92 | 80.52 91 | 82.90 139 | 87.80 139 | 63.46 196 | 87.02 143 | 91.87 68 | 79.01 25 | 78.38 105 | 89.07 114 | 65.02 83 | 93.05 148 | 70.05 145 | 76.46 219 | 92.20 83 |
|
v144192 | | | 79.47 138 | 78.37 141 | 82.78 149 | 83.35 224 | 63.96 189 | 86.96 144 | 90.36 113 | 69.99 160 | 77.50 126 | 85.67 202 | 60.66 153 | 93.77 115 | 74.27 106 | 76.58 217 | 90.62 127 |
|
Fast-Effi-MVS+-dtu | | | 78.02 165 | 76.49 172 | 82.62 154 | 83.16 232 | 66.96 123 | 86.94 145 | 87.45 200 | 72.45 127 | 71.49 208 | 84.17 223 | 54.79 193 | 91.58 190 | 67.61 161 | 80.31 176 | 89.30 173 |
|
v1192 | | | 79.59 134 | 78.43 140 | 83.07 128 | 83.55 221 | 64.52 169 | 86.93 146 | 90.58 104 | 70.83 148 | 77.78 122 | 85.90 196 | 59.15 163 | 93.94 101 | 73.96 109 | 77.19 203 | 90.76 119 |
|
EPNet_dtu | | | 75.46 207 | 74.86 197 | 77.23 243 | 82.57 246 | 54.60 277 | 86.89 147 | 83.09 240 | 71.64 137 | 66.25 265 | 85.86 198 | 55.99 184 | 88.04 247 | 54.92 256 | 86.55 116 | 89.05 179 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
原ACMM2 | | | | | | | | 86.86 148 | | | | | | | | | |
|
VPA-MVSNet | | | 80.60 106 | 80.55 90 | 80.76 194 | 88.07 131 | 60.80 226 | 86.86 148 | 91.58 79 | 75.67 71 | 80.24 83 | 89.45 109 | 63.34 94 | 90.25 215 | 70.51 143 | 79.22 187 | 91.23 108 |
|
v1921920 | | | 79.22 143 | 78.03 147 | 82.80 146 | 83.30 227 | 63.94 190 | 86.80 150 | 90.33 115 | 69.91 161 | 77.48 127 | 85.53 206 | 58.44 167 | 93.75 117 | 73.60 113 | 76.85 211 | 90.71 122 |
|
IterMVS-LS | | | 80.06 123 | 79.38 113 | 82.11 161 | 85.89 169 | 63.20 204 | 86.79 151 | 89.34 147 | 74.19 91 | 75.45 157 | 86.72 168 | 66.62 69 | 92.39 167 | 72.58 124 | 76.86 210 | 90.75 120 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
TransMVSNet (Re) | | | 75.39 209 | 74.56 201 | 77.86 232 | 85.50 176 | 57.10 256 | 86.78 152 | 86.09 216 | 72.17 133 | 71.53 207 | 87.34 153 | 63.01 104 | 89.31 228 | 56.84 249 | 61.83 298 | 87.17 233 |
|
Baseline_NR-MVSNet | | | 78.15 162 | 78.33 143 | 77.61 237 | 85.79 170 | 56.21 270 | 86.78 152 | 85.76 218 | 73.60 101 | 77.93 120 | 87.57 147 | 65.02 83 | 88.99 233 | 67.14 168 | 75.33 234 | 87.63 221 |
|
MVS_test0326 | | | 79.86 128 | 78.74 128 | 83.23 120 | 85.76 172 | 63.99 188 | 86.77 154 | 89.97 130 | 73.63 99 | 73.53 182 | 86.56 182 | 53.05 206 | 94.38 82 | 71.43 138 | 87.94 97 | 91.33 104 |
|
PAPR | | | 81.66 85 | 80.89 86 | 83.99 98 | 90.27 73 | 64.00 187 | 86.76 155 | 91.77 74 | 68.84 183 | 77.13 137 | 89.50 103 | 67.63 61 | 94.88 69 | 67.55 162 | 88.52 92 | 93.09 59 |
|
Vis-MVSNet (Re-imp) | | | 78.36 157 | 78.45 137 | 78.07 231 | 88.64 116 | 51.78 289 | 86.70 156 | 79.63 273 | 74.14 93 | 75.11 169 | 90.83 82 | 61.29 142 | 89.75 220 | 58.10 239 | 91.60 56 | 92.69 70 |
|
pmmvs6 | | | 74.69 211 | 73.39 210 | 78.61 223 | 81.38 260 | 57.48 253 | 86.64 157 | 87.95 190 | 64.99 220 | 70.18 219 | 86.61 179 | 50.43 234 | 89.52 224 | 62.12 204 | 70.18 270 | 88.83 187 |
|
v1240 | | | 78.99 148 | 77.78 153 | 82.64 153 | 83.21 228 | 63.54 193 | 86.62 158 | 90.30 117 | 69.74 166 | 77.33 129 | 85.68 201 | 57.04 180 | 93.76 116 | 73.13 118 | 76.92 208 | 90.62 127 |
|
MTAPA | | | 87.23 20 | 87.00 20 | 87.90 14 | 94.18 19 | 74.25 2 | 86.58 159 | 92.02 57 | 79.45 18 | 85.88 18 | 94.80 4 | 68.07 56 | 96.21 26 | 86.69 10 | 95.34 17 | 93.23 53 |
|
旧先验2 | | | | | | | | 86.56 160 | | 58.10 273 | 87.04 13 | | | 88.98 234 | 74.07 108 | | |
|
FMVSNet3 | | | 77.88 171 | 76.85 167 | 80.97 191 | 86.84 160 | 62.36 213 | 86.52 161 | 88.77 174 | 71.13 144 | 75.34 161 | 86.66 175 | 54.07 200 | 91.10 202 | 62.72 196 | 79.57 181 | 89.45 171 |
|
MVS_dtu | | | 79.86 128 | 78.53 135 | 83.85 103 | 86.55 164 | 64.93 160 | 86.47 162 | 87.68 195 | 73.52 104 | 74.35 178 | 87.89 140 | 51.92 218 | 94.53 77 | 71.28 140 | 87.08 109 | 92.08 87 |
|
v52 | | | 77.94 170 | 76.37 175 | 82.67 151 | 79.39 283 | 65.52 141 | 86.43 163 | 89.94 132 | 72.28 129 | 72.15 200 | 84.94 218 | 55.70 186 | 93.44 131 | 73.64 111 | 72.84 255 | 89.06 175 |
|
V4 | | | 77.95 168 | 76.37 175 | 82.67 151 | 79.40 282 | 65.52 141 | 86.43 163 | 89.94 132 | 72.28 129 | 72.14 201 | 84.95 217 | 55.72 185 | 93.44 131 | 73.64 111 | 72.86 254 | 89.05 179 |
|
pm-mvs1 | | | 77.25 189 | 76.68 170 | 78.93 219 | 84.22 194 | 58.62 239 | 86.41 165 | 88.36 182 | 71.37 143 | 73.31 184 | 88.01 139 | 61.22 144 | 89.15 231 | 64.24 189 | 73.01 253 | 89.03 181 |
|
EI-MVSNet | | | 80.52 109 | 79.98 97 | 82.12 160 | 84.28 191 | 63.19 205 | 86.41 165 | 88.95 167 | 74.18 92 | 78.69 95 | 87.54 149 | 66.62 69 | 92.43 165 | 72.57 125 | 80.57 172 | 90.74 121 |
|
CVMVSNet | | | 72.99 229 | 72.58 216 | 74.25 265 | 84.28 191 | 50.85 296 | 86.41 165 | 83.45 237 | 44.56 312 | 73.23 186 | 87.54 149 | 49.38 239 | 85.70 262 | 65.90 177 | 78.44 191 | 86.19 248 |
|
NR-MVSNet | | | 80.23 118 | 79.38 113 | 82.78 149 | 87.80 139 | 63.34 199 | 86.31 168 | 91.09 94 | 79.01 25 | 72.17 198 | 89.07 114 | 67.20 65 | 92.81 158 | 66.08 176 | 75.65 228 | 92.20 83 |
|
v148 | | | 78.72 151 | 77.80 152 | 81.47 181 | 82.73 242 | 61.96 219 | 86.30 169 | 88.08 188 | 73.26 107 | 76.18 149 | 85.47 208 | 62.46 125 | 92.36 169 | 71.92 134 | 73.82 249 | 90.09 152 |
|
æ–°å‡ ä½•2 | | | | | | | | 86.29 170 | | | | | | | | | |
|
PVSNet_BlendedMVS | | | 80.60 106 | 80.02 96 | 82.36 158 | 88.85 106 | 65.40 145 | 86.16 171 | 92.00 60 | 69.34 171 | 78.11 116 | 86.09 195 | 66.02 76 | 94.27 86 | 71.52 136 | 82.06 156 | 87.39 226 |
|
DI_MVS_plusplus_test | | | 79.89 127 | 78.58 133 | 83.85 103 | 82.89 239 | 65.32 149 | 86.12 172 | 89.55 141 | 69.64 167 | 70.55 213 | 85.82 200 | 57.24 177 | 93.81 111 | 76.85 82 | 88.55 90 | 92.41 77 |
|
MVS_Test | | | 83.15 64 | 83.06 59 | 83.41 114 | 86.86 159 | 63.21 203 | 86.11 173 | 92.00 60 | 74.31 90 | 82.87 58 | 89.44 110 | 70.03 43 | 93.21 138 | 77.39 76 | 88.50 93 | 93.81 34 |
|
BH-untuned | | | 79.47 138 | 78.60 131 | 82.05 162 | 89.19 101 | 65.91 136 | 86.07 174 | 88.52 180 | 72.18 132 | 75.42 158 | 87.69 144 | 61.15 145 | 93.54 126 | 60.38 218 | 86.83 112 | 86.70 243 |
|
MVS_111021_HR | | | 85.14 50 | 84.75 50 | 86.32 47 | 91.65 58 | 72.70 25 | 85.98 175 | 90.33 115 | 76.11 66 | 82.08 65 | 91.61 64 | 71.36 35 | 94.17 93 | 81.02 47 | 92.58 51 | 92.08 87 |
|
jason | | | 81.39 90 | 80.29 94 | 84.70 75 | 86.63 163 | 69.90 65 | 85.95 176 | 86.77 205 | 63.24 233 | 81.07 77 | 89.47 105 | 61.08 147 | 92.15 174 | 78.33 67 | 90.07 74 | 92.05 89 |
jason: jason. |
test_0402 | | | 72.79 231 | 70.44 234 | 79.84 207 | 88.13 130 | 65.99 134 | 85.93 177 | 84.29 229 | 65.57 215 | 67.40 255 | 85.49 207 | 46.92 252 | 92.61 161 | 35.88 308 | 74.38 243 | 80.94 291 |
|
OurMVSNet-221017-0 | | | 74.26 214 | 72.42 218 | 79.80 208 | 83.76 218 | 59.59 232 | 85.92 178 | 86.64 206 | 66.39 206 | 66.96 258 | 87.58 146 | 39.46 288 | 91.60 189 | 65.76 179 | 69.27 272 | 88.22 209 |
|
EG-PatchMatch MVS | | | 74.04 215 | 71.82 223 | 80.71 195 | 84.92 184 | 67.42 113 | 85.86 179 | 88.08 188 | 66.04 210 | 64.22 276 | 83.85 226 | 35.10 303 | 92.56 163 | 57.44 244 | 80.83 167 | 82.16 287 |
|
test_normal | | | 79.81 130 | 78.45 137 | 83.89 102 | 82.70 243 | 65.40 145 | 85.82 180 | 89.48 144 | 69.39 168 | 70.12 222 | 85.66 203 | 57.15 179 | 93.71 122 | 77.08 79 | 88.62 88 | 92.56 73 |
|
CLD-MVS | | | 82.31 74 | 81.65 77 | 84.29 86 | 88.47 121 | 67.73 110 | 85.81 181 | 92.35 47 | 75.78 68 | 78.33 107 | 86.58 181 | 64.01 89 | 94.35 83 | 76.05 87 | 87.48 105 | 90.79 118 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
SixPastTwentyTwo | | | 73.37 222 | 71.26 229 | 79.70 209 | 85.08 183 | 57.89 249 | 85.57 182 | 83.56 235 | 71.03 147 | 65.66 267 | 85.88 197 | 42.10 279 | 92.57 162 | 59.11 229 | 63.34 295 | 88.65 193 |
|
xiu_mvs_v1_base_debu | | | 80.80 101 | 79.72 102 | 84.03 95 | 87.35 149 | 70.19 60 | 85.56 183 | 88.77 174 | 69.06 177 | 81.83 66 | 88.16 135 | 50.91 227 | 92.85 154 | 78.29 68 | 87.56 102 | 89.06 175 |
|
xiu_mvs_v1_base | | | 80.80 101 | 79.72 102 | 84.03 95 | 87.35 149 | 70.19 60 | 85.56 183 | 88.77 174 | 69.06 177 | 81.83 66 | 88.16 135 | 50.91 227 | 92.85 154 | 78.29 68 | 87.56 102 | 89.06 175 |
|
xiu_mvs_v1_base_debi | | | 80.80 101 | 79.72 102 | 84.03 95 | 87.35 149 | 70.19 60 | 85.56 183 | 88.77 174 | 69.06 177 | 81.83 66 | 88.16 135 | 50.91 227 | 92.85 154 | 78.29 68 | 87.56 102 | 89.06 175 |
|
V42 | | | 79.38 141 | 78.24 145 | 82.83 145 | 81.10 265 | 65.50 144 | 85.55 186 | 89.82 135 | 71.57 141 | 78.21 113 | 86.12 194 | 60.66 153 | 93.18 142 | 75.64 94 | 75.46 232 | 89.81 165 |
|
lupinMVS | | | 81.39 90 | 80.27 95 | 84.76 74 | 87.35 149 | 70.21 58 | 85.55 186 | 86.41 209 | 62.85 239 | 81.32 72 | 88.61 124 | 61.68 133 | 92.24 173 | 78.41 66 | 90.26 70 | 91.83 93 |
|
Fast-Effi-MVS+ | | | 80.81 99 | 79.92 98 | 83.47 110 | 88.85 106 | 64.51 171 | 85.53 188 | 89.39 146 | 70.79 149 | 78.49 100 | 85.06 215 | 67.54 62 | 93.58 124 | 67.03 170 | 86.58 115 | 92.32 79 |
|
DELS-MVS | | | 85.41 46 | 85.30 44 | 85.77 54 | 88.49 120 | 67.93 106 | 85.52 189 | 93.44 11 | 78.70 27 | 83.63 52 | 89.03 116 | 74.57 11 | 95.71 37 | 80.26 55 | 94.04 42 | 93.66 35 |
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 |
Anonymous20231211 | | | 64.82 273 | 61.79 277 | 73.91 268 | 77.11 293 | 50.92 295 | 85.29 190 | 81.53 255 | 54.19 293 | 57.98 295 | 78.03 282 | 26.90 310 | 87.83 250 | 37.92 305 | 57.12 306 | 82.99 282 |
|
Test4 | | | 77.83 172 | 75.90 189 | 83.62 106 | 80.24 273 | 65.25 151 | 85.27 191 | 90.67 100 | 69.03 180 | 66.48 263 | 83.75 228 | 43.07 272 | 93.00 151 | 75.93 89 | 88.66 87 | 92.62 72 |
|
MVS_111021_LR | | | 82.61 72 | 82.11 70 | 84.11 90 | 88.82 109 | 71.58 41 | 85.15 192 | 86.16 214 | 74.69 86 | 80.47 82 | 91.04 76 | 62.29 127 | 90.55 212 | 80.33 54 | 90.08 73 | 90.20 146 |
|
WR-MVS | | | 79.49 137 | 79.22 122 | 80.27 202 | 88.79 112 | 58.35 241 | 85.06 193 | 88.61 179 | 78.56 28 | 77.65 124 | 88.34 131 | 63.81 92 | 90.66 211 | 64.98 185 | 77.22 202 | 91.80 96 |
|
OpenMVS_ROB | | 64.09 19 | 70.56 245 | 68.19 248 | 77.65 236 | 80.26 272 | 59.41 236 | 85.01 194 | 82.96 242 | 58.76 270 | 65.43 269 | 82.33 241 | 37.63 297 | 91.23 197 | 45.34 294 | 76.03 223 | 82.32 285 |
|
BH-RMVSNet | | | 79.61 133 | 78.44 139 | 83.14 124 | 89.38 90 | 65.93 135 | 84.95 195 | 87.15 202 | 73.56 102 | 78.19 114 | 89.79 98 | 56.67 181 | 93.36 134 | 59.53 226 | 86.74 113 | 90.13 149 |
|
BH-w/o | | | 78.21 159 | 77.33 161 | 80.84 192 | 88.81 110 | 65.13 155 | 84.87 196 | 87.85 192 | 69.75 164 | 74.52 176 | 84.74 221 | 61.34 140 | 93.11 146 | 58.24 238 | 85.84 125 | 84.27 268 |
|
TDRefinement | | | 67.49 260 | 64.34 267 | 76.92 244 | 73.47 305 | 61.07 222 | 84.86 197 | 82.98 241 | 59.77 263 | 58.30 294 | 85.13 213 | 26.06 312 | 87.89 248 | 47.92 284 | 60.59 303 | 81.81 289 |
|
TAMVS | | | 78.89 150 | 77.51 158 | 83.03 130 | 87.80 139 | 67.79 109 | 84.72 198 | 85.05 224 | 67.63 194 | 76.75 139 | 87.70 143 | 62.25 128 | 90.82 208 | 58.53 235 | 87.13 108 | 90.49 137 |
|
1314 | | | 76.53 196 | 75.30 195 | 80.21 203 | 83.93 212 | 62.32 215 | 84.66 199 | 88.81 173 | 60.23 259 | 70.16 221 | 84.07 225 | 55.30 189 | 90.73 210 | 67.37 164 | 83.21 144 | 87.59 223 |
|
1121 | | | 80.84 96 | 79.77 100 | 84.05 93 | 93.11 37 | 70.78 51 | 84.66 199 | 85.42 220 | 57.37 281 | 81.76 70 | 92.02 54 | 63.41 93 | 94.12 94 | 67.28 165 | 92.93 47 | 87.26 231 |
|
MVS | | | 78.19 161 | 76.99 165 | 81.78 167 | 85.66 173 | 66.99 120 | 84.66 199 | 90.47 108 | 55.08 291 | 72.02 202 | 85.27 211 | 63.83 91 | 94.11 96 | 66.10 175 | 89.80 76 | 84.24 269 |
|
TR-MVS | | | 77.44 186 | 76.18 181 | 81.20 186 | 88.24 128 | 63.24 202 | 84.61 202 | 86.40 210 | 67.55 196 | 77.81 121 | 86.48 187 | 54.10 199 | 93.15 143 | 57.75 242 | 82.72 151 | 87.20 232 |
|
AllTest | | | 70.96 241 | 68.09 251 | 79.58 212 | 85.15 179 | 63.62 191 | 84.58 203 | 79.83 271 | 62.31 245 | 60.32 288 | 86.73 166 | 32.02 305 | 88.96 236 | 50.28 273 | 71.57 264 | 86.15 249 |
|
EU-MVSNet | | | 68.53 257 | 67.61 259 | 71.31 280 | 78.51 287 | 47.01 306 | 84.47 204 | 84.27 230 | 42.27 313 | 66.44 264 | 84.79 220 | 40.44 286 | 83.76 271 | 58.76 233 | 68.54 278 | 83.17 277 |
|
VNet | | | 82.21 75 | 82.41 66 | 81.62 177 | 90.82 68 | 60.93 223 | 84.47 204 | 89.78 136 | 76.36 62 | 84.07 45 | 91.88 58 | 64.71 85 | 90.26 214 | 70.68 141 | 88.89 82 | 93.66 35 |
|
xiu_mvs_v2_base | | | 81.69 83 | 81.05 84 | 83.60 107 | 89.15 102 | 68.03 105 | 84.46 206 | 90.02 129 | 70.67 152 | 81.30 74 | 86.53 185 | 63.17 99 | 94.19 91 | 75.60 96 | 88.54 91 | 88.57 202 |
|
VPNet | | | 78.69 152 | 78.66 130 | 78.76 221 | 88.31 127 | 55.72 274 | 84.45 207 | 86.63 207 | 76.79 50 | 78.26 112 | 90.55 86 | 59.30 162 | 89.70 222 | 66.63 171 | 77.05 204 | 90.88 116 |
|
PVSNet_Blended | | | 80.98 93 | 80.34 92 | 82.90 139 | 88.85 106 | 65.40 145 | 84.43 208 | 92.00 60 | 67.62 195 | 78.11 116 | 85.05 216 | 66.02 76 | 94.27 86 | 71.52 136 | 89.50 77 | 89.01 182 |
|
MVP-Stereo | | | 76.12 200 | 74.46 203 | 81.13 189 | 85.37 177 | 69.79 66 | 84.42 209 | 87.95 190 | 65.03 219 | 67.46 253 | 85.33 210 | 53.28 205 | 91.73 181 | 58.01 240 | 83.27 143 | 81.85 288 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
CDS-MVSNet | | | 79.07 146 | 77.70 155 | 83.17 122 | 87.60 144 | 68.23 101 | 84.40 210 | 86.20 213 | 67.49 197 | 76.36 146 | 86.54 184 | 61.54 136 | 90.79 209 | 61.86 207 | 87.33 106 | 90.49 137 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
K. test v3 | | | 71.19 239 | 68.51 245 | 79.21 216 | 83.04 235 | 57.78 251 | 84.35 211 | 76.91 286 | 72.90 119 | 62.99 282 | 82.86 236 | 39.27 289 | 91.09 204 | 61.65 209 | 52.66 313 | 88.75 190 |
|
PS-MVSNAJ | | | 81.69 83 | 81.02 85 | 83.70 105 | 89.51 87 | 68.21 102 | 84.28 212 | 90.09 125 | 70.79 149 | 81.26 75 | 85.62 204 | 63.15 100 | 94.29 84 | 75.62 95 | 88.87 83 | 88.59 200 |
|
diffmvs | | | 79.51 135 | 78.59 132 | 82.25 159 | 83.31 226 | 62.66 211 | 84.17 213 | 88.11 184 | 67.64 193 | 76.09 152 | 87.47 151 | 64.01 89 | 91.15 198 | 71.71 135 | 84.82 131 | 92.94 66 |
|
test222 | | | | | | 91.50 59 | 68.26 100 | 84.16 214 | 83.20 239 | 54.63 292 | 79.74 84 | 91.63 63 | 58.97 164 | | | 91.42 59 | 86.77 241 |
|
testdata1 | | | | | | | | 84.14 215 | | 75.71 69 | | | | | | | |
|
testing_2 | | | 75.73 205 | 73.34 212 | 82.89 141 | 77.37 291 | 65.22 152 | 84.10 216 | 90.54 106 | 69.09 176 | 60.46 287 | 81.15 260 | 40.48 285 | 92.84 157 | 76.36 85 | 80.54 174 | 90.60 129 |
|
MVSTER | | | 79.01 147 | 77.88 150 | 82.38 157 | 83.07 233 | 64.80 163 | 84.08 217 | 88.95 167 | 69.01 181 | 78.69 95 | 87.17 161 | 54.70 194 | 92.43 165 | 74.69 103 | 80.57 172 | 89.89 161 |
|
ab-mvs | | | 79.51 135 | 78.97 126 | 81.14 188 | 88.46 122 | 60.91 224 | 83.84 218 | 89.24 153 | 70.36 156 | 79.03 91 | 88.87 118 | 63.23 98 | 90.21 216 | 65.12 182 | 82.57 153 | 92.28 81 |
|
PAPM | | | 77.68 175 | 76.40 174 | 81.51 180 | 87.29 154 | 61.85 220 | 83.78 219 | 89.59 140 | 64.74 221 | 71.23 209 | 88.70 120 | 62.59 120 | 93.66 123 | 52.66 266 | 87.03 111 | 89.01 182 |
|
1112_ss | | | 77.40 188 | 76.43 173 | 80.32 200 | 89.11 103 | 60.41 229 | 83.65 220 | 87.72 194 | 62.13 247 | 73.05 188 | 86.72 168 | 62.58 121 | 89.97 218 | 62.11 205 | 80.80 168 | 90.59 131 |
|
PCF-MVS | | 73.52 7 | 80.38 114 | 78.84 127 | 85.01 67 | 87.71 142 | 68.99 81 | 83.65 220 | 91.46 85 | 63.00 236 | 77.77 123 | 90.28 88 | 66.10 73 | 95.09 61 | 61.40 211 | 88.22 96 | 90.94 115 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
XVG-ACMP-BASELINE | | | 76.11 201 | 74.27 205 | 81.62 177 | 83.20 229 | 64.67 165 | 83.60 222 | 89.75 137 | 69.75 164 | 71.85 203 | 87.09 162 | 32.78 304 | 92.11 175 | 69.99 147 | 80.43 175 | 88.09 212 |
|
XVG-OURS-SEG-HR | | | 80.81 99 | 79.76 101 | 83.96 100 | 85.60 175 | 68.78 85 | 83.54 223 | 90.50 107 | 70.66 153 | 76.71 140 | 91.66 60 | 60.69 152 | 91.26 195 | 76.94 81 | 81.58 161 | 91.83 93 |
|
IB-MVS | | 68.01 15 | 75.85 204 | 73.36 211 | 83.31 116 | 84.76 185 | 66.03 132 | 83.38 224 | 85.06 223 | 70.21 159 | 69.40 232 | 81.05 261 | 45.76 260 | 94.66 75 | 65.10 183 | 75.49 231 | 89.25 174 |
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 |
HY-MVS | | 69.67 12 | 77.95 168 | 77.15 163 | 80.36 198 | 87.57 148 | 60.21 230 | 83.37 225 | 87.78 193 | 66.11 208 | 75.37 160 | 87.06 163 | 63.27 96 | 90.48 213 | 61.38 212 | 82.43 154 | 90.40 142 |
|
XVG-OURS | | | 80.41 110 | 79.23 121 | 83.97 99 | 85.64 174 | 69.02 79 | 83.03 226 | 90.39 109 | 71.09 146 | 77.63 125 | 91.49 68 | 54.62 196 | 91.35 193 | 75.71 93 | 83.47 141 | 91.54 99 |
|
mvs_anonymous | | | 79.42 140 | 79.11 123 | 80.34 199 | 84.45 190 | 57.97 247 | 82.59 227 | 87.62 196 | 67.40 199 | 76.17 151 | 88.56 127 | 68.47 55 | 89.59 223 | 70.65 142 | 86.05 122 | 93.47 47 |
|
DWT-MVSNet_test | | | 73.70 218 | 71.86 221 | 79.21 216 | 82.91 238 | 58.94 237 | 82.34 228 | 82.17 249 | 65.21 216 | 71.05 211 | 78.31 279 | 44.21 266 | 90.17 217 | 63.29 194 | 77.28 200 | 88.53 205 |
|
cascas | | | 76.72 195 | 74.64 199 | 82.99 132 | 85.78 171 | 65.88 137 | 82.33 229 | 89.21 154 | 60.85 255 | 72.74 190 | 81.02 262 | 47.28 250 | 93.75 117 | 67.48 163 | 85.02 127 | 89.34 172 |
|
RPSCF | | | 73.23 226 | 71.46 225 | 78.54 225 | 82.50 247 | 59.85 231 | 82.18 230 | 82.84 243 | 58.96 268 | 71.15 210 | 89.41 111 | 45.48 263 | 84.77 269 | 58.82 232 | 71.83 262 | 91.02 113 |
|
tpmp4_e23 | | | 73.45 221 | 71.17 230 | 80.31 201 | 83.55 221 | 59.56 234 | 81.88 231 | 82.33 247 | 57.94 276 | 70.51 215 | 81.62 256 | 51.19 225 | 91.63 188 | 53.96 260 | 77.51 198 | 89.75 167 |
|
pmmvs-eth3d | | | 70.50 246 | 67.83 255 | 78.52 226 | 77.37 291 | 66.18 131 | 81.82 232 | 81.51 256 | 58.90 269 | 63.90 278 | 80.42 267 | 42.69 275 | 86.28 259 | 58.56 234 | 65.30 292 | 83.11 279 |
|
MS-PatchMatch | | | 73.83 217 | 72.67 215 | 77.30 242 | 83.87 213 | 66.02 133 | 81.82 232 | 84.66 226 | 61.37 253 | 68.61 244 | 82.82 237 | 47.29 249 | 88.21 244 | 59.27 227 | 84.32 136 | 77.68 301 |
|
pmmvs5 | | | 71.55 237 | 70.20 237 | 75.61 253 | 77.83 288 | 56.39 266 | 81.74 234 | 80.89 259 | 57.76 277 | 67.46 253 | 84.49 222 | 49.26 242 | 85.32 266 | 57.08 248 | 75.29 235 | 85.11 262 |
|
Test_1112_low_res | | | 76.40 197 | 75.44 193 | 79.27 214 | 89.28 97 | 58.09 244 | 81.69 235 | 87.07 203 | 59.53 265 | 72.48 194 | 86.67 174 | 61.30 141 | 89.33 227 | 60.81 217 | 80.15 178 | 90.41 141 |
|
PatchFormer-LS_test | | | 74.50 212 | 73.05 213 | 78.86 220 | 82.95 237 | 59.55 235 | 81.65 236 | 82.30 248 | 67.44 198 | 71.62 206 | 78.15 281 | 52.34 211 | 88.92 238 | 65.05 184 | 75.90 225 | 88.12 211 |
|
IterMVS | | | 74.29 213 | 72.94 214 | 78.35 229 | 81.53 257 | 63.49 195 | 81.58 237 | 82.49 245 | 68.06 192 | 69.99 225 | 83.69 230 | 51.66 221 | 85.54 263 | 65.85 178 | 71.64 263 | 86.01 253 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
pmmvs4 | | | 74.03 216 | 71.91 220 | 80.39 197 | 81.96 252 | 68.32 98 | 81.45 238 | 82.14 250 | 59.32 266 | 69.87 228 | 85.13 213 | 52.40 210 | 88.13 246 | 60.21 220 | 74.74 240 | 84.73 266 |
|
GA-MVS | | | 76.87 193 | 75.17 196 | 81.97 164 | 82.75 241 | 62.58 212 | 81.44 239 | 86.35 212 | 72.16 134 | 74.74 174 | 82.89 235 | 46.20 256 | 92.02 176 | 68.85 155 | 81.09 164 | 91.30 107 |
|
CostFormer | | | 75.24 210 | 73.90 208 | 79.27 214 | 82.65 245 | 58.27 243 | 80.80 240 | 82.73 244 | 61.57 250 | 75.33 164 | 83.13 234 | 55.52 187 | 91.07 205 | 64.98 185 | 78.34 193 | 88.45 206 |
|
MIMVSNet1 | | | 68.58 256 | 66.78 261 | 73.98 267 | 80.07 275 | 51.82 288 | 80.77 241 | 84.37 228 | 64.40 225 | 59.75 291 | 82.16 244 | 36.47 299 | 83.63 273 | 42.73 298 | 70.33 269 | 86.48 246 |
|
MSDG | | | 73.36 224 | 70.99 231 | 80.49 196 | 84.51 189 | 65.80 138 | 80.71 242 | 86.13 215 | 65.70 213 | 65.46 268 | 83.74 229 | 44.60 264 | 90.91 207 | 51.13 270 | 76.89 209 | 84.74 265 |
|
tpm2 | | | 73.26 225 | 71.46 225 | 78.63 222 | 83.34 225 | 56.71 261 | 80.65 243 | 80.40 266 | 56.63 285 | 73.55 181 | 82.02 248 | 51.80 219 | 91.24 196 | 56.35 251 | 78.42 192 | 87.95 214 |
|
XXY-MVS | | | 75.41 208 | 75.56 192 | 74.96 258 | 83.59 220 | 57.82 250 | 80.59 244 | 83.87 233 | 66.54 205 | 74.93 173 | 88.31 132 | 63.24 97 | 80.09 286 | 62.16 203 | 76.85 211 | 86.97 238 |
|
HyFIR lowres test | | | 77.53 179 | 75.40 194 | 83.94 101 | 89.59 85 | 66.62 125 | 80.36 245 | 88.64 178 | 56.29 287 | 76.45 144 | 85.17 212 | 57.64 172 | 93.28 136 | 61.34 213 | 83.10 146 | 91.91 91 |
|
TinyColmap | | | 67.30 263 | 64.81 265 | 74.76 261 | 81.92 253 | 56.68 262 | 80.29 246 | 81.49 257 | 60.33 257 | 56.27 303 | 83.22 233 | 24.77 314 | 87.66 251 | 45.52 292 | 69.47 271 | 79.95 295 |
|
LCM-MVSNet-Re | | | 77.05 190 | 76.94 166 | 77.36 240 | 87.20 155 | 51.60 290 | 80.06 247 | 80.46 265 | 75.20 80 | 67.69 251 | 86.72 168 | 62.48 124 | 88.98 234 | 63.44 192 | 89.25 80 | 91.51 100 |
|
FMVSNet5 | | | 69.50 252 | 67.96 252 | 74.15 266 | 82.97 236 | 55.35 275 | 80.01 248 | 82.12 251 | 62.56 243 | 63.02 280 | 81.53 257 | 36.92 298 | 81.92 279 | 48.42 281 | 74.06 245 | 85.17 261 |
|
Patchmatch-test1 | | | 73.49 220 | 71.85 222 | 78.41 228 | 84.05 210 | 62.17 217 | 79.96 249 | 79.29 275 | 66.30 207 | 72.38 196 | 79.58 274 | 51.95 217 | 85.08 267 | 55.46 254 | 77.67 197 | 87.99 213 |
|
tpmrst | | | 72.39 232 | 72.13 219 | 73.18 271 | 80.54 270 | 49.91 300 | 79.91 250 | 79.08 276 | 63.11 234 | 71.69 205 | 79.95 270 | 55.32 188 | 82.77 277 | 65.66 180 | 73.89 247 | 86.87 239 |
|
PatchmatchNet | | | 73.12 227 | 71.33 227 | 78.49 227 | 83.18 230 | 60.85 225 | 79.63 251 | 78.57 277 | 64.13 227 | 71.73 204 | 79.81 273 | 51.20 224 | 85.97 261 | 57.40 245 | 76.36 221 | 88.66 192 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
PatchMatch-RL | | | 72.38 233 | 70.90 232 | 76.80 246 | 88.60 117 | 67.38 115 | 79.53 252 | 76.17 288 | 62.75 241 | 69.36 234 | 82.00 249 | 45.51 262 | 84.89 268 | 53.62 262 | 80.58 171 | 78.12 299 |
|
CMPMVS | | 51.72 21 | 70.19 249 | 68.16 249 | 76.28 248 | 73.15 307 | 57.55 252 | 79.47 253 | 83.92 232 | 48.02 310 | 56.48 302 | 84.81 219 | 43.13 271 | 86.42 258 | 62.67 199 | 81.81 160 | 84.89 263 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
GG-mvs-BLEND | | | | | 75.38 256 | 81.59 256 | 55.80 273 | 79.32 254 | 69.63 312 | | 67.19 256 | 73.67 300 | 43.24 270 | 88.90 239 | 50.41 272 | 84.50 133 | 81.45 290 |
|
LTVRE_ROB | | 69.57 13 | 76.25 199 | 74.54 202 | 81.41 182 | 88.60 117 | 64.38 180 | 79.24 255 | 89.12 156 | 70.76 151 | 69.79 230 | 87.86 141 | 49.09 243 | 93.20 140 | 56.21 252 | 80.16 177 | 86.65 244 |
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 |
tpm | | | 72.37 234 | 71.71 224 | 74.35 264 | 82.19 250 | 52.00 287 | 79.22 256 | 77.29 284 | 64.56 223 | 72.95 189 | 83.68 231 | 51.35 222 | 83.26 276 | 58.33 237 | 75.80 226 | 87.81 218 |
|
USDC | | | 70.33 247 | 68.37 246 | 76.21 249 | 80.60 269 | 56.23 269 | 79.19 257 | 86.49 208 | 60.89 254 | 61.29 284 | 85.47 208 | 31.78 307 | 89.47 226 | 53.37 263 | 76.21 222 | 82.94 284 |
|
PM-MVS | | | 66.41 268 | 64.14 268 | 73.20 270 | 73.92 302 | 56.45 264 | 78.97 258 | 64.96 323 | 63.88 232 | 64.72 273 | 80.24 268 | 19.84 320 | 83.44 274 | 66.24 172 | 64.52 294 | 79.71 296 |
|
tpmvs | | | 71.09 240 | 69.29 240 | 76.49 247 | 82.04 251 | 56.04 271 | 78.92 259 | 81.37 258 | 64.05 228 | 67.18 257 | 78.28 280 | 49.74 238 | 89.77 219 | 49.67 278 | 72.37 257 | 83.67 273 |
|
test_post1 | | | | | | | | 78.90 260 | | | | 5.43 331 | 48.81 246 | 85.44 265 | 59.25 228 | | |
|
CHOSEN 1792x2688 | | | 77.63 178 | 75.69 190 | 83.44 111 | 89.98 79 | 68.58 95 | 78.70 261 | 87.50 198 | 56.38 286 | 75.80 153 | 86.84 164 | 58.67 165 | 91.40 192 | 61.58 210 | 85.75 126 | 90.34 143 |
|
test-LLR | | | 72.94 230 | 72.43 217 | 74.48 262 | 81.35 261 | 58.04 245 | 78.38 262 | 77.46 282 | 66.66 201 | 69.95 226 | 79.00 277 | 48.06 247 | 79.24 288 | 66.13 173 | 84.83 129 | 86.15 249 |
|
TESTMET0.1,1 | | | 69.89 251 | 69.00 242 | 72.55 272 | 79.27 285 | 56.85 257 | 78.38 262 | 74.71 298 | 57.64 278 | 68.09 248 | 77.19 288 | 37.75 295 | 76.70 299 | 63.92 190 | 84.09 137 | 84.10 272 |
|
test-mter | | | 71.41 238 | 70.39 236 | 74.48 262 | 81.35 261 | 58.04 245 | 78.38 262 | 77.46 282 | 60.32 258 | 69.95 226 | 79.00 277 | 36.08 301 | 79.24 288 | 66.13 173 | 84.83 129 | 86.15 249 |
|
Anonymous20231206 | | | 68.60 255 | 67.80 256 | 71.02 281 | 80.23 274 | 50.75 297 | 78.30 265 | 80.47 264 | 56.79 284 | 66.11 266 | 82.63 239 | 46.35 254 | 78.95 290 | 43.62 297 | 75.70 227 | 83.36 276 |
|
tpm cat1 | | | 70.57 244 | 68.31 247 | 77.35 241 | 82.41 248 | 57.95 248 | 78.08 266 | 80.22 269 | 52.04 303 | 68.54 245 | 77.66 286 | 52.00 216 | 87.84 249 | 51.77 267 | 72.07 261 | 86.25 247 |
|
WTY-MVS | | | 75.65 206 | 75.68 191 | 75.57 254 | 86.40 166 | 56.82 258 | 77.92 267 | 82.40 246 | 65.10 218 | 76.18 149 | 87.72 142 | 63.13 103 | 80.90 282 | 60.31 219 | 81.96 157 | 89.00 184 |
|
test20.03 | | | 67.45 261 | 66.95 260 | 68.94 287 | 75.48 301 | 44.84 308 | 77.50 268 | 77.67 281 | 66.66 201 | 63.01 281 | 83.80 227 | 47.02 251 | 78.40 292 | 42.53 299 | 68.86 276 | 83.58 274 |
|
EPMVS | | | 69.02 254 | 68.16 249 | 71.59 275 | 79.61 279 | 49.80 302 | 77.40 269 | 66.93 319 | 62.82 240 | 70.01 223 | 79.05 275 | 45.79 259 | 77.86 296 | 56.58 250 | 75.26 236 | 87.13 235 |
|
gg-mvs-nofinetune | | | 69.95 250 | 67.96 252 | 75.94 250 | 83.07 233 | 54.51 279 | 77.23 270 | 70.29 310 | 63.11 234 | 70.32 217 | 62.33 313 | 43.62 269 | 88.69 240 | 53.88 261 | 87.76 99 | 84.62 267 |
|
MDTV_nov1_ep13 | | | | 69.97 238 | | 83.18 230 | 53.48 284 | 77.10 271 | 80.18 270 | 60.45 256 | 69.33 235 | 80.44 266 | 48.89 245 | 86.90 253 | 51.60 268 | 78.51 190 | |
|
LF4IMVS | | | 64.02 276 | 62.19 276 | 69.50 286 | 70.90 312 | 53.29 285 | 76.13 272 | 77.18 285 | 52.65 302 | 58.59 292 | 80.98 263 | 23.55 315 | 76.52 300 | 53.06 265 | 66.66 283 | 78.68 298 |
|
sss | | | 73.60 219 | 73.64 209 | 73.51 269 | 82.80 240 | 55.01 276 | 76.12 273 | 81.69 254 | 62.47 244 | 74.68 175 | 85.85 199 | 57.32 174 | 78.11 294 | 60.86 216 | 80.93 165 | 87.39 226 |
|
testgi | | | 66.67 266 | 66.53 262 | 67.08 293 | 75.62 299 | 41.69 315 | 75.93 274 | 76.50 287 | 66.11 208 | 65.20 272 | 86.59 180 | 35.72 302 | 74.71 307 | 43.71 296 | 73.38 252 | 84.84 264 |
|
CR-MVSNet | | | 73.37 222 | 71.27 228 | 79.67 210 | 81.32 263 | 65.19 153 | 75.92 275 | 80.30 267 | 59.92 262 | 72.73 191 | 81.19 258 | 52.50 208 | 86.69 254 | 59.84 222 | 77.71 195 | 87.11 236 |
|
RPMNet | | | 71.62 236 | 68.94 243 | 79.67 210 | 81.32 263 | 65.19 153 | 75.92 275 | 78.30 279 | 57.60 279 | 72.73 191 | 76.45 291 | 52.30 212 | 86.69 254 | 48.14 283 | 77.71 195 | 87.11 236 |
|
MIMVSNet | | | 70.69 243 | 69.30 239 | 74.88 259 | 84.52 188 | 56.35 268 | 75.87 277 | 79.42 274 | 64.59 222 | 67.76 249 | 82.41 240 | 41.10 283 | 81.54 281 | 46.64 288 | 81.34 162 | 86.75 242 |
|
test0.0.03 1 | | | 68.00 259 | 67.69 258 | 68.90 288 | 77.55 289 | 47.43 304 | 75.70 278 | 72.95 305 | 66.66 201 | 66.56 261 | 82.29 242 | 48.06 247 | 75.87 303 | 44.97 295 | 74.51 242 | 83.41 275 |
|
PMMVS | | | 69.34 253 | 68.67 244 | 71.35 279 | 75.67 298 | 62.03 218 | 75.17 279 | 73.46 303 | 50.00 308 | 68.68 241 | 79.05 275 | 52.07 215 | 78.13 293 | 61.16 214 | 82.77 149 | 73.90 309 |
|
UnsupCasMVSNet_eth | | | 67.33 262 | 65.99 263 | 71.37 277 | 73.48 304 | 51.47 292 | 75.16 280 | 85.19 222 | 65.20 217 | 60.78 286 | 80.93 265 | 42.35 276 | 77.20 298 | 57.12 247 | 53.69 312 | 85.44 257 |
|
MDTV_nov1_ep13_2view | | | | | | | 37.79 320 | 75.16 280 | | 55.10 290 | 66.53 262 | | 49.34 240 | | 53.98 259 | | 87.94 215 |
|
pmmvs3 | | | 57.79 285 | 54.26 289 | 68.37 291 | 64.02 319 | 56.72 260 | 75.12 282 | 65.17 321 | 40.20 315 | 52.93 309 | 69.86 308 | 20.36 319 | 75.48 305 | 45.45 293 | 55.25 311 | 72.90 310 |
|
dp | | | 66.80 264 | 65.43 264 | 70.90 282 | 79.74 278 | 48.82 303 | 75.12 282 | 74.77 296 | 59.61 264 | 64.08 277 | 77.23 287 | 42.89 273 | 80.72 283 | 48.86 280 | 66.58 284 | 83.16 278 |
|
Patchmtry | | | 70.74 242 | 69.16 241 | 75.49 255 | 80.72 267 | 54.07 281 | 74.94 284 | 80.30 267 | 58.34 272 | 70.01 223 | 81.19 258 | 52.50 208 | 86.54 256 | 53.37 263 | 71.09 266 | 85.87 255 |
|
PVSNet | | 64.34 18 | 72.08 235 | 70.87 233 | 75.69 252 | 86.21 168 | 56.44 265 | 74.37 285 | 80.73 262 | 62.06 248 | 70.17 220 | 82.23 243 | 42.86 274 | 83.31 275 | 54.77 257 | 84.45 135 | 87.32 229 |
|
MDA-MVSNet-bldmvs | | | 66.68 265 | 63.66 269 | 75.75 251 | 79.28 284 | 60.56 228 | 73.92 286 | 78.35 278 | 64.43 224 | 50.13 313 | 79.87 272 | 44.02 268 | 83.67 272 | 46.10 290 | 56.86 307 | 83.03 281 |
|
UnsupCasMVSNet_bld | | | 63.70 277 | 61.53 279 | 70.21 284 | 73.69 303 | 51.39 293 | 72.82 287 | 81.89 252 | 55.63 289 | 57.81 296 | 71.80 303 | 38.67 291 | 78.61 291 | 49.26 279 | 52.21 314 | 80.63 292 |
|
PatchT | | | 68.46 258 | 67.85 254 | 70.29 283 | 80.70 268 | 43.93 310 | 72.47 288 | 74.88 294 | 60.15 260 | 70.55 213 | 76.57 290 | 49.94 237 | 81.59 280 | 50.58 271 | 74.83 239 | 85.34 258 |
|
MVS-HIRNet | | | 59.14 282 | 57.67 284 | 63.57 298 | 81.65 255 | 43.50 311 | 71.73 289 | 65.06 322 | 39.59 317 | 51.43 311 | 57.73 317 | 38.34 293 | 82.58 278 | 39.53 303 | 73.95 246 | 64.62 316 |
|
Patchmatch-RL test | | | 70.24 248 | 67.78 257 | 77.61 237 | 77.43 290 | 59.57 233 | 71.16 290 | 70.33 309 | 62.94 238 | 68.65 242 | 72.77 301 | 50.62 232 | 85.49 264 | 69.58 150 | 66.58 284 | 87.77 219 |
|
test123 | | | 6.12 311 | 8.11 312 | 0.14 322 | 0.06 336 | 0.09 336 | 71.05 291 | 0.03 338 | 0.04 332 | 0.25 333 | 1.30 334 | 0.05 339 | 0.03 335 | 0.21 332 | 0.01 333 | 0.29 329 |
|
test1235678 | | | 58.74 284 | 56.89 287 | 64.30 295 | 69.70 313 | 41.87 314 | 71.05 291 | 74.87 295 | 54.06 294 | 50.63 312 | 71.53 304 | 25.30 313 | 74.10 310 | 31.80 316 | 63.10 296 | 76.93 305 |
|
ANet_high | | | 50.57 295 | 46.10 297 | 63.99 296 | 48.67 329 | 39.13 317 | 70.99 293 | 80.85 260 | 61.39 252 | 31.18 321 | 57.70 318 | 17.02 324 | 73.65 312 | 31.22 317 | 15.89 328 | 79.18 297 |
|
testmv | | | 53.85 290 | 51.03 292 | 62.31 299 | 61.46 321 | 38.88 319 | 70.95 294 | 74.69 299 | 51.11 307 | 41.26 315 | 66.85 310 | 14.28 326 | 72.13 315 | 29.19 318 | 49.51 316 | 75.93 308 |
|
testmvs | | | 6.04 312 | 8.02 313 | 0.10 323 | 0.08 335 | 0.03 337 | 69.74 295 | 0.04 337 | 0.05 331 | 0.31 332 | 1.68 333 | 0.02 340 | 0.04 334 | 0.24 331 | 0.02 331 | 0.25 330 |
|
testus | | | 59.00 283 | 57.91 282 | 62.25 300 | 72.25 309 | 39.09 318 | 69.74 295 | 75.02 293 | 53.04 301 | 57.21 299 | 73.72 299 | 18.76 322 | 70.33 318 | 32.86 311 | 68.57 277 | 77.35 302 |
|
N_pmnet | | | 52.79 292 | 53.26 290 | 51.40 311 | 78.99 286 | 7.68 335 | 69.52 297 | 3.89 335 | 51.63 306 | 57.01 300 | 74.98 295 | 40.83 284 | 65.96 324 | 37.78 306 | 64.67 293 | 80.56 294 |
|
FPMVS | | | 53.68 291 | 51.64 291 | 59.81 303 | 65.08 318 | 51.03 294 | 69.48 298 | 69.58 313 | 41.46 314 | 40.67 316 | 72.32 302 | 16.46 325 | 70.00 319 | 24.24 323 | 65.42 291 | 58.40 318 |
|
DSMNet-mixed | | | 57.77 286 | 56.90 286 | 60.38 302 | 67.70 317 | 35.61 321 | 69.18 299 | 53.97 326 | 32.30 323 | 57.49 298 | 79.88 271 | 40.39 287 | 68.57 321 | 38.78 304 | 72.37 257 | 76.97 304 |
|
new-patchmatchnet | | | 61.73 278 | 61.73 278 | 61.70 301 | 72.74 308 | 24.50 331 | 69.16 300 | 78.03 280 | 61.40 251 | 56.72 301 | 75.53 294 | 38.42 292 | 76.48 301 | 45.95 291 | 57.67 305 | 84.13 271 |
|
YYNet1 | | | 65.03 271 | 62.91 273 | 71.38 276 | 75.85 297 | 56.60 263 | 69.12 301 | 74.66 300 | 57.28 282 | 54.12 305 | 77.87 284 | 45.85 258 | 74.48 308 | 49.95 276 | 61.52 300 | 83.05 280 |
|
MDA-MVSNet_test_wron | | | 65.03 271 | 62.92 272 | 71.37 277 | 75.93 296 | 56.73 259 | 69.09 302 | 74.73 297 | 57.28 282 | 54.03 306 | 77.89 283 | 45.88 257 | 74.39 309 | 49.89 277 | 61.55 299 | 82.99 282 |
|
1111 | | | 57.11 287 | 56.82 288 | 57.97 305 | 69.10 314 | 28.28 326 | 68.90 303 | 74.54 301 | 54.01 295 | 53.71 307 | 74.51 296 | 23.09 316 | 67.90 322 | 32.28 313 | 61.26 301 | 77.73 300 |
|
.test1245 | | | 45.55 297 | 50.02 294 | 32.14 317 | 69.10 314 | 28.28 326 | 68.90 303 | 74.54 301 | 54.01 295 | 53.71 307 | 74.51 296 | 23.09 316 | 67.90 322 | 32.28 313 | 0.02 331 | 0.25 330 |
|
PVSNet_0 | | 57.27 20 | 61.67 279 | 59.27 280 | 68.85 289 | 79.61 279 | 57.44 254 | 68.01 305 | 73.44 304 | 55.93 288 | 58.54 293 | 70.41 306 | 44.58 265 | 77.55 297 | 47.01 285 | 35.91 319 | 71.55 311 |
|
test2356 | | | 59.50 281 | 58.08 281 | 63.74 297 | 71.23 311 | 41.88 313 | 67.59 306 | 72.42 307 | 53.72 297 | 57.65 297 | 70.74 305 | 26.31 311 | 72.40 314 | 32.03 315 | 71.06 267 | 76.93 305 |
|
ADS-MVSNet2 | | | 66.20 270 | 63.33 270 | 74.82 260 | 79.92 276 | 58.75 238 | 67.55 307 | 75.19 292 | 53.37 299 | 65.25 270 | 75.86 292 | 42.32 277 | 80.53 284 | 41.57 300 | 68.91 274 | 85.18 259 |
|
ADS-MVSNet | | | 64.36 275 | 62.88 274 | 68.78 290 | 79.92 276 | 47.17 305 | 67.55 307 | 71.18 308 | 53.37 299 | 65.25 270 | 75.86 292 | 42.32 277 | 73.99 311 | 41.57 300 | 68.91 274 | 85.18 259 |
|
LP | | | 61.36 280 | 57.78 283 | 72.09 273 | 75.54 300 | 58.53 240 | 67.16 309 | 75.22 291 | 51.90 305 | 54.13 304 | 69.97 307 | 37.73 296 | 80.45 285 | 32.74 312 | 55.63 309 | 77.29 303 |
|
LCM-MVSNet | | | 54.25 289 | 49.68 295 | 67.97 292 | 53.73 326 | 45.28 307 | 66.85 310 | 80.78 261 | 35.96 319 | 39.45 318 | 62.23 315 | 8.70 332 | 78.06 295 | 48.24 282 | 51.20 315 | 80.57 293 |
|
JIA-IIPM | | | 66.32 269 | 62.82 275 | 76.82 245 | 77.09 294 | 61.72 221 | 65.34 311 | 75.38 290 | 58.04 275 | 64.51 274 | 62.32 314 | 42.05 280 | 86.51 257 | 51.45 269 | 69.22 273 | 82.21 286 |
|
PMVS | | 37.38 22 | 44.16 299 | 40.28 300 | 55.82 306 | 40.82 332 | 42.54 312 | 65.12 312 | 63.99 324 | 34.43 320 | 24.48 323 | 57.12 319 | 3.92 334 | 76.17 302 | 17.10 326 | 55.52 310 | 48.75 319 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
new_pmnet | | | 50.91 294 | 50.29 293 | 52.78 309 | 68.58 316 | 34.94 324 | 63.71 313 | 56.63 325 | 39.73 316 | 44.95 314 | 65.47 312 | 21.93 318 | 58.48 326 | 34.98 309 | 56.62 308 | 64.92 315 |
|
Patchmatch-test | | | 64.82 273 | 63.24 271 | 69.57 285 | 79.42 281 | 49.82 301 | 63.49 314 | 69.05 316 | 51.98 304 | 59.95 290 | 80.13 269 | 50.91 227 | 70.98 317 | 40.66 302 | 73.57 250 | 87.90 216 |
|
ambc | | | | | 75.24 257 | 73.16 306 | 50.51 298 | 63.05 315 | 87.47 199 | | 64.28 275 | 77.81 285 | 17.80 323 | 89.73 221 | 57.88 241 | 60.64 302 | 85.49 256 |
|
test12356 | | | 49.28 296 | 48.51 296 | 51.59 310 | 62.06 320 | 19.11 332 | 60.40 316 | 72.45 306 | 47.60 311 | 40.64 317 | 65.68 311 | 13.84 327 | 68.72 320 | 27.29 320 | 46.67 318 | 66.94 314 |
|
PNet_i23d | | | 38.26 302 | 35.42 302 | 46.79 312 | 58.74 322 | 35.48 322 | 59.65 317 | 51.25 327 | 32.45 322 | 23.44 326 | 47.53 322 | 2.04 336 | 58.96 325 | 25.60 322 | 18.09 326 | 45.92 322 |
|
no-one | | | 51.08 293 | 45.79 298 | 66.95 294 | 57.92 324 | 50.49 299 | 59.63 318 | 76.04 289 | 48.04 309 | 31.85 319 | 56.10 320 | 19.12 321 | 80.08 287 | 36.89 307 | 26.52 321 | 70.29 312 |
|
CHOSEN 280x420 | | | 66.51 267 | 64.71 266 | 71.90 274 | 81.45 258 | 63.52 194 | 57.98 319 | 68.95 317 | 53.57 298 | 62.59 283 | 76.70 289 | 46.22 255 | 75.29 306 | 55.25 255 | 79.68 180 | 76.88 307 |
|
wuykxyi23d | | | 39.76 301 | 33.18 304 | 59.51 304 | 46.98 330 | 44.01 309 | 57.70 320 | 67.74 318 | 24.13 325 | 13.98 330 | 34.33 325 | 1.27 337 | 71.33 316 | 34.23 310 | 18.23 324 | 63.18 317 |
|
E-PMN | | | 31.77 304 | 30.64 305 | 35.15 315 | 52.87 327 | 27.67 328 | 57.09 321 | 47.86 329 | 24.64 324 | 16.40 328 | 33.05 326 | 11.23 329 | 54.90 328 | 14.46 328 | 18.15 325 | 22.87 325 |
|
EMVS | | | 30.81 305 | 29.65 306 | 34.27 316 | 50.96 328 | 25.95 330 | 56.58 322 | 46.80 330 | 24.01 326 | 15.53 329 | 30.68 327 | 12.47 328 | 54.43 329 | 12.81 329 | 17.05 327 | 22.43 326 |
|
PMMVS2 | | | 40.82 300 | 38.86 301 | 46.69 313 | 53.84 325 | 16.45 333 | 48.61 323 | 49.92 328 | 37.49 318 | 31.67 320 | 60.97 316 | 8.14 333 | 56.42 327 | 28.42 319 | 30.72 320 | 67.19 313 |
|
testpf | | | 56.51 288 | 57.58 285 | 53.30 308 | 71.99 310 | 41.19 316 | 46.89 324 | 69.32 315 | 58.06 274 | 52.87 310 | 69.45 309 | 27.99 309 | 72.73 313 | 59.59 225 | 62.07 297 | 45.98 321 |
|
wuyk23d | | | 16.82 309 | 15.94 310 | 19.46 320 | 58.74 322 | 31.45 325 | 39.22 325 | 3.74 336 | 6.84 329 | 6.04 331 | 2.70 332 | 1.27 337 | 24.29 332 | 10.54 330 | 14.40 330 | 2.63 328 |
|
tmp_tt | | | 18.61 308 | 21.40 309 | 10.23 321 | 4.82 334 | 10.11 334 | 34.70 326 | 30.74 333 | 1.48 330 | 23.91 325 | 26.07 328 | 28.42 308 | 13.41 333 | 27.12 321 | 15.35 329 | 7.17 327 |
|
Gipuma | | | 45.18 298 | 41.86 299 | 55.16 307 | 77.03 295 | 51.52 291 | 32.50 327 | 80.52 263 | 32.46 321 | 27.12 322 | 35.02 324 | 9.52 331 | 75.50 304 | 22.31 324 | 60.21 304 | 38.45 323 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
MVE | | 26.22 23 | 30.37 306 | 25.89 308 | 43.81 314 | 44.55 331 | 35.46 323 | 28.87 328 | 39.07 331 | 18.20 327 | 18.58 327 | 40.18 323 | 2.68 335 | 47.37 330 | 17.07 327 | 23.78 323 | 48.60 320 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
cdsmvs_eth3d_5k | | | 19.96 307 | 26.61 307 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 89.26 152 | 0.00 333 | 0.00 334 | 88.61 124 | 61.62 135 | 0.00 336 | 0.00 333 | 0.00 334 | 0.00 332 |
|
pcd_1.5k_mvsjas | | | 5.26 313 | 7.02 314 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 0.00 339 | 0.00 333 | 0.00 334 | 0.00 335 | 63.15 100 | 0.00 336 | 0.00 333 | 0.00 334 | 0.00 332 |
|
pcd1.5k->3k | | | 34.07 303 | 35.26 303 | 30.50 318 | 86.92 158 | 0.00 338 | 0.00 329 | 91.58 79 | 0.00 333 | 0.00 334 | 0.00 335 | 56.23 183 | 0.00 336 | 0.00 333 | 82.60 152 | 91.49 102 |
|
sosnet-low-res | | | 0.00 314 | 0.00 315 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 0.00 339 | 0.00 333 | 0.00 334 | 0.00 335 | 0.00 341 | 0.00 336 | 0.00 333 | 0.00 334 | 0.00 332 |
|
sosnet | | | 0.00 314 | 0.00 315 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 0.00 339 | 0.00 333 | 0.00 334 | 0.00 335 | 0.00 341 | 0.00 336 | 0.00 333 | 0.00 334 | 0.00 332 |
|
uncertanet | | | 0.00 314 | 0.00 315 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 0.00 339 | 0.00 333 | 0.00 334 | 0.00 335 | 0.00 341 | 0.00 336 | 0.00 333 | 0.00 334 | 0.00 332 |
|
Regformer | | | 0.00 314 | 0.00 315 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 0.00 339 | 0.00 333 | 0.00 334 | 0.00 335 | 0.00 341 | 0.00 336 | 0.00 333 | 0.00 334 | 0.00 332 |
|
ab-mvs-re | | | 7.23 310 | 9.64 311 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 0.00 339 | 0.00 333 | 0.00 334 | 86.72 168 | 0.00 341 | 0.00 336 | 0.00 333 | 0.00 334 | 0.00 332 |
|
uanet | | | 0.00 314 | 0.00 315 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 0.00 339 | 0.00 333 | 0.00 334 | 0.00 335 | 0.00 341 | 0.00 336 | 0.00 333 | 0.00 334 | 0.00 332 |
|
sam_mvs1 | | | | | | | | | | | | | 51.32 223 | | | | |
|
sam_mvs | | | | | | | | | | | | | 50.01 236 | | | | |
|
semantic-postprocess | | | | | 80.11 204 | 82.69 244 | 64.85 162 | | 83.47 236 | 69.16 175 | 70.49 216 | 84.15 224 | 50.83 231 | 88.15 245 | 69.23 152 | 72.14 260 | 87.34 228 |
|
MTGPA | | | | | | | | | 92.02 57 | | | | | | | | |
|
test_post | | | | | | | | | | | | 5.46 330 | 50.36 235 | 84.24 270 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 74.00 298 | 51.12 226 | 88.60 241 | | | |
|
MTMP | | | | | | | | | 32.83 332 | | | | | | | | |
|
gm-plane-assit | | | | | | 81.40 259 | 53.83 283 | | | 62.72 242 | | 80.94 264 | | 92.39 167 | 63.40 193 | | |
|
test9_res | | | | | | | | | | | | | | | 84.90 17 | 95.70 12 | 92.87 67 |
|
agg_prior2 | | | | | | | | | | | | | | | 82.91 38 | 95.45 14 | 92.70 68 |
|
agg_prior | | | | | | 92.85 41 | 71.94 38 | | 91.78 72 | | 84.41 39 | | | 94.93 64 | | | |
|
TestCases | | | | | 79.58 212 | 85.15 179 | 63.62 191 | | 79.83 271 | 62.31 245 | 60.32 288 | 86.73 166 | 32.02 305 | 88.96 236 | 50.28 273 | 71.57 264 | 86.15 249 |
|
test_prior | | | | | 86.33 45 | 92.61 46 | 69.59 70 | | 92.97 29 | | | | | 95.48 42 | | | 93.91 29 |
|
æ–°å‡ ä½•1 | | | | | 83.42 112 | 93.13 35 | 70.71 52 | | 85.48 219 | 57.43 280 | 81.80 69 | 91.98 55 | 63.28 95 | 92.27 171 | 64.60 188 | 92.99 46 | 87.27 230 |
|
旧先验1 | | | | | | 91.96 54 | 65.79 139 | | 86.37 211 | | | 93.08 43 | 69.31 51 | | | 92.74 49 | 88.74 191 |
|
原ACMM1 | | | | | 84.35 84 | 93.01 39 | 68.79 84 | | 92.44 42 | 63.96 231 | 81.09 76 | 91.57 65 | 66.06 75 | 95.45 44 | 67.19 167 | 94.82 29 | 88.81 188 |
|
testdata2 | | | | | | | | | | | | | | 91.01 206 | 62.37 201 | | |
|
segment_acmp | | | | | | | | | | | | | 73.08 23 | | | | |
|
testdata | | | | | 79.97 206 | 90.90 66 | 64.21 181 | | 84.71 225 | 59.27 267 | 85.40 22 | 92.91 44 | 62.02 132 | 89.08 232 | 68.95 154 | 91.37 60 | 86.63 245 |
|
test12 | | | | | 86.80 37 | 92.63 45 | 70.70 53 | | 91.79 71 | | 82.71 61 | | 71.67 32 | 96.16 28 | | 94.50 33 | 93.54 45 |
|
plane_prior7 | | | | | | 90.08 76 | 68.51 96 | | | | | | | | | | |
|
plane_prior6 | | | | | | 89.84 82 | 68.70 92 | | | | | | 60.42 157 | | | | |
|
plane_prior5 | | | | | | | | | 92.44 42 | | | | | 95.38 49 | 78.71 62 | 86.32 119 | 91.33 104 |
|
plane_prior4 | | | | | | | | | | | | 91.00 79 | | | | | |
|
plane_prior3 | | | | | | | 68.60 94 | | | 78.44 29 | 78.92 93 | | | | | | |
|
plane_prior1 | | | | | | 89.90 81 | | | | | | | | | | | |
|
n2 | | | | | | | | | 0.00 339 | | | | | | | | |
|
nn | | | | | | | | | 0.00 339 | | | | | | | | |
|
door-mid | | | | | | | | | 69.98 311 | | | | | | | | |
|
lessismore_v0 | | | | | 78.97 218 | 81.01 266 | 57.15 255 | | 65.99 320 | | 61.16 285 | 82.82 237 | 39.12 290 | 91.34 194 | 59.67 223 | 46.92 317 | 88.43 207 |
|
LGP-MVS_train | | | | | 84.50 78 | 89.23 99 | 68.76 86 | | 91.94 64 | 75.37 76 | 76.64 142 | 91.51 66 | 54.29 197 | 94.91 66 | 78.44 64 | 83.78 139 | 89.83 163 |
|
test11 | | | | | | | | | 92.23 49 | | | | | | | | |
|
door | | | | | | | | | 69.44 314 | | | | | | | | |
|
HQP5-MVS | | | | | | | 66.98 121 | | | | | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 77.47 74 | | |
|
HQP4-MVS | | | | | | | | | | | 77.24 132 | | | 95.11 57 | | | 91.03 111 |
|
HQP3-MVS | | | | | | | | | 92.19 52 | | | | | | | 85.99 123 | |
|
HQP2-MVS | | | | | | | | | | | | | 60.17 160 | | | | |
|
NP-MVS | | | | | | 89.62 84 | 68.32 98 | | | | | 90.24 89 | | | | | |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 81.95 158 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 81.25 163 | |
|
Test By Simon | | | | | | | | | | | | | 64.33 86 | | | | |
|
ITE_SJBPF | | | | | 78.22 230 | 81.77 254 | 60.57 227 | | 83.30 238 | 69.25 173 | 67.54 252 | 87.20 159 | 36.33 300 | 87.28 252 | 54.34 258 | 74.62 241 | 86.80 240 |
|
DeepMVS_CX | | | | | 27.40 319 | 40.17 333 | 26.90 329 | | 24.59 334 | 17.44 328 | 23.95 324 | 48.61 321 | 9.77 330 | 26.48 331 | 18.06 325 | 24.47 322 | 28.83 324 |
|