TDRefinement | | | 93.16 1 | 95.57 1 | 90.36 1 | 88.79 52 | 93.57 1 | 97.27 1 | 78.23 22 | 95.55 1 | 93.00 1 | 93.98 15 | 96.01 37 | 87.53 1 | 97.69 1 | 96.81 1 | 97.33 1 | 95.34 3 |
|
anonymousdsp | | | 85.62 57 | 90.53 44 | 79.88 87 | 64.64 198 | 76.35 134 | 96.28 13 | 53.53 184 | 85.63 65 | 81.59 67 | 92.81 28 | 97.71 12 | 86.88 2 | 94.56 26 | 92.83 25 | 96.35 6 | 93.84 8 |
|
LTVRE_ROB | | 86.82 1 | 91.55 3 | 94.43 3 | 88.19 11 | 83.19 105 | 86.35 63 | 93.60 36 | 78.79 19 | 95.48 3 | 91.79 2 | 93.08 24 | 97.21 19 | 86.34 3 | 97.06 2 | 96.27 3 | 95.46 23 | 95.56 2 |
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 |
PMVS | | 79.51 9 | 90.23 14 | 92.67 13 | 87.39 21 | 90.16 39 | 88.75 40 | 93.64 35 | 75.78 43 | 90.00 31 | 83.70 47 | 92.97 26 | 92.22 98 | 86.13 4 | 97.01 3 | 96.79 2 | 94.94 29 | 90.96 44 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
SixPastTwentyTwo | | | 89.14 29 | 92.19 29 | 85.58 32 | 84.62 85 | 82.56 88 | 90.53 60 | 71.93 59 | 91.95 11 | 85.89 35 | 94.22 13 | 97.25 18 | 85.42 5 | 95.73 12 | 91.71 41 | 95.08 28 | 91.89 35 |
|
CPTT-MVS | | | 89.63 25 | 90.52 45 | 88.59 7 | 90.95 31 | 90.74 21 | 95.71 17 | 79.13 15 | 87.70 47 | 85.68 38 | 80.05 130 | 95.74 44 | 84.77 6 | 94.28 30 | 92.68 27 | 95.28 26 | 92.45 29 |
|
ACMMPR | | | 91.30 4 | 92.88 10 | 89.46 4 | 91.92 11 | 91.61 5 | 96.60 5 | 79.46 14 | 90.08 29 | 88.53 14 | 89.54 61 | 95.57 46 | 84.25 7 | 95.24 20 | 94.27 13 | 95.97 11 | 93.85 7 |
|
COLMAP_ROB | | 85.66 2 | 91.85 2 | 95.01 2 | 88.16 12 | 88.98 51 | 92.86 2 | 95.51 20 | 72.17 58 | 94.95 4 | 91.27 3 | 94.11 14 | 97.77 11 | 84.22 8 | 96.49 4 | 95.27 5 | 96.79 2 | 93.60 11 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
MP-MVS | | | 90.84 6 | 91.95 32 | 89.55 3 | 92.92 5 | 90.90 19 | 96.56 6 | 79.60 11 | 86.83 56 | 88.75 13 | 89.00 69 | 94.38 74 | 84.01 9 | 94.94 25 | 94.34 11 | 95.45 24 | 93.24 22 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
CP-MVS | | | 91.09 5 | 92.33 23 | 89.65 2 | 92.16 10 | 90.41 27 | 96.46 10 | 80.38 8 | 88.26 42 | 89.17 11 | 87.00 90 | 96.34 29 | 83.95 10 | 95.77 11 | 94.72 8 | 95.81 17 | 93.78 9 |
|
SD-MVS | | | 89.91 18 | 92.23 28 | 87.19 22 | 91.31 24 | 89.79 34 | 94.31 31 | 75.34 46 | 89.26 34 | 81.79 65 | 92.68 29 | 95.08 59 | 83.88 11 | 93.10 39 | 92.69 26 | 96.54 4 | 93.02 23 |
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024 |
PGM-MVS | | | 90.42 10 | 91.58 35 | 89.05 6 | 91.77 14 | 91.06 13 | 96.51 7 | 78.94 17 | 85.41 68 | 87.67 19 | 87.02 89 | 95.26 53 | 83.62 12 | 95.01 24 | 93.94 16 | 95.79 19 | 93.40 20 |
|
ACMM | | 80.67 7 | 90.67 7 | 92.46 18 | 88.57 8 | 91.35 22 | 89.93 31 | 96.34 12 | 77.36 31 | 90.17 27 | 86.88 29 | 87.32 85 | 96.63 22 | 83.32 13 | 95.79 10 | 94.49 10 | 96.19 9 | 92.91 25 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
LGP-MVS_train | | | 90.56 9 | 92.38 20 | 88.43 10 | 90.88 32 | 91.15 11 | 95.35 22 | 77.65 26 | 86.26 61 | 87.23 24 | 90.45 52 | 97.35 16 | 83.20 14 | 95.44 16 | 93.41 21 | 96.28 8 | 92.63 26 |
|
TSAR-MVS + ACMM | | | 89.14 29 | 92.11 30 | 85.67 31 | 89.27 47 | 90.61 24 | 90.98 50 | 79.48 13 | 88.86 37 | 79.80 76 | 93.01 25 | 93.53 84 | 83.17 15 | 92.75 44 | 92.45 30 | 91.32 78 | 93.59 12 |
|
ACMMP | | | 90.63 8 | 92.40 19 | 88.56 9 | 91.24 28 | 91.60 6 | 96.49 9 | 77.53 27 | 87.89 45 | 86.87 30 | 87.24 87 | 96.46 24 | 82.87 16 | 95.59 15 | 94.50 9 | 96.35 6 | 93.51 17 |
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 |
MSLP-MVS++ | | | 86.29 55 | 89.10 53 | 83.01 56 | 85.71 78 | 89.79 34 | 87.04 100 | 74.39 50 | 85.17 70 | 78.92 82 | 77.59 144 | 93.57 82 | 82.60 17 | 93.23 37 | 91.88 40 | 89.42 101 | 92.46 28 |
|
zzz-MVS | | | 90.38 11 | 91.35 39 | 89.25 5 | 93.08 3 | 86.59 60 | 96.45 11 | 79.00 16 | 90.23 26 | 89.30 10 | 85.87 101 | 94.97 62 | 82.54 18 | 95.05 23 | 94.83 7 | 95.14 27 | 91.94 34 |
|
TSAR-MVS + MP. | | | 89.67 24 | 92.25 26 | 86.65 26 | 91.53 18 | 90.98 17 | 96.15 14 | 73.30 55 | 87.88 46 | 81.83 64 | 92.92 27 | 95.15 57 | 82.23 19 | 93.58 35 | 92.25 34 | 94.87 30 | 93.01 24 |
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition |
ACMP | | 80.00 8 | 90.12 16 | 92.30 24 | 87.58 19 | 90.83 34 | 91.10 12 | 94.96 27 | 76.06 41 | 87.47 49 | 85.33 39 | 88.91 72 | 97.65 14 | 82.13 20 | 95.31 17 | 93.44 20 | 96.14 10 | 92.22 31 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
SteuartSystems-ACMMP | | | 90.00 17 | 91.73 33 | 87.97 13 | 91.21 29 | 90.29 28 | 96.51 7 | 78.00 24 | 86.33 59 | 85.32 40 | 88.23 77 | 94.67 67 | 82.08 21 | 95.13 22 | 93.88 17 | 94.72 35 | 93.59 12 |
Skip Steuart: Steuart Systems R&D Blog. |
Gipuma | | | 86.47 53 | 89.25 52 | 83.23 53 | 83.88 97 | 78.78 115 | 85.35 110 | 68.42 85 | 92.69 9 | 89.03 12 | 91.94 35 | 96.32 31 | 81.80 22 | 94.45 27 | 86.86 78 | 90.91 84 | 83.69 95 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
HFP-MVS | | | 90.32 13 | 92.37 21 | 87.94 14 | 91.46 21 | 90.91 18 | 95.69 18 | 79.49 12 | 89.94 32 | 83.50 50 | 89.06 68 | 94.44 72 | 81.68 23 | 94.17 31 | 94.19 14 | 95.81 17 | 93.87 6 |
|
APDe-MVS | | | 89.85 20 | 92.91 9 | 86.29 27 | 90.47 38 | 91.34 7 | 96.04 15 | 76.41 40 | 91.11 16 | 78.50 84 | 93.44 19 | 95.82 41 | 81.55 24 | 93.16 38 | 91.90 39 | 94.77 33 | 93.58 14 |
|
DVP-MVS | | | 89.40 27 | 92.69 12 | 85.56 34 | 89.01 50 | 89.85 32 | 93.72 34 | 75.42 44 | 92.28 10 | 80.49 70 | 94.36 12 | 94.87 63 | 81.46 25 | 92.49 48 | 91.42 42 | 93.27 48 | 93.54 16 |
|
APD-MVS | | | 89.14 29 | 91.25 41 | 86.67 25 | 91.73 15 | 91.02 15 | 95.50 21 | 77.74 25 | 84.04 79 | 79.47 79 | 91.48 42 | 94.85 64 | 81.14 26 | 92.94 41 | 92.20 36 | 94.47 38 | 92.24 30 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
DPE-MVS | | | 89.81 22 | 92.34 22 | 86.86 24 | 89.69 44 | 91.00 16 | 95.53 19 | 76.91 34 | 88.18 43 | 83.43 53 | 93.48 18 | 95.19 54 | 81.07 27 | 92.75 44 | 92.07 37 | 94.55 36 | 93.74 10 |
|
DeepC-MVS | | 83.59 4 | 90.37 12 | 92.56 17 | 87.82 15 | 91.26 27 | 92.33 3 | 94.72 29 | 80.04 9 | 90.01 30 | 84.61 42 | 93.33 20 | 94.22 75 | 80.59 28 | 92.90 42 | 92.52 29 | 95.69 21 | 92.57 27 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
ACMH+ | | 79.05 11 | 89.62 26 | 93.08 7 | 85.58 32 | 88.58 54 | 89.26 37 | 92.18 44 | 74.23 51 | 93.55 7 | 82.66 56 | 92.32 34 | 98.35 7 | 80.29 29 | 95.28 18 | 92.34 32 | 95.52 22 | 90.43 47 |
|
HPM-MVS++ | | | 88.74 39 | 89.54 50 | 87.80 16 | 92.58 7 | 85.69 68 | 95.10 25 | 78.01 23 | 87.08 53 | 87.66 20 | 87.89 80 | 92.07 101 | 80.28 30 | 90.97 66 | 91.41 44 | 93.17 52 | 91.69 36 |
|
DeepPCF-MVS | | 81.61 6 | 87.95 46 | 90.29 47 | 85.22 38 | 87.48 63 | 90.01 30 | 93.79 33 | 73.54 53 | 88.93 36 | 83.89 45 | 89.40 63 | 90.84 115 | 80.26 31 | 90.62 69 | 90.19 52 | 92.36 66 | 92.03 33 |
|
RPSCF | | | 88.05 45 | 92.61 16 | 82.73 63 | 84.24 90 | 88.40 42 | 90.04 70 | 66.29 100 | 91.46 12 | 82.29 58 | 88.93 71 | 96.01 37 | 79.38 32 | 95.15 21 | 94.90 6 | 94.15 39 | 93.40 20 |
|
TSAR-MVS + GP. | | | 85.32 62 | 87.41 71 | 82.89 60 | 90.07 41 | 85.69 68 | 89.07 78 | 72.99 56 | 82.45 87 | 74.52 101 | 85.09 108 | 87.67 134 | 79.24 33 | 91.11 61 | 90.41 49 | 91.45 75 | 89.45 54 |
|
OPM-MVS | | | 89.82 21 | 92.24 27 | 86.99 23 | 90.86 33 | 89.35 36 | 95.07 26 | 75.91 42 | 91.16 15 | 86.87 30 | 91.07 48 | 97.29 17 | 79.13 34 | 93.32 36 | 91.99 38 | 94.12 40 | 91.49 39 |
|
SMA-MVS | | | 90.13 15 | 92.26 25 | 87.64 18 | 91.68 16 | 90.44 26 | 95.22 24 | 77.34 33 | 90.79 21 | 87.80 17 | 90.42 53 | 92.05 103 | 79.05 35 | 93.89 33 | 93.59 19 | 94.77 33 | 94.62 4 |
|
IterMVS-SCA-FT | | | 77.23 125 | 79.18 133 | 74.96 122 | 76.67 160 | 79.85 105 | 75.58 175 | 61.34 149 | 73.10 140 | 73.79 106 | 86.23 96 | 79.61 160 | 79.00 36 | 80.28 153 | 75.50 162 | 83.41 160 | 79.70 133 |
|
EG-PatchMatch MVS | | | 84.35 70 | 87.55 68 | 80.62 82 | 86.38 71 | 82.24 90 | 86.75 101 | 64.02 126 | 84.24 75 | 78.17 86 | 89.38 64 | 95.03 61 | 78.78 37 | 89.95 74 | 86.33 81 | 89.59 97 | 85.65 82 |
|
ACMMP_NAP | | | 89.86 19 | 91.96 31 | 87.42 20 | 91.00 30 | 90.08 29 | 96.00 16 | 76.61 37 | 89.28 33 | 87.73 18 | 90.04 55 | 91.80 106 | 78.71 38 | 94.36 29 | 93.82 18 | 94.48 37 | 94.32 5 |
|
CSCG | | | 88.12 44 | 91.45 36 | 84.23 47 | 88.12 59 | 90.59 25 | 90.57 57 | 68.60 83 | 91.37 14 | 83.45 52 | 89.94 56 | 95.14 58 | 78.71 38 | 91.45 55 | 88.21 70 | 95.96 12 | 93.44 19 |
|
UA-Net | | | 89.02 33 | 91.44 37 | 86.20 28 | 94.88 1 | 89.84 33 | 94.76 28 | 77.45 29 | 85.41 68 | 74.79 98 | 88.83 73 | 88.90 128 | 78.67 40 | 96.06 7 | 95.45 4 | 96.66 3 | 95.58 1 |
|
LS3D | | | 89.02 33 | 91.69 34 | 85.91 30 | 89.72 43 | 90.81 20 | 92.56 43 | 71.69 60 | 90.83 20 | 87.24 23 | 89.71 59 | 92.07 101 | 78.37 41 | 94.43 28 | 92.59 28 | 95.86 13 | 91.35 40 |
|
train_agg | | | 86.67 51 | 87.73 67 | 85.43 35 | 91.51 19 | 82.72 85 | 94.47 30 | 74.22 52 | 81.71 94 | 81.54 68 | 89.20 67 | 92.87 90 | 78.33 42 | 90.12 72 | 88.47 66 | 92.51 65 | 89.04 58 |
|
3Dnovator+ | | 83.71 3 | 88.13 43 | 90.00 48 | 85.94 29 | 86.82 68 | 91.06 13 | 94.26 32 | 75.39 45 | 88.85 38 | 85.76 37 | 85.74 103 | 86.92 137 | 78.02 43 | 93.03 40 | 92.21 35 | 95.39 25 | 92.21 32 |
|
ACMH | | 78.40 12 | 88.94 37 | 92.62 15 | 84.65 41 | 86.45 70 | 87.16 56 | 91.47 47 | 68.79 81 | 95.49 2 | 89.74 6 | 93.55 17 | 98.50 2 | 77.96 44 | 94.14 32 | 89.57 59 | 93.49 44 | 89.94 51 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
v7n | | | 87.11 48 | 90.46 46 | 83.19 54 | 85.22 81 | 83.69 79 | 90.03 71 | 68.20 88 | 91.01 18 | 86.71 33 | 94.80 9 | 98.46 4 | 77.69 45 | 91.10 62 | 85.98 85 | 91.30 79 | 88.19 64 |
|
OMC-MVS | | | 88.16 42 | 91.34 40 | 84.46 45 | 86.85 67 | 90.63 23 | 93.01 40 | 67.00 95 | 90.35 25 | 87.40 22 | 86.86 92 | 96.35 28 | 77.66 46 | 92.63 46 | 90.84 45 | 94.84 31 | 91.68 37 |
|
FPMVS | | | 81.56 96 | 84.04 109 | 78.66 96 | 82.92 107 | 75.96 138 | 86.48 104 | 65.66 109 | 84.67 74 | 71.47 119 | 77.78 142 | 83.22 150 | 77.57 47 | 91.24 58 | 90.21 51 | 87.84 119 | 85.21 84 |
|
PVSNet_Blended_VisFu | | | 83.00 83 | 84.16 107 | 81.65 69 | 82.17 115 | 86.01 64 | 88.03 86 | 71.23 62 | 76.05 132 | 79.54 78 | 83.88 114 | 83.44 147 | 77.49 48 | 87.38 94 | 84.93 96 | 91.41 76 | 87.40 72 |
|
PLC | | 76.06 15 | 85.38 61 | 87.46 69 | 82.95 59 | 85.79 77 | 88.84 39 | 88.86 80 | 68.70 82 | 87.06 54 | 83.60 48 | 79.02 133 | 90.05 120 | 77.37 49 | 90.88 67 | 89.66 57 | 93.37 47 | 86.74 74 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
PM-MVS | | | 80.42 105 | 83.63 113 | 76.67 108 | 78.04 145 | 72.37 158 | 87.14 96 | 60.18 157 | 80.13 114 | 71.75 118 | 86.12 98 | 93.92 78 | 77.08 50 | 86.56 102 | 85.12 94 | 85.83 143 | 81.18 118 |
|
NCCC | | | 86.74 50 | 87.97 66 | 85.31 36 | 90.64 35 | 87.25 55 | 93.27 38 | 74.59 48 | 86.50 57 | 83.72 46 | 75.92 160 | 92.39 96 | 77.08 50 | 91.72 51 | 90.68 47 | 92.57 63 | 91.30 41 |
|
X-MVS | | | 89.36 28 | 90.73 43 | 87.77 17 | 91.50 20 | 91.23 8 | 96.76 4 | 78.88 18 | 87.29 51 | 87.14 26 | 78.98 135 | 94.53 69 | 76.47 52 | 95.25 19 | 94.28 12 | 95.85 14 | 93.55 15 |
|
CNLPA | | | 85.50 59 | 88.58 55 | 81.91 66 | 84.55 87 | 87.52 53 | 90.89 52 | 63.56 131 | 88.18 43 | 84.06 44 | 83.85 115 | 91.34 112 | 76.46 53 | 91.27 57 | 89.00 64 | 91.96 70 | 88.88 60 |
|
MSP-MVS | | | 88.51 41 | 91.36 38 | 85.19 39 | 90.63 36 | 92.01 4 | 95.29 23 | 77.52 28 | 90.48 24 | 80.21 75 | 90.21 54 | 96.08 33 | 76.38 54 | 88.30 89 | 91.42 42 | 91.12 83 | 91.01 43 |
|
v1240 | | | 83.57 76 | 84.94 96 | 81.97 65 | 84.05 92 | 81.27 97 | 89.46 75 | 66.06 103 | 81.31 104 | 87.50 21 | 91.88 38 | 95.46 50 | 76.25 55 | 81.16 146 | 80.51 134 | 88.52 114 | 82.98 103 |
|
TAPA-MVS | | 78.00 13 | 85.88 56 | 88.37 59 | 82.96 58 | 84.69 84 | 88.62 41 | 90.62 55 | 64.22 121 | 89.15 35 | 88.05 15 | 78.83 137 | 93.71 79 | 76.20 56 | 90.11 73 | 88.22 69 | 94.00 41 | 89.97 50 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
CNVR-MVS | | | 86.93 49 | 88.98 54 | 84.54 43 | 90.11 40 | 87.41 54 | 93.23 39 | 73.47 54 | 86.31 60 | 82.25 59 | 82.96 118 | 92.15 99 | 76.04 57 | 91.69 52 | 90.69 46 | 92.17 68 | 91.64 38 |
|
PHI-MVS | | | 86.37 54 | 88.14 63 | 84.30 46 | 86.65 69 | 87.56 52 | 90.76 54 | 70.16 67 | 82.55 86 | 89.65 7 | 84.89 110 | 92.40 95 | 75.97 58 | 90.88 67 | 89.70 56 | 92.58 61 | 89.03 59 |
|
v1921920 | | | 83.49 77 | 84.94 96 | 81.80 67 | 83.78 98 | 81.20 99 | 89.50 74 | 65.91 106 | 81.64 96 | 87.18 25 | 91.70 40 | 95.39 51 | 75.85 59 | 81.56 144 | 80.27 136 | 88.60 111 | 82.80 105 |
|
DeepC-MVS_fast | | 81.78 5 | 87.38 47 | 89.64 49 | 84.75 40 | 89.89 42 | 90.70 22 | 92.74 42 | 74.45 49 | 86.02 62 | 82.16 62 | 86.05 99 | 91.99 105 | 75.84 60 | 91.16 60 | 90.44 48 | 93.41 46 | 91.09 42 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
v144192 | | | 83.43 78 | 84.97 95 | 81.63 70 | 83.43 101 | 81.23 98 | 89.42 76 | 66.04 105 | 81.45 102 | 86.40 34 | 91.46 43 | 95.70 45 | 75.76 61 | 82.14 137 | 80.23 137 | 88.74 108 | 82.57 108 |
|
CDPH-MVS | | | 86.66 52 | 88.52 57 | 84.48 44 | 89.61 45 | 88.27 44 | 92.86 41 | 72.69 57 | 80.55 112 | 82.71 55 | 86.92 91 | 93.32 86 | 75.55 62 | 91.00 65 | 89.85 54 | 93.47 45 | 89.71 52 |
|
thisisatest0515 | | | 81.18 102 | 84.32 103 | 77.52 106 | 76.73 159 | 74.84 148 | 85.06 113 | 61.37 148 | 81.05 107 | 73.95 104 | 88.79 74 | 89.25 125 | 75.49 63 | 85.98 107 | 84.78 98 | 92.53 64 | 85.56 83 |
|
MVS_111021_LR | | | 83.20 81 | 85.33 88 | 80.73 80 | 82.88 109 | 78.23 119 | 89.61 72 | 65.23 113 | 82.08 91 | 81.19 69 | 85.31 105 | 92.04 104 | 75.22 64 | 89.50 76 | 85.90 87 | 90.24 88 | 84.23 90 |
|
AdaColmap | | | 84.15 71 | 85.14 92 | 83.00 57 | 89.08 49 | 87.14 57 | 90.56 58 | 70.90 63 | 82.40 88 | 80.41 71 | 73.82 172 | 84.69 146 | 75.19 65 | 91.58 54 | 89.90 53 | 91.87 72 | 86.48 75 |
|
WR-MVS | | | 89.79 23 | 93.66 4 | 85.27 37 | 91.32 23 | 88.27 44 | 93.49 37 | 79.86 10 | 92.75 8 | 75.37 94 | 96.86 1 | 98.38 5 | 75.10 66 | 95.93 8 | 94.07 15 | 96.46 5 | 89.39 55 |
|
MCST-MVS | | | 84.79 67 | 86.48 75 | 82.83 61 | 87.30 64 | 87.03 58 | 90.46 65 | 69.33 75 | 83.14 82 | 82.21 61 | 81.69 126 | 92.14 100 | 75.09 67 | 87.27 96 | 84.78 98 | 92.58 61 | 89.30 56 |
|
PatchMatch-RL | | | 76.05 135 | 76.64 149 | 75.36 115 | 77.84 149 | 69.87 166 | 81.09 138 | 63.43 133 | 71.66 149 | 68.34 140 | 71.70 179 | 81.76 155 | 74.98 68 | 84.83 121 | 83.44 110 | 86.45 134 | 73.22 160 |
|
v1192 | | | 83.61 75 | 85.23 90 | 81.72 68 | 84.05 92 | 82.15 91 | 89.54 73 | 66.20 101 | 81.38 103 | 86.76 32 | 91.79 39 | 96.03 35 | 74.88 69 | 81.81 141 | 80.92 130 | 88.91 107 | 82.50 109 |
|
Effi-MVS+-dtu | | | 82.04 93 | 83.39 116 | 80.48 85 | 85.48 80 | 86.57 62 | 88.40 83 | 68.28 87 | 69.04 162 | 73.13 111 | 76.26 155 | 91.11 114 | 74.74 70 | 88.40 86 | 87.76 71 | 92.84 58 | 84.57 88 |
|
ambc | | | | 88.38 58 | | 91.62 17 | 87.97 50 | 84.48 117 | | 88.64 41 | 87.93 16 | 87.38 84 | 94.82 66 | 74.53 71 | 89.14 80 | 83.86 108 | 85.94 141 | 86.84 73 |
|
MVS_111021_HR | | | 83.95 73 | 86.10 81 | 81.44 71 | 84.62 85 | 80.29 103 | 90.51 61 | 68.05 89 | 84.07 78 | 80.38 73 | 84.74 111 | 91.37 111 | 74.23 72 | 90.37 71 | 87.25 74 | 90.86 85 | 84.59 87 |
|
v10 | | | 83.17 82 | 85.22 91 | 80.78 77 | 83.26 104 | 82.99 84 | 88.66 82 | 66.49 99 | 79.24 121 | 83.60 48 | 91.46 43 | 95.47 49 | 74.12 73 | 82.60 136 | 80.66 131 | 88.53 113 | 84.11 93 |
|
V42 | | | 79.59 112 | 83.59 114 | 74.93 123 | 69.61 183 | 77.05 130 | 86.59 103 | 55.84 172 | 78.42 124 | 77.29 87 | 89.84 58 | 95.08 59 | 74.12 73 | 83.05 129 | 80.11 138 | 86.12 137 | 81.59 116 |
|
v1144 | | | 83.22 80 | 85.01 93 | 81.14 73 | 83.76 99 | 81.60 94 | 88.95 79 | 65.58 111 | 81.89 93 | 85.80 36 | 91.68 41 | 95.84 40 | 74.04 75 | 82.12 138 | 80.56 133 | 88.70 110 | 81.41 117 |
|
TSAR-MVS + COLMAP | | | 85.51 58 | 88.36 60 | 82.19 64 | 86.05 74 | 87.69 51 | 90.50 62 | 70.60 66 | 86.40 58 | 82.33 57 | 89.69 60 | 92.52 94 | 74.01 76 | 87.53 93 | 86.84 79 | 89.63 96 | 87.80 69 |
|
EIA-MVS | | | 78.57 120 | 77.90 139 | 79.35 91 | 87.24 66 | 80.71 101 | 86.16 105 | 64.03 125 | 62.63 190 | 73.49 108 | 73.60 173 | 76.12 175 | 73.83 77 | 88.49 85 | 84.93 96 | 91.36 77 | 78.78 138 |
|
EU-MVSNet | | | 76.48 131 | 80.53 128 | 71.75 136 | 67.62 189 | 70.30 163 | 81.74 134 | 54.06 180 | 75.47 134 | 71.01 122 | 80.10 128 | 93.17 89 | 73.67 78 | 83.73 127 | 77.85 147 | 82.40 162 | 83.07 100 |
|
CVMVSNet | | | 75.65 139 | 77.62 143 | 73.35 131 | 71.95 175 | 69.89 165 | 83.04 124 | 60.84 153 | 69.12 160 | 68.76 135 | 79.92 131 | 78.93 163 | 73.64 79 | 81.02 147 | 81.01 129 | 81.86 165 | 83.43 97 |
|
Fast-Effi-MVS+ | | | 81.42 97 | 83.82 111 | 78.62 97 | 82.24 114 | 80.62 102 | 87.72 89 | 63.51 132 | 73.01 141 | 74.75 99 | 83.80 116 | 92.70 92 | 73.44 80 | 88.15 91 | 85.26 92 | 90.05 90 | 83.17 99 |
|
v8 | | | 82.20 91 | 84.56 101 | 79.45 89 | 82.42 112 | 81.65 93 | 87.26 94 | 64.27 120 | 79.36 120 | 81.70 66 | 91.04 49 | 95.75 43 | 73.30 81 | 82.82 132 | 79.18 143 | 87.74 121 | 82.09 112 |
|
CS-MVS | | | 79.35 115 | 77.74 140 | 81.22 72 | 85.59 79 | 79.85 105 | 88.78 81 | 66.61 97 | 67.63 165 | 80.41 71 | 67.82 194 | 75.07 180 | 73.27 82 | 88.31 88 | 84.36 102 | 92.63 60 | 81.18 118 |
|
PCF-MVS | | 76.59 14 | 84.11 72 | 85.27 89 | 82.76 62 | 86.12 73 | 88.30 43 | 91.24 49 | 69.10 76 | 82.36 89 | 84.45 43 | 77.56 145 | 90.40 119 | 72.91 83 | 85.88 108 | 83.88 106 | 92.72 59 | 88.53 62 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
ETV-MVS | | | 79.04 119 | 79.09 134 | 78.98 94 | 86.03 75 | 78.98 113 | 88.25 85 | 65.59 110 | 70.75 153 | 69.32 129 | 75.83 161 | 70.44 186 | 72.62 84 | 89.62 75 | 85.99 84 | 92.09 69 | 82.04 114 |
|
MVS_0304 | | | 84.73 68 | 86.19 79 | 83.02 55 | 88.32 55 | 86.71 59 | 91.55 46 | 70.87 64 | 73.79 139 | 82.88 54 | 85.13 107 | 93.35 85 | 72.55 85 | 88.62 83 | 87.69 72 | 91.93 71 | 88.05 67 |
|
HQP-MVS | | | 85.02 64 | 86.41 77 | 83.40 52 | 89.19 48 | 86.59 60 | 91.28 48 | 71.60 61 | 82.79 85 | 83.48 51 | 78.65 139 | 93.54 83 | 72.55 85 | 86.49 103 | 85.89 88 | 92.28 67 | 90.95 45 |
|
Effi-MVS+ | | | 82.33 89 | 83.87 110 | 80.52 84 | 84.51 88 | 81.32 96 | 87.53 91 | 68.05 89 | 74.94 137 | 79.67 77 | 82.37 123 | 92.31 97 | 72.21 87 | 85.06 115 | 86.91 77 | 91.18 81 | 84.20 91 |
|
thisisatest0530 | | | 75.54 140 | 75.95 157 | 75.05 118 | 75.08 166 | 73.56 153 | 82.15 131 | 60.31 154 | 69.17 159 | 69.32 129 | 79.02 133 | 58.78 201 | 72.17 88 | 83.88 126 | 83.08 116 | 91.30 79 | 84.20 91 |
|
tttt0517 | | | 75.86 138 | 76.23 153 | 75.42 114 | 75.55 165 | 74.06 152 | 82.73 126 | 60.31 154 | 69.24 158 | 70.24 126 | 79.18 132 | 58.79 200 | 72.17 88 | 84.49 123 | 83.08 116 | 91.54 74 | 84.80 85 |
|
EPP-MVSNet | | | 82.76 87 | 86.47 76 | 78.45 98 | 86.00 76 | 84.47 73 | 85.39 109 | 68.42 85 | 84.17 76 | 62.97 156 | 89.26 66 | 76.84 171 | 72.13 90 | 92.56 47 | 90.40 50 | 95.76 20 | 87.56 71 |
|
v2v482 | | | 82.20 91 | 84.26 104 | 79.81 88 | 82.67 111 | 80.18 104 | 87.67 90 | 63.96 128 | 81.69 95 | 84.73 41 | 91.27 46 | 96.33 30 | 72.05 91 | 81.94 140 | 79.56 140 | 87.79 120 | 78.84 137 |
|
WR-MVS_H | | | 88.99 35 | 93.28 5 | 83.99 51 | 91.92 11 | 89.13 38 | 91.95 45 | 83.23 1 | 90.14 28 | 71.92 117 | 95.85 4 | 98.01 10 | 71.83 92 | 95.82 9 | 93.19 23 | 93.07 54 | 90.83 46 |
|
Vis-MVSNet | | | 83.32 79 | 88.12 64 | 77.71 102 | 77.91 148 | 83.44 82 | 90.58 56 | 69.49 72 | 81.11 106 | 67.10 145 | 89.85 57 | 91.48 110 | 71.71 93 | 91.34 56 | 89.37 60 | 89.48 99 | 90.26 48 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
v148 | | | 79.33 116 | 82.32 121 | 75.84 112 | 80.14 126 | 75.74 139 | 81.98 132 | 57.06 169 | 81.51 100 | 79.36 80 | 89.42 62 | 96.42 26 | 71.32 94 | 81.54 145 | 75.29 163 | 85.20 148 | 76.32 146 |
|
PS-CasMVS | | | 89.07 32 | 93.23 6 | 84.21 48 | 92.44 8 | 88.23 46 | 90.54 59 | 82.95 3 | 90.50 23 | 75.31 95 | 95.80 5 | 98.37 6 | 71.16 95 | 96.30 5 | 93.32 22 | 92.88 56 | 90.11 49 |
|
HyFIR lowres test | | | 73.29 149 | 74.14 165 | 72.30 133 | 73.08 171 | 78.33 118 | 83.12 122 | 62.41 143 | 63.81 183 | 62.13 160 | 76.67 152 | 78.50 164 | 71.09 96 | 74.13 175 | 77.47 152 | 81.98 164 | 70.10 167 |
|
CP-MVSNet | | | 88.71 40 | 92.63 14 | 84.13 49 | 92.39 9 | 88.09 48 | 90.47 64 | 82.86 4 | 88.79 39 | 75.16 96 | 94.87 8 | 97.68 13 | 71.05 97 | 96.16 6 | 93.18 24 | 92.85 57 | 89.64 53 |
|
QAPM | | | 80.43 104 | 84.34 102 | 75.86 111 | 79.40 132 | 82.06 92 | 79.86 147 | 61.94 145 | 83.28 81 | 74.73 100 | 81.74 125 | 85.44 143 | 70.97 98 | 84.99 120 | 84.71 100 | 88.29 115 | 88.14 65 |
|
3Dnovator | | 79.41 10 | 82.21 90 | 86.07 82 | 77.71 102 | 79.31 133 | 84.61 72 | 87.18 95 | 61.02 151 | 85.65 64 | 76.11 90 | 85.07 109 | 85.38 144 | 70.96 99 | 87.22 97 | 86.47 80 | 91.66 73 | 88.12 66 |
|
IterMVS-LS | | | 79.79 108 | 82.56 120 | 76.56 110 | 81.83 117 | 77.85 121 | 79.90 146 | 69.42 74 | 78.93 122 | 71.21 120 | 90.47 51 | 85.20 145 | 70.86 100 | 80.54 151 | 80.57 132 | 86.15 136 | 84.36 89 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
DTE-MVSNet | | | 88.99 35 | 92.77 11 | 84.59 42 | 93.31 2 | 88.10 47 | 90.96 51 | 83.09 2 | 91.38 13 | 76.21 89 | 96.03 2 | 98.04 8 | 70.78 101 | 95.65 14 | 92.32 33 | 93.18 51 | 87.84 68 |
|
PEN-MVS | | | 88.86 38 | 92.92 8 | 84.11 50 | 92.92 5 | 88.05 49 | 90.83 53 | 82.67 5 | 91.04 17 | 74.83 97 | 95.97 3 | 98.47 3 | 70.38 102 | 95.70 13 | 92.43 31 | 93.05 55 | 88.78 61 |
|
TinyColmap | | | 83.79 74 | 86.12 80 | 81.07 74 | 83.42 102 | 81.44 95 | 85.42 108 | 68.55 84 | 88.71 40 | 89.46 8 | 87.60 82 | 92.72 91 | 70.34 103 | 89.29 78 | 81.94 123 | 89.20 102 | 81.12 120 |
|
MAR-MVS | | | 81.98 94 | 82.92 118 | 80.88 76 | 85.18 82 | 85.85 65 | 89.13 77 | 69.52 70 | 71.21 151 | 82.25 59 | 71.28 183 | 88.89 129 | 69.69 104 | 88.71 81 | 86.96 75 | 89.52 98 | 87.57 70 |
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 |
MSDG | | | 81.39 99 | 84.23 106 | 78.09 100 | 82.40 113 | 82.47 89 | 85.31 112 | 60.91 152 | 79.73 118 | 80.26 74 | 86.30 95 | 88.27 132 | 69.67 105 | 87.20 98 | 84.98 95 | 89.97 92 | 80.67 123 |
|
MDA-MVSNet-bldmvs | | | 76.51 130 | 82.87 119 | 69.09 154 | 50.71 209 | 74.72 150 | 84.05 119 | 60.27 156 | 81.62 97 | 71.16 121 | 88.21 78 | 91.58 107 | 69.62 106 | 92.78 43 | 77.48 151 | 78.75 171 | 73.69 158 |
|
CANet | | | 82.84 85 | 84.60 100 | 80.78 77 | 87.30 64 | 85.20 71 | 90.23 67 | 69.00 77 | 72.16 147 | 78.73 83 | 84.49 112 | 90.70 117 | 69.54 107 | 87.65 92 | 86.17 82 | 89.87 94 | 85.84 80 |
|
IterMVS | | | 73.62 147 | 76.53 150 | 70.23 146 | 71.83 176 | 77.18 129 | 80.69 139 | 53.22 185 | 72.23 146 | 66.62 147 | 85.21 106 | 78.96 162 | 69.54 107 | 76.28 170 | 71.63 173 | 79.45 168 | 74.25 155 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
UniMVSNet (Re) | | | 84.95 65 | 88.53 56 | 80.78 77 | 87.82 61 | 84.21 74 | 88.03 86 | 76.50 38 | 81.18 105 | 69.29 131 | 92.63 32 | 96.83 21 | 69.07 109 | 91.23 59 | 89.60 58 | 93.97 42 | 84.00 94 |
|
abl_6 | | | | | 79.30 92 | 84.98 83 | 85.78 66 | 90.50 62 | 66.88 96 | 77.08 128 | 74.02 103 | 73.29 176 | 89.34 123 | 68.94 110 | | | 90.49 86 | 85.98 78 |
|
IB-MVS | | 71.28 17 | 75.21 141 | 77.00 147 | 73.12 132 | 76.76 153 | 77.45 124 | 83.05 123 | 58.92 163 | 63.01 186 | 64.31 152 | 59.99 205 | 87.57 135 | 68.64 111 | 86.26 106 | 82.34 121 | 87.05 128 | 82.36 111 |
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 |
OpenMVS | | 75.38 16 | 78.44 121 | 81.39 126 | 74.99 121 | 80.46 124 | 79.85 105 | 79.99 144 | 58.31 166 | 77.34 127 | 73.85 105 | 77.19 148 | 82.33 154 | 68.60 112 | 84.67 122 | 81.95 122 | 88.72 109 | 86.40 77 |
|
UniMVSNet_NR-MVSNet | | | 84.62 69 | 88.00 65 | 80.68 81 | 88.18 58 | 83.83 76 | 87.06 98 | 76.47 39 | 81.46 101 | 70.49 124 | 93.24 21 | 95.56 47 | 68.13 113 | 90.43 70 | 88.47 66 | 93.78 43 | 83.02 101 |
|
DU-MVS | | | 84.88 66 | 88.27 62 | 80.92 75 | 88.30 56 | 83.59 80 | 87.06 98 | 78.35 20 | 80.64 110 | 70.49 124 | 92.67 30 | 96.91 20 | 68.13 113 | 91.79 49 | 89.29 62 | 93.20 50 | 83.02 101 |
|
pmmvs-eth3d | | | 79.64 110 | 82.06 123 | 76.83 107 | 80.05 127 | 72.64 156 | 87.47 92 | 66.59 98 | 80.83 109 | 73.50 107 | 89.32 65 | 93.20 87 | 67.78 115 | 80.78 149 | 81.64 126 | 85.58 146 | 76.01 147 |
|
CHOSEN 1792x2688 | | | 68.80 169 | 71.09 172 | 66.13 171 | 69.11 185 | 68.89 170 | 78.98 153 | 54.68 175 | 61.63 192 | 56.69 168 | 71.56 180 | 78.39 165 | 67.69 116 | 72.13 182 | 72.01 172 | 69.63 190 | 73.02 161 |
|
DPM-MVS | | | 81.42 97 | 82.11 122 | 80.62 82 | 87.54 62 | 85.30 70 | 90.18 69 | 68.96 78 | 81.00 108 | 79.15 81 | 70.45 189 | 83.29 149 | 67.67 117 | 82.81 133 | 83.46 109 | 90.19 89 | 88.48 63 |
|
IS_MVSNet | | | 81.72 95 | 85.01 93 | 77.90 101 | 86.19 72 | 82.64 87 | 85.56 107 | 70.02 68 | 80.11 115 | 63.52 153 | 87.28 86 | 81.18 156 | 67.26 118 | 91.08 64 | 89.33 61 | 94.82 32 | 83.42 98 |
|
DELS-MVS | | | 79.71 109 | 83.74 112 | 75.01 120 | 79.31 133 | 82.68 86 | 84.79 115 | 60.06 158 | 75.43 135 | 69.09 132 | 86.13 97 | 89.38 122 | 67.16 119 | 85.12 114 | 83.87 107 | 89.65 95 | 83.57 96 |
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 |
TranMVSNet+NR-MVSNet | | | 85.23 63 | 89.38 51 | 80.39 86 | 88.78 53 | 83.77 77 | 87.40 93 | 76.75 35 | 85.47 66 | 68.99 133 | 95.18 7 | 97.55 15 | 67.13 120 | 91.61 53 | 89.13 63 | 93.26 49 | 82.95 104 |
|
Baseline_NR-MVSNet | | | 82.79 86 | 86.51 74 | 78.44 99 | 88.30 56 | 75.62 142 | 87.81 88 | 74.97 47 | 81.53 98 | 66.84 146 | 94.71 11 | 96.46 24 | 66.90 121 | 91.79 49 | 83.37 114 | 85.83 143 | 82.09 112 |
|
DI_MVS_plusplus_trai | | | 77.64 124 | 79.64 130 | 75.31 116 | 79.87 129 | 76.89 131 | 81.55 136 | 63.64 130 | 76.21 131 | 72.03 116 | 85.59 104 | 82.97 151 | 66.63 122 | 79.27 157 | 77.78 148 | 88.14 117 | 78.76 139 |
|
USDC | | | 81.39 99 | 83.07 117 | 79.43 90 | 81.48 119 | 78.95 114 | 82.62 128 | 66.17 102 | 87.45 50 | 90.73 4 | 82.40 122 | 93.65 81 | 66.57 123 | 83.63 128 | 77.97 146 | 89.00 105 | 77.45 145 |
|
PVSNet_BlendedMVS | | | 76.45 132 | 78.12 137 | 74.49 124 | 76.76 153 | 78.46 116 | 79.65 148 | 63.26 135 | 65.42 177 | 73.15 109 | 75.05 166 | 88.96 126 | 66.51 124 | 82.73 134 | 77.66 149 | 87.61 122 | 78.60 140 |
|
PVSNet_Blended | | | 76.45 132 | 78.12 137 | 74.49 124 | 76.76 153 | 78.46 116 | 79.65 148 | 63.26 135 | 65.42 177 | 73.15 109 | 75.05 166 | 88.96 126 | 66.51 124 | 82.73 134 | 77.66 149 | 87.61 122 | 78.60 140 |
|
ET-MVSNet_ETH3D | | | 74.71 144 | 74.19 164 | 75.31 116 | 79.22 135 | 75.29 143 | 82.70 127 | 64.05 124 | 65.45 176 | 70.96 123 | 77.15 149 | 57.70 202 | 65.89 126 | 84.40 124 | 81.65 125 | 89.03 104 | 77.67 144 |
|
casdiffmvs | | | 79.93 107 | 84.11 108 | 75.05 118 | 81.41 121 | 78.99 112 | 82.95 125 | 62.90 139 | 81.53 98 | 68.60 138 | 91.94 35 | 96.03 35 | 65.84 127 | 82.89 131 | 77.07 154 | 88.59 112 | 80.34 129 |
|
SCA | | | 68.54 171 | 67.52 180 | 69.73 149 | 67.79 188 | 75.04 144 | 76.96 162 | 68.94 79 | 66.41 170 | 67.86 142 | 74.03 170 | 60.96 193 | 65.55 128 | 68.99 190 | 65.67 185 | 71.30 185 | 61.54 192 |
|
canonicalmvs | | | 81.22 101 | 86.04 83 | 75.60 113 | 83.17 106 | 83.18 83 | 80.29 142 | 65.82 108 | 85.97 63 | 67.98 141 | 77.74 143 | 91.51 109 | 65.17 129 | 88.62 83 | 86.15 83 | 91.17 82 | 89.09 57 |
|
CR-MVSNet | | | 69.56 166 | 68.34 178 | 70.99 140 | 72.78 174 | 67.63 172 | 64.47 199 | 67.74 92 | 59.93 196 | 72.30 113 | 80.10 128 | 56.77 204 | 65.04 130 | 71.64 183 | 72.91 169 | 83.61 158 | 69.40 170 |
|
PatchT | | | 66.25 177 | 66.76 182 | 65.67 175 | 55.87 204 | 60.75 189 | 70.17 188 | 59.00 162 | 59.80 198 | 72.30 113 | 78.68 138 | 54.12 209 | 65.04 130 | 71.64 183 | 72.91 169 | 71.63 182 | 69.40 170 |
|
pmmvs4 | | | 75.92 136 | 77.48 144 | 74.10 126 | 78.21 144 | 70.94 160 | 84.06 118 | 64.78 116 | 75.13 136 | 68.47 139 | 84.12 113 | 83.32 148 | 64.74 132 | 75.93 171 | 79.14 144 | 84.31 153 | 73.77 157 |
|
CLD-MVS | | | 82.75 88 | 87.22 72 | 77.54 105 | 88.01 60 | 85.76 67 | 90.23 67 | 54.52 177 | 82.28 90 | 82.11 63 | 88.48 76 | 95.27 52 | 63.95 133 | 89.41 77 | 88.29 68 | 86.45 134 | 81.01 121 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
GA-MVS | | | 75.01 143 | 76.39 151 | 73.39 129 | 78.37 141 | 75.66 141 | 80.03 143 | 58.40 165 | 70.51 154 | 75.85 92 | 83.24 117 | 76.14 174 | 63.75 134 | 77.28 163 | 76.62 157 | 83.97 155 | 75.30 152 |
|
UniMVSNet_ETH3D | | | 85.39 60 | 91.12 42 | 78.71 95 | 90.48 37 | 83.72 78 | 81.76 133 | 82.41 6 | 93.84 5 | 64.43 151 | 95.41 6 | 98.76 1 | 63.72 135 | 93.63 34 | 89.74 55 | 89.47 100 | 82.74 107 |
|
MVE | | 41.12 19 | 51.80 203 | 60.92 198 | 41.16 203 | 35.21 212 | 34.14 212 | 48.45 212 | 41.39 200 | 69.11 161 | 19.53 209 | 63.33 200 | 73.80 181 | 63.56 136 | 67.19 193 | 61.51 194 | 38.85 209 | 57.38 200 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
CMPMVS | | 55.74 18 | 71.56 159 | 76.26 152 | 66.08 172 | 68.11 187 | 63.91 185 | 63.17 201 | 50.52 193 | 68.79 163 | 75.49 93 | 70.78 188 | 85.67 141 | 63.54 137 | 81.58 143 | 77.20 153 | 75.63 173 | 85.86 79 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
MVS_Test | | | 76.72 129 | 79.40 132 | 73.60 127 | 78.85 139 | 74.99 146 | 79.91 145 | 61.56 147 | 69.67 156 | 72.44 112 | 85.98 100 | 90.78 116 | 63.50 138 | 78.30 159 | 75.74 161 | 85.33 147 | 80.31 130 |
|
CHOSEN 280x420 | | | 56.32 200 | 58.85 206 | 53.36 197 | 51.63 206 | 39.91 210 | 69.12 195 | 38.61 202 | 56.29 201 | 36.79 205 | 48.84 207 | 62.59 192 | 63.39 139 | 73.61 179 | 67.66 182 | 60.61 198 | 63.07 186 |
|
Fast-Effi-MVS+-dtu | | | 76.92 127 | 77.18 145 | 76.62 109 | 79.55 130 | 79.17 110 | 84.80 114 | 77.40 30 | 64.46 181 | 68.75 136 | 70.81 187 | 86.57 138 | 63.36 140 | 81.74 142 | 81.76 124 | 85.86 142 | 75.78 149 |
|
DWT-MVSNet_training | | | 63.07 182 | 60.04 201 | 66.61 168 | 71.64 177 | 65.27 181 | 76.80 163 | 53.82 181 | 55.90 202 | 63.07 155 | 62.23 203 | 41.87 213 | 62.54 141 | 64.32 200 | 63.71 188 | 71.78 179 | 66.97 175 |
|
MS-PatchMatch | | | 71.18 162 | 73.99 166 | 67.89 163 | 77.16 151 | 71.76 159 | 77.18 160 | 56.38 171 | 67.35 166 | 55.04 175 | 74.63 168 | 75.70 176 | 62.38 142 | 76.62 166 | 75.97 160 | 79.22 169 | 75.90 148 |
|
diffmvs | | | 76.74 128 | 81.61 125 | 71.06 139 | 75.64 164 | 74.45 151 | 80.68 140 | 57.57 168 | 77.48 125 | 67.62 144 | 88.95 70 | 93.94 77 | 61.98 143 | 79.74 154 | 76.18 158 | 82.85 161 | 80.50 124 |
|
MDTV_nov1_ep13_2view | | | 72.96 154 | 75.59 158 | 69.88 148 | 71.15 180 | 64.86 182 | 82.31 130 | 54.45 178 | 76.30 130 | 78.32 85 | 86.52 93 | 91.58 107 | 61.35 144 | 76.80 164 | 66.83 184 | 71.70 180 | 66.26 177 |
|
baseline2 | | | 68.71 170 | 68.34 178 | 69.14 153 | 75.69 163 | 69.70 167 | 76.60 164 | 55.53 174 | 60.13 195 | 62.07 161 | 66.76 197 | 60.35 195 | 60.77 145 | 76.53 169 | 74.03 165 | 84.19 154 | 70.88 164 |
|
PatchmatchNet | | | 64.81 180 | 63.74 190 | 66.06 173 | 69.21 184 | 58.62 192 | 73.16 181 | 60.01 159 | 65.92 172 | 66.19 149 | 76.27 154 | 59.09 197 | 60.45 146 | 66.58 195 | 61.47 195 | 67.33 194 | 58.24 197 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
RPMNet | | | 67.02 175 | 63.99 189 | 70.56 144 | 71.55 178 | 67.63 172 | 75.81 168 | 69.44 73 | 59.93 196 | 63.24 154 | 64.32 199 | 47.51 212 | 59.68 147 | 70.37 187 | 69.64 179 | 83.64 157 | 68.49 173 |
|
EPNet | | | 79.36 114 | 79.44 131 | 79.27 93 | 89.51 46 | 77.20 128 | 88.35 84 | 77.35 32 | 68.27 164 | 74.29 102 | 76.31 153 | 79.22 161 | 59.63 148 | 85.02 119 | 85.45 91 | 86.49 133 | 84.61 86 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
tpm cat1 | | | 64.79 181 | 62.74 194 | 67.17 164 | 74.61 168 | 65.91 179 | 76.18 167 | 59.32 160 | 64.88 180 | 66.41 148 | 71.21 184 | 53.56 210 | 59.17 149 | 61.53 203 | 58.16 198 | 67.33 194 | 63.95 181 |
|
gm-plane-assit | | | 71.56 159 | 69.99 173 | 73.39 129 | 84.43 89 | 73.21 154 | 90.42 66 | 51.36 191 | 84.08 77 | 76.00 91 | 91.30 45 | 37.09 214 | 59.01 150 | 73.65 178 | 70.24 177 | 79.09 170 | 60.37 193 |
|
E-PMN | | | 59.07 194 | 62.79 193 | 54.72 194 | 67.01 193 | 47.81 206 | 60.44 204 | 43.40 197 | 72.95 142 | 44.63 196 | 70.42 190 | 73.17 182 | 58.73 151 | 80.97 148 | 51.98 205 | 54.14 205 | 42.26 208 |
|
DCV-MVSNet | | | 80.04 106 | 85.67 87 | 73.48 128 | 82.91 108 | 81.11 100 | 80.44 141 | 66.06 103 | 85.01 71 | 62.53 159 | 78.84 136 | 94.43 73 | 58.51 152 | 88.66 82 | 85.91 86 | 90.41 87 | 85.73 81 |
|
PMMVS | | | 61.98 189 | 65.61 184 | 57.74 188 | 45.03 210 | 51.76 202 | 69.54 192 | 35.05 203 | 55.49 204 | 55.32 173 | 68.23 193 | 78.39 165 | 58.09 153 | 70.21 188 | 71.56 174 | 83.42 159 | 63.66 182 |
|
dps | | | 65.14 178 | 64.50 187 | 65.89 174 | 71.41 179 | 65.81 180 | 71.44 185 | 61.59 146 | 58.56 199 | 61.43 162 | 75.45 164 | 52.70 211 | 58.06 154 | 69.57 189 | 64.65 186 | 71.39 184 | 64.77 179 |
|
EMVS | | | 58.97 195 | 62.63 195 | 54.70 195 | 66.26 197 | 48.71 204 | 61.74 202 | 42.71 198 | 72.80 144 | 46.00 195 | 73.01 177 | 71.66 183 | 57.91 155 | 80.41 152 | 50.68 207 | 53.55 206 | 41.11 209 |
|
MVSTER | | | 68.08 173 | 69.73 174 | 66.16 170 | 66.33 196 | 70.06 164 | 75.71 173 | 52.36 187 | 55.18 205 | 58.64 165 | 70.23 191 | 56.72 205 | 57.34 156 | 79.68 155 | 76.03 159 | 86.61 131 | 80.20 131 |
|
MVS-HIRNet | | | 59.74 191 | 58.74 207 | 60.92 184 | 57.74 203 | 45.81 207 | 56.02 207 | 58.69 164 | 55.69 203 | 65.17 150 | 70.86 186 | 71.66 183 | 56.75 157 | 61.11 204 | 53.74 203 | 71.17 186 | 52.28 203 |
|
CostFormer | | | 66.81 176 | 66.94 181 | 66.67 167 | 72.79 173 | 68.25 171 | 79.55 151 | 55.57 173 | 65.52 175 | 62.77 157 | 76.98 150 | 60.09 196 | 56.73 158 | 65.69 198 | 62.35 189 | 72.59 178 | 69.71 169 |
|
Anonymous20231211 | | | 79.37 113 | 85.78 85 | 71.89 135 | 82.87 110 | 79.66 108 | 78.77 154 | 63.93 129 | 83.36 80 | 59.39 163 | 90.54 50 | 94.66 68 | 56.46 159 | 87.38 94 | 84.12 104 | 89.92 93 | 80.74 122 |
|
test-mter | | | 59.39 193 | 61.59 196 | 56.82 190 | 53.21 205 | 54.82 196 | 73.12 182 | 26.57 208 | 53.19 206 | 56.31 169 | 64.71 198 | 60.47 194 | 56.36 160 | 68.69 191 | 64.27 187 | 75.38 174 | 65.00 178 |
|
pmmvs6 | | | 80.46 103 | 88.34 61 | 71.26 137 | 81.96 116 | 77.51 123 | 77.54 157 | 68.83 80 | 93.72 6 | 55.92 171 | 93.94 16 | 98.03 9 | 55.94 161 | 89.21 79 | 85.61 89 | 87.36 125 | 80.38 125 |
|
Anonymous202405211 | | | | 84.68 99 | | 83.92 95 | 79.45 109 | 79.03 152 | 67.79 91 | 82.01 92 | | 88.77 75 | 92.58 93 | 55.93 162 | 86.68 101 | 84.26 103 | 88.92 106 | 78.98 136 |
|
thres600view7 | | | 74.34 146 | 78.43 136 | 69.56 151 | 80.47 123 | 76.28 135 | 78.65 155 | 62.56 141 | 77.39 126 | 52.53 181 | 74.03 170 | 76.78 172 | 55.90 163 | 85.06 115 | 85.19 93 | 87.25 126 | 74.29 154 |
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FMVSNet1 | | | 78.20 123 | 84.83 98 | 70.46 145 | 78.62 140 | 79.03 111 | 77.90 156 | 67.53 94 | 83.02 83 | 55.10 174 | 87.19 88 | 93.18 88 | 55.65 164 | 85.57 109 | 83.39 111 | 87.98 118 | 82.40 110 |
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MDTV_nov1_ep13 | | | 64.96 179 | 64.77 186 | 65.18 177 | 67.08 192 | 62.46 187 | 75.80 169 | 51.10 192 | 62.27 191 | 69.74 127 | 74.12 169 | 62.65 191 | 55.64 165 | 68.19 192 | 62.16 193 | 71.70 180 | 61.57 191 |
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thres400 | | | 73.13 152 | 76.99 148 | 68.62 156 | 79.46 131 | 74.93 147 | 77.23 159 | 61.23 150 | 75.54 133 | 52.31 184 | 72.20 178 | 77.10 170 | 54.89 166 | 82.92 130 | 82.62 120 | 86.57 132 | 73.66 159 |
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thres200 | | | 72.41 156 | 76.00 156 | 68.21 159 | 78.28 142 | 76.28 135 | 74.94 176 | 62.56 141 | 72.14 148 | 51.35 189 | 69.59 192 | 76.51 173 | 54.89 166 | 85.06 115 | 80.51 134 | 87.25 126 | 71.92 162 |
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tfpnnormal | | | 77.16 126 | 84.26 104 | 68.88 155 | 81.02 122 | 75.02 145 | 76.52 165 | 63.30 134 | 87.29 51 | 52.40 183 | 91.24 47 | 93.97 76 | 54.85 168 | 85.46 112 | 81.08 128 | 85.18 149 | 75.76 150 |
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baseline | | | 69.33 167 | 75.37 160 | 62.28 182 | 66.54 194 | 66.67 177 | 73.95 179 | 48.07 194 | 66.10 171 | 59.26 164 | 82.45 120 | 86.30 139 | 54.44 169 | 74.42 174 | 73.25 168 | 71.42 183 | 78.43 142 |
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pm-mvs1 | | | 78.21 122 | 85.68 86 | 69.50 152 | 80.38 125 | 75.73 140 | 76.25 166 | 65.04 114 | 87.59 48 | 54.47 176 | 93.16 23 | 95.99 39 | 54.20 170 | 86.37 104 | 82.98 118 | 86.64 130 | 77.96 143 |
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NR-MVSNet | | | 82.89 84 | 87.43 70 | 77.59 104 | 83.91 96 | 83.59 80 | 87.10 97 | 78.35 20 | 80.64 110 | 68.85 134 | 92.67 30 | 96.50 23 | 54.19 171 | 87.19 99 | 88.68 65 | 93.16 53 | 82.75 106 |
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pmmvs3 | | | 62.72 186 | 68.71 177 | 55.74 192 | 50.74 208 | 57.10 193 | 70.05 189 | 28.82 206 | 61.57 194 | 57.39 167 | 71.19 185 | 85.73 140 | 53.96 172 | 73.36 180 | 69.43 180 | 73.47 177 | 62.55 187 |
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CANet_DTU | | | 75.04 142 | 78.45 135 | 71.07 138 | 77.27 150 | 77.96 120 | 83.88 120 | 58.00 167 | 64.11 182 | 68.67 137 | 75.65 163 | 88.37 131 | 53.92 173 | 82.05 139 | 81.11 127 | 84.67 151 | 79.88 132 |
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TransMVSNet (Re) | | | 79.05 118 | 86.66 73 | 70.18 147 | 83.32 103 | 75.99 137 | 77.54 157 | 63.98 127 | 90.68 22 | 55.84 172 | 94.80 9 | 96.06 34 | 53.73 174 | 86.27 105 | 83.22 115 | 86.65 129 | 79.61 134 |
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UGNet | | | 79.62 111 | 85.91 84 | 72.28 134 | 73.52 169 | 83.91 75 | 86.64 102 | 69.51 71 | 79.85 117 | 62.57 158 | 85.82 102 | 89.63 121 | 53.18 175 | 88.39 87 | 87.35 73 | 88.28 116 | 86.43 76 |
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 |
Vis-MVSNet (Re-imp) | | | 76.15 134 | 80.84 127 | 70.68 142 | 83.66 100 | 74.80 149 | 81.66 135 | 69.59 69 | 80.48 113 | 46.94 194 | 87.44 83 | 80.63 158 | 53.14 176 | 86.87 100 | 84.56 101 | 89.12 103 | 71.12 163 |
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test-LLR | | | 62.15 188 | 59.46 204 | 65.29 176 | 79.07 136 | 52.66 200 | 69.46 193 | 62.93 137 | 50.76 208 | 53.81 178 | 63.11 201 | 58.91 198 | 52.87 177 | 66.54 196 | 62.34 190 | 73.59 175 | 61.87 189 |
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TESTMET0.1,1 | | | 57.21 196 | 59.46 204 | 54.60 196 | 50.95 207 | 52.66 200 | 69.46 193 | 26.91 207 | 50.76 208 | 53.81 178 | 63.11 201 | 58.91 198 | 52.87 177 | 66.54 196 | 62.34 190 | 73.59 175 | 61.87 189 |
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GBi-Net | | | 73.17 150 | 77.64 141 | 67.95 161 | 76.76 153 | 77.36 125 | 75.77 170 | 64.57 117 | 62.99 187 | 51.83 186 | 76.05 156 | 77.76 167 | 52.73 179 | 85.57 109 | 83.39 111 | 86.04 138 | 80.37 126 |
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test1 | | | 73.17 150 | 77.64 141 | 67.95 161 | 76.76 153 | 77.36 125 | 75.77 170 | 64.57 117 | 62.99 187 | 51.83 186 | 76.05 156 | 77.76 167 | 52.73 179 | 85.57 109 | 83.39 111 | 86.04 138 | 80.37 126 |
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FMVSNet2 | | | 74.43 145 | 79.70 129 | 68.27 158 | 76.76 153 | 77.36 125 | 75.77 170 | 65.36 112 | 72.28 145 | 52.97 180 | 81.92 124 | 85.61 142 | 52.73 179 | 80.66 150 | 79.73 139 | 86.04 138 | 80.37 126 |
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tfpn200view9 | | | 72.01 157 | 75.40 159 | 68.06 160 | 77.97 146 | 76.44 133 | 77.04 161 | 62.67 140 | 66.81 168 | 50.82 190 | 67.30 195 | 75.67 177 | 52.46 182 | 85.06 115 | 82.64 119 | 87.41 124 | 73.86 156 |
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EPNet_dtu | | | 71.90 158 | 73.03 170 | 70.59 143 | 78.28 142 | 61.64 188 | 82.44 129 | 64.12 122 | 63.26 185 | 69.74 127 | 71.47 181 | 82.41 152 | 51.89 183 | 78.83 158 | 78.01 145 | 77.07 172 | 75.60 151 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
FMVSNet3 | | | 71.40 161 | 75.20 162 | 66.97 165 | 75.00 167 | 76.59 132 | 74.29 177 | 64.57 117 | 62.99 187 | 51.83 186 | 76.05 156 | 77.76 167 | 51.49 184 | 76.58 167 | 77.03 155 | 84.62 152 | 79.43 135 |
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thres100view900 | | | 69.86 164 | 72.97 171 | 66.24 169 | 77.97 146 | 72.49 157 | 73.29 180 | 59.12 161 | 66.81 168 | 50.82 190 | 67.30 195 | 75.67 177 | 50.54 185 | 78.24 160 | 79.40 141 | 85.71 145 | 70.88 164 |
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FC-MVSNet-train | | | 79.20 117 | 86.29 78 | 70.94 141 | 84.06 91 | 77.67 122 | 85.68 106 | 64.11 123 | 82.90 84 | 52.22 185 | 92.57 33 | 93.69 80 | 49.52 186 | 88.30 89 | 86.93 76 | 90.03 91 | 81.95 115 |
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tpmrst | | | 59.42 192 | 60.02 202 | 58.71 187 | 67.56 190 | 53.10 199 | 66.99 197 | 51.88 188 | 63.80 184 | 57.68 166 | 76.73 151 | 56.49 206 | 48.73 187 | 56.47 207 | 55.55 200 | 59.43 201 | 58.02 198 |
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tpm | | | 62.79 185 | 63.25 191 | 62.26 183 | 70.09 182 | 53.78 197 | 71.65 184 | 47.31 195 | 65.72 174 | 76.70 88 | 80.62 127 | 56.40 207 | 48.11 188 | 64.20 201 | 58.54 196 | 59.70 200 | 63.47 183 |
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FC-MVSNet-test | | | 75.91 137 | 83.59 114 | 66.95 166 | 76.63 161 | 69.07 168 | 85.33 111 | 64.97 115 | 84.87 73 | 41.95 198 | 93.17 22 | 87.04 136 | 47.78 189 | 91.09 63 | 85.56 90 | 85.06 150 | 74.34 153 |
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baseline1 | | | 69.62 165 | 73.55 168 | 65.02 178 | 78.95 138 | 70.39 162 | 71.38 186 | 62.03 144 | 70.97 152 | 47.95 193 | 78.47 140 | 68.19 188 | 47.77 190 | 79.65 156 | 76.94 156 | 82.05 163 | 70.27 166 |
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pmmvs5 | | | 68.91 168 | 74.35 163 | 62.56 181 | 67.45 191 | 66.78 176 | 71.70 183 | 51.47 190 | 67.17 167 | 56.25 170 | 82.41 121 | 88.59 130 | 47.21 191 | 73.21 181 | 74.23 164 | 81.30 166 | 68.03 174 |
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MIMVSNet1 | | | 73.40 148 | 81.85 124 | 63.55 179 | 72.90 172 | 64.37 183 | 84.58 116 | 53.60 183 | 90.84 19 | 53.92 177 | 87.75 81 | 96.10 32 | 45.31 192 | 85.37 113 | 79.32 142 | 70.98 187 | 69.18 172 |
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EPMVS | | | 56.62 198 | 59.77 203 | 52.94 198 | 62.41 199 | 50.55 203 | 60.66 203 | 52.83 186 | 65.15 179 | 41.80 199 | 77.46 146 | 57.28 203 | 42.68 193 | 59.81 205 | 54.82 201 | 57.23 203 | 53.35 202 |
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CDS-MVSNet | | | 73.07 153 | 77.02 146 | 68.46 157 | 81.62 118 | 72.89 155 | 79.56 150 | 70.78 65 | 69.56 157 | 52.52 182 | 77.37 147 | 81.12 157 | 42.60 194 | 84.20 125 | 83.93 105 | 83.65 156 | 70.07 168 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
TAMVS | | | 63.02 183 | 69.30 175 | 55.70 193 | 70.12 181 | 56.89 194 | 69.63 191 | 45.13 196 | 70.23 155 | 38.00 204 | 77.79 141 | 75.15 179 | 42.60 194 | 74.48 173 | 72.81 171 | 68.70 192 | 57.75 199 |
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gg-mvs-nofinetune | | | 72.68 155 | 75.21 161 | 69.73 149 | 81.48 119 | 69.04 169 | 70.48 187 | 76.67 36 | 86.92 55 | 67.80 143 | 88.06 79 | 64.67 190 | 42.12 196 | 77.60 161 | 73.65 166 | 79.81 167 | 66.57 176 |
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Anonymous20231206 | | | 67.28 174 | 73.41 169 | 60.12 185 | 76.45 162 | 63.61 186 | 74.21 178 | 56.52 170 | 76.35 129 | 42.23 197 | 75.81 162 | 90.47 118 | 41.51 197 | 74.52 172 | 69.97 178 | 69.83 189 | 63.17 185 |
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test20.03 | | | 69.91 163 | 76.20 154 | 62.58 180 | 84.01 94 | 67.34 174 | 75.67 174 | 65.88 107 | 79.98 116 | 40.28 202 | 82.65 119 | 89.31 124 | 39.63 198 | 77.41 162 | 73.28 167 | 69.98 188 | 63.40 184 |
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ADS-MVSNet | | | 56.89 197 | 61.09 197 | 52.00 199 | 59.48 201 | 48.10 205 | 58.02 205 | 54.37 179 | 72.82 143 | 49.19 192 | 75.32 165 | 65.97 189 | 37.96 199 | 59.34 206 | 54.66 202 | 52.99 207 | 51.42 204 |
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MIMVSNet | | | 63.02 183 | 69.02 176 | 56.01 191 | 68.20 186 | 59.26 191 | 70.01 190 | 53.79 182 | 71.56 150 | 41.26 201 | 71.38 182 | 82.38 153 | 36.38 200 | 71.43 185 | 67.32 183 | 66.45 196 | 59.83 194 |
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FMVSNet5 | | | 56.37 199 | 60.14 200 | 51.98 200 | 60.83 200 | 59.58 190 | 66.85 198 | 42.37 199 | 52.68 207 | 41.33 200 | 47.09 208 | 54.68 208 | 35.28 201 | 73.88 176 | 70.77 175 | 65.24 197 | 62.26 188 |
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DeepMVS_CX | | | | | | | 17.78 213 | 20.40 213 | 6.69 209 | 31.41 210 | 9.80 212 | 38.61 209 | 34.88 215 | 33.78 202 | 28.41 209 | | 23.59 211 | 45.77 207 |
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test0.0.03 1 | | | 61.79 190 | 65.33 185 | 57.65 189 | 79.07 136 | 64.09 184 | 68.51 196 | 62.93 137 | 61.59 193 | 33.71 206 | 61.58 204 | 71.58 185 | 33.43 203 | 70.95 186 | 68.68 181 | 68.26 193 | 58.82 195 |
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testgi | | | 68.20 172 | 76.05 155 | 59.04 186 | 79.99 128 | 67.32 175 | 81.16 137 | 51.78 189 | 84.91 72 | 39.36 203 | 73.42 174 | 95.19 54 | 32.79 204 | 76.54 168 | 70.40 176 | 69.14 191 | 64.55 180 |
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N_pmnet | | | 54.95 201 | 65.90 183 | 42.18 202 | 66.37 195 | 43.86 209 | 57.92 206 | 39.79 201 | 79.54 119 | 17.24 211 | 86.31 94 | 87.91 133 | 25.44 205 | 64.68 199 | 51.76 206 | 46.33 208 | 47.23 206 |
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new-patchmatchnet | | | 62.59 187 | 73.79 167 | 49.53 201 | 76.98 152 | 53.57 198 | 53.46 209 | 54.64 176 | 85.43 67 | 28.81 207 | 91.94 35 | 96.41 27 | 25.28 206 | 76.80 164 | 53.66 204 | 57.99 202 | 58.69 196 |
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new_pmnet | | | 52.29 202 | 63.16 192 | 39.61 204 | 58.89 202 | 44.70 208 | 48.78 211 | 34.73 204 | 65.88 173 | 17.85 210 | 73.42 174 | 80.00 159 | 23.06 207 | 67.00 194 | 62.28 192 | 54.36 204 | 48.81 205 |
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PMMVS2 | | | 48.13 204 | 64.06 188 | 29.55 205 | 44.06 211 | 36.69 211 | 51.95 210 | 29.97 205 | 74.75 138 | 8.90 213 | 76.02 159 | 91.24 113 | 7.53 208 | 73.78 177 | 55.91 199 | 34.87 210 | 40.01 210 |
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tmp_tt | | | | | 13.54 207 | 16.73 213 | 6.42 214 | 8.49 214 | 2.36 210 | 28.69 211 | 27.44 208 | 18.40 210 | 13.51 216 | 3.70 209 | 33.23 208 | 36.26 208 | 22.54 212 | |
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test123 | | | 1.06 206 | 1.41 208 | 0.64 208 | 0.39 214 | 0.48 215 | 0.52 217 | 0.25 212 | 1.11 213 | 1.37 215 | 2.01 212 | 1.98 217 | 0.87 210 | 1.43 210 | 1.27 209 | 0.46 214 | 1.62 212 |
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testmvs | | | 0.93 207 | 1.37 209 | 0.41 209 | 0.36 215 | 0.36 216 | 0.62 216 | 0.39 211 | 1.48 212 | 0.18 216 | 2.41 211 | 1.31 218 | 0.41 211 | 1.25 211 | 1.08 210 | 0.48 213 | 1.68 211 |
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GG-mvs-BLEND | | | 41.63 205 | 60.36 199 | 19.78 206 | 0.14 216 | 66.04 178 | 55.66 208 | 0.17 213 | 57.64 200 | 2.42 214 | 51.82 206 | 69.42 187 | 0.28 212 | 64.11 202 | 58.29 197 | 60.02 199 | 55.18 201 |
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sosnet-low-res | | | 0.00 208 | 0.00 210 | 0.00 210 | 0.00 217 | 0.00 217 | 0.00 218 | 0.00 214 | 0.00 214 | 0.00 217 | 0.00 213 | 0.00 219 | 0.00 213 | 0.00 212 | 0.00 211 | 0.00 215 | 0.00 213 |
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sosnet | | | 0.00 208 | 0.00 210 | 0.00 210 | 0.00 217 | 0.00 217 | 0.00 218 | 0.00 214 | 0.00 214 | 0.00 217 | 0.00 213 | 0.00 219 | 0.00 213 | 0.00 212 | 0.00 211 | 0.00 215 | 0.00 213 |
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SR-MVS | | | | | | 91.82 13 | | | 80.80 7 | | | | 95.53 48 | | | | | |
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our_test_3 | | | | | | 73.27 170 | 70.91 161 | 83.26 121 | | | | | | | | | | |
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test_part1 | | | | | | | | | | | | | | | | | | 93.49 18 |
|
MTAPA | | | | | | | | | | | 89.37 9 | | 94.85 64 | | | | | |
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MTMP | | | | | | | | | | | 90.54 5 | | 95.16 56 | | | | | |
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Patchmatch-RL test | | | | | | | | 4.13 215 | | | | | | | | | | |
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XVS | | | | | | 91.28 25 | 91.23 8 | 96.89 2 | | | 87.14 26 | | 94.53 69 | | | | 95.84 15 | |
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X-MVStestdata | | | | | | 91.28 25 | 91.23 8 | 96.89 2 | | | 87.14 26 | | 94.53 69 | | | | 95.84 15 | |
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mPP-MVS | | | | | | 93.05 4 | | | | | | | 95.77 42 | | | | | |
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NP-MVS | | | | | | | | | | 78.65 123 | | | | | | | | |
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Patchmtry | | | | | | | 56.88 195 | 64.47 199 | 67.74 92 | | 72.30 113 | | | | | | | |
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