DeepPCF-MVS | | 80.84 1 | 88.10 1 | 88.56 1 | 86.73 5 | 92.24 5 | 69.03 11 | 89.57 12 | 93.39 3 | 77.53 2 | 89.79 1 | 94.12 2 | 78.98 1 | 96.58 4 | 85.66 1 | 95.72 1 | 94.58 2 |
|
DeepC-MVS | | 79.81 2 | 87.08 2 | 86.88 2 | 87.69 3 | 91.16 8 | 72.32 5 | 90.31 8 | 93.94 1 | 77.12 4 | 82.82 3 | 94.23 1 | 72.13 2 | 97.09 1 | 84.83 2 | 95.37 2 | 93.65 4 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
DeepC-MVS_fast | | 79.65 3 | 86.91 3 | 86.62 3 | 87.76 2 | 93.52 2 | 72.37 4 | 91.26 3 | 93.04 5 | 76.62 6 | 84.22 2 | 93.36 5 | 71.44 3 | 96.76 2 | 80.82 4 | 95.33 3 | 94.16 3 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
ACMMwithACMP | | | 85.89 4 | 85.39 4 | 87.38 4 | 93.59 1 | 72.63 3 | 92.74 2 | 93.18 4 | 76.78 5 | 80.73 5 | 93.82 3 | 64.33 8 | 96.29 5 | 82.67 3 | 90.69 11 | 93.23 5 |
|
3Dnovator+ | | 77.84 4 | 85.48 5 | 84.47 5 | 88.51 1 | 91.08 9 | 73.49 1 | 93.18 1 | 93.78 2 | 80.79 1 | 76.66 14 | 93.37 4 | 60.40 15 | 96.75 3 | 77.20 6 | 93.73 4 | 95.29 1 |
|
MG-MVS | | | 83.41 6 | 83.45 6 | 83.28 18 | 92.74 4 | 62.28 27 | 88.17 20 | 89.50 16 | 75.22 8 | 81.49 4 | 92.74 7 | 66.75 4 | 95.11 7 | 72.85 11 | 91.58 7 | 92.45 7 |
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3Dnovator | | 76.31 5 | 83.38 7 | 82.31 7 | 86.59 6 | 87.94 23 | 72.94 2 | 90.64 6 | 92.14 7 | 77.21 3 | 75.47 16 | 92.83 6 | 58.56 16 | 94.72 11 | 73.24 10 | 92.71 5 | 92.13 9 |
|
OMC-MVS | | | 82.69 8 | 81.97 8 | 84.85 9 | 88.75 18 | 67.42 18 | 87.98 22 | 90.87 14 | 74.92 9 | 79.72 6 | 91.65 9 | 62.19 13 | 93.96 16 | 75.26 8 | 86.42 18 | 93.16 6 |
|
CLD-MVS | | | 82.31 9 | 81.65 9 | 84.29 11 | 88.47 22 | 67.73 17 | 85.81 24 | 92.35 6 | 75.78 7 | 78.33 9 | 86.58 26 | 64.01 10 | 94.35 15 | 76.05 7 | 87.48 14 | 90.79 11 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
0108 | | | 81.99 10 | 81.23 10 | 84.26 12 | 90.94 10 | 70.18 8 | 91.10 4 | 89.32 18 | 71.51 11 | 78.66 7 | 88.28 20 | 65.26 7 | 95.10 8 | 64.74 13 | 91.23 8 | 87.51 23 |
|
MAR-MVS | | | 81.84 11 | 80.70 11 | 85.27 7 | 91.32 7 | 71.53 6 | 89.82 11 | 90.92 13 | 69.77 17 | 78.50 8 | 86.21 27 | 62.36 12 | 94.52 14 | 65.36 12 | 92.05 6 | 89.77 13 |
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 |
ACMP | | 74.13 6 | 81.51 12 | 80.57 12 | 84.36 10 | 89.42 15 | 68.69 14 | 89.97 10 | 91.50 10 | 74.46 10 | 75.04 17 | 90.41 12 | 53.82 20 | 94.54 12 | 77.56 5 | 82.91 21 | 89.86 12 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
PHI-MVS | | | 81.07 13 | 80.32 13 | 83.34 17 | 90.91 11 | 70.11 9 | 90.80 5 | 88.83 23 | 71.17 12 | 78.06 10 | 83.31 32 | 65.55 6 | 94.73 10 | 63.34 14 | 90.88 10 | 85.18 29 |
|
ACMM | | 73.20 8 | 80.78 14 | 79.84 14 | 83.58 15 | 89.31 16 | 68.37 15 | 89.99 9 | 91.60 8 | 70.28 14 | 77.25 12 | 89.66 15 | 53.37 22 | 93.53 20 | 74.24 9 | 82.85 22 | 88.85 15 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
AdaColmap |  | | 80.58 15 | 79.42 15 | 84.06 14 | 93.09 3 | 68.91 13 | 89.36 13 | 88.97 22 | 69.27 18 | 75.70 15 | 89.69 14 | 57.20 18 | 95.77 6 | 63.06 15 | 88.41 12 | 87.50 24 |
|
PCF-MVS | | 73.52 7 | 80.38 16 | 78.84 16 | 85.01 8 | 87.71 24 | 68.99 12 | 83.65 28 | 91.46 11 | 63.00 28 | 77.77 11 | 90.28 13 | 66.10 5 | 95.09 9 | 61.40 18 | 88.22 13 | 90.94 10 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
OpenMVS |  | 72.83 10 | 79.77 17 | 78.33 17 | 84.09 13 | 85.17 28 | 69.91 10 | 90.57 7 | 90.97 12 | 66.70 20 | 72.17 19 | 91.91 8 | 54.70 19 | 93.96 16 | 61.81 17 | 90.95 9 | 88.41 18 |
|
TAPA-MVS | | 73.13 9 | 79.15 18 | 77.94 18 | 82.79 20 | 89.59 14 | 62.99 26 | 88.16 21 | 91.51 9 | 65.77 22 | 77.14 13 | 91.09 10 | 60.91 14 | 93.21 21 | 50.26 32 | 87.05 15 | 92.17 8 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
CNLPA | | | 78.08 19 | 76.79 19 | 81.97 23 | 90.40 13 | 71.07 7 | 87.59 23 | 84.55 28 | 66.03 21 | 72.38 18 | 89.64 16 | 57.56 17 | 86.04 34 | 59.61 19 | 83.35 20 | 88.79 16 |
|
PAPM | | | 77.68 21 | 76.40 20 | 81.51 25 | 87.29 25 | 61.85 28 | 83.78 27 | 89.59 15 | 64.74 24 | 71.23 21 | 88.70 17 | 62.59 11 | 93.66 18 | 52.66 30 | 87.03 16 | 89.01 14 |
|
PLC |  | 70.83 11 | 78.05 20 | 76.37 21 | 83.08 19 | 91.88 6 | 67.80 16 | 88.19 19 | 89.46 17 | 64.33 25 | 69.87 23 | 88.38 19 | 53.66 21 | 93.58 19 | 58.86 20 | 82.73 23 | 87.86 20 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
LS3D | | | 76.95 22 | 74.82 22 | 83.37 16 | 90.45 12 | 67.36 19 | 89.15 14 | 86.94 25 | 61.87 31 | 69.52 25 | 90.61 11 | 51.71 23 | 94.53 13 | 46.38 36 | 86.71 17 | 88.21 19 |
|
LTVRE_ROB | | 69.57 12 | 76.25 24 | 74.54 23 | 81.41 26 | 88.60 20 | 64.38 25 | 79.24 33 | 89.12 19 | 70.76 13 | 69.79 24 | 87.86 21 | 49.09 25 | 93.20 22 | 56.21 24 | 80.16 25 | 86.65 28 |
Andreas Kuhn, Heiko HirschmĂĽller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016 |
F-COLMAP | | | 76.38 23 | 74.33 24 | 82.50 21 | 89.28 17 | 66.95 20 | 88.41 17 | 89.03 20 | 64.05 27 | 66.83 28 | 88.61 18 | 46.78 26 | 92.89 24 | 57.48 22 | 78.55 26 | 87.67 21 |
|
ACMH+ | | 68.96 13 | 76.01 25 | 74.01 25 | 82.03 22 | 88.60 20 | 65.31 24 | 88.86 15 | 87.55 24 | 70.25 15 | 67.75 26 | 87.47 23 | 41.27 32 | 93.19 23 | 58.37 21 | 75.94 32 | 87.60 22 |
|
ACMH | | 67.68 14 | 75.89 26 | 73.93 26 | 81.77 24 | 88.71 19 | 66.61 21 | 88.62 16 | 89.01 21 | 69.81 16 | 66.78 29 | 86.70 25 | 41.95 31 | 91.51 26 | 55.64 25 | 78.14 27 | 87.17 26 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
PatchmatchNet | | | 73.12 28 | 71.33 27 | 78.49 29 | 83.18 30 | 60.85 30 | 79.63 31 | 78.57 38 | 64.13 26 | 71.73 20 | 79.81 37 | 51.20 24 | 85.97 35 | 57.40 23 | 76.36 29 | 88.66 17 |
|
MSDG | | | 73.36 27 | 70.99 28 | 80.49 28 | 84.51 29 | 65.80 22 | 80.71 29 | 86.13 27 | 65.70 23 | 65.46 30 | 83.74 31 | 44.60 27 | 90.91 28 | 51.13 31 | 76.89 28 | 84.74 31 |
|
PVSNet | | 64.34 16 | 72.08 30 | 70.87 29 | 75.69 35 | 86.21 27 | 56.44 36 | 74.37 36 | 80.73 36 | 62.06 30 | 70.17 22 | 82.23 35 | 42.86 30 | 83.31 36 | 54.77 26 | 84.45 19 | 87.32 25 |
|
COLMAP_ROB |  | 66.92 15 | 73.01 29 | 70.41 30 | 80.81 27 | 87.13 26 | 65.63 23 | 88.30 18 | 84.19 29 | 62.96 29 | 63.80 32 | 87.69 22 | 38.04 33 | 92.56 25 | 46.66 35 | 74.91 33 | 84.24 32 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
USDC | | | 70.33 32 | 68.37 31 | 76.21 34 | 80.60 33 | 56.23 37 | 79.19 34 | 86.49 26 | 60.89 33 | 61.29 33 | 85.47 28 | 31.78 36 | 89.47 29 | 53.37 28 | 76.21 30 | 82.94 33 |
|
OpenMVS_ROB |  | 64.09 17 | 70.56 31 | 68.19 32 | 77.65 31 | 80.26 34 | 59.41 32 | 85.01 25 | 82.96 33 | 58.76 36 | 65.43 31 | 82.33 34 | 37.63 34 | 91.23 27 | 45.34 38 | 76.03 31 | 82.32 34 |
|
CMPMVS |  | 51.72 19 | 70.19 33 | 68.16 33 | 76.28 33 | 73.15 38 | 57.55 33 | 79.47 32 | 83.92 30 | 48.02 39 | 56.48 37 | 84.81 30 | 43.13 29 | 86.42 33 | 62.67 16 | 81.81 24 | 84.89 30 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
TinyColmap | | | 67.30 35 | 64.81 34 | 74.76 36 | 81.92 31 | 56.68 35 | 80.29 30 | 81.49 34 | 60.33 34 | 56.27 38 | 83.22 33 | 24.77 38 | 87.66 31 | 45.52 37 | 69.47 35 | 79.95 36 |
|
TDRefinement | | | 67.49 34 | 64.34 35 | 76.92 32 | 73.47 37 | 61.07 29 | 84.86 26 | 82.98 32 | 59.77 35 | 58.30 36 | 85.13 29 | 26.06 37 | 87.89 30 | 47.92 33 | 60.59 38 | 81.81 35 |
|
LF4IMVS | | | 64.02 36 | 62.19 36 | 69.50 37 | 70.90 39 | 53.29 38 | 76.13 35 | 77.18 39 | 52.65 38 | 58.59 34 | 80.98 36 | 23.55 39 | 76.52 38 | 53.06 29 | 66.66 36 | 78.68 38 |
|
PVSNet_0 | | 57.27 18 | 61.67 37 | 59.27 37 | 68.85 38 | 79.61 35 | 57.44 34 | 68.01 39 | 73.44 40 | 55.93 37 | 58.54 35 | 70.41 39 | 44.58 28 | 77.55 37 | 47.01 34 | 35.91 41 | 71.55 39 |
|
FPMVS | | | 53.68 38 | 51.64 38 | 59.81 40 | 65.08 40 | 51.03 40 | 69.48 38 | 69.58 41 | 41.46 40 | 40.67 39 | 72.32 38 | 16.46 41 | 70.00 43 | 24.24 42 | 65.42 37 | 58.40 41 |
|
ANet_high | | | 50.57 39 | 46.10 39 | 63.99 39 | 48.67 43 | 39.13 43 | 70.99 37 | 80.85 35 | 61.39 32 | 31.18 40 | 57.70 40 | 17.02 40 | 73.65 41 | 31.22 40 | 15.89 46 | 79.18 37 |
|
Gipuma |  | | 45.18 40 | 41.86 40 | 55.16 43 | 77.03 36 | 51.52 39 | 32.50 44 | 80.52 37 | 32.46 42 | 27.12 41 | 35.02 45 | 9.52 43 | 75.50 40 | 22.31 43 | 60.21 39 | 38.45 45 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
PMVS |  | 37.38 20 | 44.16 41 | 40.28 41 | 55.82 42 | 40.82 46 | 42.54 42 | 65.12 40 | 63.99 43 | 34.43 41 | 24.48 42 | 57.12 41 | 3.92 44 | 76.17 39 | 17.10 45 | 55.52 40 | 48.75 42 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
PNet_i23d | | | 38.26 43 | 35.42 42 | 46.79 44 | 58.74 41 | 35.48 44 | 59.65 41 | 51.25 44 | 32.45 43 | 23.44 44 | 47.53 43 | 2.04 46 | 58.96 44 | 25.60 41 | 18.09 45 | 45.92 44 |
|
wuykxyi23d | | | 39.76 42 | 33.18 43 | 59.51 41 | 46.98 44 | 44.01 41 | 57.70 42 | 67.74 42 | 24.13 44 | 13.98 46 | 34.33 46 | 1.27 47 | 71.33 42 | 34.23 39 | 18.23 44 | 63.18 40 |
|
MVE |  | 26.22 21 | 30.37 44 | 25.89 44 | 43.81 45 | 44.55 45 | 35.46 45 | 28.87 45 | 39.07 45 | 18.20 45 | 18.58 45 | 40.18 44 | 2.68 45 | 47.37 45 | 17.07 46 | 23.78 43 | 48.60 43 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
wuyk23d | | | 16.82 45 | 15.94 45 | 19.46 47 | 58.74 41 | 31.45 46 | 39.22 43 | 3.74 47 | 6.84 47 | 6.04 47 | 2.70 47 | 1.27 47 | 24.29 47 | 10.54 47 | 14.40 47 | 2.63 47 |
|
Test By Simon | | | | | | | | | | | | | 64.33 8 | | | | |
|
DeepMVS_CX |  | | | | 27.40 46 | 40.17 47 | 26.90 47 | | 24.59 46 | 17.44 46 | 23.95 43 | 48.61 42 | 9.77 42 | 26.48 46 | 18.06 44 | 24.47 42 | 28.83 46 |
|
ITE_SJBPF | | | | | 78.22 30 | 81.77 32 | 60.57 31 | | 83.30 31 | 69.25 19 | 67.54 27 | 87.20 24 | 36.33 35 | 87.28 32 | 54.34 27 | 74.62 34 | 86.80 27 |
|