This table lists the benchmark results for the high-res multi-view scenario.
The following metrics are evaluated:
Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.F1 score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.Time [s]: The runtime of the method. Smaller is better.
(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper .
The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category.
Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.
Methods with suffix _ROB may participate in the Robust Vision Challenge .
training datasets , ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.
Set : Test
Metric : F1 score [%]
Tolerance : 1cm
Method
Info all high-res indoor outdoor botani. boulde. bridge door exhibi. lectur. living. lounge observ. old co. statue terrac.
DeepPCF-MVS 69.58 1 79.03 1 79.00 1 79.13 4 84.92 4 60.32 3 83.03 4 85.33 3 62.86 3 80.17 1 90.03 1 61.76 1 88.95 4 74.21 1 88.67 1 88.12 2 DeepC-MVS 68.55 2 78.02 2 77.46 2 79.68 1 84.43 5 61.68 1 84.51 3 86.17 2 62.66 4 74.68 2 89.18 3 57.25 2 89.71 1 70.47 2 87.80 3 87.66 3 DeepC-MVS_fast 68.22 3 77.40 3 76.78 3 79.27 3 86.46 1 61.11 2 85.41 2 84.91 4 63.46 2 72.86 3 88.92 4 53.24 3 89.19 3 67.88 3 87.88 2 87.52 4 3Dnovator+ 66.72 4 75.84 4 74.57 4 79.66 2 82.40 6 59.92 4 85.83 1 86.32 1 66.92 1 67.80 8 89.24 2 42.03 9 89.38 2 64.07 6 86.50 4 89.69 1 MG-MVS 73.96 5 73.89 5 74.16 10 85.65 2 49.69 19 81.59 8 81.29 12 61.45 6 71.05 4 88.11 5 51.77 6 87.73 5 61.05 7 83.09 7 85.05 8 CLD-MVS 73.33 6 72.68 6 75.29 9 78.82 12 53.33 14 78.23 17 84.79 5 61.30 7 70.41 5 81.04 18 52.41 5 87.12 6 64.61 5 82.49 9 85.41 6 3Dnovator 64.47 5 72.49 7 71.39 7 75.79 5 77.70 15 58.99 6 80.66 11 83.15 6 62.24 5 65.46 10 86.59 6 42.38 8 85.52 9 59.59 9 84.72 5 82.85 10 ACMP 63.53 6 72.30 8 71.20 8 75.59 7 80.28 10 57.54 9 82.74 5 82.84 8 60.58 8 65.24 11 86.18 7 39.25 11 86.03 8 66.95 4 76.79 13 83.22 9 Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo . AAAI 2020 MAR-MVS 71.51 9 70.15 9 75.60 6 81.84 7 59.39 5 81.38 9 82.90 7 54.90 13 68.08 7 78.70 21 47.73 7 85.51 10 51.68 11 84.17 6 81.88 11 PCF-MVS 61.88 8 70.95 10 69.49 11 75.35 8 77.63 16 55.71 10 76.04 19 81.81 10 50.30 18 69.66 6 85.40 9 52.51 4 84.89 11 51.82 10 80.24 10 85.45 5 Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction . GCPR 2019 ACMM 61.98 7 70.80 11 69.73 10 74.02 11 80.59 9 58.59 7 82.68 6 82.02 9 55.46 12 67.18 9 84.39 12 38.51 14 83.17 14 60.65 8 76.10 14 80.30 12 Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo . CVPR 2019 AdaColmap 69.99 12 68.66 12 73.97 12 84.94 3 57.83 8 82.63 7 78.71 15 56.28 10 64.34 13 84.14 13 41.57 10 87.06 7 46.45 14 78.88 11 77.02 19 OpenMVS 61.03 9 68.85 13 67.56 13 72.70 13 74.26 20 53.99 13 81.21 10 81.34 11 52.70 15 62.75 14 85.55 8 38.86 13 84.14 13 48.41 13 83.01 8 79.97 13 TAPA-MVS 59.36 10 66.60 14 65.20 14 70.81 16 76.63 17 48.75 20 76.52 18 80.04 13 50.64 17 65.24 11 84.93 10 39.15 12 78.54 22 36.77 20 76.88 12 85.14 7 Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo . ICCV 2019 ACMH+ 57.40 11 66.12 15 64.06 15 72.30 14 77.79 14 52.83 15 80.39 12 78.03 16 57.30 9 57.47 16 82.55 14 27.68 20 84.17 12 45.54 15 69.78 20 79.90 14 ACMH 55.70 13 65.20 16 63.57 16 70.07 18 78.07 13 52.01 18 79.48 14 79.69 14 55.75 11 56.59 18 80.98 19 27.12 21 80.94 18 42.90 16 71.58 17 77.25 18 Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo . CVPR 2019 PLC 56.13 12 65.09 17 63.21 17 70.72 17 81.04 8 54.87 12 78.57 15 77.47 17 48.51 20 55.71 20 81.89 16 33.71 15 79.71 19 41.66 18 70.37 19 77.58 16 Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency . Pacific Graphics 2019 LS3D 64.71 18 62.50 18 71.34 15 79.72 11 55.71 10 79.82 13 74.72 20 48.50 21 56.62 17 84.62 11 33.59 16 82.34 15 29.65 25 75.23 15 75.97 20 LTVRE_ROB 55.42 14 63.15 19 61.23 19 68.92 20 76.57 18 47.80 21 59.92 23 76.39 18 54.35 14 58.67 15 82.46 15 29.44 19 81.49 16 42.12 17 71.14 18 77.46 17 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 MSDG 61.81 20 59.23 20 69.55 19 72.64 21 52.63 16 70.45 21 75.81 19 51.38 16 53.70 22 76.11 23 29.52 17 81.08 17 37.70 19 65.79 23 74.93 21 COLMAP_ROB 52.97 15 61.27 21 58.81 21 68.64 21 74.63 19 52.51 17 78.42 16 73.30 21 49.92 19 50.96 23 81.51 17 23.06 23 79.40 21 31.63 23 65.85 22 74.01 22 Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo . ECCV 2016 OpenMVS_ROB 52.78 16 60.03 22 58.14 22 65.69 22 70.47 22 44.82 23 75.33 20 70.86 22 45.04 23 56.06 19 76.00 24 26.89 22 79.65 20 35.36 22 67.29 21 72.60 23 CMPMVS 42.80 17 57.81 23 55.97 23 63.32 23 60.98 25 47.38 22 64.66 22 69.50 23 32.06 24 46.83 24 77.80 22 29.50 18 71.36 23 48.68 12 73.75 16 71.21 24 M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces . CVPR 2011 LF4IMVS 42.95 24 42.26 24 45.04 26 48.30 27 32.50 28 54.80 24 48.49 28 28.03 26 40.51 25 70.16 25 9.24 26 43.89 29 31.63 23 49.18 27 58.72 25 PMVS 28.69 18 36.22 25 33.29 25 45.02 27 36.82 30 35.98 27 54.68 25 48.74 27 26.31 27 21.02 27 51.61 27 2.88 29 60.10 26 9.99 31 47.58 28 38.99 29 Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis . PAMI (2010) Gipuma 34.77 26 31.91 26 43.33 29 62.05 24 37.87 26 20.39 29 67.03 24 23.23 28 18.41 28 25.84 31 4.24 27 62.73 24 14.71 28 51.32 26 29.38 31 S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion . ICCV 2015 wuykxyi23d 28.12 27 22.54 28 44.87 28 34.97 31 32.11 29 37.96 27 47.31 29 13.32 30 9.29 32 23.72 32 0.45 32 56.58 27 21.85 26 13.98 30 45.93 26 PNet_i23d 27.88 28 25.99 27 33.55 30 47.54 28 25.89 31 47.24 26 32.91 30 21.44 29 15.90 29 38.09 28 0.85 31 42.76 30 16.90 27 13.03 31 32.00 30 MVE 17.77 19 21.41 29 17.77 29 32.34 31 34.34 32 25.44 32 16.11 30 24.11 31 11.19 31 13.22 30 31.92 29 1.58 30 30.95 31 10.47 30 17.03 29 40.62 28 Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment . EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) wuyk23d 13.32 30 12.52 30 15.71 32 47.54 28 26.27 30 31.06 28 1.98 33 4.93 33 5.18 33 1.94 33 0.45 32 18.54 32 6.81 33 12.83 32 2.33 33 FPMVS 45.39 25 58.03 26 41.01 25 53.81 26 30.07 25 33.71 26 64.03 26 11.69 25 52.08 28 14.01 29 55.11 25 43.09 27 DeepMVS_CX 12.03 33 17.97 33 10.91 33 10.60 32 7.46 32 11.07 31 28.36 30 3.28 28 11.29 33 8.01 32 9.74 33 13.89 32 ITE_SJBPF 62.09 24 66.16 23 44.55 24 64.32 25 47.36 22 55.31 21 80.34 20 19.27 24 62.68 25 36.29 21 62.39 24 79.04 15
