%0 Conference Paper %1 conf/cvpr/JohannsenHGABBB17 %A Johannsen, Ole %A Honauer, Katrin %A Goldluecke, Bastian %A Alperovich, Anna %A Battisti, Federica %A Bok, Yunsu %A Brizzi, Michele %A Carli, Marco %A Choe, Gyeongmin %A Diebold, Maximilian %A Gutsche, Marcel %A Jeon, Hae-Gon %A Kweon, In So %A Park, Jaesik %A Park, Jinsun %A Schilling, Hendrik %A Sheng, Hao %A Si, Lipeng %A Strecke, Michael %A Sulc, Antonin %A Tai, Yu-Wing %A Wang, Qing %A Wang, Ting-Chun %A Wanner, Sven %A Xiong, Zhang %A Yu, Jingyi %A Zhang, Shuo %A Zhu, Hao %B Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Workshops %D 2017 %I IEEE %K 2017 B05 sfbtrr161 %P 1795-1812 %R 10.1109/CVPRW.2017.226 %T A Taxonomy and Evaluation of Dense Light Field Depth Estimation Algorithms %U https://ieeexplore.ieee.org/document/8014960 %X This paper presents the results of the depth estimation challenge for dense light fields, which took place at the second workshop on Light Fields for Computer Vision (LF4CV) in conjunction with CVPR 2017. The challenge consisted of submission to a recent benchmark 7, which allows a thorough performance analysis. While individual results are readily available on the benchmark web page http://www.lightfield-analysis.net, we take this opportunity to give a detailed overview of the current participants. Based on the algorithms submitted to our challenge, we develop a taxonomy of light field disparity estimation algorithms and give a report on the current state-of-the-art. In addition, we include more comparative metrics, and discuss the relative strengths and weaknesses of the algorithms. Thus, we obtain a snapshot of where light field algorithm development stands at the moment and identify aspects with potential for further improvement. %@ 978-1-5386-0733-6

@inproceedings{conf/cvpr/JohannsenHGABBB17, abstract = {This paper presents the results of the depth estimation challenge for dense light fields, which took place at the second workshop on Light Fields for Computer Vision (LF4CV) in conjunction with CVPR 2017. The challenge consisted of submission to a recent benchmark [7], which allows a thorough performance analysis. While individual results are readily available on the benchmark web page http://www.lightfield-analysis.net, we take this opportunity to give a detailed overview of the current participants. Based on the algorithms submitted to our challenge, we develop a taxonomy of light field disparity estimation algorithms and give a report on the current state-of-the-art. In addition, we include more comparative metrics, and discuss the relative strengths and weaknesses of the algorithms. Thus, we obtain a snapshot of where light field algorithm development stands at the moment and identify aspects with potential for further improvement.}, added-at = {2020-03-05T12:10:44.000+0100}, author = {Johannsen, Ole and Honauer, Katrin and Goldluecke, Bastian and Alperovich, Anna and Battisti, Federica and Bok, Yunsu and Brizzi, Michele and Carli, Marco and Choe, Gyeongmin and Diebold, Maximilian and Gutsche, Marcel and Jeon, Hae-Gon and Kweon, In So and Park, Jaesik and Park, Jinsun and Schilling, Hendrik and Sheng, Hao and Si, Lipeng and Strecke, Michael and Sulc, Antonin and Tai, Yu-Wing and Wang, Qing and Wang, Ting-Chun and Wanner, Sven and Xiong, Zhang and Yu, Jingyi and Zhang, Shuo and Zhu, Hao}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2fe765136c28019ee2893a8b03f42b4e1/leonkokkoliadis}, booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Workshops}, doi = {10.1109/CVPRW.2017.226}, ee = {http://doi.ieeecomputersociety.org/10.1109/CVPRW.2017.226}, interhash = {656b853713c20bf84a0819b9700cb93f}, intrahash = {fe765136c28019ee2893a8b03f42b4e1}, isbn = {978-1-5386-0733-6}, keywords = {2017 B05 sfbtrr161}, pages = {1795-1812}, publisher = {IEEE}, timestamp = {2020-03-05T11:10:44.000+0100}, title = {A Taxonomy and Evaluation of Dense Light Field Depth Estimation Algorithms}, url = {https://ieeexplore.ieee.org/document/8014960}, year = 2017 }