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Computer Science > Computer Vision and Pattern Recognition

arXiv:2101.01133 (cs)
[Submitted on 4 Jan 2021 (v1), last revised 28 Jan 2021 (this version, v4)]

Title:Stereo Correspondence and Reconstruction of Endoscopic Data Challenge

Authors:Max Allan, Jonathan Mcleod, Congcong Wang, Jean Claude Rosenthal, Zhenglei Hu, Niklas Gard, Peter Eisert, Ke Xue Fu, Trevor Zeffiro, Wenyao Xia, Zhanshi Zhu, Huoling Luo, Fucang Jia, Xiran Zhang, Xiaohong Li, Lalith Sharan, Tom Kurmann, Sebastian Schmid, Raphael Sznitman, Dimitris Psychogyios, Mahdi Azizian, Danail Stoyanov, Lena Maier-Hein, Stefanie Speidel
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Abstract:The stereo correspondence and reconstruction of endoscopic data sub-challenge was organized during the Endovis challenge at MICCAI 2019 in Shenzhen, China. The task was to perform dense depth estimation using 7 training datasets and 2 test sets of structured light data captured using porcine cadavers. These were provided by a team at Intuitive Surgical. 10 teams participated in the challenge day. This paper contains 3 additional methods which were submitted after the challenge finished as well as a supplemental section from these teams on issues they found with the dataset.
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2101.01133 [cs.CV]
  (or arXiv:2101.01133v4 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2101.01133

Submission history

From: Max Allan [view email]
[v1] Mon, 4 Jan 2021 17:58:48 UTC (44,102 KB)
[v2] Wed, 6 Jan 2021 19:36:32 UTC (44,329 KB)
[v3] Mon, 25 Jan 2021 00:27:21 UTC (44,331 KB)
[v4] Thu, 28 Jan 2021 20:09:37 UTC (44,331 KB)
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