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We present SkelNetOn 2019 Challenge and Deep Learning for Geometric ShapeUnderstanding workshop to utilize existing and develop novel deep learningarchitectures for shape understanding. We observed that unlike traditionalsegmentation and detection tasks, geometry understanding is still a new areafor deep learning techniques. SkelNetOn aims to bring together researchers fromdifferent domains to foster learning methods on global shape understandingtasks. We aim to improve and evaluate the state-of-the-art shape understandingapproaches, and to serve as reference benchmarks for future research. Similarto other challenges in computer vision, SkelNetOn proposes three datasets andcorresponding evaluation methodologies; all coherently bundled in threecompetitions with a dedicated workshop co-located with CVPR 2019 conference. Inthis paper, we describe and analyze characteristics of datasets, define theevaluation criteria of the public competitions, and provide baselines for eachtask.

SkelNetOn 2019: Dataset and Challenge on Deep Learning for Geometric Shape Understanding