  We present SkelNetOn 2019 Challenge and Deep Learning for Geometric Shape
Understanding workshop to utilize existing and develop novel deep learning
architectures for shape understanding. We observed that unlike traditional
segmentation and detection tasks, geometry understanding is still a new area
for deep learning techniques. SkelNetOn aims to bring together researchers from
different domains to foster learning methods on global shape understanding
tasks. We aim to improve and evaluate the state-of-the-art shape understanding
approaches, and to serve as reference benchmarks for future research. Similar
to other challenges in computer vision, SkelNetOn proposes three datasets and
corresponding evaluation methodologies; all coherently bundled in three
competitions with a dedicated workshop co-located with CVPR 2019 conference. In
this paper, we describe and analyze characteristics of datasets, define the
evaluation criteria of the public competitions, and provide baselines for each
task.
