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

Ilke Demir, Camilla Hahn, Kathryn Leonard, Geraldine Morin, Dana Rahbani, Athina Panotopoulou, Amelie Fondevilla, Elena Balashova, Bastien Durix, Adam Kortylewski; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0

Abstract


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.

Related Material


[pdf]
[bibtex]
@InProceedings{Demir_2019_CVPR_Workshops,
author = {Demir, Ilke and Hahn, Camilla and Leonard, Kathryn and Morin, Geraldine and Rahbani, Dana and Panotopoulou, Athina and Fondevilla, Amelie and Balashova, Elena and Durix, Bastien and Kortylewski, Adam},
title = {SkelNetOn 2019: Dataset and Challenge on Deep Learning for Geometric Shape Understanding},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}