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

Ilke Demir, Camilla Hahn, Kathryn Leonard, Géraldine Morin, Dana Rahbani, Athina Panotopoulou, Amélie Fondevilla, Elena Balashova, Bastien Durix, Adam Kortylewski

CVPRW 2019 pp. 1143-1151

doi:10.1109/CVPRW.2019.00149 /cvprw/2019/demir2019cvprw-skelneton/

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.

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Cite

Text

Demir et al. "SkelNetOn 2019: Dataset and Challenge on Deep Learning for Geometric Shape Understanding." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019. doi:10.1109/CVPRW.2019.00149

Markdown

[Demir et al. "SkelNetOn 2019: Dataset and Challenge on Deep Learning for Geometric Shape Understanding." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.](https://mlanthology.org/cvprw/2019/demir2019cvprw-skelneton/) doi:10.1109/CVPRW.2019.00149

BibTeX

@inproceedings{demir2019cvprw-skelneton,
  title     = {{SkelNetOn 2019: Dataset and Challenge on Deep Learning for Geometric Shape Understanding}},
  author    = {Demir, Ilke and Hahn, Camilla and Leonard, Kathryn and Morin, Géraldine and Rahbani, Dana and Panotopoulou, Athina and Fondevilla, Amélie and Balashova, Elena and Durix, Bastien and Kortylewski, Adam},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2019},
  pages     = {1143-1151},
  doi       = {10.1109/CVPRW.2019.00149},
  url       = {https://mlanthology.org/cvprw/2019/demir2019cvprw-skelneton/}
}