Parametric SkelNetOn

The last domain aims to push the boundaries to find true parametric representations of the skeleton of the shape, given its image. The participants are expected to output the skeleton of the shape defined by its parametric curves, together with a radius function. The main challenge of this track arises from the domain change between the input and output, so representation of the output in a deterministic way is the key motivation of this track. We expect the challengers to provide results in terms of the accuracy better than the current best parametrized skeleton. This will be a recognition problem (similar to the problem of pose estimation) to detect the geometric representation (Bezier curves) for a given shape image.

For details about other SkelNetOn challenges and the workshop: https://sites.google.com/view/dlgc-workshop-cvpr2020/home

Please refer to the following paper if you participate in this challenge or use the dataset for your approach:

@inproceedings{demir2019,
  title={Skelneton 2019: Dataset and challenge on deep learning for geometric shape understanding},
  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},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops},
  pages={0--0},
  year={2019}
}