Parametric SkelNetOn - CVPR 2020

Organized by ilkedemir - Current server time: Jan. 21, 2021, 3:39 p.m. UTC


June 1, 2020, midnight UTC


March 18, 2020, midnight UTC


Competition Ends
June 7, 2020, midnight UTC

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:

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

  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},



The parametric skeleton extraction task aims to recover the medial axis as a set of parametric curves from an input image of a shape. We evaluate our results by distance to the ground truth medial axes in our database, since the proposed skeletal representation in the dataset already guarantees the properties introduced, and are ordered in a deterministic order. We use the mean squared distance between the control points on the original and predicted branches.

The evaluation metric needs to take into account models with an incorrect number of branches, since this number is different for each shape. We penalize each missing (or extra) branch in the output with a measure on the length of the branch in the ground truth (or in the output). We use a measure called missing branch error (MBE) for each missing or extra branch b. Finally we combine these two metrics for the final score. 

There are 2 phases:

  • Phase 1: Development phase. We provide you with labeled training dataset and unlabeled validation dataset. You can submit your predictions on the validation data to CodaLab. You will receive feed-back on your performance on the validation set. The performance of your LAST submission will be displayed on the leaderboard.
  • Phase 2: Final phase. The unlabeled testing dataset will be released. You can submit your predictions on the testing dataset to CodaLab. Your performance on the test set will appear on the leaderboard when the organizers finish checking the submissions.

You only need to submit the prediction results (no code). However you need to submit your a short paper of 4 pages before April 10th to be eligible for the final phase. We will evaluate your methodology and your results in parallel. Paper submission is open at and please use the CVPR paper template.

The submissions are evaluated using the combined MSD and MBE scores.


This challenge is governed by SkelNetOn Rules. For academic use of the datasets within and outside this competition, please cite the following papers.
[1] I. Demir, C. Hahn, K. Leonard, G. Morin, D. Rahbani, A. Panotopoulou, A. Fondevilla, E. Balashova, B. Durix, and A. Kortylewski. "SkelNetOn 2019: Dataset and Challenge on Deep Learning for Geometric Shape Understanding." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2019.
[2] A. M. Bronstein, M. M. Bronstein, A. M. Bruckstein, R. Kimmel, Analysis of two- dimensional non-rigid shapes, Intl. J. Computer Vision (IJCV), Vol. 78/1, pp. 67-88, June 2008. 
[3] Sebastian TB, Klein PN, Kimia BB (2004) Recognition of shapes by editing their shock graphs. IEEE Trans Pattern Anal Mach Intell 26(5):550–571 
[4] Leonard, K., Morin, G., Hahmann, S., & Carlier, A. (2016). A 2D shape structure for decomposition and part similarity. 2016 23rd International Conference on Pattern Recognition (ICPR), 3216-3221.



Start: March 18, 2020, midnight

Description: Development phase: Please submit your results on the validation set.


Start: June 1, 2020, midnight

Description: Final phase: Please submit the output of your approach on the provided test set.

Competition Ends

June 7, 2020, midnight

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