Point SkelNetOn - CVPR 2020

Organized by ilkedemir - Current server time: Sept. 27, 2020, 2:31 p.m. UTC

Previous

Final
June 1, 2020, midnight UTC

Current

Development
March 18, 2020, midnight UTC

End

Competition Ends
June 7, 2020, midnight UTC

Points SkelNetOn

The second challenge track investigates the problem in the point domain, where the shapes will be represented by point clouds as well as the skeletons. This track also emphasizes some fundamental questions as how to process non-uniform data, how to overcome class imbalance, and some exploration in higher dimensional point clouds. We expect the challengers to provide results in terms of the accuracy better than the current best skeleton extraction from points in the system. This can be posed as a binary classification problem to assign a skeleton/non-skeleton class to all points in the given point cloud; however other formulations (i.e., as in transformer networks) are also accepted to solve this challenge.

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}
}
 

Evaluation

Given a 2D point set representing a shape, the goal of the point skeleton extraction task is to output a set of point coordinates corresponding to the given shape’s skeleton. This can be approached as a binary point classification task or a point generation task, both of which end up producing a skeleton point cloud that approximate the shape skeleton. The output set of skeletal points need not be part of the original input point set. The evaluation metric for this task needs to be invariant to the number and ordering of the points. The metric should also be flexible for different point sampling distributions representing the same skeleton. Therefore, the results are evaluated using the symmetric Chamfer distance function.

  • 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 https://cmt3.research.microsoft.com/DLGC2020 and please use the CVPR paper template.

The submissions are evaluated using the Chamfer Distance.

Rules

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.

Development

Start: March 18, 2020, midnight

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

Final

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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# Username Score
1 digitalspecialists 1.6646
2 qiyinhao 1.7511
3 sohom21d 2.2035