Acknowledgement
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (GR 2016R1D1A3B03931911).
Proceedings of the Korea Information Processing Society Conference (한국정보처리학회:학술대회논문집)
- 2019.05a
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- Pages.482-484
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- 2019
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- 2005-0011(pISSN)
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- 2671-7298(eISSN)
DOI QR Code
Projection Loss for Point Cloud Augmentation
점운증강을 위한 프로젝션 손실
- Wu, Chenmou (Div. of Computer Science & Engineering, Chonbuk National University) ;
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Lee, Hyo-Jone
(Div. of Computer Science & Engineering, Chonbuk National University)
- Published : 2019.05.10
Abstract
Learning and analyzing 3D point clouds with deep networks is challenging due to the limited and irregularity of the data. In this paper, we present a data-driven point cloud augmentation technique. The key idea is to learn multilevel features per point and to reconstruct to a similar point set. Our network is applied to a projection loss function that encourages the predicted points to remain on the geometric shapes with a particular target. We conduct various experiments using ShapeNet part data to evaluate our method and demonstrate its possibility. Results show that our generated points have a similar shape and are located closer to the object.
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