3D Point Capsule Networks

Yongheng Zhao; Tolga Birdal; Haowen Deng; Federico Tombari

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arXiv · 1812.10775

3D Point Capsule Networks

Yongheng Zhao·Tolga Birdal·Haowen Deng·Federico Tombari

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Abstract

In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data. 3D capsule networks arise as a direct consequence of our novel unified 3D auto-encoder formulation. Their dynamic routing scheme and the peculiar 2D latent space deployed by our approach bring in improvements for several common point cloud-related tasks, such as object classification, object reconstruction and part segmentation as substantiated by our extensive evaluations. Moreover, it enables new applications such as part interpolation and replacement.

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BibTeX RIS

Yongheng Zhao, Tolga Birdal, Haowen Deng, Federico Tombari. 2018-12-27. 3D Point Capsule Networks. https://arxiv.org/abs/1812.10775

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