3D Point Capsule Networks

Yongheng Zhao; Tolga Birdal; Haowen Deng; Federico Tombari

CVPR 2019poster454 citations

3D Point Capsule Networks

Yongheng Zhao, Tolga Birdal, Haowen Deng, Federico Tombari

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 unified formulation of the common 3D auto-encoders. The dynamic routing scheme and the peculiar 2D latent space deployed by our capsule networks 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.

BibTeX

@inproceedings{cvpr2019_3dpointcapsulene,
  title = {3D Point Capsule Networks},
  author = {Yongheng Zhao and Tolga Birdal and Haowen Deng and Federico Tombari},
  booktitle = {CVPR 2019},
  year = {2019}
}