Y. Zhao, T. Birdal, H. Deng, F. Tombari 3D Point-Capsule Networks IEEE Computer Vision and Pattern Recognition (CVPR), Long Beach, United States, June 2019 (bib)
	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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