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 [30] 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.
The 3D Point Capsule Network REF showed the possibility of extracting semantically meaningful parts from 3D point clouds using capsulebased representations.
3D Point Capsule Networks
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Science