# 3D Point Capsule Networks

## TL;DR

This paper introduces 3D point-capsule networks, a novel auto-encoder architecture for processing sparse 3D point clouds that enhances tasks like classification, reconstruction, and segmentation, while enabling new applications like part interpolation.

## Contribution

The paper presents a new 3D auto-encoder with capsule networks and dynamic routing, specifically designed for point clouds, improving performance and enabling novel applications.

## Key findings

- Improved object classification accuracy
- Enhanced part segmentation performance
- Enabled part interpolation and replacement

## 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.