# Explaining Hierarchical Features in Dynamic Point Cloud Processing

arXiv: https://arxiv.org/abs/2209.15557 · HTML: https://mlsanity.com/paper/2209.15557/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2022.

## Built on by (most cited first, 1 of 1)

- [AGAR: Attention Graph-RNN for Adaptative Motion Prediction of Point Clouds of Deformable Objects](https://mlsanity.com/paper/2307.09936/) (2023)

## Abstract (arXiv)

This paper aims at bringing some light and understanding to the field of deep learning for dynamic point cloud processing. Specifically, we focus on the hierarchical features learning aspect, with the ultimate goal of understanding which features are learned at the different stages of the process and what their meaning is. Last, we bring clarity on how hierarchical components of the network affect the learned features and their importance for a successful learning model. This study is conducted for point cloud prediction tasks, useful for predicting coding applications.