# Multiresolution Tree Networks for 3D Point Cloud Processing

**Authors:** Matheus Gadelha, Rui Wang, Subhransu Maji  
**arXiv:** [1807.03520](https://arxiv.org/abs/1807.03520)  
**Date:** 2018-07-10  
**Summary page:** https://www.summarizepaper.com/fr/paper/1807.03520v2/

## Abstract

We present multiresolution tree-structured networks to process point clouds for 3D shape understanding and generation tasks. Our network represents a 3D shape as a set of locality-preserving 1D ordered list of points at multiple resolutions. This allows efficient feed-forward processing through 1D convolutions, coarse-to-fine analysis through a multi-grid architecture, and it leads to faster convergence and small memory footprint during training. The proposed tree-structured encoders can be used to classify shapes and outperform existing point-based architectures on shape classification benchmarks, while tree-structured decoders can be used for generating point clouds directly and they outperform existing approaches for image-to-shape inference tasks learned using the ShapeNet dataset. Our model also allows unsupervised learning of point-cloud based shapes by using a variational autoencoder, leading to higher-quality generated shapes.