Multiresolution Tree Networks for 3D Point Cloud Processing

Matheus Gadelha, Rui Wang, Subhransu Maji; Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 103-118

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 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.

Related Material


[pdf]
[bibtex]
@InProceedings{Gadelha_2018_ECCV,
author = {Gadelha, Matheus and Wang, Rui and Maji, Subhransu},
title = {Multiresolution Tree Networks for 3D Point Cloud Processing},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}
}