Publication Type

Journal Article

Version

acceptedVersion

Publication Date

3-2022

Abstract

3D point clouds deep learning is a promising field of research that allows a neural network to learn features of point clouds directly, making it a robust tool for solving 3D scene understanding tasks. While recent works show that point cloud convolutions can be invariant to translation and point permutation, investigations of the rotation invariance property for point cloud convolution has been so far scarce. Some existing methods perform point cloud convolutions with rotation-invariant features, existing methods generally do not perform as well as translation-invariant only counterpart. In this work, we argue that a key reason is that compared to point coordinates, rotation-invariant features consumed by point cloud convolution are not as distinctive. To address this problem, we propose a simple yet effective convolution operator that enhances feature distinction by designing powerful rotation invariant features from the local regions. We consider the relationship between the point of interest and its neighbors as well as the internal relationship of the neighbors to largely improve the feature descriptiveness. Our network architecture can capture both local and global context by simply tuning the neighborhood size in each convolution layer. We conduct several experiments on synthetic and real-world point cloud classifications, part segmentation, and shape retrieval to evaluate our method, which achieves the state-of-the-art accuracy under challenging rotations.

Keywords

3D Point Cloud, Convolutional Neural, Networks, Deep Learning, Rotation Invariance

Discipline

Artificial Intelligence and Robotics | Software Engineering

Publication

International Journal of Computer Vision

Volume

130

Issue

5

First Page

1228

Last Page

1243

ISSN

0920-5691

Identifier

10.1007/s11263-022-01601-z

Publisher

Springer

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1007/s11263-022-01601-z

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