Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2019

Abstract

Deep learning with 3D data has progressed significantly since the introduction of convolutional neural networks that can handle point order ambiguity in point cloud data. While being able to achieve good accuracies in various scene understanding tasks, previous methods often have low training speed and complex network architecture. In this paper, we address these problems by proposing an efficient end-to-end permutation invariant convolution for point cloud deep learning. Our simple yet effective convolution operator named ShellConv uses statistics from concentric spherical shells to define representative features and resolve the point order ambiguity, allowing traditional convolution to perform on such features. Based on ShellConv we further build an efficient neural network named ShellNet to directly consume the point clouds with larger receptive fields while maintaining less layers. We demonstrate the efficacy of ShellNet by producing state-of-the-art results on object classification, object part segmentation, and semantic scene segmentation while keeping the network very fast to train.

Discipline

Graphics and Human Computer Interfaces | OS and Networks

Research Areas

Information Systems and Management

Publication

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea, October 27 - November 2

First Page

1607

Last Page

1616

ISBN

9781728148038

Identifier

10.1109/iccv.2019.00169

Publisher

IEEE

City or Country

Seoul, Korea

Additional URL

https://doi.org/10.1109/iccv.2019.00169

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