ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statistics
This work addresses efficiency and complexity issues in 3D scene understanding for applications like robotics and autonomous driving, representing an incremental improvement over existing methods.
The paper tackles the problems of low training speed and complex architecture in point cloud deep learning by proposing ShellNet, an efficient end-to-end permutation invariant convolution using concentric spherical shells statistics. It achieves state-of-the-art results on object classification, part segmentation, and semantic scene segmentation while maintaining fast training.
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.