ICCV2019 ShellNet

Point Cloud

Posted by Renwu Li on 27 October 2019 · 1 min read

• Title: 《ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statistics》
• Source: ICCV2019
• Subject: Point Cloud Convolution

How it works

ShellConv operator. (a) For an input point cloud with/without associated features, representative points (red dots) are randomly sampled. The nearest neighbors are then chosen to form a point set centered at the representative points. The point sets are distributed across a series of concentric spherical shells (b) and the statistics of each shell is summarized by a maxpooling over all points in the shell, the features of which are lifted by an mlp to a higher dimension. The maxpooled features are indicated as squares with different colors (c). Following the inner to the outer order, a standard 1D convolution can be performed to yield the output features (d). Thicker dot means less points but each has higher dimensional features.