A Network Architecture for Point Cloud Classification
via Automatic Depth Images Generation
Abstract
We propose a novel neural network architecture for point
cloud classification. Our key idea is to automatically transform the 3D unordered input data into a set of useful 2D
depth images, and classify them by exploiting well performing image classification CNNs. We present new differentiable module designs to generate depth images from a point
cloud. These modules can be combined with any network
architecture for processing point clouds. We utilize them
in combination with state-of-the-art classification networks,
and get results competitive with the state of the art in point
cloud classification. Furthermore, our architecture automatically produces informative images representing the input point cloud, which could be used for further applications such as point cloud visualization