Previous Research: 3D Point Cloud Parsing

Deep neural networks are widely used for understanding 3D point clouds. At each point convolution layer, features are computed from local neighborhoods of 3D points and combined for subsequent processing in order to extract semantic information. We study a novel approach to learn different non-rigid transformations of the input point cloud so that optimal local neighborhoods can be adopted at each layer.