ScatterNet: Point Cloud Learning via Scatters

Design of point cloud shape descriptors is a challenging problem in practical applications due to the sparsity and the inscrutable distribution of the point clouds. In this paper, we propose ScatterNet, a novel 3D local feature learning approach for exploring and aggregating hypothetical scatters of the point clouds. Scatters of relational points are first organized in point cloud via guided explorations, and then propagated back to extend the capacity in representing the point-wise characteristics. We provide an practical implementation of the ScatterNet, which involves an unique scatter exploration operator and a scatter convolution operator. Our method achieves the state-of-the-art performance on several point cloud analysis tasks like classification, part segmentation and normal estimation. The source code of ScatterNet is available in supplementary materials.