Local Learning in Point Clouds based on Spectral Pooling

As one of the most fundamental geometric data types for the representation of space and object shapes, a point cloud usually maintains much structural information about the spatial relationship between objects and their features. However, the relative sparseness of point clouds sampled in most practical applications make extracting information-rich features a major challenge. Traditionally, feature extraction algorithms resorted to structured feature engineering and used handcrafted representations for some specific problems. Motivated by the development of deep neural networks, many researchers started to handle the unstructured point clouds from the raw data samples of 3D scanning devices. Some important advantages that deep learning frameworks have over traditional feature engineering is generalizing complex features and associated semantic concepts in a hierarchical manner. Deep learning models have achieved significant landmarks in cognitive processing of speech, image, and video signals. However, unlike in 2D image processing, a 3D point cloud is irregular and sparse. Hence, traditional network frameworks are difficult to apply on 3D geometric data directly. In this paper, we propose to integrate a local point convolution network with spectral pooling to aggregate and learn features in 3D point clouds. The benefits of our framework are fast convergence and competitive performance on point cloud classification.