Reviews: PointCNN: Convolution On X-Transformed Points

This paper addresses the problem of representing unordered point sets for recognition applications. The key insights is a "chi-convolution" operator that learns to "permute" local points and point-features into a canonical order within a neural network. The approach is demonstrated on 3D point cloud recognition and segmentation and 2D sketch and image classification applications. Positives: The paper addresses a known hard problem - how to properly represent unordered point sets for recognition. As far as I'm aware, the paper describes a novel and interesting approach for learning to "permute" local point neighborhoods in unordered point sets.