We present a simple and general framework for feature learning from point cloud. The key to the success of CNNs is the convolution operator that is capable of leveraging spatially-local correlation in data represented densely in grids (e.g.

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.

We present a simple and general framework for feature learning from point cloud. The key to the success of CNNs is the convolution operator that is capable of leveraging spatially-local correlation in data represented densely in grids (e.g. However, point cloud are irregular and unordered, thus a direct convolving of kernels against the features associated with the points will result in deserting the shape information while being variant to the orders. To address these problems, we propose to learn a X-transformation from the input points, which is used for simultaneously weighting the input features associated with the points and permuting them into latent potentially canonical order. Then element-wise product and sum operations of typical convolution operator are applied on the X-transformed features.