  The introduction of cheap RGB-D cameras, stereo cameras, and LIDAR devices
has given the computer vision community 3D information that conventional RGB
cameras cannot provide. This data is often stored as a point cloud. In this
paper, we present a novel method to apply the concept of convolutional neural
networks to this type of data. By creating a mapping of nearest neighbors in a
dataset, and individually applying weights to spatial relationships between
points, we achieve an architecture that works directly with point clouds, but
closely resembles a convolutional neural net in both design and behavior. Such
a method bypasses the need for extensive feature engineering, while proving to
be computationally efficient and requiring few parameters.
