Title: Context Prediction for Unsupervised Deep Learning on Point Clouds,
Abstract: Point clouds provide a flexible and natural representation usable in
countless applications such as robotics or self-driving cars. Recently, deep
neural networks operating on raw point cloud data have shown promising results
on supervised learning tasks such as object classification and semantic
segmentation. While massive point cloud datasets can be captured using modern
scanning technology, manually labelling such large 3D point clouds for
supervised learning tasks is a cumbersome process. This necessitates effective
unsupervised learning methods that can produce representations such that
downstream tasks require significantly fewer annotated samples. We propose a
novel method for unsupervised learning on raw point cloud data in which a
neural network is trained to predict the spatial relationship between two point
cloud segments. While solving this task, representations that capture semantic
properties of the point cloud are learned. Our method outperforms previous
unsupervised learning approaches in downstream object classification and
segmentation tasks and performs on par with fully supervised methods.