  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 methods
that can learn from unlabelled data to significantly reduce the number of
annotated samples needed in supervised learning. We propose a self-supervised
learning task for deep learning on raw point cloud data in which a neural
network is trained to reconstruct point clouds whose parts have been randomly
rearranged. While solving this task, representations that capture semantic
properties of the point cloud are learned. Our method is agnostic of network
architecture and outperforms current unsupervised learning approaches in
downstream object classification tasks. We show experimentally, that
pre-training with our method before supervised training improves the
performance of state-of-the-art models and significantly improves sample
efficiency.
