Image convolutions have been a cornerstone of a great number of deep learning advances in computer vision. The research community is yet to settle on an equivalent operator for sparse, unstructured continuous data like point clouds and event streams however. We present an elegant sparse matrix-based interpretation of the convolution operator for these cases, which is consistent with the mathematical definition of convolution and efficient during training. On benchmark point cloud classification problems we demonstrate networks built with these operations can train an order of magnitude or more faster than top existing methods, whilst maintaining comparable accuracy and requiring a tiny fraction of the memory. We also apply our operator to event stream processing, achieving state-of-the-art results on multiple tasks with streams of hundreds of thousands of events.
@article{arxiv.2012.01170,
title = {Sparse Convolutions on Continuous Domains for Point Cloud and Event Stream Networks},
author = {Dominic Jack and Frederic Maire and Simon Denman and Anders Eriksson},
journal= {arXiv preprint arXiv:2012.01170},
year = {2020}
}