Over the last few years, advances in graph, kernel, and sparse convolutions have helped establish deep networks as the predominant methods for 3D point clouds analysis. In this talk, I will first present the very dynamic landscape of 3D deep learning, and introduce the superpoint graph approach for scaling memory-intensive algorithms to very large point clouds. Finally, I will present some of the latest developments on the subject, including our recent work on unsupervised and interpretable shape sets summarization.