Speaker: Loic Landrieu (IGN)
Title: Recent Advances in Deep Learning for 3D Point Clouds Analysis
Abstract:
Over the last few years, deep learning-based approaches have established their relevance for the automated analysis of 3d point clouds. In particular, machine learning advances in terms of graph, kernel-based, and sparse convolutions have allowed deep learning methods to take over all major benchmarks.  In this talk, I present an overview of the landscape of deep learning for 3d point cloud analysis. I will then present the superpoint graph approach that we developed for scaling memory-intensive algorithms to very large point clouds. Finally, I will introduce some of our latest work on unsupervised and interpretable analysis of point clouds.