Learning to Optimally Segment Point Clouds

We focus on the problem of class-agnostic instance segmentation of LiDAR point clouds. We propose an approach that combines graph-theoretic search with data-driven learning: it searches over a set of candidate segmentations and returns one where individual segments score well according to a data-driven point-based model of “objectness”. We prove that if we score a segmentation by the worst objectn...