2019/12/10 by Peiyun Hu, Hu, Peiyun, David Held +3
Computer Science · Earth and Planetary Sciences · Engineering · Environmental Science · #3D Shape Modeling and Analysis #3D Surveying and Cultural Heritage #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Remote Sensing and LiDAR Applications #Robotics (cs.RO) #cs.CV #cs.RO
paper · pdf · doi:10.48550/arxiv.1912.04976
arxiv created 2019/12/10 · openalex publication_date 2019/12/10 · arxiv updated 2019/12/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 objectness among its individual segments, there is an efficient algorithm that finds the optimal worst-case segmentation among an exponentially large number of candidate segmentations. We also present an efficient algorithm for the average-case. For evaluation, we repurpose KITTI 3D detection as a segmentation benchmark and empirically demonstrate that our algorithms significantly outperform past bottom-up segmentation approaches and top-down object-based algorithms on segmenting point clouds.