Bilevel Optimization for Scene Segmentation of LiDAR Point Cloud

doi:10.11947/j.AGCS.2018.20170493

Abstract

Abstract: The segmentation of point clouds obtained by light detection and ranging (LiDAR) systems is a critical step for many tasks,such as data organization,reconstruction and information extraction.In this paper,we propose a bilevel progressive optimization algorithm based on the local differentiability.First,we define the topological relation and distance metric of points in the framework of Riemannian geometry,and in the point-based level using k-means method generates over-segmentation results,e.g.super voxels.Then these voxels are formulated as nodes which consist a minimal spanning tree.High level features are extracted from voxel structures,and a graph-based optimization method is designed to yield the final adaptive segmentation results.The implementation experiments on real data demonstrate that our method is efficient and superior to state-of-the-art methods.

Key words: point cloud segmentation, Riemannian geometry, super voxel, minimal spanning tree, feature extraction

CLC Number:

P237

LI Minglei, LIU Shaochuang, YANG Huan, QI Chen. Bilevel Optimization for Scene Segmentation of LiDAR Point Cloud[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(2): 269-274.

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