双层优化的激光雷达点云场景分割方法

对激光雷达扫描的非结构化点云进行分割处理，是进行数据组织、重构和信息提取的重要步骤。本文根据点云表面的局部可微的性质，提出了一种递进形式的双层优化分割算法。首先在黎曼几何框架下计算点的拓扑关系和距离度量特性，以k均值聚类的方法获得过分割体素，作为底层分割结果。然后，将点云的体素模式化为节点，构建最小生成树，提取节点的高级特征信息，利用图优化得到对点云细节自适应的区域分割效果。通过真实数据进行验证，并与现有方法比较，证明所提算法的可行性和先进性。

Bilevel Optimization for Scene Segmentation of LiDAR Point Cloud

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.