Octree-based region growing for point cloud segmentation
Octree-based region growing for point cloud segmentation
复制标题
DOI：
10.1016/j.isprsjprs.2015.01.011
复制
发表时间：
2015-06-01
影响因子：
12.7
通讯作者：
Bertolotto, Michela
中科院分区：
文献类型：
--
作者：
Anh-Vu Vo;Linh Truong-Hong;Bertolotto, Michela
本文介绍了一种新的，区域增长算法的快速表面块分割的三维点云的城市环境。该算法是由两个阶段的基础上，由粗到精的概念。首先，在输入点云的基于八叉树的体素化表示上执行区域生长步骤以提取主要（粗）段。然后将输出通过细化过程。作为其中的一部分，存在与体素大小选择相关的两个竞争因素。为了平衡约束，自适应八叉树分两个阶段创建。对复杂建筑物和城市环境的真实的地面和机载激光扫描数据的实证研究表明，与传统的区域生长方法相比，所提出的方法至少快一个数量级，并且能够结合基于语义的特征标准，同时实现精度，召回率和适应度得分至少为75%和高达95%。(c)2015由Elsevier B.V.代表国际摄影测量与遥感学会（International Society for Photogrammetry and Remote Sensing，Inc.）（摄影测量和遥感学会）。
This paper introduces a novel, region-growing algorithm for the fast surface patch segmentation of three-dimensional point clouds of urban environments. The proposed algorithm is composed of two stages based on a coarse-to-fine concept. First, a region-growing step is performed on an octree-based voxelized representation of the input point cloud to extract major (coarse) segments. The output is then passed through a refinement process. As part of this, there are two competing factors related to voxel size selection. To balance the constraints, an adaptive octree is created in two stages. Empirical studies on real terrestrial and airborne laser scanning data for complex buildings and an urban setting show the proposed approach to be at least an order of magnitude faster when compared to a conventional region growing method and able to incorporate semantic-based feature criteria, while achieving precision, recall, and fitness scores of at least 75% and as much as 95%. (c) 2015 Published by Elsevier B.V. on behalf of International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).