3D tree modeling from incomplete point clouds via optimization and L1-MST. Issue 5 (4th May 2017)

• Record Type: Journal Article
• Authors: Mei, Jie; Zhang, Liqiang; Wu, Shihao; Wang, Zhen; Zhang, Liang
• Is Part Of: International journal of geographical information science. Volume 31:Issue 5(2017)
• Page Start: 999
• Page End: 1021
• Publication Date: 2017-05-04
• Subjects: Incomplete point cloud -- tree skeleton -- point density -- dominant direction -- optimization -- L1-MST; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2016.1264075
• Languages: English
• ISSNs: 1365-8816

Abstract

ABSTRACT: Reconstruction of 3D trees from incomplete point clouds is a challenging issue due to their large variety and natural geometric complexity. In this paper, we develop a novel method to effectively model trees from a single laser scan. First, coarse tree skeletons are extracted by utilizing the L 1 -median skeleton to compute the dominant direction of each point and the local point density of the point cloud. Then we propose a data completion scheme that guides the compensation for missing data. It is an iterative optimization process based on the dominant direction of each point and local point density. Finally, we present a L 1 -minimum spanning tree (MST) algorithm to refine tree skeletons from the optimized point cloud, which integrates the advantages of both L 1 -median skeleton and MST algorithms. The proposed method has been validated on various point clouds captured from single laser scans. The experiment results demonstrate the effectiveness and robustness of our method for coping with complex shapes of branching structures and occlusions.