Skeleton extraction from point clouds of trees with complex branches via graph contraction
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ORIGINAL ARTICLE
Skeleton extraction from point clouds of trees with complex branches via graph contraction Anling Jiang1 · Ji Liu1
· Jianling Zhou1 · Min Zhang1
Accepted: 14 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Extracting a skeleton from a 3D tree-shaped point cloud with complex branches is a challenging issue due to the diversity of branches and their natural topological complexity. In this paper, we first introduce a novel contraction method called “graph contraction” to contract 3D tree-shaped point clouds. The computation of the graph contraction is formulated as the minimization of an energy function that consists of a contraction term that minimizes the sum of the graph geodesic distances of the k-nearest geodesic neighbors and a topology-preserving term that prevents point clouds from shrinking in the local principal direction. After graph contraction, we downsample the initial skeleton and obtain skeleton nodes. Finally, we introduce an algorithm that can be used to extract a topologically correct skeleton by connecting skeleton nodes. The proposed method has been validated on various 3D tree-shaped point clouds, including reconstructed tree-shaped point clouds, artificially generated tree-shaped point clouds and raw scan data. We use the Chamfer distance to measure the correctness of the skeleton. The experimental results show that the Chamfer distance obtained using our method is smaller than that obtained using current state-of-the-art methods for skeleton extraction of 3D tree-shaped point clouds with complex branches. Keywords Geodesic neighbor · 3D tree-shaped point clouds · Complex branches · Skeleton extraction
1 Introduction Skeletons extracted from a 3D point cloud have been applied in shape animation, shape comparison and object recognition [31]. They provide an intuitive and effective representation of the underlying geometric features of shapes that facilitates knowledge-based shape understanding, shape manipulation and shape analysis [6,7]. The main challenge in extracting skeletons from 3D point clouds is preserving the topology. However, such challenges become more serious in the case of 3D point clouds with complex branches. The 3D treeshaped point cloud is one type of such a point cloud, and skeletons extracted from 3D tree-shaped point clouds play an important role in computational forestry [9], ecological Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00371-020-01983-6) contains supplementary material, which is available to authorized users.
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Ji Liu [email protected] College of Computer Science, Chongqing University, No. 174, Shazheng Street, Shapingba District, Chongqing 400044, China
assessment [16], forest management and urban planning [30], [34]. 3D tree-shaped point clouds can be acquired in a variety of ways, including 3D terrestrial laser scanning (TLS) [30], image-based 3D reconstruction methods [26] and multiple view stereo (MVS) techniques [21]. However, due to the dive
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