Abstract

One key step to the tree structure study is skeleton processing. Although there are lots of extraction approaches, the existing methods have paid less attention to extraction effectiveness, which highly use redundant points to formulate the skeleton and bring difficulties to the subsequent 3D modeling. This work proposes a four-step framework for the purpose of skeleton extraction. Firstly, candidate skeleton points are filtered from input data based on the spatial slice projection and grouped using the Euclidean distance analysis. Secondly, a key dynamic path optimization step is used to formulate a tree skeleton using the candidate point information. Thirdly, the optimized path is filled by interpolating points to achieve complete skeletons. Finally, short skeletons are removed based on the distance between branching points and ending points, and then, the extraction skeletons are smoothed for improving the visual quality. Our main contribution lies in that we find the global minimization cost path from every point to the root using a novel energy function. The formulated objective function contains a data term to constrain the distance between points and paths, and a smoothness term to constrain the direction continuities. Experimental scenes include three different types of trees, and input point clouds are collected by a portable laser scanning system. Skeleton extraction results demonstrate that we achieved completeness and correctness of 81.10% and 99.21%. respectively. Besides, our effectiveness is up to 79.26%, which uses only 5.82% of the input tree points in the skeleton representation, showing a promising effective solution for the tree skeleton and structure study.

Highlights

  • Nowadays, the Light Detection and Ranging (LiDAR) technique has played a significant role in mapping 3D space information of vegetation, such as crown delineation [1,2], wood–leaf separation [3], and tree segmentation [4]

  • Most of the existing skeleton extraction methods have been proposed for terrestrial laser scanning (TLS) and vehicle laser scanning point clouds, which requires researchers to develop new skeleton extraction approaches for portable laser scanning systems (PLS) point clouds

  • The following section includes both the visual evaluation to qualitatively demonstrate our tree skeleton extraction and the accuracy evaluation to quantitatively show the superiority in terms of the completeness, correctness, and effectiveness

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Summary

Introduction

The Light Detection and Ranging (LiDAR) technique has played a significant role in mapping 3D space information of vegetation, such as crown delineation [1,2], wood–leaf separation [3], and tree segmentation [4]. Portable laser scanning systems (PLS) have become increasingly more mature, which provide data in dense point-cloud sets and are flexible to capture the region of interest of street tree information. Portable laser scanning systems do not expect users with professional surveying experience, which brings PLS more chances in 3D structure research. This work aims to address the potential issues that lie in the existing approaches, that is, low effectiveness, completeness, and correctness, in PLS point cloud processing.

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