Tree Skeletonization for Raw Point Cloud Exploiting Cylindrical Shape Prior

Lixian Fu,Yan Lin,Ji Liu,Min Zhang,Jianling Zhou

doi:10.1109/access.2020.2971549

Abstract

Tree skeleton extraction plays a fundamental role in reconstructing both biological and structural models of trees. However, traditional approaches can be ineffective and problematic in guaranteeing the topological correctness and centeredness of the tree skeleton when the tree point clouds contain noise and occlusions. To overcome this limitation, we present a tree skeletonization method to generate topologically correct and well-centered tree skeletons. We extract an initial skeleton from the tree point clouds via an octree and level set method, use cylindrical prior constraint (CPC) optimization and the estimated radii of branches to yield corrected positions of improper joints, and finally obtain updated skeletons with improved smoothness. The good centeredness of our proposed method is intrinsically achieved by (1) exploiting the cylindrical shape prior and calculating the CPC in the local neighborhood and (2) feeding the prior knowledge regarding the radii of tree branches into a topology refinement algorithm to yield near-optimal estimates of the positions of the skeleton points. To evaluate our method, we construct a novel tree point cloud data set with known ground truth and propose three quantitative assessment methods: skeleton points deviation (SPD), bifurcation points coverage (BPC) and endpoints coverage (EPC). The quantitative assessment and visual assessment show that the proposed method clearly outperforms traditional ones in terms of topology correctness and centeredness of the extracted tree skeleton.

Highlights

Due to the important role of tree models in computational forestry [1], function-structure plant modeling [2], urban planning and many other fields, substantial research effort has been devoted to tree model reconstruction [3]–[6]
To validate the effectiveness of our proposed approach, we present a synthetic data set of tree point clouds with known ground truths and three quantitative assessment methods: skeleton points deviation (SPD), bifurcation points coverage (BPC) and endpoints coverage (EPC)
We propose a tree skeletonization algorithm that consists of four steps: (1) Tree skeleton points computation

Summary

INTRODUCTION

Due to the important role of tree models in computational forestry [1], function-structure plant modeling [2], urban planning and many other fields, substantial research effort has been devoted to tree model reconstruction [3]–[6]. Three common challenges are associated with extracting skeletons from point clouds [8]: (1) the. Many skeleton extraction algorithms have been proposed [9]–[11] to overcome these challenges Many of these algorithms fail to extract a topology-preserving skeleton from point clouds of trees that have complex structures. Because there are almost no quantitative evaluation methods that can measure topological correctness and centeredness, the real performance of existing algorithms is difficult to assess. We present a novel skeletonization method that yields well-centered and topology-preserving tree skeletons from point clouds with noise and occlusions. Our proposed method makes considerable improvements over traditional level set methods [3]–[5], [13] and greatly enhances the centeredness and topological correctness of the resulting skeleton. The visual comparisons and detailed quantitative assessments demonstrate that our method clearly outperforms traditional baselines

RELATED WORK

SKELETON POINTS COMPUTATION

RESULTS

CONCLUSION