Hierarchical Modeling of Street Trees Using Mobile Laser Scanning

Jingzhong Xu,Ge Wang,Jie Shan

doi:10.3390/rs12142321

Jingzhong Xu, Ge Wang + Show 1 more

Open Access

https://doi.org/10.3390/rs12142321

Copy DOI

Journal: Remote sensing	Publication Date: Jul 19, 2020
Citations: 10	License type: CC BY 4.0

Affiliation: Wuhan University, Purdue University West Lafayette

Abstract

This paper proposes a novel method to reconstruct hierarchical 3D tree models from Mobile Laser Scanning (MLS) point clouds. Starting with a neighborhood graph from the tree point clouds, the method treats the root point of the tree as a source point and determines an initial tree skeleton by using the Dijkstra algorithm. The initial skeleton lines are then optimized by adjusting line connectivity and branch nodes based on morphological characteristics of the tree. Finally, combined with the tree point clouds, the radius of each branch skeleton node is estimated and flat cones are used to simulate tree branches. A local triangulation method is used to connect the gaps between two joint flat cones. Demonstrated by street trees of different sizes and point densities, the proposed method can extract street tree skeletons effectively, generate tree models with higher fidelity, and reconstruct trees with different details according to the skeleton level. It is found out the tree modeling error is related to the average point spacing, with a maximum error at the coarsest level 6 being about 0.61 times the average point spacing. The main source of the modeling error is the self-occlusion of trees branches. Such findings are both theoretically and practically useful for generating high-precision tree models from point clouds. The developed method can be an alternative to the current ones that struggle to balance modeling efficiency and modeling accuracy.

Highlights

Three-dimensional tree models have great significance for cities, tourism, and ecological landscapes, both in physical and virtual worlds
This paper proposes an automatic reconstruction method of trees from mobile laser scanning (MLS) point clouds by a skeleton ranking method
Considering that tree branches have obvious grading characteristics and ranking skeleton lines into different levels can maintain the shape of trees with high precision [13,14], this paper demonstrates how the proposed method facilitates skeleton line extraction through optimization

Summary

Introduction

Three-dimensional tree models have great significance for cities, tourism, and ecological landscapes, both in physical and virtual worlds. Livny et al [19] proposed a tree reconstruction method based on global optimization of tree skeletons This method first generates a Branch Structure Graph (BSG) from the point clouds. The tree growth method by spatial distribution constraints is iteratively used to obtain a smooth tree skeleton This method can deal with trees that have leaves and generate tree models automatically, it requires point clouds with high density and its modeling results are prone to local distortion due to the iterative smoothing process. Mei et al [24] integrate the advantages of the L1-median and the Minimum Spanning Tree (MST) to improve the modeling result from incomplete TLS point clouds This method can extract tree skeletons from the optimized point cloud automatically without prior assumptions on the shape geometry or topology.

Methods

Initial Skeleton Lines Extraction

Tree Skeleton Line Ranking

Skeleton Line Optimization

Branch Radius Estimation

Findings

Evaluation and Discussion