Automated extraction of 3D vector topographic feature line from terrain point cloud

This paper presents an automated algorithm for extracting 3D trees directly from 3D mobile light detection and ranging (LiDAR) data. To reduce both computational and spatial complexities, ground points are first filtered out from a raw 3D point cloud via blockbased elevation filtering. Off-ground points are then grouped into clusters representing individual objects through Euclidean distance clustering and voxel-based normalized cut segmentation. Finally, a model-driven method is proposed to achieve the extraction of 3D trees based on a pairwise 3D shape descriptor. The proposed algorithm is tested using a set of mobile LiDAR point clouds acquired by a RIEGL VMX-450 system. The results demonstrate the feasibility and effectiveness of the proposed algorithm.

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Automatic point cloud feature-line extraction algorithm based on curvature-mutation analysis

Optics and Precision Engineering ◽

10.3788/ope.20192705.1218 ◽

2019 ◽

Vol 27 (5) ◽

pp. 1218-1228

Author(s):

陈华伟 CHEN Hua-wei ◽

袁小翠 YUAN Xiao-cui ◽

吴禄慎 WU Lu-chen ◽

王晓辉 WANG Xiao-hui

Keyword(s):

Mutation Analysis ◽

Point Cloud ◽

Line Extraction ◽

Feature Line ◽

Extraction Algorithm

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Automated Extraction of Buildings from Aerial Lidar Point Cloud and Digital Imaging Datasets for 3D Cadastre – Preliminary Results

Cadastre: Geo-Information Innovations in Land Administration ◽

10.1007/978-3-319-51216-7_14 ◽

2017 ◽

pp. 159-165

Author(s):

Pankaj Kumar ◽

Alias Abdul Rahman ◽

Gurcan Buyuksalih

Keyword(s):

Digital Imaging ◽

Point Cloud ◽

Automated Extraction ◽

Preliminary Results ◽

3D Cadastre

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Steel Structure Building Feature Line Reconstruction from Terrestrial Laser Scanning Point Cloud

2018 2nd IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC) ◽

10.1109/imcec.2018.8469526 ◽

2018 ◽

Author(s):

Xuefei Li ◽

Ningbo Liang ◽

Xiaoxin Ma ◽

Fei Deng

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Terrestrial Laser Scanning ◽

Steel Structure ◽

Structure Building ◽

Feature Line

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Apple tree canopy leaf spatial location automated extraction based on point cloud data

Computers and Electronics in Agriculture ◽

10.1016/j.compag.2019.104975 ◽

2019 ◽

Vol 166 ◽

pp. 104975 ◽

Cited By ~ 3

Author(s):

Cailing Guo ◽

Gang Liu ◽

Weijie Zhang ◽

Juan Feng

Keyword(s):

Point Cloud ◽

Apple Tree ◽

Spatial Location ◽

Tree Canopy ◽

Point Cloud Data ◽

Automated Extraction ◽

Cloud Data

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Extraction of Mountain Feature Line Based on 3D Laser Scanning

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.182-183.1821 ◽

2012 ◽

Vol 182-183 ◽

pp. 1821-1825 ◽

Cited By ~ 1

Author(s):

Cheng Hui Wan ◽

Xiao Jun Cheng

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Point Cloud Data ◽

Feature Points ◽

3D Laser Scanning ◽

Cloud Data ◽

3D Space ◽

Contour Lines ◽

Feature Line ◽

Cut Surface

This paper applies a Cut-and-Sew algorithm extracting feature line of the mountain based on laser 3D scanning. The mountain is cut at the direction of contour lines. Point cloud data of the cut surface fits curve that project the same plane. Feature points find the curve corresponding to match. Through the connection of feature points, then returning the 3D space to achieve feature line, and the establishment of the formation of the triangular feature line is reconstructed on the mountain.

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Feature curve extraction from point clouds via developable strip intersection

Journal of Computational Design and Engineering ◽

10.1016/j.jcde.2015.07.001 ◽

2015 ◽

Vol 3 (2) ◽

pp. 102-111 ◽

Cited By ~ 7

Author(s):

Kai Wah Lee ◽

Pengbo Bo

Keyword(s):

Point Cloud ◽

Point Clouds ◽

Approximation Procedure ◽

Developable Surface ◽

Line Segments ◽

Sequencing Algorithm ◽

Shape Approximation ◽

Feature Line ◽

Data Points ◽

The Given

Abstract In this paper, we study the problem of computing smooth feature curves from CAD type point clouds models. The proposed method reconstructs feature curves from the intersections of developable strip pairs which approximate the regions along both sides of the features. The generation of developable surfaces is based on a linear approximation of the given point cloud through a variational shape approximation approach. A line segment sequencing algorithm is proposed for collecting feature line segments into different feature sequences as well as sequential groups of data points. A developable surface approximation procedure is employed to refine incident approximation planes of data points into developable strips. Some experimental results are included to demonstrate the performance of the proposed method.

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