Surface reconstruction from LiDAR point cloud data with a surface growing algorithm

Ying Zhe Luo, Yi-Hsing Tseng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

LiDAR generates datasets of sub-randomly distributed points on scanned surfaces, named point cloud, which contains abundant implicit 3D spatial information. Explicit spatial information of scanned surfaces can be retrieved through a process of surface reconstruction. This paper proposes a novel algorithm for surface reconstruction based on the scheme of surface growing. It starts with a seed point and continuously merges adjacent points which are found as extensions of the surface. In order to handle randomly distributed points, LiDAR data is divided into 3D grid for the algorithm to search adjacent points. The point-set in every 3D grid was applied to estimate the normal vector. And there are two factors to conduct the growing process: the angle between two normal vectors of adjacent patches and the distance of the point to the growing surface. Finally, the planar surface is reconstructed by merging the clustered patches. The experimental datasets include point clouds acquired by both ground-based and airborne LiDARs.

Original languageEnglish
Title of host publication28th Asian Conference on Remote Sensing 2007, ACRS 2007
Pages1944-1949
Number of pages6
Publication statusPublished - 2007 Dec 1
Event28th Asian Conference on Remote Sensing 2007, ACRS 2007 - Kuala Lumpur, Malaysia
Duration: 2007 Nov 122007 Nov 16

Publication series

Name28th Asian Conference on Remote Sensing 2007, ACRS 2007
Volume3

Other

Other28th Asian Conference on Remote Sensing 2007, ACRS 2007
CountryMalaysia
CityKuala Lumpur
Period07-11-1207-11-16

Fingerprint

Surface reconstruction
Merging
Seed

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

Cite this

Luo, Y. Z., & Tseng, Y-H. (2007). Surface reconstruction from LiDAR point cloud data with a surface growing algorithm. In 28th Asian Conference on Remote Sensing 2007, ACRS 2007 (pp. 1944-1949). (28th Asian Conference on Remote Sensing 2007, ACRS 2007; Vol. 3).
Luo, Ying Zhe ; Tseng, Yi-Hsing. / Surface reconstruction from LiDAR point cloud data with a surface growing algorithm. 28th Asian Conference on Remote Sensing 2007, ACRS 2007. 2007. pp. 1944-1949 (28th Asian Conference on Remote Sensing 2007, ACRS 2007).
@inproceedings{c7d57725941c4fe0b1eb84facd8a056b,
title = "Surface reconstruction from LiDAR point cloud data with a surface growing algorithm",
abstract = "LiDAR generates datasets of sub-randomly distributed points on scanned surfaces, named point cloud, which contains abundant implicit 3D spatial information. Explicit spatial information of scanned surfaces can be retrieved through a process of surface reconstruction. This paper proposes a novel algorithm for surface reconstruction based on the scheme of surface growing. It starts with a seed point and continuously merges adjacent points which are found as extensions of the surface. In order to handle randomly distributed points, LiDAR data is divided into 3D grid for the algorithm to search adjacent points. The point-set in every 3D grid was applied to estimate the normal vector. And there are two factors to conduct the growing process: the angle between two normal vectors of adjacent patches and the distance of the point to the growing surface. Finally, the planar surface is reconstructed by merging the clustered patches. The experimental datasets include point clouds acquired by both ground-based and airborne LiDARs.",
author = "Luo, {Ying Zhe} and Yi-Hsing Tseng",
year = "2007",
month = "12",
day = "1",
language = "English",
isbn = "9781615673650",
series = "28th Asian Conference on Remote Sensing 2007, ACRS 2007",
pages = "1944--1949",
booktitle = "28th Asian Conference on Remote Sensing 2007, ACRS 2007",

}

Luo, YZ & Tseng, Y-H 2007, Surface reconstruction from LiDAR point cloud data with a surface growing algorithm. in 28th Asian Conference on Remote Sensing 2007, ACRS 2007. 28th Asian Conference on Remote Sensing 2007, ACRS 2007, vol. 3, pp. 1944-1949, 28th Asian Conference on Remote Sensing 2007, ACRS 2007, Kuala Lumpur, Malaysia, 07-11-12.

Surface reconstruction from LiDAR point cloud data with a surface growing algorithm. / Luo, Ying Zhe; Tseng, Yi-Hsing.

28th Asian Conference on Remote Sensing 2007, ACRS 2007. 2007. p. 1944-1949 (28th Asian Conference on Remote Sensing 2007, ACRS 2007; Vol. 3).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Surface reconstruction from LiDAR point cloud data with a surface growing algorithm

AU - Luo, Ying Zhe

AU - Tseng, Yi-Hsing

PY - 2007/12/1

Y1 - 2007/12/1

N2 - LiDAR generates datasets of sub-randomly distributed points on scanned surfaces, named point cloud, which contains abundant implicit 3D spatial information. Explicit spatial information of scanned surfaces can be retrieved through a process of surface reconstruction. This paper proposes a novel algorithm for surface reconstruction based on the scheme of surface growing. It starts with a seed point and continuously merges adjacent points which are found as extensions of the surface. In order to handle randomly distributed points, LiDAR data is divided into 3D grid for the algorithm to search adjacent points. The point-set in every 3D grid was applied to estimate the normal vector. And there are two factors to conduct the growing process: the angle between two normal vectors of adjacent patches and the distance of the point to the growing surface. Finally, the planar surface is reconstructed by merging the clustered patches. The experimental datasets include point clouds acquired by both ground-based and airborne LiDARs.

AB - LiDAR generates datasets of sub-randomly distributed points on scanned surfaces, named point cloud, which contains abundant implicit 3D spatial information. Explicit spatial information of scanned surfaces can be retrieved through a process of surface reconstruction. This paper proposes a novel algorithm for surface reconstruction based on the scheme of surface growing. It starts with a seed point and continuously merges adjacent points which are found as extensions of the surface. In order to handle randomly distributed points, LiDAR data is divided into 3D grid for the algorithm to search adjacent points. The point-set in every 3D grid was applied to estimate the normal vector. And there are two factors to conduct the growing process: the angle between two normal vectors of adjacent patches and the distance of the point to the growing surface. Finally, the planar surface is reconstructed by merging the clustered patches. The experimental datasets include point clouds acquired by both ground-based and airborne LiDARs.

UR - http://www.scopus.com/inward/record.url?scp=84865637523&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84865637523&partnerID=8YFLogxK

M3 - Conference contribution

SN - 9781615673650

T3 - 28th Asian Conference on Remote Sensing 2007, ACRS 2007

SP - 1944

EP - 1949

BT - 28th Asian Conference on Remote Sensing 2007, ACRS 2007

ER -

Luo YZ, Tseng Y-H. Surface reconstruction from LiDAR point cloud data with a surface growing algorithm. In 28th Asian Conference on Remote Sensing 2007, ACRS 2007. 2007. p. 1944-1949. (28th Asian Conference on Remote Sensing 2007, ACRS 2007).