Plane fitting methods of LiDAR point cloud

Chien Ming Huang, Yi-Hsing Tseng

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

7 Citations (Scopus)


LiDAR (Light detection and ranging) point-cloud data contain abundant three-dimensional (3D) spatial information. Dense distribution of scanned points on object surfaces prominently implies surface features. Particularly, plane features commonly appear in a typical LiDAR dataset of artificial structures. Plane fitting is the key process for extracting plane features from LiDAR data. This paper investigates the two mostly applied plane fitting methods: Least Squares Fitting (LSF) and Principal Component Analysis (PCA). LSF is an iterative algorithm of finding the best-fit plane with the least-squares constraint of the distances from the scanned points to the plane. The PCA method calculates the eigenvector of point cloud as the normal for finding out the parameters of the optimal plane. The goal of this paper is to compare these two methods in terms of algorithm implementation, computation time, and robust estimation. Experimental results of computations with simulated data are shown for analysis.

Original languageEnglish
Title of host publication29th Asian Conference on Remote Sensing 2008, ACRS 2008
Number of pages6
Publication statusPublished - 2008 Dec 1
Event29th Asian Conference on Remote Sensing 2008, ACRS 2008 - Colombo, Sri Lanka
Duration: 2008 Nov 102008 Nov 14

Publication series

Name29th Asian Conference on Remote Sensing 2008, ACRS 2008


Other29th Asian Conference on Remote Sensing 2008, ACRS 2008
Country/TerritorySri Lanka

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications


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