Planar feature extration from LIDAR data based on tensot analysis

Chung Cheng Lin, Rey-Jer You

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

1 Citation (Scopus)

Abstract

Recently, LIDAR (Light Detection and Ranging) technique is in widespread use for obtaining a large number of points with three-dimensional coordinates. Detection and extraction of the objects from LIDAR data is needed for practical use. The LIDAR data, however, do not explicitly contain any geometric information, and features of objects implicitly exist in point clouds. The features, such as planes, lines and corners, can be only indirectly extracted by segmentation algorithms. In this article, we present a two-step algorithm based on the tensor voting framework for the extraction of planar features. First, we infer the normal vector at each LIDAR point by the plate tensors derived from geometric relations among the LIDAR points. In the second step, these normal vectors are classified by the density cluster method based on the directions of normal vectors. The density clusters can be divided into sub-clusters if the coordinates of points are introduced. The normal vectors at points in the same sub-clusters possess the same planar feature. The results of our experiments show that our algorithm is very effective in the automatic extraction of planar features from both airborne and terrestrial LIDAR data.

Original languageEnglish
Title of host publicationAsian Association on Remote Sensing - 27th Asian Conference on Remote Sensing, ACRS 2006
Pages931-937
Number of pages7
Publication statusPublished - 2006 Dec 1
Event27th Asian Conference on Remote Sensing, ACRS 2006 - Ulaanbaatar, Mongolia
Duration: 2006 Oct 92006 Oct 13

Publication series

NameAsian Association on Remote Sensing - 27th Asian Conference on Remote Sensing, ACRS 2006

Other

Other27th Asian Conference on Remote Sensing, ACRS 2006
CountryMongolia
CityUlaanbaatar
Period06-10-0906-10-13

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Tensors
Experiments

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

Cite this

Lin, C. C., & You, R-J. (2006). Planar feature extration from LIDAR data based on tensot analysis. In Asian Association on Remote Sensing - 27th Asian Conference on Remote Sensing, ACRS 2006 (pp. 931-937). (Asian Association on Remote Sensing - 27th Asian Conference on Remote Sensing, ACRS 2006).
Lin, Chung Cheng ; You, Rey-Jer. / Planar feature extration from LIDAR data based on tensot analysis. Asian Association on Remote Sensing - 27th Asian Conference on Remote Sensing, ACRS 2006. 2006. pp. 931-937 (Asian Association on Remote Sensing - 27th Asian Conference on Remote Sensing, ACRS 2006).
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Lin, CC & You, R-J 2006, Planar feature extration from LIDAR data based on tensot analysis. in Asian Association on Remote Sensing - 27th Asian Conference on Remote Sensing, ACRS 2006. Asian Association on Remote Sensing - 27th Asian Conference on Remote Sensing, ACRS 2006, pp. 931-937, 27th Asian Conference on Remote Sensing, ACRS 2006, Ulaanbaatar, Mongolia, 06-10-09.

Planar feature extration from LIDAR data based on tensot analysis. / Lin, Chung Cheng; You, Rey-Jer.

Asian Association on Remote Sensing - 27th Asian Conference on Remote Sensing, ACRS 2006. 2006. p. 931-937 (Asian Association on Remote Sensing - 27th Asian Conference on Remote Sensing, ACRS 2006).

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

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AU - You, Rey-Jer

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N2 - Recently, LIDAR (Light Detection and Ranging) technique is in widespread use for obtaining a large number of points with three-dimensional coordinates. Detection and extraction of the objects from LIDAR data is needed for practical use. The LIDAR data, however, do not explicitly contain any geometric information, and features of objects implicitly exist in point clouds. The features, such as planes, lines and corners, can be only indirectly extracted by segmentation algorithms. In this article, we present a two-step algorithm based on the tensor voting framework for the extraction of planar features. First, we infer the normal vector at each LIDAR point by the plate tensors derived from geometric relations among the LIDAR points. In the second step, these normal vectors are classified by the density cluster method based on the directions of normal vectors. The density clusters can be divided into sub-clusters if the coordinates of points are introduced. The normal vectors at points in the same sub-clusters possess the same planar feature. The results of our experiments show that our algorithm is very effective in the automatic extraction of planar features from both airborne and terrestrial LIDAR data.

AB - Recently, LIDAR (Light Detection and Ranging) technique is in widespread use for obtaining a large number of points with three-dimensional coordinates. Detection and extraction of the objects from LIDAR data is needed for practical use. The LIDAR data, however, do not explicitly contain any geometric information, and features of objects implicitly exist in point clouds. The features, such as planes, lines and corners, can be only indirectly extracted by segmentation algorithms. In this article, we present a two-step algorithm based on the tensor voting framework for the extraction of planar features. First, we infer the normal vector at each LIDAR point by the plate tensors derived from geometric relations among the LIDAR points. In the second step, these normal vectors are classified by the density cluster method based on the directions of normal vectors. The density clusters can be divided into sub-clusters if the coordinates of points are introduced. The normal vectors at points in the same sub-clusters possess the same planar feature. The results of our experiments show that our algorithm is very effective in the automatic extraction of planar features from both airborne and terrestrial LIDAR data.

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Lin CC, You R-J. Planar feature extration from LIDAR data based on tensot analysis. In Asian Association on Remote Sensing - 27th Asian Conference on Remote Sensing, ACRS 2006. 2006. p. 931-937. (Asian Association on Remote Sensing - 27th Asian Conference on Remote Sensing, ACRS 2006).