Share Email Print
cover

Proceedings Paper

Comparison of an L1-regression-based and a RANSAC-based planar segmentation procedure for urban terrain data with many outliers
Author(s): Jian Luo; Zhibin Deng; Dimitri Bulatov; John E. Lavery; Shu-Cherng Fang
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

For urban terrain data with many outliers, we compare an ℓ1-regression-based and a RANSAC-based planar segmentation procedure. The procedure consists of 1) calculating the normal at each of the points using ℓ1 regression or RANSAC, 2) clustering the normals thus generated using DBSCAN or fuzzy c-means, 3) within each cluster, identifying segments (roofs, walls, ground) by DBSCAN-based-subclustering of the 3D points that correspond to each cluster of normals and 4) fitting the subclusters by the same method as that used in Step 1 (ℓ1 regression or RANSAC). Domain decomposition is used to handle data sets that are too large for processing as a whole. Computational results for a point cloud of a building complex in Bonnland, Germany obtained from a depth map of seven UAV-images are presented. The ℓ1-regression-based procedure is slightly over 25% faster than the RANSAC-based procedure and produces better dominant roof segments. However, the roof polygonalizations and cutlines based on these dominant segments are roughly equal in accuracy for the two procedures. For a set of artificial data, ℓ1 regression is much more accurate and much faster than RANSAC. We outline the complete building reconstruction procedure into which the ℓ1-regression-based and RANSAC-based segmentation procedures will be integrated in the future.

Paper Details

Date Published: 17 October 2013
PDF: 11 pages
Proc. SPIE 8892, Image and Signal Processing for Remote Sensing XIX, 889209 (17 October 2013); doi: 10.1117/12.2028627
Show Author Affiliations
Jian Luo, North Carolina State Univ. (United States)
Zhibin Deng, North Carolina State Univ. (United States)
Dimitri Bulatov, Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung (Germany)
John E. Lavery, U.S. Army Research Lab. (United States)
North Carolina State Univ. (United States)
Shu-Cherng Fang, North Carolina State Univ. (United States)


Published in SPIE Proceedings Vol. 8892:
Image and Signal Processing for Remote Sensing XIX
Lorenzo Bruzzone, Editor(s)

© SPIE. Terms of Use
Back to Top