17 October 2013 Comparison of an ℓ1-regression-based and a RANSAC-based planar segmentation procedure for urban terrain data with many outliers
Author Affiliations +
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.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jian Luo, Jian Luo, Zhibin Deng, Zhibin Deng, Dimitri Bulatov, Dimitri Bulatov, John E. Lavery, John E. Lavery, Shu-Cherng Fang, Shu-Cherng Fang, } "Comparison of an ℓ1-regression-based and a RANSAC-based planar segmentation procedure for urban terrain data with many outliers", Proc. SPIE 8892, Image and Signal Processing for Remote Sensing XIX, 889209 (17 October 2013); doi: 10.1117/12.2028627; https://doi.org/10.1117/12.2028627
PROCEEDINGS
11 PAGES


SHARE
Back to Top