Comparison of an L1-regression-based and a RANSAC-based planar segmentation procedure for urban terrain data with many outliers

Jian Luo; Zhibin Deng; Dimitri Bulatov; John E. Lavery; Shu-Cherng Fang

doi:10.1117/12.2028627

17 October 2013 Comparison of an ℓ¹-regression-based and a RANSAC-based planar segmentation procedure for urban terrain data with many outliers

Jian Luo, Zhibin Deng, Dimitri Bulatov, John E. Lavery, Shu-Cherng Fang

Author Affiliations +

Proceedings Volume 8892, Image and Signal Processing for Remote Sensing XIX; 889209 (2013) https://doi.org/10.1117/12.2028627
Event: SPIE Remote Sensing, 2013, Dresden, Germany

Abstract

For urban terrain data with many outliers, we compare an ℓ^1-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 (ℓ¹regression 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 ℓ^1-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.

Citation Download Citation

Jian Luo, Zhibin Deng, Dimitri Bulatov, John E. Lavery, and 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); https://doi.org/10.1117/12.2028627

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