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An Integrated Octree-RANSAC Technique for Automated LiDAR Building Data Segmentation for Decorative Buildings
Author(s)
Date Issued
10 December 2016
Date Available
25T11:12:55Z October 2018
Abstract
This paper introduces a new method for the automated segmentation of laser scanning data for decorative urban buildings. The method combines octree indexing and RANSAC - two previously established but heretofore not integrated techniques. The approach was successfully applied to terrestrial point clouds of the facades of five highly decorative urban structures for which existing approaches could not provide an automated pipeline. The segmentation technique was relatively efficient and wholly scalable requiring only 1 second per 1,000 points, regardless of the façade’s level of ornamentation or non-recti-linearity. While the technique struggled with shallow protrusions, its ability to process a wide range of building types and opening shapes with data densities as low as 400 pts/m2 demonstrate its inherent potential as part of a large and more sophisticated processing approach.
Sponsorship
European Research Council
Type of Material
Book Chapter
Publisher
Springer
Volume
10073
Series
Lecture Notes in Computer Science
Copyright (Published Version)
2016 Springer
Web versions
Language
English
Status of Item
Peer reviewed
Conference Details
The 12th International Symposium on Visual Computing (ISVC 2016), Las Vegas, United States of America, 12-14 December 2016
This item is made available under a Creative Commons License
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