An Integrated Octree-RANSAC Technique for Automated LiDAR Building Data Segmentation for Decorative Buildings

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-rectilinearity. 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/m 2 demonstrate its inherent potential as part of a large and more sophisticated processing approach.