3D reconstruction of irregular spaced LIDAR

  • Authors:

  • Nicholas Shorter;Takis Kasparis

  • Affiliations:

  • Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL;Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL

  • Venue:
  • ISTASC'06 Proceedings of the 6th WSEAS International Conference on Systems Theory & Scientific Computation
  • Year:
  • 2006

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Abstract

Preprocessing techniques are proposed for the development of a 3D Reconstruction algorithm designed for autonomously reconstructing three dimensional models from urban and residential buildings depicted in raw LIDAR data. First, a greedy insertion triangulation algorithm, modified with a novel noise filtering technique, triangulates the raw LIDAR data. Second, the normal vectors of the triangulated raw LIDAR data are then passed to an unsupervised clustering algorithm - Fuzzy Simplified Adaptive Resonance Theory (Fuzzy SART). Fuzzy SART returns a rough grouping of the coplanar triangles. Then, a proposed multiple regression algorithm further refines the coplanar grouping by further removing outliers and deriving an improved planar segmentation of the raw LIDAR data. Finally, further refinement is achieved by calculating the intersection of the best fit roof planes and moving nearby points close to that intersection to exist at the intersection, resulting in straight, straight roof ridges.