Authors:
Sebastian Ochmann
1
;
Richard Vock
1
;
Raoul Wessel
1
;
Martin Tamke
2
and
Reinhard Klein
1
Affiliations:
1
University of Bonn, Germany
;
2
Centre for Information Technology and Architecture (CITA), Denmark
Keyword(s):
Scene Understanding, Building Structure, Point Cloud, Laser Scanning, Segmentation.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Geometric Computing
;
Geometry and Modeling
;
Modeling and Algorithms
Abstract:
We present a new method for automatic semantic structuring of 3D point clouds representing buildings. In
contrast to existing approaches which either target the outside appearance like the facade structure or rather
low-level geometric structures, we focus on the building’s interior using indoor scans to derive high-level
architectural entities like rooms and doors. Starting with a registered 3D point cloud, we probabilistically
model the affiliation of each measured point to a certain room in the building. We solve the resulting clustering
problem using an iterative algorithm that relies on the estimated visibilities between any two locations within
the point cloud. With the segmentation into rooms at hand, we subsequently determine the locations and
extents of doors between adjacent rooms. In our experiments, we demonstrate the feasibility of our method by
applying it to synthetic as well as to real-world data.