Rapid clustering of colorized 3D point cloud data for reconstructing building interiors

Range scanning of building interiors generates very large, partially spurious and unstructured point cloud data. Accurate information extraction from such data sets is a complex task due to the presence of multiple objects, diversity of their shapes, large disparity in the feature sizes, and the spatial uncertainty due to occluded regions. A fast segmentation of such data is necessary for quick understanding of the scanned scene. Unfortunately, traditional range segmentation methodologies are computationally expensive because they rely almost exclusively on shape parameters (normal, curvature) and are highly sensitive to small geometric distortions in the captured data. This paper introduces a quick and effective segmentation technique for large volumes of colorized range scans from unknown building interiors and labelling clusters of points that represent distinct surfaces and objects in the scene. Rather than computing geometric parameters, the proposed technique uses a robust Hue, Saturation and Value (HSV) color model as an effective means of id entifying rough clusters (objects) that are further refined by eliminating spurious and outlier points through region growth an d a fixed distance neighbors (FDNs) analysis. The results demonstrate that the proposed method is effective in identifying continuous clusters and can extract meaningful object clusters, even from geometrically similar regions.