Segmentation of terrestrial laser scanning data using geometry and image information

Terrestrial laser scanning is becoming a standard technology for 3D modeling of complex scenes. Laser scans contain detailed geometric information, but still require interpretation of the data for making it useable for mapping purposes. A fundamental step in the transformation of the data into objects involves their segmentation into consistent units. Such units should follow some predefined rules, and result in salient regions guided by the desire that the individual segments represent object or object-parts within the scene. Nonetheless, due to the scene complexity and the variety of objects, a segmentation using only a single cue does not suffice. Considering the availability of additional data sources such as color images, more information can be integrated in the data partitioning process and ultimately into the reconstruction scheme. We propose segmentation of terrestrial laser scanning data by the integration of range and color content and by using multiple cues. This concept raises questions regarding their mode of integration, and definition of the expected outcome. We show, that while individual segmentation based on given cues have their own limitations, their integration provide a more coherent partitioning that has better potential for further processing. Experiments show that the proposed segmentation methodology yield physically meaningful segments, which surpass those obtained via segmentation of the individual channels.