Towards Automated Segmentation of Dense Range Scans

This paper addresses the problem of segmenting dense range data containing curved surfaces. Segmentation is a crucial step in the processing of range data for applications in object recognition, measurement, reengineering and modeling. We propose a two stage process using model-based curvature classification as an initial grouping. Features based on differential geometry, mainly curvature features, are ideally suited for processing objects of arbitrary shape including of course curved surfaces. The second stage uses a modified region growing algorithm to perform the final segmentation. The approach is demonstrated on a test scene acquired with a stripe projection sensor.