Automated Interpretation of Dense Range Data

We address the problem of automating the processing of dense range data, specifically the automated interpretation of such data containing curved surfaces. This is a crucial step in the automated processing of range data for applications in object recognition, measurement, re-engineering and modeling. We propose a two stage process using model-based curvature classification as the first step. 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 results of the proposed approach are demonstrated on different range data sets.