Range Data Segmentation Based on a Probabilistic Approach Using Principal Curvatures

Abstract

We propose a new method for modeling complex range data using a parametric model. We first compute local surface curvatures by quadratic fitting of a local surfaces, and then extract the curved surfaces and plane regions from the range data by examining the probability distribution of the local surface curvatures. After extracting these regions, one shape can be divided into multiple regions using the characteristics of the feature space. Then, after the parameters of each region have been estimated, the fitting error between the regions of the integration candidates are calculated. Next, by applying statistical testing techniques to the fitting error, we establish the integration criteria and integrate the regions that satisfy the criteria. Using this process, it is possible to perform a robust surface extraction. Experimental results on real range data show the effectiveness of the proposed method.