A probabilistic approach to plane extraction and polyhedral approximation of range data
In this paper, we propose a probabilistic approach to derive an approximate polyhedral description from range data. We. first compare several least-squares-based methods for estimation of local normal vectors and select the most robust out, based on a reasonable noise model of the, range data. Second, we extract the stable planar regions from the range data by examining the distributions of the local normal vectors together with their spatial information in the 2D range image. Instead of segmenting the range data completely, we use only the geometries of the extracted stable planar regions to derive a polyhedral description of the range data. The curved surfaces in the range data are approximated by their extracted plane patches. With a probabilistic approach, the proposed method can be expected to be robust against the noise. Experimental results on real range data from different sources show the effectiveness of the proposed method.
probabilistic approach plane extraction range data polyhedral model scene description