A primitive-based 3D segmentation algorithm for mechanical CAD models

This paper presents a novel segmentation algorithm for mechanical CAD models (represented by either mesh or point cloud) constructed from planes, cylinders, cones, spheres, tori and easily extendable to surfaces of revolution. Our proposed approach differs from existing techniques in the following aspects. First, by assuming that common mechanical models only have a limited number of dominant orientations that their primitives are either parallel or orthogonal to, we narrow down the search space for detecting the primitives to the automatically estimated major orientations of the input model. Second, we employ a dimension reduction method which transforms the problem of detecting 3D primitives into the classical 2D problems such as circle and line detection in images. Third, we generate an over-complete set of primitives and formulate the segmentation as a set cover optimization problem. We demonstrate our method's robustness to noise and show that it compares favorably with state-of-the-art solutions such as the RANSAC-based (Schnabel et al., 2007) and GlobFit (Li et al., 2011) approaches on many synthetic and real scanned examples.