Surface and volumetric segmentation of range images using biquadrics and superquadrics

Presents an integrated framework for segmenting dense range data of complex 3-D scenes into surface (bi-quadrics) and volumetric (superquadrics) primitives, without a priori domain knowledge or stored models. Surface segmentation is performed by a novel local-to-global iterative regression approach of searching for the best piecewise description of the data in terms of biquadric models. Region adjacency information, surface discontinuities, and global shape properties are extracted and used to guide the volumetric segmentation. Superquadric models are recovered by a global-to-local residual-driven procedure, which recursively segments the scene to derive the part-structure. A set of acceptance criteria provide the objective evaluation of intermediate descriptions, and decide whether to terminate the procedure, or selectively refine the segmentation. The control module generates hypotheses about superquadric models at clusters of underestimated data and performs controlled extrapolation of part-models by shrinking the global model. The authors present results on real range images of scenes of varying complexity, including objects with occluding parts, and scenes where surface segmentation is not sufficient to guide the volumetric segmentation