Computing Parametric Geon Descriptions of 3D Multi-Part Objects

Abstract

A new approach for computing qualitative part-based descriptions of 3D objects from single- and multi-view range data is presented. This research is motivated by both a theory of human image understanding (Recognition-by-Components) and the need for qualitative recognition by an autonomous robot in order for it to e ciently interact with its environ-ment. Object descriptions are obtained in two consecutive steps: (1) object segmentation into parts and (2) part model identi cation. Segmentation is achieved by rst computing the simulated electrical charge density distribution on a tessellated triangular mesh of the object surface. The algorithm then detects the object part boundaries where the the charge density achieves a local minimum. The charge density distribution can simultaneously provide an indication of the gross and ne object structures. Parametric geons are introduced as the part models, which indicate both qualitative shape and quantitative attribute information. Model recovery is achieved by tting all parametric geons to a part and then selecting the best model based on the minimum tting residual. A new objective function used for model recovery is optimised by a global optimisation technique (Very Fast Simulated Re-Annealing). The advantages of this approach are demonstrated through experimentation. By using aphysical analogy to the well known transversality principle, part segmentation does not require an assumption of surface smoothness or the choice of a particular scale to com-pute local surface features. The formulation for parametric geons provides a global shape constraint, which ensures reliable part model recovery even when the part shape is not an exact instance of a parametric geon. By directly comparing a part with all candidate models, this approach explicitly veri es the shape of the resultant part descriptions. The computed part-based descriptions are well suited for the object recognition task carried out by an autonomous robot. ii