Signal-to-Symbol Mapping for Laser Rangefinders

Abstract

A new approach for computing qualitative part-based descriptions of 3D objects is presented. The object descriptions are obtained in two steps: Object segmentation into parts and part model identi cation. Beginning with single-or multi-view range data of a 3D object, we simulate the charge density distribution over an object's surface which has been tessellated by a triangular mesh. We detect the deep surface concavities by tracing local charge density minima and then decompose the object into parts at these points. The individual parts are then modelled by parametric geons. The latter are seven qualitative shapes, each of which is formulated by a restricted globally deformed superellipsoid. Model recovery is performed by tting all parametric geons to a part and selecting the best model for the part, based on the minimum tting residual. A newly de ned objective function and a fast global optimisation technique are employed to obtain robust model tting results. Experiments demonstrate that this approach can successfully recover qualitative shape models from input data, especially when part shapes are not fully consistent with model shapes. The resultant object descriptions are well suited for symbolic reasoning and fast object recognition.