POINT SIGNATURES - A NEW REPRESENTATION FOR 3D OBJECT RECOGNITION

Few systems capable of recognizing complex objects with free-form (scu lptured) surfaces have been developed. The apparent lack of success is mainly due to the lack of a competent modelling scheme for representi ng such complex objects. In this paper, a new form of point representa tion for describing 3D free-form surfaces is proposed. This representa tion, which we call the point signature, serves to describe the struct ural neighbourhood of a point in a more complete manner than just usin g the 3D coordinates of the point. Being invariant to rotation and tra nslation, the point signature can be used directly to hypothesize the correspondence to model points with similar signatures. Recognition is achieved by matching the signatures of data points representing the s ensed surface to the signatures of data points representing the model surface. The use of point signatures is not restricted to the recognit ion of a single-object scene to a small library of models. Instead, it can be extended naturally to the recognition of scenes containing mul tiple partially-overlapping objects (which may also be juxtaposed with each other) against a large model library. No preliminary phase of se gmenting the scene into the component objects is required. In searchin g for the appropriate candidate model, recognition need not proceed in a linear order which can become prohibitive for a large model library . For a given scene, signatures are extracted at arbitrarily spaced se ed points. Each of these signatures is used to vote for models that co ntain points having similar signatures. Inappropriate models with low votes can be rejected while the remaining candidate models are ordered according to the votes they received. In this way, efficient verifica tion of the hypothesized candidates can proceed by testing the most li kely model first. Experiments using real data obtained from a range fi nder have shown fast recognition from a library of fifteen models whos e complexities vary from that of simple piecewise quadric shapes to co mplicated face masks. Results from the recognition of both single-obje ct and multiple-object scenes are presented.