INVARIANT CATEGORISATION OF POLYGONAL OBJECTS USING MULTI-RESOLUTION SIGNATURES

With the increasing use of 3D objects and models, mining of 3D databases is becoming an important issue. However, 3D object recognition is very time consuming because of variations due to position, rotation, size and mesh resolution. A fast categorisation can be used to discard non-similar objects, such that only few objects need to be compared in full detail. We present a simple method for characterising 3D objects with the goal of performing a fast similarity search in a set of polygonal mesh models. The method constructs, for each object, two sets of multi-scale signatures: (a) the progression of deformation due to iterative mesh smoothing and, similarly, (b) the influence of mesh dilation and erosion using a sphere with increasing radius. The signatures are invariant to 3D translation, rotation and scaling, also to mesh resolution because of proper normalisation. The method was validated on a set of 31 complex objects, each object being represented with three mesh resolutions. The results were measured in terms of Euclidian distance for ranking all objects, with an overall average ranking rate of 1.29.