3D wavelet-based multiresolution object representation

This paper presents a technique for computing multiresolution shape models of 3D objects acquired as clouds of 3D points. The procedure is fully automated and is able to compute approximations for any object, overcoming sampling irregularity if present (sampling irregularity is a common feature of most 3D acquisition techniques; a typical example is stereo vision). The method described here starts by computing an intermediate mesh that meets the subdivision connectivity requirement needed to allow the computation of the wavelet transform. The mesh is then adjusted to the 3D input data using an iterative deformation process. Finally, a spherical wavelet transform is computed to obtain the object’s 3D multiresolution model. This paper shows a number of real objects acquired with different techniques, including hand-held 3D digitizers. The paper also gives some examples of how multiresolution representations can be used in tasks such as acquisition noise filtering, mesh simplification and shape labelling.