Résumé
We present a method for the segmentation of unstructured and unfiltered 3D data. The core of this approach is based on the construction of a local neighborhood structure and its recursive subdivision. 3D points will be organized into groups according to their spatial proximity, but also to their similarity in the attribute space. Thanks to this hierarchical approach, this method provides high flexibility to the final level of segmentation. We assume that the 3D image is composed of regions homogeneous according to some criterion (color, curvature, etc.), but no assumption about noise, nor spatial repartition/shape of the regions or points is made. Thus, this approach can be applied to a wide variety of segmentation problems, unlike most existing specialized methods. We demonstrate the performance of our algorithm with experimental results on real range images.