Scale-dependent/invariant local 3D shape descriptors for fully automatic registration of multiple sets of range images

Abstract. Despite the ubiquitous use of range images in various com-puter vision applications, little has been investigated about the size vari-ation of the local geometric structures captured in the range images. In this paper, we show that, through canonical geometric scale-space anal-ysis, this geometric scale-variability embedded in a range image can be exploited as a rich source of discriminative information regarding the captured geometry. We extend previous work on geometric scale-space analysis of 3D models to analyze the scale-variability of a range image and to detect scale-dependent 3D features – geometric features with their inherent scales. We derive novel local 3D shape descriptors that encode the local shape information within the inherent support region of each feature. We show that the resulting set of scale-dependent local shape descriptors can be used in an efficient hierarchical registration algorithm for aligning range images with the same global scale. We also show that local 3D shape descriptors invariant to the scale variation can be de-rived and used to align range images with significantly different global scales. Finally, we demonstrate that the scale-dependent/invariant local 3D shape descriptors can even be used to fully automatically register multiple sets of range images with varying global scales corresponding to multiple objects.