Learn More
Permission is granted to quote short excerpts and to reproduce figures and tables from this report, provided that the source of such material is fully acknowledged. Abstract Characterizing the variations of the human body shape is fundamentally important to many applications ranging from animation to product design. 3-D scanning technology makes it possible(More)
—When reconstructing a specific type or class of object using stereo, we can leverage prior knowledge of the shape of that type of object. A popular class of object to reconstruct is the human face. In this paper we learn a statistical wavelet prior of the shape of the human face and use it to constrain stereo reconstruction within a Bayesian framework. We(More)
Non-rigid shape matching is one of the most challenging fields in content-based 3D object retrieval. The aim of the SHREC 2010-Shape Retrieval Contest of Non-rigid 3D Models is to evaluate and compare the effectiveness of different methods run on a non-rigid 3D shape benchmark consisting of 200 watertight triangular meshes. Three groups with six methods(More)
We present an algorithm for automatic locating of an-thropometric landmarks on 3D human scans. Our method is based on learning landmark characteristics and the spatial relationships between them from a set of human scans where the landmarks are identified. The learned information is formulated by a pairwise Markov network. Each node of the network is a(More)
Characterizing the variations of the human body shape is fundamentally important in many applications ranging from animation to product design. 3D scanning technology makes it possible to digitize the complete surfaces of a large number of human bodies, providing much richer information about the body shape than traditional anthropometric measurements. This(More)
Communicated by (xxxxxxxxxx) We present an approach to find dense point-to-point correspondences between two deformed surfaces corresponding to different postures of the same non-rigid object in a fully automatic way. The approach requires no prior knowledge about the shapes being registered or the initial alignment of the shapes. We consider surfaces that(More)
We propose a posture invariant surface descriptor for triangular meshes. Using intrinsic geometry, the surface is first transformed into a representation that is independent of the posture. Spin image is then adapted to derive a descriptor for the representation. The descriptor is used for extracting surface features automatically. It is invariant with(More)