This paper investigates compression of 3D objects in computer graphics using manifold learning. Spectral compression uses the eigenvectors of the graph Laplacian of an object'...
In this paper we propose an approximated structured prediction framework for large scale graphical models and derive message-passing algorithms for learning their parameters effic...
This paper describes a method to localize 3D objects, which is the extension of the segment-based object recognition method to use on a STL CAD model. Models for localization are ...
We present a method to extract principal deformation modes from a set of articulated models describing the human spine. The spine was expressed as a set of rigid transforms that su...
Jonathan Boisvert, Xavier Pennec, Hubert Labelle, ...
We present an example-based surface reconstruction method for scanned point sets. Our approach uses a database of local shape priors built from a set of given context models that ...
Ran Gal, Ariel Shamir, Tal Hassner, Mark Pauly, Da...