A Bayesian network formulation for relational shape matching is presented. The main advantage of the relational shape matching approach is the obviation of the non-rigid spatial m...
Anand Rangarajan, James M. Coughlan, Alan L. Yuill...
We present an effective framework for segmenting 3D shapes into meaningful components using the curve skeleton. Our algorithm identifies a number of a critical points on the curv...
In this paper we study interest point descriptors for image matching and 3D reconstruction. We examine the building blocks of descriptor algorithms and evaluate numerous combinati...
This paper studies the inference of 3D shape from a set of ? noisy photos. We derive a probabilistic framework to specify what one can infer about 3D shape for arbitrarily-shaped, ...
Rahul Bhotika, David J. Fleet, Kiriakos N. Kutulak...
We present physically based algorithms for interactive deformable shape and motion modeling. We coarsely sample the objects with simulation nodes, and apply a meshless finite elem...
Bart Adams, Martin Wicke, Maks Ovsjanikov, Michael...