We propose a novel probabilistic framework for learning
visual models of 3D object categories by combining appearance
information and geometric constraints. Objects are
represen...
In this paper we present a novel approach for expanding spherical 3D-tensor fields of arbitrary order in terms of a tensor valued local Fourier basis. For an efficient implementati...
Henrik Skibbe, Marco Reisert, Thorsten Schmidt, Kl...
While supervised learning approaches for 3D shape retrieval have been successfully used to incorporate human knowledge about object classes based on global shape features, the inc...
This paper describes work done as part of the Oxford AGV (Autonomous Guided Vehicle) project [2] towards recognition of classes of objects to be encountered in a factory environme...
—This paper proposes a set of methods for building informative and robust feature point representations, used for accurately labeling points in a 3D point cloud, based on the typ...
Radu Bogdan Rusu, Zoltan Csaba Marton, Nico Blodow...