We introduce a novel parametric BRDF model that can
accurately encode a wide variety of real-world isotropic
BRDFs with a small number of parameters. The key observation
we make...
Object detection in cluttered, natural scenes has a high
complexity since many local observations compete for object
hypotheses. Voting methods provide an efficient solution
to ...
We present a manifold learning approach to dimensionality
reduction that explicitly models the manifold as a mapping
from low to high dimensional space. The manifold is
represen...
Context is critical for minimising ambiguity in object de-
tection. In this work, a novel context modelling framework
is proposed without the need of any prior scene segmen-
tat...
In this paper, we present a novel algorithm for partial
intrinsic symmetry detection in 3D geometry. Unlike previous
work, our algorithm is based on a conceptually simple
and st...
Ruxandra Lasowski, Art Tevs, Hans-Peter Seidel, Mi...