Daniel Glazner (Faculty of Mathematics and Computer Science
The Weizmann Institute of Science)
We describe an approach to category-level detection and viewpoint estimation
for rigid 3D objects from single 2D images. In contrast to many existing
methods, we directly integrate 3D reasoning with an appearance-based voting
architecture. Our method relies on a nonparametric representation of a
joint distribution of shape and appearance of the object class. Our voting
method employs a novel parametrization of joint detection and viewpoint hypothesis
space, allowing efficient accumulation of evidence. We combine this with a
re-scoring and refinement mechanism, using an ensemble of view-specific
Support Vector Machines. We evaluate the performance of our approach in
detection and pose estimation of cars on a number of benchmark datasets.
This is joint work with Meirav Galun, Sharon Alpert, Ronen Basri and Gregory Shakhnarovich.