The problem of multi-view/view-invariant recognition remains one of the most fundamental challenges to the progress of the computer vision. In this paper we consider the problem of modeling the combined object-viewpoint manifold. The shape and appearance of an object in a given image is a function of its category, style within category, viewpoint, and several other factors. The visual manifold (in any chosen feature representation space) given all these variability collectively is very hard and even impossible to model. We propose an efficient computational framework that can untangle such a complex manifold, and achieve a model that separates a view-invariant category representation, from category-invariant pose representation. We outperform the state of the art in the three widely used multiview dataset, for both category recognition, and pose estimation.
REF learns a visual manifold which captures large variations in object appearances and proposes a method which can untangle such a visual manifold into a view-invariant category representation and a category-invariant pose representation.
Untangling Object-View Manifold for Multiview Recognition and Pose Estimation
ECCV
Computer Science