Factorization of View-Object Manifolds for Joint Object Recognition and Pose Estimation
Abstract
Due to large variations in shape, appearance, and viewing conditions, object recognition is a key precursory challenge in the fields of object manipulation and robotic/AI visual reasoning in general. Recognizing object categories, particular instances of objects and viewpoints/poses of objects are three critical subproblems robots must solve in order to accurately grasp/manipulate objects and reason about their environments. Multi-view images of the same object lie on intrinsic low-dimensional manifolds in descriptor spaces (e.g. visual/depth descriptor spaces). These object manifolds share the same topology despite being geometrically different. Each object manifold can be represented as a deformed version of a unified manifold. The object manifolds can thus be parameterized by its homeomorphic mapping/reconstruction from the unified manifold. In this work, we develop a novel framework to jointly solve the three challenging recognition sub-problems, by explicitly modeling the deformations of object manifolds and factorizing it in a view-invariant space for recognition. We perform extensive experiments on several challenging datasets and achieve state-of-the-art results.
Cite
@article{arxiv.1503.06813,
title = {Factorization of View-Object Manifolds for Joint Object Recognition and Pose Estimation},
author = {Haopeng Zhang and Tarek El-Gaaly and Ahmed Elgammal and Zhiguo Jiang},
journal= {arXiv preprint arXiv:1503.06813},
year = {2015}
}