Untangling Object-View Manifold for Multiview Recognition and Pose Estimation Supplementary Materials

Untangling Object-View Manifold for Multiview Recognition and Pose Estimation

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 ...

A Comparative Analysis and Study of Multiview CNN Models for Joint Object Categorization and Pose Estimation Supplementary Materials

A Comparative Analysis and Study of Multiview CNN Models for Joint Object Categorization and Pose Estimation

In the Object Recognition task, there exists a dichotomy between the categorization of objects and estimating object pose, where the former necessitates a view-invariant representation, while the latter requires a representation capable of capturing pose information over different categories of objects. With the rise of deep architectures, the prime focus has been on object category recognition...

Integration of Probabilistic Pose Estimates from Multiple Views

We propose an approach to multi-view object detection and pose estimation that considers combinations of single-view estimates. It can be used with most existing single-view pose estimation systems, and can produce improved results even if the individual pose estimates are incoherent. The method is introduced in the context of an existing, probabilistic, view-based detection and pose estimation...

Construction of Appearance Manifold with Embedded View-Dependent Covariance Matrix for 3D Object Recognition

We propose the construction of an appearance manifold with embedded view-dependent covariance matrix to recognize 3D objects which are influenced by geometric distortions and quality degradation effects. The appearance manifold is used to capture the pose variability, while the covariance matrix is used to learn the distribution of samples for gaining noise-invariance. However, since the appear...