Locating 3D objects from a single RGB image via Perspective-n-Points (PnP) is a long-
standing problem in computer vision. Driven by end-to-end deep learning, recent studies …

Deterministic neural networks (NNs) are increasingly being deployed in safety critical
domains, where calibrated, robust, and efficient measures of uncertainty are crucial. In this …

While neural networks achieve state-of-the-art performance for many molecular modeling
and structure–property prediction tasks, these models can struggle with generalization to out …

We describe a data-driven method for inferring the camera viewpoints given multiple images
of an arbitrary object. This task is a core component of classic geometric pipelines such as …

We address the task of estimating 6D camera poses from sparse-view image sets (2-8
images). This task is a vital pre-processing stage for nearly all contemporary (neural) …

Despite various probabilistic methods for modeling the uncertainty and ambiguity in human
mesh recovery, their overall precision is limited because existing formulations for joint …

This paper addresses the problem of 3D human body shape and pose estimation from an
RGB image. This is often an ill-posed problem, since multiple plausible 3D bodies may …

Single image pose estimation is a fundamental problem in many vision and robotics tasks,
and existing deep learning approaches suffer by not completely modeling and handling: i) …

Symmetric orthogonalization via SVD, and closely related procedures, are well-known
techniques for projecting matrices onto O (n) or SO (n). These tools have long been used for …

Modern deep learning techniques that regress the relative camera pose between two
images have difficulty dealing with challenging scenarios, such as large camera motions …