Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Capturing uncertainty in object detection is indispensable for safe autonomous driving. In
recent years, deep learning has become the de-facto approach for object detection, and …

Algorithmic transparency entails exposing system properties to various stakeholders for
purposes that include understanding, improving, and contesting predictions. Until now, most …

Augmented reality (AR) has proven to be an invaluable interactive medium to reduce
cognitive load by bridging the gap between the task-at-hand and relevant information by …

Visual localization is the task of accurate camera pose estimation in a known scene. It is a
key problem in computer vision and robotics, with applications including self-driving cars …

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 …

Deep learning has shown to be effective for robust and real-time monocular image
relocalisation. In particular, PoseNet is a deep convolutional neural network which learns to …

In this paper we formulate structure from motion as a learning problem. We train a
convolutional network end-to-end to compute depth and camera motion from successive …

We present GLNet, a self-supervised framework for learning depth, optical flow, camera
pose and intrinsic parameters from monocular video--addressing the difficulty of acquiring …

We present a robust and real-time monocular six degree of freedom relocalization system.
Our system trains a convolutional neural network to regress the 6-DOF camera pose from a …