Object pose detection and tracking has recently attracted increasing attention due to its wide
applications in many areas, such as autonomous driving, robotics, and augmented reality …

Nowadays, computer vision with 3D (dimension) object detection and 6D (degree of
freedom) pose assumptions are widely discussed and studied in the field. In the 3D object …

In recent years, the task of estimating the 6D pose of object instances and complete scenes,
ie camera localization, from a single input image has received considerable attention …

Tracking 6-D poses of objects from videos provides rich information to a robot in performing
different tasks such as manipulation and navigation. In this article, we formulate the 6-D …

In this work, we tackle the problem of category-level online pose tracking for objects from
point cloud sequences. For the first time, we propose a unified framework that can handle …

We present a novel approach for model-based 6D pose refinement in color data. Building on
the established idea of contour-based pose tracking, we teach a deep neural network to …

We present a new dataset, called Falling Things (FAT), for advancing the state-of-the-art in
object detection and 3D pose estimation in the context of robotics. By synthetically …

Tracking the 6D pose of objects in video sequences is important for robot manipulation. This
task, however, introduces multiple challenges:(i) robot manipulation involves significant …

Abstract Analysis-by-synthesis has been a successful approach for many tasks in computer
vision, such as 6D pose estimation of an object in an RGB-D image which is the topic of this …

One-shot pose estimation for tasks such as body joint localization, camera pose estimation,
and object tracking are generally noisy, and temporal filters have been extensively used for …