Learning based 6D object pose estimation methods rely on computing large intermediate
pose representations and/or iteratively refining an initial estimation with a slow render …

Most recent 6D object pose estimation methods first use object detection to obtain 2D
bounding boxes before actually regressing the pose. However, the general object detection …

Most self-supervised 6D object pose estimation methods can only work with additional depth
information or rely on the accurate annotation of 2D segmentation masks, limiting their …

Estimating the pose of an unseen object is the goal of the challenging one-shot pose
estimation task. Previous methods have heavily relied on feature matching with great …

We propose a single-shot approach to determining 6-DoF pose of an object with available
3D computer-aided design (CAD) model from a single RGB image. Our method dubbed …

Estimating the 6-DoF pose of a rigid object from a single RGB image is a crucial yet
challenging task. Recent studies have shown the great potential of dense correspondence …

Object pose estimation is a fundamental computer vision problem with broad applications in
augmented reality and robotics. Over the past decade, deep learning models, due to their …

This paper introduces GS-Pose, an end-to-end framework for locating and estimating the 6D
pose of objects. GS-Pose begins with a set of posed RGB images of a previously unseen …

Retinal surgery is a complex medical procedure that requires high precision dexterity to
perform delicate instrument maneuvers with sub-millimeter accuracy. Minimizing the manual …

Directly regressing 6 degrees of freedom for all the objects from a single RGB image is not
well explored. Even end-to-end pose estimation approaches for a single object are inferior …