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 …

Object pose estimation is a core perception task that enables, for example, object
manipulation and scene understanding. The widely available, inexpensive, and high …

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 …

Establishing correspondences from image to 3D has been a key task of 6DoF object pose
estimation for a long time. To predict pose more accurately, deeply learned dense maps …

Regression-based methods for 3D human pose estimation directly predict the 3D pose
parameters from a 2D image using deep networks. While achieving state-of-the-art …

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 …

The two-stage object pose estimation paradigm first detects semantic keypoints on the
image and then estimates the 6D pose by minimizing reprojection errors. Despite performing …

DoF object pose estimation from a monocular image is challenging, and a post-refinement
procedure is generally needed for high-precision estimation. In this paper, we propose a …

Current 6D pose estimation methods focus on handling objects that are previously trained,
which limits their applications in real dynamic world. To this end, we propose a geometry …

Abstract 6D object pose estimation is one of the fundamental problems in computer vision
and robotics research. While a lot of recent efforts have been made on generalizing pose …