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 …

Abstract 6D pose estimation from a single RGB image is a fundamental task in computer
vision. The current top-performing deep learning-based methods rely on an indirect strategy …

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 …

6D pose estimation is a common and important task in industry. Obtaining the 6D pose of
objects is the basis for many other functions such as bin picking, autopilot, etc. Therefore …

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 …

While 6D object pose estimation has recently made a huge leap forward, most methods can
still only handle a single or a handful of different objects, which limits their applications. To …

We present a new method for estimating the 6D pose of rigid objects with available 3D
models from a single RGB input image. The method is applicable to a broad range of …

We introduce HybridPose, a novel 6D object pose estimation approach. HybridPose utilizes
a hybrid intermediate representation to express different geometric information in the input …

Directly regressing all 6 degrees-of-freedom (6DoF) for the object pose (ie the 3D rotation
and translation) in a cluttered environment from a single RGB image is a challenging …

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 …