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 …

The 6D pose estimation of an object from an image is a central problem in many domains of
Computer Vision (CV) and researchers have struggled with this issue for several years …

We present a near real-time (10Hz) method for 6-DoF tracking of an unknown object from a
monocular RGBD video sequence, while simultaneously performing neural 3D …

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 …

We propose a new method named OnePose for object pose estimation. Unlike existing
instance-level or category-level methods, OnePose does not rely on CAD models and can …

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 …

We present the evaluation methodology, datasets and results of the BOP Challenge 2022,
the fourth in a series of public competitions organized with the goal to capture the status quo …

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 …

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 propose a simple yet powerful method to segment novel objects in RGB images from
their CAD models. Leveraging recent foundation models, Segment Anything and DINOv2 …