This paper presents a comprehensive survey on vision-based robotic grasping. We
conclude three key tasks during vision-based robotic grasping, which are object localization …

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 …

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 …

In this work, we present FFB6D, a full flow bidirectional fusion network designed for 6D pose
estimation from a single RGBD image. Our key insight is that appearance information in the …

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 …

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 …

The 3D CAD shapes in current 3D benchmarks are mostly collected from online model
repositories. Thus, they typically have insufficient geometric details and less informative …

We propose a benchmark for 6D pose estimation of a rigid object from a single RGB-D input
image. The training data consists of a texture-mapped 3D object model or images of the …