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 …

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 …

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 …

A key technical challenge in performing 6D object pose estimation from RGB-D image is to
fully leverage the two complementary data sources. Prior works either extract information …

We propose a real-time RGB-based pipeline for object detection and 6D pose estimation.
Our novel 3D orientation estimation is based on a variant of the Denoising Autoencoder that …

Estimating the 6D pose of objects using only RGB images remains challenging because of
problems such as occlusion and symmetries. It is also difficult to construct 3D models with …

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 …

Tracking 6-D poses of objects from videos provides rich information to a robot in performing
different tasks such as manipulation and navigation. In this article, we formulate the 6-D …

We present a novel, data driven approach for solving the problem of registration of two point
cloud scans. Our approach is direct in the sense that a single pair of corresponding local …

We introduce a scalable approach for object pose estimation trained on simulated RGB
views of multiple 3D models together. We learn an encoding of object views that does not …