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 …

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 …

Limited training data is one of the biggest challenges in the industrial application of deep
learning. Generating synthetic training images is a promising solution in computer vision; …

Six-dimensional object detection of rigid objects is a problem especially relevant for quality
control and robotic manipulation in industrial contexts. This work is a survey of the state of …

In this work we present a novel dense-correspondence method for 6DoF object pose
estimation from a single RGB-D image. While many existing data-driven methods achieve …

The assisted assembly of customized products supported by collaborative robots combined
with mixed reality devices is the current trend in the Industry 4.0 concept. This article …

This study offers a systematic literature review on the application of Convolutional Neural
Networks in Virtual Reality, Augmented Reality, Mixed Reality, and Extended Reality …

Generating synthetic data is a promising solution to the challenge of limited training data for
industrial deep learning applications. However, training on synthetic data and testing on real …

Six degrees of freedom (6-DoF) object pose estimation is essential for robotic gras** and
autonomous driving. While estimating pose from a single RGB image is highly desirable for …

Purpose Multiple applications in open surgical environments may benefit from adoption of
markerless computer vision depending on associated speed and accuracy requirements …