This paper addresses the problems of object detection and 6 DoF pose estimation from a
sequence of RGB images. Our deep learning-based approach uses only synthetic non …

This paper presents an approach to estimating the continuous 6-DoF pose of an object from
a single RGB image. The approach combines semantic keypoints predicted by a …

6D pose estimation of objects is an important part of robot grasping. The latest research
trend on 6D pose estimation is to train a deep neural network to directly predict the 2D …

We present a learning-based method for 6 DoF pose estimation of rigid objects in point
cloud data. Many recent learning-based approaches use primarily RGB information for …

While much progress has been made in 6-DoF object pose estimation from a single RGB
image, the current leading approaches heavily rely on real-annotation data. As such, they …

3-D pose estimation for texture-less objects remains a challenging problem. Previous works
either focus on a template matching method to find the nearest template as a candidate, or …

This paper presents a novel approach to estimating the continuous six degree of freedom (6-
DoF) pose (3D translation and rotation) of an object from a single RGB image. The approach …

Object recognition and 6DoF pose estimation are quite challenging tasks in computer vision
applications. Despite efficiency in such tasks, standard methods deliver far from real-time …

We propose a method for 6DoF pose estimation of rigid objects that uses a state-of-the-art
deep learning based instance detector to segment object instances in an RGB image …

We address the task of 6D pose estimation of known rigid objects from single input images
in scenarios where the objects are partly occluded. Recent RGB-D-based methods are …