Recently, 6DoF object pose estimation has become increasingly important for a broad range
of applications in the fields of virtual reality, augmented reality, autonomous driving, and …

Object pose estimation is a fundamental computer vision problem with broad applications in
augmented reality and robotics. Over the past decade, deep learning models, due to their …

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 …

One critical challenge in 6D object pose estimation from a single RGBD image is efficient
integration of two different modalities, ie, color and depth. In this work, we tackle this problem …

This paper proposes a universal framework, called OVE6D, for model-based 6D object pose
estimation from a single depth image and a target object mask. Our model is trained using …

Category-level 6DoF object pose estimation intends to estimate the rotation, translation, and
size of unseen objects. Many previous works use point clouds as a pre-learned shape prior …

We propose a novel keypoint voting scheme based on intersecting spheres, that is more
accurate than existing schemes and allows for fewer, more disperse keypoints. The scheme …

Recent works on 6D object pose estimation focus on learning keypoint correspondences
between images and object models, and then determine the object pose through RANSAC …

Six-dimensional (6-D) object pose estimation plays a critical role in robotic grasp, which
performs extensive usage in manufacturing. The current state-of-the-art pose estimation …

Abstract 6D pose estimation for certain targets from RGB-D images is a fundamental
problem in computer vision. Current methods emphasize learning the overall expression of …