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 …

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 …

Given a single scene image, this paper proposes a method of Category-level 6D Object
Pose and Size Estimation (COPSE) from the point cloud of the target object, without external …

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 …

While category-level 9DoF object pose estimation has emerged recently, previous
correspondence-based or direct regression methods are both limited in accuracy due to the …

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 …

Due to the fact that point clouds are always corrupted by significant noise and large
transformations, aligning two point clouds by deep neural networks is still challenging. This …

Efficient and accurate estimation of objects' pose is essential in numerous practical
applications. Due to the depth data contains abundant geometric information, some existing …

The 6D object pose estimation is a forward-looking technology in the field of computer
vision, which has great application potential in metaverse, VRI AR, robot operation …

Current state-of-the-art pose estimation methods are almost launched on segmented RGB-D
images. However, these methods may not apply to more general industrial parts due to a …