Abstract 6DoF Pose estimation has been gaining increased importance in vision for over a
decade however it does not yet meet the reliability and accuracy standards for mass …

We propose a novel keypoint voting 6DoF object pose estimation method, which takes pure
unordered point cloud geometry as input without RGB information. The proposed cascaded …

We address the problem of keypoint selection, and find that the performance of 6DoF pose
estimation methods can be improved when pre-defined keypoint locations are learned …

Existing Object Pose Estimation (OPE) methods for stacked scenarios are not robust to
changes in object scale. This paper proposes a new 6DoF OPE network (NormNet) for …

We address the simulation-to-real domain gap in six degree-of-freedom pose estimation
(6DoF PE), and propose a novel self-supervised keypoint voting-based 6DoF PE framework …

Efficient and accurate object pose estimation is an essential component for modern vision
systems in many applications such as Augmented Reality, autonomous driving, and …

In this work, we propose a data generation pipeline by leveraging the 3D suite Blender to
produce synthetic RGBD image datasets with 6D poses for robotic picking. The proposed …

Bin picking, essential in various industries, depends on accurate object segmentation and
6D pose estimation for successful grasping and manipulation. Existing datasets for deep …

Industrial automation depends on accurate object recognition and localization tasks, such as
depth estimation, instance segmentation, object detection, and 6D pose estimation. Despite …