Object Pose Estimation in Point Clouds

Object pose estimation aims at estimating the 3D location and 3D orientation of an object in a 3D environment. It is an important prerequisite for object manipulation of a robot or for better understanding the spatial arrangement of objects in a scene. We develop deep-learning based methods for object pose estimation on 3D point cloud data.

Our corresponding publications can be found below.

Publications

Ge Gao: Learning 6D Object Pose from Point Clouds, PhD thesis, 2021, [PDF]
Ge Gao, Mikko Lauri, Xiaolin Hu, Jianwei Zhang, Simone Frintrop: CloudAAE: Learning 6D Object Pose Regression with On-line Data Synthesis on Point Clouds, Proceeding of International Conference on Robotics and Automation (ICRA), 2021, [PDF], [Code]
Ge Gao, Mikko Lauri, Yulong Wang, Xiaolin Hu, Jianwei Zhang and Simone Frintrop: 6D Object Pose Regression via Supervised Learning on Point Clouds, Proceeding of International Conference on Robotics and Automation (ICRA) 2020, [PDF], [Code]
Ge Gao, Mikko Lauri, Jianwei Zhang, Simone Frintrop: Occlusion Resistant Object Rotation Regression from Point Cloud Segments, Proceeding of the ECCV workshop on Recovering 6D Object Pose, 2018, [PDF], [arXiv]