Category-Level 6D Object Pose Estimation via Cascaded Relation and Recurrent Reconstruction Networks

Understand

Category-level 6D pose estimation, aiming to predict the location and orientation of unseen object instances, is fundamental to many scenarios such as robotic manipulation and augmented reality, yet still remains unsolved.

• Precisely recovering instance 3D model in the canonical space and accurately matching it with the observation is an essential point when estimating 6D pose for unseen objects.
• In this paper, we achieve accurate category-level 6D pose estimation via cascaded relation and recurrent reconstruction networks.
• Specifically, a novel cascaded relation network is dedicated for advanced representation learning to explore the complex and informative relations among instance RGB image, instance point cloud and category shape prior.