Self-Supervised Geometric Correspondence for Category-Level 6D Object Pose Estimation in the Wild

Understand

While 6D object pose estimation has wide applications across computer vision and robotics, it remains far from being solved due to the lack of annotations.

• The problem becomes even more challenging when moving to category-level 6D pose, which requires generalization to unseen instances.
• Current approaches are restricted by leveraging annotations from simulation or collected from humans.
• In this paper, we overcome this barrier by introducing a self-supervised learning approach trained directly on large-scale real-world object videos for category-level 6D pose estimation in the wild.