Pose from Shape: Deep Pose Estimation for Arbitrary 3D Objects

Understand

Most deep pose estimation methods need to be trained for specific object instances or categories.

• In this work we propose a completely generic deep pose estimation approach, which does not require the network to have been trained on relevant categories, nor objects in a category to have a canonical pose.
• We believe this is a crucial step to design robotic systems that can interact with new objects in the wild not belonging to a predefined category.
• Our main insight is to dynamically condition pose estimation with a representation of the 3D shape of the target object.