You Only Look at One: Category-Level Object Representations for Pose Estimation From a Single Example
This addresses the challenge of robots interacting with diverse objects in real-world settings, offering a more efficient and scalable approach compared to methods requiring large labeled datasets.
The paper tackles the problem of category-level 6D pose estimation for robot manipulation by enabling accurate pose inference for unseen objects using only a single example from a category, achieving real-time performance and outperforming prior work.
In order to meaningfully interact with the world, robot manipulators must be able to interpret objects they encounter. A critical aspect of this interpretation is pose estimation: inferring quantities that describe the position and orientation of an object in 3D space. Most existing approaches to pose estimation make limiting assumptions, often working only for specific, known object instances, or at best generalising to an object category using large pose-labelled datasets. In this work, we present a method for achieving category-level pose estimation by inspection of just a single object from a desired category. We show that we can subsequently perform accurate pose estimation for unseen objects from an inspected category, and considerably outperform prior work by exploiting multi-view correspondences. We demonstrate that our method runs in real-time, enabling a robot manipulator equipped with an RGBD sensor to perform online 6D pose estimation for novel objects. Finally, we showcase our method in a continual learning setting, with a robot able to determine whether objects belong to known categories, and if not, use active perception to produce a one-shot category representation for subsequent pose estimation.