  Object pose estimation is an important component of most vision pipelines for
embodied agents, as well as in 3D vision more generally. In this paper we
tackle the problem of estimating the pose of novel object categories in a
zero-shot manner. This extends much of the existing literature by removing the
need for pose-labelled datasets or category-specific CAD models for training or
inference. Specifically, we make the following contributions. First, we
formalise the zero-shot, category-level pose estimation problem and frame it in
a way that is most applicable to real-world embodied agents. Secondly, we
propose a novel method based on semantic correspondences from a self-supervised
vision transformer to solve the pose estimation problem. We further re-purpose
the recent CO3D dataset to present a controlled and realistic test setting.
Finally, we demonstrate that all baselines for our proposed task perform
poorly, and show that our method provides a six-fold improvement in average
rotation accuracy at 30 degrees. Our code is available at
https://github.com/applied-ai-lab/zero-shot-pose.
