  Category-level pose estimation is a challenging task with many potential
applications in computer vision and robotics. Recently, deep-learning-based
approaches have made great progress, but are typically hindered by the need for
large datasets of either pose-labelled real images or carefully tuned
photorealistic simulators. This can be avoided by using only geometry inputs
such as depth images to reduce the domain-gap but these approaches suffer from
a lack of semantic information, which can be vital in the pose estimation
problem. To resolve this conflict, we propose to utilize both geometric and
semantic features obtained from a pre-trained foundation model.Our approach
projects 2D features from this foundation model into 3D for a single object
model per category, and then performs matching against this for new single view
observations of unseen object instances with a trained matching network. This
requires significantly less data to train than prior methods since the semantic
features are robust to object texture and appearance. We demonstrate this with
a rich evaluation, showing improved performance over prior methods with a
fraction of the data required.
