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.

类别级别姿态估计是一项具有挑战性的任务，近期深度学习方法取得了很大进展，但常常受到需要大量数据集或精心调整的逼真模拟器的限制。为了解决这个冲突，我们提出利用从预训练基础模型中获取的几何和语义特征，通过将 2D 特征从基础模型投影到三维空间来对单个类别的物体模型进行匹配，并在训练好的匹配网络上处理对未见物体实例的新的单视图观测，这比先前方法需要的数据量显著减少。我们通过丰富的评估结果表明了这一点，并展示了比先前方法更好的性能。