  Estimating the pose of objects from images is a crucial task of 3D scene
understanding, and recent approaches have shown promising results on very large
benchmarks. However, these methods experience a significant performance drop
when dealing with unseen objects. We believe that it results from the limited
generalizability of image features. To address this problem, we have an
in-depth analysis on the features of diffusion models, e.g. Stable Diffusion,
which hold substantial potential for modeling unseen objects. Based on this
analysis, we then innovatively introduce these diffusion features for object
pose estimation. To achieve this, we propose three distinct architectures that
can effectively capture and aggregate diffusion features of different
granularity, greatly improving the generalizability of object pose estimation.
Our approach outperforms the state-of-the-art methods by a considerable margin
on three popular benchmark datasets, LM, O-LM, and T-LESS. In particular, our
method achieves higher accuracy than the previous best arts on unseen objects:
98.2% vs. 93.5% on Unseen LM, 85.9% vs. 76.3% on Unseen O-LM, showing the
strong generalizability of our method. Our code is released at
https://github.com/Tianfu18/diff-feats-pose.
