  Object pose refinement is essential for robust object pose estimation.
Previous work has made significant progress towards instance-level object pose
refinement. Yet, category-level pose refinement is a more challenging problem
due to large shape variations within a category and the discrepancies between
the target object and the shape prior. To address these challenges, we
introduce a novel architecture for category-level object pose refinement. Our
approach integrates an HS-layer and learnable affine transformations, which
aims to enhance the extraction and alignment of geometric information.
Additionally, we introduce a cross-cloud transformation mechanism that
efficiently merges diverse data sources. Finally, we push the limits of our
model by incorporating the shape prior information for translation and size
error prediction. We conducted extensive experiments to demonstrate the
effectiveness of the proposed framework. Through extensive quantitative
experiments, we demonstrate significant improvement over the baseline method by
a large margin across all metrics.
