  Category-level object pose estimation involves estimating the 6D pose and the
3D metric size of objects from predetermined categories. While recent
approaches take categorical shape prior information as reference to improve
pose estimation accuracy, the single-stage network design and training manner
lead to sub-optimal performance since there are two distinct tasks in the
pipeline. In this paper, the advantage of two-stage pipeline over single-stage
design is discussed. To this end, we propose a two-stage deformation-and
registration pipeline called DR-Pose, which consists of completion-aided
deformation stage and scaled registration stage. The first stage uses a point
cloud completion method to generate unseen parts of target object, guiding
subsequent deformation on the shape prior. In the second stage, a novel
registration network is designed to extract pose-sensitive features and predict
the representation of object partial point cloud in canonical space based on
the deformation results from the first stage. DR-Pose produces superior results
to the state-of-the-art shape prior-based methods on both CAMERA25 and REAL275
benchmarks. Codes are available at https://github.com/Zray26/DR-Pose.git.
