  Recently, category-level 6D object pose estimation has achieved significant
improvements with the development of reconstructing canonical 3D
representations. However, the reconstruction quality of existing methods is
still far from excellent. In this paper, we propose a novel Adversarial
Canonical Representation Reconstruction Network named ACR-Pose. ACR-Pose
consists of a Reconstructor and a Discriminator. The Reconstructor is primarily
composed of two novel sub-modules: Pose-Irrelevant Module (PIM) and Relational
Reconstruction Module (RRM). PIM tends to learn canonical-related features to
make the Reconstructor insensitive to rotation and translation, while RRM
explores essential relational information between different input modalities to
generate high-quality features. Subsequently, a Discriminator is employed to
guide the Reconstructor to generate realistic canonical representations. The
Reconstructor and the Discriminator learn to optimize through adversarial
training. Experimental results on the prevalent NOCS-CAMERA and NOCS-REAL
datasets demonstrate that our method achieves state-of-the-art performance.
