  In this paper, we propose a novel 3D graph convolution based pipeline for
category-level 6D pose and size estimation from monocular RGB-D images. The
proposed method leverages an efficient 3D data augmentation and a novel
vector-based decoupled rotation representation. Specifically, we first design
an orientation-aware autoencoder with 3D graph convolution for latent feature
learning. The learned latent feature is insensitive to point shift and size
thanks to the shift and scale-invariance properties of the 3D graph
convolution. Then, to efficiently decode the rotation information from the
latent feature, we design a novel flexible vector-based decomposable rotation
representation that employs two decoders to complementarily access the rotation
information. The proposed rotation representation has two major advantages: 1)
decoupled characteristic that makes the rotation estimation easier; 2) flexible
length and rotated angle of the vectors allow us to find a more suitable vector
representation for specific pose estimation task. Finally, we propose a 3D
deformation mechanism to increase the generalization ability of the pipeline.
Extensive experiments show that the proposed pipeline achieves state-of-the-art
performance on category-level tasks. Further, the experiments demonstrate that
the proposed rotation representation is more suitable for the pose estimation
tasks than other rotation representations.
