  In this paper, we focus on category-level 6D pose and size estimation from
monocular RGB-D image. Previous methods suffer from inefficient category-level
pose feature extraction which leads to low accuracy and inference speed. To
tackle this problem, we propose a fast shape-based network (FS-Net) with
efficient category-level feature extraction for 6D pose estimation. First, we
design an orientation aware autoencoder with 3D graph convolution for latent
feature extraction. The learned latent feature is insensitive to point shift
and object size thanks to the shift and scale-invariance properties of the 3D
graph convolution. Then, to efficiently decode category-level rotation
information from the latent feature, we propose a novel decoupled rotation
mechanism that employs two decoders to complementarily access the rotation
information. Meanwhile, we estimate translation and size by two residuals,
which are the difference between the mean of object points and ground truth
translation, and the difference between the mean size of the category and
ground truth size, respectively. Finally, to increase the generalization
ability of FS-Net, we propose an online box-cage based 3D deformation mechanism
to augment the training data. Extensive experiments on two benchmark datasets
show that the proposed method achieves state-of-the-art performance in both
category- and instance-level 6D object pose estimation. Especially in
category-level pose estimation, without extra synthetic data, our method
outperforms existing methods by 6.3% on the NOCS-REAL dataset.
