VI-Net: Boosting Category-level 6D Object Pose Estimation via Learning
  Decoupled Rotations on the Spherical Representations
Jiehong Lin Zewei Wei Yabin Zhang Kui Jia
Rotation estimation of high precision from an RGB-D object observation is a
huge challenge in 6D object pose estimation, due to the difficulty of learning
in the non-linear space of SO(3). In this paper, we propose a novel rotation
estimation network, termed as VI-Net, to make the task easier by decoupling the
rotation as the combination of a viewpoint rotation and an in-plane rotation.
More specifically, VI-Net bases the feature learning on the sphere with two
individual branches for the estimates of two factorized rotations, where a
V-Branch is employed to learn the viewpoint rotation via binary classification
on the spherical signals, while another I-Branch is used to estimate the
in-plane rotation by transforming the signals to view from the zenith
direction. To process the spherical signals, a Spherical Feature Pyramid
Network is constructed based on a novel design of SPAtial Spherical Convolution
(SPA-SConv), which settles the boundary problem of spherical signals via
feature padding and realizesviewpoint-equivariant feature extraction by
symmetric convolutional operations. We apply the proposed VI-Net to the
challenging task of category-level 6D object pose estimation for predicting the
poses of unknown objects without available CAD models; experiments on the
benchmarking datasets confirm the efficacy of our method, which outperforms the
existing ones with a large margin in the regime of high precision.