  Category-level 6D object pose and size estimation is to predict full pose
configurations of rotation, translation, and size for object instances observed
in single, arbitrary views of cluttered scenes. In this paper, we propose a new
method of Dual Pose Network with refined learning of pose consistency for this
task, shortened as DualPoseNet. DualPoseNet stacks two parallel pose decoders
on top of a shared pose encoder, where the implicit decoder predicts object
poses with a working mechanism different from that of the explicit one; they
thus impose complementary supervision on the training of pose encoder. We
construct the encoder based on spherical convolutions, and design a module of
Spherical Fusion wherein for a better embedding of pose-sensitive features from
the appearance and shape observations. Given no testing CAD models, it is the
novel introduction of the implicit decoder that enables the refined pose
prediction during testing, by enforcing the predicted pose consistency between
the two decoders using a self-adaptive loss term. Thorough experiments on
benchmarks of both category- and instance-level object pose datasets confirm
efficacy of our designs. DualPoseNet outperforms existing methods with a large
margin in the regime of high precision. Our code is released publicly at
https://github.com/Gorilla-Lab-SCUT/DualPoseNet.
