  It is difficult to precisely annotate object instances and their semantics in
3D space, and as such, synthetic data are extensively used for these tasks,
e.g., category-level 6D object pose and size estimation. However, the easy
annotations in synthetic domains bring the downside effect of synthetic-to-real
(Sim2Real) domain gap. In this work, we aim to address this issue in the task
setting of Sim2Real, unsupervised domain adaptation for category-level 6D
object pose and size estimation. We propose a method that is built upon a novel
Deep Prior Deformation Network, shortened as DPDN. DPDN learns to deform
features of categorical shape priors to match those of object observations, and
is thus able to establish deep correspondence in the feature space for direct
regression of object poses and sizes. To reduce the Sim2Real domain gap, we
formulate a novel self-supervised objective upon DPDN via consistency learning;
more specifically, we apply two rigid transformations to each object
observation in parallel, and feed them into DPDN respectively to yield dual
sets of predictions; on top of the parallel learning, an inter-consistency term
is employed to keep cross consistency between dual predictions for improving
the sensitivity of DPDN to pose changes, while individual intra-consistency
ones are used to enforce self-adaptation within each learning itself. We train
DPDN on both training sets of the synthetic CAMERA25 and real-world REAL275
datasets; our results outperform the existing methods on REAL275 test set under
both the unsupervised and supervised settings. Ablation studies also verify the
efficacy of our designs. Our code is released publicly at
https://github.com/JiehongLin/Self-DPDN.
