  Intra-class variations, distribution shifts among source and target domains
are the major challenges of category-level tasks. In this study, we address
category-level full 6D object pose estimation in the context of depth modality,
introducing a novel part-based architecture that can tackle the above-mentioned
challenges. Our architecture particularly adapts the distribution shifts
arising from shape discrepancies, and naturally removes the variations of
texture, illumination, pose, etc., so we call it as "Intrinsic Structure
Adaptor (ISA)". We engineer ISA based on the followings: i) "Semantically
Selected Centers (SSC)" are proposed in order to define the "6D pose" at the
level of categories. ii) 3D skeleton structures, which we derive as
shape-invariant features, are used to represent the parts extracted from the
instances of given categories, and privileged one-class learning is employed
based on these parts. iii) Graph matching is performed during training in such
a way that the adaptation/generalization capability of the proposed
architecture is improved across unseen instances. Experiments validate the
promising performance of the proposed architecture on both synthetic and real
datasets.
