  Most learning-based approaches to category-level 6D pose estimation are
design around normalized object coordinate space (NOCS). While being
successful, NOCS-based methods become inaccurate and less robust when handling
objects of a category containing significant intra-category shape variations.
This is because the object coordinates induced by global and rigid alignment of
objects are semantically incoherent, making the coordinate regression hard to
learn and generalize. We propose Semantically-aware Object Coordinate Space
(SOCS) built by warping-and-aligning the objects guided by a sparse set of
keypoints with semantically meaningful correspondence. SOCS is semantically
coherent: Any point on the surface of a object can be mapped to a semantically
meaningful location in SOCS, allowing for accurate pose and size estimation
under large shape variations. To learn effective coordinate regression to SOCS,
we propose a novel multi-scale coordinate-based attention network. Evaluations
demonstrate that our method is easy to train, well-generalizing for large
intra-category shape variations and robust to inter-object occlusions.
