  Category-level object pose estimation aims to predict the 6D pose as well as
the 3D metric size of arbitrary objects from a known set of categories. Recent
methods harness shape prior adaptation to map the observed point cloud into the
canonical space and apply Umeyama algorithm to recover the pose and size.
However, their shape prior integration strategy boosts pose estimation
indirectly, which leads to insufficient pose-sensitive feature extraction and
slow inference speed. To tackle this problem, in this paper, we propose a novel
geometry-guided Residual Object Bounding Box Projection network RBP-Pose that
jointly predicts object pose and residual vectors describing the displacements
from the shape-prior-indicated object surface projections on the bounding box
towards the real surface projections. Such definition of residual vectors is
inherently zero-mean and relatively small, and explicitly encapsulates spatial
cues of the 3D object for robust and accurate pose regression. We enforce
geometry-aware consistency terms to align the predicted pose and residual
vectors to further boost performance.
