  While 6D object pose estimation has recently made a huge leap forward, most
methods can still only handle a single or a handful of different objects, which
limits their applications. To circumvent this problem, category-level object
pose estimation has recently been revamped, which aims at predicting the 6D
pose as well as the 3D metric size for previously unseen instances from a given
set of object classes. This is, however, a much more challenging task due to
severe intra-class shape variations. To address this issue, we propose
GPV-Pose, a novel framework for robust category-level pose estimation,
harnessing geometric insights to enhance the learning of category-level
pose-sensitive features. First, we introduce a decoupled confidence-driven
rotation representation, which allows geometry-aware recovery of the associated
rotation matrix. Second, we propose a novel geometry-guided point-wise voting
paradigm for robust retrieval of the 3D object bounding box. Finally,
leveraging these different output streams, we can enforce several geometric
consistency terms, further increasing performance, especially for non-symmetric
categories. GPV-Pose produces superior results to state-of-the-art competitors
on common public benchmarks, whilst almost achieving real-time inference speed
at 20 FPS.
