  While category-level 9DoF object pose estimation has emerged recently,
previous correspondence-based or direct regression methods are both limited in
accuracy due to the huge intra-category variances in object shape and color,
etc. Orthogonal to them, this work presents a category-level object pose and
size refiner CATRE, which is able to iteratively enhance pose estimate from
point clouds to produce accurate results. Given an initial pose estimate, CATRE
predicts a relative transformation between the initial pose and ground truth by
means of aligning the partially observed point cloud and an abstract shape
prior. In specific, we propose a novel disentangled architecture being aware of
the inherent distinctions between rotation and translation/size estimation.
Extensive experiments show that our approach remarkably outperforms
state-of-the-art methods on REAL275, CAMERA25, and LM benchmarks up to a speed
of ~85.32Hz, and achieves competitive results on category-level tracking. We
further demonstrate that CATRE can perform pose refinement on unseen category.
Code and trained models are available.
