Category-level 6D pose estimation, aiming to predict the location and
orientation of unseen object instances, is fundamental to many scenarios such
as robotic manipulation and augmented reality, yet still remains unsolved.
Precisely recovering instance 3D model in the canonical space and accurately
matching it with the observation is an essential point when estimating 6D pose
for unseen objects. In this paper, we achieve accurate category-level 6D pose
estimation via cascaded relation and recurrent reconstruction networks.
Specifically, a novel cascaded relation network is dedicated for advanced
representation learning to explore the complex and informative relations among
instance RGB image, instance point cloud and category shape prior. Furthermore,
we design a recurrent reconstruction network for iterative residual refinement
to progressively improve the reconstruction and correspondence estimations from
coarse to fine. Finally, the instance 6D pose is obtained leveraging the
estimated dense correspondences between the instance point cloud and the
reconstructed 3D model in the canonical space. We have conducted extensive
experiments on two well-acknowledged benchmarks of category-level 6D pose
estimation, with significant performance improvement over existing approaches.
On the representatively strict evaluation metrics of $3D_{75}$ and $5^{\circ}2
cm$, our method exceeds the latest state-of-the-art SPD by $4.9\%$ and $17.7\%$
on the CAMERA25 dataset, and by $2.7\%$ and $8.5\%$ on the REAL275 dataset.
Codes are available at this https URL

本文提出利用级联关系网络和递归重建网络进行精确的类别级别 6D 姿态估计，并通过稠密对应关系获得实例的 6D 姿态。