  The challenges of learning a robust 6D pose function lie in 1) severe
occlusion and 2) systematic noises in depth images. Inspired by the success of
point-pair features, the goal of this paper is to recover the 6D pose of an
object instance segmented from RGB-D images by locally matching pairs of
oriented points between the model and camera space. To this end, we propose a
novel Bi-directional Correspondence Mapping Network (BiCo-Net) to first
generate point clouds guided by a typical pose regression, which can thus
incorporate pose-sensitive information to optimize generation of local
coordinates and their normal vectors. As pose predictions via geometric
computation only rely on one single pair of local oriented points, our BiCo-Net
can achieve robustness against sparse and occluded point clouds. An ensemble of
redundant pose predictions from locally matching and direct pose regression
further refines final pose output against noisy observations. Experimental
results on three popularly benchmarking datasets can verify that our method can
achieve state-of-the-art performance, especially for the more challenging
severe occluded scenes. Source codes are available at
https://github.com/Gorilla-Lab-SCUT/BiCo-Net.
