  Due to the visual properties of reflection and refraction, RGB-D cameras
cannot accurately capture the depth of transparent objects, leading to
incomplete depth maps. To fill in the missing points, recent studies tend to
explore new visual features and design complex networks to reconstruct the
depth, however, these approaches tremendously increase computation, and the
correlation of different visual features remains a problem. To this end, we
propose an efficient depth completion network named DistillGrasp which
distillates knowledge from the teacher branch to the student branch.
Specifically, in the teacher branch, we design a position correlation block
(PCB) that leverages RGB images as the query and key to search for the
corresponding values, guiding the model to establish correct correspondence
between two features and transfer it to the transparent areas. For the student
branch, we propose a consistent feature correlation module (CFCM) that retains
the reliable regions of RGB images and depth maps respectively according to the
consistency and adopts a CNN to capture the pairwise relationship for depth
completion. To avoid the student branch only learning regional features from
the teacher branch, we devise a distillation loss that not only considers the
distance loss but also the object structure and edge information. Extensive
experiments conducted on the ClearGrasp dataset manifest that our teacher
network outperforms state-of-the-art methods in terms of accuracy and
generalization, and the student network achieves competitive results with a
higher speed of 48 FPS. In addition, the significant improvement in a
real-world robotic grasping system illustrates the effectiveness and robustness
of our proposed system.
