  Exploiting internal spatial geometric constraints of sparse LiDARs is
beneficial to depth completion, however, has been not explored well. This paper
proposes an efficient method to learn geometry-aware embedding, which encodes
the local and global geometric structure information from 3D points, e.g.,
scene layout, object's sizes and shapes, to guide dense depth estimation.
Specifically, we utilize the dynamic graph representation to model generalized
geometric relationship from irregular point clouds in a flexible and efficient
manner. Further, we joint this embedding and corresponded RGB appearance
information to infer missing depths of the scene with well structure-preserved
details. The key to our method is to integrate implicit 3D geometric
representation into a 2D learning architecture, which leads to a better
trade-off between the performance and efficiency. Extensive experiments
demonstrate that the proposed method outperforms previous works and could
reconstruct fine depths with crisp boundaries in regions that are over-smoothed
by them. The ablation study gives more insights into our method that could
achieve significant gains with a simple design, while having better
generalization capability and stability. The code is available at
https://github.com/Wenchao-Du/GAENet.
