  Depth completion is a popular research direction in the field of depth
estimation. The fusion of color and depth features is the current critical
challenge in this task, mainly due to the asymmetry between the rich scene
details in color images and the sparse pixels in depth maps. To tackle this
issue, we design an efficient Gated Cross-Attention Network that propagates
confidence via a gating mechanism, simultaneously extracting and refining key
information in both color and depth branches to achieve local spatial feature
fusion. Additionally, we employ an attention network based on the Transformer
in low-dimensional space to effectively fuse global features and increase the
network's receptive field. With a simple yet efficient gating mechanism, our
proposed method achieves fast and accurate depth completion without the need
for additional branches or post-processing steps. At the same time, we use the
Ray Tune mechanism with the AsyncHyperBandScheduler scheduler and the
HyperOptSearch algorithm to automatically search for the optimal number of
module iterations, which also allows us to achieve performance comparable to
state-of-the-art methods. We conduct experiments on both indoor and outdoor
scene datasets. Our fast network achieves Pareto-optimal solutions in terms of
time and accuracy, and at the time of submission, our accurate network ranks
first among all published papers on the KITTI official website in terms of
accuracy.
