The KITTI Vision Benchmark Suite

Depth completion is a popular research direction
in the
field of depth estimation. The fusion of color and
depth features
is the 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
incorporate a Transformer-based attention network
in low-dimensional
space to effectively fuse global features and
increase the network’s
receptive field. At the same time, we use the Ray
Tune mechanism
with the AsyncHyperBandScheduler 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

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