  Depth completion aims to recover a dense depth map from the sparse depth data
and the corresponding single RGB image. The observed pixels provide the
significant guidance for the recovery of the unobserved pixels' depth. However,
due to the sparsity of the depth data, the standard convolution operation,
exploited by most of existing methods, is not effective to model the observed
contexts with depth values. To address this issue, we propose to adopt the
graph propagation to capture the observed spatial contexts. Specifically, we
first construct multiple graphs at different scales from observed pixels. Since
the graph structure varies from sample to sample, we then apply the attention
mechanism on the propagation, which encourages the network to model the
contextual information adaptively. Furthermore, considering the mutli-modality
of input data, we exploit the graph propagation on the two modalities
respectively to extract multi-modal representations. Finally, we introduce the
symmetric gated fusion strategy to exploit the extracted multi-modal features
effectively. The proposed strategy preserves the original information for one
modality and also absorbs complementary information from the other through
learning the adaptive gating weights. Our model, named Adaptive Context-Aware
Multi-Modal Network (ACMNet), achieves the state-of-the-art performance on two
benchmarks, {\it i.e.}, KITTI and NYU-v2, and at the same time has fewer
parameters than latest models. Our code is available at:
\url{https://github.com/sshan-zhao/ACMNet}.
