  Given sparse depths and the corresponding RGB images, depth completion aims
at spatially propagating the sparse measurements throughout the whole image to
get a dense depth prediction. Despite the tremendous progress of
deep-learning-based depth completion methods, the locality of the convolutional
layer or graph model makes it hard for the network to model the long-range
relationship between pixels. While recent fully Transformer-based architecture
has reported encouraging results with the global receptive field, the
performance and efficiency gaps to the well-developed CNN models still exist
because of its deteriorative local feature details. This paper proposes a Joint
Convolutional Attention and Transformer block (JCAT), which deeply couples the
convolutional attention layer and Vision Transformer into one block, as the
basic unit to construct our depth completion model in a pyramidal structure.
This hybrid architecture naturally benefits both the local connectivity of
convolutions and the global context of the Transformer in one single model. As
a result, our CompletionFormer outperforms state-of-the-art CNNs-based methods
on the outdoor KITTI Depth Completion benchmark and indoor NYUv2 dataset,
achieving significantly higher efficiency (nearly 1/3 FLOPs) compared to pure
Transformer-based methods. Code is available at
\url{https://github.com/youmi-zym/CompletionFormer}.
