Title: CompletionFormer: Depth Completion with Convolutions and Vision Transformers. Abstract: This paper proposes a joint convolutional attention and Transformer block, which deeply couples the convolutional layer and Vision Transformer into one block, as the basic unit to construct our depth completion model in a pyramidal structure. This hybrid structure 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 (near 1/3 FLOPs) compared to pure Transformer-based methods. Especially when the captured depth is highly sparse, the performance gap with other methods gets much larger.