Depth completion deals with the problem of recovering dense depth maps from sparse ones, where color images are often used to facilitate this completion. Recent approaches mainly focus on image guided learning to predict dense results. However, blurry image guidance and object structures in depth still impede the performance of image guided frameworks. To tackle these problems, we explore a repetitive design in our image guided network to sufficiently and gradually recover depth values. Specifically, the repetition is embodied in a color image guidance branch and a depth generation branch. In the former branch, we design a repetitive hourglass network to extract higher-level image features of complex environments, which can provide powerful context guidance for depth prediction. In the latter branch, we design a repetitive guidance module based on dynamic convolution where the convolution factorization is applied to simultaneously reduce its complexity and progressively model high-frequency structures, e.g., boundaries. Further, in this module, we propose an adaptive fusion mechanism to effectively aggregate multi-step depth features. Extensive experiments show that our method achieves state-of-the-art result on the NYUv2 dataset and ranks 1st on the KITTI benchmark at the time of submission.

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