  Depth completion aims to recover dense depth maps from sparse ones, where
color images are often used to facilitate this task. Recent depth methods
primarily focus on image guided learning frameworks. However, blurry guidance
in the image and unclear structure in the depth still impede their performance.
To tackle these challenges, we explore an efficient repetitive design in our
image guided network to gradually and sufficiently recover depth values.
Specifically, the efficient repetition is embodied in both the image guidance
branch and depth generation branch. In the former branch, we design a dense
repetitive hourglass network to extract discriminative image features of
complex environments, which can provide powerful contextual instruction for
depth prediction. In the latter branch, we introduce a repetitive guidance
module based on dynamic convolution, in which an efficient convolution
factorization is proposed to reduce the complexity while modeling
high-frequency structures progressively. Extensive experiments indicate that
our approach achieves superior or competitive results on KITTI, VKITTI, NYUv2,
3D60, and Matterport3D datasets.

Computer Vision and Pattern Recognition