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Depth completion aims to recover dense depth maps from sparse ones, wherecolor images are often used to facilitate this task. Recent depth methodsprimarily focus on image guided learning frameworks. However, blurry guidancein the image and unclear structure in the depth still impede their performance.To tackle these challenges, we explore an efficient repetitive design in ourimage guided network to gradually and sufficiently recover depth values.Specifically, the efficient repetition is embodied in both the image guidancebranch and depth generation branch. In the former branch, we design a denserepetitive hourglass network to extract discriminative image features ofcomplex environments, which can provide powerful contextual instruction fordepth prediction. In the latter branch, we introduce a repetitive guidancemodule based on dynamic convolution, in which an efficient convolutionfactorization is proposed to reduce the complexity while modelinghigh-frequency structures progressively. Extensive experiments indicate thatour approach achieves superior or competitive results on KITTI, VKITTI, NYUv2,3D60, and Matterport3D datasets.

RigNet++: Efficient Repetitive Image Guided Network for Depth Completion