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.

翻译：深度补全旨在从稀疏深度图中恢复稠密深度图，常借助彩色图像来辅助该任务。近期深度补全方法主要聚焦于图像引导学习框架。然而，图像中的模糊引导信息及深度中的不清晰结构仍制约着其性能。为应对这些挑战，我们探索了图像引导网络中的高效重复设计，以逐步且充分地恢复深度值。具体而言，高效重复性体现在图像引导分支与深度生成分支中。在前者中，我们设计了密集重复沙漏网络，以提取复杂环境中具有判别力的图像特征，从而为深度预测提供强大的上下文指导。在后者中，我们引入了基于动态卷积的重复引导模块，并提出高效卷积分解以降低复杂度，同时逐步建模高频结构。大量实验表明，我们的方法在KITTI、VKITTI、NYUv2、3D60及Matterport3D数据集上取得了优越或具有竞争力的结果。