LAUSR

Depth completion aims to recover dense depth maps from sparse depth maps using the corresponding RGB images as guides. Learning guided convolutional network (GuideNet) is one of the state-of-the-art (SoTA) depth completion methods. In this letter, we propose a robust and efficient end-to-end guided spatial propagation network (GSPN), which further improves the effectiveness and efficiency of GuideNet through spatial propagation. Specifically, we expand the receptive field of the content-dependent guided kernels through a spatial propagation network without adding additional parameters. And the resources required by GSPN can be adjusted according to the actual situation. Furthermore, the proposed algorithm can better fuse the information from different sensors, which is one of the main problems of depth completion. We demonstrate the effectiveness of GSPN compared to other SoTA methods on KITTI depth completion and NYUv2 datasets.