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In order to acquire precise depth maps, depth completion is a fundamental method for autonomous vehicles and robotics. Recent methods mainly focus on fusing multi-model information from sparse depth maps and color images to recover dense depth maps. Previous researches have made remarkable contributions in predicting depth values, but how to better fuse multi-model features, and how to better restore details are still two main issues. Aiming at these two issues, we propose a fusion net composed of two-branch backbone and depth refinement module. The backbone aims to extract and combine the features of sparse depths and color images, in which we adopt the strategies of symmetric gated fusion and pixel-shuffle for cross-branch and branch-wise fusion respectively. Then, we designed a new module named dilation-pyramid convolution spatial propagation network (DP-CSPN) for depth refinement which enlarges the propagation neighborhoods and obtains more local affinities than CSPN. Finally, to better process details, we designed loss functions to achieve clearer edges as well as to be aware of tiny structures. Our method achieves the state-of-the-art (SoTA) performance in NYU-Depth-v2 Dataset and KITTI Depth Completion Dataset, and we got the achievement of top 5 in mobile intelligent photography and imaging (MIPI) challenge held by European Conference on Computer Vision (ECCV) 2022.

AF 2 R Net: Adaptive Feature Fusion and Robust Network for Efficient and Precise Depth Completion