Depth completion method based on multi-guided structure-aware networks

Aiming at the problem of sparse depth information observation in 3D scenes, this paper proposes a multi-guided structure-aware network model fused with color images to complement the sparse depth. Using the mapping relationship between the 3D plane normal vector and the scene gradient information, we design a two-branch backbone network framework and combine image features and geometric features for depth prediction to fully extract the feature representation of spatial location information. Secondly, considering the structural differences of different objects in large-scale scenes, a network channel attention mechanism is designed. A structure-aware module with an adaptive receptive field is used to characterize information at different scales. Finally, in the process of network upsampling, the predicted sub-depth maps are filtered and the edge details of objects are repaired with the guidance of images of different sizes. The experimental results on public datasets show that the designed depth completion algorithm can obtain accurate dense depth. At the same time, through the in-depth evaluation of two downstream sensing tasks, the results show that the propsed method can effectively improve the effect of other sensing tasks. © 2024 Northeast University. All rights reserved.