EDRNet: Edge-Enhanced Dynamic Routing Adaptive for Depth Completion

Fuyun Sun (), Baoquan Li and Qiaomei Zhang
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Fuyun Sun: Tianjin Key Laboratory of Intelligent Control of Electrical Equipment, School of Control Science and Engineering, Tiangong University, Tianjin 300387, China
Baoquan Li: Tianjin Key Laboratory of Intelligent Control of Electrical Equipment, School of Control Science and Engineering, Tiangong University, Tianjin 300387, China
Qiaomei Zhang: Tianjin Key Laboratory of Intelligent Control of Electrical Equipment, School of Control Science and Engineering, Tiangong University, Tianjin 300387, China

Mathematics, 2025, vol. 13, issue 6, 1-28

Abstract: Depth completion is a technique to densify the sparse depth maps acquired by depth sensors (e.g., RGB-D cameras, LiDAR) to generate complete and accurate depth maps. This technique has important application value in autonomous driving, robot navigation, and virtual reality. Currently, deep learning has become a mainstream method for depth completion. Therefore, we propose an edge-enhanced dynamically routed adaptive depth completion network, EDRNet, to achieve efficient and accurate depth completion through lightweight design and boundary optimisation. Firstly, we introduce the Canny operator (a classical image processing technique) to explicitly extract and fuse the object contour information and fuse the acquired edge maps with RGB images and sparse depth map inputs to provide the network with clear edge-structure information. Secondly, we design a Sparse Adaptive Dynamic Routing Transformer block called SADRT, which can effectively combine the global modelling capability of the Transformer and the local feature extraction capability of CNN. The dynamic routing mechanism introduced in this block can dynamically select key regions for efficient feature extraction, and the amount of redundant computation is significantly reduced compared with the traditional Transformer. In addition, we design a loss function with additional penalties for the depth error of the object edges, which further enhances the constraints on the edges. The experimental results demonstrate that the method presented in this paper achieves significant performance improvements on the public datasets KITTI DC and NYU Depth v2, especially in the edge region’s depth prediction accuracy and computational efficiency.

Keywords: depth completion; depth map; deep learning; edge guide; neural network; computer vision (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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