High-Resolution LiDAR Depth Completion Algorithm Guided by Image Topography Maps

Abstract

The process of recovering dense depth maps from sparse depth information is prone to edge blurring. This paper proposes an image-guided depth completion algorithm to address this issue. The method uses the edges of the color image as prior constraints to construct an image topography map as an intermediate representation and performs nonlinear adaptive reconstruction based on the image content to adjust the position and scale of the pixel-weighted neighborhood. This approach avoids incorporating depth information with different distributions when estimating missing values, resulting in a full-resolution dense depth map with sharp edges. We conducted a quantitative comparison with state-of-the-art models on the KITTI and MidAir datasets, demonstrating that our algorithm has better performance and robustness in terms of completion accuracy. We also analyzed the impact of sparsity on the algorithm’s performance and its ability to recover fine structures in dense depth results and demonstrated the reconstruction results for sparse data in real-world scenarios.