Finer monocular depth estimation with long range in various driving lighting environments

Depth estimation methods for autonomous driving application face numerous challenges, such as capturing fine details and handling varying lighting conditions. Based on these challenges, LRDepth is proposed to improve the depth estimation task, which includes a simple high frequency enhancement module (HFEM) and a progressive residual denoising diffusion (PRDD) module. HFEM aids in extracting high-frequency components and amplifying the features, such as object edge details, generating more precise depth predictions. Inspired by the strong performance of diffusion models in various vision tasks, PRDD is designed to refine the depth predictions by reducing noise and enhancing edge details, which ensures the accurate representation of distant objects and subtle features. Extensive experiments on the KITTI and DIODE datasets demonstrated that the proposed network boosts the performance of monocular depth estimation, achieving more accurate long range depth predictions and improving model robustness in various lighting environments. The experiment results verified the method's adaptability, and the model is potential for real-world applications, which is beneficial for the optimization of visual perception module in intelligent driving system.