Infrared Monocular Depth Estimation Based on Radiation Field Detail Enhancement

Monocular depth estimation (MDE) offers cost-effective approach to 3D perception, while thermal infrared sensors show high robustness under varying illumination conditions. Their combination is crucial for day and night 3D perception, yet it faces two key challenges: 1) inadequate processing of 14-bit RAW data, leading to loss of subtle infrared information, and 2) the resolution limitation of current infrared datasets (existing $640\times 512$ sensor collections restrict the camera’s ability to capture critical scene details). To this end, in this paper, we introduce the IRSL dataset, which integrates infrared, stereo RGB, and LiDAR modalities. Data were acquired using a high-resolution thermal infrared camera ( $1280\times 1024$ pixels), synchronized stereo cameras, and LiDAR (500K points per frame). The dataset aims to enhance the precision and robustness of depth estimation in both daytime and nighttime environments. Secondly, this paper proposes Radiation Field Detail Enhancement (RFDE), a novel method for enhancing infrared radiation field details in RAW data. This method performs gray mapping based on radiation field intensity and spatial context awareness and uses a bilateral filter to enhance the details in the image. Experimental results show that the proposed RFDE method fully leverages the advantages of high-resolution infrared images, effectively improving the quantitative and qualitative performance of the MDE model ( $\delta _{1}$ =0.964, SqRel = 0.441).