Bayesian-Inspired Cross-Spectral Fusion Network for Robust Depth Estimation

Depth estimation is a critical task in multimedia, with widespread applications in autonomous driving, robotics, and 3D reconstruction. Traditional methods relying on single-spectral data suffer from low-light and nighttime conditions. In view of this, we propose BCSFDepth, a bayesian-inspired cross-spectral fusion network fuses infrared and visible images to enhance depth estimation. Our method incorporates a frequency-domain interaction enhancement module and a Bayesian uncertainty-weighted fusion mechanism to effectively integrate complementary information across modalities. The proposed framework dynamically adjusts modality weights, mitigating the impact of low-quality features. We collected and calibrated a real-world cross-spectral dataset, IVLD, to evaluate our method under diverse weather conditions. Experimental results demonstrate substantial improvements in depth estimation accuracy, particularly in noisy and complex environments.