RTFusion: A depth estimation network based on multimodal fusion in challenging scenarios

Depth estimation in complex real-world scenarios is a challenging task, especially when relying solely on a single modality such as visible light or thermal infrared (THR) imagery. This paper proposes a novel multimodal depth estimation model, RTFusion, which enhances depth estimation accuracy and robustness by integrating the complementary strengths of RGB and THR data. The RGB modality provides rich texture and color information, while the THR modality captures thermal patterns, ensuring stability under adverse lighting conditions such as extreme illumination. The model incorporates a unique fusion mechanism, EGFusion, consisting of the Mutual Complementary Attention (MCA) module for cross-modal feature alignment and the Edge Saliency Enhancement Module (ESEM) to improve edge detail preservation. Comprehensive experiments on the MS2 and ViViD++ datasets demonstrate that the proposed model consistently produces high-quality depth maps across various challenging environments, including nighttime, rainy, and high-glare conditions. The experimental results highlight the potential of the proposed method in applications requiring reliable depth estimation, such as autonomous driving, robotics, and augmented reality.