Technique Report of CVPR 2024 PBDL Challenges
This is an incremental technical report summarizing a workshop challenge for researchers in computer vision, focusing on specific domain applications.
The report summarizes the outcomes of the CVPR 2024 PBDL challenge, which tackled low-light enhancement, detection, and HDR imaging by combining physics-based vision with deep learning, highlighting top-performing solutions and their results.
The intersection of physics-based vision and deep learning presents an exciting frontier for advancing computer vision technologies. By leveraging the principles of physics to inform and enhance deep learning models, we can develop more robust and accurate vision systems. Physics-based vision aims to invert the processes to recover scene properties such as shape, reflectance, light distribution, and medium properties from images. In recent years, deep learning has shown promising improvements for various vision tasks, and when combined with physics-based vision, these approaches can enhance the robustness and accuracy of vision systems. This technical report summarizes the outcomes of the Physics-Based Vision Meets Deep Learning (PBDL) 2024 challenge, held in CVPR 2024 workshop. The challenge consisted of eight tracks, focusing on Low-Light Enhancement and Detection as well as High Dynamic Range (HDR) Imaging. This report details the objectives, methodologies, and results of each track, highlighting the top-performing solutions and their innovative approaches.