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.

物理学基础视觉与深度学习的交叉领域在推动计算机视觉技术方面具有激动人心的前景。通过利用物理学原理来指导和增强深度学习模型，我们可以开发出更强大和准确的视觉系统。