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.

翻译：基于物理的视觉与深度学习的交叉融合，为推进计算机视觉技术发展呈现了一个激动人心的前沿领域。通过利用物理原理来指导和增强深度学习模型，我们可以开发出更鲁棒、更精确的视觉系统。基于物理的视觉旨在逆向求解成像过程，从图像中恢复出形状、反射率、光照分布和介质属性等场景属性。近年来，深度学习在各种视觉任务中展现出显著的改进潜力，当其与基于物理的视觉相结合时，这些方法能够进一步提升视觉系统的鲁棒性与准确性。本技术报告总结了在CVPR 2024研讨会上举办的"基于物理的视觉遇见深度学习"（PBDL）2024挑战赛的成果。该挑战赛包含八个赛道，聚焦于低光增强与检测以及高动态范围（HDR）成像。本报告详细阐述了各赛道的目标、方法与结果，重点介绍了表现最优的解决方案及其创新性方法。