Physics-based differentiable rendering (PBDR) has become an efficient method in computer vision, graphics, and machine learning for addressing an array of inverse problems. PBDR allows patterns to be generated from perceptions which can be applied to enhance object attributes like geometry, substances, and lighting by adding physical models of light propagation and materials interaction. Due to these capabilities, distinguished rendering has been employed in a wider range of sectors such as autonomous navigation, scene reconstruction, and material design. We provide an extensive overview of PBDR techniques in this study, emphasizing their creation, effectiveness, and limitations while managing inverse situations. We demonstrate modern techniques and examine their value in everyday situations.

翻译：基于物理的可微分渲染（PBDR）已成为计算机视觉、图形学和机器学习领域中解决一系列逆问题的高效方法。PBDR能够通过融合光传播与材质相互作用的物理模型，从感知数据生成可优化物体几何、材质与光照等属性的模式。凭借这些能力，可微分渲染技术已广泛应用于自动驾驶导航、场景重建和材料设计等领域。本研究对PBDR技术进行了全面综述，重点阐述其在逆问题处理中的构建原理、效能表现与局限性，同时展示了前沿技术方法并探讨其在实际应用场景中的价值。