Recent Trends in Inverse Rendering

Inverse rendering is the problem of recovering scene geometry, material properties, and illumination from one or more observed images. It has experienced rapid progress in recent years, driven by advances in differentiable rendering, neural scene representations, and generative priors. This paper presents a comprehensive review of inverse rendering methods published between 2020 and 2025. In contrast to prior surveys on neural rendering, which review methods primarily focused on novel-view synthesis without explicit material and lighting decomposition, and intrinsic image decomposition surveys, which review approaches for reflectance and shading separation at the image level, we focus on methods that aim to solve the complete inverse rendering problem. We organize the surveyed approaches using a structured taxonomy that categorizes methods by input and output representations, computational strategies, and evaluation protocols. We further analyze commonly used datasets, evaluation strategies, and performance trade-offs, and discuss the strengths and limitations of existing approaches in terms of accuracy, efficiency, and generalization. Finally, we identify open challenges, including the ill-posed nature of material and illumination disentanglement, the lack of standardized benchmarks for joint evaluation, and the limited exploration of downstream applications such as relighting and scene editing.