Recent advancements in the field of Neural Radiance Fields (NeRF) have significantly advanced the capabilities of 3D scene reconstruction and rendering. A notable trend is the integration of physics-based rendering principles into NeRF models, enabling more accurate estimation of scene materials and illumination. This approach not only enhances the realism of novel view synthesis but also broadens the applicability of NeRF techniques to various inverse rendering tasks. Another key area of innovation is the synthesis of textures within NeRF frameworks, addressing the limitations of traditional 2D texture methods by incorporating meso-structure details from 3D geometry. This advancement allows for more realistic and versatile texture generation, particularly on curved surfaces, which is crucial for applications in computer graphics. Efforts to improve the robustness and efficiency of NeRF models are also evident, with methods focusing on reducing artifacts and enhancing the quality of unseen areas. These approaches leverage global priors and optimization techniques to clean up artifacts without compromising the model's performance, resulting in faster and more accurate novel view synthesis. In the realm of material generation, recent work has emphasized multi-view consistency and physical accuracy, using advanced diffusion models to produce high-quality PBR materials. These materials are critical for realistic rendering and relighting applications, showcasing the potential of combining multi-view data with physically-based principles. Noteworthy papers include: - PBR-NeRF: Introduces physics-based priors to enhance material estimation in inverse rendering. - NeRF-Texture: Proposes a novel method for synthesizing textures with meso-structure details. - Bright-NeRF: Achieves color restoration and denoising from low-light raw images, expanding NeRF applicability. - MCMat: Demonstrates state-of-the-art performance in generating multi-view consistent PBR materials.