NeRF's high-quality scene synthesis capability was quickly accepted by scholars in the years after it was proposed, and significant progress has been made in 3D scene representation and synthesis. However, the high computational cost limits intuitive and efficient editing of scenes, making NeRF's development in the scene editing field facing many challenges. This paper reviews the preliminary explorations of scholars on NeRF in the scene or object editing field in recent years, mainly changing the shape and texture of scenes or objects in new synthesized scenes; through the combination of residual models such as GaN and Transformer with NeRF, the generalization ability of NeRF scene editing has been further expanded, including realizing real-time new perspective editing feedback, multimodal editing of text synthesized 3D scenes, 4D synthesis performance, and in-depth exploration in light and shadow editing, initially achieving optimization of indirect touch editing and detail representation in complex scenes. Currently, most NeRF editing methods focus on the touch points and materials of indirect points, but when dealing with more complex or larger 3D scenes, it is difficult to balance accuracy, breadth, efficiency, and quality. Overcoming these challenges may become the direction of future NeRF 3D scene editing technology.

本研究综述了近年来学者们在NeRF场景或对象编辑领域的初步探索，并通过将GaN和Transformer等残差模型与NeRF相结合，进一步扩展了NeRF场景编辑的泛化能力，其中包括实现实时新视角编辑反馈、文本合成的多模编辑、4D合成性能，以及在光影编辑方面的深入探索，首次实现了复杂场景中间接触编辑和细节表达的优化。然而，在处理更复杂或更大的3D场景时，很难在准确性、广度、效率和质量之间取得平衡，克服这些挑战可能成为未来NeRF 3D场景编辑技术的方向。

基于Nerf的3D场景编辑技术的探索与改进