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.

翻译：NeRF的高质量场景合成能力在提出后迅速被学者接受，并在三维场景表示与合成方面取得了显著进展。然而，高昂的计算成本限制了场景的直观高效编辑，使得NeRF在场景编辑领域的发展面临诸多挑战。本文综述了近年来学者在场景或物体编辑领域对NeRF的初步探索，主要改变新合成场景中场景或物体的形状和纹理；通过GaN、Transformer等残差模型与NeRF的结合，进一步拓展了NeRF场景编辑的泛化能力，包括实现实时新视角编辑反馈、文本合成三维场景的多模态编辑、4D合成性能以及光影编辑方面的深入探索，初步实现了复杂场景中非直接触编辑和细节表示的优化。目前大多数NeRF编辑方法侧重于非直接点的触摸点和材质，但在处理更复杂或更大的三维场景时，难以平衡精度、广度、效率和质量。克服这些挑战可能成为未来NeRF三维场景编辑技术的发展方向。