基于 NeRF 的三维场景编辑技术探索与改进
Exploration and Improvement of Nerf-based 3D Scene Editing Techniques
AI总结：
本文综述基于 NeRF 的三维场景编辑研究，梳理形状纹理编辑及 GAN、Transformer 等融合方法，并指出复杂大场景下精度、广度、效率与质量难以兼顾的未来挑战。
摘要：
NeRF 高质量的场景合成能力在其提出后的数年间迅速获得学者认可，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直观、高效的场景编辑，使 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们在场景或物体编辑领域对 NeRF 的初步探索，这些工作主要改变新合成场景中场景或物体的形状与纹理；通过将 GAN、Transformer 等残差模型与 NeRF 相结合，进一步拓展了 NeRF 场景编辑的泛化能力，包括实现实时的新视角编辑反馈、文本合成三维场景的多模态编辑、4D 合成性能，以及在光影编辑方面的深入探索，初步实现了间接接触编辑和复杂场景中细节表示的优化。目前，大多数 NeRF 编辑方法聚焦于接触点和间接点的材质，但在处理更复杂或更大规模的三维场景时，难以兼顾准确性、广度、效率与质量。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。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
