ViCA-NeRF：面向神经辐射场的视图一致性感知三维编辑
ViCA-NeRF: View-Consistency-Aware 3D Editing of Neural Radiance Fields
AI总结：
本研究提出首个文本驱动的视图一致性感知NeRF三维编辑方法ViCA-NeRF，通过几何与学习型两类正则化保证多视图一致性，分两阶段实现编辑，相较现有方法更灵活高效、效果更优。
摘要：
我们提出了ViCA-NeRF，这是首个支持文本指令的视图一致性感知三维编辑方法。除了隐式神经辐射场（NeRF）建模外，我们的核心思路是利用两类正则化源，将编辑信息在不同视图间显式传播，从而保证多视图一致性。在几何正则化方面，我们利用从NeRF中提取的深度信息建立不同视图间的图像对应关系。在学习型正则化方面，我们对齐2D扩散模型中编辑图像与未编辑图像的隐编码，实现对关键视图的编辑并将更新传播至整个场景。结合这两种策略，ViCA-NeRF分为两个阶段运行：第一阶段融合不同视图的编辑结果，生成初步的三维编辑效果；随后进入第二阶段的NeRF训练，专门用于进一步优化场景的外观。实验结果表明，与现有最优方法相比，ViCA-NeRF的编辑更灵活、效率更高（速度快3倍），且一致性和细节表现更优。我们的代码已公开。
英文摘要：
We introduce ViCA-NeRF, the first view-consistency-aware method for 3D editing with text instructions. In addition to the implicit neural radiance field (NeRF) modeling, our key insight is to exploit two sources of regularization that explicitly propagate the editing information across different views, thus ensuring multi-view consistency. For geometric regularization, we leverage the depth information derived from NeRF to establish image correspondences between different views. For learned regularization, we align the latent codes in the 2D diffusion model between edited and unedited images, enabling us to edit key views and propagate the update throughout the entire scene. Incorporating these two strategies, our ViCA-NeRF operates in two stages. In the initial stage, we blend edits from different views to create a preliminary 3D edit. This is followed by a second stage of NeRF training, dedicated to further refining the scene's appearance. Experimental results demonstrate that ViCA-NeRF provides more flexible, efficient (3 times faster) editing with higher levels of consistency and details, compared with the state of the art. Our code is publicly available.
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