RehearsalNeRF：解耦动态光照的内在神经场以实现场景编辑
RehearsalNeRF: Decoupling Intrinsic Neural Fields of Dynamic Illuminations for Scene Editing
AI总结：
RehearsalNeRF通过利用稳定光照下的场景数据，解耦动态光照下的场景辐射，提升动态光照下的视图合成和场景编辑性能。
摘要：
尽管神经辐射场在动态光照变化方面取得显著进展，但该问题仍未解决。与相关工作不同，本研究提出RehearsalNeRF方法，通过利用稳定光照下的场景数据，强制不同光照条件下的几何一致性。核心思想是利用可学习的向量表示光照效果，以时间维度表示光照颜色，并用于分离投影光照颜色与场景辐射。此外，RehearsalNeRF还能通过采用现成的交互掩码重建动态物体的神经场。为解耦动态物体，我们提出一种新的正则化方法，利用光流提供粗略监督以促进颜色解耦。通过在动态光照条件下展示鲁棒的视图合成和场景编辑性能，证明了RehearsalNeRF的有效性。源代码和视频数据集将公开发布。
英文摘要：
Although there has been significant progress in neural radiance fields, an issue on dynamic illumination changes still remains unsolved. Different from relevant works that parameterize time-variant/-invariant components in scenes, subjects' radiance is highly entangled with their own emitted radiance and lighting colors in spatio-temporal domain. In this paper, we present a new effective method to learn disentangled neural fields under the severe illumination changes, named RehearsalNeRF. Our key idea is to leverage scenes captured under stable lighting like rehearsal stages, easily taken before dynamic illumination occurs, to enforce geometric consistency between the different lighting conditions. In particular, RehearsalNeRF employs a learnable vector for lighting effects which represents illumination colors in a temporal dimension and is used to disentangle projected light colors from scene radiance. Furthermore, our RehearsalNeRF is also able to reconstruct the neural fields of dynamic objects by simply adopting off-the-shelf interactive masks. To decouple the dynamic objects, we propose a new regularization leveraging optical flow, which provides coarse supervision for the color disentanglement. We demonstrate the effectiveness of RehearsalNeRF by showing robust performances on novel view synthesis and scene editing under dynamic illumination conditions. Our source code and video datasets will be publicly available.
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