Neural Radiance Field (NeRF), as an implicit 3D scene representation, lacks inherent ability to accommodate changes made to the initial static scene. If objects are reconfigured, it is difficult to update the NeRF to reflect the new state of the scene without time-consuming data re-capturing and NeRF re-training. To address this limitation, we develop the first update method for NeRFs to physical changes. Our method takes only sparse new images (e.g. 4) of the altered scene as extra inputs and update the pre-trained NeRF in around 1 to 2 minutes. Particularly, we develop a pipeline to identify scene changes and update the NeRF accordingly. Our core idea is the use of a second helper NeRF to learn the local geometry and appearance changes, which sidesteps the optimization difficulties in direct NeRF fine-tuning. The interpolation power of the helper NeRF is the key to accurately reconstruct the un-occluded objects regions under sparse view supervision. Our method imposes no constraints on NeRF pre-training, and requires no extra user input or explicit semantic priors. It is an order of magnitude faster than re-training NeRF from scratch while maintaining on-par and even superior performance.

我们开发了第一个用于NeRF更新的方法，可通过只提供少量新的图像来更新预训练的NeRF，以便适应物理变化，而无需耗时的数据重新捕获和重新训练。我们使用辅助的第二个NeRF来学习场景的局部几何和外观变化，用于准确重建稀疏视角下未遮挡物体区域。该方法比从头开始重新训练NeRF快一个数量级，并且具有相当甚至更好的性能。

基于快速稀疏视角引导的对象重构 NeRF 更新