Fast Sparse View Guided NeRF Update for Object Reconfigurations
This addresses the limitation of NeRFs in accommodating scene reconfigurations, enabling faster updates for applications like robotics or AR, though it is incremental as it builds on existing NeRF methods.
The paper tackles the problem of updating Neural Radiance Fields (NeRFs) to reflect physical changes in a scene without re-capturing data or re-training from scratch, achieving updates in 1-2 minutes using only 4 sparse new images while maintaining or improving performance.
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.