NeIF: Generalizable Illumination Neural Radiance Fields with Implicit Feature Volume

We present Neural Illumination Fields (NeIF), a neural rendering approach for novel illumination view synthesis. Existing generalizable Neural Radiance Fields methods are designed for scenes under static lighting conditions, and it’s difficult to produce high-quality results when input views are from untrained lighting conditions. To address this limitation, NeIF is proposed to render novel scene representations under unknown lighting conditions in an unsupervised manner. The core of our method involves using plane sweep volume to construct an implicit illumination feature volume for geometry representation and modeling the physical illumination imaging process. The feature volume encodes information about the scene’s illumination and geometry. An implicit function is used to simulate the process of mapping light to the camera film under various lighting conditions, which is called the tone mapper. We train our network on a dataset with multi-exposure times and evaluate its lighting generalizability on diverse datasets. Our approach demonstrates the ability to generalize scenes under unknown lighting conditions and generate novel view synthesis results by using only three arbitrary inputs during testing. Experimental results validate that our method accurately generalizes the illumination of synthesized views and effectively renders novel views.