Adaptive Multi-Exposure Fusion for Enhanced Neural Radiance Fields

Neural Radiance Fields (NeRF) have revolutionized 3D scene modeling and rendering. However, their performance dips when handling images with diverse exposure levels, mainly due to the intricate luminance dynamics. Addressing this, we present an innovative method that proficiently models and renders images across a spectrum of exposure conditions. Our approach utilizes an unsupervised classifier-generator structure for HDR fusion, significantly enhancing NeRF's ability to comprehend and adjust to light variations, leading to the generation of images with appropriate brightness. Extensive evaluations on the LOM1 and LOL2 datasets underscore our method's edge. Our approach significantly improves the task of novel view synthesis for multi-exposure images, attaining state-of-the-art results.