Recent advances in implicit neural representation have demonstrated the ability to recover detailed geometry and material from multi-view images. However, the use of simplified lighting models such as environment maps to represent non-distant illumination, or using a network to fit indirect light modeling without a solid basis, can lead to an undesirable decomposition between lighting and material. To address this, we propose a fully differentiable framework named neural ambient illumination (NeAI) that uses Neural Radiance Fields (NeRF) as a lighting model to handle complex lighting in a physically based way. Together with integral lobe encoding for roughness-adaptive specular lobe and leveraging the pre-convoluted background for accurate decomposition, the proposed method represents a significant step towards integrating physically based rendering into the NeRF representation. The experiments demonstrate the superior performance of novel-view rendering compared to previous works, and the capability to re-render objects under arbitrary NeRF-style environments opens up exciting possibilities for bridging the gap between virtual and real-world scenes. The project and supplementary materials are available at https://yiyuzhuang.github.io/NeAI/.

翻译：摘要：近期隐式神经表示方面的进展展示了从多视角图像恢复详细几何与材质的能力。然而，使用诸如环境贴图等简化光照模型来表示非远距离照明，或采用缺乏坚实基础的网络拟合间接光照建模，可能导致光照与材质之间的分解不理想。为解决此问题，我们提出一种名为神经环境照明（NeAI）的全微分框架，该框架利用神经辐射场（NeRF）作为照明模型，以基于物理的方式处理复杂光照。结合针对粗糙度自适应高光瓣的积分瓣编码，并利用预卷积背景实现精确分解，所提方法在将基于物理的渲染集成到NeRF表示方面迈出了重要一步。实验表明，与先前研究相比，本方法在新视角渲染方面具有优越性能，且能够在任意NeRF风格环境下重新渲染对象，这为弥合虚拟与现实世界场景之间的差距开辟了令人兴奋的可能性。项目及补充材料见 https://yiyuzhuang.github.io/NeAI/。