We present a novel approach for digitizing real-world objects by estimating their geometry, material properties, and environmental lighting from a set of posed images with fixed lighting. Our method incorporates into Neural Radiance Field (NeRF) pipelines the split sum approximation used with image-based lighting for real-time physical-based rendering. We propose modeling the scene's lighting with a single scene-specific MLP representing pre-integrated image-based lighting at arbitrary resolutions. We achieve accurate modeling of pre-integrated lighting by exploiting a novel regularizer based on efficient Monte Carlo sampling. Additionally, we propose a new method of supervising self-occlusion predictions by exploiting a similar regularizer based on Monte Carlo sampling. Experimental results demonstrate the efficiency and effectiveness of our approach in estimating scene geometry, material properties, and lighting. Our method is capable of attaining state-of-the-art relighting quality after only ${\sim}1$ hour of training in a single NVIDIA A100 GPU.

我们提出了一种新颖的方法来通过估计来自一组拍摄图片的几何、材质特性和环境光照来数字化真实世界物体。我们的方法将用于实时基于物理的渲染的基于图像的光照的分数和逼近方法纳入到神经辐射场（NeRF）管道中。我们提出了使用单个场景特定的MLP模型来建模场景的光照，该模型表示任意分辨率的预集成图像光照。我们通过利用基于高效蒙特卡洛采样的新型正则化器实现了对预集成光照的准确建模。此外，我们提出了一种利用类似蒙特卡洛采样的新型正则化器来监督自遮挡预测的方法。实验结果证明了我们的方法在估计场景的几何、材质特性和光照方面的效率和效果。我们的方法在单个NVIDIA A100 GPU上进行仅约1小时的训练后能够达到最先进的重光质量。

SplitNeRF: 切分总和逼近神经场用于联合几何、照明和材料估计