We present a novel single-stage framework, Neural Photon Field (NePF), to address the ill-posed inverse rendering from multi-view images. Contrary to previous methods that recover the geometry, material, and illumination in multiple stages and extract the properties from various multi-layer perceptrons across different neural fields, we question such complexities and introduce our method - a single-stage framework that uniformly recovers all properties. NePF achieves this unification by fully utilizing the physical implication behind the weight function of neural implicit surfaces and the view-dependent radiance. Moreover, we introduce an innovative coordinate-based illumination model for rapid volume physically-based rendering. To regularize this illumination, we implement the subsurface scattering model for diffuse estimation. We evaluate our method on both real and synthetic datasets. The results demonstrate the superiority of our approach in recovering high-fidelity geometry and visual-plausible material attributes.

翻译：我们提出了一种新颖的单阶段框架——神经光子场（NePF），以解决多视角图像中病态逆渲染问题。与以往通过多阶段恢复几何、材质与光照，并从不同神经场中多个多层感知器提取属性的方法不同，我们对这种复杂性提出质疑，并引入了一种统一恢复所有属性的单阶段框架。NePF通过充分利用神经隐式曲面的权重函数与视角相关辐射背后的物理含义实现了这种统一。此外，我们提出了一种创新的基于坐标的光照模型，用于快速体素化物理渲染。为约束该光照，我们采用次表面散射模型进行漫反射估计。我们在真实与合成数据集上评估了该方法，结果表明我们的方法在恢复高保真几何与视觉合理的材质属性方面具有优越性。