This paper proposes a practical photometric solution for the challenging problem of in-the-wild inverse rendering under unknown ambient lighting. Our system recovers scene geometry and reflectance using only multi-view images captured by a smartphone. The key idea is to exploit smartphone's built-in flashlight as a minimally controlled light source, and decompose image intensities into two photometric components -- a static appearance corresponds to ambient flux, plus a dynamic reflection induced by the moving flashlight. Our method does not require flash/non-flash images to be captured in pairs. Building on the success of neural light fields, we use an off-the-shelf method to capture the ambient reflections, while the flashlight component enables physically accurate photometric constraints to decouple reflectance and illumination. Compared to existing inverse rendering methods, our setup is applicable to non-darkroom environments yet sidesteps the inherent difficulties of explicit solving ambient reflections. We demonstrate by extensive experiments that our method is easy to implement, casual to set up, and consistently outperforms existing in-the-wild inverse rendering techniques. Finally, our neural reconstruction can be easily exported to PBR textured triangle mesh ready for industrial renderers.

翻译：本文提出了一种实用的光度学解决方案，用于解决未知环境光照下极具挑战性的野外逆渲染问题。我们的系统仅利用智能手机拍摄的多视角图像，即可恢复场景几何与反射率。核心思想在于将智能手机内置的手电筒作为最小化控制光源，并将图像强度分解为两个光度学分量——一个对应环境光通量的静态外观，加上一个由移动手电筒激发的动态反射。我们的方法无需将闪光/非闪光图像成对捕捉。基于神经光场的成功，我们采用现成方法捕捉环境反射，而手电筒分量则提供了物理精确的光度学约束，以解耦反射率和光照。与现有逆渲染方法相比，我们的设置适用于非暗室环境，同时规避了显式求解环境反射的固有难题。通过大量实验证明，我们的方法易于实现、设置简便，并且始终优于现有野外逆渲染技术。最后，我们的神经重建结果可轻松导出为PBR材质纹理三角网格，直接适用于工业渲染器。