Recovering the physical attributes of an object's appearance from its images captured under an unknown illumination is challenging yet essential for photo-realistic rendering. Recent approaches adopt the emerging implicit scene representations and have shown impressive results.However, they unanimously adopt a surface-based representation,and hence can not well handle scenes with very complex geometry, translucent object and etc.In this paper, we propose to conduct inverse volume rendering, in contrast to surface-based, by representing a scene using microflake volume, which assumes the space is filled with infinite small flakes and light reflects or scatters at each spatial location according to microflake distributions. We further adopt the coordinate networks to implicitly encode the microflake volume, and develop a differentiable microflake volume renderer to train the network in an end-to-end way in principle.Our NeMF enables effective recovery of appearance attributes for highly complex geometry and scattering object, enables high-quality relighting, material editing, and especially simulates volume rendering effects, such as scattering, which is infeasible for surface-based approaches.

翻译：从未知光照条件下拍摄的图像中恢复物体外观的物理属性，对于实现照片级真实感渲染而言至关重要但极具挑战性。近期方法采用新兴的隐式场景表示并取得了显著成果，然而这些方法均采用基于表面的表示方式，因此难以处理几何结构极其复杂、半透明物体等场景。本文提出采用逆向体绘制方法（与基于表面的方法相对），通过微片体表示场景——该表示假设空间由无限小的微片填充，光线根据微片分布在每个空间位置发生反射或散射。我们进一步采用坐标网络隐式编码微片体，并开发了一种可微分微片体渲染器，原则上能够以端到端方式训练网络。我们的NeMF能够有效恢复具有高度复杂几何与散射特性的物体的外观属性，实现高质量的重光照、材质编辑，尤其能够模拟表面方法无法实现的散射等体绘制效果。