  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.
