NeMF: Inverse Volume Rendering with Neural Microflake Field

TL;DR

NeMF introduces a volume-based neural microflake field approach for inverse volume rendering, effectively capturing complex geometries and scattering effects, enabling high-quality relighting and material editing.

Contribution

It proposes a novel microflake volume representation and differentiable renderer for inverse volume rendering, addressing limitations of surface-based methods in complex scenes.

Findings

Effective recovery of appearance attributes for complex geometries.

Enables high-quality relighting and material editing.

Simulates volume scattering effects infeasible for surface-based approaches.

Abstract

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.