Traditional inverse rendering techniques are based on textured meshes, which naturally adapts to modern graphics pipelines, but costly differentiable multi-bounce Monte Carlo (MC) ray tracing poses challenges for modeling global illumination. Recently, neural fields has demonstrated impressive reconstruction quality but falls short in modeling indirect illumination. In this paper, we introduce a simple yet efficient inverse rendering framework that combines the strengths of both methods. Specifically, given pre-trained neural field representing the scene, we can obtain an initial estimate of the signed distance field (SDF) and create a Neural Radiance Cache (NRC), an enhancement over the traditional radiance cache used in real-time rendering. By using the former to initialize differentiable marching tetrahedrons (DMTet) and the latter to model indirect illumination, we can compute the global illumination via single-bounce differentiable MC ray tracing and jointly optimize the geometry, material, and light through back propagation. Experiments demonstrate that, compared to previous methods, our approach effectively prevents indirect illumination effects from being baked into materials, thus obtaining the high-quality reconstruction of triangle mesh, Physically-Based (PBR) materials, and High Dynamic Range (HDR) light probe.

翻译：传统逆渲染技术基于纹理网格，能自然适配现代图形管线，但高成本的微分多弹跳蒙特卡洛光线追踪对全局光照建模构成挑战。近年神经场虽展现出卓越的重建质量，却在间接光照建模方面存在不足。本文提出一种兼具两类方法优势的简洁高效逆渲染框架：给定表征场景的预训练神经场，可获取符号距离场的初始估计并构建神经辐射缓存（传统实时渲染辐射缓存的增强版本）。通过前者初始化可微行进四面体，后者建模间接光照，借助单弹跳可微蒙特卡洛光线追踪计算全局光照，并通过反向传播联合优化几何、材质与光照。实验表明，相较现有方法，本文方法有效避免了间接光照效应被烘焙至材质中，从而获得三角网格、基于物理的材质与高动态范围光照探针的高质量重建。