  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.
