NePF: Neural Photon Field for Single-Stage Inverse Rendering
Tuen-Yue Tsui Qin Zou
We present a novel single-stage framework, Neural Photon Field (NePF), to
address the ill-posed inverse rendering from multi-view images. Contrary to
previous methods that recover the geometry, material, and illumination in
multiple stages and extract the properties from various multi-layer perceptrons
across different neural fields, we question such complexities and introduce our
method - a single-stage framework that uniformly recovers all properties. NePF
achieves this unification by fully utilizing the physical implication behind
the weight function of neural implicit surfaces and the view-dependent
radiance. Moreover, we introduce an innovative coordinate-based illumination
model for rapid volume physically-based rendering. To regularize this
illumination, we implement the subsurface scattering model for diffuse
estimation. We evaluate our method on both real and synthetic datasets. The
results demonstrate the superiority of our approach in recovering high-fidelity
geometry and visual-plausible material attributes.