Factored-NeuS: Reconstructing Surfaces, Illumination, and Materials of
  Possibly Glossy Objects
Yue Fan Ivan Skorokhodov Oleg Voynov Savva Ignatyev Evgeny Burnaev Peter Wonka Yiqun Wang
We develop a method that recovers the surface, materials, and illumination of
a scene from its posed multi-view images. In contrast to prior work, it does
not require any additional data and can handle glossy objects or bright
lighting. It is a progressive inverse rendering approach, which consists of
three stages. First, we reconstruct the scene radiance and signed distance
function (SDF) with our novel regularization strategy for specular reflections.
Our approach considers both the diffuse and specular colors, which allows for
handling complex view-dependent lighting effects for surface reconstruction.
Second, we distill light visibility and indirect illumination from the learned
SDF and radiance field using learnable mapping functions. Third, we design a
method for estimating the ratio of incoming direct light represented via
Spherical Gaussians reflected in a specular manner and then reconstruct the
materials and direct illumination of the scene. Experimental results
demonstrate that the proposed method outperforms the current state-of-the-art
in recovering surfaces, materials, and lighting without relying on any
additional data.