Implicit neural representation has opened up new possibilities for inverse
rendering. However, existing implicit neural inverse rendering methods struggle
to handle strongly illuminated scenes with significant shadows and indirect
illumination. The existence of shadows and reflections can lead to an
inaccurate understanding of scene geometry, making precise factorization
difficult. To this end, we present SIRe-IR, an implicit neural inverse
rendering approach that uses non-linear mapping and regularized visibility
estimation to decompose the scene into environment map, albedo, and roughness.
By accurately modeling the indirect radiance field, normal, visibility, and
direct light simultaneously, we are able to remove both shadows and indirect
illumination in materials without imposing strict constraints on the scene.
Even in the presence of intense illumination, our method recovers high-quality
albedo and roughness with no shadow interference. SIRe-IR outperforms existing
methods in both quantitative and qualitative evaluations.

SIRe-IR 是一种基于非线性映射和正则化能见度估计的隐式神经反渲染方法，能够准确地将场景分解为环境贴图、反射率和粗糙度，并能够同时模拟间接辐射场、法线、能见度和直接光，从而消除材质中的阴影和间接照明。SIRe-IR 在定量和定性评估中均优于现有方法。