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

SIRe-IR: Inverse Rendering for BRDF Reconstruction with Shadow and Illumination Removal in High-Illuminance Scenes