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

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arxiv 2310.13030 v2 pith:ENPE54LF submitted 2023-10-19 cs.CV

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

Ziyi Yang , Yanzhen Chen , Xinyu Gao , Yazhen Yuan , Yu Wu , Xiaowei Zhou , Xiaogang Jin This is my paper
classification cs.CV
keywords illuminationinverserenderingimplicitindirectneuralsceneshadows
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 4 Pith papers

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