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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

Ziyi Yang , Yanzhen Chen , Xinyu Gao , Yazhen Yuan , Yu Wu , Xiaowei Zhou , Xiaogang Jin This is my paper · ORCID
classification cs.CV
keywords illuminationinverserenderingimplicitindirectneuralsceneshadows
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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

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. LSRM: High-Fidelity Object-Centric Reconstruction via Scaled Context Windows

    cs.CV 2026-04 conditional novelty 6.0 of 10

    Scaling transformer context with sparse attention and 3D-aware block routing improves feed-forward 3D reconstruction and inverse rendering, closing much of the quality gap with dense-view optimization.

  2. LIRM: Large Inverse Rendering Model for Progressive Reconstruction of Shape, Materials and View-dependent Radiance Fields

    cs.CV 2025-04 conditional novelty 6.0 of 10

    A feed-forward transformer reconstructs shape, PBR materials, and view-dependent radiance from 3 to 6 posed images in under a second, rivaling slower optimization-based inverse rendering.

  3. Deformable Radial Kernel Splatting

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    A new 2D planar kernel primitive with learnable radial bases, mixed L1/L2 norms, and edge sharpening generalizes Gaussian splatting and claims better rendering quality with fewer primitives.

  4. MLI-NeRF: Multi-Light Intrinsic-Aware Neural Radiance Fields

    cs.CV 2024-11 conditional novelty 6.0 of 10

    MLI-NeRF generates physics-based pseudo reflectance and shading labels from multi-light images to train an intrinsic-aware neural radiance field without ground truth intrinsic data.

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