Differentiable Inverse Rendering with Interpretable Basis BRDFs
Hoon-Gyu Chung, Seokjun Choi, Seung-Hwan Baek
Abstract
and normals under novel view and lighting (a) Input multi-view flash images (c) Our interpretable basis BRDFs and weight maps 0.0 1.0 (d) Reflectance editing with environment relighting Figure 1. We propose a differentiable inverse rendering method with (a) multi-view flash photography inputs. Our analysis-by-synthesis method achieves not only (b) novel-view relighting and accurate geometry reconstruction, but also (c) interpretable basis BRDFs and their spatially-separated weights. This allows for (d) intuitive scene editing.
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Install the CLIlune papers fulltext 65b0ada5-1e93-4a80-be12-a988eb76f2f8Cited by top-tier papers2
- A Real-World Display Inverse Rendering DatasetSeokjun Choi, Hoon-Gyu Chung, Yujin Jeon, Giljoo Nam et al.ICCV 2025 · 2 citations
- Ambient-robust Inverse Rendering using Active RGB-NIR ImagingHoon-Gyu Chung, Jinnyeong Kim, Hyunwoo Kang, Seung-Hwan BaekSIGGRAPH 2026
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- 3D Gaussian Splatting for Real-Time Radiance Field RenderingBernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, George DrettakisSIGGRAPH 2023 · 5,687 citations
- 2D Gaussian Splatting for Geometrically Accurate Radiance FieldsBinbin Huang, Zehao Yu, Anpei Chen, Andreas Geiger et al.SIGGRAPH 2024 · 660 citations
- NeRD: Neural Reflectance Decomposition from Image CollectionsMark Boss, Raphael Braun, Varun Jampani, Jonathan T. Barron et al.ICCV 2021 · 608 citations
- Neural-PIL: Neural Pre-Integrated Lighting for Reflectance DecompositionMark Boss, Varun Jampani, Raphael Braun, Ce Liu et al.NeurIPS 2021 · 270 citations
- Neural Inverse Rendering of an Indoor Scene From a Single ImageSoumyadip Sengupta, Jinwei Gu, Kihwan Kim, Guilin Liu et al.ICCV 2019 · 172 citations
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