Ambient-robust Inverse Rendering using Active RGB-NIR Imaging
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Abstract (arXiv)
Inverse rendering aims to reconstruct geometry and reflectance of objects from images. Despite recent progress, existing methods often produces inaccurate reconstructions that are sensitive to ambient illumination conditions. Here we introduce an ambient-robust inverse rendering method enabled by active RGB-NIR imaging. Our key insight is to leverage near-infrared (NIR) flash illumination-imperceptible to human observers-to obtain stable point-light shading that is largely invariant to ambient illumination. By using multi-view RGB images illuminated by ambient light and NIR images acquired with active NIR flash illumination, we reconstruct accurate geometry and reflectance by exploiting the complementary benefits of RGB and NIR images via a three-stage inverse rendering method. To enable dense multi-view acquisition, we develop an active imaging system equipped with a RGB-NIR camera and a NIR flash mounted on a mobile base. Using this system, we collect the first multi-view RGB-NIR inverse rendering dataset captured under multiple ambient illumination conditions. Experiments demonstrate that our method outperforms prior approaches, achieving accurate geometry and reflectance estimation across multiple ambient lighting scenarios.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 28 of its 62 references that are in the map. First posted on arXiv 28 May 2026.
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References · 28
- 20242D Gaussian Splatting for Geometrically Accurate Radiance Fieldscited by 1,076
- 2021PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relightingcited by 335
- 2023GaussianShader: 3D Gaussian Splatting with Shading Functions for Reflective Surfacescited by 252
- 2023GS-IR: 3D Gaussian Splatting for Inverse Renderingcited by 224
- 2022Modeling Indirect Illumination for Inverse Renderingcited by 168
- 2023Relightable 3D Gaussians: Realistic Point Cloud Relighting with BRDF Decomposition and Ray Tracingcited by 142
- 2021NeRFactor: Neural Factorization of Shape and Reflectance Under an Unknown Illuminationcited by 135
- 2022IRON: Inverse Rendering by Optimizing Neural SDFs and Materials from Photometric Imagescited by 111
- 2022NeILF: Neural Incident Light Field for Physically-based Material Estimationcited by 105
- 2019Neural Inverse Rendering of an Indoor Scene from a Single Imagecited by 101
- 2024IntrinsicAnything: Learning Diffusion Priors for Inverse Rendering Under Unknown Illuminationcited by 67
- 2022IRISformer: Dense Vision Transformers for Single-Image Inverse Rendering in Indoor Scenescited by 62
16 more
- 2024IRGS: Inter-Reflective Gaussian Splatting with 2D Gaussian Ray Tracingcited by 54
- 2022PANDORA: Polarization-Aided Neural Decomposition Of Radiancecited by 52
- 2023Stanford-ORB: A Real-World 3D Object Inverse Rendering Benchmarkcited by 51
- 2024GS^3: Efficient Relighting with Triple Gaussian Splattingcited by 44
- 2023Relighting Neural Radiance Fields with Shadow and Highlight Hintscited by 42
- 2023NeFII: Inverse Rendering for Reflectance Decomposition with Near-Field Indirect Illuminationcited by 38
- 2024MaterialFusion: Enhancing Inverse Rendering with Material Diffusion Priorscited by 28
- 2023NeISF: Neural Incident Stokes Field for Geometry and Material Estimationcited by 25
- 2023WildLight: In-the-wild Inverse Rendering with a Flashlightcited by 21
- 2024Neural LightRig: Unlocking Accurate Object Normal and Material Estimation with Multi-Light Diffusioncited by 19
- 2024Objects With Lighting: A Real-World Dataset for Evaluating Reconstruction and Rendering for Object Relightingcited by 18
- 2024Cross-spectral Gated-RGB Stereo Depth Estimationcited by 8
- 2016Refining Geometry from Depth Sensors using IR Shading Imagescited by 5
- 2025A Real-world Display Inverse Rendering Datasetcited by 4
- 2016Fine-scale Surface Normal Estimation using a Single NIR Imagecited by 2
- 2025Mobile Robotic Multi-View Photometric Stereocited by 1
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