mlsanityData to 14 Oct 2026

WildLight: In-the-wild Inverse Rendering with a Flashlight

arXiv 2303.14190 ↗First posted 24 Mar 2023cs.CVcited by 21

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Abstract (arXiv)

This paper proposes a practical photometric solution for the challenging problem of in-the-wild inverse rendering under unknown ambient lighting. Our system recovers scene geometry and reflectance using only multi-view images captured by a smartphone. The key idea is to exploit smartphone's built-in flashlight as a minimally controlled light source, and decompose image intensities into two photometric components -- a static appearance corresponds to ambient flux, plus a dynamic reflection induced by the moving flashlight. Our method does not require flash/non-flash images to be captured in pairs. Building on the success of neural light fields, we use an off-the-shelf method to capture the ambient reflections, while the flashlight component enables physically accurate photometric constraints to decouple reflectance and illumination. Compared to existing inverse rendering methods, our setup is applicable to non-darkroom environments yet sidesteps the inherent difficulties of explicit solving ambient reflections. We demonstrate by extensive experiments that our method is easy to implement, casual to set up, and consistently outperforms existing in-the-wild inverse rendering techniques. Finally, our neural reconstruction can be easily exported to PBR textured triangle mesh ready for industrial renderers.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). Builds on 22 of its 49 references that are in the map. First posted on arXiv 24 Mar 2023.

Builds on

22 of 49 references.

References · 22

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2021NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstructioncited by 1,712
  3. 2020NeRF++: Analyzing and Improving Neural Radiance Fieldscited by 861
  4. 2019Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervisioncited by 700
  5. 2021UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstructioncited by 646
  6. 2020NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesiscited by 496
  7. 2020NeRD: Neural Reflectance Decomposition from Image Collectionscited by 449
  8. 2019SAL: Sign Agnostic Learning of Shapes from Raw Datacited by 390
  9. 2021Extracting Triangular 3D Models, Materials, and Lighting From Imagescited by 366
  10. 2021PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relightingcited by 335
  11. 2021Neural-PIL: Neural Pre-Integrated Lighting for Reflectance Decompositioncited by 205
  12. 2020Neural Reflectance Fields for Appearance Acquisitioncited by 196
10 more
  1. 2021NeRFactor: Neural Factorization of Shape and Reflectance Under an Unknown Illuminationcited by 135
  2. 2022IRON: Inverse Rendering by Optimizing Neural SDFs and Materials from Photometric Imagescited by 111
  3. 2020Deep Reflectance Volumes: Relightable Reconstructions from Multi-View Photometric Imagescited by 98
  4. 2021Unified Shape and SVBRDF Recovery using Differentiable Monte Carlo Renderingcited by 71
  5. 2020Deep 3D Capture: Geometry and Reflectance from Sparse Multi-View Imagescited by 65
  6. 2020Multi-View Photometric Stereo: A Robust Solution and Benchmark Dataset for Spatially Varying Isotropic Materialscited by 56
  7. 2017Intrinsic3D: High-Quality 3D Reconstruction by Joint Appearance and Geometry Optimization with Spatially-Varying Lightingcited by 31
  8. 2021Neural Radiance Fields Approach to Deep Multi-View Photometric Stereocited by 25
  9. 2018A Differential Volumetric Approach to Multi-View Photometric Stereocited by 24
  10. 2021Multi-view 3D Reconstruction of a Texture-less Smooth Surface of Unknown Generic Reflectancecited by 17

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 10; 2025: 3; 2026: 4.

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  1. 2024Advances in 3D Generation: A Surveycited by 63
  2. 2024MaterialFusion: Enhancing Inverse Rendering with Material Diffusion Priorscited by 28
  3. 2023NeISF: Neural Incident Stokes Field for Geometry and Material Estimationcited by 25
  4. 2023High-Quality Facial Geometry and Appearance Capture at Homecited by 9
  5. 2024NeISF++: Neural Incident Stokes Field for Polarized Inverse Rendering of Conductors and Dielectricscited by 9
  6. 2024SuperMat: Physically Consistent PBR Material Estimation at Interactive Ratescited by 9
  7. 2024Sparse Views, Near Light: A Practical Paradigm for Uncalibrated Point-light Photometric Stereocited by 6
  8. 2024Differentiable Inverse Rendering with Interpretable Basis BRDFscited by 6
  9. 2023Differentiable Display Photometric Stereocited by 4
  10. 2024MetaGS: A Meta-Learned Gaussian-Phong Model for Out-of-Distribution 3D Scene Relightingcited by 4
  11. 2024GaNI: Global and Near Field Illumination Aware Neural Inverse Renderingcited by 2
  12. 2024PIR: Photometric Inverse Rendering with Shading Cues Modeling and Surface Reflectance Regularizationcited by 2
9 more
  1. 2025Mobile Robotic Multi-View Photometric Stereocited by 1
  2. 2023Shape from Shading for Robotic Manipulationcited by 0
  3. 2024Incorporating dense metric depth into neural 3D representations for view synthesis and relightingcited by 0
  4. 2025Neural Multi-View Self-Calibrated Photometric Stereo without Photometric Stereo Cuescited by 0
  5. 2025GLOW: Global Illumination-Aware Inverse Rendering of Indoor Scenes Captured with Dynamic Co-Located Light & Cameracited by 0
  6. 2026Rotated Lights for Consistent and Efficient 2D Gaussians Inverse Renderingcited by 0
  7. 2026EvalMVX: A Unified Benchmarking for Neural 3D Reconstruction under Diverse Multiview Setupscited by 0
  8. 2026Ambient-robust Inverse Rendering using Active RGB-NIR Imagingcited by 0
  9. 2026Casual Flash Lighting for Gaussian Splat Inverse Renderingcited by 0