# Neural Microfacet Fields for Inverse Rendering

arXiv: https://arxiv.org/abs/2303.17806 · HTML: https://mlsanity.com/paper/2303.17806/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 50 references that are in the map.
- First posted on arXiv 31 Mar 2023.

## Builds on (most cited first, 28 of 28)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields](https://mlsanity.com/paper/2103.13415/) (2021)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [TensoRF: Tensorial Radiance Fields](https://mlsanity.com/paper/2203.09517/) (2022)
- [Volume Rendering of Neural Implicit Surfaces](https://mlsanity.com/paper/2106.12052/) (2021)
- [NeRF++: Analyzing and Improving Neural Radiance Fields](https://mlsanity.com/paper/2010.07492/) (2020)
- [Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields](https://mlsanity.com/paper/2112.03907/) (2021)
- [NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis](https://mlsanity.com/paper/2012.03927/) (2020)
- [NeRD: Neural Reflectance Decomposition from Image Collections](https://mlsanity.com/paper/2012.03918/) (2020)
- [Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoning](https://mlsanity.com/paper/1904.01786/) (2019)
- [Extracting Triangular 3D Models, Materials, and Lighting From Images](https://mlsanity.com/paper/2111.12503/) (2021)
- [PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relighting](https://mlsanity.com/paper/2104.00674/) (2021)
- [Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer](https://mlsanity.com/paper/1908.01210/) (2019)
- [Neural-PIL: Neural Pre-Integrated Lighting for Reflectance Decomposition](https://mlsanity.com/paper/2110.14373/) (2021)
- [Neural Reflectance Fields for Appearance Acquisition](https://mlsanity.com/paper/2008.03824/) (2020)
- [Shape, Light, and Material Decomposition from Images using Monte Carlo Rendering and Denoising](https://mlsanity.com/paper/2206.03380/) (2022)
- [IRON: Inverse Rendering by Optimizing Neural SDFs and Materials from Photometric Images](https://mlsanity.com/paper/2204.02232/) (2022)
- [Object-Centric Neural Scene Rendering](https://mlsanity.com/paper/2012.08503/) (2020)
- [NerfAcc: A General NeRF Acceleration Toolbox](https://mlsanity.com/paper/2210.04847/) (2022)
- [Ref-NeuS: Ambiguity-Reduced Neural Implicit Surface Learning for Multi-View Reconstruction with Reflection](https://mlsanity.com/paper/2303.10840/) (2023)
- [Inverse Path Tracing for Joint Material and Lighting Estimation](https://mlsanity.com/paper/1903.07145/) (2019)
- [Neural Radiance Transfer Fields for Relightable Novel-view Synthesis with Global Illumination](https://mlsanity.com/paper/2207.13607/) (2022)
- [NeROIC: Neural Rendering of Objects from Online Image Collections](https://mlsanity.com/paper/2201.02533/) (2022)
- [Seeing the World in a Bag of Chips](https://mlsanity.com/paper/2001.04642/) (2020)
- [Neural apparent BRDF fields for multiview photometric stereo](https://mlsanity.com/paper/2207.06793/) (2022)

## Built on by (most cited first, 30 of 38)

- [GIR: 3D Gaussian Inverse Rendering for Relightable Scene Factorization](https://mlsanity.com/paper/2312.05133/) (2023)
- [DeferredGS: Decoupled and Editable Gaussian Splatting with Deferred Shading](https://mlsanity.com/paper/2404.09412/) (2024)
- [NeRF-Casting: Improved View-Dependent Appearance with Consistent Reflections](https://mlsanity.com/paper/2405.14871/) (2024)
- [SpecNeRF: Gaussian Directional Encoding for Specular Reflections](https://mlsanity.com/paper/2312.13102/) (2023)
- [IllumiNeRF: 3D Relighting Without Inverse Rendering](https://mlsanity.com/paper/2406.06527/) (2024)
- [IntrinsicAvatar: Physically Based Inverse Rendering of Dynamic Humans from Monocular Videos via Explicit Ray Tracing](https://mlsanity.com/paper/2312.05210/) (2023)
- [StochasticSplats: Stochastic Rasterization for Sorting-Free 3D Gaussian Splatting](https://mlsanity.com/paper/2503.24366/) (2025)
- [Neural Directional Encoding for Efficient and Accurate View-Dependent Appearance Modeling](https://mlsanity.com/paper/2405.14847/) (2024)
- [Generative Multiview Relighting for 3D Reconstruction under Extreme Illumination Variation](https://mlsanity.com/paper/2412.15211/) (2024)
- [Flash Cache: Reducing Bias in Radiance Cache Based Inverse Rendering](https://mlsanity.com/paper/2409.05867/) (2024)
- [Manifold Sampling for Differentiable Uncertainty in Radiance Fields](https://mlsanity.com/paper/2409.12661/) (2024)
- [Spherical Voronoi: Directional Appearance as a Differentiable Partition of the Sphere](https://mlsanity.com/paper/2512.14180/) (2025)
- [TraM-NeRF: Tracing Mirror and Near-Perfect Specular Reflections through Neural Radiance Fields](https://mlsanity.com/paper/2310.10650/) (2023)
- [Flying with Photons: Rendering Novel Views of Propagating Light](https://mlsanity.com/paper/2404.06493/) (2024)
- [ROGR: Relightable 3D Objects using Generative Relighting](https://mlsanity.com/paper/2510.03163/) (2025)
- [Interactive Rendering of Relightable and Animatable Gaussian Avatars](https://mlsanity.com/paper/2407.10707/) (2024)
- [Eye2Eye: A Simple Approach for Monocular-to-Stereo Video Synthesis](https://mlsanity.com/paper/2505.00135/) (2025)
- [Progressive Radiance Distillation for Inverse Rendering with Gaussian Splatting](https://mlsanity.com/paper/2408.07595/) (2024)
- [GR3EN: Generative Relighting for 3D Environments](https://mlsanity.com/paper/2601.16272/) (2026)
- [From Blobs to Spokes: High-Fidelity Surface Reconstruction via Oriented Gaussians](https://mlsanity.com/paper/2604.07337/) (2026)
- [Unveiling the Ambiguity in Neural Inverse Rendering: A Parameter Compensation Analysis](https://mlsanity.com/paper/2404.12819/) (2024)
- [RRM: Relightable assets using Radiance guided Material extraction](https://mlsanity.com/paper/2407.06397/) (2024)
- [RISE-SDF: a Relightable Information-Shared Signed Distance Field for Glossy Object Inverse Rendering](https://mlsanity.com/paper/2409.20140/) (2024)
- [RGS-DR: Deferred Reflections and Residual Shading in 2D Gaussian Splatting](https://mlsanity.com/paper/2504.18468/) (2025)
- [ESR-NeRF: Emissive Source Reconstruction Using LDR Multi-view Images](https://mlsanity.com/paper/2404.15707/) (2024)
- [PIR: Photometric Inverse Rendering with Shading Cues Modeling and Surface Reflectance Regularization](https://mlsanity.com/paper/2408.06828/) (2024)
- [NeRFs are Mirror Detectors: Using Structural Similarity for Multi-View Mirror Scene Reconstruction with 3D Surface Primitives](https://mlsanity.com/paper/2501.04074/) (2025)
- [ROSA: Reconstructing Object Shape and Appearance Textures by Adaptive Detail Transfer](https://mlsanity.com/paper/2501.18595/) (2025)
- [Twinner: Shining Light on Digital Twins in a Few Snaps](https://mlsanity.com/paper/2503.08382/) (2025)
- [RT-Splatting: Joint Reflection-Transmission Modeling with Gaussian Splatting](https://mlsanity.com/paper/2605.18263/) (2026)

## Abstract (arXiv)

We present Neural Microfacet Fields, a method for recovering materials, geometry, and environment illumination from images of a scene. Our method uses a microfacet reflectance model within a volumetric setting by treating each sample along the ray as a (potentially non-opaque) surface. Using surface-based Monte Carlo rendering in a volumetric setting enables our method to perform inverse rendering efficiently by combining decades of research in surface-based light transport with recent advances in volume rendering for view synthesis. Our approach outperforms prior work in inverse rendering, capturing high fidelity geometry and high frequency illumination details; its novel view synthesis results are on par with state-of-the-art methods that do not recover illumination or materials.