# MLI-NeRF: Multi-Light Intrinsic-Aware Neural Radiance Fields

arXiv: https://arxiv.org/abs/2411.17235 · HTML: https://mlsanity.com/paper/2411.17235/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 51 references that are in the map.
- First posted on arXiv 26 Nov 2024.

## Builds on (most cited first, 30 of 36)

- [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)
- [Implicit Geometric Regularization for Learning Shapes](https://mlsanity.com/paper/2002.10099/) (2020)
- [NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis](https://mlsanity.com/paper/2012.03927/) (2020)
- [Neuralangelo: High-Fidelity Neural Surface Reconstruction](https://mlsanity.com/paper/2306.03092/) (2023)
- [NeRD: Neural Reflectance Decomposition from Image Collections](https://mlsanity.com/paper/2012.03918/) (2020)
- [PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relighting](https://mlsanity.com/paper/2104.00674/) (2021)
- [Modeling Indirect Illumination for Inverse Rendering](https://mlsanity.com/paper/2204.06837/) (2022)
- [TensoIR: Tensorial Inverse Rendering](https://mlsanity.com/paper/2304.12461/) (2023)
- [Single Image Portrait Relighting](https://mlsanity.com/paper/1905.00824/) (2019)
- [NeRFactor: Neural Factorization of Shape and Reflectance Under an Unknown Illumination](https://mlsanity.com/paper/2106.01970/) (2021)
- [NeRF for Outdoor Scene Relighting](https://mlsanity.com/paper/2112.05140/) (2021)
- [Learning Physics-guided Face Relighting under Directional Light](https://mlsanity.com/paper/1906.03355/) (2019)
- [SAMURAI: Shape And Material from Unconstrained Real-world Arbitrary Image collections](https://mlsanity.com/paper/2205.15768/) (2022)
- [CGIntrinsics: Better Intrinsic Image Decomposition through Physically-Based Rendering](https://mlsanity.com/paper/1808.08601/) (2018)
- [Learning Intrinsic Image Decomposition from Watching the World](https://mlsanity.com/paper/1804.00582/) (2018)
- [LightIt: Illumination Modeling and Control for Diffusion Models](https://mlsanity.com/paper/2403.10615/) (2024)
- [Unsupervised Learning for Intrinsic Image Decomposition from a Single Image](https://mlsanity.com/paper/1911.09930/) (2019)
- [Intrinsic Image Decomposition via Ordinal Shading](https://mlsanity.com/paper/2311.12792/) (2023)
- [IntrinsicNeRF: Learning Intrinsic Neural Radiance Fields for Editable Novel View Synthesis](https://mlsanity.com/paper/2210.00647/) (2022)
- [A Dataset of Multi-Illumination Images in the Wild](https://mlsanity.com/paper/1910.08131/) (2019)
- [Face Relighting with Geometrically Consistent Shadows](https://mlsanity.com/paper/2203.16681/) (2022)
- [ReLight My NeRF: A Dataset for Novel View Synthesis and Relighting of Real World Objects](https://mlsanity.com/paper/2304.10448/) (2023)
- [Relighting Neural Radiance Fields with Shadow and Highlight Hints](https://mlsanity.com/paper/2308.13404/) (2023)
- [A Survey on Intrinsic Images: Delving Deep Into Lambert and Beyond](https://mlsanity.com/paper/2112.03842/) (2021)
- [OpenIllumination: A Multi-Illumination Dataset for Inverse Rendering Evaluation on Real Objects](https://mlsanity.com/paper/2309.07921/) (2023)
- [PIE-Net: Photometric Invariant Edge Guided Network for Intrinsic Image Decomposition](https://mlsanity.com/paper/2203.16670/) (2022)
- [NTIRE 2021 Depth Guided Image Relighting Challenge](https://mlsanity.com/paper/2104.13365/) (2021)
- [Unsupervised Deep Single-Image Intrinsic Decomposition using Illumination-Varying Image Sequences](https://mlsanity.com/paper/1803.00805/) (2018)
- [SIRe-IR: Inverse Rendering for BRDF Reconstruction with Shadow and Illumination Removal in High-Illuminance Scenes](https://mlsanity.com/paper/2310.13030/) (2023)

## Built on by (most cited first, 1 of 1)

- [Cast and Attached Shadow Detection via Iterative Light and Geometry Reasoning](https://mlsanity.com/paper/2512.06179/) (2025)

## Abstract (arXiv)

Current methods for extracting intrinsic image components, such as reflectance and shading, primarily rely on statistical priors. These methods focus mainly on simple synthetic scenes and isolated objects and struggle to perform well on challenging real-world data. To address this issue, we propose MLI-NeRF, which integrates Multiple Light information in Intrinsic-aware Neural Radiance Fields. By leveraging scene information provided by different light source positions complementing the multi-view information, we generate pseudo-label images for reflectance and shading to guide intrinsic image decomposition without the need for ground truth data. Our method introduces straightforward supervision for intrinsic component separation and ensures robustness across diverse scene types. We validate our approach on both synthetic and real-world datasets, outperforming existing state-of-the-art methods. Additionally, we demonstrate its applicability to various image editing tasks. The code and data are publicly available.