# A Neural Rendering Framework for Free-Viewpoint Relighting

arXiv: https://arxiv.org/abs/1911.11530 · HTML: https://mlsanity.com/paper/1911.11530/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2019.

## Built on by (most cited first, 27 of 27)

- [MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View Stereo](https://mlsanity.com/paper/2103.15595/) (2021)
- [Spacetime Gaussian Feature Splatting for Real-Time Dynamic View Synthesis](https://mlsanity.com/paper/2312.16812/) (2023)
- [NeRFactor: Neural Factorization of Shape and Reflectance Under an Unknown Illumination](https://mlsanity.com/paper/2106.01970/) (2021)
- [HDR-NeRF: High Dynamic Range Neural Radiance Fields](https://mlsanity.com/paper/2111.14451/) (2021)
- [NeuTex: Neural Texture Mapping for Volumetric Neural Rendering](https://mlsanity.com/paper/2103.00762/) (2021)
- [NeuRBF: A Neural Fields Representation with Adaptive Radial Basis Functions](https://mlsanity.com/paper/2309.15426/) (2023)
- [Free-viewpoint Indoor Neural Relighting from Multi-view Stereo](https://mlsanity.com/paper/2106.13299/) (2021)
- [Neural Radiance Transfer Fields for Relightable Novel-view Synthesis with Global Illumination](https://mlsanity.com/paper/2207.13607/) (2022)
- [NeMF: Inverse Volume Rendering with Neural Microflake Field](https://mlsanity.com/paper/2304.00782/) (2023)
- [Sort-free Gaussian Splatting via Weighted Sum Rendering](https://mlsanity.com/paper/2410.18931/) (2024)
- [GMLight: Lighting Estimation via Geometric Distribution Approximation](https://mlsanity.com/paper/2102.10244/) (2021)
- [Convolutional Neural Opacity Radiance Fields](https://mlsanity.com/paper/2104.01772/) (2021)
- [NeuFace: Realistic 3D Neural Face Rendering from Multi-view Images](https://mlsanity.com/paper/2303.14092/) (2023)
- [MirrorNeRF: One-shot Neural Portrait Radiance Field from Multi-mirror Catadioptric Imaging](https://mlsanity.com/paper/2104.02607/) (2021)
- [Semantic-aware Occlusion Filtering Neural Radiance Fields in the Wild](https://mlsanity.com/paper/2303.03966/) (2023)
- [Perceptual Assessment and Optimization of HDR Image Rendering](https://mlsanity.com/paper/2310.12877/) (2023)
- [Controllable Data Augmentation Through Deep Relighting](https://mlsanity.com/paper/2110.13996/) (2021)
- [Light Sampling Field and BRDF Representation for Physically-based Neural Rendering](https://mlsanity.com/paper/2304.05472/) (2023)
- [SFDM: Robust Decomposition of Geometry and Reflectance for Realistic Face Rendering from Sparse-view Images](https://mlsanity.com/paper/2312.06085/) (2023)
- [Data Augmentation for Object Detection via Differentiable Neural Rendering](https://mlsanity.com/paper/2103.02852/) (2021)
- [Joint Learning of Portrait Intrinsic Decomposition and Relighting](https://mlsanity.com/paper/2106.15305/) (2021)
- [Designing An Illumination-Aware Network for Deep Image Relighting](https://mlsanity.com/paper/2207.10582/) (2022)
- [Appearance Editing with Free-viewpoint Neural Rendering](https://mlsanity.com/paper/2110.07674/) (2021)
- [MixLight: Borrowing the Best of both Spherical Harmonics and Gaussian Models](https://mlsanity.com/paper/2404.12768/) (2024)
- [NeuGen: Amplifying the 'Neural' in Neural Radiance Fields for Domain Generalization](https://mlsanity.com/paper/2505.06894/) (2025)
- [3D Gaussian Splatting for Efficient Retrospective Dynamic Scene Novel View Synthesis with a Standardized Benchmark](https://mlsanity.com/paper/2605.12437/) (2026)
- [Mobile-4DGS: Unified Static-Dynamic Real-time Mobile Gaussian Splatting](https://mlsanity.com/paper/2610.05289/) (2026)

## Abstract (arXiv)

We present a novel Relightable Neural Renderer (RNR) for simultaneous view synthesis and relighting using multi-view image inputs. Existing neural rendering (NR) does not explicitly model the physical rendering process and hence has limited capabilities on relighting. RNR instead models image formation in terms of environment lighting, object intrinsic attributes, and light transport function (LTF), each corresponding to a learnable component. In particular, the incorporation of a physically based rendering process not only enables relighting but also improves the quality of view synthesis. Comprehensive experiments on synthetic and real data show that RNR provides a practical and effective solution for conducting free-viewpoint relighting.