# PRTGS: Precomputed Radiance Transfer of Gaussian Splats for Real-Time High-Quality Relighting

arXiv: https://arxiv.org/abs/2408.03538 · HTML: https://mlsanity.com/paper/2408.03538/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 42 references that are in the map.
- First posted on arXiv 7 Aug 2024.

## Builds on (most cited first, 19 of 19)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [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)
- [D-NeRF: Neural Radiance Fields for Dynamic Scenes](https://mlsanity.com/paper/2011.13961/) (2020)
- [Nerfies: Deformable Neural Radiance Fields](https://mlsanity.com/paper/2011.12948/) (2020)
- [Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes](https://mlsanity.com/paper/2011.13084/) (2020)
- [NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis](https://mlsanity.com/paper/2012.03927/) (2020)
- [Real-time Photorealistic Dynamic Scene Representation and Rendering with 4D Gaussian Splatting](https://mlsanity.com/paper/2310.10642/) (2023)
- [Extracting Triangular 3D Models, Materials, and Lighting From Images](https://mlsanity.com/paper/2111.12503/) (2021)
- [GaussianEditor: Swift and Controllable 3D Editing with Gaussian Splatting](https://mlsanity.com/paper/2311.14521/) (2023)
- [GS-IR: 3D Gaussian Splatting for Inverse Rendering](https://mlsanity.com/paper/2311.16473/) (2023)
- [Modeling Indirect Illumination for Inverse Rendering](https://mlsanity.com/paper/2204.06837/) (2022)
- [NeRF-DS: Neural Radiance Fields for Dynamic Specular Objects](https://mlsanity.com/paper/2303.14435/) (2023)
- [NeILF: Neural Incident Light Field for Physically-based Material Estimation](https://mlsanity.com/paper/2203.07182/) (2022)
- [InverseRenderNet: Learning single image inverse rendering](https://mlsanity.com/paper/1811.12328/) (2018)
- [NeILF++: Inter-Reflectable Light Fields for Geometry and Material Estimation](https://mlsanity.com/paper/2303.17147/) (2023)
- [Inverse Path Tracing for Joint Material and Lighting Estimation](https://mlsanity.com/paper/1903.07145/) (2019)
- [Single-image Full-body Human Relighting](https://mlsanity.com/paper/2107.07259/) (2021)

## Built on by (most cited first, 18 of 18)

- [RTR-GS: 3D Gaussian Splatting for Inverse Rendering with Radiance Transfer and Reflection](https://mlsanity.com/paper/2507.07733/) (2025)
- [3D Gaussian Inverse Rendering with Approximated Global Illumination](https://mlsanity.com/paper/2504.01358/) (2025)
- [MVInverse: Feed-forward Multi-view Inverse Rendering in Seconds](https://mlsanity.com/paper/2512.21003/) (2025)
- [Splats in Splats: Robust and Effective 3D Steganography towards Gaussian Splatting](https://mlsanity.com/paper/2412.03121/) (2024)
- [Real-time Global Illumination for Dynamic 3D Gaussian Scenes](https://mlsanity.com/paper/2503.17897/) (2025)
- [Splatography: Sparse multi-view dynamic Gaussian Splatting for filmmaking challenges](https://mlsanity.com/paper/2511.05152/) (2025)
- [Can Protective Watermarking Safeguard the Copyright of 3D Gaussian Splatting?](https://mlsanity.com/paper/2511.22262/) (2025)
- [Radiometrically Consistent Gaussian Surfels for Inverse Rendering](https://mlsanity.com/paper/2603.01491/) (2026)
- [RT-Splatting: Joint Reflection-Transmission Modeling with Gaussian Splatting](https://mlsanity.com/paper/2605.18263/) (2026)
- [TranSplat: Instant Object Relighting in Gaussian Splatting via Spherical Harmonic Radiance Transfer](https://mlsanity.com/paper/2503.22676/) (2025)
- [Image-Conditioned 3D Gaussian Splat Quantization](https://mlsanity.com/paper/2508.15372/) (2025)
- [UTrice: Unifying Primitives in Differentiable Ray Tracing and Rasterization via Triangles for Particle-Based 3D Scenes](https://mlsanity.com/paper/2512.04421/) (2025)
- [Rotated Lights for Consistent and Efficient 2D Gaussians Inverse Rendering](https://mlsanity.com/paper/2602.08724/) (2026)
- [Splats in Splats++: Robust and Generalizable 3D Gaussian Splatting Steganography](https://mlsanity.com/paper/2604.15862/) (2026)
- [Relightable Gaussian Splatting for Virtual Production Using Image-Based Illumination](https://mlsanity.com/paper/2605.09024/) (2026)
- [DR-GS: Physically-Based Deformable and Relightable 2D Gaussians](https://mlsanity.com/paper/2606.29379/) (2026)
- [LiteTex-GS: Fast and Lightweight Texturing for Gaussian Splatting](https://mlsanity.com/paper/2609.23380/) (2026)
- [VolS-GS: Relightable Gaussian Splatting with Volumetric Subsurface Scattering](https://mlsanity.com/paper/2610.04007/) (2026)

## Abstract (arXiv)

We proposed Precomputed RadianceTransfer of GaussianSplats (PRTGS), a real-time high-quality relighting method for Gaussian splats in low-frequency lighting environments that captures soft shadows and interreflections by precomputing 3D Gaussian splats' radiance transfer. Existing studies have demonstrated that 3D Gaussian splatting (3DGS) outperforms neural fields' efficiency for dynamic lighting scenarios. However, the current relighting method based on 3DGS still struggles to compute high-quality shadow and indirect illumination in real time for dynamic light, leading to unrealistic rendering results. We solve this problem by precomputing the expensive transport simulations required for complex transfer functions like shadowing, the resulting transfer functions are represented as dense sets of vectors or matrices for every Gaussian splat. We introduce distinct precomputing methods tailored for training and rendering stages, along with unique ray tracing and indirect lighting precomputation techniques for 3D Gaussian splats to accelerate training speed and compute accurate indirect lighting related to environment light. Experimental analyses demonstrate that our approach achieves state-of-the-art visual quality while maintaining competitive training times and allows high-quality real-time (30+ fps) relighting for dynamic light and relatively complex scenes at 1080p resolution.