# R3GW: Relightable 3D Gaussians for Outdoor Scenes in the Wild

arXiv: https://arxiv.org/abs/2603.02801 · HTML: https://mlsanity.com/paper/2603.02801/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 43 references that are in the map.
- First posted on arXiv 3 Mar 2026.

## Builds on (most cited first, 21 of 21)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [TensoRF: Tensorial Radiance Fields](https://mlsanity.com/paper/2203.09517/) (2022)
- [2D Gaussian Splatting for Geometrically Accurate Radiance Fields](https://mlsanity.com/paper/2403.17888/) (2024)
- [Vision Transformer Adapter for Dense Predictions](https://mlsanity.com/paper/2205.08534/) (2022)
- [GaussianShader: 3D Gaussian Splatting with Shading Functions for Reflective Surfaces](https://mlsanity.com/paper/2311.17977/) (2023)
- [GS-IR: 3D Gaussian Splatting for Inverse Rendering](https://mlsanity.com/paper/2311.16473/) (2023)
- [WildGaussians: 3D Gaussian Splatting in the Wild](https://mlsanity.com/paper/2407.08447/) (2024)
- [NeuSG: Neural Implicit Surface Reconstruction with 3D Gaussian Splatting Guidance](https://mlsanity.com/paper/2312.00846/) (2023)
- [Gaussian in the Wild: 3D Gaussian Splatting for Unconstrained Image Collections](https://mlsanity.com/paper/2403.15704/) (2024)
- [NeRF for Outdoor Scene Relighting](https://mlsanity.com/paper/2112.05140/) (2021)
- [Neural Fields meet Explicit Geometric Representation for Inverse Rendering of Urban Scenes](https://mlsanity.com/paper/2304.03266/) (2023)
- [Self-supervised Outdoor Scene Relighting](https://mlsanity.com/paper/2107.03106/) (2021)
- [GI-GS: Global Illumination Decomposition on Gaussian Splatting for Inverse Rendering](https://mlsanity.com/paper/2410.02619/) (2024)
- [Splatfacto-W: A Nerfstudio Implementation of Gaussian Splatting for Unconstrained Photo Collections](https://mlsanity.com/paper/2407.12306/) (2024)
- [LumiGauss: Relightable Gaussian Splatting in the Wild](https://mlsanity.com/paper/2408.04474/) (2024)
- [The Sky's the Limit: Re-lightable Outdoor Scenes via a Sky-pixel Constrained Illumination Prior and Outside-In Visibility](https://mlsanity.com/paper/2311.16937/) (2023)
- [GaRe: Relightable 3D Gaussian Splatting for Outdoor Scenes from Unconstrained Photo Collections](https://mlsanity.com/paper/2507.20512/) (2025)
- [3D Gaussian Inverse Rendering with Approximated Global Illumination](https://mlsanity.com/paper/2504.01358/) (2025)
- [Improving Adaptive Density Control for 3D Gaussian Splatting](https://mlsanity.com/paper/2503.14274/) (2025)
- [Fast Sun-aligned Outdoor Scene Relighting based on TensoRF](https://mlsanity.com/paper/2311.03965/) (2023)

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

- [Relightable Gaussian Splatting for Virtual Production Using Image-Based Illumination](https://mlsanity.com/paper/2605.09024/) (2026)

## Abstract (arXiv)

3D Gaussian Splatting (3DGS) has established itself as a leading technique for 3D reconstruction and novel view synthesis of static scenes, achieving outstanding rendering quality and fast training. However, the method does not explicitly model the scene illumination, making it unsuitable for relighting tasks. Furthermore, 3DGS struggles to reconstruct scenes captured in the wild by unconstrained photo collections featuring changing lighting conditions. In this paper, we present R3GW, a novel method that learns a relightable 3DGS representation of an outdoor scene captured in the wild. Our approach separates the scene into a relightable foreground and a non-reflective background (the sky), using two distinct sets of Gaussians. R3GW models view-dependent lighting effects in the foreground reflections by combining Physically Based Rendering with the 3DGS scene representation in a varying illumination setting. We evaluate our method quantitatively and qualitatively on the NeRF-OSR dataset, offering state-of-the-art performance and enhanced support for physically-based relighting of unconstrained scenes. Our method synthesizes photorealistic novel views under arbitrary illumination conditions. Additionally, our representation of the sky mitigates depth reconstruction artifacts, improving rendering quality at the sky-foreground boundary