Liao, Lianjun, Zhang, Chunhui, Wu, Tong, Lv, Henglei, Deng, Bailin  ORCID: https://orcid.org/0000-0002-0158-7670 and Gao, Lin
2026.
ROS-GS: relightable outdoor scenes with Gaussian Splatting.
IEEE Transactions on Visualization and Computer Graphics
32
(9)
, pp. 7965-7981.
10.1109/tvcg.2026.3709027
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Abstract
Image data captured outdoors often exhibit unbounded scenes and unconstrained, varying lighting conditions, making it challenging to decompose them into geometry, reflectance, and illumination. Recent works have focused on achieving this decomposition using Neural Radiance Fields (NeRF) or the 3D Gaussian Splatting (3DGS) representation but remain hindered by two key limitations: the high computational overhead associated with neural networks of NeRF and the use of lowfrequency lighting representations, which often result in inefficient rendering and suboptimal relighting accuracy. We propose ROSGS, a two-stage pipeline designed to efficiently reconstruct relightable outdoor scenes using the Gaussian Splatting representation. By leveraging monocular normal priors, ROS-GS first reconstructs the scene's geometry with the compact 2D Gaussian Splatting (2DGS) representation, providing an efficient and accurate geometric foundation. Building upon this reconstructed geometry, ROS-GS then decomposes the scene's texture and lighting through a hybrid lighting model. This model effectively represents typical outdoor lighting by employing a spherical Gaussian function to capture the directional, high-frequency components of sunlight, while learning a radiance transfer function via Spherical Harmonic coefficients to model the remaining lowfrequency skylight comprehensively. Both quantitative metrics and qualitative comparisons demonstrate that ROS-GS achieves state-of-the-art performance in relighting outdoor scenes and highlight its ability to deliver superior relighting accuracy and rendering efficiency.
| Item Type: | Article |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics |
| Publisher: | Institute of Electrical and Electronics Engineers |
| ISSN: | 1077-2626 |
| Date of First Compliant Deposit: | 12 August 2026 |
| Last Modified: | 03 Sep 2026 10:00 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/187960 |
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