One-light-at-a-time (OLAT) images sample a broader range of object appearance changes than images captured under constant lighting and are superior as input to object relighting. Although existing methods have produced reasonable relighting quality using OLAT images, they utilize surface-like representations, limiting their capacity to model volumetric objects, such as furs. Besides, their rendering process is time-consuming and still far from being used in real-time applications. To address these issues, we propose OLAT Gaussians to build relightable representations of objects from multi-view OLAT images. We build our pipeline on 3D Gaussian Splatting (3DGS), which achieves real-time high-quality rendering. To augment 3DGS with relighting capability, we assign each Gaussian a learnable feature vector, serving as an index to query the objects' appearance field. Specifically, we decompose the appearance field into an incident illumination function and a scattering function. The former accounts for light transmittance and fore-shortening effects, while the latter represents the object's material properties to scatter light. Rather than using an off-the-shelf physically-based parametric rendering formulation, we model both functions using multi-layer perceptrons (MLPs). This makes our method suitable for various objects, e.g., opaque surfaces, semi-transparent volumes, furs, fabrics, etc. Given a camera view and a point light position, we compute each Gaussian's color as the product of the light intensity, the incident illumination value, and the scattering value, and then render the target image through the 3DGS rasterizer. To enhance rendering quality, we further utilize a proxy mesh to provide OLAT Gaussians with normals to improve highlights and visibility cues to improve shadows. Extensive experiments demonstrate that our method produces state-of-the-art rendering quality with significantly more details in texture-rich areas than previous methods. Our method also achieves real-time rendering, allowing users to interactively modify camera views and point light positions to get immediate rendering results, which are not available from the offline rendering of previous methods.
Trained models of OLAT Gaussians can be directly extended to environmental relighting.
Environment Map
Relighting
Environment Map
Relighting
Environment Map
Relighting
Environment Map
Relighting
Rendering is decomposed into incident illumination an scattering.
Incident Illumination
Scattering
Rendering
Incident Illumination
Scattering
Rendering
OLAT Gaussians render at ~30 FPS on a single NVIDIA 2080ti GPU and allow real-time interactions.
There's a lot of excellent work that was introduced around the same time as ours.
GS^3: Efficient Relighting with Triple Gaussian Splatting
A Diffusion Approach to Radiance Field Relighting using Multi-Illumination Synthesis
@inproceedings{kuang2024olat,
title={OLAT Gaussians for Generic Relightable Appearance Acquisition},
author={Kuang, Zhiyi and Yang, Yanchao and Dong, Siyan and Ma, Jiayue and Fu, Hongbo and Zheng, Youyi},
booktitle={SIGGRAPH Asia 2024 Conference Papers},
pages={1--11},
year={2024}
}