BiGS: Bidirectional Primitives for Relightable 3D Gaussian Splatting

We present BiGS, an image-based novel view synthesis technique designed to model and render 3D objects with surface and volumetric materials under dynamic illumination, achieving real-time relighting of 3D objects with the rasterization algorithm of Gaussian Splatting. Our method represents light- and view-dependent scattering via bidirectional spherical harmonics that does not use a specific surface normal-related reflectance function, making it more compatible with volumetric representations like Gaussian splatting, where the normals are undefined. We demonstrate our method by reconstructing and rendering objects with complex materials on synthetic and captured One-Light-At-a-Time (OLAT) datasets, showcasing various photorealistic appearances our method captures and the real-time performance.

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Publication Details

Published
2025-03-25
DOI
https://doi.org/10.1109/3dv66043.2025.00099
Citations
1
Primary Topic
3D Surveying and Cultural Heritage
Type
article
Field-Weighted Citation Impact
2.35
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BiGS: Bidirectional Primitives for Relightable 3D Gaussian Splatting

Bernd Bickel, Zhenyuan Liu, Ran Zhang, Yu Guo et al.
1 citations
3D Surveying and Cultural Heritage
2.35
article

BiGS: Bidirectional Primitives for Relightable 3D Gaussian Splatting

Bernd Bickel, Zhenyuan Liu, Ran Zhang, Yu Guo, Xinyuan Li
article en
1 citations

Abstract

We present BiGS, an image-based novel view synthesis technique designed to model and render 3D objects with surface and volumetric materials under dynamic illumination, achieving real-time relighting of 3D objects with the rasterization algorithm of Gaussian Splatting. Our method represents light- and view-dependent scattering via bidirectional spherical harmonics that does not use a specific surface normal-related reflectance function, making it more compatible with volumetric representations like Gaussian splatting, where the normals are undefined. We demonstrate our method by reconstructing and rendering objects with complex materials on synthetic and captured One-Light-At-a-Time (OLAT) datasets, showcasing various photorealistic appearances our method captures and the real-time performance.

George Mason University (US), Tencent (China) (CN), ETH Zurich (CH)
Openalex Percentile: Top 11%
3D Surveying and Cultural Heritage
2.35
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