Relightable 3D Gaussians: Realistic Point Cloud Relighting with BRDF Decomposition and Ray Tracing

Jian Gao, Chun Gu, Youtian Lin, Zhihao Li, Hao Zhu +3 more
11/27/2023
cs.CVcs.GR

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Abstract

In this paper, we present a novel differentiable point-based rendering framework to achieve photo-realistic relighting. To make the reconstructed scene relightable, we enhance vanilla 3D Gaussians by associating extra properties, including normal vectors, BRDF parameters, and incident lighting from various directions. From a collection of multi-view images, the 3D scene is optimized through 3D Gaussian Splatting while BRDF and lighting are decomposed by physically based differentiable rendering. To produce plausible shadow effects in photo-realistic relighting, we introduce an innovative point-based ray tracing with the bounding volume hierarchies for efficient visibility pre-computation. Extensive experiments demonstrate our improved BRDF estimation, novel view synthesis and relighting results compared to state-of-the-art approaches. The proposed framework showcases the potential to revolutionize the mesh-based graphics pipeline with a point-based pipeline enabling editing, tracing, and relighting.

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Cite this paper

@article{gao2023relightable,
  title  = {Relightable 3D Gaussians: Realistic Point Cloud Relighting with BRDF Decomposition and Ray Tracing},
  author = {Jian Gao and Chun Gu and Youtian Lin and Zhihao Li and Hao Zhu and Xun Cao and Li Zhang and Yao Yao},
  year   = {2023},
  eprint = {2311.16043},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2311.16043v2}
}

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