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

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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.

Jian Gao, Chun Gu, Youtian Lin, Zhihao Li, Hao Zhu, Xun Cao, Li Zhang, Yao Yao• 2023

Related benchmarks

TaskDatasetResultRank
Novel View SynthesisDTU
PSNR31.82
115
Novel View SynthesisRelightObj NVS (test)
PSNR (bridge)30.61
13
RelightingRelightObj (Unseen Relights)
PSNR (night)23.91
13
RelightingRelightObj (Seen Relights)
PSNR (bridge)22.32
13
Novel View SynthesisFIPT synthetic dataset
PSNR18.85
11
Shape ReconstructionRMVP3D
MAE (SHISA)16.69
11
Shape ReconstructionPISR
MAE (L-RABBIT)29.19
11
Roughness EstimationSynthetic dataset
MSE0.013
10
Novel View SynthesisTensoIR Synthetic
PSNR33.35
10
Novel View SynthesisMipNeRF
PSNR18.61
10
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