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Reflective Gaussian Splatting

About

Novel view synthesis has experienced significant advancements owing to increasingly capable NeRF- and 3DGS-based methods. However, reflective object reconstruction remains challenging, lacking a proper solution to achieve real-time, high-quality rendering while accommodating inter-reflection. To fill this gap, we introduce a Reflective Gaussian splatting (Ref-Gaussian) framework characterized with two components: (I) Physically based deferred rendering that empowers the rendering equation with pixel-level material properties via formulating split-sum approximation; (II) Gaussian-grounded inter-reflection that realizes the desired inter-reflection function within a Gaussian splatting paradigm for the first time. To enhance geometry modeling, we further introduce material-aware normal propagation and an initial per-Gaussian shading stage, along with 2D Gaussian primitives. Extensive experiments on standard datasets demonstrate that Ref-Gaussian surpasses existing approaches in terms of quantitative metrics, visual quality, and compute efficiency. Further, we show that our method serves as a unified solution for both reflective and non-reflective scenes, going beyond the previous alternatives focusing on only reflective scenes. Also, we illustrate that Ref-Gaussian supports more applications such as relighting and editing.

Yuxuan Yao, Zixuan Zeng, Chun Gu, Xiatian Zhu, Li Zhang• 2024

Related benchmarks

TaskDatasetResultRank
Novel View SynthesisRef-NeRF Real Scenes 50 (test)
PSNR24.61
8
Novel View SynthesisNeRF-Casting Shiny Scenes 51 (test)
PSNR30.17
8
Roughness EstimationSynRotLight Toy (test)
MSE0.007
7
Roughness EstimationSynRotLight Table (test)
MSE0.008
7
Roughness EstimationSynRotLight Hotdog (test)
MSE0.017
7
Albedo EstimationSynRotLight Hotdog (test)
PSNR17.282
7
Albedo EstimationSynRotLight Table (test)
PSNR15.96
7
Albedo EstimationSynRotLight Toy (test)
PSNR15.107
7
Albedo EstimationSynRotLight Counter (test)
PSNR15.978
7
Roughness EstimationSynRotLight Counter (test)
MSE0.056
7
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