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Spec-Gaussian: Anisotropic View-Dependent Appearance for 3D Gaussian Splatting

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arxiv 2402.15870 v2 pith:PHHNTO55 submitted 2024-02-24 cs.CV

classification cs.CV
keywords anisotropicgaussianrenderingappearanced-gsqualityspecularability
verification ladder T0 review T1 audit T2 compute T3 formal
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The recent advancements in 3D Gaussian splatting (3D-GS) have not only facilitated real-time rendering through modern GPU rasterization pipelines but have also attained state-of-the-art rendering quality. Nevertheless, despite its exceptional rendering quality and performance on standard datasets, 3D-GS frequently encounters difficulties in accurately modeling specular and anisotropic components. This issue stems from the limited ability of spherical harmonics (SH) to represent high-frequency information. To overcome this challenge, we introduce Spec-Gaussian, an approach that utilizes an anisotropic spherical Gaussian (ASG) appearance field instead of SH for modeling the view-dependent appearance of each 3D Gaussian. Additionally, we have developed a coarse-to-fine training strategy to improve learning efficiency and eliminate floaters caused by overfitting in real-world scenes. Our experimental results demonstrate that our method surpasses existing approaches in terms of rendering quality. Thanks to ASG, we have significantly improved the ability of 3D-GS to model scenes with specular and anisotropic components without increasing the number of 3D Gaussians. This improvement extends the applicability of 3D GS to handle intricate scenarios with specular and anisotropic surfaces. Project page is https://ingra14m.github.io/Spec-Gaussian-website/.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SuperGS: Consistent and Detailed 3D Super-Resolution Scene Reconstruction via Gaussian Splatting

    cs.CV 2025-05 conditional novelty 5.0 of 10

    SuperGS outperforms prior Gaussian-splatting methods on high-resolution novel view synthesis by combining a latent feature field, multi-view voting densification, and variational uncertainty weighting.

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