PEP-GS: Perceptually-Enhanced Precise Structured 3D Gaussians for View-Adaptive Rendering

• URL: http://arxiv.org/abs/2411.05731v1
• Date: Fri, 08 Nov 2024 17:42:02 GMT
• Title: PEP-GS: Perceptually-Enhanced Precise Structured 3D Gaussians for View-Adaptive Rendering
• Authors: Junxi Jin, Xiulai Li, Haiping Huang, Lianjun Liu, Yujie Sun,
• Abstract summary: Recent advances in structured 3D Gaussians for view-adaptive rendering have demonstrated promising results in neural scene representation. We present PEP-GS, a novel framework that enhances structured 3D Gaussians through three key innovations. Our comprehensive evaluation across multiple datasets indicates that, compared to the current state-of-the-art methods, these improvements are particularly evident in challenging scenarios.
• Score: 3.285531771049763
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in structured 3D Gaussians for view-adaptive rendering, particularly through methods like Scaffold-GS, have demonstrated promising results in neural scene representation. However, existing approaches still face challenges in perceptual consistency and precise view-dependent effects. We present PEP-GS, a novel framework that enhances structured 3D Gaussians through three key innovations: (1) a Local-Enhanced Multi-head Self-Attention (LEMSA) mechanism that replaces spherical harmonics for more accurate view-dependent color decoding, and (2) Kolmogorov-Arnold Networks (KAN) that optimize Gaussian opacity and covariance functions for enhanced interpretability and splatting precision. (3) a Neural Laplacian Pyramid Decomposition (NLPD) that improves perceptual similarity across views. Our comprehensive evaluation across multiple datasets indicates that, compared to the current state-of-the-art methods, these improvements are particularly evident in challenging scenarios such as view-dependent effects, specular reflections, fine-scale details and false geometry generation.

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