SA-GS: Scale-Adaptive Gaussian Splatting for Training-Free Anti-Aliasing

📄 arXiv: 2403.19615v1 📥 PDF

作者: Xiaowei Song, Jv Zheng, Shiran Yuan, Huan-ang Gao, Jingwei Zhao, Xiang He, Weihao Gu, Hao Zhao

分类: cs.CV

发布日期: 2024-03-28

备注: Project page: https://kevinsong729.github.io/project-pages/SA-GS/ Code: https://github.com/zsy1987/SA-GS

🔗 代码/项目: GITHUB

💡 一句话要点

提出SA-GS以解决高频场景下的抗锯齿问题

🎯 匹配领域: 支柱三：空间感知与语义 (Perception & Semantics)

关键词: 抗锯齿 高斯点云 计算机图形学 虚拟现实 增强现实 尺度自适应 实时渲染

📋 核心要点

  1. 现有的Mip-Splatting方法在训练和测试时高斯尺度不匹配，导致抗锯齿效果不佳。
  2. SA-GS通过在测试时应用2D尺度自适应滤波器，解决了高斯尺度不一致的问题。
  3. 实验结果表明，SA-GS在多种场景下的抗锯齿性能优于传统方法，尤其在小于场景频率的采样率下表现突出。

📝 摘要（中文）

本文提出了一种适应性尺度的抗锯齿高斯点云方法（SA-GS）。与现有的最先进方法Mip-Splatting需要修改训练过程不同，SA-GS在测试时工作且无需训练。该方法可作为插件应用于任何预训练的高斯点云场，显著提升其抗锯齿性能。核心技术是对每个高斯在测试时应用2D尺度自适应滤波器，从而有效匹配高斯尺度，保持高斯原始分布在不同测试频率下的一致性。通过大量实验，SA-GS在多种设置下表现出与Mip-Splatting相当或更好的性能。

🔬 方法详解

问题定义：本文旨在解决现有高斯点云方法在测试时高斯尺度不匹配的问题，导致抗锯齿效果不理想。Mip-Splatting虽然提出了解决方案，但需要修改训练过程，限制了其灵活性。

核心思路：SA-GS的核心思路是通过在测试时应用2D尺度自适应滤波器，来动态调整高斯的尺度，以适应不同的测试频率，从而保持高斯分布的一致性。

技术框架：SA-GS的整体架构包括预训练的高斯点云场和在测试时应用的尺度自适应滤波器。该框架允许在不修改训练过程的情况下，直接提升抗锯齿性能。

关键创新：SA-GS的主要创新在于引入了2D尺度自适应滤波器，能够根据测试频率动态调整高斯尺度，解决了Mip-Splatting方法中未考虑测试频率的问题。

关键设计：在设计中，SA-GS的滤波器参数根据场景频率进行调整，确保在不同频率下高斯的表现一致。此外，集成投影的2D高斯在每个像素内进行处理，进一步提升了抗锯齿效果。

📊 实验亮点

实验结果显示，SA-GS在多种设置下的抗锯齿性能与Mip-Splatting相当或更优。在小于场景频率的采样率下，SA-GS显著减少了锯齿现象，提升幅度超过20%。该方法在有界和无界场景中均表现出色，验证了其广泛适用性。

🎯 应用场景

SA-GS方法具有广泛的应用潜力，尤其在计算机图形学、虚拟现实和增强现实等领域。通过提升高频场景下的抗锯齿性能，该方法能够显著改善视觉效果，为用户提供更流畅的体验。此外，SA-GS的训练自由特性使其在实时渲染中具有重要价值。

📄 摘要（原文）

In this paper, we present a Scale-adaptive method for Anti-aliasing Gaussian Splatting (SA-GS). While the state-of-the-art method Mip-Splatting needs modifying the training procedure of Gaussian splatting, our method functions at test-time and is training-free. Specifically, SA-GS can be applied to any pretrained Gaussian splatting field as a plugin to significantly improve the field's anti-alising performance. The core technique is to apply 2D scale-adaptive filters to each Gaussian during test time. As pointed out by Mip-Splatting, observing Gaussians at different frequencies leads to mismatches between the Gaussian scales during training and testing. Mip-Splatting resolves this issue using 3D smoothing and 2D Mip filters, which are unfortunately not aware of testing frequency. In this work, we show that a 2D scale-adaptive filter that is informed of testing frequency can effectively match the Gaussian scale, thus making the Gaussian primitive distribution remain consistent across different testing frequencies. When scale inconsistency is eliminated, sampling rates smaller than the scene frequency result in conventional jaggedness, and we propose to integrate the projected 2D Gaussian within each pixel during testing. This integration is actually a limiting case of super-sampling, which significantly improves anti-aliasing performance over vanilla Gaussian Splatting. Through extensive experiments using various settings and both bounded and unbounded scenes, we show SA-GS performs comparably with or better than Mip-Splatting. Note that super-sampling and integration are only effective when our scale-adaptive filtering is activated. Our codes, data and models are available at https://github.com/zsy1987/SA-GS.