SA-3DGS：一种用于3D Gaussian Splatting的自适应压缩方法
SA-3DGS: A Self-Adaptive Compression Method for 3D Gaussian Splatting
AI总结：
本文提出SA-3DGS方法，通过学习重要性分数自动识别并剪枝不重要的Gaussian点，结合重要性感知聚类和码本修复模块压缩模型并恢复属性，实现高达66倍压缩且保持或提升渲染质量，同时改进了其他剪枝方法。
摘要：
3D Gaussian Splatting的最新进展提升了高效且高质量的全新视角合成。然而，表示场景需要大量Gaussian点，导致高存储需求并限制了实际部署。最新方法促进了Gaussian模型的压缩，但难以识别场景中真正不重要的Gaussian点，导致后续Gaussian剪枝、压缩质量和渲染性能下降。为解决此问题，我们提出了SA-3DGS，该方法在保持渲染质量的同时显著降低存储成本。SA-3DGS学习重要性分数以自动识别场景重建中最不重要的Gaussian，从而实现有效剪枝和冗余减少。接着，重要性感知聚类模块将Gaussian属性更准确地压缩到码本中，提升码本表达能力并减小模型尺寸。最后，码本修复模块利用上下文场景信息修复码本，从而恢复原始Gaussian点属性并缓解因信息损失导致的渲染质量退化。在多个基准数据集上的实验结果表明，我们的方法实现了高达66倍的压缩，同时保持甚至提升了渲染质量。所提出的Gaussian剪枝方法不仅适应于而且改进了其他基于剪枝的方法（如LightGaussian），展现出卓越的性能和强大的泛化能力。
英文摘要：
Recent advancements in 3D Gaussian Splatting have enhanced efficient and high-quality novel view synthesis. However, representing scenes requires a large number of Gaussian points, leading to high storage demands and limiting practical deployment. The latest methods facilitate the compression of Gaussian models but struggle to identify truly insignificant Gaussian points in the scene, leading to a decline in subsequent Gaussian pruning, compression quality, and rendering performance. To address this issue, we propose SA-3DGS, a method that significantly reduces storage costs while maintaining rendering quality. SA-3DGS learns an importance score to automatically identify the least significant Gaussians in scene reconstruction, thereby enabling effective pruning and redundancy reduction. Next, the importance-aware clustering module compresses Gaussians attributes more accurately into the codebook, improving the codebook's expressive capability while reducing model size. Finally, the codebook repair module leverages contextual scene information to repair the codebook, thereby recovering the original Gaussian point attributes and mitigating the degradation in rendering quality caused by information loss. Experimental results on several benchmark datasets show that our method achieves up to 66x compression while maintaining or even improving rendering quality. The proposed Gaussian pruning approach is not only adaptable to but also improves other pruning-based methods (e.g., LightGaussian), showcasing excellent performance and strong generalization ability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
