用最优传输理论压缩3D高斯点云，仅用10%点数保持画质
Gaussian Herding across Pens: An Optimal Transport Perspective on Global Gaussian Reduction for 3DGS
- 将3DGS压缩建模为全局高斯混合缩减问题，基于KD树分区最小化传输散度
- 仅用10%的高斯点数，渲染质量损失极小（PSNR/SSIM/LPIPS几乎不变）
- 适用于任意阶段的3DGS流程，对轻量级神经渲染有普适价值
3D高斯点阵（3DGS）已成为辐射场渲染的强大工具，但通常需要数百万冗余的高斯原语，严重占用内存和渲染资源。现有压缩方法依赖启发式重要性评分剪枝，缺乏全局保真保证。为此，我们提出一种新的最优传输视角，将3DGS压缩视为全局高斯混合物的缩减问题。首先，在KD树划分上最小化复合传输散度，生成紧凑几何表示；随后通过微调颜色与不透明度属性，实现外观与几何解耦，使用更少高斯原语。在基准数据集上的实验表明，本方法（i）仅使用10%的高斯原语，渲染质量损失可忽略（PSNR、SSIM、LPIPS接近原始3DGS）；（ii）持续优于当前最优的3DGS压缩技术。值得注意的是，该方法可应用于任意阶段的原始或加速3DGS流水线，为轻量级神经渲染提供高效且无侵入的路径。代码已公开于https://github.com/DrunkenPoet/GHAP
原文摘要 · Abstract (English)
3D Gaussian Splatting (3DGS) has emerged as a powerful technique for radiance field rendering, but it typically requires millions of redundant Gaussian primitives, overwhelming memory and rendering budgets. Existing compaction approaches address this by pruning Gaussians based on heuristic importance scores, without global fidelity guarantee. To bridge this gap, we propose a novel optimal transport perspective that casts 3DGS compaction as global Gaussian mixture reduction. Specifically, we first minimize the composite transport divergence over a KD-tree partition to produce a compact geometric representation, and then decouple appearance from geometry by fine-tuning color and opacity attributes with far fewer Gaussian primitives. Experiments on benchmark datasets show that our method (i) yields negligible loss in rendering quality (PSNR, SSIM, LPIPS) compared to vanilla 3DGS with only 10% Gaussians; and (ii) consistently outperforms state-of-the-art 3DGS compaction techniques. Notably, our method is applicable to any stage of vanilla or accelerated 3DGS pipelines, providing an efficient and agnostic pathway to lightweight neural rendering. The code is publicly available at https://github.com/DrunkenPoet/GHAP
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。