无需重训练，秒级实现96.4%压缩率的3D高斯点云轻量化方法
FlexGaussian: Flexible and Cost-Effective Training-Free Compression for 3D Gaussian Splatting
- 混合精度量化+属性判别剪枝，无须重训练
- 最高96.4%压缩率，PSNR下降不足1dB
- 适配移动端部署，比主流方法快10-100倍
3D高斯点云因其高保真与高效渲染成为复杂场景表示的主流技术，但大规模模型对内存和计算资源要求高，尤其在移动与边缘设备上。现有压缩方法虽能减少参数量，但通常需大量重训练或微调，难以适应不同压缩需求。本文提出FlexGaussian，一种无需训练的灵活轻量化方法，结合混合精度量化与属性判别剪枝，可快速适配多样压缩目标。实验表明，该方法在不到一秒内实现最高96.4%压缩率，保持渲染质量（PSNR下降<1 dB），且可在移动设备部署。其速度较当前最优无训练方法快1.7-2.1倍，比需训练的方法快10-100倍。代码即将开源。
原文摘要 · Abstract (English)
3D Gaussian splatting has become a prominent technique for representing and rendering complex 3D scenes, due to its high fidelity and speed advantages. However, the growing demand for large-scale models calls for effective compression to reduce memory and computation costs, especially on mobile and edge devices with limited resources. Existing compression methods effectively reduce 3D Gaussian parameters but often require extensive retraining or fine-tuning, lacking flexibility under varying compression constraints. In this paper, we introduce FlexGaussian, a flexible and cost-effective method that combines mixed-precision quantization with attribute-discriminative pruning for training-free 3D Gaussian compression. FlexGaussian eliminates the need for retraining and adapts easily to diverse compression targets. Evaluation results show that FlexGaussian achieves up to 96.4% compression while maintaining high rendering quality (<1 dB drop in PSNR), and is deployable on mobile devices. FlexGaussian delivers high compression ratios within seconds, being 1.7-2.1x faster than state-of-the-art training-free methods and 10-100x faster than training-involved approaches. The code is being prepared and will be released soon at: https://github.com/Supercomputing-System-AI-Lab/FlexGaussian
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