通过时空压缩提升动态3D高斯点云的实时渲染效率
Temporally Compressed 3D Gaussian Splatting for Dynamic Scenes
- 按时间相关性筛选并动态量化高斯点参数，减少冗余
- 实现最高67倍压缩，视觉质量几乎无损失
- 适合AR/VR、低功耗设备上的动态场景实时渲染
近期高保真动态场景重建利用动态3D高斯和4D高斯点绘技术实现了逼真的场景表示。然而，为使这些方法适用于AR/VR、游戏及低功耗设备的实时应用，仍需大幅降低内存占用并提升渲染效率。尽管现有先进方法追求轻量化，但在处理复杂运动或长序列场景时表现不佳。本文提出时空压缩3D高斯点绘（TC3DGS），专门用于有效压缩动态3D高斯表示。TC3DGS基于时间相关性选择性剔除高斯点，并采用梯度感知的混合精度量化动态压缩高斯参数；同时，引入改进的Ramer-Douglas-Peucker算法对帧间高斯轨迹进行插值以进一步降低存储开销。多数据集实验表明，TC3DGS可实现最高67倍压缩，且视觉质量基本无损。更多结果与视频见附录。项目主页：https://ahmad-jarrar.github.io/tc-3dgs/
原文摘要 · Abstract (English)
Recent advancements in high-fidelity dynamic scene reconstruction have leveraged dynamic 3D Gaussians and 4D Gaussian Splatting for realistic scene representation. However, to make these methods viable for real-time applications such as AR/VR, gaming, and rendering on low-power devices, substantial reductions in memory usage and improvements in rendering efficiency are required. While many state-of-the-art methods prioritize lightweight implementations, they struggle in handling {scenes with complex motions or long sequences}. In this work, we introduce Temporally Compressed 3D Gaussian Splatting (TC3DGS), a novel technique designed specifically to effectively compress dynamic 3D Gaussian representations. TC3DGS selectively prunes Gaussians based on their temporal relevance and employs gradient-aware mixed-precision quantization to dynamically compress Gaussian parameters. In addition, TC3DGS exploits an adapted version of the Ramer-Douglas-Peucker algorithm to further reduce storage by interpolating Gaussian trajectories across frames. Our experiments on multiple datasets demonstrate that TC3DGS achieves up to 67$\times$ compression with minimal or no degradation in visual quality. More results and videos are provided in the supplementary. Project Page: https://ahmad-jarrar.github.io/tc-3dgs/
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。