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. It additionally relies on a variation of the Ramer-Douglas-Peucker algorithm in a post-processing step to further reduce storage by interpolating Gaussian trajectories across frames. Our experiments across multiple datasets demonstrate that TC3DGS achieves up to 67$\times$ compression with minimal or no degradation in visual quality.

翻译：近期高保真动态场景重建的进展利用动态3D高斯与4D高斯泼溅技术实现了逼真的场景表征。然而，为使此类方法能够应用于AR/VR、游戏及低功耗设备渲染等实时场景，亟需大幅降低内存占用并提升渲染效率。尽管现有前沿方法多侧重于轻量化实现，但在处理复杂运动或长序列场景时仍面临挑战。本研究提出时间压缩3D高斯泼溅（TC3DGS）技术，该创新方法专为高效压缩动态3D高斯表征而设计。TC3DGS基于高斯单元的时间相关性进行选择性剪枝，并采用梯度感知混合精度量化动态压缩高斯参数。此外，该方法在后处理阶段引入改进的Ramer-Douglas-Peucker算法，通过跨帧插值高斯轨迹进一步降低存储需求。我们在多个数据集上的实验表明，TC3DGS在视觉质量无损或微损前提下可实现高达67$\times$的压缩比。