AI 中文总结
研究针对3D高斯图元压缩解码慢不适用于交互式应用的问题，提出SpeedyGS，通过两阶段优化，即结构形成阶段联合优化自适应量化和剪枝，统计编码阶段转换并编码高斯几何和属性，在多方面取得良好平衡，压缩性能优且解码快。
AI 中文摘要
近期在压缩大规模3D高斯图元（3DGS）数据方面取得的进展大幅减少了存储占用、网络传输带宽以及渲染前到GPU缓存的内存流量。然而，使用先进的3DGS编解码器解码仍需数秒，不适用于交互式应用。为系统应对这一挑战，我们提出SpeedyGS，一种内容感知的3DGS压缩器，它分别优化结构形成和统计编码。在结构形成阶段，在统一的率失真目标下联合优化自适应量化和剪枝，用轻量级率代理估计下一阶段熵编码成本来调节高斯密度和精度。在统计编码阶段，将高斯几何转换为稀疏八叉树令牌并进行多阶段编码，高斯属性通过复杂度可控的局部自回归模型序列化进行熵编码。SpeedyGS在优化效率、压缩性能、解码延迟和渲染速度之间实现了良好平衡。与普通3DGS相比，在常见数据集上模型大小最多可减少160倍且质量下降可忽略不计。与现有压缩方法相比，解码速度显著更快，在消费级硬件上优化加速9倍。为进一步减少解码开销，统计编码阶段还支持高斯的逐通道、固定长度编码，使SpeedyGS能更好适应底层应用并将解码延迟降至近零。
英文摘要
Recent progress in compressing large-scale 3D Gaussian Splatting (3DGS) data has substantially reduced storage footprint, network transmission bandwidth, and memory traffic to GPU caches before rendering. Yet decoding with advanced 3DGS codecs still takes seconds, making them unsuitable for interactive applications. To systematically address this challenge, we propose SpeedyGS, a Content-Aware 3DGS Compressor that separately optimizes the structural formation and statistical coding. First, in structural formation, we jointly optimize adaptive quantization and pruning under a unified rate-distortion objective, where the rate term is replaced by a lightweight rate proxy that estimates entropy coding cost of the next stage, thereby efficiently regulating Gaussian density and precision to yield a compact scene representation. Then, in the statistical coding phase, Gaussian geometry is converted into sparse octree tokens and subsequently undergoes multi-stage coding, while Gaussian attributes are serialized into a 1D token stream for entropy coding via a complexity-controllable local autoregressive model. SpeedyGS achieves a favorable balance among optimization efficiency, compression performance, decoding latency, and rendering speed. Compared to vanilla 3DGS, SpeedyGS achieves up to 160$\times$ model size reduction with negligible quality degradation across common datasets. Compared to state-of-the-art compression methods, it also offers significantly faster decoding and accelerates optimization by 9$\times$ on consumer-grade hardware. To further reduce decoding overhead, the statistical coding stage also supports channel-wise, fixed-length coding for Gaussian as a simpler alternative, enabling SpeedyGS to better adapt to the underlying application and reduce decoding latency to nearly zero.
