With 3D Gaussian Splatting (3DGS) advancing real-time and high-fidelity rendering for novel view synthesis, storage requirements pose challenges for their widespread adoption. Although various compression techniques have been proposed, previous art suffers from a common limitation: for any existing 3DGS, per-scene optimization is needed to achieve compression, making the compression sluggish and slow. To address this issue, we introduce Fast Compression of 3D Gaussian Splatting (FCGS), an optimization-free model that can compress 3DGS representations rapidly in a single feed-forward pass, which significantly reduces compression time from minutes to seconds. To enhance compression efficiency, we propose a multi-path entropy module that assigns Gaussian attributes to different entropy constraint paths for balance between size and fidelity. We also carefully design both inter- and intra-Gaussian context models to remove redundancies among the unstructured Gaussian blobs. Overall, FCGS achieves a compression ratio of over 20X while maintaining fidelity, surpassing most per-scene SOTA optimization-based methods.
随着3D高斯点（3DGS）在新视图合成的实时高保真渲染中取得进展，存储需求成为其广泛应用的挑战。尽管已经提出了多种压缩技术，但现有方法存在一个共同的局限性：任何现有的3DGS都需要针对每个场景进行优化才能实现压缩，这使得压缩过程缓慢而低效。为了解决这一问题，我们提出了Fast Compression of 3D Gaussian Splatting（FCGS），这是一种无需优化的模型，可以在单次前馈过程中快速压缩3DGS表示，将压缩时间从几分钟大幅缩短到几秒钟。为了提高压缩效率，我们提出了一个多路径熵模块，该模块将高斯属性分配到不同的熵约束路径，以在压缩大小和保真度之间取得平衡。我们还精心设计了高斯间和高斯内的上下文模型，以去除非结构化高斯点之间的冗余。总体而言，FCGS在保持保真度的同时实现了超过20倍的压缩比，超越了大多数基于场景优化的最新方法。