构建统一框架，提升3D/4D高斯点云压缩效率与可复现性。
GSCodec Studio: A Modular Framework for Gaussian Splat Compression
- 模块化设计支持多种重建与压缩方法灵活组合。
- 静态与动态场景下均实现媲美现有方案的率失真性能。
- 开源框架促进社区协作，推动高斯点云压缩研究发展。
3D高斯点云及其扩展至4D动态场景的技术，能够从真实采集数据中实现逼真的实时渲染，使高斯点云（GS）成为下一代沉浸式媒体的有前景格式。然而，其高存储需求给共享、传输和存储带来显著挑战。尽管已有研究从不同角度探索高斯点云压缩，但这些工作分散于独立仓库，难以统一评估与整合最佳实践。为此，我们提出GSCodec Studio，一个统一且模块化的高斯点云重建、压缩与渲染框架。该框架集成多样化的3D/4D高斯点云重建方法与压缩技术作为模块化组件，支持灵活组合与全面比较。通过融合社区研究成果与自身探索，框架支持静态与动态高斯点云的紧凑表示与压缩方案开发，即Static和Dynamic GSCodec，实现在静态与动态场景下具有竞争力的率失真性能。代码已公开于https://github.com/JasonLSC/GSCodec_Studio，以推进高斯点云压缩研究。
原文摘要 · Abstract (English)
3D Gaussian Splatting and its extension to 4D dynamic scenes enable photorealistic, real-time rendering from real-world captures, positioning Gaussian Splats (GS) as a promising format for next-generation immersive media. However, their high storage requirements pose significant challenges for practical use in sharing, transmission, and storage. Despite various studies exploring GS compression from different perspectives, these efforts remain scattered across separate repositories, complicating benchmarking and the integration of best practices. To address this gap, we present GSCodec Studio, a unified and modular framework for GS reconstruction, compression, and rendering. The framework incorporates a diverse set of 3D/4D GS reconstruction methods and GS compression techniques as modular components, facilitating flexible combinations and comprehensive comparisons. By integrating best practices from community research and our own explorations, GSCodec Studio supports the development of compact representation and compression solutions for static and dynamic Gaussian Splats, namely our Static and Dynamic GSCodec, achieving competitive rate-distortion performance in static and dynamic GS compression. The code for our framework is publicly available at https://github.com/JasonLSC/GSCodec_Studio , to advance the research on Gaussian Splats compression.
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