GSCodec Studio: A Modular Framework for Gaussian Splat Compression

TL;DR

GSCodec Studio is a modular framework that unifies various methods for reconstructing, compressing, and rendering Gaussian Splats in static and dynamic scenes, enabling better benchmarking and development of compact representations.

Contribution

It introduces a flexible, modular framework that consolidates GS compression and reconstruction techniques, facilitating comprehensive comparisons and advancing practical compression solutions.

Findings

Achieves competitive rate-distortion performance in static GS compression.

Supports dynamic GS compression with promising results.

Provides an open-source platform for benchmarking and development.

Abstract

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.