HybridGS通过双通道稀疏表示加速3D高斯点云压缩，兼顾速度与标准格式输出。
HybridGS: High-Efficiency Gaussian Splatting Data Compression using Dual-Channel Sparse Representation and Point Cloud Encoder
- 采用双通道稀疏表示控制位置与特征比特深度，提升编码效率
- 使用标准点云编码器生成通用比特流，支持快速编解码
- 相较现有方法速度显著提升，适合实时部署场景
现有3D高斯点云（3DGS）压缩方案多依赖隐式数据嵌入，编码耗时长且格式自定义，难以推广。本文提出HybridGS框架，先生成紧凑显式的3DGS数据，引入双通道稀疏表示以监督原始位置和特征比特深度，再通过标准点云编码器进行进一步压缩，生成标准化比特流。设计了简单有效的码率控制机制，实现可解释的压缩流程。当前版本未包含生成阶段质量优化模块，但实验表明其重建性能仍可媲美顶尖方法，且编码解码速度明显更快。代码已开源：https://github.com/Qi-Yangsjtu/HybridGS。
原文摘要 · Abstract (English)
Most existing 3D Gaussian Splatting (3DGS) compression schemes focus on producing compact 3DGS representation via implicit data embedding. They have long coding times and highly customized data format, making it difficult for widespread deployment. This paper presents a new 3DGS compression framework called HybridGS, which takes advantage of both compact generation and standardized point cloud data encoding. HybridGS first generates compact and explicit 3DGS data. A dual-channel sparse representation is introduced to supervise the primitive position and feature bit depth. It then utilizes a canonical point cloud encoder to perform further data compression and form standard output bitstreams. A simple and effective rate control scheme is proposed to pivot the interpretable data compression scheme. At the current stage, HybridGS does not include any modules aimed at improving 3DGS quality during generation. But experiment results show that it still provides comparable reconstruction performance against state-of-the-art methods, with evidently higher encoding and decoding speed. The code is publicly available at https://github.com/Qi-Yangsjtu/HybridGS.
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