HybridGS：利用双通道稀疏表示与点云编码器的高效高斯泼溅数据压缩
HybridGS: High-Efficiency Gaussian Splatting Data Compression using Dual-Channel Sparse Representation and Point Cloud Encoder
AI总结：
HybridGS提出结合紧凑生成与标准化点云编码的3DGS压缩框架，通过双通道稀疏表示和码率控制，在保持重建质量的同时显著提升编解码速度。
摘要：
现有的大多数三维高斯泼溅（3DGS）压缩方案侧重于通过隐式数据嵌入生成紧凑的3DGS表示。这些方案编码时间长且数据格式高度定制化，难以广泛部署。本文提出了一种名为HybridGS的新型3DGS压缩框架，该框架同时利用了紧凑生成和标准化点云数据编码的优势。HybridGS首先生成紧凑且显式的3DGS数据。引入双通道稀疏表示来监督基元位置和特征位深。然后利用规范点云编码器进行进一步的数据压缩并形成标准输出比特流。提出了一种简单有效的码率控制方案来支撑这一可解释的数据压缩方案。在现阶段，HybridGS不包含任何旨在生成过程中提升3DGS质量的模块。但实验结果表明，与最先进的方法相比，它仍能提供相当的重建性能，同时编码和解码速度明显更高。代码公开于 https://github.com/Qi-Yangsjtu/HybridGS。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
