HybridGS: High-Efficiency Gaussian Splatting Data Compression using Dual-Channel Sparse Representation and Point Cloud Encoder (2505.01938v1)

Abstract: 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.

HybridGS: High-Efficiency Gaussian Splatting Data Compression using Dual-Channel Sparse Representation and Point Cloud Encoder

The paper introduces HybridGS, a novel framework for compressing three-dimensional Gaussian Splatting (3DGS) data. This method offers a significant advancement in 3DGS data compression by merging the advantages of generative and traditional compression techniques. The primary objective is to achieve efficient data compression while maintaining fast encoding and decoding speeds, using standardized point cloud encoding formats.

Overview

The motivation behind HybridGS stems from the limitations observed in existing 3DGS methods which require lengthy coding times and proprietary formats, hindering broader application. HybridGS mitigates these issues through a dual-channel sparse representation and point cloud encoding. The dual-channel approach supervises primitive positioning and feature bit depth, producing data compatible with conventional point cloud codecs, notably enhancing speed and flexibility. By integrating lossy processing operations at the 3DGS generation stage, HybridGS elegantly balances distortion mitigation, prediction of compressed quality, and processing efficiency.

HybridGS is characterized by its use of canonical point cloud encoders, such as the Geometry-based Point Cloud Compression (GPCC), allowing seamless deployment and inheriting compression efficiency from existing codecs. While this method does not directly improve 3DGS quality during generation, it showcases reconstructive performance on par with state-of-the-art generative compression methods.

Methodology

HybridGS consists of two primary steps:

1. Dual-Channel Sparse Representation:
   • Sparse Attribute Representation: Attributes undergo dimensionality reduction using a learnable low-dimensional latent feature combined with a lightweight decoder. This method mimics and generalizes PCA, focusing on attributes like color and rotation, which are statistically compressible. Additionally, feature precision has been accounted for, introducing quantization during generation with methods such as Robust Quantizer (RQ).
   • Primitive Sparsification: Position uniqueness is ensured, and a learning-based quantizer manages primitive positions without de-quantity before rendering. This aligns the scale of scenes with 3DGS, effectively reducing primitive numbers and providing effective rate control strategies.
2. High-Efficiency Coding:
   • The framework supports lossy and lossless compression through GPCC or similar. HybridGS splits 3DGS data for point cloud compression, employing parallel computing techniques where feasible to expedite processing.

Results

Experimental evaluations demonstrate HybridGS's capacity to match or outperform previous methods in compression efficiency. It significantly improves encoding and decoding speeds to real-time levels, an accomplishment pivotal for dynamic streaming applications. Furthermore, its rate control capabilities are robust, ensuring precise bandwidth requirements while accommodating lossy conditions flexibly.

Implications and Future Work

HybridGS offers substantial practical and theoretical implications. Its capability to pair traditional point cloud encoders with generative compression philosophies lays the groundwork for novel implementations in the visualization and streaming sectors. Future research could explore adaptive parameter selection methodologies to optimize trade-offs between quality and bitstream size. Moreover, enhancements to the feature compression methods and alternative encoder optimizations might further boost efficiency.

The paper sets the stage for profound developments in 3D data management, establishing HybridGS as a pivotal framework for future 3DGS and neural rendering applications.