HEMGS: A Hybrid Entropy Model for 3D Gaussian Splatting Data Compression (2411.18473v2)

Abstract: In this work, we propose a novel compression framework for 3D Gaussian Splatting (3DGS) data. Building on anchor-based 3DGS methodologies, our approach compresses all attributes within each anchor by introducing a novel Hybrid Entropy Model for 3D Gaussian Splatting (HEMGS) to achieve hybrid lossy-lossless compression. It consists of three main components: a variable-rate predictor, a hyperprior network, and an autoregressive network. First, unlike previous methods that adopt multiple models to achieve multi-rate lossy compression, thereby increasing training overhead, our variable-rate predictor enables variable-rate compression with a single model and a hyperparameter $\lambda$ by producing a learned Quantization Step feature for versatile lossy compression. Second, to improve lossless compression, the hyperprior network captures both scene-agnostic and scene-specific features to generate a prior feature, while the autoregressive network employs an adaptive context selection algorithm with flexible receptive fields to produce a contextual feature. By integrating these two features, HEMGS can accurately estimate the distribution of the current coding element within each attribute, enabling improved entropy coding and reduced storage. We integrate HEMGS into a compression framework, and experimental results on four benchmarks indicate that HEMGS achieves about a 40% average reduction in size while maintaining rendering quality over baseline methods and achieving state-of-the-art compression results.

• The paper introduces HEMGS, a hybrid entropy model that reduces 3D Gaussian Splatting data storage by approximately 40% while maintaining high rendering quality.
• It integrates a hyperprior network with domain-aware and instance-aware architectures to efficiently capture spatial dependencies and attribute redundancies.
• Experimental results show superior PSNR and SSIM compared to HAC and Context-GS methods, underscoring its potential for future advancements in 3D data compression.

Summary of "HEMGS: A Hybrid Entropy Model for 3D Gaussian Splatting Data Compression"

The paper "HEMGS: A Hybrid Entropy Model for 3D Gaussian Splatting Data Compression" introduces a novel approach to compress 3D Gaussian Splatting (3DGS) data, which has become a prevalent method for 3D scene representation. The growing popularity of 3DGS in capturing 3D scene geometry and appearance has led to substantial challenges in data storage, making efficient compression imperative. The authors propose the HEMGS as a solution to these challenges, aiming to achieve high compression rates without significant loss of rendering quality.

Key Contributions

The HEMGS model integrates two main components for efficient 3DGS data compression: a hyperprior network and an autoregressive network. This combination is central to reducing redundancies both between different attributes and within individual attributes of the 3DGS data.

1. Hyperprior Network: The approach utilizes a progressive coding algorithm to leverage spatial dependencies across 3DGS attributes. By encoding anchor positions and attributes in a sequential manner and using previously compressed data as priors, the model effectively exploits inter-attribute redundancies.
2. Domain-Aware and Instance-Aware Architecture: To optimize location feature extraction, the authors incorporate both a pre-trained domain-aware network and an instance-aware network, capturing comprehensive structural relations without additional storage overhead. This dual-path approach enhances the model's ability to generate efficient hyperpriors for subsequent attribute coding.
3. Autoregressive Network with Adaptive Context Coding: The autoregressive network introduces a novel adaptive context coding algorithm that adjusts its receptive fields based on anchor density, thereby maximizing contextual information and reducing attribute redundancy.
4. End-to-End Compression Framework: HEMGS is integrated into a comprehensive 3DGS data compression framework, which combines entropy modeling with quantization and arithmetic coding for an optimized, end-to-end solution.

Experimental Results

The authors benchmark HEMGS against contemporary 3DGS data compression methods across multiple datasets. The results demonstrate a considerable reduction in storage size—approximately 40% on average—while maintaining high rendering quality. Specifically, when compared to the baseline methods such as HAC and Context-GS, HEMGS achieves superior PSNR and SSIM metrics, underscoring the significance of effective entropy modeling in optimizing data compression.

Implications and Future Directions

The development of HEMGS represents a significant advance in the domain of 3D data compression, particularly in terms of efficiently managing storage without degrading rendering performance. The findings indicate strong potential for further exploration in entropy models tailored to 3D data structures. Future research may extend these methods to broader applications, including anchor-free structures and dynamic scenes, to fully capitalize on the advantages of entropy-based compression techniques.

Overall, the paper offers a substantial contribution to the computer graphics and data compression communities by presenting a model that effectively balances fidelity and compression efficiency, setting a foundation for future innovations in 3D graphics processing.