ProGS: Towards Progressive Coding for 3D Gaussian Splatting

2026-03-10

AI Summary

This work addresses the challenge of excessive data volume and lack of progressive encoding support in existing 3D Gaussian Splatting (3DGS) methods, which hinders their applicability to streaming under dynamically varying bandwidth conditions. To overcome this limitation, we introduce the first progressive encoding framework for 3DGS by organizing Gaussians into an octree hierarchy. Our approach integrates a mutual information enhancement mechanism and a dynamic anchor adjustment strategy to effectively reduce structural redundancy and improve representation efficiency. The proposed method enables scalable compression, achieving over 10% improvement in visual fidelity while compressing the storage footprint to 1/45 of the original size—significantly outperforming current compression techniques for 3DGS.

Technology Category

• Computer Vision: 3D Computer Vision
• Data Mining & Knowledge Management: Data Compression
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

With the emergence of 3D Gaussian Splatting (3DGS), numerous pioneering efforts have been made to address the effective compression issue of massive 3DGS data. 3DGS offers an efficient and scalable representation of 3D scenes by utilizing learnable 3D Gaussians, but the large size of the generated data has posed significant challenges for storage and transmission. Existing methods, however, have been limited by their inability to support progressive coding, a crucial feature in streaming applications with varying bandwidth. To tackle this limitation, this paper introduce a novel approach that organizes 3DGS data into an octree structure, enabling efficient progressive coding. The proposed ProGS is a streaming-friendly codec that facilitates progressive coding for 3D Gaussian splatting, and significantly improves both compression efficiency and visual fidelity. The proposed method incorporates mutual information enhancement mechanisms to mitigate structural redundancy, leveraging the relevance between nodes in the octree hierarchy. By adapting the octree structure and dynamically adjusting the anchor nodes, ProGS ensures scalable data compression without compromising the rendering quality. ProGS achieves a remarkable 45X reduction in file storage compared to the original 3DGS format, while simultaneously improving visual performance by over 10%. This demonstrates that ProGS can provide a robust solution for real-time applications with varying network conditions.

Problem

• 3D Gaussian Splatting
• progressive coding
• data compression
• streaming
• octree

Innovation

• Progressive Coding
• 3D Gaussian Splatting
• Octree Structure
• Mutual Information Enhancement
• Scalable Compression