Compression in 3D Gaussian Splatting: A Survey of Methods, Trends, and Future Directions

TL;DR

This survey reviews methods for compressing 3D Gaussian Splatting (3DGS), highlighting current techniques, challenges, and future directions to improve scalability and efficiency in 3D scene representation.

Contribution

It provides a comprehensive taxonomy and analysis of existing 3DGS compression methods, and discusses future research directions for scalable 3D scene compression.

Findings

Existing methods vary in fidelity and compression ratios.

Memory and storage challenges remain significant for resource-limited devices.

Future directions include leveraging advancements in efficient NeRF representations.

Abstract

3D Gaussian Splatting (3DGS) has recently emerged as a pioneering approach in explicit scene rendering and computer graphics. Unlike traditional neural radiance field (NeRF) methods, which typically rely on implicit, coordinate-based models to map spatial coordinates to pixel values, 3DGS utilizes millions of learnable 3D Gaussians. Its differentiable rendering technique and inherent capability for explicit scene representation and manipulation positions 3DGS as a potential game-changer for the next generation of 3D reconstruction and representation technologies. This enables 3DGS to deliver real-time rendering speeds while offering unparalleled editability levels. However, despite its advantages, 3DGS suffers from substantial memory and storage requirements, posing challenges for deployment on resource-constrained devices. In this survey, we provide a comprehensive overview focusing on the scalability and compression of 3DGS. We begin with a detailed background overview of 3DGS, followed by a structured taxonomy of existing compression methods. Additionally, we analyze and compare current methods from the topological perspective, evaluating their strengths and limitations in terms of fidelity, compression ratios, and computational efficiency. Furthermore, we explore how advancements in efficient NeRF representations can inspire future developments in 3DGS optimization. Finally, we conclude with current research challenges and highlight key directions for future exploration.