Compression in 3D Gaussian Splatting: A Survey of Methods, Trends, and Future Directions (2502.19457v1)

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.

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

The paper "Compression in 3D Gaussian Splatting: A Survey of Methods, Trends, and Future Directions" offers an extensive survey on the emerging technique of 3D Gaussian Splatting (3DGS) in the field of explicit scene rendering. At its core, 3DGS diverges from the traditional Neural Radiance Fields (NeRFs) by employing millions of learnable 3D Gaussians instead of implicit function-based mappings. This technique facilitates explicit scene representation, rendering in real time, and advanced editability, promising advancements in 3D reconstruction and representation technologies.

Despite its advantages, one of the major challenges with 3DGS is its significant memory and storage demands, complicating its applicability in environments with limited resources. The survey provides a detailed taxonomy of 3DGS compression methods aimed at addressing these challenges. It emphasizes the necessity for scalable solutions to optimize 3DGS's computation, memory footprint, and deployment efficiency.

Key Contributions and Findings:

1. Background and Taxonomy:
   • The paper begins with an introduction to 3DGS, elucidating its process from SfM-derived initialization to the rendering of dense point representations through differentiable rendering.
   • It categorizes compression methods into unstructured (focusing on attribute pruning, quantization, and entropy coding) and structured approaches (leveraging Gaussian relationships via anchors and graph models).
2. Unstructured Compression:
   • Unstructured methods optimize Gaussian representations through pruning techniques that assess Gaussian attributes such as opacity and gradient contribution.
   • Quantization methods are deployed to reduce memory footprint, with vector quantization being prominent but scalar quantization recommended for hardware efficiency.
   • Entropy coding is used to further compact the data, with various methods leveraging rate-distortion considerations for balanced quality retention.
3. Structured Compression:
   • Structured compression embraces architectural innovations like anchor-based models (e.g., ScaffoldGS) and hierarchical structures like hash grids to enforce organization within 3DGS datasets.
   • Contextual modeling and graph-based methods further optimize the relations between Gaussians, allowing for nuanced scene interpretations and efficient memory usage.
4. Evaluation Metrics and Performance:
   • The paper highlights the evaluation of compression techniques using datasets like Mip-NeRF360, Tanks {content} Temples, and Deep Blending.
   • Fidelity metrics (SSIM, PSNR) and efficiency attributes (memory usage, FPS) form the basis for assessing these methods.
   • Structured compression methods, while achieving higher compression ratios, often exhibit increased computational complexity compared to unstructured methods, which maintain nimble real-time rendering capabilities.
5. Challenges and Future Directions:
   • The scalability challenge remains prevalent, calling for more efficient optimization processes, especially in scenes with large-scale requirements.
   • Integration of scalar quantization holds promise for hardware optimization on edge devices.
   • The exploration of adaptive loss functions, inspired by image compression, could enhance quality in compressed 3DGS frameworks.
6. Cross-Domain Inspirations:
   • Future research is recommended to draw inspiration from advancements in NeRFs and point cloud compression, leveraging adaptive hierarchical representations and sparsity-driven techniques.
   • There is potential for further research to bridge the gap between the flexibility of unstructured methods and the organizational efficiency of structured approaches through hybrid frameworks.

Overall, this survey underscores 3D Gaussian Splatting as a formidable technique in 3D scene representation, projecting its relevance across domains such as VR/AR applications and autonomous systems. By addressing existing challenges in compression techniques, there lies an opportunity for 3DGS to become an integral tool in next-generation 3D rendering solutions.