Fast Feedforward 3D Gaussian Splatting Compression (2410.08017v3)

Abstract: With 3D Gaussian Splatting (3DGS) advancing real-time and high-fidelity rendering for novel view synthesis, storage requirements pose challenges for their widespread adoption. Although various compression techniques have been proposed, previous art suffers from a common limitation: for any existing 3DGS, per-scene optimization is needed to achieve compression, making the compression sluggish and slow. To address this issue, we introduce Fast Compression of 3D Gaussian Splatting (FCGS), an optimization-free model that can compress 3DGS representations rapidly in a single feed-forward pass, which significantly reduces compression time from minutes to seconds. To enhance compression efficiency, we propose a multi-path entropy module that assigns Gaussian attributes to different entropy constraint paths for balance between size and fidelity. We also carefully design both inter- and intra-Gaussian context models to remove redundancies among the unstructured Gaussian blobs. Overall, FCGS achieves a compression ratio of over 20X while maintaining fidelity, surpassing most per-scene SOTA optimization-based methods. Our code is available at: https://github.com/YihangChen-ee/FCGS.

• The paper presents FCGS, a fast feedforward compression model for 3D Gaussian Splatting that reduces processing time from minutes to seconds while preserving visual fidelity.
• It introduces a Multi-path Entropy Module (MEM) and autoregressive context models to efficiently encode both value and structural characteristics of Gaussian primitives.
• FCGS achieves over 20x compression, demonstrates robustness across diverse datasets, and eliminates the need for scene-specific optimization.

Fast Feedforward 3D Gaussian Splatting Compression: A Summary

The paper "Fast Feedforward 3D Gaussian Splatting Compression" introduces a novel approach to compress 3D Gaussian Splatting (3DGS) without relying on per-scene optimization. Traditional methods of 3DGS compression often require time-consuming optimization for each scene, hindering their practicality and efficiency. The authors propose an innovative feed-forward model, named FCGS (Fast Compression of 3D Gaussian Splatting), which dramatically reduces compression time from minutes to seconds while maintaining a high level of fidelity.

Overview of 3DGS and Compression Challenges

3D Gaussian Splatting has advanced the rendering of novel views by effectively utilizing explicit Gaussian representations for scenes. However, this approach often requires significant storage due to the sheer number of Gaussian primitives involved. Compression techniques have sought to address this but typically depend on scene-specific optimization, which is resource-intensive. FCGS circumvents this by utilizing a compression pipeline that operates independently of scene-specific details.

Methodology

FCGS employs a combination of value-based and structure-based principles to achieve efficient compression:

• Value-Based Principle: FCGS introduces a Multi-path Entropy Module (MEM) to assess which Gaussian attributes should directly undergo quantization or pass through an autoencoder for compression. This selective approach allows FCGS to reduce redundancies while preserving critical geometric and color data.
• Structure-Based Principle: The method leverages inter- and intra-Gaussian context models to encode structural relationships among Gaussian attributes effectively. By using autoregressive context models, the system predicts values based on learned dependencies, further enhancing compression efficiency.

These components allow FCGS to achieve a compression ratio exceeding 20x compared to uncompressed data while maintaining visual fidelity comparable to optimization-heavy methods.

Experimental Results

The authors evaluate FCGS across several datasets including DL3DV-GS and MipNeRF360. Notably, the model achieves high fidelity with significant storage reductions, outperforming several state-of-the-art (SOTA) methods in terms of both compression ratio and computational efficiency. The results demonstrate FCGS's capability to generalize across various scenes without the need for scene-specific tuning, showcasing its robustness and versatility.

Implications and Future Directions

FCGS's optimization-free compression pipeline represents a substantial step forward in practical applications of 3DGS by allowing for rapid and effective data compression without sacrificing quality. This advancement opens up potential for enhanced storage solutions in virtual reality applications, gaming, and real-time rendering systems.

Future research directions could include extending the FCGS model to handle diverse scene characteristics dynamically and exploring integration with other rendering techniques. Moreover, improving the model's adaptability to various types of 3D data assets may further broaden its applicability in realistic and complex environments.

In conclusion, the FCGS approach efficiently addresses key limitations of current 3DGS compression methodologies, paving the way for more accessible and streamlined implementation of large-scale 3D rendering systems.