- The paper presents a novel framework that compresses 3D Gaussian Splatting by merging optimized feature planes with standard video codecs.
- It employs frequency-domain entropy modeling and channel-wise bit allocation to achieve up to 146× compression with minimal quality loss.
- Experimental results confirm high PSNR, SSIM, and LPIPS scores on benchmarks like Mip-NeRF360 and Tanks and Temples, key for resource-constrained applications.
Compression of 3D Gaussian Splatting with Optimized Feature Planes and Standard Video Codecs
The paper presents a sophisticated framework for compressing 3D scene representations, particularly those represented by 3D Gaussian Splatting (3DGS). This technique is known for its rendering quality and speed, but it traditionally demands substantial data storage, making deployment in limited-resource environments challenging. The authors propose a strategic integration of compact feature plane representations with standard video codecs to achieve a notable reduction in storage overhead while preserving visual integrity.
Methodology Overview
The proposed method introduces a unified architecture that leverages 2D feature planes in a tri-plane structure to represent 3D Gaussian primitives. This structure facilitates a continuous spatial representation and enhances the correlation within feature planes, which is crucial for effective compression. A critical innovation in the paper is the incorporation of entropy modeling in the frequency domain—a technique designed to optimize compatibility with existing video codec infrastructure, such as High Efficiency Video Coding (HEVC).
The authors further augment this compression technique by implementing a channel-wise bit allocation strategy. This approach prioritizes the allocation of bitrates based on the importance of the channels, thereby optimizing the trade-off between bitrate consumption and representation efficiency. These enhancements are designed to effectively manage spatial redundancies within the attributes of Gaussian primitives.
Experimental Results
The experimental evaluation demonstrates the efficacy of this compression strategy. The authors report a compression ratio of up to 146 times compared to traditional 3DGS, with only negligible losses in image quality. This remarkable compression efficiency is achieved without substantial deviation from the high rendering speeds characteristic of the original 3DGS pipeline. Key metrics such as PSNR, SSIM, and LPIPS confirm the method's capability to maintain high visual fidelity across various datasets, including Mip-NeRF360 and Tanks and Temples.
Implications and Future Work
The implications of this work are significant in the context of applications involving mobile devices or head-mounted displays, where storage and computational resources are constrained. The ability to compress 3D scene representations efficiently while using standard video codecs suggests practical avenues for deploying high-quality visual content across diverse platforms with minimal overhead.
The proposed method not only optimizes storage efficiency but also opens pathways for future research in video codec-integrated 3D scene representation. As video codec technologies advance, further performance optimizations are anticipated. Future work could explore the integration of adaptive learning techniques, where codecs dynamically adjust to varying scene complexities, potentially enhancing the balance between compression and quality even further.
In conclusion, this paper presents a well-formulated approach to overcoming the data demands of 3D Gaussian splatting. By innovatively leveraging feature plane architecture combined with frequency-domain entropy modeling and standardized video coding technologies, the authors provide a viable solution for managing complex 3D scenes in resource-constrained environments. This work not only contributes to the domain of 3D scene compression but also lays a foundation for future enhancements in integrating traditional and neural compression methodologies.