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CompGS: Efficient 3D Scene Representation via Compressed Gaussian Splatting (2404.09458v1)

Published 15 Apr 2024 in cs.CV and cs.GR

Abstract: Gaussian splatting, renowned for its exceptional rendering quality and efficiency, has emerged as a prominent technique in 3D scene representation. However, the substantial data volume of Gaussian splatting impedes its practical utility in real-world applications. Herein, we propose an efficient 3D scene representation, named Compressed Gaussian Splatting (CompGS), which harnesses compact Gaussian primitives for faithful 3D scene modeling with a remarkably reduced data size. To ensure the compactness of Gaussian primitives, we devise a hybrid primitive structure that captures predictive relationships between each other. Then, we exploit a small set of anchor primitives for prediction, allowing the majority of primitives to be encapsulated into highly compact residual forms. Moreover, we develop a rate-constrained optimization scheme to eliminate redundancies within such hybrid primitives, steering our CompGS towards an optimal trade-off between bitrate consumption and representation efficacy. Experimental results show that the proposed CompGS significantly outperforms existing methods, achieving superior compactness in 3D scene representation without compromising model accuracy and rendering quality. Our code will be released on GitHub for further research.

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Citations (6)
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Summary

  • The paper introduces a hybrid primitive structure that predicts coupled primitives via anchor primitives for efficient 3D scene representation.
  • The paper employs a rate-distortion optimization scheme to balance compression and rendering fidelity effectively.
  • The paper demonstrates up to 110× compression on standard datasets without compromising model accuracy or visual quality.

Efficient 3D Scene Representation with Compressed Gaussian Splatting (CompGS)

Introduction to CompGS

The novel approach, Compressed Gaussian Splatting (CompGS), represents a significant stride in 3D scene representation efficiency. Addressing the challenges posed by the data-intensiveness of traditional Gaussian splatting techniques, CompGS introduces a hybrid primitive structure that leverages compact Gaussian primitives. This structure, combined with a rate-constrained optimization scheme, exhibits superior capabilities in rendering quality maintenance while substantially reducing data size. Through these innovations, CompGS achieves an impressive compression ratio, significantly outperforming existing methods in both compactness and rendering fidelity.

Key Contributions of CompGS

  • Introduction of a Hybrid Primitive Structure: Composed of anchor and coupled primitives, this structure enables highly efficient scene representation. The sparse set of anchor primitives serve as references, from which the attributes of the more numerous coupled primitives are predicted, resulting in compact residual representations.
  • Rate-Constrained Optimization Scheme: This technique further enhances the compactness of 3D scene representations by optimizing the trade-off between bitrate consumption and representation efficacy. It incorporates a rate-distortion cost minimization for end-to-end optimization, allowing for an optimal balance between compactness and rendering quality.
  • Superior Compression Ratios Achieved: In comparative experiments, CompGS dramatically outperforms existing methods, delivering an up to 110× compression ratio on standard datasets without sacrificing model accuracy or rendering quality.

Insights on Methodology

CompGS introduces a hybrid structure that capitalizes on the predictive relationships among Gaussian primitives. By employing a set of anchor primitives for prediction, coupled primitives can be reduced to contain only essential residual information, substantially decreasing the overall data footprint. This reduction is catalyzed by a novel rate-constrained optimization scheme, which steers the representation towards an optimal balance of rendering quality and data efficiency.

Inter-Primitive Prediction

The methodology revolves around accurate and efficient prediction mechanisms for deriving the geometry and appearance attributes of coupled primitives from anchor primitives. This technique utilizes affine transforms and neural networks to predict the primitives' attributes while minimizing the information needed for their representation.

Rate-Distortion Optimization

At the core of CompGS is the employment of a rate-distortion optimization scheme, focusing on the minimization of bitrate costs while maintaining rendering fidelity. This process involves entropy estimation to model the bitrates of primitives and rate-distortion cost formulation to guide the optimization.

Experimental Validation

The efficacy of CompGS was rigorously evaluated against existing methods on prevalent 3D scene datasets. These evaluations demonstrate CompGS's superior performance in compressing 3D Gaussian datasets significantly more than current methods while maintaining or improving rendering quality. Notably, the compression efficiency achieved does not come at the expense of rendering time, which remains competitive with current methods.

Future Directions in 3D Scene Representation

CompGS's advancements suggest several avenues for further research in the domain of 3D scene representation. The potential for improving the hybrid primitive structure and the rate-constrained optimization scheme can pave the way for even more efficient and accurate 3D scene representations. Furthermore, exploring the application of these foundational principles in other modalities of 3D data compression could yield significant benefits across various domains of computer vision and graphics.

Conclusion

CompGS marks a notable advancement in the efficient representation of 3D scenes, providing a compelling solution to the challenges posed by the data-heavy nature of Gaussian splatting techniques. By optimizing the compactness of Gaussian primitives through predictive relationships and rate-constrained optimization, CompGS achieves unparalleled compression ratios without compromising the rendering quality. This balance of efficiency and effectiveness establishes a new benchmark for future developments in 3D scene representation technology.

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