Core Concepts
The proposed Compressed Gaussian Splatting (CompGS) method leverages compact primitives to efficiently represent 3D scenes, achieving significant compression ratios without compromising rendering quality.
Abstract
The paper proposes a novel 3D scene representation method called Compressed Gaussian Splatting (CompGS) that utilizes compact primitives to achieve efficient 3D scene representation. The key highlights are:
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Hybrid Primitive Structure:
- CompGS employs a hybrid primitive structure that consists of anchor primitives and coupled primitives.
- Anchor primitives serve as references, while coupled primitives are efficiently predicted from the anchor primitives and represented using compact residual embeddings.
- This structure enables the majority of primitives to be encoded in a highly compact form, leading to significant size reduction.
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Rate-constrained Optimization:
- CompGS devises a rate-constrained optimization scheme to further improve the compactness of the primitives.
- It establishes a primitive rate model via entropy estimation and formulates a rate-distortion loss to jointly optimize the primitives for an optimal trade-off between rendering quality and bitrate consumption.
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Experimental Results:
- CompGS outperforms existing compression methods, achieving superior compression ratios up to 110x on prevalent 3D scene datasets without compromising rendering quality.
- Detailed ablation studies demonstrate the effectiveness of the hybrid primitive structure and the rate-constrained optimization in enhancing the compactness of the 3D scene representation.
- The computational complexity analysis shows that CompGS is practical for real-world applications, with efficient encoding, decoding, and rendering times.
Overall, the proposed CompGS method significantly advances the state-of-the-art in 3D scene representation by leveraging compact primitives and a rate-constrained optimization scheme, enabling efficient and high-quality 3D scene modeling.
Stats
The paper reports the following key metrics:
On the Tanks&Templates dataset, the proposed method achieves a compression ratio up to 73.75x, reducing the model size from 434.38 MB to 5.89 MB.
On the Deep Blending dataset, the proposed method achieves a compression ratio up to 110.45x, reducing the model size from 665.99 MB to 6.03 MB.
On the Mip-NeRF 360 dataset, the proposed method achieves a compression ratio up to 89.35x, reducing the model size from 788.98 MB to 8.83 MB.
Quotes
"The proposed CompGS method significantly advances the state-of-the-art in 3D scene representation by leveraging compact primitives and a rate-constrained optimization scheme, enabling efficient and high-quality 3D scene modeling."
"Owing to the proposed hybrid primitive structure and the rate-constrained optimization scheme, our CompGS achieves not only high-quality rendering but also compact representations compared to prior works."