CompGS：透過壓縮高斯點陣法有效地呈現3D場景

CompGS: Efficient 3D Scene Representation via Compressed Gaussian Splatting

April 15, 2024

作者: Xiangrui Liu, Xinju Wu, Pingping Zhang, Shiqi Wang, Zhu Li, Sam Kwong

cs.AI

摘要

高斯點塗，以其出色的渲染質量和效率而聞名，在3D場景表示中已成為一項傑出的技術。然而，高斯點塗的龐大數據量阻礙了其在現實應用中的實用性。在這裡，我們提出了一種高效的3D場景表示，名為壓縮高斯點塗（CompGS），利用緊湊的高斯基元對3D場景進行忠實建模，並極大地減少了數據大小。為確保高斯基元的緊湊性，我們設計了一種捕捉彼此之間預測關係的混合基元結構。然後，我們利用一小組錨基元進行預測，使大多數基元被封裝為高度緊湊的剩餘形式。此外，我們開發了一種受速率限制的優化方案，以消除這些混合基元中的冗餘，將我們的CompGS引向在比特率消耗和表示效能之間取得最佳平衡的方向。實驗結果顯示，所提出的CompGS明顯優於現有方法，在3D場景表示中實現了卓越的緊湊性，而不影響模型準確性和渲染質量。我們的代碼將在GitHub上發布供進一步研究使用。

English

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.