Title: CompGS++: Compressed Gaussian Splatting for Static and Dynamic Scene Representation Show Chinese title

Title: CompGS++：静态和动态场景表示的压缩高斯泼溅

Abstract: Gaussian splatting demonstrates proficiency for 3D scene modeling but suffers from substantial data volume due to inherent primitive redundancy. To enable future photorealistic 3D immersive visual communication applications, significant compression is essential for transmission over the existing Internet infrastructure. Hence, we propose Compressed Gaussian Splatting (CompGS++), a novel framework that leverages compact Gaussian primitives to achieve accurate 3D modeling with substantial size reduction for both static and dynamic scenes. Our design is based on the principle of eliminating redundancy both between and within primitives. Specifically, we develop a comprehensive prediction paradigm to address inter-primitive redundancy through spatial and temporal primitive prediction modules. The spatial primitive prediction module establishes predictive relationships for scene primitives and enables most primitives to be encoded as compact residuals, substantially reducing the spatial redundancy. We further devise a temporal primitive prediction module to handle dynamic scenes, which exploits primitive correlations across timestamps to effectively reduce temporal redundancy. Moreover, we devise a rate-constrained optimization module that jointly minimizes reconstruction error and rate consumption. This module effectively eliminates parameter redundancy within primitives and enhances the overall compactness of scene representations. Comprehensive evaluations across multiple benchmark datasets demonstrate that CompGS++ significantly outperforms existing methods, achieving superior compression performance while preserving accurate scene modeling. Our implementation will be made publicly available on GitHub to facilitate further research. Show Chinese abstract

Abstract: 高斯点云在3D场景建模中表现出色，但由于固有的原始冗余，导致数据量较大。 为了实现未来的逼真3D沉浸式视觉通信应用，通过现有互联网基础设施传输时，显著的压缩是必不可少的。 因此，我们提出了压缩高斯点云（CompGS++），这是一种新的框架，利用紧凑的高斯原始体，实现了静态和动态场景的准确3D建模，并大幅减小了数据规模。 我们的设计基于消除原始体之间和内部冗余的原则。 具体而言，我们开发了一个全面的预测范式，通过空间和时间原始体预测模块来解决原始体间的冗余。 空间原始体预测模块为场景原始体建立了预测关系，并使大多数原始体能够以紧凑的残差形式进行编码，显著减少了空间冗余。 我们进一步设计了一个时间原始体预测模块，用于处理动态场景，该模块利用时间戳之间的原始体相关性，有效减少时间冗余。 此外，我们设计了一个速率约束优化模块，联合最小化重建误差和速率消耗。 该模块有效地消除了原始体内的参数冗余，并增强了场景表示的整体紧凑性。 在多个基准数据集上的综合评估表明，CompGS++显著优于现有方法，在保持准确场景建模的同时实现了优越的压缩性能。 我们的实现将在GitHub上公开，以促进进一步的研究。