提升3D高斯点云实时渲染效率与存储密度
Perceive-Sample-Compress: Towards Real-Time 3D Gaussian Splatting
- 分层感知-采样-压缩框架，动态优化关键区域细节
- 支持真实场景下实时渲染，内存占用降低显著
- 适合需要高效3D重建的AR/VR与机器人应用
近年来，3D高斯点云（3DGS）在实时、逼真新视角合成方面展现出强大能力。然而，传统3DGS表示在大规模场景管理与高效存储方面仍存在挑战，尤其在复杂环境或计算资源受限时表现不佳。为此，我们提出一种全新的感知-采样-压缩框架。首先，设计场景感知补偿算法，在各层级智能优化高斯参数，优先保障视觉重要区域的渲染质量，同时提升资源利用效率。其次，引入金字塔采样表示，实现多层级高斯原语的有序管理。最后，为高效存储所提分层金字塔结构，开发广义高斯混合模型压缩算法，在不损失视觉保真度的前提下实现显著压缩比。大量实验表明，该方法在保持实时渲染速度的同时，大幅提升了内存效率与视觉质量。
原文摘要 · Abstract (English)
Recent advances in 3D Gaussian Splatting (3DGS) have demonstrated remarkable capabilities in real-time and photorealistic novel view synthesis. However, traditional 3DGS representations often struggle with large-scale scene management and efficient storage, particularly when dealing with complex environments or limited computational resources. To address these limitations, we introduce a novel perceive-sample-compress framework for 3D Gaussian Splatting. Specifically, we propose a scene perception compensation algorithm that intelligently refines Gaussian parameters at each level. This algorithm intelligently prioritizes visual importance for higher fidelity rendering in critical areas, while optimizing resource usage and improving overall visible quality. Furthermore, we propose a pyramid sampling representation to manage Gaussian primitives across hierarchical levels. Finally, to facilitate efficient storage of proposed hierarchical pyramid representations, we develop a Generalized Gaussian Mixed model compression algorithm to achieve significant compression ratios without sacrificing visual fidelity. The extensive experiments demonstrate that our method significantly improves memory efficiency and high visual quality while maintaining real-time rendering speed.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。