Perceive-Sample-Compress: Towards Real-Time 3D Gaussian Splatting
This addresses storage and computational bottlenecks for real-time 3D rendering applications, representing an incremental improvement to existing 3DGS methods.
The paper tackles the problem of large-scale scene management and storage inefficiency in 3D Gaussian Splatting (3DGS) for novel view synthesis, introducing a perceive-sample-compress framework that achieves significant compression ratios while maintaining real-time rendering speed and high visual quality.
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.