Over the past year, 3D Gaussian Splatting (3DGS) has received significant attention for its ability to represent 3D scenes in a perceptually accurate manner. However, it can require a substantial amount of storage since each splat's individual data must be stored. While compression techniques offer a potential solution by reducing the memory footprint, they still necessitate retrieving the entire scene before any part of it can be rendered. In this work, we introduce a novel approach for progressively rendering such scenes, aiming to display visible content that closely approximates the final scene as early as possible without loading the entire scene into memory. This approach benefits both on-device rendering applications limited by memory constraints and streaming applications where minimal bandwidth usage is preferred. To achieve this, we approximate the contribution of each Gaussian to the final scene and construct an order of prioritization on their inclusion in the rendering process. Additionally, we demonstrate that our approach can be combined with existing compression methods to progressively render (and stream) 3DGS scenes, optimizing bandwidth usage by focusing on the most important splats within a scene. Overall, our work establishes a foundation for making remotely hosted 3DGS content more quickly accessible to end-users in over-the-top consumption scenarios, with our results showing significant improvements in quality across all metrics compared to existing methods.
在过去一年中，3D 高斯点喷射（3DGS）因其以感知准确的方式表示 3D 场景而受到了广泛关注。然而，由于每个点喷射的单独数据必须被存储，这可能需要大量的存储空间。虽然压缩技术提供了通过减少内存占用的潜在解决方案，但它们仍然需要在渲染任何部分之前检索整个场景。在这项工作中，我们提出了一种新颖的方法来逐步渲染这些场景，旨在尽早显示接近最终场景的可见内容，而无需将整个场景加载到内存中。这种方法对受内存限制的设备渲染应用程序和对带宽使用有最低要求的流式应用程序都具有优势。为此，我们近似每个高斯点对最终场景的贡献，并建立了一个优先级排序，以确定它们在渲染过程中的包含顺序。此外，我们展示了我们的方法可以与现有的压缩方法结合使用，以逐步渲染（和流式传输）3DGS 场景，通过关注场景中最重要的点喷射来优化带宽使用。总体而言，我们的工作为在超高清视频消费场景中更快地访问远程托管的 3DGS 内容奠定了基础，我们的结果在所有指标上相比现有方法显示了显著的质量提升。