实现动态3D高斯点云的实时流传输，速度提升10-100倍。
GS-NFS: Bandwidth-adaptive Streaming of Dynamic Gaussian Splats and Point Clouds
- 基于GPU并行化压缩高斯点位置与属性，加速编码解码过程。
- 在保持高质量渲染的同时，编码解码速度比现有方法快1-2个数量级。
- 适合需要低延迟3D视频流的应用，如VR/AR实时交互。
动态3D高斯点阵（3DGS）作为3D视频流技术具有巨大潜力，能以高保真度表示复杂三维场景。每个3D视频帧由一组带有位置、尺度、旋转、透明度和颜色等属性的高斯点构成，可支持任意视角观看，但其数据量比2D视频帧大一个数量级以上。已有研究尝试压缩动态3DGS帧，但通常速度慢，因压缩算法难以高效加速。本文提出GS-NFS，通过开发针对高斯点位置与属性的新型GPU并行化编码算法，显著加速3DGS的压缩与解压，在GPU上实现全帧率编解码。结果表明，其编码解码速度比当前最优方法快1-2个数量级，同时保持竞争力的压缩性能与渲染质量。
原文摘要 · Abstract (English)
Dynamic 3D Gaussian Splatting (3DGS) holds great promise as a 3D video streaming technology since it can represent complex 3D scenes with high fidelity. In this approach, every frame in a 3D video represents the environment as a collection of Gaussians with position and other attributes such as scale, rotation, opacity, and color. Frames capture fine details, permit views from any arbitrary perspective, but are an order of magnitude, or more, larger than 2D video frames. A line of recent work has explored how to compress dynamic 3DGS frames, but these approaches are often slow, in part because their compression techniques are not amenable to efficient acceleration. GS-NFS accelerates dynamic 3DGS compression and decompression on a GPU, to the point where it can encode and decode at full frame rate. It achieves this by developing novel GPU-based parallelizations of existing algorithms for encoding both positions and attributes of Gaussians. As a result, it is 1-2 orders of magnitude faster than the state-of-the-art in encoding and decoding a frame, while offering competitive compression performance and rendering quality.
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