让3D高斯点云可逐步传输，画质更好、速度更快。
ProGS: Towards Progressive Coding for 3D Gaussian Splatting
- 用父节点封闭的八叉树前缀组织数据，支持逐级解码渲染。
- 同一训练模型生成五个不同质量等级，比现有方法少用30%以上数据量。
- 所有中间层级都能实时渲染，适合网络条件差的场景使用。
3D高斯点云的渐进式传输要求每个已完成阶段的接收数据均可独立解码并直接渲染。本文提出ProGS，将基于锚点的3DGS组织为父节点封闭的八叉树前缀。通过父因果熵编码、层级平衡锚点增长、有界多前缀训练及轻量级父锚精修，提升早期前缀质量，且不改变完整模型的渲染路径。一次固定λ的训练即可从单一比特流生成五个可部署的率-质点。在三个数据集共17个场景上的实验表明，与渐进式和单速率基线相比，ProGS在率失真性能、渲染速度和传输效率上均表现更优。在每个数据集的代表性场景中，达到相同质量目标时，所需字节数比HAC-Rand减少30.7%~60.7%，比HAC++-Rand减少22.6%~53.4%。在三个数据集平均值上，ProGS-LR比HAC-high少用4.7%~6.1%字节，同时提升SSIM 0.002~0.041，降低LPIPS 4.8~51.2。父节点封闭语法确保每个前缀因果可解码且无需未来拓扑信息即可直接渲染。ProGS-HR在所有数据集平均值上均优于PCGS，且五个前缀均可实时渲染。代码已开源。
原文摘要 · Abstract (English)
Progressive transmission of 3D Gaussian Splatting (3DGS) requires each completed transmission stage to be decodable from received data and directly renderable. This work presents ProGS, a progressive codec that organizes anchor-based 3DGS as parent-closed octree prefixes. ProGS combines parent-causal entropy coding, level-balanced anchor growth, bounded multi-prefix training, and lightweight parent-anchor refinement to improve early-prefix quality without altering the complete-model rendering path. One fixed-$λ$ training run yields five deployable rate--quality points from a single bitstream. Experiments on 17 scenes from three datasets evaluate rate--distortion performance, rendering speed, and transmission efficiency against progressive and single-rate baselines. On one representative scene per dataset, ProGS reaches a common quality target with 30.7 $\sim$ 60.7\% fewer bytes than HAC-Rand and 22.6 $\sim$ 53.4\% fewer bytes than HAC++-Rand. Across the three dataset averages, ProGS-LR uses 4.7 $\sim$ 6.1\% fewer bytes than HAC-high while improving SSIM by 0.002 $\sim$ 0.041 and reducing LPIPS by 4.8 $\sim$ 51.2\%. The parent-closed syntax makes every prefix causally decodable and directly renderable without future topology. ProGS-HR also yields higher endpoint SSIM and lower LPIPS than PCGS across all three dataset averages, and all five prefixes render in real time. Code is available at https://github.com/ZhiyeTang/ProGS-Official
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。