发表机构
Central South University Changsha China; Malanshan Audio \& Video Laboratory Changsha China; Central South University; Malanshan Audio \& Video Laboratory（; ; ; ）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究3D高斯压缩在网络流传输中丢包问题，提出通过原子封装、分层随机分组及基于GNN的错误隐藏框架，改进丢包时的渲染效果，相比无隐藏传输有显著提升，平均PSNR退化限制在约3dB。
AI 中文摘要
3D高斯渲染（3DGS）和HAC++等压缩方案能实现高保真实时神经渲染，但网络流传输中其比特流在丢包时很脆弱。现有方法常将相关锚点属性分离成独立流，丢包会导致属性不一致和严重渲染伪影。我们提出抗丢包的3DGS传输和错误隐藏框架。编码器端，锚点级原子封装将每个锚点所有属性封装在一起，分层随机分组分散丢包。解码器端，将恢复视为先验感知属性修复。实验表明该方法相比无隐藏传输有显著改进，相对于无损HAC++参考，平均PSNR退化限制在约3dB。
英文摘要
3D Gaussian Splatting (3DGS) and recent compression schemes such as HAC++ enable high-fidelity real-time neural rendering, but their bitstreams are fragile under packet loss during network streaming. Existing compression methods often separate correlated anchor attributes into independent streams, so losing one packet can create attribute-inconsistent broken anchors and severe rendering artifacts. We propose a packet-loss robust 3DGS transmission and error concealment framework. On the encoder side, anchor-level atomic packaging jointly encapsulates all attributes of each anchor, converting corrupted-attribute failures into clean missing-anchor erasures. Stratified random grouping further disperses packet losses across the spatial domain to avoid large contiguous voids. On the decoder side, we formulate recovery as prior-aware attribute inpainting. A Context-Aware Residual Interpolation (CARI) branch uses hash-grid prior predictions and neighboring residuals to build a robust baseline, while a lightweight two-layer graph neural network with cross-attention over hash-grid priors refines high-frequency attribute residuals. Attribute-wise confidence control falls back to interpolation when learned predictions are unreliable. Experiments under 20 percent random packet loss on BungeeNeRF, Mip-NeRF 360, and Tanks and Temples show that the proposed method substantially improves over no-concealment transmission and limits average PSNR degradation to about 3 dB relative to the lossless HAC++ reference.
Comments21 pages, 3 figures, 3 tables
