AI 中文总结
针对有损网络中点云压缩的数据包丢失问题，提出具备感知丢包率能力的ResPCC编解码器，通过CALM、SCI、MGLR、DBR等模块提升稳定性与率失真性能，在5%-30%丢包率下优于基线方法，为3D数据传输提供可靠方案。
AI 中文摘要
点云压缩（PCC）对3D数据的高效存储与传输至关重要。尽管近期基于学习的PCC方法在率失真（R-D）性能上表现良好，但它们通常依赖理想的传输条件。实际应用中，数据包丢失是常见问题，会严重破坏潜在特征，导致坐标漂移与几何退化。为应对这一挑战，我们提出ResPCC，这是首个专为应对数据丢失提供内在抗损能力设计的端到端神经点云编解码器。我们的框架具备感知丢包率的能力，可适配不同的数据包丢失条件：在编码器端，我们引入条件自适应潜在调制（CALM）模块，根据感知到的丢包率调整潜在特征分布，还提出空间-通道交织（SCI）机制，将通道级数据丢失转化为空间分散的元素级缺失模式；在解码器端，我们开发掩码感知图基潜在恢复（MGLR）模块，随后是基于字典的细化（DBR）阶段，以恢复受损特征并使其与规范先验对齐。在ShapeNet和SemanticKITTI数据集上，针对5%至30%的数据包丢失率开展的评估显示，ResPCC相较于基线方法始终展现出更优的稳定性与R-D性能。我们的框架在有损条件下仍能保持高重建保真度，为实际网络中的3D数据传输提供了可靠解决方案。代码可在该https网址获取。
英文摘要
Point cloud compression (PCC) is critical for efficient storage and transmission of 3D data. While recent learning-based PCC methods achieve good rate-distortion (R-D) performance, they generally rely on ideal transmission conditions. In practice, packet loss is a common issue and can severely distort latent features, causing coordinate drift and geometric degradation. To address this challenge, we present ResPCC, the first end-to-end neural point cloud codec designed to offer intrinsic resilience against data loss. Our framework is loss-rate-aware and adapts to diverse packet loss conditions. At the encoder, we introduce a Condition-Adaptive Latent Modulation (CALM) module to adjust latent feature distributions according to the perceived loss rate, as well as a Spatial-Channel Interleaving (SCI) mechanism that transforms channel-wise data extinction into spatially scattered element-wise missing patterns. At the decoder, we develop a Mask-Aware Graph-based Latent Restoration (MGLR) module, followed by a Dictionary-based Refinement (DBR) stage to recover corrupted features and align them with canonical priors. Evaluations on ShapeNet and SemanticKITTI under 5\% to 30\% packet loss rates show that ResPCC consistently delivers superior stability and R-D performance over baselines. Our framework maintains high reconstruction fidelity under lossy conditions, providing a reliable solution for 3D data transmission over practical networks. Code is available at https://github.com/starrynight314/ResPCC.
