针对点云检测的云边协同传输难题，提出高效压缩与可靠传输方案。
Point Cloud Feature Coding for Object Detection over an Error-Prone Cloud-Edge Collaborative System
- 设计轻量级特征压缩模块，去除无关区域并降维相关通道。
- 结合自适应编码与LDPC纠错，在0dB信噪比下实现172倍压缩率。
- 适合低延迟、低功耗场景下的3D目标检测系统部署。
云边协同通过融合边缘与云端计算优势提升机器感知能力。边缘设备采集原始数据（如3D点云）并提取显著特征，传输至云端进行深度分析与数据融合。然而，如何在低延迟、低功耗条件下高效可靠地传输特征仍具挑战。本文聚焦点云目标检测，提出基于源编码与信道编码的任务驱动压缩与传输框架。为满足边缘设备的低时延与低功耗需求，设计了轻量但有效的特征压缩模块，通过剔除多尺度表示中最深层中任务无关区域，并对任务相关区域进行通道维度降维。随后，采用信噪比自适应信道编码动态编码压缩特征的属性信息，同时使用低密度奇偶校验（LDPC）编码保障几何信息的可靠传输。云端侧，信噪比自适应解码器指导属性信息解码，LDPC解码器纠正几何错误。最后，通过特征解压缩模块恢复通道维度，结合扩散模型的特征上采样模块重建浅层特征，实现多尺度特征重构。在KITTI数据集上，该方法在0dB信噪比无线信道下实现172倍特征压缩，对易、中、难三类目标的3D平均精度分别达到93.17%、86.96%和77.25%。源代码将发布于GitHub：https://github.com/yuanhui0325/T-PCFC。
原文摘要 · Abstract (English)
Cloud-edge collaboration enhances machine perception by combining the strengths of edge and cloud computing. Edge devices capture raw data (e.g., 3D point clouds) and extract salient features, which are sent to the cloud for deeper analysis and data fusion. However, efficiently and reliably transmitting features between cloud and edge devices remains a challenging problem. We focus on point cloud-based object detection and propose a task-driven point cloud compression and reliable transmission framework based on source and channel coding. To meet the low-latency and low-power requirements of edge devices, we design a lightweight yet effective feature compaction module that compresses the deepest feature among multi-scale representations by removing task-irrelevant regions and applying channel-wise dimensionality reduction to task-relevant areas. Then, a signal-to-noise ratio (SNR)-adaptive channel encoder dynamically encodes the attribute information of the compacted features, while a Low-Density Parity-Check (LDPC) encoder ensures reliable transmission of geometric information. At the cloud side, an SNR-adaptive channel decoder guides the decoding of attribute information, and the LDPC decoder corrects geometry errors. Finally, a feature decompaction module restores the channel-wise dimensionality, and a diffusion-based feature upsampling module reconstructs shallow-layer features, enabling multi-scale feature reconstruction. On the KITTI dataset, our method achieved a 172-fold reduction in feature size with 3D average precision scores of 93.17%, 86.96%, and 77.25% for easy, moderate, and hard objects, respectively, over a 0 dB SNR wireless channel. Our source code will be released on GitHub at: https://github.com/yuanhui0325/T-PCFC.
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