Due to the limited computational capabilities of edge devices, deep learning inference can be quite expensive. One remedy is to compress and transmit point cloud data over the network for server-side processing. Unfortunately, this approach can be sensitive to network factors, including available bitrate. Luckily, the bitrate requirements can be reduced without sacrificing inference accuracy by using a machine task-specialized codec. In this paper, we present a scalable codec for point-cloud data that is specialized for the machine task of classification, while also providing a mechanism for human viewing. In the proposed scalable codec, the "base" bitstream supports the machine task, and an "enhancement" bitstream may be used for better input reconstruction performance for human viewing. We base our architecture on PointNet++, and test its efficacy on the ModelNet40 dataset. We show significant improvements over prior non-specialized codecs.

翻译：由于边缘设备计算能力有限，深度学习推理成本较高。一种解决方案是通过网络压缩并传输点云数据至服务器端处理。然而，该方法易受网络因素（如可用比特率）影响。幸运的是，通过采用面向机器任务的专用编解码器，可在不牺牲推理准确率的前提下降低比特率需求。本文提出一种面向点云数据的可扩展编解码器，该编解码器专门针对机器分类任务优化，同时提供人工查看机制。在所提出的可扩展编解码器中，“基础”比特流支持机器任务，而“增强”比特流可用于提升输入重建质量以支持人工查看。我们基于PointNet++构建架构，并在ModelNet40数据集上验证其有效性。实验结果表明，该方法较现有非专用编解码器具有显著性能提升。