In the context of Intelligent Transportation Systems (ITS), efficient data compression is crucial for managing large-scale point cloud data acquired by roadside LiDAR sensors. The demand for efficient storage, streaming, and real-time object detection capabilities for point cloud data is substantial. This work introduces PointCompress3D, a novel point cloud compression framework tailored specifically for roadside LiDARs. Our framework addresses the challenges of compressing high-resolution point clouds while maintaining accuracy and compatibility with roadside LiDAR sensors. We adapt, extend, integrate, and evaluate three cutting-edge compression methods using our real-world-based TUMTraf dataset family. We achieve a frame rate of 10 FPS while keeping compression sizes below 105 Kb, a reduction of 50 times, and maintaining object detection performance on par with the original data. In extensive experiments and ablation studies, we finally achieved a PSNR d2 of 94.46 and a BPP of 6.54 on our dataset. Future work includes the deployment on the live system. The code is available on our project website: https://pointcompress3d.github.io.

翻译：在智能交通系统（ITS）背景下，高效的数据压缩对于管理路侧激光雷达传感器获取的大规模点云数据至关重要。点云数据对高效存储、流式传输以及实时目标检测能力的需求十分巨大。本文提出了PointCompress3D，一种专为路侧激光雷达定制的新型点云压缩框架。我们的框架解决了压缩高分辨率点云同时保持精度以及与路侧激光雷达传感器兼容性的挑战。我们基于真实世界数据构建的TUMTraf数据集系列，对三种前沿压缩方法进行了适配、扩展、集成与评估。我们实现了10 FPS的帧率，同时将压缩后数据大小保持在105 Kb以下（压缩率达到50倍），并保持了与原始数据相当的目标检测性能。在大量实验与消融研究中，我们最终在数据集上取得了94.46的PSNR d2和6.54的BPP。未来的工作包括在实际系统中进行部署。代码可在我们的项目网站获取：https://pointcompress3d.github.io。