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：在智能交通系统背景下，高效数据压缩对于管理路边激光雷达传感器获取的大规模点云数据至关重要。点云数据在高效存储、流式传输以及实时目标检测能力方面存在巨大需求。本文提出PointCompress3D——一种专为路边激光雷达量身定制的新型点云压缩框架。该框架解决了高分辨率点云压缩的挑战，同时保持与路边激光雷达传感器的精度和兼容性。我们基于实际场景的TUMTraf数据集家族，对三种前沿压缩方法进行了适配、扩展、集成与评估。在保持压缩体积低于105 Kb（实现50倍缩减）且目标检测性能与原数据持平的前提下，我们实现了10 FPS的帧率。通过大量实验与消融研究，最终在数据集中达到PSNR d2为94.46、BPP为6.54的性能。未来工作包括在实时系统上的部署。代码已发布在项目网站：https://pointcompress3d.github.io。