Abstract
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: this https URL.
Abstract (translated)
在智能交通系统（ITS）的背景下，高效的数据压缩对于通过路边激光雷达传感器获取的大规模点云数据的管理至关重要。对于点云数据的高效存储、流式处理和实时物体检测功能的需求非常大。本文介绍了一个专门为路边激光雷达设计的点云压缩框架——PointCompress3D。我们的框架通过在TUMTraf现实数据集家族上使用，解决了对高分辨率点云压缩保持准确性和与路边激光雷达传感器兼容性的挑战。我们使用基于现实数据的TUMTraf数据集家族，采用三种最先进的压缩方法进行调整、扩展、集成并评估。我们达到10 FPS的帧率，同时将压缩大小保持在105 Kb以下，压缩比降低了50倍，并保持与原始数据相同的物体检测性能。在广泛的实验和消融研究中，最终我们在数据集上实现了94.46 PSNR和6.54 BPP的值。未来的工作包括将该系统部署到实际环境中。代码可在本项目网站上获取：https://www.tum.de/。
URL
https://arxiv.org/abs/2405.01750
