PointCompress3D – A Point Cloud Compression Framework for Roadside LiDARs in Intelligent Transportation Systems
arxiv(2024)
摘要
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.
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