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PointCompress3D -- A Point Cloud Compression Framework for Roadside LiDARs in Intelligent Transportation Systems
May 6, 2024, 4:45 a.m. | Walter Zimmer, Ramandika Pranamulia, Xingcheng Zhou, Mingyu Liu, Alois C. Knoll
cs.CV updates on arXiv.org arxiv.org
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. …
arxiv cloud compression cs.cv eess.iv framework intelligent intelligent transportation systems transportation type
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