PointCompress3D: A Point Cloud Compression Framework for Roadside LiDARs in Intelligent Transportation Systems

TL;DR

PointCompress3D is a new point cloud compression framework designed for roadside LiDARs in intelligent transportation, achieving high compression ratios, real-time processing, and preserving object detection accuracy.

Contribution

It introduces a specialized compression framework for roadside LiDAR point clouds, extending and evaluating multiple methods on real-world data for the first time.

Findings

Achieves 50x compression reduction

Maintains object detection performance

Operates at 10 FPS with small data sizes

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: https://pointcompress3d.github.io.