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

Walter Zimmer, Ramandika Pranamulia, Xingcheng Zhou, Mingyu Liu, Alois C. Knoll

TL;DR

This work introduces PointCompress3D, a novel point cloud compression framework tailored specifically for roadside LiDARs, which addresses the challenges of compressing high-resolution point clouds while maintaining accuracy and compatibility with roadside LiDAR sensors.

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.

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

TL;DR

This work introduces PointCompress3D, a novel point cloud compression framework tailored specifically for roadside LiDARs, which addresses the challenges of compressing high-resolution point clouds while maintaining accuracy and compatibility with roadside LiDAR sensors.

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.
Paper Structure (31 sections, 3 equations, 11 figures, 4 tables, 1 algorithm)

This paper contains 31 sections, 3 equations, 11 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Our contribution is the following:
  3. Related Work
  4. Geometry compression
  5. Octree representation
  6. Voxelization
  7. 3D Transformation
  8. Methodology
  9. Method Selection
  10. Extensions
  11. Evaluation
  12. Performance Metrics
  13. Experiment Setup
  14. Quantitative Results
  15. Qualitative Results
  16. ...and 16 more sections

Figures (11)

  • Figure 1: Visualization of the processing pipeline of our PointCompress3D point cloud compression and streaming framework. Our framework takes raw roadside LiDAR point clouds as input, processes them, and outputs compressed point clouds to facilitate downstream application tasks like 3D object detection, data storage, and real-time point cloud streaming on an ITS test bed for autonomous driving.
  • Figure 2: Visualization of ablation study results for Depoco on the TUMTraf V2X Cooperative Perceptionzimmer2024tumtrafv2x dataset. From left to right: a) Reference image b) View of the original point cloud. b) Experiment E1: Reconstructed point cloud with a subsampling distance of 3.0 and a minimum kernel radius of 1.5. c) Experiment E3: Reconstructed point cloud with a subsampling distance of 1.0 and a minimum kernel radius of 1.2. Both reconstructed point clouds are generated with max. 30,000 points and a grid size of 40x40x15 m.
  • Figure 3: Ablation study results for Depoco: Adjusting the minimum kernel radius parameters by 0.5 times and 2 times the original value, respectively.
  • Figure 4: Ablation study results for Depoco: We set the kernel radius in encoding block to 10 and 0.05 (top row) while keeping the decoding block values constant at 0.05. Subsequently, we set kernel radius in the decoding block to 1 and 0.01 (bottom row) while keeping the encoding block values constant at 1.0.
  • Figure 5: Visualization of the ablation study results for Depoco for the max. number of points. The point cloud shows a side view of a truck and a car with 5,000, 12,500, 25,000, 50,000, 100,000, and 200,000 points.
  • ...and 6 more figures