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An Efficient LiDAR Point Cloud Map Coding Scheme Based on Segmentation and Frame-Inserting Network.

Qiang Wang1,2, Liuyang Jiang1,3, Xuebin Sun4

  • 1College of Information and Control Engineering, Qingdao University of Technology, Qingdao 266525, China.

Sensors (Basel, Switzerland)
|July 27, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient coding scheme for LiDAR point cloud maps, compressing them to 1/24th their original size with millimeter precision. The method effectively reduces data redundancy for improved storage and transmission.

Keywords:
LiDARcodinginterpolationpoint cloud mapsegmentation

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Area of Science:

  • Computer Vision
  • Robotics
  • Data Compression

Background:

  • LiDAR point cloud maps are crucial for autonomous systems but are large and data-intensive.
  • Existing compression methods struggle to balance efficiency and precision for dynamic point cloud data.

Purpose of the Study:

  • To develop an efficient coding scheme for compressing LiDAR point cloud maps.
  • To reduce temporal and spatial redundancies in point cloud data for improved storage and transmission.

Main Methods:

  • The proposed scheme treats point cloud map compression as a sequence compression problem.
  • It employs intra-coding (segmentation-based prediction) and inter-coding (interpolation network) techniques.
  • Only the differences between original and predicted data are coded, enabling efficient reconstruction.

Main Results:

  • The coding scheme achieved a compression ratio of 1/24 with 2 mm-level precision on the KITTI dataset.
  • It significantly reduced temporal and spatial redundancies in the point cloud maps.
  • Outperformed existing octree and Google Draco compression algorithms.

Conclusions:

  • The developed coding scheme offers a highly efficient and precise method for LiDAR point cloud map compression.
  • This advancement is vital for applications requiring large-scale 3D data management, such as autonomous driving.
  • The approach demonstrates superior performance compared to current state-of-the-art algorithms.