A Task-Driven Scene-Aware LiDAR Point Cloud Coding Framework for Autonomous Vehicles

Xuebin Sun, Miaohui Wang, Jingxin Du, Yuxiang Sun, Shing Shin Cheng, Wuyuan Xie

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)


LiDAR sensors are almost indispensable for autonomous robots to perceive the surrounding environment. However, the transmission of large-scale LiDAR point clouds is highly bandwidth-intensive, which can easily lead to transmission problems, especially for unstable communication networks. Meanwhile, existing LiDAR data compression is mainly based on rate-distortion optimization, which ignores the semantic information of ordered point clouds and the task requirements of autonomous robots. To address these challenges, this article presents a task-driven <underline>S</underline>cene-<underline>A</underline>ware <underline>L</underline>iDAR <underline>P</underline>oint <underline>C</underline>louds <underline>C</underline>oding (SA-LPCC) framework for autonomous vehicles. Specifically, a semantic segmentation model is developed based on multi-dimension information, in which both 2D texture and 3D topology information are fully utilized to segment movable objects. Further, a prediction-based deep network is explored to remove the spatial-temporal redundancy. The experimental results on the benchmark semantic KITTI dataset validate that our SA-LPCC achieves state-of-the-art performance in terms of the reconstruction quality and storage space for downstream tasks. We believe that SA-LPCC jointly considers the scene-aware characteristics of movable objects and removes the spatial-temporal redundancy from an end-to-end learning mechanism, which will boost the related applications from algorithm optimization to industrial products.

Original languageEnglish
Pages (from-to)1-11
Number of pages11
JournalIEEE Transactions on Industrial Informatics
Publication statusAccepted/In press - 2022


  • Autonomous vehicles
  • autonomous vehicles
  • Encoding
  • Feature extraction
  • Laser radar
  • LiDAR point clouds
  • Point cloud compression
  • semantic segmentation
  • Task analysis
  • Three-dimensional displays

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering


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