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Updated: Mar 15, 2026

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Lightweight LiDAR-Based 3D Human Pose Estimation via 2D Depth Images for Autonomous Driving.

Gyu-Yeon Kim1, Somi Park2, Sunkyung Lee2

  • 1Graduate Program in Smart Factory, Ewha Womans University, Seoul 03760, Republic of Korea.

Sensors (Basel, Switzerland)
|March 14, 2026
PubMed
Summary

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This study introduces a lightweight 3D human pose estimation method using LiDAR for autonomous driving. It enhances safety by efficiently predicting pedestrian movements, even with self-occlusions.

Area of Science:

  • Robotics and Computer Vision
  • Autonomous Systems
  • Sensor Fusion

Background:

  • Pedestrian pose estimation is vital for autonomous driving safety.
  • LiDAR sensors offer accurate 3D data and privacy benefits for pose estimation.
  • Existing LiDAR methods are computationally intensive, limiting real-time application.

Purpose of the Study:

  • To develop a computationally efficient LiDAR-based 3D human pose estimation method for autonomous driving.
  • To address the high computational cost and memory requirements of current methods.
  • To improve robustness in challenging poses like side-views and bending.

Main Methods:

  • Projecting LiDAR point clouds into 2D depth images to reduce complexity.
  • Utilizing a lightweight MoveNet model for initial pose estimation.
Keywords:
2D depth image3D human pose estimationLiDARautonomous drivinglightweight

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  • Implementing an efficient 3D lifting technique.
  • Introducing a self-occlusion correction algorithm for improved accuracy.

    • Main Results:

    • Achieved competitive 3D human pose estimation accuracy.
    • Significantly improved computational efficiency compared to direct 3D processing methods.
    • Demonstrated robustness under side-view and bending poses through self-occlusion correction.

    Conclusions:

    • The proposed lightweight method is practical and scalable for real-time autonomous vehicle applications.
    • The approach balances accuracy and efficiency, making it suitable for resource-constrained systems.
    • Enhanced pedestrian detection and tracking capabilities in autonomous driving.