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Towards Minimizing the LiDAR Sim-to-Real Domain Shift: Object-Level Local Domain Adaptation for 3D Point Clouds of

Sebastian Huch¹, Markus Lienkamp¹

¹Institute of Automotive Technology, Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich, 85748 Garching, Germany.

Sensors (Basel, Switzerland)

|December 23, 2023

Summary

This study introduces a new method to reduce the domain shift between simulated and real-world LiDAR data for autonomous vehicles. The approach significantly improves object detection performance by adapting 3D point clouds using domain invariance techniques.

Keywords:

LIDAR autonomous vehicles deep learning domain adaptation object detection point cloud synthetic data

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Area of Science:

Computer Vision
Robotics
Machine Learning

Background:

Autonomous vehicles require large labeled datasets for perception algorithms.
Synthetic data from simulations offers a cost-effective alternative to real-world data acquisition.
Domain shift between simulated and real-world data degrades model performance.

Purpose of the Study:

To minimize the domain shift between simulated and real-world LiDAR data.
To improve the performance of perception algorithms in autonomous vehicles.
To develop a scalable domain adaptation method for 3D point cloud data.

Main Methods:

Object-level adaptation of 3D point clouds by learning target domain characteristics.
Downsampling for domain invariance of input data.
Adversarial training using a point completion network and a discriminator.

Main Results:

Reduced sim-to-real domain shift by nearly 50% (from 8.63% to 4.36% 3D average precision).
Demonstrated significant improvement in object detection accuracy on a distribution-aligned dataset.
Validated the method's effectiveness in reducing domain shift.

Conclusions:

The proposed domain adaptation approach effectively minimizes the sim-to-real domain shift for LiDAR data.
The method is scalable and can adapt point clouds from any source domain using only target data.
This work contributes to more robust and reliable perception systems for autonomous vehicles.