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L-DIG: A GAN-Based Method for LiDAR Point Cloud Processing under Snow Driving Conditions.

Yuxiao Zhang¹, Ming Ding^1,2, Hanting Yang¹

¹Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-Ward, Nagoya 464-8601, Japan.

Sensors (Basel, Switzerland)

|November 14, 2023

Summary

This summary is machine-generated.

LiDAR depth images GAN (L-DIG) effectively removes snow noise from autonomous driving point clouds and synthesizes snow effects. This novel generative adversarial network model improves perception system reliability in adverse weather conditions.

Keywords:

CycleGAN LiDAR point cloud processing snow effect generation snow noise removal

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Area of Science:

Computer Vision
Autonomous Driving Systems
Sensor Data Processing

Background:

Snowfall significantly degrades LiDAR point cloud data quality in driving scenarios.
Existing snow removal methods, primarily outlier filters, are limited in effectiveness.
Autonomous driving systems require robust perception capabilities, even in snowy conditions.

Purpose of the Study:

To introduce a novel generative adversarial network (GAN) model, L-DIG (LiDAR depth images GAN), for LiDAR point cloud de-snowing and snow synthesis.
To enhance the perception capabilities of autonomous driving systems in snowy environments.
To develop a model capable of both removing and generating snow effects in LiDAR data.

Main Methods:

Utilized depth image representations of point clouds from unpaired datasets for training.
Implemented customized loss functions for depth images to maintain scale and structure consistency.
Developed a dual-discriminator architecture with a pixel-attention discriminator for snow capture near the ego vehicle and a downsampling convolutional discriminator for snow clusters.
Employed a 3D clustering algorithm for adaptive evaluation of various snow conditions.

Main Results:

The L-DIG model demonstrated superior performance in capturing snow and object features in LiDAR point clouds.
Experimental results showed a clear de-snowing effect on corrupted data.
The model successfully synthesized realistic snow effects onto clear LiDAR data.
The dual-discriminator approach effectively handled diverse snow conditions, from scattered points to dense clusters.

Conclusions:

The proposed L-DIG model offers an effective solution for mitigating snow interference in LiDAR data for autonomous driving.
The ability to both remove and synthesize snow provides a versatile tool for research and development.
The model's performance indicates a significant advancement in robust perception for autonomous vehicles in winter conditions.