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LiGenCam: Reconstruction of Color Camera Images from Multimodal LiDAR Data for Autonomous Driving.

Minghao Xu¹, Yanlei Gu², Igor Goncharenko³

¹Graduate School of Interdisciplinary Information Studies, The University of Tokyo, Tokyo 113-0033, Japan.

Sensors (Basel, Switzerland)

|July 30, 2025

Summary

Researchers developed LiGenCam, a deep learning model that reconstructs camera images from LiDAR data. This enhances perception system redundancy for automated driving, improving safety and reliability.

Keywords:

LiDAR autonomous driving generative adversarial networks image reconstruction

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Area of Science:

Computer Vision
Autonomous Driving Systems
Sensor Fusion

Background:

Automated driving systems rely on sensors like LiDAR and cameras for environmental perception.
Redundancy in perception systems is crucial for safety in Level 4+ autonomous vehicles.
Reconstructing camera images from LiDAR data is an underexplored area compared to the reverse process.

Purpose of the Study:

To propose a deep learning model, LiGenCam, for reconstructing camera images from multimodal LiDAR data.
To enhance the redundancy and robustness of perception systems in automated driving.
To investigate the effectiveness of using LiDAR data for image reconstruction.

Main Methods:

Developed LiGenCam, a Generative Adversarial Network (GAN)-based model.
Utilized multimodal LiDAR data (reflectance, ambient light, range).
Incorporated pixel-wise loss and semantic segmentation loss for guided reconstruction.

Main Results:

Multimodal LiDAR data improved realism and semantic consistency of reconstructed images.
Semantic segmentation loss further enhanced semantic consistency.
Ablation studies validated the model's components and performance.

Conclusions:

LiGenCam successfully reconstructs camera images from LiDAR data.
Multimodal LiDAR input and semantic loss contribute to improved image reconstruction quality.
The approach offers a viable method for sensor fusion and redundancy in autonomous driving perception.