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

TL;DR

This paper introduces LiGenCam, a GAN-based model that reconstructs realistic camera images from LiDAR data, improving redundancy and safety in autonomous driving systems.

Contribution

The novel use of multimodal LiDAR data and a segmentation-based loss to reconstruct semantically consistent camera images.

Findings

Multimodal LiDAR data improves the realism and semantic consistency of reconstructed images.

Adding a segmentation-based loss enhances the semantic fidelity of the reconstructions.

LiGenCam demonstrates potential for data augmentation and sensor redundancy in autonomous vehicles.

Abstract

What are the main findings?
Color camera images can be realistically and semantically reconstructed from multimodal LiDAR data using a GAN-based model.The fusion of multiple LiDAR modalities enhances reconstruction quality, and the incorporation of a segmentation-based loss further improves the reconstruction fidelity.

Color camera images can be realistically and semantically reconstructed from multimodal LiDAR data using a GAN-based model.

The fusion of multiple LiDAR modalities enhances reconstruction quality, and the incorporation of a segmentation-based loss further improves the reconstruction fidelity.

What is the implication of the main finding?
LiDAR can serve as a backup to cameras by reconstructing semantically meaningful visual information, enhancing system redundancy and safety in autonomous driving.LiGenCam has the potential to perform data augmentation by generating virtual camera viewpoints using panoramic LiDAR data.

LiDAR can serve as a backup to cameras by reconstructing semantically meaningful visual information, enhancing system redundancy and safety in autonomous driving.

LiGenCam has the potential to perform data augmentation by generating virtual camera viewpoints using panoramic LiDAR data.

The automotive industry is advancing toward fully automated driving, where perception systems rely on complementary sensors such as LiDAR and cameras to interpret the vehicle’s surroundings. For Level 4 and higher vehicles, redundancy is vital to prevent safety-critical failures. One way to achieve this is by using data from one sensor type to support another. While much research has focused on reconstructing LiDAR point cloud data using camera images, limited work has been conducted on the reverse process—reconstructing image data from LiDAR. This paper proposes a deep learning model, named LiDAR Generative Camera (LiGenCam), to fill this gap. The model reconstructs camera images by utilizing multimodal LiDAR data, including reflectance, ambient light, and range information. LiGenCam is developed based on the Generative Adversarial Network framework, incorporating pixel-wise loss and semantic segmentation loss to guide reconstruction, ensuring both pixel-level similarity and semantic coherence. Experiments on the DurLAR dataset demonstrate that multimodal LiDAR data enhances the realism and semantic consistency of reconstructed images, and adding segmentation loss further improves semantic consistency. Ablation studies confirm these findings.

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