Abstract
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.