SG-LDM: Semantic-Guided LiDAR Generation via Latent-Aligned Diffusion

Understand

Lidar point cloud synthesis based on generative models offers a promising solution to augment deep learning pipelines, particularly when real-world data is scarce or lacks diversity.

• By enabling flexible object manipulation, this synthesis approach can significantly enrich training datasets and enhance discriminative models.
• However, existing methods focus on unconditional lidar point cloud generation, overlooking their potential for real-world applications.
• In this paper, we propose SG-LDM, a Semantic-Guided Lidar Diffusion Model that employs latent alignment to enable robust semantic-to-lidar synthesis.