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

2025-06-30

AI Summary

Existing LiDAR point cloud generation methods primarily focus on unconditional synthesis, failing to effectively leverage semantic priors and thus inadequately supporting data augmentation for downstream perception tasks. To address this, we propose SG-LDM—the first semantic-guided LiDAR diffusion model—which enables high-fidelity, controllable generation directly in the native point cloud space via a latent-space-aligned, semantic-conditioned diffusion mechanism. Furthermore, we introduce an end-to-end diffusion-based cross-domain translation framework that jointly integrates semantic conditional generation with domain-adaptive data augmentation. Experiments demonstrate that SG-LDM achieves state-of-the-art performance in generation quality; moreover, our translation framework significantly improves downstream task performance—e.g., LiDAR point cloud segmentation—establishing a novel paradigm for semantic-driven autonomous driving perception systems.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Generation
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

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. By directly operating in the native lidar space and leveraging explicit semantic conditioning, SG-LDM achieves state-of-the-art performance in generating high-fidelity lidar point clouds guided by semantic labels. Moreover, we propose the first diffusion-based lidar translation framework based on SG-LDM, which enables cross-domain translation as a domain adaptation strategy to enhance downstream perception performance. Systematic experiments demonstrate that SG-LDM significantly outperforms existing lidar diffusion models and the proposed lidar translation framework further improves data augmentation performance in the downstream lidar segmentation task.

Problem

• Generates high-fidelity lidar point clouds using semantic guidance
• Enables cross-domain lidar translation for domain adaptation
• Enhances training datasets when real-world data is scarce

Innovation

• Semantic-guided latent-aligned diffusion for LiDAR
• Native LiDAR space with explicit semantic conditioning
• Diffusion-based cross-domain LiDAR translation framework