An Auto-Labeling tool for Occupancy Grid and BEV in Autonomous Driving Dataset

Accurate 3D semantic labeling is critical for scene understanding and navigation in autonomous driving systems. However, manual annotation of 3D point clouds is labor-intensive and difficult to scale across diverse environments. To address this challenge, we propose a modular-based automated labeling framework that leverages 2D semantic cues to facilitate 3D scene understanding. The system integrates state-of-the-art models—Grounding DINO, SAM2, YOLOv7, and Generative AI (GAI) —to perform high-quality 2D semantic segmentation, which is then processing into semantic LiDAR point clouds. Afterward, the framework employs multi-frame fusion and static-dynamic scene separation to construct dense 3D semantic occupancy grids. Additionally, GAI serves as a prompt-refinement module, improving segmentation accuracy at object boundaries and in distant regions. Experiments on real-world street-view datasets demonstrate that AutoLabel-2D significantly enhances labeling efficiency and segmentation completeness, offering strong generalization across scenes. This framework provides a scalable and effective solution for high-definition mapping and semantic perception in autonomous driving applications.