La La LiDAR: Large-Scale Layout Generation from LiDAR Data

• URL: http://arxiv.org/abs/2508.03691v1
• Date: Tue, 05 Aug 2025 17:59:55 GMT
• Title: La La LiDAR: Large-Scale Layout Generation from LiDAR Data
• Authors: Youquan Liu, Lingdong Kong, Weidong Yang, Xin Li, Ao Liang, Runnan Chen, Ben Fei, Tongliang Liu,
• Abstract summary: Controllable generation of realistic LiDAR scenes is crucial for applications such as autonomous driving and robotics.<n>We propose Large-scale Layout-guided LiDAR generation model ("La La LiDAR"), a novel layout-guided generative framework.<n>La La LiDAR achieves state-of-the-art performance in both LiDAR generation and downstream perception tasks.
• Score: 45.5317990948996
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Controllable generation of realistic LiDAR scenes is crucial for applications such as autonomous driving and robotics. While recent diffusion-based models achieve high-fidelity LiDAR generation, they lack explicit control over foreground objects and spatial relationships, limiting their usefulness for scenario simulation and safety validation. To address these limitations, we propose Large-scale Layout-guided LiDAR generation model ("La La LiDAR"), a novel layout-guided generative framework that introduces semantic-enhanced scene graph diffusion with relation-aware contextual conditioning for structured LiDAR layout generation, followed by foreground-aware control injection for complete scene generation. This enables customizable control over object placement while ensuring spatial and semantic consistency. To support our structured LiDAR generation, we introduce Waymo-SG and nuScenes-SG, two large-scale LiDAR scene graph datasets, along with new evaluation metrics for layout synthesis. Extensive experiments demonstrate that La La LiDAR achieves state-of-the-art performance in both LiDAR generation and downstream perception tasks, establishing a new benchmark for controllable 3D scene generation.

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