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.
Abstract (translated)
可控生成逼真的激光雷达场景对于自动驾驶和机器人技术等应用至关重要。尽管最近基于扩散模型的方案在高保真激光雷达生成方面取得了成功，但这些模型缺乏对前景物体及其空间关系的显式控制，这限制了它们在场景模拟和安全验证中的实用性。为了解决这些问题，我们提出了一种新的布局引导生成框架——大规模布局引导激光雷达生成模型（"La La LiDAR"），该模型引入了语义增强的场景图扩散，并结合了感知上下文关系的情境条件，用于结构化的激光雷达布局生成。随后通过前景感知控制注入来完成完整场景的生成，从而在确保空间和语义一致性的同时实现对象放置的自定义化控制。 为了支持我们的结构化激光雷达生成工作，我们还引入了Waymo-SG和nuScenes-SG两个大规模激光雷达场景图数据集，并提出了新的布局合成评估指标。大量的实验表明，La La LiDAR在激光雷达生成以及下游感知任务中均达到了最先进的性能水平，为可控的3D场景生成设定了一个新的基准标准。
URL
https://arxiv.org/abs/2508.03691
