Abstract
LiDAR scene generation is critical for mitigating real-world LiDAR data collection costs and enhancing the robustness of downstream perception tasks in autonomous driving. However, existing methods commonly struggle to capture geometric realism and global topological consistency. Recent LiDAR Diffusion Models (LiDMs) predominantly embed LiDAR points into the latent space for improved generation efficiency, which limits their interpretable ability to model detailed geometric structures and preserve global topological consistency. To address these challenges, we propose TopoLiDM, a novel framework that integrates graph neural networks (GNNs) with diffusion models under topological regularization for high-fidelity LiDAR generation. Our approach first trains a topological-preserving VAE to extract latent graph representations by graph construction and multiple graph convolutional layers. Then we freeze the VAE and generate novel latent topological graphs through the latent diffusion models. We also introduce 0-dimensional persistent homology (PH) constraints, ensuring the generated LiDAR scenes adhere to real-world global topological structures. Extensive experiments on the KITTI-360 dataset demonstrate TopoLiDM's superiority over state-of-the-art methods, achieving improvements of 22.6% lower Frechet Range Image Distance (FRID) and 9.2% lower Minimum Matching Distance (MMD). Notably, our model also enables fast generation speed with an average inference time of 1.68 samples/s, showcasing its scalability for real-world applications. We will release the related codes at this https URL.
Abstract (translated)
LiDAR场景生成对于降低真实世界中的激光雷达数据采集成本以及增强自动驾驶下游感知任务的鲁棒性至关重要。然而，现有方法通常难以捕捉几何真实性及全局拓扑一致性。最近提出的激光雷达扩散模型（LiDMs）主要通过将激光雷达点嵌入到潜在空间来提高生成效率，这限制了它们在建模详细几何结构和保持全局拓扑一致方面的解释能力。为了解决这些挑战，我们提出了一种新框架TopoLiDM，该框架整合了图神经网络（GNNs）与扩散模型，并通过拓扑正则化实现高保真度的激光雷达生成。 我们的方法首先训练一个保持拓扑结构的变分自编码器（VAE），通过构建图和多个图卷积层来提取潜在图表示。然后，我们将该VAE冻结并利用潜扩散模型生成新的潜在拓扑图。此外，我们还引入了零维持久同调（PH）约束，确保所生成的激光雷达场景符合真实世界的全局拓扑结构。 在KITTI-360数据集上进行的大量实验表明，TopoLiDM相比现有最先进方法有明显优势，在Frechet范围图像距离（FRID）和最小匹配距离（MMD）方面分别取得了22.6%和9.2%的改进。值得注意的是，我们的模型还具备快速生成速度，平均推断时间为每秒1.68个样本，展示了其在实际应用中的可扩展性。 相关代码将在以下网址发布：[链接] (请提供URL)。
URL
https://arxiv.org/abs/2507.22454
