Abstract
Constructing high-definition (HD) maps is a crucial requirement for enabling autonomous driving. In recent years, several map segmentation algorithms have been developed to address this need, leveraging advancements in Bird's-Eye View (BEV) perception. However, existing models still encounter challenges in producing realistic and consistent semantic map layouts. One prominent issue is the limited utilization of structured priors inherent in map segmentation masks. In light of this, we propose DiffMap, a novel approach specifically designed to model the structured priors of map segmentation masks using latent diffusion model. By incorporating this technique, the performance of existing semantic segmentation methods can be significantly enhanced and certain structural errors present in the segmentation outputs can be effectively rectified. Notably, the proposed module can be seamlessly integrated into any map segmentation model, thereby augmenting its capability to accurately delineate semantic information. Furthermore, through extensive visualization analysis, our model demonstrates superior proficiency in generating results that more accurately reflect real-world map layouts, further validating its efficacy in improving the quality of the generated maps.
Abstract (translated)
构建高清晰度（HD）地图是实现自动驾驶的关键要求。近年来，为了解决这个问题，已经开发了几种地图分割算法，利用了Bird's-Eye View（BEV）感知技术的发展。然而，现有的模型在生成逼真的且一致的语义图布局方面仍然遇到困难。一个突出的问题是，地图分割掩码中固有结构的优先利用程度有限。鉴于这一点，我们提出了DiffMap，一种专门利用潜在扩散模型来建模地图分割掩码结构优先级的全新方法。通过引入这项技术，现有语义分割方法的性能可以显著提高，而且分割输出中存在的某些结构错误可以通过有效矫正来消除。值得注意的是，所提出的模块可以轻松地集成到任何地图分割模型中，从而增强其准确描绘语义信息的能力。此外，通过广泛的可视化分析，我们的模型证明了其在生成更准确反映真实世界地图布局的结果方面具有卓越的性能，进一步验证了其在提高生成的地图质量方面的有效性。
URL
https://arxiv.org/abs/2405.02008
