DiffMap: Enhancing Map Segmentation with Map Prior Using Diffusion Model

TL;DR

DiffMap introduces a diffusion model-based method to incorporate structured priors into map segmentation, significantly improving the realism and accuracy of semantic map layouts for autonomous driving applications.

Contribution

It presents a novel diffusion model approach that enhances existing map segmentation models by modeling structured priors, leading to more realistic and consistent HD maps.

Findings

Enhanced map segmentation accuracy

Better structural consistency in generated maps

Seamless integration with existing models

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.