MapSeg: Segmentation guided structured model for online HD map
  construction
Mingchao Jiang Yin Cheng Linghai Liu
The development of online high-definition maps is significant since they
provide real-time, accurate, and updatable geographic information for
location-based applications, such as autonomous driving and intelligent
transportation, thus improving the performance and reliability of these
applications. Previous works, such as VectorMapNet and MapTR, show that direct
model generation of vectorized HD maps is a promising solution. However, these
methods did not take into account the usage of global semantic information to
improve map construction accuracy. To address this limitation, we propose a
segmentation-guided structured model (MapSeg) for online HD map construction.
Specifically, we added a UV segmentation module (USM) and a BEV segmentation
module (BSM) based on the MapTR structure, enabling the model to better capture
the semantic information.