MapSeg: Segmentation guided structured model for online HD map construction
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Autonomous DrivingBEV SegmentationSegmentationSimilar Papers 제목 키워드 기반
MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling
Robust segmentation is critical for deriving quantitative measures from large-scale, multi-center, and longitudinal medical scans. Manually annotating medical scans, however, is expensive and labor-intensive and may not …
Domain AdaptationDomain GeneralizationImage SegmentationMedical Image Segmentation+5SMOL-MapSeg: Show Me One Label as prompt
Historical maps offer valuable insights into changes on Earth's surface but pose challenges for modern segmentation models due to inconsistent visual styles and symbols. While deep learning models such as UNet and pre-tr…
Autonomous DrivingICDAR 2021 Competition on Historical Map Segmentation
This paper presents the final results of the ICDAR 2021 Competition on Historical Map Segmentation (MapSeg), encouraging research on a series of historical atlases of Paris, France, drawn at 1/5000 scale between 1894 and…
Contour DetectionDocument Layout AnalysisInstance SegmentationLine Detection+2Active Scene Understanding via Online Semantic Reconstruction
We propose a novel approach to robot-operated active understanding of unknown indoor scenes, based on online RGBD reconstruction with semantic segmentation. In our method, the exploratory robot scanning is both driven by…
Scene ParsingScene UnderstandingSemantic SegmentationMapFusion: A General Framework for 3D Object Detection with HDMaps
3D object detection is a key perception component in autonomous driving. Most recent approaches are based on Lidar sensors only or fused with cameras. Maps (e.g., High Definition Maps), a basic infrastructure for intelli…
3D Object DetectionAutonomous DrivingObjectobject-detection+1