KPMapNet: Keypoint Representation Learning for Online Vectorized High-Definition Map Construction Article Swipe
Bicheng Jin
,
Wenyu Hao
,
W. QIU
,
Shanmin Pang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/s25061897
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/s25061897
Vectorized high-definition (HD) map construction is a critical task in the autonomous driving domain. The existing methods typically represent vectorized map elements with a fixed number of points, establishing robust baselines for this task. However, the inherent shape priors introduce additional shape errors, which in turn lead to error accumulation in the downstream tasks. Moreover, the subtle and sparse nature of the annotations limits detection-based frameworks in accurately extracting the relevant features, often resulting in the loss of fine structural details in the predictions. To address these challenges, this work presents KPMapNet, an end-to-end framework that redefines the ground truth training representation of vectorized map elements to achieve precise topological representations. Specifically, the conventional equidistant sampling method is modified to better preserve the geometric features of the original instances while maintaining a fixed number of points. In addition, a map mask fusion module and an enhanced hybrid attention module are incorporated to mitigate the issues introduced by the new representation. Moreover, a novel point-line matching loss function is introduced to further refine the training process. Extensive experiments on the nuScenes and Argoverse2 datasets demonstrate that KPMapNet achieves state-of-the-art performance, with 75.1 mAP on nuScenes and 74.2 mAP on Argoverse2. The visualization results further corroborate the enhanced accuracy of the map generation outcomes.
Related Topics
Concepts
Computer science
Representation (politics)
Artificial intelligence
Task (project management)
Process (computing)
Function (biology)
Ground truth
Equidistant
Visualization
Matching (statistics)
Realization (probability)
Pattern recognition (psychology)
Mathematics
Evolutionary biology
Statistics
Biology
Economics
Politics
Operating system
Political science
Management
Geometry
Law
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s25061897
- https://www.mdpi.com/1424-8220/25/6/1897/pdf?version=1742313617
- OA Status
- gold
- References
- 41
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408569706
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4408569706Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s25061897Digital Object Identifier
- Title
-
KPMapNet: Keypoint Representation Learning for Online Vectorized High-Definition Map ConstructionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-03-18Full publication date if available
- Authors
-
Bicheng Jin, Wenyu Hao, W. QIU, Shanmin PangList of authors in order
- Landing page
-
https://doi.org/10.3390/s25061897Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/25/6/1897/pdf?version=1742313617Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/25/6/1897/pdf?version=1742313617Direct OA link when available
- Concepts
-
Computer science, Representation (politics), Artificial intelligence, Task (project management), Process (computing), Function (biology), Ground truth, Equidistant, Visualization, Matching (statistics), Realization (probability), Pattern recognition (psychology), Mathematics, Evolutionary biology, Statistics, Biology, Economics, Politics, Operating system, Political science, Management, Geometry, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
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41Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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