SuperMapNet for long-range and high-accuracy vectorized HD map construction.
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| Title: | SuperMapNet for long-range and high-accuracy vectorized HD map construction. |
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| Authors: | Zhou, Ruqin1 (AUTHOR), Dai, Chenguang1 (AUTHOR), Jiang, Wanshou2 (AUTHOR), Zhang, Yongsheng1 (AUTHOR), Zhang, Zhenchao1 (AUTHOR), Jiang, San1,3 (AUTHOR) jiangsan@szu.edu.cn |
| Source: | ISPRS Journal of Photogrammetry & Remote Sensing. Mar2026, Vol. 233, p89-103. 15p. |
| Subjects: | Vector data, Multisensor data fusion, Autonomous vehicles, Remote sensing devices |
| Abstract: | Vectorized high-definition (HD) map construction is formulated as the task of classifying and localizing typical map elements based on features in a bird's-eye view (BEV). This is essential for autonomous driving systems, providing interpretable environmental structured representations for decision and planning. Remarkable work has been achieved in recent years, but several major issues remain: (1) in the generation of the BEV features, single modality methods suffer from limited perception capability and range, while existing multi-modal fusion approaches underutilize cross-modal synergies and fail to resolve spatial disparities between modalities, resulting in misaligned BEV features with holes; (2) in the classification and localization of map elements, existing methods heavily rely on point-level modeling information while neglecting the information between elements and between point and element, leading to low accuracy with erroneous shapes and element entanglement. To address these limitations, we propose SuperMapNet, a multi-modal framework designed for long-range and high-accuracy vectorized HD map construction. This framework uses both camera images and LiDAR point clouds as input. It first tightly couples semantic information from camera images and geometric information from LiDAR point clouds by a cross-attention based synergy enhancement module and a flow-based disparity alignment module for long-range BEV feature generation. Subsequently, local information acquired by point queries and global information acquired by element queries are tightly coupled by three-level interactions for high-accuracy classification and localization, where Point2Point interaction captures local geometric consistency between points of the same element, Element2Element interaction learns global semantic relationships between elements, and Point2Element interaction complement element information for its constituent points. Experiments on the nuScenes and Argoverse2 datasets demonstrate high accuracy, surpassing previous state-of-the-art methods (SOTAs) by 14.9%/8.8% and 18.5%/3.1% mAP under the hard/easy settings, respectively, even over the double perception ranges (up to 120 m in the X-axis and 60 m in the Y-axis). The code is made publicly available at https://github.com/zhouruqin/SuperMapNet. [ABSTRACT FROM AUTHOR] |
| Copyright of ISPRS Journal of Photogrammetry & Remote Sensing is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 191634079 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: SuperMapNet for long-range and high-accuracy vectorized HD map construction. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhou%2C+Ruqin%22">Zhou, Ruqin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dai%2C+Chenguang%22">Dai, Chenguang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jiang%2C+Wanshou%22">Jiang, Wanshou</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Yongsheng%22">Zhang, Yongsheng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Zhenchao%22">Zhang, Zhenchao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jiang%2C+San%22">Jiang, San</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<i> jiangsan@szu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22ISPRS+Journal+of+Photogrammetry+%26+Remote+Sensing%22">ISPRS Journal of Photogrammetry & Remote Sensing</searchLink>. Mar2026, Vol. 233, p89-103. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Vector+data%22">Vector data</searchLink><br /><searchLink fieldCode="DE" term="%22Multisensor+data+fusion%22">Multisensor data fusion</searchLink><br /><searchLink fieldCode="DE" term="%22Autonomous+vehicles%22">Autonomous vehicles</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing+devices%22">Remote sensing devices</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Vectorized high-definition (HD) map construction is formulated as the task of classifying and localizing typical map elements based on features in a bird's-eye view (BEV). This is essential for autonomous driving systems, providing interpretable environmental structured representations for decision and planning. Remarkable work has been achieved in recent years, but several major issues remain: (1) in the generation of the BEV features, single modality methods suffer from limited perception capability and range, while existing multi-modal fusion approaches underutilize cross-modal synergies and fail to resolve spatial disparities between modalities, resulting in misaligned BEV features with holes; (2) in the classification and localization of map elements, existing methods heavily rely on point-level modeling information while neglecting the information between elements and between point and element, leading to low accuracy with erroneous shapes and element entanglement. To address these limitations, we propose SuperMapNet, a multi-modal framework designed for long-range and high-accuracy vectorized HD map construction. This framework uses both camera images and LiDAR point clouds as input. It first tightly couples semantic information from camera images and geometric information from LiDAR point clouds by a cross-attention based synergy enhancement module and a flow-based disparity alignment module for long-range BEV feature generation. Subsequently, local information acquired by point queries and global information acquired by element queries are tightly coupled by three-level interactions for high-accuracy classification and localization, where Point2Point interaction captures local geometric consistency between points of the same element, Element2Element interaction learns global semantic relationships between elements, and Point2Element interaction complement element information for its constituent points. Experiments on the nuScenes and Argoverse2 datasets demonstrate high accuracy, surpassing previous state-of-the-art methods (SOTAs) by 14.9%/8.8% and 18.5%/3.1% mAP under the hard/easy settings, respectively, even over the double perception ranges (up to 120 m in the X-axis and 60 m in the Y-axis). The code is made publicly available at https://github.com/zhouruqin/SuperMapNet. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of ISPRS Journal of Photogrammetry & Remote Sensing is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.isprsjprs.2026.01.023 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 89 Subjects: – SubjectFull: Vector data Type: general – SubjectFull: Multisensor data fusion Type: general – SubjectFull: Autonomous vehicles Type: general – SubjectFull: Remote sensing devices Type: general Titles: – TitleFull: SuperMapNet for long-range and high-accuracy vectorized HD map construction. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhou, Ruqin – PersonEntity: Name: NameFull: Dai, Chenguang – PersonEntity: Name: NameFull: Jiang, Wanshou – PersonEntity: Name: NameFull: Zhang, Yongsheng – PersonEntity: Name: NameFull: Zhang, Zhenchao – PersonEntity: Name: NameFull: Jiang, San IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 09242716 Numbering: – Type: volume Value: 233 Titles: – TitleFull: ISPRS Journal of Photogrammetry & Remote Sensing Type: main |
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