MapTCL: Temporal Consistency Learning via Bidirectional Alignment for Vectorized HD Map Construction
arXiv:2608.05209v1 Announce Type: new Abstract: Constructing reliable online HD maps remains challenging in dynamic urban environments due to moving objects and occlusions. While recent works employ feature-level temporal fusion to address this, they rely solely on per-frame ground truth supervision. Consequently, they lack an explicit objective to directly penalize the geometric noise and temporal jitter between consecutive online HD maps. To address this, we propose MapTCL, an auxiliary training strategy that formulates temporal consistency loss between current and past frames via bidirectional alignment. Specifically, Bidirectional Vector Consistency Learning (BVCL) models the geometric and semantic discrepancies between associated past and current vector instances as an auxiliary loss. We also employ Raster map Consistency Learning (RCL) as an additional loss to stabilize dense BEV features. By jointly training with these dual losses, MapTCL improves the temporal stability of generated HD maps. Extensive experiments on two standard benchmarks demonstrate the effectiveness of our approach. As a versatile plug-and-play module, MapTCL consistently enhances existing baseline models, achieving gains of +3.7 mAP & +2.8 C-mAP on nuScenes and +3.1 mAP & +2.5 C-mAP on Argoverse 2 without additional inference overhead.
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[Submitted on 5 Aug 2026]
Title:MapTCL: Temporal Consistency Learning via Bidirectional Alignment for Vectorized HD Map Construction
View a PDF of the paper titled MapTCL: Temporal Consistency Learning via Bidirectional Alignment for Vectorized HD Map Construction, by Hyeonseo Kim and 4 other authors
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Abstract:Constructing reliable online HD maps remains challenging in dynamic urban environments due to moving objects and occlusions. While recent works employ feature-level temporal fusion to address this, they rely solely on per-frame ground truth supervision. Consequently, they lack an explicit objective to directly penalize the geometric noise and temporal jitter between consecutive online HD maps. To address this, we propose MapTCL, an auxiliary training strategy that formulates temporal consistency loss between current and past frames via bidirectional alignment. Specifically, Bidirectional Vector Consistency Learning (BVCL) models the geometric and semantic discrepancies between associated past and current vector instances as an auxiliary loss. We also employ Raster map Consistency Learning (RCL) as an additional loss to stabilize dense BEV features. By jointly training with these dual losses, MapTCL improves the temporal stability of generated HD maps. Extensive experiments on two standard benchmarks demonstrate the effectiveness of our approach. As a versatile plug-and-play module, MapTCL consistently enhances existing baseline models, achieving gains of +3.7 mAP & +2.8 C-mAP on nuScenes and +3.1 mAP & +2.5 C-mAP on Argoverse 2 without additional inference overhead.
Comments: Accepted at 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2608.05209 [cs.CV]
(or arXiv:2608.05209v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.05209
arXiv-issued DOI via DataCite
Submission history
From: Hyeonseo Kim [view email] [v1] Wed, 5 Aug 2026 08:53:26 UTC (1,389 KB)
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