FlexMap: Robust HD Map Construction under Flexible Camera Configurations

arXiv:2601.22376v2 Announce Type: replace Abstract: High-definition (HD) maps provide essential semantic information about road structures for autonomous driving, but existing HD map construction methods typically require calibrated multi-camera rigs and explicit 2D-to-BEV transformations. Such pipelines degrade when camera views are missing or pose estimates are inaccurate, limiting their use across heterogeneous fleet configurations. We introduce FlexMap, a vectorized HD mapping framework that adapts to varying camera configurations without architectural changes or per-configuration retraining and does not require camera parameters as model input. FlexMap replaces explicit geometric projection with a geometry foundation model that encodes cross-view 3D structure. A spatial-temporal enhancement module then separates cross-view spatial reasoning from temporal aggregation, while a camera-aware decoder uses the token produced for each input view to adapt its attention without camera poses. Experiments on nuScenes and Argoverse 2 show that FlexMap outperforms pose-dependent baselines using estimated poses and maintains comparable accuracy across all evaluated camera configurations, including those with missing views.

Related

• MapRF: Weakly Supervised Online HD Map Construction via NeRF-Guided Self-Training
• LIE: LiDAR-only HD Map Construction with Intensity Enhancement via Online Knowledge Distillation
• RoGS: Adaptive Meshgrid Gaussian for Large-Scale Road Surface Mapping
• GSMap: 2D Gaussians for Online HD Mapping

Source: arXiv cs.CV | 2026-09-04