# MGMap: Mask-Guided Learning for Online Vectorized HD Map Construction

arXiv: https://arxiv.org/abs/2404.00876 · HTML: https://mlsanity.com/paper/2404.00876/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- Builds on 48 of its 52 references that are in the map.
- First posted on arXiv 1 Apr 2024.

## Builds on (most cited first, 30 of 48)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers](https://mlsanity.com/paper/2203.17270/) (2022)
- [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://mlsanity.com/paper/2107.06278/) (2021)
- [Planning-oriented Autonomous Driving](https://mlsanity.com/paper/2212.10156/) (2022)
- [Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D](https://mlsanity.com/paper/2008.05711/) (2020)
- [BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation](https://mlsanity.com/paper/2205.13542/) (2022)
- [On the Convergence of Adam and Beyond](https://mlsanity.com/paper/1904.09237/) (2019)
- [BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View](https://mlsanity.com/paper/2112.11790/) (2021)
- [Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting](https://mlsanity.com/paper/2301.00493/) (2023)
- [DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries](https://mlsanity.com/paper/2110.06922/) (2021)
- [PETR: Position Embedding Transformation for Multi-View 3D Object Detection](https://mlsanity.com/paper/2203.05625/) (2022)
- [VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation](https://mlsanity.com/paper/2005.04259/) (2020)
- [FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detection](https://mlsanity.com/paper/2104.10956/) (2021)
- [BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework](https://mlsanity.com/paper/2205.13790/) (2022)
- [MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2208.14437/) (2022)
- [HDMapNet: An Online HD Map Construction and Evaluation Framework](https://mlsanity.com/paper/2107.06307/) (2021)
- [VectorMapNet: End-to-end Vectorized HD Map Learning](https://mlsanity.com/paper/2206.08920/) (2022)
- [MapTRv2: An End-to-End Framework for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2308.05736/) (2023)
- [BEVerse: Unified Perception and Prediction in Birds-Eye-View for Vision-Centric Autonomous Driving](https://mlsanity.com/paper/2205.09743/) (2022)
- [LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping](https://mlsanity.com/paper/2007.00258/) (2020)
- [Predicting Semantic Map Representations from Images using Pyramid Occupancy Networks](https://mlsanity.com/paper/2003.13402/) (2020)
- [Cross-view Semantic Segmentation for Sensing Surroundings](https://mlsanity.com/paper/1906.03560/) (2019)
- [Gated-SCNN: Gated Shape CNNs for Semantic Segmentation](https://mlsanity.com/paper/1907.05740/) (2019)

## Built on by (most cited first, 30 of 52)

- [MapExpert: Online HD Map Construction with Simple and Efficient Sparse Map Element Expert](https://mlsanity.com/paper/2412.12704/) (2024)
- [Data Scaling Laws for End-to-End Autonomous Driving](https://mlsanity.com/paper/2504.04338/) (2025)
- [SafeMap: Robust HD Map Construction from Incomplete Observations](https://mlsanity.com/paper/2507.00861/) (2025)
- [MapDiffusion: Generative Diffusion for Vectorized Online HD Map Construction and Uncertainty Estimation in Autonomous Driving](https://mlsanity.com/paper/2507.21423/) (2025)
- [PrevPredMap: Exploring Temporal Modeling with Previous Predictions for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2407.17378/) (2024)
- [Unveiling the Hidden: Online Vectorized HD Map Construction with Clip-Level Token Interaction and Propagation](https://mlsanity.com/paper/2411.11002/) (2024)
- [DTCLMapper: Dual Temporal Consistent Learning for Vectorized HD Map Construction](https://mlsanity.com/paper/2405.05518/) (2024)
- [Label-efficient Semantic Scene Completion with Scribble Annotations](https://mlsanity.com/paper/2405.15170/) (2024)
- [MemFusionMap: Working Memory Fusion for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2409.18737/) (2024)
- [MGMapNet: Multi-Granularity Representation Learning for End-to-End Vectorized HD Map Construction](https://mlsanity.com/paper/2410.07733/) (2024)
- [What Really Matters for Robust Multi-Sensor HD Map Construction?](https://mlsanity.com/paper/2507.01484/) (2025)
- [HisTrackMap: Global Vectorized High-Definition Map Construction via History Map Tracking](https://mlsanity.com/paper/2503.07168/) (2025)
- [InteractionMap: Improving Online Vectorized HDMap Construction with Interaction](https://mlsanity.com/paper/2503.21659/) (2025)
- [TopoMaskV2: Enhanced Instance-Mask-Based Formulation for the Road Topology Problem](https://mlsanity.com/paper/2409.11325/) (2024)
- [Stability Under Scrutiny: Benchmarking Representation Paradigms for Online HD Mapping](https://mlsanity.com/paper/2510.10660/) (2025)
- [Improving Hierarchical Representations of Vectorized HD Maps with Perspective Clues](https://mlsanity.com/paper/2404.11155/) (2024)
- [Exploring Semi-Supervised Learning for Online Mapping](https://mlsanity.com/paper/2410.10279/) (2024)
- [AugMapNet: Improving Spatial Latent Structure via BEV Grid Augmentation for Enhanced Vectorized Online HD Map Construction](https://mlsanity.com/paper/2503.13430/) (2025)
- [Delving into Mapping Uncertainty for Mapless Trajectory Prediction](https://mlsanity.com/paper/2507.18498/) (2025)
- [DAMap: Distance-aware MapNet for High Quality HD Map Construction](https://mlsanity.com/paper/2510.22675/) (2025)
- [LaneDiffusion: Improving Centerline Graph Learning via Prior Injected BEV Feature Generation](https://mlsanity.com/paper/2511.06272/) (2025)
- [Panacea+: Panoramic and Controllable Video Generation for Autonomous Driving](https://mlsanity.com/paper/2408.07605/) (2024)
- [TopoBDA: Towards Bezier Deformable Attention for Road Topology Understanding](https://mlsanity.com/paper/2412.18951/) (2024)
- [RTMap: Real-Time Recursive Mapping with Change Detection and Localization](https://mlsanity.com/paper/2507.00980/) (2025)
- [GenMapping: Unleashing the Potential of Inverse Perspective Mapping for Robust Online HD Map Construction](https://mlsanity.com/paper/2409.08688/) (2024)
- [Uni-PrevPredMap: Extending PrevPredMap to a Unified Framework of Prior-Informed Modeling for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2504.06647/) (2025)
- [SparseMeXT Unlocking the Potential of Sparse Representations for HD Map Construction](https://mlsanity.com/paper/2505.08808/) (2025)
- [FastMap: Fast Queries Initialization Based Vectorized HD Map Reconstruction Framework](https://mlsanity.com/paper/2503.05492/) (2025)
- [Uncertainty-Instructed Structure Injection for Generalizable HD Map Construction](https://mlsanity.com/paper/2503.23109/) (2025)
- [Coherent Online Road Topology Estimation and Reasoning with Standard-Definition Maps](https://mlsanity.com/paper/2507.01397/) (2025)

## Abstract (arXiv)

Currently, high-definition (HD) map construction leans towards a lightweight online generation tendency, which aims to preserve timely and reliable road scene information. However, map elements contain strong shape priors. Subtle and sparse annotations make current detection-based frameworks ambiguous in locating relevant feature scopes and cause the loss of detailed structures in prediction. To alleviate these problems, we propose MGMap, a mask-guided approach that effectively highlights the informative regions and achieves precise map element localization by introducing the learned masks. Specifically, MGMap employs learned masks based on the enhanced multi-scale BEV features from two perspectives. At the instance level, we propose the Mask-activated instance (MAI) decoder, which incorporates global instance and structural information into instance queries by the activation of instance masks. At the point level, a novel position-guided mask patch refinement (PG-MPR) module is designed to refine point locations from a finer-grained perspective, enabling the extraction of point-specific patch information. Compared to the baselines, our proposed MGMap achieves a notable improvement of around 10 mAP for different input modalities. Extensive experiments also demonstrate that our approach showcases strong robustness and generalization capabilities. Our code can be found at https://github.com/xiaolul2/MGMap.