# InstaGraM: Instance-level Graph Modeling for Vectorized HD Map Learning

arXiv: https://arxiv.org/abs/2301.04470 · HTML: https://mlsanity.com/paper/2301.04470/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 53 references that are in the map.
- First posted on arXiv 10 Jan 2023.

## Builds on (most cited first, 30 of 40)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [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)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers](https://mlsanity.com/paper/2203.17270/) (2022)
- [Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D](https://mlsanity.com/paper/2008.05711/) (2020)
- [Argoverse: 3D Tracking and Forecasting with Rich Maps](https://mlsanity.com/paper/1911.02620/) (2019)
- [Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting](https://mlsanity.com/paper/2301.00493/) (2023)
- [VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation](https://mlsanity.com/paper/2005.04259/) (2020)
- [HDMapNet: An Online HD Map Construction and Evaluation Framework](https://mlsanity.com/paper/2107.06307/) (2021)
- [Cross-view Transformers for real-time Map-view Semantic Segmentation](https://mlsanity.com/paper/2205.02833/) (2022)
- [VectorMapNet: End-to-end Vectorized HD Map Learning](https://mlsanity.com/paper/2206.08920/) (2022)
- [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)
- [Towards End-to-End Lane Detection: an Instance Segmentation Approach](https://mlsanity.com/paper/1802.05591/) (2018)
- [Vision-Centric BEV Perception: A Survey](https://mlsanity.com/paper/2208.02797/) (2022)
- [BEVSegFormer: Bird's Eye View Semantic Segmentation From Arbitrary Camera Rigs](https://mlsanity.com/paper/2203.04050/) (2022)
- [Monocular Semantic Occupancy Grid Mapping with Convolutional Variational Encoder-Decoder Networks](https://mlsanity.com/paper/1804.02176/) (2018)
- [Efficient and Robust 2D-to-BEV Representation Learning via Geometry-guided Kernel Transformer](https://mlsanity.com/paper/2206.04584/) (2022)
- [A Sim2Real Deep Learning Approach for the Transformation of Images from Multiple Vehicle-Mounted Cameras to a Semantically Segmented Image in Bird's Eye View](https://mlsanity.com/paper/2005.04078/) (2020)
- [Inter-Region Affinity Distillation for Road Marking Segmentation](https://mlsanity.com/paper/2004.05304/) (2020)
- [VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition](https://mlsanity.com/paper/1710.06288/) (2017)

## Built on by (most cited first, 30 of 40)

- [MapTRv2: An End-to-End Framework for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2308.05736/) (2023)
- [CRN: Camera Radar Net for Accurate, Robust, Efficient 3D Perception](https://mlsanity.com/paper/2304.00670/) (2023)
- [Augmenting Lane Perception and Topology Understanding with Standard Definition Navigation Maps](https://mlsanity.com/paper/2311.04079/) (2023)
- [Graph-based Topology Reasoning for Driving Scenes](https://mlsanity.com/paper/2304.05277/) (2023)
- [MGMap: Mask-Guided Learning for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2404.00876/) (2024)
- [Lane Graph as Path: Continuity-preserving Path-wise Modeling for Online Lane Graph Construction](https://mlsanity.com/paper/2303.08815/) (2023)
- [Leveraging Enhanced Queries of Point Sets for Vectorized Map Construction](https://mlsanity.com/paper/2402.17430/) (2024)
- [Localization Is All You Evaluate: Data Leakage in Online Mapping Datasets and How to Fix It](https://mlsanity.com/paper/2312.06420/) (2023)
- [Producing and Leveraging Online Map Uncertainty in Trajectory Prediction](https://mlsanity.com/paper/2403.16439/) (2024)
- [Complementing Onboard Sensors with Satellite Map: A New Perspective for HD Map Construction](https://mlsanity.com/paper/2308.15427/) (2023)
- [Mask2Map: Vectorized HD Map Construction Using Bird's Eye View Segmentation Masks](https://mlsanity.com/paper/2407.13517/) (2024)
- [Grid-Centric Traffic Scenario Perception for Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2303.01212/) (2023)
- [InsMapper: Exploring Inner-instance Information for Vectorized HD Mapping](https://mlsanity.com/paper/2308.08543/) (2023)
- [RoadPainter: Points Are Ideal Navigators for Topology transformER](https://mlsanity.com/paper/2407.15349/) (2024)
- [MapExpert: Online HD Map Construction with Simple and Efficient Sparse Map Element Expert](https://mlsanity.com/paper/2412.12704/) (2024)
- [ScalableMap: Scalable Map Learning for Online Long-Range Vectorized HD Map Construction](https://mlsanity.com/paper/2310.13378/) (2023)
- [DTCLMapper: Dual Temporal Consistent Learning for Vectorized HD Map Construction](https://mlsanity.com/paper/2405.05518/) (2024)
- [MGMapNet: Multi-Granularity Representation Learning for End-to-End Vectorized HD Map Construction](https://mlsanity.com/paper/2410.07733/) (2024)
- [Accelerating Online Mapping and Behavior Prediction via Direct BEV Feature Attention](https://mlsanity.com/paper/2407.06683/) (2024)
- [TopoMaskV2: Enhanced Instance-Mask-Based Formulation for the Road Topology Problem](https://mlsanity.com/paper/2409.11325/) (2024)
- [TopoMask: Instance-Mask-Based Formulation for the Road Topology Problem via Transformer-Based Architecture](https://mlsanity.com/paper/2306.05419/) (2023)
- [Continuity Preserving Online CenterLine Graph Learning](https://mlsanity.com/paper/2407.11337/) (2024)
- [Neural HD Map Generation from Multiple Vectorized Tiles Locally Produced by Autonomous Vehicles](https://mlsanity.com/paper/2409.03445/) (2024)
- [TopoBDA: Towards Bezier Deformable Attention for Road Topology Understanding](https://mlsanity.com/paper/2412.18951/) (2024)
- [Spatial Retrieval Augmented Autonomous Driving](https://mlsanity.com/paper/2512.06865/) (2025)
- [BroadBEV: Collaborative LiDAR-camera Fusion for Broad-sighted Bird's Eye View Map Construction](https://mlsanity.com/paper/2309.11119/) (2023)
- [GenMapping: Unleashing the Potential of Inverse Perspective Mapping for Robust Online HD Map Construction](https://mlsanity.com/paper/2409.08688/) (2024)
- [Learning to Predict Navigational Patterns from Partial Observations](https://mlsanity.com/paper/2304.13242/) (2023)
- [Geometry-Guided Representations for Coherent Lane and Traffic Topology Reasoning in Driving Scenes](https://mlsanity.com/paper/2506.13553/) (2025)
- [HierDAMap: Towards Universal Domain Adaptive BEV Mapping via Hierarchical Perspective Priors](https://mlsanity.com/paper/2503.06821/) (2025)

## Abstract (arXiv)

For scalable autonomous driving, a robust map-based localization system, independent of GPS, is fundamental. To achieve such map-based localization, online high-definition (HD) map construction plays a significant role in accurate estimation of the pose. Although recent advancements in online HD map construction have predominantly investigated on vectorized representation due to its effectiveness, they suffer from computational cost and fixed parametric model, which limit scalability. To alleviate these limitations, we propose a novel HD map learning framework that leverages graph modeling. This framework is designed to learn the construction of diverse geometric shapes, thereby enhancing the scalability of HD map construction. Our approach involves representing the map elements as an instance-level graph by decomposing them into vertices and edges to facilitate accurate and efficient end-to-end vectorized HD map learning. Furthermore, we introduce an association strategy using a Graph Neural Network to efficiently handle the complex geometry of various map elements, while maintaining scalability. Comprehensive experiments on public open dataset show that our proposed network outperforms state-of-the-art model by 1.6 mAP. We further showcase the superior scalability of our approach compared to state-of-the-art methods, achieving a 4.8 mAP improvement in long range configuration. Our code is available at https://github.com/juyebshin/InstaGraM.