# ScalableMap: Scalable Map Learning for Online Long-Range Vectorized HD Map Construction

arXiv: https://arxiv.org/abs/2310.13378 · HTML: https://mlsanity.com/paper/2310.13378/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 37 references that are in the map.
- First posted on arXiv 20 Oct 2023.

## Builds on (most cited first, 30 of 33)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [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)
- [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)
- [DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries](https://mlsanity.com/paper/2110.06922/) (2021)
- [VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation](https://mlsanity.com/paper/2005.04259/) (2020)
- [MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2208.14437/) (2022)
- [DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets](https://mlsanity.com/paper/2108.09640/) (2021)
- [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)
- [End-to-end Interpretable Neural Motion Planner](https://mlsanity.com/paper/2101.06679/) (2021)
- [CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers](https://mlsanity.com/paper/2207.02202/) (2022)
- [Predicting Semantic Map Representations from Images using Pyramid Occupancy Networks](https://mlsanity.com/paper/2003.13402/) (2020)
- [PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark](https://mlsanity.com/paper/2203.11089/) (2022)
- [Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection](https://mlsanity.com/paper/2010.12035/) (2020)
- [Towards End-to-End Lane Detection: an Instance Segmentation Approach](https://mlsanity.com/paper/1802.05591/) (2018)
- [Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++](https://mlsanity.com/paper/1803.09693/) (2018)
- [3D-LaneNet: End-to-End 3D Multiple Lane Detection](https://mlsanity.com/paper/1811.10203/) (2018)
- [PolyTransform: Deep Polygon Transformer for Instance Segmentation](https://mlsanity.com/paper/1912.02801/) (2019)
- [Annotating Object Instances with a Polygon-RNN](https://mlsanity.com/paper/1704.05548/) (2017)
- [Rethinking Efficient Lane Detection via Curve Modeling](https://mlsanity.com/paper/2203.02431/) (2022)
- [HDNET: Exploiting HD Maps for 3D Object Detection](https://mlsanity.com/paper/2012.11704/) (2020)
- [Line Segment Detection Using Transformers without Edges](https://mlsanity.com/paper/2101.01909/) (2021)
- [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)
- [DAGMapper: Learning to Map by Discovering Lane Topology](https://mlsanity.com/paper/2012.12377/) (2020)
- [PolyWorld: Polygonal Building Extraction with Graph Neural Networks in Satellite Images](https://mlsanity.com/paper/2111.15491/) (2021)
- [InstaGraM: Instance-level Graph Modeling for Vectorized HD Map Learning](https://mlsanity.com/paper/2301.04470/) (2023)
- [SuperFusion: Multilevel LiDAR-Camera Fusion for Long-Range HD Map Generation](https://mlsanity.com/paper/2211.15656/) (2022)

## Built on by (most cited first, 16 of 16)

- [Leveraging Enhanced Queries of Point Sets for Vectorized Map Construction](https://mlsanity.com/paper/2402.17430/) (2024)
- [Enhancing Vectorized Map Perception with Historical Rasterized Maps](https://mlsanity.com/paper/2409.00620/) (2024)
- [Is Your HD Map Constructor Reliable under Sensor Corruptions?](https://mlsanity.com/paper/2406.12214/) (2024)
- [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)
- [ADMap: Anti-disturbance framework for reconstructing online vectorized HD map](https://mlsanity.com/paper/2401.13172/) (2024)
- [PrevPredMap: Exploring Temporal Modeling with Previous Predictions for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2407.17378/) (2024)
- [TopoMaskV2: Enhanced Instance-Mask-Based Formulation for the Road Topology Problem](https://mlsanity.com/paper/2409.11325/) (2024)
- [TopoBDA: Towards Bezier Deformable Attention for Road Topology Understanding](https://mlsanity.com/paper/2412.18951/) (2024)
- [EAN-MapNet: Efficient Vectorized HD Map Construction with Anchor Neighborhoods](https://mlsanity.com/paper/2402.18278/) (2024)
- [HeightMapNet: Explicit Height Modeling for End-to-End HD Map Learning](https://mlsanity.com/paper/2411.01408/) (2024)
- [Geometry-Guided Representations for Coherent Lane and Traffic Topology Reasoning in Driving Scenes](https://mlsanity.com/paper/2506.13553/) (2025)
- [Coherent Online Road Topology Estimation and Reasoning with Standard-Definition Maps](https://mlsanity.com/paper/2507.01397/) (2025)
- [SatMap: Revisiting Satellite Maps as Prior for Online HD Map Construction](https://mlsanity.com/paper/2601.10512/) (2026)
- [NavMapFusion: Diffusion-based Fusion of Navigation Maps for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2512.03317/) (2025)
- [Learning Global Representation from Queries for Vectorized HD Map Construction](https://mlsanity.com/paper/2510.06969/) (2025)

## Abstract (arXiv)

We propose a novel end-to-end pipeline for online long-range vectorized high-definition (HD) map construction using on-board camera sensors. The vectorized representation of HD maps, employing polylines and polygons to represent map elements, is widely used by downstream tasks. However, previous schemes designed with reference to dynamic object detection overlook the structural constraints within linear map elements, resulting in performance degradation in long-range scenarios. In this paper, we exploit the properties of map elements to improve the performance of map construction. We extract more accurate bird's eye view (BEV) features guided by their linear structure, and then propose a hierarchical sparse map representation to further leverage the scalability of vectorized map elements and design a progressive decoding mechanism and a supervision strategy based on this representation. Our approach, ScalableMap, demonstrates superior performance on the nuScenes dataset, especially in long-range scenarios, surpassing previous state-of-the-art model by 6.5 mAP while achieving 18.3 FPS. Code is available at https://github.com/jingy1yu/ScalableMap.