# Convolutional Recurrent Network for Road Boundary Extraction

arXiv: https://arxiv.org/abs/2012.12160 · HTML: https://mlsanity.com/paper/2012.12160/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2020.

## Built on by (most cited first, 30 of 33)

- [VectorMapNet: End-to-end Vectorized HD Map Learning](https://mlsanity.com/paper/2206.08920/) (2022)
- [Structured Bird's-Eye-View Traffic Scene Understanding from Onboard Images](https://mlsanity.com/paper/2110.01997/) (2021)
- [Semantics for Robotic Mapping, Perception and Interaction: A Survey](https://mlsanity.com/paper/2101.00443/) (2021)
- [InstaGraM: Instance-level Graph Modeling for Vectorized HD Map Learning](https://mlsanity.com/paper/2301.04470/) (2023)
- [Topology Preserving Local Road Network Estimation from Single Onboard Camera Image](https://mlsanity.com/paper/2112.10155/) (2021)
- [PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation](https://mlsanity.com/paper/2103.06564/) (2021)
- [RNGDet: Road Network Graph Detection by Transformer in Aerial Images](https://mlsanity.com/paper/2202.07824/) (2022)
- [RNGDet++: Road Network Graph Detection by Transformer with Instance Segmentation and Multi-scale Features Enhancement](https://mlsanity.com/paper/2209.10150/) (2022)
- [iCurb: Imitation Learning-based Detection of Road Curbs using Aerial Images for Autonomous Driving](https://mlsanity.com/paper/2103.17118/) (2021)
- [OmniCity: Omnipotent City Understanding with Multi-level and Multi-view Images](https://mlsanity.com/paper/2208.00928/) (2022)
- [Learning and Aggregating Lane Graphs for Urban Automated Driving](https://mlsanity.com/paper/2302.06175/) (2023)
- [Topo-boundary: A Benchmark Dataset on Topological Road-boundary Detection Using Aerial Images for Autonomous Driving](https://mlsanity.com/paper/2103.17119/) (2021)
- [Lane Graph Estimation for Scene Understanding in Urban Driving](https://mlsanity.com/paper/2105.00195/) (2021)
- [A Simple and Efficient Multi-task Network for 3D Object Detection and Road Understanding](https://mlsanity.com/paper/2103.04056/) (2021)
- [CenterLineDet: CenterLine Graph Detection for Road Lanes with Vehicle-mounted Sensors by Transformer for HD Map Generation](https://mlsanity.com/paper/2209.07734/) (2022)
- [csBoundary: City-scale Road-boundary Detection in Aerial Images for High-definition Maps](https://mlsanity.com/paper/2111.06020/) (2021)
- [VMA: Divide-and-Conquer Vectorized Map Annotation System for Large-Scale Driving Scene](https://mlsanity.com/paper/2304.09807/) (2023)
- [VideoClick: Video Object Segmentation with a Single Click](https://mlsanity.com/paper/2101.06545/) (2021)
- [Improving Online Lane Graph Extraction by Object-Lane Clustering](https://mlsanity.com/paper/2307.10947/) (2023)
- [Pix2Map: Cross-modal Retrieval for Inferring Street Maps from Images](https://mlsanity.com/paper/2301.04224/) (2023)
- [PolyR-CNN: R-CNN for end-to-end polygonal building outline extraction](https://mlsanity.com/paper/2407.14912/) (2024)
- [CurbNet: Curb Detection Framework Based on LiDAR Point Cloud Segmentation](https://mlsanity.com/paper/2403.16794/) (2024)
- [Hierarchical Road Topology Learning for Urban Map-less Driving](https://mlsanity.com/paper/2104.00084/) (2021)
- [How to Build a Curb Dataset with LiDAR Data for Autonomous Driving](https://mlsanity.com/paper/2110.03968/) (2021)
- [Monocular BEV Perception of Road Scenes via Front-to-Top View Projection](https://mlsanity.com/paper/2211.08144/) (2022)
- [Prior Based Online Lane Graph Extraction from Single Onboard Camera Image](https://mlsanity.com/paper/2307.13344/) (2023)
- [Learning Isometric Embeddings of Road Networks using Multidimensional Scaling](https://mlsanity.com/paper/2504.17534/) (2025)
- [UniVector: Unified Vector Extraction via Instance-Geometry Interaction](https://mlsanity.com/paper/2510.13234/) (2025)
- [The Oxford Road Boundaries Dataset](https://mlsanity.com/paper/2106.08983/) (2021)
- [Semi-Supervised Domain Adaptation for Semantic Segmentation of Roads from Satellite Images](https://mlsanity.com/paper/2212.13079/) (2022)

## Abstract (arXiv)

Creating high definition maps that contain precise information of static elements of the scene is of utmost importance for enabling self driving cars to drive safely. In this paper, we tackle the problem of drivable road boundary extraction from LiDAR and camera imagery. Towards this goal, we design a structured model where a fully convolutional network obtains deep features encoding the location and direction of road boundaries and then, a convolutional recurrent network outputs a polyline representation for each one of them. Importantly, our method is fully automatic and does not require a user in the loop. We showcase the effectiveness of our method on a large North American city where we obtain perfect topology of road boundaries 99.3% of the time at a high precision and recall.