# 3D-LaneNet+: Anchor Free Lane Detection using a Semi-Local Representation

arXiv: https://arxiv.org/abs/2011.01535 · HTML: https://mlsanity.com/paper/2011.01535/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2020.

## Built on by (most cited first, 30 of 32)

- [MapTRv2: An End-to-End Framework for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2308.05736/) (2023)
- [Anchor3DLane: Learning to Regress 3D Anchors for Monocular 3D Lane Detection](https://mlsanity.com/paper/2301.02371/) (2023)
- [ONCE-3DLanes: Building Monocular 3D Lane Detection](https://mlsanity.com/paper/2205.00301/) (2022)
- [LATR: 3D Lane Detection from Monocular Images with Transformer](https://mlsanity.com/paper/2308.04583/) (2023)
- [CurveFormer: 3D Lane Detection by Curve Propagation with Curve Queries and Attention](https://mlsanity.com/paper/2209.07989/) (2022)
- [Ultra Fast Deep Lane Detection with Hybrid Anchor Driven Ordinal Classification](https://mlsanity.com/paper/2206.07389/) (2022)
- [Topology Preserving Local Road Network Estimation from Single Onboard Camera Image](https://mlsanity.com/paper/2112.10155/) (2021)
- [Lane Graph Estimation for Scene Understanding in Urban Driving](https://mlsanity.com/paper/2105.00195/) (2021)
- [LaneCPP: Continuous 3D Lane Detection using Physical Priors](https://mlsanity.com/paper/2406.08381/) (2024)
- [GroupLane: End-to-End 3D Lane Detection with Channel-wise Grouping](https://mlsanity.com/paper/2307.09472/) (2023)
- [CurveFormer++: 3D Lane Detection by Curve Propagation with Temporal Curve Queries and Attention](https://mlsanity.com/paper/2402.06423/) (2024)
- [WS-3D-Lane: Weakly Supervised 3D Lane Detection With 2D Lane Labels](https://mlsanity.com/paper/2209.11523/) (2022)
- [Monocular 3D lane detection for Autonomous Driving: Recent Achievements, Challenges, and Outlooks](https://mlsanity.com/paper/2404.06860/) (2024)
- [DV-3DLane: End-to-end Multi-modal 3D Lane Detection with Dual-view Representation](https://mlsanity.com/paper/2406.16072/) (2024)
- [Anchor3DLane++: 3D Lane Detection via Sample-Adaptive Sparse 3D Anchor Regression](https://mlsanity.com/paper/2412.16889/) (2024)
- [M2-3DLaneNet: Exploring Multi-Modal 3D Lane Detection](https://mlsanity.com/paper/2209.05996/) (2022)
- [BEV-LaneDet: a Simple and Effective 3D Lane Detection Baseline](https://mlsanity.com/paper/2210.06006/) (2022)
- [An Efficient Transformer for Simultaneous Learning of BEV and Lane Representations in 3D Lane Detection](https://mlsanity.com/paper/2306.04927/) (2023)
- [Improving Online Lane Graph Extraction by Object-Lane Clustering](https://mlsanity.com/paper/2307.10947/) (2023)
- [Advancements in 3D Lane Detection Using LiDAR Point Clouds: From Data Collection to Model Development](https://mlsanity.com/paper/2309.13596/) (2023)
- [Monocular Lane Detection Based on Deep Learning: A Survey](https://mlsanity.com/paper/2411.16316/) (2024)
- [Geometry-Guided Representations for Coherent Lane and Traffic Topology Reasoning in Driving Scenes](https://mlsanity.com/paper/2506.13553/) (2025)
- [RATopo: Improving Lane Topology Reasoning via Redundancy Assignment](https://mlsanity.com/paper/2508.15272/) (2025)
- [Reconstruct from BEV: A 3D Lane Detection Approach based on Geometry Structure Prior](https://mlsanity.com/paper/2206.10098/) (2022)
- [Rethinking Lanes and Points in Complex Scenarios for Monocular 3D Lane Detection](https://mlsanity.com/paper/2503.06237/) (2025)
- [3D Lane Detection from Front or Surround-View using Joint-Modeling & Matching](https://mlsanity.com/paper/2401.08036/) (2024)
- [LOID: Lane Occlusion Inpainting and Detection for Enhanced Autonomous Driving Systems](https://mlsanity.com/paper/2408.09117/) (2024)
- [Learning Isometric Embeddings of Road Networks using Multidimensional Scaling](https://mlsanity.com/paper/2504.17534/) (2025)
- [Unified Modeling of Lane and Lane Topology for Driving Scene Reasoning](https://mlsanity.com/paper/2605.08911/) (2026)
- [Online Lane Graph Extraction from Onboard Video](https://mlsanity.com/paper/2304.00930/) (2023)

## Abstract (arXiv)

3D-LaneNet+ is a camera-based DNN method for anchor free 3D lane detection which is able to detect 3d lanes of any arbitrary topology such as splits, merges, as well as short and perpendicular lanes. We follow recently proposed 3D-LaneNet, and extend it to enable the detection of these previously unsupported lane topologies. Our output representation is an anchor free, semi-local tile representation that breaks down lanes into simple lane segments whose parameters can be learnt. In addition we learn, per lane instance, feature embedding that reasons for the global connectivity of locally detected segments to form full 3d lanes. This combination allows 3D-LaneNet+ to avoid using lane anchors, non-maximum suppression, and lane model fitting as in the original 3D-LaneNet. We demonstrate the efficacy of 3D-LaneNet+ using both synthetic and real world data. Results show significant improvement relative to the original 3D-LaneNet that can be attributed to better generalization to complex lane topologies, curvatures and surface geometries.