# CurveFormer: 3D Lane Detection by Curve Propagation with Curve Queries and Attention

arXiv: https://arxiv.org/abs/2209.07989 · HTML: https://mlsanity.com/paper/2209.07989/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 36 references that are in the map.
- First posted on arXiv 16 Sep 2022.

## Builds on (most cited first, 30 of 31)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [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)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries](https://mlsanity.com/paper/2110.06922/) (2021)
- [DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR](https://mlsanity.com/paper/2201.12329/) (2022)
- [Spatial As Deep: Spatial CNN for Traffic Scene Understanding](https://mlsanity.com/paper/1712.06080/) (2017)
- [Learning Lightweight Lane Detection CNNs by Self Attention Distillation](https://mlsanity.com/paper/1908.00821/) (2019)
- [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)
- [BEVSegFormer: Bird's Eye View Semantic Segmentation From Arbitrary Camera Rigs](https://mlsanity.com/paper/2203.04050/) (2022)
- [Ultra Fast Structure-aware Deep Lane Detection](https://mlsanity.com/paper/2004.11757/) (2020)
- [End-to-end Lane Shape Prediction with Transformers](https://mlsanity.com/paper/2011.04233/) (2020)
- [3D-LaneNet: End-to-End 3D Multiple Lane Detection](https://mlsanity.com/paper/1811.10203/) (2018)
- [Structured Bird's-Eye-View Traffic Scene Understanding from Onboard Images](https://mlsanity.com/paper/2110.01997/) (2021)
- [PolyLaneNet: Lane Estimation via Deep Polynomial Regression](https://mlsanity.com/paper/2004.10924/) (2020)
- [CLRNet: Cross Layer Refinement Network for Lane Detection](https://mlsanity.com/paper/2203.10350/) (2022)
- [CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending](https://mlsanity.com/paper/2007.12147/) (2020)
- [Key Points Estimation and Point Instance Segmentation Approach for Lane Detection](https://mlsanity.com/paper/2002.06604/) (2020)
- [RESA: Recurrent Feature-Shift Aggregator for Lane Detection](https://mlsanity.com/paper/2008.13719/) (2020)
- [Gen-LaneNet: A Generalized and Scalable Approach for 3D Lane Detection](https://mlsanity.com/paper/2003.10656/) (2020)
- [A Keypoint-based Global Association Network for Lane Detection](https://mlsanity.com/paper/2204.07335/) (2022)
- [ONCE-3DLanes: Building Monocular 3D Lane Detection](https://mlsanity.com/paper/2205.00301/) (2022)
- [End-to-end Lane Detection through Differentiable Least-Squares Fitting](https://mlsanity.com/paper/1902.00293/) (2019)
- [3D-LaneNet+: Anchor Free Lane Detection using a Semi-Local Representation](https://mlsanity.com/paper/2011.01535/) (2020)
- [Learning to Predict 3D Lane Shape and Camera Pose from a Single Image via Geometry Constraints](https://mlsanity.com/paper/2112.15351/) (2021)
- [Laneformer: Object-aware Row-Column Transformers for Lane Detection](https://mlsanity.com/paper/2203.09830/) (2022)

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

- [Online Map Vectorization for Autonomous Driving: A Rasterization Perspective](https://mlsanity.com/paper/2306.10502/) (2023)
- [HIMap: HybrId Representation Learning for End-to-end Vectorized HD Map Construction](https://mlsanity.com/paper/2403.08639/) (2024)
- [LATR: 3D Lane Detection from Monocular Images with Transformer](https://mlsanity.com/paper/2308.04583/) (2023)
- [Enhancing 3D Lane Detection and Topology Reasoning with 2D Lane Priors](https://mlsanity.com/paper/2406.03105/) (2024)
- [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)
- [SeqGrowGraph: Learning Lane Topology as a Chain of Graph Expansions](https://mlsanity.com/paper/2507.04822/) (2025)
- [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)
- [Transformer-Based Autonomous Driving Models and Deployment-Oriented Compression: A Survey](https://mlsanity.com/paper/2304.10891/) (2023)
- [Anchor3DLane++: 3D Lane Detection via Sample-Adaptive Sparse 3D Anchor Regression](https://mlsanity.com/paper/2412.16889/) (2024)
- [An Efficient Transformer for Simultaneous Learning of BEV and Lane Representations in 3D Lane Detection](https://mlsanity.com/paper/2306.04927/) (2023)
- [CLFT: Camera-LiDAR Fusion Transformer for Semantic Segmentation in Autonomous Driving](https://mlsanity.com/paper/2404.17793/) (2024)
- [TopoMaskV2: Enhanced Instance-Mask-Based Formulation for the Road Topology Problem](https://mlsanity.com/paper/2409.11325/) (2024)
- [BezierFormer: A Unified Architecture for 2D and 3D Lane Detection](https://mlsanity.com/paper/2404.16304/) (2024)
- [RepVF: A Unified Vector Fields Representation for Multi-task 3D Perception](https://mlsanity.com/paper/2407.10876/) (2024)
- [Continuity Preserving Online CenterLine Graph Learning](https://mlsanity.com/paper/2407.11337/) (2024)
- [TopoBDA: Towards Bezier Deformable Attention for Road Topology Understanding](https://mlsanity.com/paper/2412.18951/) (2024)
- [Decoupling the Curve Modeling and Pavement Regression for Lane Detection](https://mlsanity.com/paper/2309.10533/) (2023)
- [A Survey of Vision Transformers in Autonomous Driving: Current Trends and Future Directions](https://mlsanity.com/paper/2403.07542/) (2024)
- [Monocular Lane Detection Based on Deep Learning: A Survey](https://mlsanity.com/paper/2411.16316/) (2024)
- [SparseLaneSTP: Leveraging Spatio-Temporal Priors with Sparse Transformers for 3D Lane Detection](https://mlsanity.com/paper/2601.04968/) (2026)
- [GLane3D : Detecting Lanes with Graph of 3D Keypoints](https://mlsanity.com/paper/2503.23882/) (2025)
- [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)
- [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)
- [Depth3DLane: Monocular 3D Lane Detection via Depth Prior Distillation](https://mlsanity.com/paper/2504.18325/) (2025)
- [DB3D-L: Depth-aware BEV Feature Transformation for Accurate 3D Lane Detection](https://mlsanity.com/paper/2505.13266/) (2025)

## Abstract (arXiv)

3D lane detection is an integral part of autonomous driving systems. Previous CNN and Transformer-based methods usually first generate a bird's-eye-view (BEV) feature map from the front view image, and then use a sub-network with BEV feature map as input to predict 3D lanes. Such approaches require an explicit view transformation between BEV and front view, which itself is still a challenging problem. In this paper, we propose CurveFormer, a single-stage Transformer-based method that directly calculates 3D lane parameters and can circumvent the difficult view transformation step. Specifically, we formulate 3D lane detection as a curve propagation problem by using curve queries. A 3D lane query is represented by a dynamic and ordered anchor point set. In this way, queries with curve representation in Transformer decoder iteratively refine the 3D lane detection results. Moreover, a curve cross-attention module is introduced to compute the similarities between curve queries and image features. Additionally, a context sampling module that can capture more relative image features of a curve query is provided to further boost the 3D lane detection performance. We evaluate our method for 3D lane detection on both synthetic and real-world datasets, and the experimental results show that our method achieves promising performance compared with the state-of-the-art approaches. The effectiveness of each component is validated via ablation studies as well.