mlsanityData to 7 Oct 2026

M2-3DLaneNet: Exploring Multi-Modal 3D Lane Detection

arXiv 2209.05996 ↗First posted 13 Sep 2022cs.CVcited by 7

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Abstract (arXiv)

Estimating accurate lane lines in 3D space remains challenging due to their sparse and slim nature. Previous works mainly focused on using images for 3D lane detection, leading to inherent projection error and loss of geometry information. To address these issues, we explore the potential of leveraging LiDAR for 3D lane detection, either as a standalone method or in combination with existing monocular approaches. In this paper, we propose M2-3DLaneNet to integrate complementary information from multiple sensors. Specifically, M2-3DLaneNet lifts 2D features into 3D space by incorporating geometry information from LiDAR data through depth completion. Subsequently, the lifted 2D features are further enhanced with LiDAR features through cross-modality BEV fusion. Extensive experiments on the large-scale OpenLane dataset demonstrate the effectiveness of M2-3DLaneNet, regardless of the range (75m or 100m).

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 39 of its 49 references that are in the map. First posted on arXiv 13 Sep 2022.

Builds on

39 of 49 references.

References · 39

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27 more
  1. 2019PointPainting: Sequential Fusion for 3D Object Detectioncited by 449
  2. 2022BEVFusion: A Simple and Robust LiDAR-Camera Fusion Frameworkcited by 334
  3. 2022DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detectioncited by 224
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  11. 2018In Defense of Classical Image Processing: Fast Depth Completion on the CPUcited by 96
  12. 2020PolyLaneNet: Lane Estimation via Deep Polynomial Regressioncited by 95
  13. 2021CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolutioncited by 95
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  16. 2022Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completioncited by 82
  17. 2022CLRNet: Cross Layer Refinement Network for Lane Detectioncited by 82
  18. 2020Gen-LaneNet: A Generalized and Scalable Approach for 3D Lane Detectioncited by 75
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 2; 2026: 1.

7 papers.

  1. 2024Monocular 3D lane detection for Autonomous Driving: Recent Achievements, Challenges, and Outlookscited by 9
  2. 2024SparseFusion: Efficient Sparse Multi-Modal Fusion Framework for Long-Range 3D Perceptioncited by 8
  3. 2024TopoBDA: Towards Bezier Deformable Attention for Road Topology Understandingcited by 5
  4. 2024Monocular Lane Detection Based on Deep Learning: A Surveycited by 4
  5. 2025GLane3D : Detecting Lanes with Graph of 3D Keypointscited by 3
  6. 2025Depth3DLane: Fusing Monocular 3D Lane Detection with Self-Supervised Monocular Depth Estimationcited by 0
  7. 2026ReManNet: A Riemannian Manifold Network for Monocular 3D Lane Detectioncited by 0