# LaneCPP: Continuous 3D Lane Detection using Physical Priors

arXiv: https://arxiv.org/abs/2406.08381 · HTML: https://mlsanity.com/paper/2406.08381/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 54 references that are in the map.
- First posted on arXiv 12 Jun 2024.

## Builds on (most cited first, 30 of 40)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [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)
- [HDMapNet: An Online HD Map Construction and Evaluation Framework](https://mlsanity.com/paper/2107.06307/) (2021)
- [Spatial As Deep: Spatial CNN for Traffic Scene Understanding](https://mlsanity.com/paper/1712.06080/) (2017)
- [Predicting Semantic Map Representations from Images using Pyramid Occupancy Networks](https://mlsanity.com/paper/2003.13402/) (2020)
- [Cross-view Semantic Segmentation for Sensing Surroundings](https://mlsanity.com/paper/1906.03560/) (2019)
- [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)
- [Translating Images into Maps](https://mlsanity.com/paper/2110.00966/) (2021)
- [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)
- [EqMotion: Equivariant Multi-agent Motion Prediction with Invariant Interaction Reasoning](https://mlsanity.com/paper/2303.10876/) (2023)
- [PolyLaneNet: Lane Estimation via Deep Polynomial Regression](https://mlsanity.com/paper/2004.10924/) (2020)
- [Rethinking Efficient Lane Detection via Curve Modeling](https://mlsanity.com/paper/2203.02431/) (2022)
- [CLRNet: Cross Layer Refinement Network for Lane Detection](https://mlsanity.com/paper/2203.10350/) (2022)
- [Deep Declarative Networks: A New Hope](https://mlsanity.com/paper/1909.04866/) (2019)
- [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)
- [Focus on Local: Detecting Lane Marker from Bottom Up via Key Point](https://mlsanity.com/paper/2105.13680/) (2021)
- [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)
- [Robust Lane Detection from Continuous Driving Scenes Using Deep Neural Networks](https://mlsanity.com/paper/1903.02193/) (2019)
- [CurveFormer: 3D Lane Detection by Curve Propagation with Curve Queries and Attention](https://mlsanity.com/paper/2209.07989/) (2022)
- [An Empirical Evaluation of Deep Learning on Highway Driving](https://mlsanity.com/paper/1504.01716/) (2015)

## Built on by (most cited first, 17 of 17)

- [SeqGrowGraph: Learning Lane Topology as a Chain of Graph Expansions](https://mlsanity.com/paper/2507.04822/) (2025)
- [TopoBDA: Towards Bezier Deformable Attention for Road Topology Understanding](https://mlsanity.com/paper/2412.18951/) (2024)
- [PseudoMapTrainer: Learning Online Mapping without HD Maps](https://mlsanity.com/paper/2508.18788/) (2025)
- [HeightLane: BEV Heightmap guided 3D Lane Detection](https://mlsanity.com/paper/2408.08270/) (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)
- [Rethinking Lanes and Points in Complex Scenarios for Monocular 3D Lane Detection](https://mlsanity.com/paper/2503.06237/) (2025)
- [Depth3DLane: Monocular 3D Lane Detection via Depth Prior Distillation](https://mlsanity.com/paper/2504.18325/) (2025)
- [SC-Lane: Slope-aware and Consistent Road Height Estimation Framework for 3D Lane Detection](https://mlsanity.com/paper/2508.10411/) (2025)
- [Collaborative Learning for Semi-Supervised LiDAR Semantic Segmentation](https://mlsanity.com/paper/2605.17135/) (2026)
- [Breaking Down Monocular Ambiguity: Exploiting Temporal Evolution for 3D Lane Detection](https://mlsanity.com/paper/2504.20525/) (2025)
- [Fine-Grained Representation for Lane Topology Reasoning](https://mlsanity.com/paper/2511.12590/) (2025)
- [Monocular 3D Lane Detection via Structure Uncertainty-Aware Network with Curve-Point Queries](https://mlsanity.com/paper/2511.13055/) (2025)
- [ReManNet: A Riemannian Manifold Network for Monocular 3D Lane Detection](https://mlsanity.com/paper/2603.19776/) (2026)
- [HSDF-Lane: Height-Aligned Signed Distance Field with Semantic Lane Prior for 3D Lane Detection](https://mlsanity.com/paper/2606.31172/) (2026)
- [3D Lane Detection with Odometry for High-Speed Vehicle Racing](https://mlsanity.com/paper/2607.14248/) (2026)

## Abstract (arXiv)

Monocular 3D lane detection has become a fundamental problem in the context of autonomous driving, which comprises the tasks of finding the road surface and locating lane markings. One major challenge lies in a flexible but robust line representation capable of modeling complex lane structures, while still avoiding unpredictable behavior. While previous methods rely on fully data-driven approaches, we instead introduce a novel approach LaneCPP that uses a continuous 3D lane detection model leveraging physical prior knowledge about the lane structure and road geometry. While our sophisticated lane model is capable of modeling complex road structures, it also shows robust behavior since physical constraints are incorporated by means of a regularization scheme that can be analytically applied to our parametric representation. Moreover, we incorporate prior knowledge about the road geometry into the 3D feature space by modeling geometry-aware spatial features, guiding the network to learn an internal road surface representation. In our experiments, we show the benefits of our contributions and prove the meaningfulness of using priors to make 3D lane detection more robust. The results show that LaneCPP achieves state-of-the-art performance in terms of F-Score and geometric errors.