# Laneformer: Object-aware Row-Column Transformers for Lane Detection

arXiv: https://arxiv.org/abs/2203.09830 · HTML: https://mlsanity.com/paper/2203.09830/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 33 references that are in the map.
- First posted on arXiv 18 Mar 2022.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2012.15840/) (2020)
- [MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers](https://mlsanity.com/paper/2012.00759/) (2020)
- [Recent Trends in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/1708.02709/) (2017)
- [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)
- [End-to-end Lane Shape Prediction with Transformers](https://mlsanity.com/paper/2011.04233/) (2020)
- [PolyLaneNet: Lane Estimation via Deep Polynomial Regression](https://mlsanity.com/paper/2004.10924/) (2020)
- [CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending](https://mlsanity.com/paper/2007.12147/) (2020)
- [Line Segment Detection Using Transformers without Edges](https://mlsanity.com/paper/2101.01909/) (2021)
- [Robust Lane Detection from Continuous Driving Scenes Using Deep Neural Networks](https://mlsanity.com/paper/1903.02193/) (2019)
- [Lane Detection and Classification using Cascaded CNNs](https://mlsanity.com/paper/1907.01294/) (2019)
- [Robust Lane Detection via Expanded Self Attention](https://mlsanity.com/paper/2102.07037/) (2021)
- [Dynamic Approach for Lane Detection using Google Street View and CNN](https://mlsanity.com/paper/1909.00798/) (2019)
- [Agnostic Lane Detection](https://mlsanity.com/paper/1905.03704/) (2019)

## Built on by (most cited first, 25 of 25)

- [CurveFormer: 3D Lane Detection by Curve Propagation with Curve Queries and Attention](https://mlsanity.com/paper/2209.07989/) (2022)
- [CLRerNet: Improving Confidence of Lane Detection with LaneIoU](https://mlsanity.com/paper/2305.08366/) (2023)
- [ADNet: Lane Shape Prediction via Anchor Decomposition](https://mlsanity.com/paper/2308.10481/) (2023)
- [BSNet: Lane Detection via Draw B-spline Curves Nearby](https://mlsanity.com/paper/2301.06910/) (2023)
- [CurveFormer++: 3D Lane Detection by Curve Propagation with Temporal Curve Queries and Attention](https://mlsanity.com/paper/2402.06423/) (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)
- [End-to-End Lane detection with One-to-Several Transformer](https://mlsanity.com/paper/2305.00675/) (2023)
- [Lane2Seq: Towards Unified Lane Detection via Sequence Generation](https://mlsanity.com/paper/2402.17172/) (2024)
- [BezierFormer: A Unified Architecture for 2D and 3D Lane Detection](https://mlsanity.com/paper/2404.16304/) (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)
- [Sketch and Refine: Towards Fast and Accurate Lane Detection](https://mlsanity.com/paper/2401.14729/) (2024)
- [LDTR: Transformer-based Lane Detection with Anchor-chain Representation](https://mlsanity.com/paper/2403.14354/) (2024)
- [Sparse Laneformer](https://mlsanity.com/paper/2404.07821/) (2024)
- [Rethinking Lanes and Points in Complex Scenarios for Monocular 3D Lane Detection](https://mlsanity.com/paper/2503.06237/) (2025)
- [Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective](https://mlsanity.com/paper/2607.05783/) (2026)
- [Breaking Down Monocular Ambiguity: Exploiting Temporal Evolution for 3D Lane Detection](https://mlsanity.com/paper/2504.20525/) (2025)
- [Depth3DLane: Fusing Monocular 3D Lane Detection with Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/2507.13857/) (2025)
- [Adapt, But Don't Forget: Fine-Tuning and Contrastive Routing for Lane Detection under Distribution Shift](https://mlsanity.com/paper/2507.18653/) (2025)
- [DiffusionLane: Diffusion Model for Lane Detection](https://mlsanity.com/paper/2510.22236/) (2025)
- [Fine-Grained Representation for Lane Topology Reasoning](https://mlsanity.com/paper/2511.12590/) (2025)
- [Row-Column Separated Attention Based Low-Light Image/Video Enhancement](https://mlsanity.com/paper/2602.07428/) (2026)
- [Structure-Enhanced Features and Quality-Aware Dynamic Anchor Scoring for Robust Lane Detection](https://mlsanity.com/paper/2608.09610/) (2026)

## Abstract (arXiv)

We present Laneformer, a conceptually simple yet powerful transformer-based architecture tailored for lane detection that is a long-standing research topic for visual perception in autonomous driving. The dominant paradigms rely on purely CNN-based architectures which often fail in incorporating relations of long-range lane points and global contexts induced by surrounding objects (e.g., pedestrians, vehicles). Inspired by recent advances of the transformer encoder-decoder architecture in various vision tasks, we move forwards to design a new end-to-end Laneformer architecture that revolutionizes the conventional transformers into better capturing the shape and semantic characteristics of lanes, with minimal overhead in latency. First, coupling with deformable pixel-wise self-attention in the encoder, Laneformer presents two new row and column self-attention operations to efficiently mine point context along with the lane shapes. Second, motivated by the appearing objects would affect the decision of predicting lane segments, Laneformer further includes the detected object instances as extra inputs of multi-head attention blocks in the encoder and decoder to facilitate the lane point detection by sensing semantic contexts. Specifically, the bounding box locations of objects are added into Key module to provide interaction with each pixel and query while the ROI-aligned features are inserted into Value module. Extensive experiments demonstrate our Laneformer achieves state-of-the-art performances on CULane benchmark, in terms of 77.1% F1 score. We hope our simple and effective Laneformer will serve as a strong baseline for future research in self-attention models for lane detection.