# Progressive LiDAR Adaptation for Road Detection

arXiv: https://arxiv.org/abs/1904.01206 · HTML: https://mlsanity.com/paper/1904.01206/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2019.

## Built on by (most cited first, 30 of 37)

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- [SNE-RoadSeg+: Rethinking Depth-Normal Translation and Deep Supervision for Freespace Detection](https://mlsanity.com/paper/2107.14599/) (2021)
- [Stagewise Unsupervised Domain Adaptation with Adversarial Self-Training for Road Segmentation of Remote Sensing Images](https://mlsanity.com/paper/2108.12611/) (2021)
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- [Fast Road Segmentation via Uncertainty-aware Symmetric Network](https://mlsanity.com/paper/2203.04537/) (2022)
- [Pose-disentangled Contrastive Learning for Self-supervised Facial Representation](https://mlsanity.com/paper/2211.13490/) (2022)
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- [JPerceiver: Joint Perception Network for Depth, Pose and Layout Estimation in Driving Scenes](https://mlsanity.com/paper/2207.07895/) (2022)
- [Monocular 3D lane detection for Autonomous Driving: Recent Achievements, Challenges, and Outlooks](https://mlsanity.com/paper/2404.06860/) (2024)
- [SNE-RoadSegV2: Advancing Heterogeneous Feature Fusion and Fallibility Awareness for Freespace Detection](https://mlsanity.com/paper/2402.18918/) (2024)
- [Pseudo-LiDAR Based Road Detection](https://mlsanity.com/paper/2107.13279/) (2021)
- [Multi-Modal Multi-Task (3MT) Road Segmentation](https://mlsanity.com/paper/2308.11983/) (2023)
- [UFO: Uncertainty-aware LiDAR-image Fusion for Off-road Semantic Terrain Map Estimation](https://mlsanity.com/paper/2403.02642/) (2024)
- [Self-Supervised 3D Traversability Estimation with Proxy Bank Guidance](https://mlsanity.com/paper/2211.11201/) (2022)
- [Polarimetric Imaging for Perception](https://mlsanity.com/paper/2305.14787/) (2023)
- [SkipcrossNets: Adaptive Skip-cross Fusion for Road Detection](https://mlsanity.com/paper/2308.12863/) (2023)
- [TiCoSS: Tightening the Coupling between Semantic Segmentation and Stereo Matching within A Joint Learning Framework](https://mlsanity.com/paper/2407.18038/) (2024)
- [Fusion of neural networks, for LIDAR-based evidential road mapping](https://mlsanity.com/paper/2102.03326/) (2021)
- [OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System](https://mlsanity.com/paper/2303.00501/) (2023)
- [Homography Guided Temporal Fusion for Road Line and Marking Segmentation](https://mlsanity.com/paper/2404.07626/) (2024)
- [Composition and Application of Current Advanced Driving Assistance System: A Review](https://mlsanity.com/paper/2105.12348/) (2021)
- [Enabling Efficient Deep Convolutional Neural Network-based Sensor Fusion for Autonomous Driving](https://mlsanity.com/paper/2202.11231/) (2022)
- [Cross-Modality Domain Adaptation for Freespace Detection: A Simple yet Effective Baseline](https://mlsanity.com/paper/2210.02991/) (2022)
- [Towards Modality-agnostic Label-efficient Segmentation with Entropy-Regularized Distribution Alignment](https://mlsanity.com/paper/2408.16520/) (2024)
- [Erase, then Redraw: A Novel Data Augmentation Approach for Free Space Detection Using Diffusion Model](https://mlsanity.com/paper/2409.20164/) (2024)
- [Task-Oriented Pre-Training for Drivable Area Detection](https://mlsanity.com/paper/2409.20166/) (2024)
- [Annotation-Free Curb Detection Leveraging Altitude Difference Image](https://mlsanity.com/paper/2409.20171/) (2024)

## Abstract (arXiv)

Despite rapid developments in visual image-based road detection, robustly identifying road areas in visual images remains challenging due to issues like illumination changes and blurry images. To this end, LiDAR sensor data can be incorporated to improve the visual image-based road detection, because LiDAR data is less susceptible to visual noises. However, the main difficulty in introducing LiDAR information into visual image-based road detection is that LiDAR data and its extracted features do not share the same space with the visual data and visual features. Such gaps in spaces may limit the benefits of LiDAR information for road detection. To overcome this issue, we introduce a novel Progressive LiDAR Adaptation-aided Road Detection (PLARD) approach to adapt LiDAR information into visual image-based road detection and improve detection performance. In PLARD, progressive LiDAR adaptation consists of two subsequent modules: 1) data space adaptation, which transforms the LiDAR data to the visual data space to align with the perspective view by applying altitude difference-based transformation; and 2) feature space adaptation, which adapts LiDAR features to visual features through a cascaded fusion structure. Comprehensive empirical studies on the well-known KITTI road detection benchmark demonstrate that PLARD takes advantage of both the visual and LiDAR information, achieving much more robust road detection even in challenging urban scenes. In particular, PLARD outperforms other state-of-the-art road detection models and is currently top of the publicly accessible benchmark leader-board.