# EEPNet: Efficient Edge Pixel-based Matching Network for Cross-Modal Dynamic Registration between LiDAR and Camera

arXiv: https://arxiv.org/abs/2409.19305 · HTML: https://mlsanity.com/paper/2409.19305/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 43 references that are in the map.
- First posted on arXiv 28 Sep 2024.

## Builds on (most cited first, 19 of 19)

- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [KPConv: Flexible and Deformable Convolution for Point Clouds](https://mlsanity.com/paper/1904.08889/) (2019)
- [LightGlue: Local Feature Matching at Light Speed](https://mlsanity.com/paper/2306.13643/) (2023)
- [A Survey of Deep Learning Techniques for Autonomous Driving](https://mlsanity.com/paper/1910.07738/) (2019)
- [D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features](https://mlsanity.com/paper/2003.03164/) (2020)
- [Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe](https://mlsanity.com/paper/2209.05324/) (2022)
- [RegNet: Multimodal Sensor Registration Using Deep Neural Networks](https://mlsanity.com/paper/1707.03167/) (2017)
- [2D3D-MatchNet: Learning to Match Keypoints Across 2D Image and 3D Point Cloud](https://mlsanity.com/paper/1904.09742/) (2019)
- [DeepI2P: Image-to-Point Cloud Registration via Deep Classification](https://mlsanity.com/paper/2104.03501/) (2021)
- [Is Geometry Enough for Matching in Visual Localization?](https://mlsanity.com/paper/2203.12979/) (2022)
- [CorrI2P: Deep Image-to-Point Cloud Registration via Dense Correspondence](https://mlsanity.com/paper/2207.05483/) (2022)
- [P2-Net: Joint Description and Detection of Local Features for Pixel and Point Matching](https://mlsanity.com/paper/2103.01055/) (2021)
- [Differentiable Registration of Images and LiDAR Point Clouds with VoxelPoint-to-Pixel Matching](https://mlsanity.com/paper/2312.04060/) (2023)
- [LCD: Learned Cross-Domain Descriptors for 2D-3D Matching](https://mlsanity.com/paper/1911.09326/) (2019)
- [AA-ICP: Iterative Closest Point with Anderson Acceleration](https://mlsanity.com/paper/1709.05479/) (2017)
- [LCE-Calib: Automatic LiDAR-Frame/Event Camera Extrinsic Calibration With A Globally Optimal Solution](https://mlsanity.com/paper/2303.09825/) (2023)
- [End-to-end 2D-3D Registration between Image and LiDAR Point Cloud for Vehicle Localization](https://mlsanity.com/paper/2306.11346/) (2023)
- [LiDAR-Generated Images Derived Keypoints Assisted Point Cloud Registration Scheme in Odometry Estimation](https://mlsanity.com/paper/2309.10436/) (2023)

## Built on by (most cited first, 1 of 1)

- [EEPNet-V2: Patch-to-Pixel Solution for Efficient Cross-Modal Registration between LiDAR Point Cloud and Camera Image](https://mlsanity.com/paper/2503.15285/) (2025)

## Abstract (arXiv)

Multisensor fusion is essential for autonomous vehicles to accurately perceive, analyze, and plan their trajectories within complex environments. This typically involves the integration of data from LiDAR sensors and cameras, which necessitates high-precision and real-time registration. Current methods for registering LiDAR point clouds with images face significant challenges due to inherent modality differences and computational overhead. To address these issues, we propose EEPNet, an advanced network that leverages reflectance maps obtained from point cloud projections to enhance registration accuracy. The introduction of point cloud projections substantially mitigates cross-modality differences at the network input level, while the inclusion of reflectance data improves performance in scenarios with limited spatial information of point cloud within the camera's field of view. Furthermore, by employing edge pixels for feature matching and incorporating an efficient matching optimization layer, EEPNet markedly accelerates real-time registration tasks. Experimental validation demonstrates that EEPNet achieves superior accuracy and efficiency compared to state-of-the-art methods. Our contributions offer significant advancements in autonomous perception systems, paving the way for robust and efficient sensor fusion in real-world applications.