LiDAR as a Geometric Prior: Enhancing Camera Pose Tracking Through High-Fidelity View Synthesis
This paper presents a robust framework for monocular camera pose estimation by leveraging high-fidelity, pre-built 3D LiDAR maps. The core of our approach is a render-and-match pipeline that synthesizes photorealistic views from a dense LiDAR point cloud. By detecting and matching keypoints between...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/15/8743 |
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| author | Rafael Muñoz-Salinas Jianheng Liu Francisco J. Romero-Ramirez Manuel J. Marín-Jiménez Fu Zhang |
| author_facet | Rafael Muñoz-Salinas Jianheng Liu Francisco J. Romero-Ramirez Manuel J. Marín-Jiménez Fu Zhang |
| author_sort | Rafael Muñoz-Salinas |
| collection | DOAJ |
| description | This paper presents a robust framework for monocular camera pose estimation by leveraging high-fidelity, pre-built 3D LiDAR maps. The core of our approach is a render-and-match pipeline that synthesizes photorealistic views from a dense LiDAR point cloud. By detecting and matching keypoints between these synthetic images and the live camera feed, we establish reliable 3D–2D correspondences for accurate pose estimation. We evaluate two distinct strategies: an Online Rendering and Tracking method that renders views on the fly, and an Offline Keypoint-Map Tracking method that precomputes a keypoint map for known trajectories, optimizing for computational efficiency. Comprehensive experiments demonstrate that our framework significantly outperforms several state-of-the-art visual SLAM systems in both accuracy and tracking consistency. By anchoring localization to the stable geometric information from the LiDAR map, our method overcomes the reliance on photometric consistency that often causes failures in purely image-based systems, proving particularly effective in challenging real-world environments. |
| format | Article |
| id | doaj-art-5a15987c741a42c8a4745051e4c417a0 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-5a15987c741a42c8a4745051e4c417a02025-08-20T03:04:42ZengMDPI AGApplied Sciences2076-34172025-08-011515874310.3390/app15158743LiDAR as a Geometric Prior: Enhancing Camera Pose Tracking Through High-Fidelity View SynthesisRafael Muñoz-Salinas0Jianheng Liu1Francisco J. Romero-Ramirez2Manuel J. Marín-Jiménez3Fu Zhang4Departamento de Informática e Inteligencia Artificial, Edificio Albert Einstein, Campus de Rabanales, Universidad de Córdoba, 14071 Córdoba, SpainMechatronics and Robotic Systems (MaRS) Laboratory, Department of Mechanical Engineering, University of Hong Kong (HKU), Hong Kong, ChinaDepartamento de Teoría de la Señal y Comunicaciones y Sistemas Telemáticos y Computación, Campus de Fuenlabrada, Universidad Rey Juan Carlos, 28942 Fuenlabrada, SpainDepartamento de Informática e Inteligencia Artificial, Edificio Albert Einstein, Campus de Rabanales, Universidad de Córdoba, 14071 Córdoba, SpainMechatronics and Robotic Systems (MaRS) Laboratory, Department of Mechanical Engineering, University of Hong Kong (HKU), Hong Kong, ChinaThis paper presents a robust framework for monocular camera pose estimation by leveraging high-fidelity, pre-built 3D LiDAR maps. The core of our approach is a render-and-match pipeline that synthesizes photorealistic views from a dense LiDAR point cloud. By detecting and matching keypoints between these synthetic images and the live camera feed, we establish reliable 3D–2D correspondences for accurate pose estimation. We evaluate two distinct strategies: an Online Rendering and Tracking method that renders views on the fly, and an Offline Keypoint-Map Tracking method that precomputes a keypoint map for known trajectories, optimizing for computational efficiency. Comprehensive experiments demonstrate that our framework significantly outperforms several state-of-the-art visual SLAM systems in both accuracy and tracking consistency. By anchoring localization to the stable geometric information from the LiDAR map, our method overcomes the reliance on photometric consistency that often causes failures in purely image-based systems, proving particularly effective in challenging real-world environments.https://www.mdpi.com/2076-3417/15/15/8743camera pose estimationLiDAR mapping3D reconstructionvisual SLAM |
| spellingShingle | Rafael Muñoz-Salinas Jianheng Liu Francisco J. Romero-Ramirez Manuel J. Marín-Jiménez Fu Zhang LiDAR as a Geometric Prior: Enhancing Camera Pose Tracking Through High-Fidelity View Synthesis Applied Sciences camera pose estimation LiDAR mapping 3D reconstruction visual SLAM |
| title | LiDAR as a Geometric Prior: Enhancing Camera Pose Tracking Through High-Fidelity View Synthesis |
| title_full | LiDAR as a Geometric Prior: Enhancing Camera Pose Tracking Through High-Fidelity View Synthesis |
| title_fullStr | LiDAR as a Geometric Prior: Enhancing Camera Pose Tracking Through High-Fidelity View Synthesis |
| title_full_unstemmed | LiDAR as a Geometric Prior: Enhancing Camera Pose Tracking Through High-Fidelity View Synthesis |
| title_short | LiDAR as a Geometric Prior: Enhancing Camera Pose Tracking Through High-Fidelity View Synthesis |
| title_sort | lidar as a geometric prior enhancing camera pose tracking through high fidelity view synthesis |
| topic | camera pose estimation LiDAR mapping 3D reconstruction visual SLAM |
| url | https://www.mdpi.com/2076-3417/15/15/8743 |
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