# LiDAR as a Geometric Prior: Enhancing Camera Pose Tracking Through High-Fidelity View Synthesis
## 6 Conclusions  
In this paper, we presented a robust and accurate framework for monocular camera pose estimation by leveraging high-fidelity, pre-built LiDAR maps. Our core contribution is a render-and-match pipeline that bridges the domain gap between a sparse 3D point cloud and a 2D camera image. By generating synthetic views from the dense LiDAR reconstruction, we establish reliable keypoint correspondences for precise pose estimation. The integration of IMU data as a motion prior provides a strong initial guess for each frame, significantly enhancing the robustness and efficiency of the tracking process.  
We demonstrated the effectiveness of this framework through two distinct strategies: an Online Rendering and Tracking method that renders synthetic views in real-time, and an Offline Keypoint-Map Tracking method that precomputes a 3D keypoint map from the known trajectory to enable lightweight localization. Both approaches leverage the pre-existing, geometrically accurate LiDAR map to anchor the localization process, overcoming the reliance on photometric consistency that is a primary failure point for purely image-based methods.  
Our experimental validation showed marked improvements in both accuracy and robustness compared to several state-of-the-art visual SLAM systems. The proposed methods maintained consistent, low-error tracking in challenging real-world conditions where visual-only systems often failed. The effectiveness of both the Online Rendering and Tracking and the Offline Keypoint-Map Tracking strategies confirms that the rich geometric detail from LiDAR is a powerful and consistent source of information for camera localization.  
Future work could extend this framework in several promising directions. One key area is exploring methods to update the LiDAR map online to account for dynamic objects or long-term environmental changes. Additionally, investigating more advanced rendering techniques could further improve the realism of the synthetic views and, consequently, the matching performance. Finally, adapting the system for use with other sensor modalities, such as thermal or event cameras, could open up new applications for robust localization in even more extreme environments.  
Conceptualization, R.M.-S. and M.J.M.-J.; methodology, R.M.-S. and F.J.R.-R.; software, R.M.-S. and J.L.; validation, R.M.-S., J.L. and F.J.R.-R.; formal analysis, R.M.-S. and M.J.M.-J.; investigation, R.M.-S.; resources, R.M.-S., J.L. and F.J.R.-R.; data curation, R.M.-[PERSON], [PERSON] and [PERSON]; writing--original draft preparation, [PERSON]-[PERSON]; writing--review and editing, [PERSON]-[PERSON], [PERSON], [PERSON], F.Z. and [PERSON]; visualization, [PERSON]-[PERSON]; supervision, [PERSON]-[PERSON]; project administration, [PERSON]-[PERSON], [PERSON] and F.Z.; funding acquisition, [PERSON]-S., [PERSON]J. and F.Z. All authors have read and agreed to the published version of the manuscript.  
This research has been funded under the projects PID2023-147296 NB-I00 of the Ministry of Science, Innovation and Universities of Spain and the Hong Kong Research Grants Council (UGC) General Research Fund (GRF), project number 17205924.  
Publicly archived datasets can be found at [[https://www.uco.es/investiga/grupos/ava/](https://www.uco.es/investiga/grupos/ava/)]([https://www.uco.es/investiga/grupos/ava/](https://www.uco.es/investiga/grupos/ava/)) (accessed on 31 July 2025).  
The authors declare no conflicts of interest.