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State-of-the-art techniques for monocular camera reconstruction predominantlyrely on the Structure from Motion (SfM) pipeline. However, such methods oftenyield reconstruction outcomes that lack crucial scale information, and overtime, accumulation of images leads to inevitable drift issues. In contrast,mapping methods based on LiDAR scans are popular in large-scale urban scenereconstruction due to their precise distance measurements, a capabilityfundamentally absent in visual-based approaches. Researchers have made attemptsto utilize concurrent LiDAR and camera measurements in pursuit of precisescaling and color details within mapping outcomes. However, the outcomes aresubject to extrinsic calibration and time synchronization precision. In thispaper, we propose a novel cost-effective reconstruction pipeline that utilizesa pre-established LiDAR map as a fixed constraint to effectively address theinherent scale challenges present in monocular camera reconstruction. To ourknowledge, our method is the first to register images onto the point cloud mapwithout requiring synchronous capture of camera and LiDAR data, granting us theflexibility to manage reconstruction detail levels across various areas ofinterest. To facilitate further research in this domain, we have releasedColmap-PCD${^{3}}$, an open-source tool leveraging the Colmap algorithm, thatenables precise fine-scale registration of images to the point cloud map.

Colmap-PCD: An Open-source Tool for Fine Image-to-point cloud Registration