  State-of-the-art techniques for monocular camera reconstruction predominantly
rely on the Structure from Motion (SfM) pipeline. However, such methods often
yield reconstruction outcomes that lack crucial scale information, and over
time, accumulation of images leads to inevitable drift issues. In contrast,
mapping methods based on LiDAR scans are popular in large-scale urban scene
reconstruction due to their precise distance measurements, a capability
fundamentally absent in visual-based approaches. Researchers have made attempts
to utilize concurrent LiDAR and camera measurements in pursuit of precise
scaling and color details within mapping outcomes. However, the outcomes are
subject to extrinsic calibration and time synchronization precision. In this
paper, we propose a novel cost-effective reconstruction pipeline that utilizes
a pre-established LiDAR map as a fixed constraint to effectively address the
inherent scale challenges present in monocular camera reconstruction. To our
knowledge, our method is the first to register images onto the point cloud map
without requiring synchronous capture of camera and LiDAR data, granting us the
flexibility to manage reconstruction detail levels across various areas of
interest. To facilitate further research in this domain, we have released
Colmap-PCD${^{3}}$, an open-source tool leveraging the Colmap algorithm, that
enables precise fine-scale registration of images to the point cloud map.
