Colmap-PCD: An Open-source Tool for Fine Image-to-point cloud Registration
Chunge Bai, Ruijie Fu, Xiang Gao

TL;DR
This paper introduces Colmap-PCD, an open-source tool that enables precise registration of images to pre-existing LiDAR point cloud maps without requiring synchronized data, improving scale accuracy in monocular reconstruction.
Contribution
It presents a novel, cost-effective pipeline using a fixed LiDAR map as a constraint, and releases Colmap-PCD, a tool based on Colmap for fine image-to-point cloud registration without synchronized data.
Findings
First method to register images onto point cloud without synchronized capture
Enables flexible reconstruction detail levels across areas
Open-source tool facilitates further research
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsRemote Sensing and LiDAR Applications · 3D Surveying and Cultural Heritage · Advanced Vision and Imaging
