# CP+: Camera Poses Augmentation with Large-scale LiDAR Maps

## TL;DR

This paper introduces CP+, a framework that enhances camera pose accuracy and colorization by aligning images with large-scale LiDAR point clouds through feature extraction and iterative optimization.

## Contribution

The novel CP+ framework improves camera pose estimation and colorization by directly aligning images with LiDAR point clouds using 2D-3D line correspondences.

## Key findings

- Enhanced camera pose accuracy in large-scale scenes
- Improved colorization quality of point clouds
- Effective alignment of images with LiDAR data

## Abstract

Large-scale colored point clouds have many advantages in navigation or scene display. Relying on cameras and LiDARs, which are now widely used in reconstruction tasks, it is possible to obtain such colored point clouds. However, the information from these two kinds of sensors is not well fused in many existing frameworks, resulting in poor colorization results, thus resulting in inaccurate camera poses and damaged point colorization results. We propose a novel framework called Camera Pose Augmentation (CP+) to improve the camera poses and align them directly with the LiDAR-based point cloud. Initial coarse camera poses are given by LiDAR-Inertial or LiDAR-Inertial-Visual Odometry with approximate extrinsic parameters and time synchronization. The key steps to improve the alignment of the images consist of selecting a point cloud corresponding to a region of interest in each camera view, extracting reliable edge features from this point cloud, and deriving 2D-3D line correspondences which are used towards iterative minimization of the re-projection error.