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.

翻译：大规模彩色点云在导航或场景展示中具有诸多优势。借助目前广泛应用于重建任务中的相机和激光雷达（LiDAR），可以获取此类彩色点云。然而，在许多现有框架中，这两种传感器信息未能有效融合，导致着色效果不佳，从而产生不准确的相机位姿和受损的点云着色结果。我们提出了一种名为相机位姿增强（CP+）的新型框架，用于优化相机位姿并使其直接与基于LiDAR的点云对齐。初始粗略相机位姿由LiDAR-惯性或LiDAR-惯性-视觉里程计结合近似外参和时间同步提供。提升图像对齐的关键步骤包括：为每个相机视野中的感兴趣区域选择对应的点云，从该点云中提取可靠的边缘特征，并推导出用于迭代最小化重投影误差的2D-3D线对应关系。