Hybrid Calibration between a Laser Scanner and Smartphone Camera Using hourglass targets and Deep Learning

This paper presents a novel hybrid calibration pipeline that jointly estimates the spatial and temporal alignment between a handheld laser scanner and a smartphone camera without any hardware synchronization. The method combines deep-learning-based target detection with classical geometric calibration using 2D-3D correspondences derived from black and white hourglass planar targets. Target centers are precisely localized in both the RGB images and the 3D point cloud using a symmetric templatematching scheme, enabling robust solution of the perspective-n-point (PnP) problem for spatial calibration. To address the lack of hardware synchronization, we introduce a temporal calibration method that exploits geometric correspondences between rendered intensity images and camera frames. On a Lixel L2 Pro scanner with a Huawei P20 Pro camera, the pipeline achieves a median Reprojection error of 0.76 px for static calibration and 2.19 px across 91 dynamic evaluations. The approach enables accurate image-pointcloud fusion for scanners without syncronisation interfaces and provides a foundation for colorization, image analysis, and redensification of laser data.