Accurate hand-eye calibration is crucial for robotics and computer vision tasks, such as robotic manipulation and 3D object recognition, as it directly affects operational precision and visual perception. However, existing methods rely solely on point clouds and neglect geometric features like plane normals, leading to suboptimal spatial alignment. Additionally, the lack of an intelligent view selection strategy worsens initial alignment errors, causing cumulative inaccuracies and degraded performance. We propose HCNV, a novel framework that combines optimized point cloud alignment with intelligent view selection to overcome these limitations. Our method uses plane normals and a hybrid genetic algorithm to refine the point cloud transformation matrix, significantly improving spatial alignment accuracy. Furthermore, the intelligent view selection strategy enhances point cloud matching and optimizes view coverage, increasing robustness across various conditions. Experimental results show that HCNV improves calibration accuracy by 20% and reduces computational time by 30% compared to state-of-the-art methods, demonstrating its effectiveness and practicality in real-world applications.

This paper addresses the linear auto-calibration problem of a pan-tilt-zoom (PTZ) camera. Unlike existing methods, we take full advantage of the offset of the camera center from the rotation center, which is usually non-negligible in bullet-type PTZ cameras. Without any prior assumption, we propose a linear method to recover all intrinsic parameters. First, we successively acquired at least four images using the zoom and rotation capabilities of the PTZ camera. Second, using the homography of two images at the same location but different scales, the principal point and zoom scalar can be linearly recovered. Finally, based on the unknown offset of the camera center and rotation center, we propose a linear method to solve the scale factor in the Kruppa equation and recover the remaining camera intrinsic parameters, namely focal lengths and skew. Synthetic and real experiments demonstrate the feasibility of our approach.