Su Po-Chang, Shen Ju, Xu Wanxin, Cheung Sen-Ching S, Luo Ying
Center for Visualization and Virtual Environments, University of Kentucky, Lexington, KY 40506, USA.
Interactive Visual Media (IVDIA) Lab, University of Dayton, Dayton, OH 45469, USA.
Sensors (Basel). 2018 Jan 15;18(1):235. doi: 10.3390/s18010235.
From object tracking to 3D reconstruction, RGB-Depth (RGB-D) camera networks play an increasingly important role in many vision and graphics applications. Practical applications often use sparsely-placed cameras to maximize visibility, while using as few cameras as possible to minimize cost. In general, it is challenging to calibrate sparse camera networks due to the lack of shared scene features across different camera views. In this paper, we propose a novel algorithm that can accurately and rapidly calibrate the geometric relationships across an arbitrary number of RGB-D cameras on a network. Our work has a number of novel features. First, to cope with the wide separation between different cameras, we establish view correspondences by using a spherical calibration object. We show that this approach outperforms other techniques based on planar calibration objects. Second, instead of modeling camera extrinsic calibration using rigid transformation, which is optimal only for pinhole cameras, we systematically test different view transformation functions including rigid transformation, polynomial transformation and manifold regression to determine the most robust mapping that generalizes well to unseen data. Third, we reformulate the celebrated bundle adjustment procedure to minimize the global 3D reprojection error so as to fine-tune the initial estimates. Finally, our scalable client-server architecture is computationally efficient: the calibration of a five-camera system, including data capture, can be done in minutes using only commodity PCs. Our proposed framework is compared with other state-of-the-arts systems using both quantitative measurements and visual alignment results of the merged point clouds.
从目标跟踪到三维重建，RGB-D（深度RGB）相机网络在许多视觉和图形应用中发挥着越来越重要的作用。实际应用中常常使用稀疏放置的相机以最大化可见性，同时尽可能少用相机以最小化成本。一般来说，由于不同相机视图之间缺乏共享场景特征，校准稀疏相机网络具有挑战性。在本文中，我们提出了一种新颖的算法，它能够准确且快速地校准网络上任意数量RGB-D相机之间的几何关系。我们的工作具有许多新颖的特点。首先，为了应对不同相机之间的大间距，我们使用球形校准物体来建立视图对应关系。我们表明这种方法优于基于平面校准物体的其他技术。其次，我们不是使用仅适用于针孔相机的刚性变换来对相机外部校准进行建模，而是系统地测试不同的视图变换函数，包括刚性变换、多项式变换和流形回归，以确定对未见数据具有良好泛化能力的最稳健映射。第三，我们重新制定了著名的光束平差法程序，以最小化全局三维重投影误差，从而对初始估计进行微调。最后，我们可扩展的客户端 - 服务器架构计算效率高：一个五相机系统的校准，包括数据采集，仅使用商用个人电脑在几分钟内即可完成。我们提出的框架与其他最先进的系统进行了比较，使用了合并点云的定量测量和视觉对齐结果。
