High-precision measurement based on multi-view geometry benefits from aligning a template CAD model to the multi-view observations of a target object. It not only improves the multi-camera calibration but also assists the measurement by serving as a “scaffold.” A straightforward approach is to reconstruct the target object and perform a 3D registration with the CAD model. However, the accuracy of such 3D alignment cannot meet the high-precision requirement. We formulate the problem as a bundle adjustment where we jointly optimize the 6DoF poses of both the template model and the multiple cameras. To accommodate the manufacturing error of products, we propose a simple and robust solution based on a discrete–continuous optimization which interleaves between correspondence selection and pose optimization. In the discrete step, a robust RANSAC-based selection process selects well-matched 2D–3D feature points according to the current model/camera poses. In the continuous step, it jointly optimizes the 6D poses of the CAD model and the multiple cameras. This interleaving optimization constitutes a novel hybrid bundle adjustment (HBA). In HBA, the reprojection error of a feature point is measured either with the 2D–3D correspondence between the observation image and the CAD model or with the cross-view correspondence between multiple images, whichever is more reliable according to the discrete selection step. Through extensive evaluation on real-world data, we demonstrate that HBA achieves high-precision multi-camera calibration, outperforming alternative approaches significantly.

通过将模板CAD模型与目标对象的多视图观测值对齐，可以基于多视图几何图形进行高精度测量。它不仅可以改善多摄像机校准，还可以通过充当“支架”来辅助测量。一种简单的方法是重建目标对象，并使用CAD模型执行3D配准。但是，这种3D对准的精度不能满足高精度要求。我们将问题表述为捆绑调整，其中我们共同优化模板模型和多个摄像机的6DoF姿势。为了适应产品的制造误差，我们提出了一种基于离散连续优化的简单而强大的解决方案，该方案在对应选择和姿势优化之间进行插值。在离散步骤中，强大的基于RANSAC的选择过程会根据当前的模型/相机姿势来选择匹配度高的2D–3D特征点。在连续的步骤中，它将联合优化CAD模型和多台摄像机的6D姿态。这种交织优化构成了一种新颖的混合束调整（HBA）。在HBA中，特征点的重投影误差是通过观察图像和CAD模型之间的2D–3D对应关系或通过多个图像之间的横断面对应关系来测量的，根据离散选择步骤中哪个更可靠。通过对真实数据的广泛评估，我们证明了HBA可以实现高精度的多摄像机校准，大大优于其他方法。