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.
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