Calibration method for sparse multi-view cameras by bridging with a mobile camera

Camera calibration that estimates the projective relationship between 3D and 2D image spaces is one of the most crucial processes for such 3D image processing as 3D reconstruction and 3D tracking. A strong calibration method, which needs to place landmarks with known 3D positions, is a common technique. However, as the target space becomes large, landmark placement becomes more complicated. Although a weak-calibration method does not need known landmarks to estimate a projective transformation matrix from the correspondence information among multi-view images, the estimation precision depends on the accuracy of the correspondence. When multiple cameras are arranged sparsely, detecting sufficient corresponding points is difficult. In this research, we propose a calibration method that bridges sparse multiple cameras with mobile camera images. The mobile camera captures video images while moving among sparse multi-view cameras. The captured video resembles dense multi-view images and includes sparse multi-view images so that weak-calibration is effective. We confirmed the appropriate spacing between the images through comparative experiments of camera calibration accuracy by changing the number of bridging images and applied our proposed method to multiple capturing experiments in a large-scale space and verified its robustness.