Robust multi-view camera calibration for wide-baseline camera networks
Real-world camera networks are often characterized by very wide baselines covering a wide range of viewpoints. We describe a method not only calibrating each camera sequence added to the system automatically, but also taking advantage of multi-view correspondences to make the entire calibration framework more robust. Novel camera sequences can be seamlessly integrated into the system at any time, adding to the robustness of future computations. One of the challenges consists in establishing correspondences between cameras. Initializing a bag of features from a calibrated frame, correspondences between cameras are established in a two-step procedure. First, affine invariant features of camera sequences are warped into a common coordinate frame and a coarse matching is obtained between the collected features and the incrementally built and updated bag of features. This allows us to warp images to a common view. Second, scale invariant features are extracted from the warped images. This leads to both more numerous and more accurate correspondences. Finally, the parameters are optimized in a bundle adjustment. Adding the feature descriptors and the optimized 3D positions to the bag of features, we obtain a feature-based scene abstraction, allowing for the calibration of novel sequences and the correction of drift in single-view calibration tracking. We demonstrate that our approach can deal with wide baselines. Novel sequences can seamlessly be integrated in the calibration framework.