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The advent of high-resolution digital cameras and sophisticated multi-view stereo algorithms offers the promises of unprecedented geometric fidelity in image-based modeling tasks, but it also puts unprecedented demands on camera calibration to fulfill these promises. This paper presents a novel approach to camera calibration where top-down information from rough camera parameter estimates and the output of a publicly available multiview-stereo system (Furukawa et al.) on scaled-down input images are used to effectively guide the search for additional image correspondences and significantly improve camera calibration parameters using a standard bundle adjustment algorithm (Lourakis et al.). The proposed method has been tested on several real datasets-including objects without salient features for which image correspondences cannot be found in a purely bottom-up fashion, and image-based modeling tasks-including the construction of visual hulls where thin structures are lost without our calibration procedure.