We address the problem of single-image geo-calibration, in which an estimate of the geographic location, viewing direction and field of view is sought for the camera that captured an image. The dominant approach to this problem is to match features of the query image, using color and texture, against a reference database of nearby ground imagery. However, this fails when such imagery is not available. We propose to overcome this limitation by matching against a geographic database that contains the locations of known objects, such as houses, roads and bodies of water. Since we are unable to find one-to-one correspondences between image locations and objects in our database, we model the problem probabilistically based on the geometric configuration of multiple such weak correspondences. We propose a Markov Chain Monte Carlo (MCMC) sampling approach to approximate the underlying probability distribution over the full geo-calibration of the camera.
Zhai, M., Workman, S., & Jacobs, N. (2016). Camera Geo-Calibration using an MCMC Approach. In IEEE International Conference on Image Processing (ICIP). bibtex