Image matching and the epipolar geometry for a stereo pair has been a well-studied topic in the field of computer vision. There is a strong foundation for matching techniques between two planar images, and the case of two spherical panoramas has been more recently explored. This work establishes the geometry for a pair consisting of one planar image and one spherical panorama, while exploring matching techniques that will perform well for scenes with repetitive features. A pseudo-fundamental matrix is defined for use with one calibrated image and one uncalibrated. This allows a photograph to be used without calibration while a panorama can be more easily considered as a whole. A global context descriptor for Speeded Up Robust Features and Maximally Stable Extremal Regions improves matching results and automatically computed epipolar geometry for scenes with buildings having repetitive features.