In this thesis, we study object ranking in mobile 3D visual search. The conventional methods of object ranking achieve ranking results based on the appearance of objects in images captured by mobile devices while ignoring the underlying 3D geometric information. Thus, we propose to use the method of mobile 3D visual search to improve the ranking by using the underlying 3D geometry of the objects. We develop an algorithm of fast 3D geometric verication to re-rank the objects at low computational complexity. In that scene, the geometry of the objects such as round corners, sharp edges, or planar surfaces as well as the appearance of objects will be considered for 3D object ranking. On the other hand, we also investigate flaws of conventional vocabulary trees and improve the ranking results by introducing a credibility value to the TF-IDF scheme. By combining novel vocabulary trees and fast 3D geometric verification, we can improve the recall-datarate performance as well as the subjective ranking results for mobile 3D visual search.