Registration is a fundamental problem in a myriad of applications ranging from heritage reconstruction to industrial applications. Descriptors are an important part of the registration pipeline as well as a very active research field. However, the sets used to illustrate descriptor performance have often undergone several preprocessing steps such as noise filtering, hole filling or outlier removal. These steps simplify the problem but are not readily available in many applications. In this paper we compare the performances of 4 state of the art shape descriptors: SHOT , Spin Image , FPFH  and 3DSC . Experiments were carried out with real as well as synthetic data paying special attention to issues commonly present in real data (noise, outliers and low overlap). The method obtaining a best result overall is SHOT, based mostly on the results with synthetic data. Experiments with real data showed how state of the art descriptors are not yet able to produce optimal results in the most challenging scenarios.