In this paper we provide a method for evaluating interest point detectors independently of image descriptors. This is possible because we have compiled a unique data set enabling us to determine if common interest points are found. The data contains 60 scenes of a wide range of object types, and for each scene we have 119 precisely located camera positions obtained from a camera mounted on an industrial robot arm. The scene surfaces have been scanned using structured light, providing precise 3D
ground truth. We have investigated a number of the most popular interest point detectors where we systematically have varied camera position, light and model parameters. The overall conclusion is that the Harris and Hessian corner detectors perform well followed by MSER, whereas the FAST corner
detector, IBR and EBR performs poorly. Furthermore, only the number of interest points change with changing parameters - not the correct matches.