In fields of intelligent robots and computer vision, the capability to select a few points representing salient structures has always been focused and investigated. In this paper, we present a novel interest point detector for 3D range images, which can be used with good results in applications of surface registration and object recognition. A local shape description around each point in the range image is firstly constructed based on the distribution map of the signed distances to the tangent plane in its local support region. Using this shape description, the interest value is computed for indicating the probability of a point being the interest point. Lastly a Non-Maxima Suppression procedure is performed to select stable interest points on positions that have large surface variation in the vicinity. Our method is robust to noise, occlusion and clutter, which can be seen from the higher repeatability values compared with the state-of-the-art 3D interest point detectors in experiments. In addition, the method can be implemented easily and requires low computation time.