A Performance Evaluation of 3D Keypoint Detectors

Abstract

Intense research activity on 3D data analysis tasks, such as object recognition and shape retrieval, has recently fostered the proposal of many techniques to perform detection of repeatable and distinctive key points in 3D surfaces. This high number of proposals has not been accompanied yet by a comprehensive comparative evaluation of the methods. Motivated by this, our work proposes a performance evaluation of the state-of-the-art in 3D key point detection, mainly addressing the task of 3D object recognition. The evaluation is carried out by analyzing the performance of several prominent methods in terms of robustness to noise (real and synthetic), presence of clutter, occlusions and point-of-view variations.