A Comparative Study of Object Classification Methods Using 3D Zernike Moment on 3D Point Clouds

Abstract

The point clouds provide responsive geometric representation on many applications. The classification of objects through point clouds is one of the popular subjects of recent years. In this study, we introduce the potential of the 3D Zernike Moment approach for the object classification on the 3D point cloud. Zernike Moment (ZM) has utilized as a feature extractor of point clouds. This paper presents a comparative study of the state-of-the-art classification methods with respect to machine learning algorithms and PointNet which have been developed for classification by Stanford University. Object classification has been applied to a dataset with labeled 3D Zernike Moment features inferences obtained from the 3D point cloud. The performance of the developed method is verified by comparing the experimental results on the Washington RGB-D Object Dataset which consists of forty-five different household objects as point cloud data. Fine Gaussian SVM gives the best results in accuracy (96.0%) according to the results obtained with built-in cross-validation results. The results of the proposed classification of 3D Point Clouds on the 3D Zernike Moment features have significantly higher accuracy. The classification of 3D Zernike Moments on point cloud compared to directly point cloud classification is efficient and effective and lower complexity computation is obtained. It is emphasized that the 3D Zernike Moment features can be optimized for classification. In general, the comparative validation results have been reached a high accuracy in the proposed method. In the future, 3D Zernike Moment feature extractions are emphasized for the usability of classification operations on 3D data.