Narrow Results

Manual selection of features from massive unstructured point cloud data is a very time-consuming task that requires a considerable amount of human intervention. This work is motivated by the need of fast and simple algorithm to obtain robust, stable and well-localized interest points that are used for subsequent processing in computer vision real-time applications. This paper presents an algorithm for detection of interest points in three-dimensional (3D) point cloud data by using a combined 3D Sobel–Harris operator. The proposed algorithm is compared with six state-of-the-art approaches used to identify the true feature points. Extensive experiments were carried out using synthetic benchmark and real datasets. The datasets were selected with different sizes, features and scales. The results were evaluated against human generated ground truth and predefined feature points. Three measures were used to evaluate the algorithm accuracy, namely localization accuracy L_e, False Positive Error (FPE) and False Negative Errors (FNE). Also, the complexity analysis of the proposed algorithm is presented. The results show that the proposed algorithm can identify the interest points with accepted accuracy. It works directly on point cloud datasets and shows superiority when compared with other methods work on 3D mesh data.