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论文类型：期刊论文

发表时间：2018-03-01

发表刊物：NEURAL COMPUTING & APPLICATIONS

收录刊物：SCIE、EI、Scopus

卷号：29

期号：5,SI

页面范围：1209-1224

ISSN号：0941-0643

关键字：VR; Point cloud; Keypoints detector; 3D feature descriptor; 3D object detection

摘要：3D point clouds are important for the reconstruction of environment. However, comparing to the artificial VR scene representation methods, 3D point clouds are more difficult to correspond to real scenes. In this paper, a method for detecting keypoints and describing scale invariant point feature of 3D point clouds is proposed. To detect, we first select keypoints as the saliency points with fast changing speed along with all principal directions of the searching area of the point cloud. The searching area is a searching keyscale which represents the unique scale size of the point cloud. Then, the descriptor is encoded based on the shape of a border or silhouette of an object to be detected or recognized. We also introduce a vote-casting-based 3D multi-scale object detection method. Experimental results based on synthetic data, real data and vote-casting scheme show that we can easily deal with the different tasks without additional information.