Indexed by:期刊论文

Date of Publication:2018-03-01

Journal:NEURAL COMPUTING & APPLICATIONS

Included Journals:SCIE、EI、Scopus

Volume:29

Issue:5,SI

Page Number:1209-1224

ISSN No.:0941-0643

Key Words:VR; Point cloud; Keypoints detector; 3D feature descriptor; 3D object detection

Abstract: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.