Point Cloud Key Point Extraction Algorithm Based on Feature Space Value Filtering

Abstract

With the rapid development of 3-dimensional (3D) acquisition technology, point clouds have a wide range of application prospects in the fields of computer vision, autonomous driving, and robotics. Point cloud data is widely used in many 3D scenes, and deep learning has become a mainstream research method for classification with the advantages of automatic feature extraction and strong generalization ability. In this paper, a hierarchical key point extraction framework is proposed to solve the problem of modeling the local geometric structure between points. Various point cloud models such as PointNet, PointNet++, and DGCNN are analyzed and their features in local key point are extracted. Based on these analyses, an indexed edge geometric feature spatial value screening neural network (IEGCNN) is proposed. This network extracts features from each point and its neighborhood, calculates the distance between the center point and the points within its neighborhood, and adds the point orientation information to the edge feature spatial value screening network. The relationship between points in the edge network architecture is projected onto a 3D coordinate system and decomposed into three orthogonal bases. The geometric structure between two points is modeled by feature aggregation based on the angle between the edge vector and the base vector and the distance between the center point and the neighboring points. The proposed method has the capability of fast processing of point cloud data by significantly reducing the training and recognition time. The experimental results show that this method achieved high classification accuracy value. This work also provides an idea to solve the problem of real-time target detection network, which has a broad applications prospect in the deployment of movable devices and real-time processing.