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.

A wellbore deformation monitoring method combining inertial navigation and 3D laser scanning technology

“…So in this paper, we adopt the principle of the cylinder model in the RANSAC algorithm to carry out point cloud filtering processing, and the wellbore's wellbore mathematical model is shown in Fig. 3, and its model mathematical formula can be expressed as follows [9][10]:…”

Research on denoising of skinned point cloud based on multi-feature point parameter weight optimization

“…The larger the average distance, the greater number of point clouds in the area, and vice versa. Therefore, the distance is also used as the evaluation scale of feature points, and the calculation process is as follows [13]. 𝑑 ∑ (10) Among them, 𝑑 is the average distance; k is the number of neighborhood points; 𝑝 and 𝑝 have the same meanings as before.…”

“…However, the degree of influence of each feature point parameter on the point cloud denoising algorithm is not discussed. Chen [13] used the principal component analysis method to solve the curvature of the point cloud, and construct a denoising model considering multiple feature parameters, but also did not analyze the influence of different feature parameter weights on the denoising algorithm.…”