#Element 3d 2.2 auto normals how to
#Element 3d 2.2 auto normals registration

The algorithm uses the fast point feature histogram (FPFH) descriptor and Hausdorff distance to extract and locate the corresponding feature points in order to minimize the number of registration points, thereby improving its speed. In this paper, a point cloud registration algorithm based on feature extraction and matching is proposed to improve the speed while maintaining the accuracy of the registration of a large amount of cloud data.

#Element 3d 2.2 auto normals how to

How to achieve a balance between time and accuracy when dealing with a large volume of cloud data is a problem worth studying. With a large amount of point cloud data, the existing registration algorithms either improve the registration accuracy at the cost of time or increase the registration speed. It is an important step to integrate the point cloud in different coordinate systems into a unified coordinate system to obtain the complete data model of the object being tested. Due to the influence of the measuring equipment, object shape, and environment, a complete point cloud data model must be measured from multiple perspectives. Registration technology plays an important role in 3D point cloud reconstruction. The continuous development of three-dimensional (3D) scanning equipment has promoted the application of 3D point cloud reconstruction technology in the fields of mechanical manufacturing, medicine, robot navigation, and other industries. Experimental results show that given a large amount of point cloud data, this algorithm has advantages in both time and precision. In the precise registration phase, the algorithm uses an improved normal distribution transformation (INDT) algorithm. In the registration process, FPFH features and Hausdorff distance are used to search for corresponding point pairs, and the RANSAC algorithm is used to eliminate incorrect point pairs, thereby improving the accuracy of the corresponding relationship. In the rough registration stage, the algorithm extracts feature points based on the judgment of retention points and bumps, which improves the speed of feature point extraction.

In this paper, we propose a point cloud registration algorithm based on feature extraction and matching the algorithm helps alleviate problems of precision and speed. The existing registration algorithms suffer from low precision and slow speed when registering a large amount of point cloud data.

Element 3d 2.2 auto-normals