Open3D feature point extraction point cloud based on normal vector angle
A point cloud is a data structure representing the surface geometry of a three-dimensional object, which consists of a large number of discrete points. Point cloud processing plays an important role in areas such as 3D reconstruction, object recognition, and pose estimation. Extracting feature points in the point cloud can help us understand the structure and shape of the scene. This article will introduce how to use the Open3D library to extract feature points in the point cloud based on the angle between normal vectors.
First, we need to install the Open3D library. The latest version of Open3D can be installed via the pip command:
pip install open3d
After the installation is complete, we can import the Open3D library and load the point cloud dataset. Here we use an example dataset for demonstration:
import open3d as o3d
# 加载点云数据集
point_cloud = o3d.io.read_point_cloud("point_cloud.pcd")
Next, we can use the normal vector estimation method provided by Open3D to calculate the normal vector of each point in the point cloud. Here we use the covariance matrix with normal estimates to compute the normal vectors: