Open3D point cloud fast clustering algorithm implementation and detailed code

Open3D point cloud fast clustering algorithm implementation and detailed code

Point cloud data is a commonly used form of 3D data that is widely used in fields such as computer vision, robotics, and autonomous driving. Point cloud clustering is the process of grouping point cloud data, classifying points with similar attributes into the same category. This article will introduce how to use the fast European clustering algorithm in the Open3D library to realize point cloud clustering, and give the corresponding source code.

First, we need to install the Open3D library and create a Python environment. Open3D can be installed with the following command:

pip install open3d

Next, import the required libraries:

import open3d as o3d
import numpy as np
from sklearn.cluster import DBSCAN
from sklearn.preprocessing import StandardScaler

To demonstrate, we create a random point cloud dataset and visualize it: