Using Open3D to Realize Nonlinear Least Squares Fitting of 2D Circle Point Cloud

Using Open3D to Realize Nonlinear Least Squares Fitting of 2D Circle Point Cloud

In the field of computer vision and 3D graphics, nonlinear least squares fitting is a common data fitting method, which can be used to estimate parameters in 2D or 3D data. This article will introduce how to use the Open3D library to implement the nonlinear least squares fitting of the two-dimensional point cloud, and attach the corresponding source code.

First, we need to import the Open3D library and load the 2D point cloud data to be fitted. Suppose we have saved the point cloud data of circles in the file "circle.xyz", each line contains the x and y coordinates of a point. It can be loaded with the following code:

import open3d as o3d

# 加载点云数据
point_cloud = o3d.io.read_point_cloud("circle.xyz")

Next, we can use the method in Open3D to realize the fitting of the point cloud. Open3D provides compute_point_cloud_to_point_cloud_distancethe method to calculate the distance between point clouds. We can use this function to define a residual function that represents the sum of the squares of the distances from each point in the point cloud to the fitted circle. The specific code is as follows: