How to Get Better Precision with Point Cloud Optimization

Point cloud optimization, also known as point cloud smoothing, is a method of manipulating point cloud data in 3D space, whose primary purpose is to improve the quality and usability of point cloud data by reducing noise and irregularities.

Specifically, point cloud optimization can achieve the following goals:

Reduce noise: Point cloud data typically contains noise, which can affect the accuracy and usability of the data. Point cloud optimization can reduce noise by filtering out small outliers and smoothing the data using methods such as Gaussian filtering.

Remove irregularities: Point cloud data may contain irregularities such as sharp corners or non-smooth surfaces, which can make it difficult to process and analyze. Point cloud optimization can remove these irregularities by using smoothing algorithms, making the point cloud data more regular and easier to handle.

Improve fitting accuracy: The accuracy and quality of data are crucial when using point cloud data for fitting or reconstruction. Point cloud optimization can improve fitting accuracy by increasing the smoothness of the point cloud, producing more accurate and reliable fitting results.

After using point cloud optimization,the thickness is only 1cm,but the density of point cloud did not decrease.