{"id":6728,"date":"2019-02-19T12:22:13","date_gmt":"2019-02-19T12:22:13","guid":{"rendered":"https:\/\/www.pre-scient.com\/?p=6728"},"modified":"2025-11-17T06:52:10","modified_gmt":"2025-11-17T06:52:10","slug":"point-cloud-operations","status":"publish","type":"post","link":"https:\/\/www.pre-scient.com\/us\/point-cloud-operations\/","title":{"rendered":"Point Cloud Operations"},"content":{"rendered":"\n

No output is always perfect no matter how much the technology has evolved. Even though point cloud generation has eased up manufacturing process, it comes with its own anomaly. Generally, a point cloud data is accompanied by Noises and Outliers.<\/p>\n\n\n\n

Noises or Noisy data means the data information is contaminated by unwanted information; such unwanted information contributes to the impurity of the data while the underlying information still dominates. A noisy point cloud data can be filtered and the noise can be absolutely discarded to produce a much refined result.<\/p>\n\n\n\n

If we carefully examine the image below, it illustrates a point cloud data with noises. The surface area is usually filled with extra features which can be eliminated.<\/p>\n\n\n

\n
\"\"<\/figure>\n<\/div>\n\n\n

<\/p>\n\n\n\n

After carrying out Noise Reduction process, the image below illustrates the outcome, which a lot smoother data without any unwanted elements. There are many algorithms and processes for noise reduction.<\/p>\n\n\n

\n
\"\"<\/figure>\n<\/div>\n\n\n

<\/p>\n\n\n\n

Outlier, on the contrary, is a type of data which is not totally meaningless, but might turn out to be of interest. Outlier is a data value that differs considerably from the main set of data. It is mostly different from the existing group. Unlike noises, outliers are not removed outright but rather, it is put under analysis sometimes.<\/p>\n\n\n\n

The images below clearly portray what outliers are and how the point cloud data looks like once the outliers are removed.<\/p>\n\n\n

\n
\"\"<\/figure>\n<\/div>\n\n
\n
\"\"<\/figure>\n<\/div>\n\n\n

Point Cloud Decimation<\/strong> <\/p>\n\n\n\n

We have learned how a point cloud data obtained comes with noise and outliers and the methods to reduce them to make the data more executable for meshing. Point cloud data undergoes several operations to treat the anomalies existing within. Two of the commonly used operations are Point Cloud Decimation and Point Cloud Registration.<\/p>\n\n\n\n

A point cloud data consists of millions of small points, sometimes even more than what is necessary. Decimation is the process of discarding points from the data to improve performance and reduce usage of disk. Decimate point cloud command reduces the size of point clouds.<\/p>\n\n\n\n

The following example shows how a point cloud underwent decimation to reduce the excess points.<\/p>\n\n\n

\n
\"\"<\/figure>\n<\/div>\n\n
\n
\"\"<\/figure>\n<\/div>\n\n\n

Point Cloud Registration<\/strong><\/p>\n\n\n\n

Scanning a commodity is not a one step process. A lot of time, scanning needs to be done separately from different angles to get views. Each of the acquired data view is called a dataset. Every dataset obtained from different views needs to be aligned together into a single point cloud data model, so that subsequent processing steps can be applied. The process of aligning various 3D point cloud data views into a complete point cloud model is known as registration. The purpose is to find the relative positions and orientations of the separately acquired views, such that the intersecting regions between them overlap perfectly.<\/p>\n\n\n\n

Take a look at the example given below. The car door data sets have been merged to get a complete model.<\/p>\n\n\n

\n
\"\"<\/figure>\n<\/div>\n\n
\n
\"\"<\/figure>\n<\/div>\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

No output is always perfect no matter how much the technology has evolved. Even though point cloud generation has eased up manufacturing process, it comes with its own anomaly. Generally, a point cloud data is accompanied by Noises and Outliers. Noises or Noisy data means the data information is contaminated by unwanted information; such unwanted information contributes to the impurity of the data while the underlying information still dominates. A noisy point cloud data can be filtered and the noise can be absolutely discarded to produce a much refined result. If we carefully examine the image below, it illustrates a point cloud data with noises. The surface area is usually filled with extra features which can be eliminated. After carrying out Noise Reduction process, the image below illustrates the outcome, which a lot smoother data without any unwanted elements. There are many algorithms and processes for noise reduction. Outlier, on the contrary, is a type of data which is not totally meaningless, but might turn out to be of interest. Outlier is a data value that differs considerably from the main set of data. It is mostly different from the existing group. Unlike noises, outliers are not removed outright but rather, it is put under analysis sometimes. The images below clearly portray what outliers are and how the point cloud data looks like once the outliers are removed. Point Cloud Decimation We have learned how a point cloud data obtained comes with noise and outliers and the methods to reduce them to make the data more executable for meshing. Point cloud data undergoes several operations to treat the anomalies existing within. Two of the commonly used operations are Point Cloud Decimation and Point Cloud Registration. A point cloud data consists of millions of small points, sometimes even more than what is necessary. Decimation is the process of discarding points from the data to improve performance and reduce usage of disk. Decimate point cloud command reduces the size of point clouds. The following example shows how a point cloud underwent decimation to reduce the excess points. Point Cloud Registration Scanning a commodity is not a one step process. A lot of time, scanning needs to be done separately from different angles to get views. Each of the acquired data view is called a dataset. Every dataset obtained from different views needs to be aligned together into a single point cloud data model, so that subsequent processing steps can be applied. The process of aligning various 3D point cloud data views into a complete point cloud model is known as registration. The purpose is to find the relative positions and orientations of the separately acquired views, such that the intersecting regions between them overlap perfectly. Take a look at the example given below. The car door data sets have been merged to get a complete model.<\/p>\n","protected":false},"author":1,"featured_media":11351,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[109],"tags":[197,206,210,201,209],"class_list":["post-6728","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-product-development-by-reverse-engineering","tag-algorithms","tag-manufacturing","tag-mesh","tag-point-cloud","tag-product-development"],"yoast_head":"\nPoint Cloud Processing & Reverse Engineering for U.S. Engineering Teams<\/title>\n<meta name=\"description\" content=\"No output is always perfect no matter how much the technology has evolved. Even though point cloud generation has eased up manufacturing process, it comes with its own anomaly. 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