Compression method of three-dimensional space measurement result data

Abstract

The invention discloses a compression method for three-dimensional space measurement result data, and the method comprises the steps: obtaining point cloud data: obtaining initial data after scanningan object through employing laser radar equipment, and converting the initial data through employing a trigonometric function to obtain rectangular coordinate system data; carrying out uniform mesh generation of the point cloud data, and carryig out proportional generation on the point cloud data according to the transmitted parameters, wherein a uniform generation mode is adopted for point clouddata generation; removing discrete abnormal points of the point cloud data, wherein discrete points deviating from the whole and abnormal points exceeding a normal value are generated in the laser radar operation scanning process, and the discrete abnormal points are removed by applying a difference value; extracting feature points, performing judging according to the point location data values inthe grids, and if the maximum and minimum difference values in the grids are greater than the height values, indicating that the feature points exist in the grids; carrying out point cloud compression, carrying out compression in a certain proportion according to the divided grids, and adding the extracted feature points to the compressed point cloud data.

Description

technical field [0001] The invention relates to the technical field of three-dimensional space data processing, in particular to a method for compressing three-dimensional space measurement result data. Background technique [0002] In recent years, with the rapid development of 3D scanning equipment, the scanning cost of 3D data has been greatly reduced, and the acquisition of 3D data has become easier and faster. As an effective and high-quality 3D data acquisition and storage method, point cloud has gradually been adopted It is widely accepted and used, and the processing technology of point cloud data is also mature. During the application process, a large amount of point cloud data needs to be compressed and streamlined for preprocessing. Most of the 3D point cloud data compression in the prior art adopts mean value compression or random sampling compression. Although the mean value compression algorithm can improve the data compression rate very well, it will also comp...

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Problems solved by technology

[0002] In recent years, with the rapid development of 3D scanning equipment, the scanning cost of 3D data has been greatly reduced, and the acquisition of 3D data has become easier and faster. As an effective and high-quality 3D data acquisition and storage method, point cloud has gradually been adopted It is widely accepted and used, and the processing technology of point cloud data is also mature. During the application process, a large amount of point cloud data needs to be compressed and streamlined for preprocessing. Most of the 3D point cloud data compression in the prior art adopts mean value compression or random sampling compression. Although the mean value compression algorithm can improve the data compression rate very well, it will also compress some data with more characteristic points, which will increase the error value and cause deviations in later calculations. Random sampling compression is a It is a very simple compression method, but when using out-of-order compression, the compression rate and error value of the data cannot be well guaranteed. The above methods all have low and uncontrollable point cloud data compression efficiency, large error values, and messy point order question

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