A Simplification Method of Digital Geometric Image Based on Point Cloud Model

Abstract

The invention discloses a digital geometric image simplification method based on a point cloud model, comprising the following steps: step 1: selecting the geometric center of the point cloud model as the origin of polar coordinates; step 2: converting the Cartesian coordinate value of the point cloud model into polar coordinates Coordinate value; Step 3: Normalize and quantize the obtained polar coordinate value and map it to a gray value; Step 4: Delete some data points based on the simplification of the gray value; Step 5: Randomly sample and restore some deleted data points . It combines the two methods of geometric image and random sampling. First, the geometric image is simplified for the point cloud data. The simplified point cloud model can maintain the main geometric features in the original model. Then, the idea of ​​random sampling is introduced, and the deleted point set Random sampling is carried out, and the sampled points are added to the simplified model, so as to avoid the influence of holes on the subsequent surface reconstruction method, and realize a fast feature-preserving point cloud model simplification.

Description

technical field [0001] The invention relates to the field of simplified processing of digital geometric images, in particular to a method for simplifying digital geometric images based on point cloud models. Background technique [0002] With the rapid development of digital technology and electronic technology, digital geometric media has received more and more attention from related industries. Large-scale real-time processing of digital geometric media, complex types of data mining and knowledge extraction, interactive graphical modeling and 3D digital reconstruction of dynamic objects pose new challenges to information technology. In recent years, with the significant development of laser scanning technology, the accuracy of data acquired by 3D laser scanning equipment is getting higher and higher, and the amount of data is also increasing. Here comes some questions. Not all data points are useful for subsequent modeling. Redundant data will not only reduce the calcula...

AI Technical Summary

Problems solved by technology

However, this simplification method has the following technical disadvantages: First, by analyzing the above-mentioned geometric image production process, it is found that points with close spatial distances appear as adjacent pixels on the geometric image, so that the result can be further simplified; in addition, the above-mentioned The geometric image simplification process is a simplification based on the image resolution, which is a simplification based on the spatial distance, which does not preserve the characteristic, and it is easy to cause a partial vacuum phenomenon for more complex graphics.

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