Characteristic analytical method for product point clouds surface based on dynamic access model

Abstract

This invention provides one product cloud shape property analysis method based on data dynamic memory module, which comprises the following steps: reading the output data from digital device into memory and establishing linear memory structure for data; then adopting 3D R*-tree for rapid space polymer sorting on data and to establish data dynamic memory; then in the browsing process, using current data point as aim point to get the local reference data set by use of Bezier curve accuracy near to point set to estimate curve rate ; finally mapping the data curve rate as RGB color mode.

Description

Technical field [0001] The invention provides a product point cloud surface feature analysis method based on a data dynamic access model, which belongs to the technical field of product reverse engineering. Background technique [0002] In reverse engineering, point cloud surface feature analysis can estimate the surface feature parameters of all measurement points based on product local surface data to realize the overall evaluation of point cloud surface features, which is the process of data preprocessing and surface model reconstruction. It is one of the key technologies, and there are three main implementation methods: triangular patch method vector estimation method, local data point set paraboloid approximation estimation method and sampling point 4D Shepard interpolation calculation method. [0003] The triangular surface method vector estimation method uses the triangular surface model to interpolate or approximate the point cloud, and evaluates the overall surface ...

AI Technical Summary

Problems solved by technology

This method can well avoid the interference of data noise for point clouds with uniform data distribution, and the calculation process of point cloud curvature is stable. In the case of uneven and complex surface feature structures, the accuracy of sampling results is low, resulting in low precision of the generated 4D Shepard surface model, which affects the accuracy of the final surface feature analysis results

In addition, a large number of parabolic surface approximation local data point set processing is performed in the grid space octree subdivision and 4D Shepard surface interpolation, which reduces the overall execution efficiency of the algorithm

[0006] To sum up, the existing point cloud surface feature analysis methods can only obtain good analysis results under specific conditions, and lack of versatility for point cloud data with different data sizes, topology types, and surface complexity, resulting in point cloud analysis. Neither the stability of the cloud surface parameter calculation process nor the accuracy of the calculation results can meet the engineering requirements

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