A Sectional Curve Reconstruction Method for 3D Model Reconstruction

Abstract

The invention discloses a cross-section curve reconstruction method for three-dimensional model reconstruction. According to the curvature information of discrete data, data points are segmented, corresponding features are determined, and the area where ideal segment points are located; reference data sampling Density, determine a reasonable grid division interval, and grid the target area; for each node of the current grid, first fit a straight line, then fit a B-spline curve based on boundary constraints, and count the distance between all data points and the curve Total error and number of control vertices for B-spline curves. Comprehensively analyze the two sets of statistical data to determine the current optimal segmentation point. If the density of the current grid nodes satisfies the accuracy, then output the optimal segmentation point; otherwise, take the current optimal segmentation point as the center, narrow the target area, reduce the spacing, and divide the grid according to the current optimal segmentation point For, reconstruct the section curve.

Description

technical field [0001] The invention belongs to the technical field of three-dimensional reconstruction, in particular to a section curve reconstruction method for three-dimensional model reconstruction. Background technique [0002] With the wide application of computer application technology, data detection technology, and numerical control technology, reverse engineering has become an effective way for rapid development of new products. The development of new products using reverse engineering technology is often more complicated, and software such as CAD / CAE / CAM / CAT must be used. The form in which products exist in these software is a 3D CAD model. Therefore, the primary task of reverse engineering is to reconstruct the 3D CAD model containing the original design intent. [0003] In the feature-based reverse engineering technology, the reconstruction of two-dimensional cross-section data is also the basis of surface reconstruction, and the accuracy and quality of the r...

AI Technical Summary

Problems solved by technology

The segment point is dynamically searched by the discrete variable ordinary grid method, so that the extraction accuracy of the segment point is greatly increased; and in the process of searching for the optimal segment point, the straight line features are updated in real time, which also avoids the problem of segment point extraction. Inaccuracy leads to the problem of poor reconstruction accuracy of straight line features, thereby improving the fitting quality of the entire cross-sectional data, making the reconstruction results more in line with the original intention of product design, and improving the accuracy of CAD model construction

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