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Three-dimensional complex modeling method utilizing rotating TIN (triangulated irregular network) and distance and included angle dual-weight interpolation method

A modeling method and interpolation technology, applied in the field of three-dimensional modeling, can solve the problems of insufficient dispersion and interleaving between data, insufficient degree of echo and coordination, etc.

Inactive Publication Date: 2012-10-31
黄桂芝
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0003] The purpose of the present invention is to provide a three-dimensional complex modeling method using a rotating TIN network and a double-weighted interpolation method of distance and angle, so as to solve the dispersion and interlacing of data in the data structure of the existing three-dimensional complex modeling method Insufficient, insufficient response and cooperation, the defect that the existing algorithm does not directly consider the influence of the angle between the adjacent triangular planes formed by adjacent data

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  • Three-dimensional complex modeling method utilizing rotating TIN (triangulated irregular network) and distance and included angle dual-weight interpolation method
  • Three-dimensional complex modeling method utilizing rotating TIN (triangulated irregular network) and distance and included angle dual-weight interpolation method
  • Three-dimensional complex modeling method utilizing rotating TIN (triangulated irregular network) and distance and included angle dual-weight interpolation method

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specific Embodiment approach 1

[0088] Specific implementation mode one: the following combination Figure 8 to Figure 13 This embodiment will be specifically described. The present embodiment comprises the following steps: one, set the resolution to be 15mm, and use twice 30mm of 15mm as the side length of the basic square; two, arrange a control point at each of the four vertices of the basic square, and distribute them in the basic square Three control points, see illustration figure 1 , the plane coordinates of the four vertices are (0, 0), (30, 0), (30, 30) and (0, 30), point a is (9, 9), point b is (12, 22.5) , point c is (24, 12), the above-mentioned three points a, b, c are connected to form a triangle, and the above-mentioned three internal control points are connected to form a triangle, and then a vertex is selected from the four vertices of the basic square, and the distance between the vertex and The sum of the distances between the two vertices of the adjacent inner triangles is the smallest,...

specific Embodiment approach 2

[0153] Specific implementation mode two; see illustration figure 1 to indicate Image 6 , the difference between this embodiment and Embodiment 1 is: in step 2, the three control points distributed in the basic square are a(6,6), b(15,24), c(24, 15); other steps are the same as in Embodiment 1.

[0154] Compared with Embodiment 1, this embodiment has better dispersibility along the direction and tendency of the control points in the basic unit in Embodiment 1; this embodiment has better dispersibility along the direction and tendency of the control points in the supporting units; 2. The length between two adjacent control points in the sampling network is reduced compared with Embodiment 1, within the range of 0.43-0.67 times the side length of the basic square.

specific Embodiment approach 3

[0155] Specific implementation mode three: see Figure 14 to Figure 19 , the difference between this embodiment and Embodiment 1 is: in step 2, one control point is arranged at each of the four vertices of the basic square, and three control points are scattered and arranged in the basic square, and the above-mentioned internal three points are connected to form a triangle , and then arrange a control point at the midpoint on the right side of the basic square, and add a total of eight control points to the control points on the four vertices of the basic square and the three control points scattered in the basic square. signal Figure 14 The eight control points are (0, 0), (30, 0), (30, 30), (0, 30), a (8.31, 13.62), b (15, 24), c (20.37, 6 ), d(30, 15). Connect the control point at the midpoint on the right side of the square with the two control points of the inner nearest triangle respectively, and connect the two square vertices on the same straight line as the control...

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Abstract

The invention provides a three-dimensional complex modeling method utilizing a rotating TIN (triangulated irregular network) and a distance and included angle dual-weight interpolation method, and relates to a three-dimensional modeling method. The shortcomings that dispersibility and alternativity among data in a data structure of an existing three-dimensional complex modeling method are insufficient, echo matched degree is also insufficient, and influence of an include angle between each two adjacent triangular planes formed by adjacent data is not directly considered in an existing algorithm are overcome. The three-dimensional complex modeling method comprises the following steps of arranging sampling points at joints of the rotating network; adjusting a certain sampling point to a new position if the sampling point in the rotating TIN can be used as a feature point of a modeling body after the sampling point is adjusted within a range , which is centered at the sampling point, of 0.15 times of the side length of a basic square; adding other feature points according to features of the modeling body; connecting each added feature point with three vertexes of a triangle with the feature point to form a local secondary encryption network; performing feature value interpolation by utilizing the distance and included angle dual-weight interpolation method; making contour lines; and making a three-dimensional perspective drawing of the modeling body.

Description

technical field [0001] The invention relates to a three-dimensional complex modeling method in the field of three-dimensional modeling. Background technique [0002] The 3D complex modeling method in the field of 3D modeling must first have a good data structure, and on this basis, a good algorithm should be required. There are two types of data structures in the existing 3D complex modeling methods: regular shape and irregular shape. Their common shortcomings are that they do not take better consideration of the control distance segmentation in the 3D direction, and the dispersion and interlacing of data Insufficient sex, insufficient response and coordination. There are two types of interpolation method and approximation method in the existing algorithms, none of which directly consider the influence of the angle between the adjacent triangle planes formed by adjacent data. Due to the deficiencies in the above two aspects, the quality and accuracy of 3D complex modeling ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T17/00
Inventor 黄桂芝
Owner 黄桂芝
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