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Mean quadratic error metrics edge contraction simplifying method

A secondary error measurement and edge shrinkage technology, applied in image data processing, 3D modeling, instruments, etc., can solve problems such as long initialization time and time-consuming initialization phase

Active Publication Date: 2015-03-25
ZHEJIANG KELAN INFORMATION TECH CO LTD
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Problems solved by technology

[0005] The present invention aims at the shortcomings of the QEM edge contraction simplification algorithm in the prior art that the initialization time is long and the time-consuming operation of the initialization stage is to calculate the secondary error matrix of the vertices, and provides a method that can greatly shorten the time required for the initialization stage in the secondary error measurement. A Simplified Method of Edge Shrinkage for Average Quadratic Error Metrics that Need Time

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  • Mean quadratic error metrics edge contraction simplifying method
  • Mean quadratic error metrics edge contraction simplifying method
  • Mean quadratic error metrics edge contraction simplifying method

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Embodiment 1

[0025] A side contraction simplification method for average quadratic error measurement, including a side contraction simplification method for average quadratic error measurement, including an average quadratic error measurement method and a side contraction simplification method based on the average quadratic error measurement method, the average quadratic error measurement method Secondary error metrics include:

[0026] According to the QEM algorithm, the vertex error Δ(ν) is defined as the sum of the squares of the distances from the vertex to all triangles adjacent to the vertex, and Δ(ν) is calculated according to the following formula: Among them: p represents the triangular surface of ax+by+cz+d=0, p=[a b c d] T , and a 2 +b 2 +c 2 = 1;

[0027] The calculation of the vertex error Δ(ν) is moved to the GPU side, thereby transforming the calculation formula of the vertex error Δ(ν) into the following formula: Δ ( v ...

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Abstract

The invention relates to the field of computer graphics and discloses a mean quadratic error metrics edge contraction simplifying method. The mean quadratic error metrics edge contraction simplifying method comprises a mean quadratic error metrics method and an edge contraction simplifying method based on the mean quadratic error metrics method. The mean quadratic error metrics method comprises the steps of collecting all vertices in a model and updating the vector quantities of all triangular surfaces. The edge contraction simplifying method comprises the steps of selecting all proper vertex pairs, calculating the contraction target vertex and error value of each vertex pair, ranking the vertex pairs in an ascending order according to the error values, placing the ranked vertex pairs in a vertex pair list, and finally traversing the list from the start, removing the vertex pairs, contracting the vertices and updating the error values of corresponding vertex pairs. According to the edge contraction simplifying method adopting mean quadratic error metrics, the mean quadratic error matrix of the vertices is calculated by means of the parallel computing power of a GPU, and time required by the initialization phase during quadratic error metrics is shortened greatly.

Description

technical field [0001] The invention relates to the field of receiver graphics, in particular to an edge contraction simplification method for an average quadratic error measure. Background technique [0002] With the development of 3D scanning technology, 3D models with tens of thousands or even millions of vertices have become more common, and such model data has brought enormous pressure to the storage, rendering and transmission of computer systems, especially it is difficult to meet real-time rendering requirements. requirements. Model simplification is a key technology to solve these problems. [0003] Garland et al. proposed in 1997 that the edge contraction simplification algorithm based on QEM (Quadric Error Metrics) is a model simplification algorithm that has performed very well in terms of comprehensive performance so far. The QEM edge shrinkage simplification algorithm first needs to calculate the quadratic error matrix of each vertex, then select all appropri...

Claims

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Application Information

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IPC IPC(8): G06T17/00G06T7/40
CPCG06T17/00
Inventor 俞蔚
Owner ZHEJIANG KELAN INFORMATION TECH CO LTD
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