Three-dimensional spot sample data reduction method based on Gaussian sphere

A technology of sampling data and 3D points, applied in image data processing, 3D modeling, instruments, etc., can solve the problems of complex calculation, large approximation error, and time-consuming, and achieve the effect of simplifying calculation, reducing calculation time, and reducing approximation error.

Inactive Publication Date: 2011-03-09
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0006] In order to overcome the shortcomings of the existing three-dimensional point sampling data simplification method, which are complex in calculation, time-consuming, and large in approximation erro

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  • Three-dimensional spot sample data reduction method based on Gaussian sphere
  • Three-dimensional spot sample data reduction method based on Gaussian sphere
  • Three-dimensional spot sample data reduction method based on Gaussian sphere

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Embodiment Construction

[0019] The present invention will be further described below in conjunction with the accompanying drawings.

[0020] refer to figure 1 and figure 2 , a Gaussian sphere-based three-dimensional point sampling data simplification method, the simplification method comprising:

[0021] 1) The three-dimensional sampling point data on the L 2,1 The shape metric is defined as the Euclidean distance between the Gaussian mapping images of the normal vector field of the sampling points. According to the subdivision level specified by the user, the regular triangulation of the Gaussian sphere is obtained by recursively subdividing the inscribed regular polyhedron of the unit sphere;

[0022] 2) Using large-scale discrete surface elements as input data, the input data includes the position and normal information of the sampling points, and the neighborhood of each sampling point is determined according to the normal deviation of the sampling points;

[0023] 3) First select a seed sa...

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Abstract

A three-dimensional spot sample data reduction method based on Gaussian sphere comprises the following steps: 1) defining L2,1 shape measurement on three-dimensional sampling point data as Euclidean distance between sampling point normal vector field Gaussian mapping images, and carrying out regular triangulation for the Gaussian sphere; 2) taking discrete surface in a large scale as input data comprising position of sampling point and normal information, and determining the neighborhood of each sampling point according to the normal deviation of the sampling point; 3) initially clustering through index diffusion technique and stack data structure; 4) separating the irregular cluster for generating regular cluster, and absorbing the isolated sampling point of the normal vector projection on the top point of the Gaussian triangle into the adjacent cluster with the minimum deviation normal vector. The invention simplifies the calculation, reduces the calculation time, and reduces the approximate error.

Description

technical field [0001] The invention relates to a method for simplifying three-dimensional point sampling data. Background technique [0002] In the acquisition of 3D large-scale point sampling data, the uniform sampling point data acquired by 3D scanning equipment usually does not depend on the intrinsic characteristics of the model, so that the acquired 3D sampling point data usually has a lot of redundant information. Therefore, in practical applications such as remote transmission of 3D models, fast reconstruction of implicit surfaces, real-time display in digital entertainment and virtual reality, it is usually necessary to simplify and resample the original 3D sampling point data. At the same time, due to the large memory requirements and high time complexity required for the processing of large-scale three-dimensional point sampling data, it brings great challenges to effectively process large-scale sampling data in shape modeling and real-time rendering. How to effe...

Claims

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Application Information

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IPC IPC(8): G06T17/00
Inventor 缪永伟
Owner ZHEJIANG UNIV OF TECH
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