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Ray principle based cloud data compaction algorithm with boundary reservation

A cloud data and boundary-based technology, applied in the field of data processing, can solve problems such as time-consuming algorithms, and achieve good streamlining and efficiency effects

Inactive Publication Date: 2015-02-18
NORTHWEST A & F UNIV
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Problems solved by technology

[0003] At present, the algorithms for 3D point cloud data reduction include cluster-based simplification and curvature-based simplification. Recursive operations or curvature estimation are required, so for models with a large amount of point cloud data, such algorithms are very time-consuming

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  • Ray principle based cloud data compaction algorithm with boundary reservation
  • Ray principle based cloud data compaction algorithm with boundary reservation
  • Ray principle based cloud data compaction algorithm with boundary reservation

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Embodiment Construction

[0041] The above and other technical features and advantages of the present invention will be described in more detail below with reference to the accompanying drawings.

[0042] The present invention first assumes that rays are uniformly generated in all directions from the center point of the three-dimensional point cloud model, so that the rays fill the entire three-dimensional space. For the point cloud model in this space, if the distance between a point in the model and its nearest ray is less than a given threshold, the point is regarded as a point that needs to be simplified. It is easy to know: the denser the rays, the larger the threshold, The data points in the 3D point cloud model are easier to be reduced, so the number of rays and other thresholds can be controlled to achieve different degrees of reduction.

[0043] see figure 1 As shown, it is a flow chart of the cloud data reduction algorithm with boundary retention based on the ray principle, and the specific ...

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Abstract

The invention relates to a ray principle based cloud data compaction algorithm with boundary reservation. According to the ray principle based cloud data compaction algorithm, rays are hypothetically generated from the center of a three-dimensional point cloud model to all directions evenly, and a whole three-dimensional space is filled with the rays; for the point cloud model in the space, if the distance between a certain point in the model and the nearest ray is smaller than a given threshold value, the point is taken as a point required to be compacted; it is easily known that the more intensive the rays are, the larger the threshold value is, and the more easily the data point in the three-dimensional point cloud model is compacted; therefore, compaction effects at different degrees can be achieved through control of the number of the rays and other threshold values. A boundary reservation method is provided to effectively guarantee model integrity. In a certain compaction degree range, the three-dimensional point cloud data compaction algorithm not based on curvature calculation has good compaction effect and high compaction efficiency and can well maintain model boundary characteristics.

Description

technical field [0001] The invention relates to the field of data processing, in particular to a cloud data reduction algorithm with boundary retention based on the ray principle. Background technique [0002] In recent years, with the reduction of the cost of 3D scanners and the improvement of accuracy, 3D point cloud data has become an important form of data representation in the fields of graphics, reverse engineering and industry. However, the amount of originally collected 3D point cloud data is very large, which brings great difficulties to the later 3D reconstruction. Therefore, the denoising and simplification of 3D point cloud is a crucial link in point cloud processing. [0003] At present, the simplification algorithms for 3D point cloud data include cluster-based simplification, curvature-based simplification, etc. Although these methods can reduce the number of point clouds to a certain extent, retain the characteristics of the point cloud model, and improve the...

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Application Information

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IPC IPC(8): G06T17/00
CPCG06T17/00G06T2210/56
Inventor 王美丽廖昌粟张宏鸣胡少军何东健牛晓静
Owner NORTHWEST A & F UNIV
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