A 3D point cloud data reduction method based on ray principle
A 3D point cloud and data simplification technology, applied in the field of data processing, can solve the problems of large amount of 3D point cloud data, difficulty of 3D reconstruction, and time-consuming algorithm, and achieve good simplification effect and efficiency
Inactive Publication Date: 2017-11-21
NORTHWEST A & F UNIV
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However, the amount of 3D point cloud data collected originally 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 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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[0035] The above and other technical features and advantages of the present invention will be described in more detail below in conjunction with the accompanying drawings.
[0036] 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 certain 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 ray, 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.
[0037] see figure 1 As shown, it is a flow chart of the 3D point cloud data reduction method based on the ray principle of the present invention, a...
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The present invention relates to a 3D point cloud data simplification method based on the ray principle. The present invention first assumes that the ray is uniformly generated from the center point of the 3D point cloud model in all directions, so that the ray fills the entire 3D space. For the point cloud in this space model, 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, and it is easy to know: the denser the ray, the larger the threshold, the data in the 3D point cloud model The easier it is to simplify the point, so you can achieve different degrees of simplification by controlling the number of rays and other thresholds. The invention proposes a three-dimensional point cloud data simplification algorithm not based on curvature calculation, and within a certain simplification range, the algorithm has good simplification effect and efficiency.
Description
technical field [0001] The invention relates to the field of data processing, in particular to a ray-based three-dimensional point cloud data reduction method. Background technique [0002] In recent years, with the reduction of cost and the improvement of precision of 3D scanners, 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, denoising and simplification of 3D point cloud is a crucial link in point cloud processing. [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. [0004] In view of the abo...
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IPC IPC(8): G06T17/00
Inventor 王美丽廖昌粟张宏鸣胡少军何东健牛晓静
Owner NORTHWEST A & F UNIV