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Point cloud data sparse representation method based on compressed sensing

A point cloud data, sparse representation technology, applied in image data processing, 3D image processing, instruments, etc., can solve the problem of high complexity

Inactive Publication Date: 2014-06-25
BEIJING UNIV OF TECH
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Problems solved by technology

In general, MOD only needs a small number of iterations to converge, which is generally more effective. However, this method needs to calculate the inverse of the matrix during the solution process, and its complexity is relatively high. Therefore, the main purpose of subsequent scholars' research is In order to reduce the time complexity, some more practical methods are introduced

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  • Point cloud data sparse representation method based on compressed sensing
  • Point cloud data sparse representation method based on compressed sensing
  • Point cloud data sparse representation method based on compressed sensing

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Embodiment Construction

[0017] This sparse representation method for point cloud data based on compressed sensing includes the following steps:

[0018] (1) Point cloud data normalization;

[0019] (2) Overcomplete dictionary sparse representation based on K-SVD algorithm;

[0020] (3) Standardized point cloud data observation, transmission and storage;

[0021] (4) Point cloud data reconstruction based on l1 norm minimization;

[0022] (5) Normalized point cloud data recovery.

[0023] Since this method preprocesses the point cloud data before doing sparse solution to the point cloud data, that is, the normalization of the point cloud data, and the over-complete dictionary training method based on sparse representation is different from the traditional complete dictionary (such as FFT , DCT, wavelet, Gabor dictionary) is adaptive to extract its features according to the training signal, so it has a stronger sparse representation ability, so as to compress the massive point cloud data under the pr...

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Abstract

The invention provides a point cloud data sparse representation method based on compressed sensing. Under the premise of ensuring certain precision, massive point cloud data is compressed, thus the sparsity of the point cloud data is greatly raised, and a good foundation is laid for point cloud data compression and reconstruction based on compressed sensing. The method comprises the steps of (1) point cloud data normalization, (2) over-complete dictionary sparse representation based on a K-SVD algorithm, (3) normalized point cloud data observation, transmission and storage, (4) point cloud data reconstruction based on l1 norm minimization, and (5) normalized point cloud data recovery.

Description

technical field [0001] The invention belongs to the technical field of compression coding of three-dimensional point cloud data, and in particular relates to a sparse representation method of point cloud data based on compressed sensing. Background technique [0002] With the rapid development of 3D scanning technology, point cloud data has gradually become a very important type of data in multimedia data. Today's scanning equipment can efficiently obtain discrete and scattered mass point cloud data to represent objects, so efficient compression and encoding of point cloud data has gradually become one of the research hotspots. The main research goal of point cloud data compression is to reduce the size of data files while retaining the geometric features of the original model as much as possible, so that point cloud data can be stored and transmitted more quickly under limited bandwidth. Although many scholars are committed to the compression and reconstruction of complex ...

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Application Information

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IPC IPC(8): G06T9/00G06T15/00
Inventor 张勇吴鑫薛娟尹宝才孔德慧
Owner BEIJING UNIV OF TECH
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