Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

3D model feature extraction method based on compressed sensing

A 3D model and compressed sensing technology, which is applied in character and pattern recognition, special data processing applications, instruments, etc., can solve the problems of large feature storage space, long time for feature extraction and feature matching, and high computational complexity. Accuracy and efficiency, guaranteed speed and quality effect

Active Publication Date: 2017-05-10
广东宜教通教育有限公司
View PDF5 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] At present, most of the content-based 3D model retrieval methods still have some problems: such as the extracted features cannot fully express the 3D model information, the computational complexity is high, the time for feature extraction and feature matching is long, the feature storage space is large, feature information is easily missing, and cannot Realize user interaction, etc.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • 3D model feature extraction method based on compressed sensing
  • 3D model feature extraction method based on compressed sensing
  • 3D model feature extraction method based on compressed sensing

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0039] like figure 1 Shown, the present invention is based on the three-dimensional model feature extraction method of compressed sensing like this:

[0040] First, the 3D model is selected as a 3D model in a discrete voxel format, and then the orientation of each viewing angle is selected as a reference plane, and a contour transformation function is designed to realize spatial layering of the 3D model according to the contour transformation function;

[0041] Second, project each spatial layered model to a reference plane, construct a projection matrix, and extract the information entropy of the projection matrix;

[0042] Finally, each projection matrix is ​​subjected to sparse processing and two-dimensional compressed sensing processing to obtain spatially hierarchical features.

[0043] The method specifically includes the following steps:

[0044] Step s101: select the 3D model as a 3D model in a discrete voxel format, and perform voxel preprocessing on the 3D model to...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention provides a 3D model feature extraction method based on compressed sensing. The method comprises the following steps: first, selecting a 3D model in a discrete voxel format, selecting the orientation of each angle as a reference plane, designing an equal-altitude transformation function, and spatially layering the 3D model according to the equal-altitude transformation function; then, projecting each spatial layering model onto the reference plane, constructing projection matrixes, and extracting the information entropy of the projection matrixes; and finally, sparsely processing each projection matrix, and carrying out 2D compressed sensing treatment to get spatial layering features. Through the method, the features of a 3D model can be reflected from multiple angles, spatial layering of a 3D model in a voxel format is realized, and a complex 3D model can be spatially decomposed. The accuracy and efficiency of 3D model feature extraction are improved. Moreover, low-dimensional efficient space geometric features are extracted, and feature redundancy is avoided. Thus, the speed and quality of 3D model retrieval are ensured.

Description

technical field [0001] The present invention relates to the field of three-dimensional model processing, and more specifically, relates to a three-dimensional model feature extraction method based on compressed sensing. Background technique [0002] With the rapid development of information retrieval technology and the improvement of computer performance, information processing has changed from traditional mode to new mode. Compared with text information and two-dimensional images, more realistic and rich three-dimensional models are more and more widely used. In today's massive 3D model database, how to realize the management and retrieval based on 3D model reuse, and quickly and accurately find the 3D model that meets the requirements has become an important research topic in the retrieval field. [0003] As the fourth multimedia data type after sound, image and video, the development of content-based 3D model retrieval technology has attracted much attention. How to qui...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06K9/46G06F17/30
CPCG06F16/5838G06V10/40G06V10/513
Inventor 周燕曾凡智杨跃武
Owner 广东宜教通教育有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Eureka Blog
Learn More
PatSnap group products