Deep convolutional neural network-based three-dimensional model retrieval algorithm

A three-dimensional model, neural network technology, applied in the field of computer vision, can solve problems such as difficulty in expanding to unknown data sets, weak algorithm generalization ability, etc.

Inactive Publication Date: 2017-09-01
NORTHWEST UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

And the present invention adopts the convolutional neural network to automatically learn a super-complete feature filter group to form a feature extractor, and extract high-level abstract features. In addition to being able to tolerate nonlinear deformati

Method used

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  • Deep convolutional neural network-based three-dimensional model retrieval algorithm
  • Deep convolutional neural network-based three-dimensional model retrieval algorithm
  • Deep convolutional neural network-based three-dimensional model retrieval algorithm

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Embodiment 1

[0061] This embodiment provides a 3D model retrieval algorithm based on a deep convolutional neural network, including the following steps:

[0062] Step 1, set the 3D model database M={m 1 ,m 2 ,...,m n};

[0063] This embodiment selects SHREC, 13 data sets;

[0064] Step 11, set the unit spherical triangular mesh U={V, T}, V is a triangle vertex set, and T is a triangle set;

[0065] Step 12, randomly select d vertices from the triangular vertex set V as seed vertices, and the d seed vertices form the seed vertex set Se,

[0066] Step 131, using all seed vertices in Seeds as the seeds of the Lloyd relaxation algorithm to obtain d Voronoi cells whose centers are respectively Cent 1 , Cent 2 ,...,Cent d ;

[0067] The Lloyd relaxation algorithm adopted in this embodiment is: Lloyd S. Least squares quantization in PCM [J]. IEEE transactions on information theory, 1982, 28 (2): 129-137.

[0068] The method used in this embodiment to calculate the centers of d Voronoi ...

Embodiment 2

[0112] On the basis of Embodiment 1, this embodiment also includes:

[0113] Step 6, set the hand-drawn sketch to be tested as x s ;

[0114] Step 61, adopt the embedding function E(x) to convert x s Embedded in the Euclidean feature space, get x s The feature point E(x in the Euclidean feature space s );

[0115] Step 62, search and test the hand-drawn sketch in the Euclidean feature space as x s The feature point set F of the projection map with the same category label, calculate each feature point in F and feature point E(x s ) between the Euclidean distance;

[0116] Step 63, select the model corresponding to the first K projection map feature points with the smallest Euclidean distance as the hand-drawn sketch x to be tested s The closest K models.

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Abstract

The invention discloses a deep convolutional neural network-based three-dimensional model retrieval algorithm. According to the method, a Euclidean embedded space is obtained by adopting a measurement learning algorithm; a free-hand sketch and model projection are embedded in a same feature space; a Euclidean distance in the feature embedded space can directly represent the similarity between the sketch and the model projection; and the problem of cross-domain matching between the sketch and a model projection drawing is solved. Meanwhile, a sorting mechanism is designed, so that a distance between images with the same type in the feature space is smaller than a distance between images with different types, subtle difference among different types can be distinguished, and variants same in type and different in style can be adapted; and in addition, a convolutional neural network is adopted to learn an overcomplete feature filter set to form a feature extractor for extracting advanced abstract features, so that the problems that a low-level geometric feature descriptor of manual design is weak in algorithm generalization capability and is difficultly expanded to an unknown data set are effectively solved.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and relates to a three-dimensional model retrieval algorithm based on a deep convolutional neural network. Background technique [0002] At present, 3D models have been widely used in fields such as virtual reality, industrial design, 3D games and visual design. With the development of 3D graphics modeling technology and 3D data acquisition technology, massive 3D model databases have been produced. Therefore, in order to make full use of the existing 3D models and help users obtain 3D models that meet their needs conveniently and efficiently, 3D retrieval technology has become a current hot research issue. [0003] The workflow of 3D model retrieval is to search out the relevant model collection in the model database according to the query request input by the user, and finally feed it back to the user. One class of algorithms uses existing 3D models as input to express query intentions...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/583
Inventor 安勃卿史维峰
Owner NORTHWEST UNIV
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