3D Model Retrieval Method Based on Deep Convolutional Neural Network
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A three-dimensional model, neural network technology, applied in the field of computer vision, can solve the problem of weak generalization ability of the algorithm, difficult to expand to unknown data sets, etc.
Inactive Publication Date: 2019-10-29
NORTHWEST UNIV
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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 deformation, this feature also has a strong generality in unknown data sets. It effectively solves the problem that the hand-designed low-level geometric feature descriptor algorithm has weak generalization ability and is difficult to extend to unknown data sets.
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Embodiment 1
[0059] This embodiment provides a three-dimensional model retrieval method based on a deep convolutional neural network, including the following steps:
[0060] Step 1, set the 3D model database M={m 1 ,m 2 ,...,m n};
[0061] This embodiment selects the SHREC'13 data set;
[0062] Step 11, set the unit spherical triangular mesh U={V, T}, V is a triangle vertex set, and T is a triangle set;
[0063] Step 12, randomly select d vertices from the triangle vertex set V as seed vertices, and the d seed vertices form the seed vertex set Seeds,
[0064] 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 ;
[0065] 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.
[0066] The method used in this embodiment to calculate the center...
Embodiment 2
[0109] On the basis of Embodiment 1, this embodiment also includes:
[0110] Step 6, set the hand-drawn sketch to be tested as x s ;
[0111] 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 );
[0112] 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;
[0113] 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 three-dimensional model retrieval algorithm based on a deep convolutional neural network. This method uses a metric learning algorithm to obtain a Euclidean embedding space, embeds hand-drawn sketches and model projections in the same feature space, and the Euclidean distance in the feature embedding space It can directly represent the similarity between the sketch and the model projection, solving the cross-domain matching problem between the sketch and the model projection. At the same time, a sorting mechanism is designed so that the distance between images of the same category in the feature space is smaller than the distance between images of different categories, which can distinguish subtle differences between different categories and adapt to variants of different styles in the same category; and the present invention The convolutional neural network is used to learn an ultra-complete feature filter group to form a feature extractor to extract high-level abstract features, which effectively solves the problem of weak generalization ability of hand-designed low-level geometric feature descriptors and difficulty in extending to unknown data sets. The problem.
Description
technical field [0001] The invention belongs to the technical field of computer vision, and relates to a three-dimensional model retrieval method 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, b...
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