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Multi-view three-dimensional model retrieval method and system based on pairing depth feature learning

A 3D model and depth feature technology, applied in digital data information retrieval, special data processing applications, instruments, etc., can solve problems such as inability to fully utilize 3D model feature representation and limit the performance of shape descriptors

Active Publication Date: 2020-07-07
SHANDONG NORMAL UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The latent relationship and category information between views has not been mined, which greatly limits the performance of shape descriptors, resulting in the inability to fully utilize the ability of 3D model feature representation

Method used

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  • Multi-view three-dimensional model retrieval method and system based on pairing depth feature learning
  • Multi-view three-dimensional model retrieval method and system based on pairing depth feature learning
  • Multi-view three-dimensional model retrieval method and system based on pairing depth feature learning

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Embodiment 1

[0037] In one or more implementations, a group-pair deep feature learning based multi-view Figure three Dimensional model retrieval method, refer to figure 1 , including the following steps:

[0038] (1) Use scalable convolutional neural networks to extract initial view descriptors for 3D models;

[0039] (2) Use the maximum view pool to aggregate multiple initial view descriptors to obtain the final view descriptor;

[0040] (3) Using autoencoders to mine potential features of 2D view descriptors;

[0041] (4) Use the discriminator of the generated confrontation network to extract the category features of the two-dimensional view according to the discriminant score;

[0042] (5) carry out weighted combination with described latent feature and class feature, form shape descriptor;

[0043] (6) The cosine distance measurement function is used to calculate the similarity of the shape descriptors of the query 3D model and the 3D model of the database, and arrange the 3D mode...

Embodiment 2

[0116] Multi-view based group-pair deep feature learning Figure three Dimensional model retrieval system, including:

[0117] Means for extracting initial view descriptors of 3D models using scalable convolutional neural networks;

[0118] means for aggregating a plurality of initial view descriptors using maximum view pooling to obtain a final view descriptor;

[0119] Apparatus for mining latent features of two-dimensional view descriptors using autoencoders;

[0120] means for extracting class features of a two-dimensional view based on a discriminant score using a discriminator of a generative adversarial network;

[0121] means for weighting and combining the latent features and category features to form a shape descriptor;

[0122] It is used to calculate the similarity between the obtained shape descriptor and the shape descriptor of the 3D model in the database to realize multi-view Figure three A device for dimensional model retrieval.

[0123] The specific imple...

Embodiment 3

[0125] In one or more embodiments, a terminal device is disclosed, including a server, the server includes a memory, a processor, and a computer program stored on the memory and operable on the processor, and the processor executes the The program realizes the multi-view based on group pair deep feature learning in the first embodiment Figure three Dimensional model retrieval method. For the sake of brevity, details are not repeated here.

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Abstract

The invention discloses a multi-view three-dimensional model retrieval method and a multi-view three-dimensional model retrieval system based on pairing depth feature learning. The multi-view three-dimensional model retrieval method comprises the steps of: acquiring two-dimensional views of a to-be-retrieved three-dimensional model at different angles, and extracting an initial view descriptor ofeach two-dimensional view; aggregating the plurality of initial view descriptors to obtain a final view descriptor; extracting potential features and category features of the final view descriptor respectively; performing weighted combination on the potential features and the category features to form a shape descriptor; and performing similarity calculation on the obtained shape descriptor and ashape descriptor of the three-dimensional model in a database to realize retrieval of the multi-view three-dimensional model. According to the multi-view three-dimensional model retrieval method, a multi-view three-dimensional model retrieval framework GPDFL is provided, potential features and category features of the model are fused, and the feature recognition capability and the model retrievalperformance can be improved.

Description

technical field [0001] The invention relates to the technical field of three-dimensional model retrieval, in particular to a multi-view model based on group-pair deep feature learning. Figure three Dimensional model retrieval method and system. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] With the continuous improvement of computer graphics processing capabilities and 3D modeling technology, 3D models have been widely used in games, virtual reality environments, medical diagnosis, computer-aided design and other fields, becoming a new generation of multimedia after images, sounds, and texts. data. Faced with a huge 3D model database, 3D model retrieval has become an important research topic in the field of computer vision and computer graphics, and has attracted great attention in recent years. With the popularity of deep learning, v...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/583
CPCG06F16/5854
Inventor 刘丽陈秀秀张龙张化祥高爽刘冬梅
Owner SHANDONG NORMAL UNIV
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