A 3D Object Fusion Feature Representation Method Based on Multimodal Feature Fusion

A feature fusion and feature fusion technology, applied in neural learning methods, character and pattern recognition, biological neural network models, etc., to achieve the effect of excellent multimodal information fusion

Active Publication Date: 2022-04-01
HANGZHOU DIANZI UNIV
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  • Claims
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Problems solved by technology

Despite these issues, grids have stronger 3D shape description capabilities than other types of data

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  • A 3D Object Fusion Feature Representation Method Based on Multimodal Feature Fusion
  • A 3D Object Fusion Feature Representation Method Based on Multimodal Feature Fusion
  • A 3D Object Fusion Feature Representation Method Based on Multimodal Feature Fusion

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Embodiment Construction

[0022] Below in conjunction with accompanying drawing, the present invention is further described;

[0023] A 3D object fusion feature representation method based on multimodal feature fusion, the steps are as follows:

[0024] Step (1), process multi-view through multi-view neural network Figure three dimension information;

[0025] Such as figure 1 As shown, multiple independent CNNs that do not share weights are used to input multi-view information, and then through Max-pooling, the outputs of multiple CNNs are unified into one output, and a discriminator (that is, a non-linear network based on a fully connected layer) is added. classifier) ​​to classify the model.

[0026] First, convert the 3D model data into multi-view data. The specific method is to place 12 cameras evenly around the 3D model (that is, at intervals of 30 degrees) on the middle horizontal plane of the 3D model, and take a group of 12 pictures as the 3D model. multi-view representation of . Then use...

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Abstract

The invention provides a three-dimensional object fusion feature representation method based on multi-modal feature fusion. The present invention realizes and reproduces the three-dimensional model feature representations of the three modes in a very detailed way: multi-view feature representation, point cloud feature representation and grid feature representation of the three-dimensional model. The multi-modal 3D data is processed, enhanced and fused, and based on this, the fused feature representation of the 3D object is extracted. The invention can realize excellent multi-modal information fusion, realize more robust three-dimensional model feature representation, and provide it for other three-dimensional object tasks.

Description

technical field [0001] The invention belongs to the technical field of computer images and artificial intelligence, and provides a more efficient three-dimensional object fusion feature representation form that integrates three-dimensional multi-modal information. Background technique [0002] 3D data recognition and analysis is a fundamental and interesting field in multimedia and computer vision, covering a wide range of applications from environment understanding to autonomous driving. How to understand 3D data, such as recognizing 3D shapes, has attracted a lot of attention in recent years. With the development of deep learning, various deep networks are used to process different types of 3D data: point cloud (PointCloud), multi-view (Multi-view) and volume (Volumetric) data. While it is natural and reasonable to extend 2D convolutional neural networks to volumetric data, these methods suffer from large computational complexity and data sparsity, making it difficult to ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06V10/82G06V10/764G06V10/40G06N3/04G06N3/08
CPCG06F18/2453G06F18/253
Inventor 颜成钢龚镖白俊杰孙垚棋张继勇张勇东沈韬
Owner HANGZHOU DIANZI UNIV
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