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Convolutional network-based stereo matching method and related device

A convolutional network and stereo matching technology, applied in the field of image processing, can solve problems such as poor accuracy and slow speed, and achieve the effect of improving accuracy and avoiding slow speed.

Pending Publication Date: 2022-03-01
GUANGZHOU VOCATIONAL COLLEGE OF SCI & TECH
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Problems solved by technology

The calculation speed of the two-volume convolutional network is faster than that of the three-dimensional convolutional network, but the accuracy is slightly worse than that of the three-dimensional convolutional network.
Although the three-dimensional convolutional network has higher precision than the two-dimensional convolutional network, it is slower

Method used

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  • Convolutional network-based stereo matching method and related device
  • Convolutional network-based stereo matching method and related device
  • Convolutional network-based stereo matching method and related device

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Embodiment Construction

[0033] The method and technical effects of the present invention will be described below through specific examples.

[0034] refer to figure 1 and figure 2 , the present invention is based on the convolutional network stereo matching method, including obtaining the left and right images of the binocular camera or video images containing the left and right images, and adopting the method of combining the scale-balanced pyramid convolutional network and the ConvLSTM convolutional long-short memory network, for binocular camera images Feature extraction is performed, and a preliminary disparity map is generated through threshold filtering similarity calculation, and then a final high-precision disparity map is generated through a convolutional long-short memory network.

[0035] The concrete process of this method includes:

[0036] Use the scale-balanced pyramid convolutional network to perform feature extraction on the left and right images obtained through the binocular cam...

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Abstract

The invention discloses a three-dimensional matching method based on a convolutional network and a related device, and a new end-to-end network model is formed by combining a scale equalization pyramid convolutional network and a ConvLSTM convolutional long-short memory network model. According to the network model, multi-scale image features are extracted by using a scale-balanced pyramid convolutional network, information between feature layers is fused, and a preliminary disparity map is generated through threshold filtering similarity calculation and is used for ConvLSTM training. And for the whole network, the problem of low speed of the three-dimensional convolutional network is avoided, and the matching precision is also improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a convolution network-based stereo matching method and a related device. Background technique [0002] Image depth calculation is of great significance in the fields of autonomous driving, 3D face recognition, robot navigation, and 3D reconstruction. Stereo matching calculation methods are mainly divided into two types: traditional methods and deep learning methods. In the traditional method, it is mainly divided into four steps: cost calculation, cost aggregation, disparity estimation, and disparity optimization. Two branches are formed in deep learning methods: 2D convolution-based methods and 3D convolution-based methods. Two-dimensional convolutional networks mainly include: DispNet, CRL, and FADNet, and three-dimensional convolutional networks mainly include: GCNet, PSMNet, and GANet. The two-volume convolutional network is faster than the three-dimen...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/55G06N3/04G06N3/08G06V10/52G06V10/74G06V10/80
CPCG06T7/55G06N3/08G06T2207/20228G06T2207/20081G06T2207/20084G06T2207/20016G06N3/044G06N3/045G06F18/22G06F18/253
Inventor 杨玉卢爱芬
Owner GUANGZHOU VOCATIONAL COLLEGE OF SCI & TECH
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