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23 April 2019 Disparity estimation using multilevel and global information
Yaru Zhang, Hongbin Lin, Chao Wu, Bin Liu
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Journal of Electronic Imaging, Vol. 28, Issue 2, 023035 (April 2019). https://doi.org/10.1117/1.JEI.28.2.023035
Abstract
Deep convolutional neural networks have shown prominent performance in stereo matching. However, current network architectures lack performance in exploiting context and global information to finding corresponding points in ill-posed regions. A stereo matching network without postprocessing is proposed to solve this problem. This network combines the improved multilevel feature pyramid pooling module with the light two-dimensional (2-D) convolution subnetwork to efficiently utilize multilevel information and global information. In the multilevel feature pyramid pooling module, the base image feature is extracted by cascading three small convolution filters. Features of a stereo image pair are calculated by hierarchically fusing and pooling features information of the same scale after using the residual network. Multilevel semantic information is fully utilized to improve the robustness of image feature representation in multilevel feature pyramid pooling module. In the light 2-D convolution subnetwork, low-level structural information is obtained from the target image by three concatenated convolution layers with small convolution filters. Low-level information is used to rectify matching cost with global view to improve matching accuracy. The experimental results on the Scene Flow dataset, the MPI Sintel dataset, and the Middlebury dataset show that the performance obtained by the proposed network can be improved in the ill-posed regions. Matching accuracy is competitive compared to other results obtained by end-to-end networks without postprocessing.
© 2019 SPIE and IS&T 1017-9909/2019/$25.00 © 2019 SPIE and IS&T
Citation Download Citation
Yaru Zhang, Hongbin Lin, Chao Wu, and Bin Liu "Disparity estimation using multilevel and global information," Journal of Electronic Imaging 28(2), 023035 (23 April 2019). https://doi.org/10.1117/1.JEI.28.2.023035
Received: 8 November 2018; Accepted: 2 April 2019; Published: 23 April 2019
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KEYWORDS
Convolution
Feature extraction
Network architectures
3D acquisition
Image filtering
Image fusion
Computer programming
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