Dr. Chao Wu Profile

Dr. Chao Wu

at Yanshan Univ

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SPIE Journal Paper | 23 April 2019

Disparity estimation using multilevel and global information

Yaru Zhang, Hongbin Lin, Chao Wu, Bin Liu

JEI, Vol. 28, Issue 02, 023035, (April 2019) https://doi.org/10.1117/12.10.1117/1.JEI.28.2.023035

KEYWORDS: Convolution, Feature extraction, Network architectures, 3D acquisition, Image filtering, Image fusion, Computer programming, Image resolution, Convolutional neural networks, Deconvolution

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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.

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