Journal of Northeastern University Natural Science ›› 2020, Vol. 41 ›› Issue (8): 1065-1069.DOI: 10.12068/j.issn.1005-3026.2020.08.001

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A View Reconstruction Method Based on Deep Network

ZHANG Zhi-min, QIAO Jian-zhong, LIN Shu-kuan, WANG Pin-he   

  1. School of Computer Science & Engineering, Northeastern University, Shenyang 110169, China.
  • Received:2020-01-18 Revised:2020-01-18 Online:2020-08-15 Published:2020-08-28
  • Contact: QIAO Jian-zhong
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Abstract: To deal with stereo matching in the environment of only a single view, a full convolution reconstruction model with weighted local contrast normalization constraint is proposed on the basis of the existing view reconstruction network model Deep3D. This model adopts the improved full convolutional neural network architecture as the feature extraction module of the model to reduce the training parameters and training time, and to increase the nonlinearity of the model. In order to further improve the accuracy of reconstruction, a new constraint condition based on weighted local comparison normalization is designed, and a loss optimization function combining structural similarity (SSIM) cost and L1 cost is used to optimize the model. Experiments were carried out on the KITTI 2015 dataset, and compared with the Deep3D model and subsequent improvements. The experimental results show that the generated right view has a great improvement in SSIM and peak signal to noise ratio when only the left view is used as the training data, which can meet the accuracy requirements of the right view in the stereo matching method.

Key words: view reconstruction, convolutional neural network, stereo matching, fully convolutional network, weighted local contrast normalization

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