Depth prediction is essential for three-dimensional optical displays. The accuracy of the depth map influences the quality of virtual viewpoint synthesis. Due to the relatively simple end-to-end structures of CNNs, the performance for poor and repetitive texture is barely satisfactory. In consideration of the shortage of existing network structures, the two main structures are proposed to optimize the depth map. (i) Inspired by GoogLeNet, the inception module is added at the beginning of the network. (ii) Assuming that the disparity map has only horizontal disparity, two sizes of rectangular convolution kernels are introduced to the network structure. Experimental results demonstrate that our structures of the CNN reduce the error rate from 19.23% to 14.08%.
The deep convolution neural network has been widely tackled for optical flow estimation in recent works. Due to advantages of extracting abstract features and efficiency, the accuracy of optical flow estimation using CNN is improved steadily. However, the edge information for most flow predictions is vague. Here, two methods are presented to add extra useful information in training our optical flow network, for the purpose of enhancing edge information of the result. The edges map is added into the input section, and the motion boundary is considered for the input section. Experimental result shows that the accuracy with both methods is higher than the control experiment. 3.71% and 7.54% are improved by comparing just a pair of frames in the input section respectively.
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