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7 November 2018 The enhancement of depth estimation based on multi-scale convolution kernels
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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%.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Heng Hua, Xinzhu Sang, Xiyu Tian, Wanqi Sun, Duo Chen, and Peng Wang "The enhancement of depth estimation based on multi-scale convolution kernels", Proc. SPIE 10817, Optoelectronic Imaging and Multimedia Technology V, 1081710 (7 November 2018);

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