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Volume: 31 | Article ID: art00003
Depth-map estimation using combination of global deep network and local deep random forest
  DOI :  10.2352/ISSN.2470-1173.2019.16.3DMP-004  Published OnlineJanuary 2019

This study propose a robust 3D depth-map generation algorithm using a single image. Unlike previous related works estimating global depth-map using deep neural networks, this study uses the global and local feature of image together to reflect local changes in the depth map instead of using only global feature. A coarse-scale network is designed to predict the global-coarse depth map structure using a global view of the scene and the finer-scale random forest (RF) is to be designed to refine the depth map based on combination of original image and coarse depth map. As the first step, we use a partial structure of the multi-scale deep network (MSDN) to predict the depth of the scene at a global level. As the second step, we propose local patchbased deep RF to estimate the local depth and smoothen noise of local depth map by combining MSDN global-coarse network. The proposed algorithm was successfully applied to various single images and yielded a more accurate depthmap estimation performance than other existing methods.

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SangJun Kim, Sangwon Kim, Deokwoo Lee, ByoungChul Ko, "Depth-map estimation using combination of global deep network and local deep random forestin Proc. IS&T Int’l. Symp. on Electronic Imaging: 3D Measurement and Data Processing,  2019,  pp 4-1 - 4-5,

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