Learning-Based, Automatic 2D-To-3D Image And Video Conversion

Authors(1) :-Hattarki Pooja

This work is to present a new method based on the radically different approach of learning the 2D-to-3D conversion from examples. It is based on locally estimating the entire depth map of query image directly from a repository of 3D images using a nearest neighbor regression type idea. Among 2D-to-3D conversion methods, those involving human operators have been most successful but also time consuming and costly. Automatic methods that typically make use of a deterministic 3D scene model, have not yet achieved the same level of quality as they often rely on assumptions that are easily violated in practice. Despite a significant growth in the last few years, the availability of 3D content is still dwarfed by that of its 2D counterpart. To close this gap, many 2D-to-3D image and video conversion methods have been proposed. In this paper we adopt the radically different approach of learning the 3D scene structure. We develop a simplified and computationally efficient version of our recent 2D-to-3Dconversion algorithm. A repository of 3D images, either as stereo pairs or image+depth pairs, we find K pairs whose photometric content most closely matches that of a 2D query to be converted. Then, we fuse the K corresponding depth fields and align the fused depth with the 2D query.

Authors and Affiliations

Hattarki Pooja
Department of Computer Science, Appa Institute Of Engineering and Technology Gulbarga, Karnataka, India

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Publication Details

Published in : Volume 3 | Issue 3 | March-April 2018
Date of Publication : 2018-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 832-838
Manuscript Number : CSEIT1833260
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Hattarki Pooja, "Learning-Based, Automatic 2D-To-3D Image And Video Conversion", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 3, pp.832-838, March-April-2018.
Journal URL : http://ijsrcseit.com/CSEIT1833260

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