Edge-based depth gradient refinement for 2D to 3D learned prior conversion

Herrera Conejero, José Luis and Blanco Adán, Carlos Roberto del and García Santos, Narciso (2015). Edge-based depth gradient refinement for 2D to 3D learned prior conversion. In: "3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON)", 08/07/2015 - 10/07/2015, Lisbon, Portugal. pp. 1-4. https://doi.org/10.1109/3DTV.2015.7169364.

Description

Title: Edge-based depth gradient refinement for 2D to 3D learned prior conversion
Author/s:
  • Herrera Conejero, José Luis
  • Blanco Adán, Carlos Roberto del
  • García Santos, Narciso
Item Type: Presentation at Congress or Conference (Article)
Event Title: 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON)
Event Dates: 08/07/2015 - 10/07/2015
Event Location: Lisbon, Portugal
Title of Book: 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON)
Date: 2015
Subjects:
Freetext Keywords: 2D-to-3D conversion, depth maps, depth prior, clustering, machine learning
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

2D-to-3D conversion is an important task for reducing the current gap between the number of 3D displays and the available 3D content. Here, we present an automatic 2D-to-3D image conversion approach based on machine learning principles. Stemming from the hypothesis that images with a similar structure have likely a similar 3D structure, the depth of a query color image is estimated using a color plus depth image dataset. Clusters with common scene structure are computed offline. Then, a matching process is performed to select the cluster centroid which is the most similar to the query image. A prior depth map is computed fusing the depth maps of the images in this cluster. Then, an edge-based post-processing stage is applied to the prior depth map estimation to enhance the final scene depth estimation. Promising results are obtained in two commonly used databases achieving a similar performance to other much complex state-of-the-art approaches.

More information

Item ID: 42761
DC Identifier: http://oa.upm.es/42761/
OAI Identifier: oai:oa.upm.es:42761
DOI: 10.1109/3DTV.2015.7169364
Deposited by: Memoria Investigacion
Deposited on: 16 Jul 2016 10:53
Last Modified: 16 Jul 2016 10:53
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