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Proceedings Paper

Accurate and consistent depth estimation for light field camera arrays
Author(s): Sang-Heon Shim; Jae Woo Kim; Sang-Eek Hyun; Do Hyung Kim; Jae-Pil Heo
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Paper Abstract

In this paper, we propose a depth estimation framework for light field camera arrays. The goal of the proposed framework is to compute consistent depth information over the multiple cameras which is hardly achieved by conventional approaches based on the pairwise stereo matching. We first perform stereo matchings on adjacent image pairs using a convolutional neural network-based correspondence scoring model. Once the local disparity maps are estimated, we consolidate the disparity values to make them globally sharable over the multiple views. We finally refine the depth values in the image domain by introducing a novel image segmentation method considering edges in the image to obtain a semantic-aware global depth map. The proposed framework is evaluated on three different real world scenarios, and the experimental results validate that our proposed method produces accurate and consistent depth maps for images captured by the light field camera arrays.

Paper Details

Date Published: 14 May 2019
PDF: 6 pages
Proc. SPIE 10997, Three-Dimensional Imaging, Visualization, and Display 2019, 109970E (14 May 2019); doi: 10.1117/12.2518581
Show Author Affiliations
Sang-Heon Shim, Sungkyunkwan Univ. (Korea, Republic of)
Jae Woo Kim, Electronics and Telecommunications Research Institute (Korea, Republic of)
Sang-Eek Hyun, Sungkyunkwan Univ. (Korea, Republic of)
Do Hyung Kim, Electronics and Telecommunications Research Institute (Korea, Republic of)
Jae-Pil Heo, Sungkyunkwan Univ. (Korea, Republic of)


Published in SPIE Proceedings Vol. 10997:
Three-Dimensional Imaging, Visualization, and Display 2019
Bahram Javidi; Jung-Young Son; Osamu Matoba, Editor(s)

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