Robotics and Computer Vision Lab

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International Conference
2015.03.12 20:30

Accurate Depth Map Estimation from a Lenslet Light Field Camera

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저 자 Hae-Gon Jeon, Jaesik Park, Gyeongmin Choe, Jinsun Park, Yunsu Bok, Yu-Wing Tai, In So Kweon
학 회 IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)
Notes This work was supported by the Study on Imaging Systems for the next generation cameras funded by the Samsung Electronics Co., Ltd (DMC R&D center) (IO130806-00717), and by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (No.2010- 0028680).
논문일시(Year) 2015
논문일시(Month) 06
https://sites.google.com/site/hgjeoncv/home/depthfromlf_cvpr15This paper introduces an algorithm to estimate accurate depth map from a lenslet
light field camera. Our algorithm estimates multi-view stereo correspondences at
sub-pixel accuracy using a cost volume.
Our key idea to build accurate costs is threefold.
First, sub-aperture images are exactly displaced using the phase shift theorem.
Second, gradient costs are adaptively aggregated using the angular coordinate of the light field.
Third, feature correspondences between the sub-aperture images are utilized as an additional constraint.
With the cost volume, a multi-label optimization propagates and corrects depth map at weak texture regions.
Finally, we iteratively refine local depth map by fitting local quadratic
function to estimate a non-discrete depth map. Since a micro-lens image contains
unexpected distortions, we also present a method to correct the error.
The effectiveness of our algorithm is demonstrated
through challenging real world examples, with comparisons to the performance
of state-of-the-art depth estimation algorithms for light field images.

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