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Cited by 506

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Depth from Combining Defocus and Correspondence Using Light-Field Cameras

Tao, Michael W., Hadap, Sunil, Malik, Jitendra, Ramamoorthi, Ravi·2013·2013 IEEE International Conference on Computer Vision

Abstract

Light-field cameras have recently become available to the consumer market. An array of micro-lenses captures enough information that one can refocus images after acquisition, as well as shift one's viewpoint within the sub-apertures of the main lens, effectively obtaining multiple views. Thus, depth cues from both defocus and correspondence are available simultaneously in a single capture. Previously, defocus could be achieved only through multiple image exposures focused at different depths, while correspondence cues needed multiple exposures at different viewpoints or multiple cameras, moreover, both cues could not easily be obtained together. In this paper, we present a novel simple and principled algorithm that computes dense depth estimation by combining both defocus and correspondence depth cues. We analyze the x-u 2D epipolar image (EPI), where by convention we assume the spatial x coordinate is horizontal and the angular u coordinate is vertical (our final algorithm uses the full 4D EPI). We show that defocus depth cues are obtained by computing the horizontal (spatial) variance after vertical (angular) integration, and correspondence depth cues by computing the vertical (angular) variance. We then show how to combine the two cues into a high quality depth map, suitable for computer vision applications such as matting, full control of depth-of-field, and surface reconstruction.

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Metadata

Authors
Tao, M. W., Hadap, S., Malik, J., & Ramamoorthi, R.
Venue
2013 IEEE International Conference on Computer Vision
Publisher
IEEE
Published
2013-12
Pages
673-680
OpenAlex
W2149694199
Cited by
506
References
1
FWCI
20.69
Type
conference paper
Language
en
Open access
closed

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Tao, M. W., Hadap, S., Malik, J., & Ramamoorthi, R. (2013). Depth from Combining Defocus and Correspondence Using Light-Field Cameras. 2013 IEEE International Conference on Computer Vision. https://doi.org/10.1109/iccv.2013.89

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