國立虎尾科技大學 |

Beyond Photo-Consistency : = Shape, Reflectance, and Material Estimation Using Light-Field Cameras.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Beyond Photo-Consistency :/
Reminder of title:	Shape, Reflectance, and Material Estimation Using Light-Field Cameras.
Author:	Wang, Ting-Chun.
Description:	1 online resource (111 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
Contained By:	Dissertation Abstracts International78-11B(E).
Subject:	Electrical engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9780355034493

Beyond Photo-Consistency : = Shape, Reflectance, and Material Estimation Using Light-Field Cameras.
Wang, Ting-Chun.

Beyond Photo-Consistency :Shape, Reflectance, and Material Estimation Using Light-Field Cameras. - 1 online resource (111 pages)

Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

Light-field cameras have recently become easily accessible in the consumer market, making applications such as post-shot refocusing and viewpoint parallax possible. An important benefit of light-field cameras for computer vision is that multiple viewpoints or subapertures are available in a single light-field image, enabling passive depth estimation. However, most existing approaches are based on photo-consistency, i.e., all viewpoints exhibit the same color when focused to the correct depth. This assumption does not hold in a number of circumstances, e.g., in the presence of occlusions, and when the surface is not Lambertian. In this thesis, we refrain from assuming photo-consistency, and explicitly deal with the situations where it fails to hold. First, we propose a novel framework that can handle occlusions when estimating depth, so we are able to get sharper occlusion boundaries in the obtained depth maps. Next, we extend traditional optical flow to the case of glossy surfaces, and derive a spatially-varying (SV) BRDF invariant equation. Using this equation, we can then recover both shape and reflectance simultaneously using light-field cameras. Finally, we show an application of recognizing materials in light-field images, based on the reflectance information extracted using learning-based methods. By looking beyond photo-consistency, we are able to estimate better depths and recover reflectance and material types, which can be useful for a variety of vision and graphics applications.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355034493Subjects--Topical Terms:

596380
Electrical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Beyond Photo-Consistency : = Shape, Reflectance, and Material Estimation Using Light-Field Cameras.
LDR:02853ntm a2200349Ki 4500 001 910453
005 20180517123955.5
006 m o u
007 cr mn||||a|a||
008 190606s2017 xx obm 000 0 eng d
020 $a 9780355034493
035 $a (MiAaPQ)AAI10282553
035 $a (MiAaPQ)berkeley:17018
035 $a AAI10282553
040 $a MiAaPQ $b eng $c MiAaPQ
099 $a TUL $f hyy $c available through World Wide Web
100 1 $a Wang, Ting-Chun. $3 1181754
245 1 0 $a Beyond Photo-Consistency : $b Shape, Reflectance, and Material Estimation Using Light-Field Cameras.
264 0 $c 2017
300 $a 1 online resource (111 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
500 $a Advisers: Ravi Ramamoorthi; Alexei A. Efros.
502 $a Thesis (Ph.D.) $c University of California, Berkeley $d 2017.
504 $a Includes bibliographical references
520 $a Light-field cameras have recently become easily accessible in the consumer market, making applications such as post-shot refocusing and viewpoint parallax possible. An important benefit of light-field cameras for computer vision is that multiple viewpoints or subapertures are available in a single light-field image, enabling passive depth estimation. However, most existing approaches are based on photo-consistency, i.e., all viewpoints exhibit the same color when focused to the correct depth. This assumption does not hold in a number of circumstances, e.g., in the presence of occlusions, and when the surface is not Lambertian. In this thesis, we refrain from assuming photo-consistency, and explicitly deal with the situations where it fails to hold. First, we propose a novel framework that can handle occlusions when estimating depth, so we are able to get sharper occlusion boundaries in the obtained depth maps. Next, we extend traditional optical flow to the case of glossy surfaces, and derive a spatially-varying (SV) BRDF invariant equation. Using this equation, we can then recover both shape and reflectance simultaneously using light-field cameras. Finally, we show an application of recognizing materials in light-field images, based on the reflectance information extracted using learning-based methods. By looking beyond photo-consistency, we are able to estimate better depths and recover reflectance and material types, which can be useful for a variety of vision and graphics applications.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 596380
650 4 $a Computer science. $3 573171
655 7 $a Electronic books. $2 local $3 554714
690 $a 0544
690 $a 0984
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of California, Berkeley. $b Electrical Engineering & Computer Sciences. $3 845564
773 0 $t Dissertation Abstracts International $g 78-11B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10282553 $z click for full text (PQDT)