國立虎尾科技大學 |

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

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Beyond Photo-Consistency :/
其他題名:	Shape, Reflectance, and Material Estimation Using Light-Field Cameras.
作者:	Wang, Ting-Chun.
面頁冊數:	1 online resource (111 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
Contained By:	Dissertation Abstracts International78-11B(E).
標題:	Electrical engineering. -
電子資源:	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.
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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.
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