Fast Physically Correct Refocusing for Sparse Light Fields Using Block-Based Multi-Rate View Interpolation

2017 ◽  
Vol 26 (2) ◽  
pp. 603-618
Author(s):  
Chao-Tsung Huang ◽  
Yu-Wen Wang ◽  
Li-Ren Huang ◽  
Jui Chin ◽  
Liang-Gee Chen
Keyword(s):  
Light Fields ◽  
View Interpolation ◽  
Block Based

Optimized sampling for view interpolation in light fields using local dictionaries

2018 ◽  
Vol 168 ◽  
pp. 93-103 ◽  
Author(s):  
David C. Schedl ◽  
Clemens Birklbauer ◽  
Oliver Bimber
Keyword(s):  
Light Fields ◽  
View Interpolation

LIGHTING AND SHADOW INTERPOLATION USING INTRINSIC LUMIGRAPHS

2004 ◽  
Vol 04 (04) ◽  
pp. 585-604 ◽  
Author(s):  
YASUYUKI MATSUSHITA ◽  
STEPHEN LIN ◽  
HEUNG-YEUNG SHUM ◽  
XIN TONG ◽  
SING BING KANG
Keyword(s):  
Light Field ◽  
Image Synthesis ◽  
Light Fields ◽  
Surface Reflectance ◽  
High Quality ◽  
View Interpolation ◽  
Lighting Conditions ◽  
3D Geometry ◽  
Intrinsic Images ◽  
Image Representations

Densely-sampled image representations such as the light field or Lumigraph have been effective in enabling photorealistic image synthesis. Unfortunately, lighting interpolation with such representations has not been shown to be possible without the use of accurate 3D geometry and surface reflectance properties. In this paper, we propose an approach to image-based lighting interpolation that is based on estimates of geometry and shading from relatively few images. We decompose light fields captured at different lighting conditions into intrinsic images (reflectance and illumination images), and estimate view-dependent scene geometries using multi-view stereo. We call the resulting representation an Intrinsic Lumigraph. In the same way that the Lumigraph uses geometry to permit more accurate view interpolation, the Intrinsic Lumigraph uses both geometry and intrinsic images to allow high-quality interpolation at different views and lighting conditions. The joint use of geometry and intrinsic images is effective in computing shadow masks for shadow prediction at new lighting conditions. We illustrate our approach with images of real scenes.

A Structure Learning Method for Block-Based Neural Networks

2013 ◽  
Vol 133 (10) ◽  
pp. 1976-1982 ◽  
Author(s):  
Hidetaka Watanabe ◽  
Seiichi Koakutsu ◽  
Takashi Okamoto ◽  
Hironori Hirata
Keyword(s):  
Neural Networks ◽  
Structure Learning ◽  
Learning Method ◽  
Block Based

Block-Based Incremental Caching for Information Centric Networking

2016 ◽  
Vol E99.B (12) ◽  
pp. 2550-2558
Author(s):  
Sung-Hwa LIM ◽  
Yeo-Hoon YOON ◽  
Young-Bae KO ◽  
Huhnkuk LIM
Keyword(s):  
Information Centric Networking ◽  
Block Based

Real-time Depth Estimation and View Interpolation using Quasar

2016 ◽  
Vol 2016 (19) ◽  
pp. 1-6 ◽  
Author(s):  
Bart Goossens ◽  
Simon Donné ◽  
Jan Aelterman ◽  
Jonas De Vylder ◽  
Dirk Van Haerenborgh ◽  
...  
Keyword(s):  
Real Time ◽  
Depth Estimation ◽  
View Interpolation
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