Paper Info
Title
Parallax View Generation for Static Scenes Using Parallax-Interpolation Adaptive Separable Convolution
Abstract
Reconstructing a Densely-Sampled Light Field (DSLF) from a Sparsely-Sampled Light Field (SSLF) is a challenging problem, for which various kinds of algorithms have been proposed. However, very few of them treat the angular information in a light field as the temporal information of a video from a virtual camera, i.e. the parallax views of a SSLF for a static scene can be turned into the key frames of a video captured by a virtual camera moving along the parallax axis. To this end, in this paper, a novel parallax view generation method, Parallax-Interpolation Adaptive Separable Convolution (PIASC), is proposed. The presented PIASC method takes full advantage of the motion coherence of static objects captured by a SSLF device to enhance the motion-sensitive convolution kernels of a state-of-the-art video frame interpolation method, i.e. Adaptive Separable Convolution (AdaSep-Conv). Experimental results on three development datasets of the grand challenge demonstrate the superior performance of PIASC for DSLF reconstruction of static scenes.
Year
DOI
Venue
2018
10.1109/ICMEW.2018.8551583
2018 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)
Keywords
Field
DocType
Parallax View Generation,View Synthesis,Densely-Sampled Light Field Reconstruction,Sparsely-Sampled Light Field Capture,Parallax-Interpolation Adaptive Separable Convolution
Computer vision,Parallax,Convolution,Computer science,Interpolation,Separable space,View synthesis,Light field,Coherence (physics),Motion interpolation,Artificial intelligence
Conference
ISSN
ISBN
Citations 
2330-7927
978-1-5386-4196-5
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Yuan Gao101.01
Reinhard Koch22038170.17