Parallax View Generation for Static Scenes Using Parallax-Interpolation Adaptive Separable Convolution
Yuan Gao, Reinhard Koch · 2018
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.