Parallax View Generation for Static Scenes Using Parallax-Interpolation Adaptive Separable Convolution
Parallax View Generation for Static Scenes Using Parallax-Interpolation Adaptive Separable Convolution
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DOI：
10.1109/icmew.2018.8551583
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发表时间：
2018-07
期刊：
影响因子：
--
通讯作者：
Yuan Gao;R. Koch
中科院分区：
文献类型：
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作者：
Yuan Gao;R. Koch
从稀疏采样光场重构密集采样光场是一个具有挑战性的问题，已经提出了各种算法。然而，很少有人将光场中的角度信息作为虚拟摄像机拍摄视频的时间信息，即将静态场景的SSLF的视差视图转化为虚拟摄像机沿视差轴沿着移动所拍摄视频的关键帧。为此，本文提出了一种新的视差视图生成方法--视差插值自适应可分离卷积（PIASC）。所提出的PIASC方法充分利用了SSLF设备捕获的静态对象的运动相干性，以增强最先进的视频帧内插方法，即自适应可分离卷积（AdaSep-Conv）的运动敏感卷积核。在大挑战的三个开发数据集上的实验结果证明了PIASC对静态场景DSLF重建的上级性能。
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.