Joint view expansion and filtering for automultiscopic 3D displays

Abstract

Multi-view autostereoscopic displays provide an immersive, glassesfree 3D viewing experience, but they require correctly filtered content from multiple viewpoints. This, however, cannot be easily obtained with current stereoscopic production pipelines. We provide a practical solution that takes a stereoscopic video as an input and converts it to multi-view and filtered video streams that can be used to drive multi-view autostereoscopic displays. The method combines a phase-based video magnification and an interperspective antialiasing into a single filtering process. The whole algorithm is simple and can be efficiently implemented on current GPUs to yield a near real-time performance. Furthermore, the ability to retarget disparity is naturally supported. Our method is robust and works well for challenging video scenes with defocus blur, motion blur, transparent materials, and specularities. We show that our results are superior when compared to the state-of-the-art depth-based rendering methods. Finally, we showcase the method in the context of a real-time 3D videoconferencing system that requires only two cameras.

Dynamic mapping for multiview autostereoscopic displays

“…27,32,33 These methods are compatible with our work since they produce a set of input views. In addition, with dynamic mapping the number of synthesized views need not match the number of device zones, and if eye tracking is available, computation can be reduced by synthesizing only the required viewpoints.…”

“…25 Didyk et al combine phase based video magnification and antialiasing into a single filtering process. 26,27 With methods that produce a set of greatly improved static views, our work could be used in conjunction to further improve the display quality.…”

The Light Field Attachment: Turning a DSLR into a Light Field Camera Using a Low Budget Camera Ring

“…Signal processing frameworks using phase-based analysis [30] or Fourier analysis [31] have also been proposed recently for the synthesis of light field images using only a few or a small subset of light field images. Depth-aided phase-based synthesis (DAPS) [32] combines the advantages of both depth-based and phasebased method by integrating the depth information into the phasebased processing framework to compensate for the spatial effects of floating texture deformation across views.…”

Improving visual quality of view transitions in automultiscopic displays

“…Due to the discrete nature of acquisition and display stages, light fields are usually aliased. Several techniques have been developed to correctly reconstruct light fields from recorded data [Isaksen et al 2000;Stewart et al 2003] and avoid both spatial and inter-view aliasing on automultiscopic displays [Zwicker et al 2006;Konrad and Agniel 2006;Didyk et al 2013]. To further adjust content to a particular device, a few techniques for multi-view content depth manipulation have been proposed [Zwicker et al 2006;Didyk et al 2012;Masia et al 2013].…”

“…A similar, manual correction is a common practice to reduce the need of inter-view antialiasing [Zwicker et al 2006;Didyk et al 2013] and visual discomfort [Shibata et al 2011].…”