Joint View Expansion and Filtering for Automultiscopic 3D Displays

摘要

Multi-view autostereoscopic displays provide an immersive, glasses-free 3D viewing experience, but they require correctly .ltered con-tent from multiple viewpoints. This, however, cannot be easily obtained with current stereoscopic production pipelines. We pro-vide a practical solution that takes a stereoscopic video as an input and converts it to multi-view and .ltered video streams that can be used to drive multi-view autostereoscopic displays. The method combines a phase-based video magni.cation and an interperspective antialiasing into a single .ltering process. The whole algorithm is simple and can be ef.ciently 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 ren-dering methods. Finally, we showcase the method in the context of a real-time 3D videoconferencing system that requires only two cameras.