Adaptive light field synthesis for a dual-layer compressive display. By combining sampling, rendering, and display-specific optimization into a single framework, the proposed algorithm facilitates light field synthesis with significantly reduced computational resources. Redundancy in the light field as well as limitations of display hardware are exploited to generate high-quality reconstructions (center left column) for a high-resolution target light field of 85x21 views with 840x525 pixels each (center). Our adaptive econstruction uses only 3.82% of the rays in the full target light field (left column), thus providing significant savings both during rendering and during the computation of the display parameters. The proposed framework allows for higher-resolution light fields, better 3D effects, and perceptually correct animations to be presented on emerging compressive displays (right columns).


Recent years have seen proposals for exciting new computational display technologies that are compressive in the sense that they generate high resolution images or light fields with relatively few display parameters. Image synthesis for these types of displays involves two major tasks: sampling and rendering high-dimensional target imagery, such as light fields or time-varying light fields, as well as optimizing the display parameters to provide a good approximation of the target content. In this paper, we introduce an adaptive optimization framework for compressive displays that generates high quality images and light fields using only a fraction of the total plenoptic samples. We demonstrate the framework for a large set of display technologies, including several types of auto-stereoscopic displays, high dynamic range displays, and high-resolution displays. We achieve significant performance gains, and in some cases are able to process data that would be infeasible with existing methods.


High-resolution paper: [AdaptiveImageSynthesis_Heide2013.pdf (21MB)]

Code, Results and Video

Code and Scenes (42MB): []
Supplemental material (479MB): []
Technical papers video (129MB): [AdaptiveImageSynthesis.avi]

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