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IEEE VR2025顶会

Privacy-Enabled Parallax Display

Sizhuo Ma, Karl Bayer, Gurunandan Krishnan, Mohit Gupta, Shree K. Nayar

2025年份

摘要

Privacy filters for displays are designed to obfuscate or hide visual content from unintended observers, while making the displayed information visible only to selected viewers. Existing privacy filters suffer from either wide viewing field (low selectivity) or limited user positioning. To solve this dilemma, we propose a display technology that allows a narrow but adaptive viewing field that can be directed to arbitrary user location. While conventional parallax barriers provide such capability of modulating the light field according to the user location, it suffers from repeated views. Our key observation is that this view repetition originates from the periodicity of barrier patterns, and we propose a privacy-enabled parallax display based on randomized barrier design. In addition to randomizing the locations of 1D slits, we also propose breaking down the slits into pinholes and randomizing their 2D locations, which results in privacy-preservation along the vertical direction as well. We build a hardware prototype using two off-the-shelf liquid-crystal displays. Experiments show that the proposed randomized parallax barrier can direct to the user a narrow viewing field of about ±6°, providing a significantly improved privacy protection as compared to traditional privacy screens.

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