A Perception-driven Hybrid Decomposition for Multi-layer Accommodative Displays.

IF 4.7 1区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Hyeonseung Yu, Mojtaba Bemana, Marek Wernikowski, Michal Chwesiuk, Okan Tarhan Tursun, Gurprit Singh, Karol Myszkowski, Radoslaw Mantiuk, Hans-Peter Seidel, Piotr Didyk
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引用次数: 11

Abstract

Multi-focal plane and multi-layered light-field displays are promising solutions for addressing all visual cues observed in the real world. Unfortunately, these devices usually require expensive optimizations to compute a suitable decomposition of the input light field or focal stack to drive individual display layers. Although these methods provide near-correct image reconstruction, a significant computational cost prevents real-time applications. A simple alternative is a linear blending strategy which decomposes a single 2D image using depth information. This method provides real-time performance, but it generates inaccurate results at occlusion boundaries and on glossy surfaces. This paper proposes a perception-based hybrid decomposition technique which combines the advantages of the above strategies and achieves both real-time performance and high-fidelity results. The fundamental idea is to apply expensive optimizations only in regions where it is perceptually superior, e.g., depth discontinuities at the fovea, and fall back to less costly linear blending otherwise. We present a complete, perception-informed analysis and model that locally determine which of the two strategies should be applied. The prediction is later utilized by our new synthesis method which performs the image decomposition. The results are analyzed and validated in user experiments on a custom multi-plane display.

多层可调节显示的感知驱动混合分解。
多焦平面和多层光场显示是解决现实世界中观察到的所有视觉线索的有前途的解决方案。不幸的是,这些设备通常需要昂贵的优化来计算输入光场或焦点堆栈的适当分解,以驱动单个显示层。尽管这些方法提供了接近正确的图像重建,但显著的计算成本阻碍了实时应用。一个简单的替代方案是线性混合策略,它使用深度信息分解单个2D图像。这种方法提供了实时性能,但在遮挡边界和光滑表面上产生不准确的结果。本文提出了一种基于感知的混合分解技术,该技术结合了上述策略的优点,实现了实时性和高保真度的结果。其基本思想是只在感知上优越的区域应用昂贵的优化,例如,中央凹处的深度不连续,并返回到成本较低的线性混合。我们提出了一个完整的,感知知情的分析和模型,在当地确定哪两种策略应该应用。然后利用我们的新合成方法进行图像分解。在用户自定义的多平面显示器上对结果进行了分析和验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Visualization and Computer Graphics 工程技术-计算机:软件工程
CiteScore
10.40
自引率
19.20%
发文量
946
审稿时长
4.5 months
期刊介绍: TVCG is a scholarly, archival journal published monthly. Its Editorial Board strives to publish papers that present important research results and state-of-the-art seminal papers in computer graphics, visualization, and virtual reality. Specific topics include, but are not limited to: rendering technologies; geometric modeling and processing; shape analysis; graphics hardware; animation and simulation; perception, interaction and user interfaces; haptics; computational photography; high-dynamic range imaging and display; user studies and evaluation; biomedical visualization; volume visualization and graphics; visual analytics for machine learning; topology-based visualization; visual programming and software visualization; visualization in data science; virtual reality, augmented reality and mixed reality; advanced display technology, (e.g., 3D, immersive and multi-modal displays); applications of computer graphics and visualization.
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