DeepFocus: learned image synthesis for computational display
DeepFocus: learned image synthesis for computational display
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DeepFocus：用于计算显示的学习图像合成
DOI：
10.1145/3214745.3214769
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Douglas Lanman
中科院分区：
文献类型：
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作者：
Lei Xiao;Anton Kaplanyan;Alexander Fix;Matthew Chapman;Douglas Lanman
再现准确的视网膜散焦模糊对于正确驱动调节并解决头戴式显示器 (HMD) 中的聚散调节冲突非常重要。已经提出了许多支持调节的头显。三种架构受到了特别关注：变焦、多焦点和光场显示器。这些设计都扩展了焦深，但依赖于计算成本高昂的渲染和优化算法来再现准确的视网膜模糊（通常限制内容复杂性和交互式应用程序）。迄今为止，尚未提出统一的计算框架来支持使用商品内容驱动这些新兴的 HMD。在本文中，我们介绍了 Deep-Focus，这是一种通用的端到端可训练卷积神经网络，旨在有效解决支持调节的 HMD 的全部计算任务。该网络被证明可以使用常用的 RGB-D 图像准确地合成散焦模糊、焦点堆栈、多层分解和多视图图像。利用 GPU 硬件的最新进展和图像合成网络的最佳实践，DeepFocus 通过一系列支持调节的 HMD 实现实时、近乎正确的视网膜模糊描绘。
Reproducing accurate retinal defocus blur is important to correctly drive accommodation and address vergence-accommodation conflict in head-mounted displays (HMDs). Numerous accommodation-supporting HMDs have been proposed. Three architectures have received particular attention: varifocal, multifocal, and light field displays. These designs all extend depth of focus, but rely on computationally expensive rendering and optimization algorithms to reproduce accurate retinal blur (often limiting content complexity and interactive applications). To date, no unified computational framework has been proposed to support driving these emerging HMDs using commodity content. In this paper, we introduce Deep-Focus, a generic, end-to-end trainable convolutional neural network designed to efficiently solve the full range of computational tasks for accommodation-supporting HMDs. This network is demonstrated to accurately synthesize defocus blur, focal stacks, multilayer decompositions, and multiview imagery using commonly available RGB-D images. Leveraging recent advances in GPU hardware and best practices for image synthesis networks, DeepFocus enables real-time, near-correct depictions of retinal blur with a broad set of accommodation-supporting HMDs.