![]() Kaplanyan, Christoph Schied, Marco Salvi, Aaron Lefohn, Derek Nowrouzezahrai, and Timo Aila. ![]() Optimal defocus estimation in individual natural images. Algorithms for Rendering Depth of Field Effects in Computer Graphics. A Stereo Display Prototype with Multiple Focal Distances. Near-eye Varifocal Augmented Reality Display Using See-through Screens. Kaan Akşit, Ward Lopes, Jonghyun Kim, Peter Shirley, and David Luebke.TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. This network is demonstrated to accurately synthesize defocus blur, focal stacks, multilayer decompositions, and multiview imagery using only commonly available RGB-D images, enabling real-time, near-correct depictions of retinal blur with a broad set of accommodation-supporting HMDs. In this paper, we introduce DeepFocus, a generic, end-to-end convolutional neural network designed to efficiently solve the full range of computational tasks for accommodation-supporting HMDs. To date, no unified framework has been proposed to support driving these emerging HMDs using commodity content. These designs all extend depth of focus, but rely on computationally expensive rendering and optimization algorithms to reproduce accurate defocus blur (often limiting content complexity and interactive applications). A multitude of accommodation-supporting HMDs have been proposed, with three architectures receiving particular attention: varifocal, multifocal, and light field displays. Second, HMDs should accurately reproduce retinal defocus blur to correctly drive accommodation. First, the hardware must support viewing sharp imagery over the full accommodation range of the user. Addressing vergence-accommodation conflict in head-mounted displays (HMDs) requires resolving two interrelated problems.
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