虚拟现实,或虚拟实境(Virtual Reality),简称 VR 技术,是指利用电脑模拟产生一个三度空间的虚拟世界,提供使用者关于视觉、听觉、触觉等感官的模拟,让使用者如同身历其境一般,可以及时、没有限制地观察三度空间内的事物。 实际上现在实用的民用VR技术只有带头部追踪功能的头戴式显示器,只能有限的勉强模拟视觉感官。近年来火爆的VR就是这个。 VR技术重点在硬件方面,尤其是头部追踪技术是重中之重。VR必须要结合硬件与软件一起使用。和大多数人想象的不同,VR在软件方面实现起来简单,几乎只需要很少的一点代码即可实现。

In augmented reality (AR), correct and precise estimations of user's visual fixations and head movements can enhance the quality of experience by allocating more computation resources for the analysing, rendering and 3D registration on the areas of interest. However, there is no research about understanding the visual exploration of users when using an AR system or modeling AR visual attention. To bridge the gap between the real-world scene and the scene augmented by virtual information, we construct the ARVR saliency dataset with 100 diverse videos evaluated by 20 people. The virtual reality (VR) technique is employed to simulate the real-world, and annotations of object recognition and tracking as augmented contents are blended into the omnidirectional videos. Users can get the sense of experiencing AR when watching the augmented videos. The saliency annotations of head and eye movements for both original and augmented videos are collected which constitute the ARVR dataset.

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#恋上冬日#又来深圳讲课 上周刚来过,这周又来了,很感慨工作的快节奏和交通的便捷。不止老师飞来飞去,学员也全国各地来深圳汇集,细想一下成本挺高。将来基于6G的全息影像虚拟现实授课一定会成为主流。 绿洲 深圳·宝立方中心

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Image space feature detection is the act of selecting points or parts of an image that are easy to distinguish from the surrounding image region. By combining a repeatable point detection with a descriptor, parts of an image can be matched with one another, which is useful in applications like estimating pose from camera input or rectifying images. Recently, precise indoor tracking has started to become important for Augmented and Virtual reality as it is necessary to allow positioning of a headset in 3D space without the need for external tracking devices. Several modern feature detectors use homographies to simulate different viewpoints, not only to train feature detection and description, but test them as well. The problem is that, often, views of indoor spaces contain high depth disparity. This makes the approximation that a homography applied to an image represents a viewpoint change inaccurate. We claim that in order to train detectors to work well in indoor environments, they must be robust to this type of geometry, and repeatable under true viewpoint change instead of homographies. Here we focus on the problem of detecting repeatable feature locations under true viewpoint change. To this end, we generate labeled 2D images from a photo-realistic 3D dataset. These images are used for training a neural network based feature detector. We further present an algorithm for automatically generating labels of repeatable 2D features, and present a fast, easy to use test algorithm for evaluating a detector in an 3D environment.

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