Handheld Augmented Reality commonly implements some variant of magic lens rendering, which turns only a fraction of the user's real environment into AR while the rest of the environment remains unaffected. Since handheld AR devices are commonly equipped with video see-through capabilities, AR magic lens applications often suffer from spatial distortions, because the AR environment is presented from the perspective of the camera of the mobile device. Recent approaches counteract this distortion based on estimations of the user's head position, rendering the scene from the user's perspective. To this end, approaches usually apply face-tracking algorithms on the front camera of the mobile device. However, this demands high computational resources and therefore commonly affects the performance of the application beyond the already high computational load of AR applications. In this paper, we present a method to reduce the computational demands for user perspective rendering by applying lightweight optical flow tracking and an estimation of the user's motion before head tracking is started. We demonstrate the suitability of our approach for computationally limited mobile devices and we compare it to device perspective rendering, to head tracked user perspective rendering, as well as to fixed point of view user perspective rendering.
evaluated the effects of display size on UPR and found that, using a simulation, a tablet-sized display allows for significantly faster performance of a selection task compared to a handheld display and that UPR outperformed DPR for a selection task @cite_26 . They also prototyped a UPR system with geometric reconstruction using a Kinect for reconstruction of the physical surrounding and a Wiimote for head tracking. In follow up works, they proposed to replace an active depth sensor by gradient domain image-based rendering method combined with semi-dense stereo matching @cite_21 @cite_19 . Other authors also employ depth-sensors for scene reconstruction in UPR @cite_10 .
@cite_19 @cite_26 @cite_10 @cite_21
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In this paper we present a user study evaluating the benefits of geometrically correct user-perspective rendering using an Augmented Reality (AR) magic lens. In simulation we compared a user-perspective magic lens against the common device-perspective magic lens on both phone-sized and tablet-sized displays. Our results indicate that a tablet-sized display allows for significantly faster performance of a selection task and that a user-perspective lens has benefits over a device-perspective lens for a selection task. Based on these promising results, we created a proof-of-concept prototype, engineered with current off-the-shelf devices and software. To our knowledge, this is the first geometrically correct user-perspective magic lens. In this paper, we propose an interaction system which displays see-through images on the mobile display and that allows a user to interact with virtual objects overlaid on the see-through image using the user's hand. In this system, the camera which tracks the user's viewpoint is attached to the front of the mobile display and the depth camera which captures color and depth images of the user's hand and the background scene is attached to the back of the mobile display. Natural interaction with virtual objects using the user's hand is realized by displaying images so that the appearance of a space through the mobile display is consistent with that of the real space from the user's viewpoint. We implemented two applications to the system and showed the usefulness of this system in various AR applications. We present a new approach to rendering a geometrically-correct user-perspective view for a magic lens interface, based on leveraging the gradients in the real world scene. Our approach couples a recent gradient-domain image-based rendering method with a novel semi-dense stereo matching algorithm. Our stereo algorithm borrows ideas from PatchMatch, and adapts them to semi-dense stereo. This approach is implemented in a prototype device build from off-the-shelf hardware, with no active depth sensing. Despite the limited depth data, we achieve high-quality rendering for the user-perspective magic lens.