<p></p><p>Augmented Reality (AR) is a powerful computer to human visual interface
that displays data overlaid onto the user's view of the real world. Compared to
conventional visualization on a computer display, AR has the advantage of
saving the user the cognitive effort of mapping the visualization to the real
world. For example, a user wearing AR glasses can find a destination in an
urban setting by following a virtual green line drawn by the AR system on the
sidewalk, which is easier to do than having to rely on navigational directions
displayed on a phone. Similarly, a surgeon looking at an operating field
through an AR display can see graphical annotations authored by a remote mentor
as if the mentor actually drew on the patient's body.</p>
<p> </p>
<p>However, several challenges remain to be addressed before AR can reach
its full potential. This research contributes solutions to four such
challenges. A first challenge is achieving visualization continuity for AR
displays. Since truly transparent displays are not feasible, AR relies on
simulating transparency by showing a live video on a conventional display. For
correct transparency, the display should show exactly what the user would see
if the display were not there. Since the video is not captured from the user
viewpoint, simply displaying each frame as acquired results in visualization
discontinuity and redundancy. A second challenge is providing the remote mentor
with an effective visualization of the mentee's workspace in AR telementoring.
Acquiring the workspace with a camera built into the mentee's AR headset is
appealing since it captures the workspace from the mentee's viewpoint, and
since it does not require external hardware. However, the workspace
visualization is unstable as it changes frequently, abruptly, and substantially
with each mentee head motion. A third challenge is occluder removal in
diminished reality. Whereas in conventional AR the user's visualization of a
real world scene is augmented with graphical annotations, diminished reality
aims to aid the user's understanding of complex real world scenes by removing
objects from the visualization. The challenge is to paint over occluder pixels
using auxiliary videos acquired from different viewpoints, in real time, and
with good visual quality. A fourth challenge is to acquire scene geometry from
the user viewpoint, as needed in AR, for example, to integrate virtual
annotations seamlessly into the real world scene through accurate depth
compositing, and shadow and reflection casting and receiving.</p>
<p> </p>
<p>Our solutions are based on the thesis that images acquired from
different viewpoints should not always be connected by computing a dense,
per-pixel set of correspondences, but rather by devising custom, lightweight,
yet sufficient connections between them, for each unique context. We have
developed a self-contained phone-based AR display that aligns the phone camera
and the user by views, reducing visualization discontinuity to less than 5% for
scene distances beyond 5m. We have developed and validated in user studies an
effective workspace visualization method by stabilizing the mentee first-person
video feed through reprojection on a planar proxy of the workspace. We have
developed a real-time occluder in-painting method for diminished reality based
on a two-stage coarse-then-fine mapping between the user and the auxiliary
view. The mapping is established in time linear with occluder contour length,
and it achieves good continuity across the occluder boundary. We have developed
a method for 3D scene acquisition from the user viewpoint based on single-image
triangulation of correspondences between left and right eye corneal
reflections. The method relies on a subpixel accurate calibration of the
catadioptric imaging system defined by two corneas and a camera, which enables
the extension of conventional epipolar geometry for a fast connection between
corneal reflections.</p><p></p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/12234899 |
| Date | 05 May 2020 |
| Creators | Chengyuan Lin (8793044) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/Lightweight_and_Sufficient_Two_Viewpoint_Connections_for_Augmented_Reality/12234899 |
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