Tech Briefs Magazine - March 2021 - PIT-28

gets a meaningful result. In this way,
they were able to show that the
method not only works but is even
optimal in a physical sense. They
found that the precision of their
method is only limited by the so-called

quantum noise. This noise results from
the fact that light consists of photons
- nothing can be done about that.
But within the limits of what quantum
physics allows for a coherent laser
beam, we can actually calculate the

optimal waves to measure different
things. Not only the position, but also
the movement or the direction of rotation of objects.
For more information on this, contact
mediarelations@osa.com.

Researchers Acquire 3D Images with LED Room Lighting and a
Smartphone
The Optical Society (Washington, D.C.)
s LEDs replace traditional lighting
systems, they bring more smart capabilities to everyday lighting. While you
might use your smartphone to dim LED
lighting at home, researchers have taken
this further by tapping into dynamically
controlled LEDs to create a simple illumination system for 3D imaging.
Researchers have demonstrated that 3D
optical imaging can be performed with a
cell phone and LEDs without requiring
any complex manual processes to synchronize the camera with the lighting.
" Deploying a smart-illumination system in an indoor area allows any camera in the room to use the light and
retrieve the 3D information from the
surrounding environment, " said Emma
Le Francois. " LEDs are being explored
for a variety of different applications,
such as optical communication, visible
light positioning and imaging. One day
the LED smart-lighting system used for

lighting an indoor area might be used
for all of these applications at the same
time. "
Human vision relies on the brain to
reconstruct depth information when we
view a scene from two slightly different
directions with our two eyes. Depth
information can also be acquired using a
method called photometric stereo imaging in which one detector, or camera, is
combined with illumination that comes
from multiple directions. This lighting
setup allows images to be recorded with
different shadowing, which can then be
used to reconstruct a 3D image.
Photometric stereo imaging traditionally requires four light sources,
such as LEDs, which are deployed symmetrically around the viewing axis of a
camera. In the new work, the
researchers show that 3D images can
also be reconstructed when objects are
illuminated from the top down but

Researchers developed a way to use overhead LED lighting and a smartphone to create 3D images of
a small figurine. (Credit: Emma Le Francois, University of Strathclyde)

imaged from the side. This setup allows
overhead room lighting to be used for
illumination.
The researchers developed algorithms
that modulate each LED in a unique
way. This acts like a fingerprint that
allows the camera to determine which
LED generated which image to facilitate
the 3D reconstruction. The new modulation approach also carries its own clock
signal so that the image acquisition can
be self-synchronized with the LEDs by
simply using the camera to passively
detect the LED clock signal.
To demonstrate this new approach, the
researchers used their modulation
scheme with a photometric stereo setup
based on commercially available LEDs. A
simple Arduino board provided the electronic control for the LEDs. Images were
captured using the high-speed video
mode of a smartphone. They imaged a 48millimeter-tall figurine that they 3D printed with a matte material to avoid any shiny
surfaces that might complicate imaging.
After identifying the best position for
the LEDs and the smartphone, the
researchers achieved a reconstruction
error of just 2.6 millimeters for the figurine when imaged from 42 centimeters
away. This error rate shows that the quality of the reconstruction was comparable
to that of other photometric stereo
imaging approaches. They were also
able to reconstruct images of a moving
object and showed that the method is
not affected by ambient light.
In the current system, the image
reconstruction takes a few minutes on a
laptop. To make the system practical, the
researchers are working to decrease the
computational time to just a few seconds
by incorporating a deep-learning neural
network that would learn to reconstruct
the shape of the object from the raw
image data.
For more information on this, contact
mediarelations@osa.org.

Photonics & Imaging Technology, March 2021

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Tech Briefs Magazine - March 2021

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