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In the future,
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self-driving cars will be safer
and more reliable than humans.
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But for this to happen,
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we need technologies
that allow cars to respond
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faster than humans,
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we need algorithms
that can drive better than humans,
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and we need cameras
that can see more than humans can see.
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For example, imagine a self-driving car
is about to make a blind turn
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and there's an oncoming car
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or perhaps there's a child
about to run into the street.
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Fortunately, our future car
will have this superpower,
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a camera that can see around corners
to detect these potential hazards.
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For the past few years as a PhD student
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in the Stanford Computational Imaging lab,
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I've been working on a camera
that can do just this.
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A camera that can image objects
hidden around corners or blocked
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from direct line of sight.
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So let me give you an example
of what our camera can see.
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This is an outdoor experiment we conducted
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where our camera system is scanning
the side of this building with a laser,
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and the scene that we want to capture
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is hidden around the corner
behind this curtain.
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So our camera system
can't actually see it directly.
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And yet, somehow,
our camera can still capture
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the 3D geometry of this scene.
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So how do we do this?
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The magic happens here
in this camera system.
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You can think of this
as a type of high-speed camera.
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Not one that operates
at 1,000 frames per second,
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or even a million frames per second,
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but a trillion frames per second.
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So fast that it can actually capture
the movement of light itself.
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And to give you an example
of just how fast light travels,
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let's compare it to the speed
of a fast-running comic book superhero
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who can move at up to three times
the speed of sound.
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It takes a pulsive light
about 3.3 billionths of a second,
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or 3.3 nanoseconds,
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to travel the distance of a meter.
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Well, in that same time,
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our superhero has moved
less than the width of a human hair.
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that's pretty fast.
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But actually we need to image much faster
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if we want to capture light light
moving at subcentimeter scales.
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So our camera system can capture photons
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at time frames of just
50 trillionths of a second,
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or 50 picoseconds.
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So we take this ultra-high-speed camera
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and we pair it with a laser
that sends out short pulses of light.
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Each pulse travels to this visible wall
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and some light scatters
back to our camera,
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but we also use the wall
to scatter light around the corner
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to the hidden object and back.
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We repeat this measurement many times
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to capture the arrival times
of many photons
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from different locations on the wall.
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And after we capture these measurements,
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we can create a trillion-frame-per-second
video of the wall.
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While this wall may look
ordinary to our own eyes,
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at a trillion frames per second
we can see something truly incredible.
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We can actually see waves of light
scattered back from the hidden scene
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and splashing against the wall.
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And each of these waves
carries information
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about the hidden object that sent it.
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So we can take these measurements
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and pass them into
a reconstruction algorithm
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to then recover the 3D geometry
of this hidden scene.
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Now I want to show you one more example
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of an indoor scene that we captured,
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this time with a variety
of different hidden objects.
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And these objects
have different appearances,
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so they reflect light differently.
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For example, this glossy dragon statue
reflects light differently
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that the mirror disco ball
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or the white discus thrower statue.
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And we can actually see the differences
in the reflected light
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by visualizing it as this 3D volume,
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where we've just taken the video frames
and stacked them together.
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And time here is represented
as the depth dimension of this cube.
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These bright dots that you see
are reflections of light
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from each of the mirrored
facets of the disco ball,
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scattering against the wall overtime.
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The bright streaks of light that you see
arriving soonest in time
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are from the glossy dragon statue
that's closest to the wall.
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And the other streaks of light come from
reflections of light from the bookcase
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and from the statue.
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Now we can also visualize
these measurements frame by frame,
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as a video,
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to directly see the scattered light.
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And again, here we see first
reflections of light from the dragon,
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closest to the wall,
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followed by bright dots
from the disco ball
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and other reflections from the bookcase.
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And finally, we see the reflected
waves of light from he statue.
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These waves of light illuminating the wall
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are like fireworks that last
for just trillionths of a second.
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And even though these objects
reflect light differently,
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we can still reconstruct their shapes.
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And this is what you can see
from around the corner.
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Now I want to show you one more example
that's slightly different.
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In this video, you see me
dressed in this reflective suit
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and our camera system is scanning the wall
at a rate of four times every second.
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The suit is reflective,
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so we can actually capture enough photons
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that we can see where I am
and what I'm doing,
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without the camera
actually directly imaging me.
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By capturing photons that scatter
from the wall to my track suit,
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back to the wall and back to the camera,
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we can capture this indirect
video in real time.
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And we think that this type
of practical non-line-of-sight imaging
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could be useful for applications
including for self-driving cars,
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but also for biomedical imaging,
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where we need to see
into the tiny structures of the body.
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And perhaps we could also put
similar camera systems on the robots
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that we send to explore other planets.
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Now you may have heard
about seeing around corners before,
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but what I showed you today
would have been impossible
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just two years ago.
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For example, we can now image large,
room-sized hidden scenes outdoors
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and at real-time rates
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and we've made significant advancements
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towards making this a practical technology
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that you could actually see
on a car someday.
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But of course, there's still
challenges remaining.
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For example, can we image
hidden scenes at long distances
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where we're collecting
very, very few photons,
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with lasers that are low-power
and that are eye-safe.
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Or can we create images from photons
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that have scattered around many more times
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than just a single bounce
around the corner?
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Can we take our prototype system
that's currently large and bulky,
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and miniaturize it into something
that could be useful
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for biomedical imaging
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or perhaps a sort of improved
home security system,
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or can we take this new imaging modality
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and use it for other applications?
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I think it's an exciting new technology
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and there could be other things
that we haven't thought of yet
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to use it for.
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And so, well, a future
with self-driving cars
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may seem distant to us now,
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we're already developing the technologies
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that could make cars safer
and more intelligent.
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And with the rapid pace
of scientific discovery and innovation,
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you never know what new
and exciting capabilities
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could be just around the corner.
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(Applause)