Post on 04-Apr-2018
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Crocotta Research & Development Ltd
Be ambitious of climbing up to the difficult,
in a manner inaccessible...
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ONCE UPON ANOTHER
FANTASTIC DAY IN THE UKwe started to think about visualization, other than
polygonized surface rendering, to bring us closer to reality.
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NEXT DAY WE TURNED TO
OUR DEMON:
We quickly realized that lots of people had been dealing with
the problem already*, so we had to set a more future
oriented goal.
What if we traveled 10 - 20 years ahead in time, and
mimicked the real environment with the equipment of the
future as much as possible.
Neither the demon criticized our project, no relevant search results
were found.
* happens just too often
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The idea was born!
VIRTUAL UNIVERSE
A virtual world solely made of particles
BUT, there are fundamental questions:
A. What is universe?
B. What bottlenecks do we need to face?
C. Can we do any part(s) of the experiment on todays
machines?
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We want to know
the building blocks
and their interaction rules.Standard model of physics
predicts
12 fundamental particles
which interact via 4 elementalforces.
Seems modelable, so far
A. WHAT IS UNIVERSE?
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1. 1014 atoms in a cell
2. 109 cells per cm3
3. 3x1011 stars in a galaxy
4. Observable universe
Diameter is estimated at
about 93 billion light-years.
Contains 1024 stars (1
septillion stars).
Approximate number of
atoms is close to 1080.
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GIGANTIC NUMBERS ALL AROUNDObviously we need to do some compromise here.
Possible modeling options:
Stay on subatomic/atomic level and model nanostructures Organic material provided that a living cell is the smallest
element
Galactic phenomena and have starts/planets as smallest
elements
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B. WHAT BOTTLENECKS?
Simulation speed:
Particle interaction
Measuring, scanning (visualization for instance)Even if we imagined 100,000 parallel cores, with fast common
memory access, petabyte storage devices, etc., we could
always enlarge/expand our simulation scenario to make the
hardware struggle again.
Amount of data:
Obviously we are forced to think in smaller scale, even in 10 -
20 years term, as the amount of data is enormous.
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C. WHAT CAN WE DO ON TODAYS
MACHINES?
Well, probably a lot, because:
If we designed the system scalable, we could deal with the
problem - in small scale - straight away.
Due to the enormous task we cant solely rely on hardware
performance growth.
We need to invent better algorithms anyway.
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Lets start!
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DEFINE AREAS OF DEVELOPMENT
We split up the work to 3 major areas:
A. Scanning & visualization
B. PhysicsC. Data compression, representation
In the current presentation we focus on the Scanning &
visualization part.
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A. SCANNING & VISUALIZATION
PARTICLES IN 3D SPACE
We deal with many particles, so a raster representation may
be more feasible than working with individual points (point
clouds).
3D VOLUMETRIC TEXTURES
(similar to 2D textures + 1 extra spatial dimension)
Definitions:
2D textures have pixels
3D textures have voxels
Texel means a pixel in 2D, a voxel in 3D.
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Pros:
Easy to scale
up/down
Opportunities for
cheap
interpolation,
patternreconstruction
Cons:
Difficult to scan,
visualize Large data-size
(empty space is
also stored)
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Instead of conventional intersection testing in ray-tracing, we
march forward in tiny steps along the ray.
RAY-MARCHING
Pro: Can access all texels/matter
Con: Damn slow
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ACCELERATED RAY-MARCHING
Spatial data structures,
adaptive grids:
Binary-trees
KD-trees
Oct-trees
Better, but still not effective
enough.
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Sphere tracing
distance fields
The trick is to estimate the
distance to the closest
surface or sharp change in
the volumetric texture at
any point in space.
This allows to march in
large steps along the ray.
ACCELERATED RAY-MARCHING
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Possible replacement for particles?
Provided field construction vs. ray-marching speed up is a win.
Is that possible? Yes.
Weve been successfully deploying gradient fields, and not for
visualization purposes only, but to accelerate physics
calculations too.
Further benefits:
Scale extremely well (down/up).
Give lots of opportunities for guessing, interpolating.
INTRODUCING GRADIENT FIELDS
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Gradient fields can be
well used for physics:
Distance fields.
Dramatic speed up at
photon-tracing.
Force fields, like
gravity.
Energy fields, like
kinetic energy.
etc.
B. PHYSICS
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Our failed approaches:
Lossless compression
Conventional lossy
compression , like wavelet or
similar
Current approaches: Adaptive representation
Focus on interesting areas
Contour & pattern analysis
Reconstruction
C. COMPRESSION
The figure below highlights that a compression method has to
be deployed.
Textures side
in texels
Size in bytes
side3
1 byte per texel
32 32K
64 256K
128 2M
256 16M
512 128M
1024 1G
2048 8G
4096 64G
8192 512G
16384 4T
32768 32T
65536 256T
131072 2P
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We traversed an exciting path so far, and the next months are
going to be even more exciting for us.
We don't want to close out the possibility of 2 - 3 magnitudes
speed up comparing to brute force methods, once we get all
our theories into practice.
And we hope our friends at the hardware department wont
rest either
SUMMARY
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To be continuedThank you!
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Crocotta Research & Development LtdSuite 5, 39 Irish Town, Gibraltar
We are a small team of international researchers with the aim ofconducting technology leaps in exciting fields of exploration like virtual
reality, virtual synthesis of matter, artificial intelligence, and robotics.
www.crocotta.co.ukcrocotta@crocotta.co.uk
+44 20 3239 7007