Virtual Universe

7/31/2019 Virtual Universe

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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.

[email protected]

+44 20 3239 7007

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Virtual Universe