Photo Cloud Modeling

8/10/2019 Photo Cloud Modeling

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Yoshinori Dobashi (Hokkaido University)

Yusuke Shinzo (Hokkaido University)

Tsuyoshi Yamamoto (Hokkaido University)

Modeling of Clouds from

a Single hotogra!h


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Overview

"ntrodu#tion$elated %orkro!osed Method$esultsCon#lusion


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Introduction

ro#edural a!!roa#h

Use of sim!le heuristi#ally'defined rules

&o #om!utational #ost

Diffi#ult to adust !arameters

hysi#ally'based a!!roa#h

Simulating !hysi#al !ro#ess of #loud formation

$ealisti# #louds

High #om!utational #ost


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Our Approach

"mage'based a!!roa#h Use of a single !hotogra!h

*ot to re#onstru#t the same #louds

Using the !hoto as a guide to synthesizesimilar #louds

Three ty!es of #louds

#irrus alto#umulus #umulus


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Overview



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Related Work

ro#edural modeling +ra#tals ,-os./0

Te1tured elli!soids ,2ar.30

Metaballs 4 noise fun#tion ,5be670

S!e#tral synthesis ,Sak6/0

$eal'time modeling8rendering system ,SS5H9/0

,2ar.30 ,5be670 ,SS5H9/0

Diffi#ult to adust !arameters


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Related Work

hysi#ally'based modeling

*umeri#al solution of atmos!heri# fluid dynami#s

,:H.;< MYD*9=< MYD*9>0

Controlling #loud simulation ,D:*Y9.0

,:H.;0 ,MYD*9=0 ,MYD*9>0 ,D:*Y9.0

High #om!utational #ost


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Related Work

"mage'based modeling Use of infrared satellite images ,D*Y?6.0

*ot a!!li#able to !hoto taken from the ground

*o #loud ty!es

satellite image synthesized #louds


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Overview



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Overview of Our Method

#irrus

alto'

#umulus

#umulus


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#irrus

alto'

#umulus

#umulus

Cal#ulation of #loud image

(#ommon !ro#ess)

Cal#ulation of #loud image

(#ommon !ro#ess)


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Calculation of Cloud Image

?vervie

#irrus

alto#umulus

#umulus

intensity o!a#ity

intensity o!a#ity

intensity o!a#ity

(in!ut !hotogra!hs) (#loud images)


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ro#esses

in!ut image sky image

intensity o!a#ity

=@ $emoving #loud !i1els >@ "nter!olating sky #olor

/@ #al#ulation of intensity8o!a#ity


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=@ $emoving #loud !i1els


ro#esses


intensity o!a#ity

>@ "nter!olating sky #olor



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ro#esses


intensity o!a#ity

=@ $emoving #loud !i1els >@ "nter!olating sky #olor


= cloud

image


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Removing Cloud Pixels

Use of #hroma to identify #loud !i1els Clouds are generally hite (or gray)

$emove !i1el if #hroma A threshold

"n!ut !hotogra!h removed #loud !i1els


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Calculation of k! Image

"nter!olation of sky #olors by solving

oisson eBuation

0),( = csky pI

sky image

#loud !i1els pc

pc: cloud pixel

= R, G, B


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"ntensity of #louds (single s#attering)

viewpoint

sun

sk!

cloud


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viewpoint

sun

sk!

Isun

Isuncloud


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viewpoint

sun

sk!

Isky

Isun

Isky

Isuncloud


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viewpoint

sun

sk!

IskyIcld

Isun

Isky

Isuncloud

(p: pixel, = R, G, B)

),()()()(),( pIpIppI skysuncld +=


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#irrus

alto'

#umulus

#umulus

Modeling of #irrusModeling of #irrus


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Modeling of Cirrus

Cirrus #louds Thin and no self'shados

To'dimensional te1ture

in!ut !hotogra!h


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Modeling of Cirrus

Use of #loud image as >D te1ture $emoving effe#t of !ers!e#tive transformation by

s!e#ifying #loud !lane

in!ut image#loud image#loud !lane

#irrus #loud te1ture


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#irrus

alto'

#umulus

#umulus

Modeling of alto#umulusModeling of alto#umulus


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"enerating Meta#alls

Converting #loud image into binary image

binary image#loud image


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Distan#e transform of binary image Distan#e from a hite !i1el to the nearest

bla#k !i1el

binary image distan#e image


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binary image


2enerating >D metaballs at hite !i1els Use distan#e for radius of metaball

distan#e image


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Optimi$ing Meta#all %ensit!

Minimizing differen#e beteen #umulativedensity and #loud intensity

= =

N

p

M

l

lpl rfqp1

2

1

min)()(

#loud image #umulative density at !i1el p

#enter density field fun#tion ,%yvill690


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Optimi$ing Meta#all %ensit!

51am!le

#loud image #umulative density image


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pecif!ing Cloud Plane

"ntera#tive s!e#ifi#ation ?rientation of #loud !lane and vieing angle

#loud !lane


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Computing %ensit! %istri#ution

Cal#ulating bounding bo1 of all metaballsSubdividing bounding bo1 into gridCom!uting density at ea#h grid !oint

bounding bo1 density distribution


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#irrus

alto'

#umulus

#umulus

#umulus#umulus


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Computing urface hape

Distan#e transform of binary image51tra#ting medial a1es

i1els here distan#es are lo#al ma1ima

binary image distan#e imagedistan#e image

medial a1is


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Use distan#e at medial a1is as thi#knessof #louds

ro!agate thi#kness by o!timization

#loud image

medial a1is

thi#kness image

#olorization by o!timization ,&evin 9;0


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Constru#ting surfa#e sha!eEssuming symmetri# sha!e ith res!e#t to

image !lane

front vie side vie


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Overview



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Results

Com!uter CPU: Intel Corei7 (3.33 GH)

!"in memor#: $G%

GPU: &'II Ge*orce G+ 2-

Com!utation time

ithin =9 se#onds


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Results

Elto#umulus

in!ut synthesized

in!ut

synthesized


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Results

Cumulus

in!ut

synthesized

in!ut

synthesized


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Results

E #loud s#ene


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Conclusion

Modeling of #louds from a !hotogra!h Methods for three ty!es of #louds

(#irrus< alto#umulus< #umulus)

2enerating realisti# /D #louds similar to thein!ut !hotogra!h

+uture %ork "m!roving sha!es of #umulus #louds


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Photo Cloud Modeling

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