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Texturering & Modeling
                 a Procedual Approach



               ๊น€์ •๊ทผ
Chapter 16
Procedural Fractal Terrains
Agenda
           Advantages Of Point Evaluation

                              The Height Field

         Homogeneous fbm Terrain Models
                                 Fractal Dimension

              Visual Effects Of The Basis Function

            Heterogeneous Terrain Models
                              Statistics By Altitude

                               A Hybrid Multifractal

                 Multiplicative Multifractal Terrains

                                     Conclusion

                                                   3
๋“ค์–ด๊ฐ€๊ธฐ ์ „์—

        โ€œ14์ฑ•ํ„ฐ์—์„œ ๋‹ค๋ฃจ์—ˆ๋“ฏ์ด
๋˜‘๊ฐ™์€ ์ ˆ์ฐจ์  ๊ตฌ์ถ•์„ ํ†ตํ•ด ํ…์Šค์ณ์ฒ˜๋Ÿผ ์‚ฌ์šฉํ•˜๋Š” ๊ฒƒ์ฒ˜๋Ÿผ
     ํ„ฐ๋ ˆ์ธ์„ ์ƒ์„ฑํ•  ๋•Œ๋„ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค.โ€


          โ€œ์ฐจ์ด์ ์€ Function์ด Color ํ˜น์€
    Surface ์†์„ฑ์„ Return ํ•œ ๊ฒƒ์„ ํ•ด์„ํ•˜๋Š” ๋Œ€์‹ 
 altitude(๊ณ ๋„)๋ฅผ Return ํ•˜๊ณ  ๊ทธ๊ฒƒ์„ ํ•ด์„ํ•œ๋‹ค๋Š” ๊ฒƒโ€

                                    4
Advantages
Of Point Evaluation
์ปดํ“จํ„ฐ๊ทธ๋ž˜ํ”ฝ๊ณผ์˜ ๊ด€๊ณ„




              6
์ปดํ“จํ„ฐ๊ทธ๋ž˜ํ”ฝ๊ณผ์˜ ๊ด€๊ณ„
์ดˆ๊ธฐ์ž‘์—…์˜ ์˜ˆ
hexagon subdivision
The Science of Fractal Image ์˜ appendix ์— ์žˆ๋Š” ๋งŒ๋ธ๋ธŒ๋กœ(Mandelbrot)๊ฐ€ ๋ฐœํ‘œ
ํด๋ฆฌ๊ณค ๋ถ„ํ•  ํ„ฐ๋ ˆ์ธ์€ ์‚์ฃฝ์‚์ฃฝํ•œ ํŠน์„ฑ์„ ๊ฐ€์ง€๊ณ  ์žˆ๊ณ ,
(homogeneous ํ”„๋ž™ํƒˆ ์ฐจ์›์œผ๋กœ ์–ด๋””์—์„œ๋‚˜ ํ•ญ์ƒ ๋˜‘๊ฐ™์€ ๊ฑฐ์น ์€ ํŠน์„ฑ์„ ๊ฐ€์ง



Erosion ํŠน์„ฑ์„ ์œ„ํ•œ ์ž‘์—…
ํ„ฐ๋ ˆ์ธ์˜ ์ง€์—ญ ํŠน์„ฑ์„ ๋‹ค์–‘ํ•˜๊ธฐ ์œ„ํ•ด ๋ช‡๊ฐ€์ง€ ๊ฐ€์ •์„ ํ•˜์˜€๊ณ 
๋‘๊ฐœ์˜ ๋ฉ€ํ‹ฐ ํ”„๋ž™ํƒˆ์„ ๊ตฌ์ถ•ํ•จ (additive / multiplicative)




                                                                    7
8
๋ฉ€ํ‹ฐํ”„๋ž™ํƒˆ ํ„ฐ๋ ˆ์ธ
ํ•˜๋‚˜์˜ ๋ฉ€ํ‹ฐ ํ”„๋ž™ํƒˆ ๋ชจ๋ธ
ํ‰์ง€, ์–ธ๋•, ์‚ฐ ์ด ๋ชจ๋‘ ํ•œ ๊ฐœ์˜ ํ”„๋ž™ํƒˆ ๋ชจ๋ธ๋กœ ๋งŒ๋“ค์–ด์ง„ ๊ฒƒ




  โ€œ์ด๋ฏธ ์กด์žฌํ•˜๋Š” ๋ฉ€ํ‹ฐํ”„๋ž™ํƒˆ์„ ๋‹ค์‹œ ๋งŒ๋“œ๋Š๋ผ
        ์‹œ๊ฐ„์„ ๋‚ญ๋น„ํ•จ์„ ๋Š๋‚Œโ€


Perlin ๋…ธ์ด์ฆˆ ๊ธฐ๋ฐ˜ ์ ˆ์ฐจ์  ํ”„๋ž™ํƒˆ ๊ตฌ์ถ•๋ฐฉ๋ฒ•
๊ทœ๋ชจ๋ฅผ ๋ฆฌ์Šค์ผ€์ผํ•˜๊ณ  ๋”ํ•œ๊ฒƒ(Saupe 1989)



                                     9
Point Evaluation
ํ•˜๋‚˜์˜ Point์—์„œ ํ‰๊ฐ€
ํ”„๋ž™ํƒˆ ํ„ฐ๋ ˆ์ธ ์ƒ์„ฑ ์•Œ๊ณ ๋ฆฌ์ฆ˜์—์„œ ๊ฝค๋‚˜ ํฐ์ฐจ์ด์ (ํŠน์„ฑ)



ํด๋ฆฌ๊ณค ๋ถ„ํ• ์—์„œ๋Š”
์ฃผ์–ด์ง„ ๊ณ ๋„๋Š” ์ฃผํŒŒ์ˆ˜๊ฐ€๊ฐ€ ๋” ๋‚ฎ์€ ์ง€์  ์ด์›ƒ๊ทผ๋ฐฉ์ง€์ ๋“ค ์‚ฌ์ด์—์„œ ๋ณด๊ฐ„์„ ํ†ตํ•ด ๊ฒฐ์ •



ํ‘ธ๋ฆฌ์— ํ†ตํ•ฉ์—์„œ
์ „์ฒด ํ„ฐ๋ ˆ์ธ Patch๋Š” ํ•œ๋ฒˆ์— ์ƒ์„ฑ๋˜์–ด์•ผ ํ•˜๋ฉฐ ์–ด๋–ค ์ƒ˜ํ”Œ๋„ ํ˜ผ์ž์„œ ๊ณ ๋ฆฝ๋˜์–ด ๊ณ„์‚ฐ ํ• ์ˆ˜ ์—†์Œ


์ ˆ์ฐจ์  ๋ฐฉ๋ฒ•์—์„œ๋Š”
์ฃผ์–ด์ง„ Point๋กœ๋ถ€ํ„ฐ ์ด์›ƒ๋“ค์„ ์ฐธ์กฐํ•˜์ง€ ์•Š๊ณ  Point ํ‰๊ฐ€๋ฅผ ํ•˜๋Š” ๋…๋ฆฝ์„ฑ์„ ๊ฐ€์ง

                                                 10
11
Point Evaluation
๋˜๋‹ค๋ฅธ ์ฐจ์ด์ (Noise Function)
์–ธ๋•์ด๋‚˜ ์˜ค๋ž˜๋œ ์‚ฐ๊ณผ ๊ฐ™์ด ๋ถ€๋“œ๋Ÿฝ๊ฒŒ ์นจ์‹๋œ ๋ชจ์–‘์„ ํ‘œํ˜„ํ• ์ˆ˜ ์žˆ์Œ




ํด๋ฆฌ๊ณค ๋ถ„ํ• ์—์„œ ํ‘œํ˜„ํ• ๋ ค๋ฉด
Lewis ์˜ "ํ™•๋ฅ ๋ก ์ ์œผ๋กœ ์ƒ์„ฑ๋˜๋Š” ๋ถ„ํ• โ€ ๊ณผ ๊ฐ™์€ ์Šคํ‚ค๋งˆ์˜ ๋ณต์žก์„ฑ์ •๋„์— ๊ธฐ๋Œ€




                                               12
์ ˆ์ฐจ์  ์ ‘๊ทผ์˜
๋˜ ๋‹ค๋ฅธ ์ฐจ๋ณ„์ ์ธ ํŠน์ง•


 โ€œQAEB ๋ Œ๋”๋ง ๋‚ด์—์„œ ์ ์šฉํ• ๋งŒํ•œ LOD๋กœ
 ๋ Œ๋”๋ง์— ํ•„์š”ํ•œ ํ„ฐ๋ ˆ์ธ Geometry๋‚ด์—์„œ
์ ์šฉํ• ๋งŒํ•œ ๋Œ€์—ญ์ด ์ œํ•œ๋œ ์ฃผํŒŒ์ˆ˜๋ฅผ ์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ
         ์ˆ˜์šฉํ•œ๋‹ค๋Š” ์ .โ€



                       13
14
15
16
17
18
19
20
21
22
The Height Field
Height Field


โ€œ์ปดํ“จํ„ฐ ๊ทธ๋ž˜ํ”ฝ์Šค์—์„œ ํ„ฐ๋ ˆ์ธ ๋ชจ๋ธ๋“ค์€
 ์ผ๋ฐ˜์ ์œผ๋กœ Height Field ํ˜•ํƒœ๋ฅผ ๊ฐ€์งโ€




                          24
Height Field
์ผ์ •ํ•œ ๊ฐ„๊ฒฉ๋งˆ๋‹ค ๊ณ ๋„๊ฐ’์ด ์ €์žฅ(2์ฐจ์›๋ฐฐ์—ด)
์„ ๋“ค์ด ๊ต์ฐจํ•˜๋Š” ๋ชจ๋“  ์ง€์ ์— ๋Œ€ํ•œ ๊ณ ๋„๊ฐ’์ด ์ €์žฅ๋˜์–ด ์žˆ๋Š” ๊ทธ๋ž˜ํ”„ ์ข…์ด์˜ ์ผ๋ถ€๋ถ„ ๊ฐ™์€ ๊ฒƒ



Grid Point์—์„œ ๊ณ ๋„๊ฐ’์ด 1:1๋กœ ๋งค์นญ
height field ์—์„œ๋Š” ๋™๊ตด์ด๋‚˜ overhangs ์™€ ๊ฐ™์€ ๊ฒƒ์€ ์กด์žฌํ•  ์ˆ˜ ์—†๋‹ค



     โ€œ์ด๋Ÿฌํ•œ ์ œ์•ฝ์€ ์‰ฝ๊ฒŒ ๊ทน๋ณตํ• ์ˆ˜ ์žˆ๋Š” ๊ฒƒ์ฒ˜๋Ÿผ
           ๋ณด์ด์ง€๋งŒ ๊ทธ๋ ‡์ง€ ์•Š๋‹ค.โ€


                                                   25
26
Non Height Field
QAEB-trace ๋ฅผ ์‚ฌ์šฉ
๋ฌผ๋ณด๋ผ์™€ ๋ฌผ๊ฑฐํ’ˆ์€ QAEB hyper texture



๋ Œ๋”๋ง ์‹œ๊ฐ„์€ ํ•˜๋ฃจ




                               27
Height Field์˜
๊ท ๋“ฑํ•œ ์ƒ˜ํ”Œ(๊ฐ„๊ฒฉ)
ํšจ์œจ์ ์ธ ray-tracing ๋“ค์„ ์ˆ˜์šฉ
grid tracing (Musgrave 1988) ๊ณผ quad tree (์ฟผ๋“œํŠธ๋ฆฌ)(Kajiya 1983) ๊ณต๊ฐ„๋ถ„ํ• 




                                                                    28
ํ„ฐ๋ ˆ์ธ ๋ Œ๋”๋Ÿฌ
Ray-Trace๋ฅผ ์‚ฌ์šฉํ•˜์ง€ ์•Š๋Š” ๋ Œ๋”๋Ÿฌ
VistaPro


Ray-Trace๋ฅผ ์‚ฌ์šฉํ•˜๋Š” World Tool Set
MetaCreations ์˜ Bryce, Animatek World Builder,
                                               ๋ Œ๋”๋Ÿฌ

Grid Tracing
Craig Kolb ์˜ public domain ์ธ Rayshade ray tracer
๊ณ„์ธต์ ์ธ ์ ‘๊ทผ๋ฐฉ์‹์œผ๋กœ Grid Tracing์˜ ๊ฒฝ์šฐ ๋ฉ”๋ชจ๋ฆฌ ์˜ค๋ฒ„ํ—ค๋“œ๊ฐ€ ์ 
๊ณ  ์ฟผ๋“œํŠธ๋ฆฌ ๋ฐฉ์‹์ด ์†๋„๋ฉด์—์„œ ์ด์ ์ด ์žˆ์Œ


PPT ์•Œ๊ณ ๋ฆฌ์ฆ˜(Paglieroni 1994)
๋‚ ๊ณ ์žˆ๋Š” ์ƒํƒœ์™€ ๊ฐ™์€ ๊ฒฝ์šฐ,static ํ•œ height field ๋ฅผ ์—ฌ๋Ÿฌ๋ฒˆ ๋ Œ๋”๋ง ํ•˜๋Š” ๊ฒฝ์šฐ์— ๊ฐ€์žฅ ๋น 
๋ฅด๊ฒŒ ๋ Œ๋”๋ง ํ• ์ˆ˜ ์žˆ์Œ
                                                             29
Height Field ํŒŒ์ผํฌ๋งท
DEM (digital elevation map)

Geological Survey (USGS) height field
๋ฏธ๊ตญ ์ „์ฒด๋ฅผ ํฌํ•จํ•˜๋Š” USGS ๋กœ๋ถ€ํ„ฐ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋Š” "quad" ์ง€ํ˜• ๋งต์— ๋Œ€์‘๋˜๋Š” ์ธก์ •
๋œ ๋ฐ์ดํ„ฐ๋“ค์„ ํฌํ•จ


DTED (digital terrain elevation data)
๋ฏธ๊ตญ ๊ตฐ์‚ฌ๊ธฐ๊ด€, ๋ฏธ๊ตญ ๋ฐ”๊นฅ์˜ ํ„ฐ๋ ˆ์ธ๊ณผ ์˜ํ† ๋“ค๋„ ํ•จ๊ป˜ ํฌํ•จ




                                                     30
Synthesist ์ž…์žฅ์—์„œ

โ€œ์‰ฝ๊ฒŒ ์ด๋Ÿฐ ๋ฐ์ดํ„ฐ๋ฅผ ์ด์šฉํ•ด์„œ ๋ Œ๋”๋งํ•  ์ˆ˜ ์žˆ์ง€๋งŒ
์‹ค์ œ ๋ฐ์ดํ„ฐ๋ฅผ Cheating ํ•ด์„œ ์‚ฌ์šฉํ•  ๊ฒƒ์„ ๊ณ ๋ คโ€
    โ€œ๊ธ์–ด์˜จ ๋ฐ์ดํ„ฐ๋“ค๋กœ๋ถ€ํ„ฐ ์‹ค์ œ์ฒ˜๋Ÿผ
   ์นœ์ˆ™ํ•ด ๋ณด์ด๋„๋ก ํ†ตํ•ฉํ•˜๋Š” ๊ฒƒ์ด ๋ชฉ์ โ€


                             31
Kenton์˜ ํฌ๋งท
์‚ฌ์ง„๋“ค์„ ์ฐ์€ ๊ฒƒ์œผ๋กœ๋ถ€ํ„ฐ ๋ฐ์ดํ„ฐ๋ฅผ ์–ป์Œ

์•„๋ž˜์˜ ์ •๋ณด๋ฅผ ํฌํ•จ
integer ( 4 bytes) ์—๋Š” height field ์˜ ๊ฐ€๋กœ์„ธ๋กœ ํฌ๊ธฐ๋ฅผ ๊ธฐ์ˆ 
๊ทธ๋‹ค์Œ n^2 floats (๊ฐ๊ฐ 4 bytes) ์—์„œ n ์€ leading integer ๊ฐ’
์ €์žฅํ•˜๊ณ  ์‹ถ์€ ์ถ”๊ฐ€์ ์ธ ๋ฐ์ดํ„ฐ- height field ์˜ ์ตœ๋Œ€ ์ตœ์†Œ๊ฐ’๊ณผ ๊ฐ™์€๊ฒƒ๊ณผ random
number ์ƒ์„ฑ์— ํ•„์š”ํ•œ seed ๊ฐ™์€๊ฒƒ๋“ค์„ ์ถ”๊ฐ€๋กœ ํฌํ•จ
๋˜‘๊ฐ™์€ ๋ฐ์ดํ„ฐ์— ์•„์Šคํ‚ค ํฌ๋งท๋ณด๋‹ค๋Š” ๋ณด๋‹ค ์••์ถ•๋œ ๊ฒƒ๋“ค์„ ์‚ฌ์šฉํ•  ์ˆ˜๋„ ์žˆ์„ ๊ฒƒ์ด๊ณ , ์ด๋Ÿฐ
์ด์œ ๋“ค๋กœ ์ €์žฅ๊ณต๊ฐ„ ๊ตฌ์กฐ์— ์ผ๋ถ€ ๋น„ํšจ์œจ์ ์ธ ๋ถ€๋ถ„๋“ค์ด ์กด์žฌ




                                                       32
Matt Pharr์˜ ๊ฐœ์„ ๋œ ํฌ๋งท

600 byte ํ—ค๋”๋ธ”๋Ÿญ์ด ์กด์žฌ

๊ณ ๋„ ๋ฐ์ดํ„ฐ๋Š” short(2 byte) ๋กœ ์ €์žฅ
์ด ๋ฐ์ดํ„ฐ๋“ค์€ normalize ๋˜๊ณ  0 ๋ถ€ํ„ฐ 2^16 -1 ๋ฒ”์œ„์˜ ์ •์ˆ˜๋กœ ์–‘์žํ™”


๊ณ ๋„์˜ ์ตœ์†Œ, ์ตœ๋Œ€๊ฐ’๋„ ์ €์žฅ
๊ณ ๋„๊ฐ’์€ ๋ Œ๋”๋งํƒ€์ž„๋•Œ ๋ณ€ํ™˜์— ์˜ํ•ด ๋ถ€๋™์†Œ์ˆ˜์ ์œผ๋กœ ๋‹ค์‹œ ์ €์žฅ๋ ์ˆ˜ ์žˆ๊ธฐ๋•Œ๋ฌธ




                                                33
Matt Pharr์˜ ๊ฐœ์„ ๋œ ํฌ๋งท



 a ๊ฐ€ quantized(์–‘์žํ™”)๋˜๊ณ , ํ•ด๋ฐœ๊ณ ๋„ ๊ฐ’์„ scale

       z ๋Š” ๋ถ€๋™ ์†Œ์ˆ˜์  ๊ฐ’์œผ๋กœ ๋””์ฝ”๋”ฉ

   zmin ๊ณผ zmax ๋Š” height field ์˜ min/max ๊ฐ’

                                            34
Matt Pharr์˜ ๊ฐœ์„ ๋œ ํฌ๋งท

big-endian/little-endian
byte-order ๋ฌธ์ œ๋„ ์ œ๊ฑฐ
32-bit ์™€ 64-bit ์šด์˜์ฒด์ œ๊ฐ„์—๋„ ๋ฐ”์ด๋„ˆ๋ฆฌํŒŒ์ผ์„ ์•Œ์•„์„œ ์ž๋™์œผ๋กœ ์ „์†กํ•  ์ˆ˜ ์žˆ์„๋ฟ๋งŒ
์•„๋‹ˆ๋ผ, ์„œ๋กœ ๋‹ค๋ฅธ ์ปดํ“จํ„ฐ๊ฐ„์— ๋ฐ”์ด๋„ˆ๋ฆฌ ํŒŒ์ผ๋“ค์„ ์ „์†กํ•  ๋•Œ์—๋„ ๋ฌธ์ œ์—†์Œ




                                                      35
Homogeneous
fbm Terrain Models
37
์ปดํ“จํ„ฐ ๊ทธ๋ž˜ํ”ฝ์Šค์—์„œ ํ”„๋ž™ํƒˆ ์‚ฐ์˜ ๊ธฐ์›์€
๋งŒ๋ธ๋ธŒ๋กœ๊ฐ€ fBm์„ ๊ฐ€์ง€๊ณ  1์ฐจ์›(ํ˜น์€ 1.xx ์ฐจ์›์—์„œ)
        ๊ฐ™์ด ์ž‘์—…ํ•œ ๊ฒƒ์—์„œ ์‹œ์ž‘




                              38
HOMOGENEOUS
fBm TERRAIN MODELS




    โ€œ์ด ํ•จ์ˆ˜์˜ Trace๋Š” ๋พฐ์กฑํ•œ ์‚ฐ์—
   ๋งž๋‹ฟ์€ Skyline ๋‹ฎ์•˜๋‹ค๋Š” ๊ฒƒ์— ์ฃผ๋ชฉโ€


                             39
HOMOGENEOUS
fBm TERRAIN MODELS


       โ€œ์œ ์ „์ž ๋ชจ๋ธ๋ง์— ๋น„์ถ”์–ด๋ณด์ž๋ฉด,
   ๋งŒ์•ฝ ์ด function์ด 2์ฐจ์›์œผ๋กœ ํ™•์žฅ๋๋‹ค๋ฉด,
๊ทธ ๊ฒฐ๊ณผ surface๋Š” ์‚ฐ๊ณผ ๋‹ฎ์„๊ฒƒ ์ž„์ด ํ‹€๋ฆผ์—†๋‹ค๊ณ  ์œ ์ถ”โ€




                               40
41
42
์ปดํ“จํ„ฐ ๊ทธ๋ž˜ํ”ฝ์Šค์—์„œ ํ”„๋ž™ํƒˆ ์‚ฐ์˜ ๊ธฐ์›์€
๋งŒ๋ธ๋ธŒ๋กœ๊ฐ€ fBm์„ ๊ฐ€์ง€๊ณ  1์ฐจ์›(ํ˜น์€ 1.xx ์ฐจ์›์—์„œ)
        ๊ฐ™์ด ์ž‘์—…ํ•œ ๊ฒƒ์—์„œ ์‹œ์ž‘




                              43
โ€œ์‹ค์ œ ์‚ฐ์˜ ๋ชจ์–‘์ด ํ”„๋ž™ํƒˆ ํ•จ์ˆ˜์˜
   ํ˜•ํƒœ ์‚ฌ์ด์— ์•Œ๋ ค์ง„ ์ธ๊ณผ ๊ด€๊ณ„๋ฅผ ์—†๋‹ค.โ€

 โ€œ๊ทธ ํ•จ์ˆ˜๊ฐ€ ๋งˆ์นจ ์‚ฐ๋“ค๊ณผ ๋‹จ์ˆœํžˆ ๋‹ฎ์•„๋ณด์ธ๊ฑฐ๊ณ 
     ํ•ด๋ณด๋‹ˆ ์‹ค์ œ๋กœ๋„ ๋„ˆ๋ฌด ๋งŽ์ด ๋‹ฎ์€๊ฒƒโ€

โ€œ๋ฌผ๋ก  ์‹ค์ œ ์‚ฐ๋“ค์„ ๋ณด๋ฉด, ๋งŽ์€ ํŠน์ง•๋“ค์ด ์กด์žฌ ํ•˜์ง€๋งŒ
  (ํ•˜๊ณ„๋ง์ด๋ผ๋“ ์ง€ ์นจ์‹๋œ ๋ถ€๋ถ„๋“ค์— ๋Œ€ํ•œ ํŠน์ง•)

์ด ์ฒซ๋ฒˆ์งธ ๋ชจ๋ธ์—๋Š” ๊ทธ๋Ÿฌํ•œ ํŠน์ง•์€ ์กด์žฌํ•˜์ง€ ์•Š์Œโ€œ


                            44
Fractal Demension

    โ€œํ”„๋ž™ํƒˆ ์ฐจ์›์€ ํ‘œ๋ฉด์˜ ๊ฑฐ์น ๊ธฐ์˜
       ์ฒ™๋„๋กœ ์ƒ๊ฐ ๋  ์ˆ˜ ์žˆ๋‹คโ€


     โ€œํ”„๋ž™ํƒˆ ์ฐจ์›์ด ์ปค์งˆ์ˆ˜๋ก,
    ์„œํ”ผ์Šค(ํ‘œ๋ฉด)๋„ ๋” ๊ฑฐ์น ์–ด์ง„๋‹คโ€


                        45
46
์ด ํŒจ์น˜์— ๋Œ€ํ•ด ๊ฐ€์žฅ ํฅ๋ฏธ๋กœ์šด ๊ฒƒ์€,
์™ผ์ชฝ์€ ์™„๋งŒํ•ด๋ณด์—ฌ๋„ ์™„์ „ํžˆ ํ‰ํ‰ํ•˜์ง€๋Š” ์•Š๊ณ 
      ์˜ค๋ฅธ์ชฝ์€ ๋นผ๊ณกํ•ด๋ณด์—ฌ๋„
 ๊ณต๊ฐ„์„ ์™„์ „ํžˆ ๋‹ค ์ฑ„์šฐ๊ณ  ์žˆ์ง€๋Š” ์•Š๋‹ค๋Š” ๊ฒƒ




                           47
๊ทธ๋ž˜์„œ ์šฐ๋ฆฌ๋Š” fBm ์— ๋Œ€ํ•œ ํ”„๋ž™ํƒˆ ์ฐจ์›์˜
 ๊ณต์‹์ ์ธ ์ •์˜๋Š” ๊ตฌ์กฐ์—์„œ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋Š”
   ์œ ์šฉํ•œ ํ”„๋ž™ํƒˆ ํ–‰๋™์„ ๋ชจ๋‘ ์บก์ฒ˜
    ํ•˜์ง€ ์•Š๋Š” ๋‹ค๋Š” ๊ฒƒ์„ ์•Œ์ˆ˜ ์žˆ๋‹ค

   ์ด patch ์˜ ์™ผ์ชฝ ๋์— ํ‘œํ˜„๋˜๋Š”
    ์™„๋งŒํ•œ ํ‰์›๊ณผ ๊ฐ™์€ ์ข…๋ฅ˜๋“ค์€
  ์‚ฌ์‹ค ์ž๊ธฐ์œ ์‚ฌ์„ฑ์„ ๊ฐ€์ง€๊ธฐ ๋•Œ๋ฌธ์—,
์šฐ๋ฆฌ์˜ ์ฒดํ—˜์ ์ธ "ํ”„๋ž™ํƒˆ" ์ •์˜์— ๋“ค์–ด๋งž๋Š”๋‹ค.


                            48
๊ทธ๋Ÿฌ๋‚˜ ์ด ํ‰์›๋“ค์€
  ํ”„๋ž™ํƒˆ ์ฐจ์›์˜
๊ณต์‹ ์ˆ˜ํ•™ ์ •์˜์—๋Š”
 ๋“ค์–ด๋งž์ง€ ์•Š๋Š”๋‹ค.



             49
์ •์˜๋‚ด๋ฆฌ๊ธฐ ํž˜๋“  โ€œํ”„๋ž™
ํƒˆโ€
   โ€œ์ด๊ฒƒ์€ ํ”„๋ž™ํƒˆ๋“ค์ด ์ •ํ™•ํ•œ ์ •์˜๋ฅผ
๋‚ด๋ฆฌ๊ธฐ๊ฐ€ ์–ผ๋งˆ๋‚˜ ํž˜๋“ ๊ฒƒ์ธ์ง€๋ฅผ ๋ณด์—ฌ์ฃผ๋Š” ์ข‹์€ ์˜ˆโ€


  โ€œ์šฐ๋ฆฌ๋Š” ๋•Œ๋•Œ๋กœ ์•„์ฃผ ํญ์ด ๋„“์€ ๋ธŒ๋Ÿฌ์‰ฌ๋ฅผ
     ๊ฐ€์ง€๊ณ  ํŽ˜์ธํŠธํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค.
     ์™œ๋ƒํ•˜๋ฉด ๊ตณ์ด ๊ด€๋ จ๋œ ํ˜„์ƒ์„
      ์ œ์™ธ์‹œํ‚ฌ ํ•„์š”๊ฐ€ ์—†๊ธฐ๋•Œ๋ฌธโ€

                           50
์ •์˜๋‚ด๋ฆฌ๊ธฐ ํž˜๋“  โ€œํ”„๋ž™
ํƒˆโ€
 โ€œ์ปดํ“จํ„ฐ ๊ทธ๋ž˜ํ”ฝ์Šค์˜ ๋งŽ์€ ์—ฐ๊ตฌ๊ฐ€๋“ค๊ณผ
 ๋‹ค๋ฅธ ๋ถ„์•ผ์‚ฌ๋žŒ๋“ค์€ "fractal" ์— ๋Œ€ํ•˜์—ฌ
      "stochastic"(ํ™•๋ฅ ์ ์ธ) ๊ณผ
    "self-similar"(์ž๊ธฐ์œ ์‚ฌ์„ฑ) ๊ณผ
   ๊ฐ™์€ ์šฉ์–ด๋“ค๋กœ ๋Œ€์ฒดํ•˜์—ฌ ์‚ฌ์šฉโ€

 โ€œ๊ณต์‹ ์ •์˜๊ฐ€ ๋“ค์–ด๋งž๋Š”๊ฒŒ ์—†๊ธฐ๋•Œ๋ฌธ์ธ๋ฐ,
  ์ด๊ฒƒ๋„ ๊ทธ๋ ‡๊ฒŒ ์ ๋‹นํ•œ ์šฉ์–ด๋Š” ์•„๋‹๊ฒƒโ€

                              51
์ •์˜๋‚ด๋ฆฌ๊ธฐ ํž˜๋“  โ€œํ”„๋ž™
ํƒˆโ€
   โ€œ๋น„์ฅฌ์–ผ์ ์œผ๋กœ ์ž์—ฐ ํ˜„์ƒ์— ๋Œ€ํ•œ
 ์œ ์šฉํ•œ srochastic(ํ™•๋ฅ ) ๋ชจ๋ธ๋“ค ์ค‘์—์„œ
์ž๊ธฐ ์œ ์‚ฌ์„ฑ ํŠน์ง•์ด ์—†๋Š” ๋ชจ๋ธ๋“ค์€ ๊ฑฐ์˜ ์—†๊ณ 
์ž๊ธฐ์œ ์‚ฌ์„ฑ ๋ชจ๋ธ๋“ค์€ ์ง€์†์ ์ด์ง„ ์•Š๋”๋ผ๋„
       ๊ณต์‹์ ์œผ๋กœ ํ™•์‹คํ•œ,
ํ”„๋ž™ํƒˆ๋กœ์จ ๊ฐ€์žฅ ๊ทธ ์„ฑ์งˆ์„ ์ž˜ ํ‘œํ˜„ํ•œ ๋ชจ๋ธโ€



                              52
Visual Effects of
 the Basis Function

    โ€œ์ปดํ“จํ„ฐ ๊ทธ๋ž˜ํ”ฝ์— ๋Œ€ํ•œ ์ž„์˜์˜ ํ”„๋ž™ํƒˆ์„ ๋งŒ๋“ค ๋•Œ
             ์‚ฌ์šฉํ•œ ์ž‘์€ ์ŠคํŽ™ํŠธ๋Ÿผ ํ•ฉ๊ณ„๋Š”
Basis Function์˜ ํŠน์„ฑ์ด ๊ฒฐ๊ณผ์— ๋ช…ํ™•ํ•˜๊ฒŒ ํ‘œ์‹œํ•  ์ˆ˜ ์žˆ์Œโ€



 โ€œ๋ณดํ†ต, ๊ธฐ์ € ํ•จ์ˆ˜์˜ ์„ ํƒ์€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์—์„œ ์ž„ํ”Œ๋ฆฌ์‹œํŠธโ€


                                  53
Visual Effects of
the Basis Function

     โ€œํ‘ธ๋ฆฌ์— ํ†ตํ•ฉ์„ ์œ„ํ•œ ์‚ฌ์ธ ๊ณก์„ ,
  ํด๋ฆฌ๊ณค ์„œ๋ธŒ๋””๋น„์ ผ์˜ ํ•˜๋‚˜์˜ ํ†ฑ๋‹ˆ๋ฐ”ํ€ด ๊ณก์„ 
   ๊ทธ๋ฆฌ๊ณ  Noise์˜ Cubic Hermite Spline
        ๊ธฐ๋ฐ˜์˜ ์ ˆ์ฐจ์  fBMโ€




                                     54
Visual Effects of
the Basis Function
Walsh ๋ณ€ํ™˜
Basis Function์œผ๋กœ square waves๋ฅผ ์–ป์„์ˆ˜ ์žˆ์Œ




                                        55
Visual Effects of
the Basis Function
Wavelets (Ruskai 1992)
์œ ํ•œํ•œ Basis Function์œผ๋กœ ๊ฐ•๋ ฅํ•œ ์ƒˆ๋กœ์šด ์„ธํŠธ๋ฅผ ์ œ๊ณต




                                      56
Visual Effects of
the Basis Function
sparse convolution (Lewis 1989)
fractal sum of pulses
(Lovejoy and Mandelbrot 1985)
Basis Function ์„ ํƒ์— ํฐ ์œ ์—ฐ์„ฑ์„ ์ œ๊ณต


   โ€œ์ด๋Ÿฌํ•œ ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜๋ฉด ์ง€ํ˜•์˜ sinkholes๋กœ
          ์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ ์ด์–ด์ง€๋Š”,
       Basis Function๋กœ ๊ทน๋‹จ์ ์œผ๋กœ
         ์‚ฌ์‹ค์ ์ธ ํ”„๋กœํ•„์„ ์‚ฌ์šฉโ€
                                  57
Visual Effects of
the Basis Function
Gavin Miller (1986)
subdivision ์˜ ๋” ์ผ๋ฐ˜์ ์ธ ํ˜•์‹์˜ ํ„ฐ๋ ˆ์ธ ๊ตฌ์กฐ์—์„œ ์ฃผ๋ฆ„์ด ์žกํžŒ ๊ฒƒ์„ ๋ณด์—ฌ์คŒ
์„œ๋ธŒ๋””๋น„์ ผ ์•Œ๊ณ ๋ฆฌ์ฆ˜์•ˆ์— implicit ๋ณด๊ฐ„๋ฒ•์˜ ์•„ํ‹ฐํŒฉํŠธ์˜€์Œ



     โ€œํ•˜์ง€๋งŒ Basis Function์˜ ํŠน์„ฑ๊ณผ
fBm๊ตฌ์กฐ์•ˆ์˜ Implicit ํ•œ Basis Function์ด๋ผ๋Š” ๊ฒƒ์„
           ๋Œ€๋ถ€๋ถ„ ๊ฐ„๊ณผํ•œ๊ฒƒโ€
       โ€œํŠน์ • ๋ฏธ์ ํšจ๊ณผ๋ฅผ ์–ป์„๋•Œ
์ด๋Ÿฐ ์ธ์‹(์•„ํ‹ฐํŒฉํŠธ๋ฅผ ์ด์šฉํ•˜๋Š”)๋ฅผ ์‚ฌ์šฉํ• ์ˆ˜ ์žˆ์Œโ€

                                                  58
Visual Effects of
the Basis Function
Hermite spline ๋ณด๊ฐ„
๋…ธ์ด์ฆˆ ํ•จ์ˆ˜์—์„œ Hermite spline ๋ณด๊ฐ„๋ฒ•์˜ ๋ถ€๋“œ๋Ÿฌ์›€์€ ์šฐ๋ฆฌ๊ฐ€ ๋” ๋งŽ์ด ์ด์ „์— ์ปดํ“จํ„ฐ
๊ทธ๋ž˜ํ”ฝ์—์„œ ๋ณผ ์ˆ˜์žˆ๋Š” ๊ฒƒ๋ณด๋‹ค ์„ธ๋ จ๋œ ์ง€ํ˜•์„ ์ƒ์„ฑํ• ์ˆ˜ ์žˆ๊ฒŒ ํ•จ




                                                      59
60
61
62
Visual Effects of
the Basis Function
Ridged Basis Function
๊ฐ€ํŒŒ๋ฅธ ์‚ฐ๋“ฑ์„ฑ์ด์˜ ๋Šฅ์„ ์™€ ์ง€ํ˜•์„ ์ƒ์„ฑํ•˜๋Š” ๋ฐ ์‚ฌ์šฉ




                              63
64
65
66
67
68
69
70
Heterogeneous
Terrain Models
Heterogeneous
Terrain Models
Even Vossโ€™s heterogeneous terrains              (Voss 1988)
๊ท ๋“ฑํ•œ ํ”„๋ž™ํƒˆ ์ฐจ์›์˜ ํ•˜๋‚˜์˜ ์„œํ”ผ์Šค์˜ ๊ฐ„๋‹จํ•œ ์ง€์ˆ˜ํ•จ์ˆ˜์ ์ธ ์Šค์ผ€์ผ๋ง์„ ํ‘œํ˜„


์‹ค์ œ ๋žœ๋“œ์„ธ์ดํ”„ ์•„์ฃผ heterogeneous
ํŠนํžˆ ์ง€๋‚˜์น˜๊ฒŒ ํฐ ์Šค์ผ€์ผ(์˜ˆ๋ฅผ ๋“ค์–ด ํ‚ฌ๋กœ๋ฏธํ„ฐ)
์ƒ๋Œ€์ ์œผ๋กœ ํ”Œ๋žซํ•œ ํ‰์›์—์„œ ๋กํ‚ค์‚ฐ์˜ ๋“œ๋ผ๋งˆํ‹ฑํ•œ ์ƒ์Šน์„ ๋ณผ์ˆ˜ ์žˆ์Œ
๋กœํ‚ค ์‚ฐ๋งฅ, Sierras, ๊ทธ๋ฆฌ๊ณ  ์•Œํ”„์Šค์™€ ๊ฐ™์€ ๋†’์€ ๋ฒ”์œ„๋Š” ์ผ๋ฐ˜์ ์œผ๋กœ ๋น™ํ•˜๋กœ ์•Œ๋ ค์ง„ ๊ฑฐ
๋Œ€ํ•œ ์ง€๊ตฌ์˜ ์›€์ง์ž„์— ์˜ํ•ด ์ฃผ๋กœ ํ˜•์„ฑ๋œ ๋กค๋ง ํ’‹ํž์„ ๊ฐ€์ง„๋‹ค.
๋ชจ๋“  ์ž์—ฐ์ ์ธ ์ง€ํ˜•์€ ์•„๋งˆ๋„ ์ตœ๊ทผ์˜ ํ™”์‚ฐ ๊ฒƒ๋“ค์„ ์ œ์™ธํ•˜๊ณ , ์นจ์‹ ํ˜„์ƒ์ด ์žˆ์„๊ฒƒ




                                                      72
73
Heterogeneous
Terrain Models

       โ€œ์นจ์‹ ๊ทธ๋ฆฌ๊ณ  ๊ตฌ์กฐ ์ง€์งˆํ•™์€
      ๋˜๋‹ค๋ฅธ ํ™”์‚ฐ ์ž‘์šฉ์— ์˜ํ•œ ํŠน์„ฑ,
์ถฉ๋Œ ๋ถ„ํ™”๊ตฌ, ๊ทธ๋ฆฌ๊ณ  ์ƒ๋ฌผ ๊ต๋ž€(์ธ๊ฐ„์„ ํฌํ•จํ•œ)์— ์˜ํ•œ
          ๋‹ค์–‘ํ•œ ํŠน์„ฑ ๋“ค๋ณด๋‹ค
์šฐ๋ฆฌ ํ–‰์„ฑ์˜ ๊ฑฐ์˜ ๋ชจ๋“  ์ง€ํ˜•ํ•™์˜ ํŠน์ง•์— ์ฑ…์ž„์ด ์žˆ์Œโ€




                            74
Heterogeneous
Terrain Models

   โ€œ์นจ์‹ ๊ธฐ๋Šฅ์€ ๋ชจ๋ธ๋กœ ํ‘œํ˜„ํ•˜๊ธฐ ์šฉ์ดํ•˜๋‹คโ€




                            75
76
Heterogeneous
Terrain Models

 โ€œ์ด ์žฅ์˜ ๋‚˜๋จธ์ง€๋Š” ์ง€๋‚˜์น˜๊ฒŒ ์›๋ž˜ fBm ๋ชจ๋ธ์˜
  ์šฐ์•„ํ•จ๊ณผ ์ „์‚ฐ ํšจ์œจ์„ฑ์„ ์†์ƒ์‹œํ‚ค์ง€ ์•Š๊ณ ,
ํŠน์ • ์นจ์‹ ๊ธฐ๋Šฅ์˜ ์ฒซ ๋ฒˆ์งธ ๊ทผ์‚ฌ์น˜๋ฅผ ์–ป์„ ์ˆ˜ ์žˆ๋„๋ก
    ๊ณ ์•ˆ๋œ ํŠน์ • ontogenetic ๋ชจ๋ธ์„ ์„ค๋ช…โ€




                             77
Statistics by Altitude

    โ€œ์ €์ง€๋Œ€๋Š” ํ™๋”๋ฏธ์™€ ํ•จ๊ป˜ ์ฑ„์›Œ์ ธ
  ์ง€ํ˜•์ ์œผ๋กœ ๋ถ€๋“œ๋Ÿฌ์›Œ์ง€๋Š” ๊ฒฝํ–ฅ์ด ์žˆ์œผ๋ฉฐ
    ๋ฐ˜๋ฉด ๋ถ€์‹ ๊ณผ์ •์€ ๋†’์€ ์ง€์—ญ๋ณด๋‹ค
   ๋“ค์ญ‰๋‚ ์ญ‰ํ•จ์„ ์œ ์ง€ํ•˜๋Š” ๊ฒฝํ–ฅ์ด ์žˆ๋‹คโ€




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A Hybrid Multifractal

  โ€œ๊ณ„๊ณก๋“ค์ด ๋ชจ๋“  ๊ณ ๋„์—์„œ ๋ถ€๋“œ๋Ÿฌ์šด ๋ฐ”๋‹ฅ์„
     ๊ฐ€์ง€๊ณ  ์žˆ์–ด์•ผ ํ• ๊ฒƒ์ด๋ผ๊ณ  ํ–ˆ๋‹ค.
       ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ํ•ด์ €๋ ˆ๋ฒจ๋„
    ์ดํ•ฉ์— ์ด์ „ ์ฃผํŒŒ์ˆ˜์˜ ๋กœ์ปฌ ๊ฐ’์œผ๋กœ
    ์Šค์ผ€์ผ๋งํ•˜์—ฌ ๋งŒ๋“  ๋” ๋†’์€ ์ฃผํŒŒ์ˆ˜๋กœ
      ์™„์„ฑ๋  ์ˆ˜ ์žˆ์„๊ฑฐ๋ผ ์ƒ๊ฐํ–ˆ๋‹คโ€


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์งˆ์ ์œผ๋กœ ํ„ฐ๋ ˆ์ธ ํŒจ์น˜๋Š”
ํ†ต๊ณ„ ํŒจ์น˜์™€ ๋งค์šฐ ์œ ์‚ฌํ•œ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚จ




                        92
Multiplicative
Multifractal Terrains

 โ€œ์šฐ๋ฆฌ์˜ ๊ณต์‹? ์ฆ‰, ์˜ˆ์ˆ ์ด๋ผ๊ธฐ ๋ณด๋‹ค๋Š” ์ˆ˜ํ•™์ ์ธ?
      ์ด๋Ÿฌํ•œ multifractal ์ง€ํ˜• ๋ชจ๋ธ์˜
     ์ˆ˜ํ•™์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ๋งค์šฐ ์ดˆ๊ธฐ์ด๋ฉฐ,
๊ทธ๋ž˜์„œ ๋ณด๊ณ ํ•˜๊ธฐ ์œ„ํ•ด ์‚ฌ์šฉํ•˜๋Š” ๊ฒƒ์ด ์กฐ๊ธˆ ๋ฐ–์— ์—†๋‹คโ€




                            93
Multiplicative
Multifractal Terrains

         โ€œmultifractal construction๋Š”
  ์ผ๋ถ€ ํ˜ธ๊ธฐ์‹ฌ์„ ๋„๋Š” ์†์„ฑ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋Š”๋ฐ
 ์Šค์ผ€์ผ์˜ ๊ฐ’ ์ฒ˜๋Ÿผ ์ œ๋กœ๋ถ€ํ„ฐ ๋ฌดํ•œ์œผ๋กœ ๊ฐ€๊ณ  ์žˆ๊ณ ,
ํ•จ์ˆ˜๋Š” highly heterogeneous (0)๋กœ๋ถ€ํ„ฐ flat(๋ฌดํ•œ๋Œ€
        ๋กœ ๋ถ„๊ธฐํ•˜๋Š”)์œผ๋กœ ๊ฐ€๊ณ  ์žˆ๋‹คโ€




                                       94
๋‹น๋ถ„๊ฐ„์€
   โ€œ์ˆœ์ˆ˜ํ•œ multifractal ํ•จ์ˆ˜๋ฅผ
  ์‚ฌ์šฉํ•˜๋ ค๊ณ  ์‹œ๋„ํ•˜๋Š” ๊ฒƒ ๋ณด๋‹ค๋Š”

 ์ด ์žฅ์—์„œ ์ œ์‹œ๋œ ๋‘ ํ•˜์ด๋ธŒ๋ฆฌ๋“œ
 additive / multiplicative multifractal
   ๊ตฌ์กฐ์™€ ํ•จ๊ป˜ ์žˆ์–ด์•ผ ์ตœ์ƒ์˜ ๊ฒƒโ€



                                          95
๊ฒฐ๋ก 
 โ€œํ”„๋ž™ํƒˆ์€ ์šฐ๋ฆฌ๊ฐ€ ์‚ฌ๋Š” ์„ธ๊ณ„๋ฅผ ๋ฌ˜์‚ฌ์˜
    ๋งˆ์ง€๋ง‰ ๋‹จ์–ด ์ผ์ˆ˜๋Š” ์—†์ง€๋งŒ,
๊ทธ๋“ค์€ ํ†ตํ•ฉ ์ด๋ฏธ์ง€์— ๋Œ€ํ•œ ์‹œ๊ฐ์  ๋ณต์žก์„ฑ์˜
   ์šฐ์•„ํ•œ ์†Œ์Šค์„ ์ œ๊ณตํ•  ์ˆ˜ ์žˆ๋‹คโ€




                          96
๊ฒฐ๋ก 
  โ€œ์ž์—ฐ ํ˜„์ƒ์˜ ํ”„๋ž™ํƒˆ ๋ชจ๋ธ์˜ ์ •ํ™•๋„๋Š”
  ๋‹ค์†Œ ๋ฌผ๋ฆฌ์ , ํŠน์„ฑ ๋ณด๋‹ค๋Š” ontogeneticํ•จ

     ๊ฝค ์ž˜ ํ˜•ํƒœํ•™์  ๋ฐ˜์˜์ด์ง€๋งŒ
         ์šฐ๋ฆฌ๊ฐ€ ์•„๋Š” ํ•œ,
๋ฌผ๋ฆฌ์  ๋ฒ•์น™์˜ ์ฒซ ๋ฒˆ์งธ ์›์น™์—์„œ ๋ฐœํ–‰ํ•˜์ง€ ์•Š์Œโ€



                                97
์•ž์œผ๋กœ
โ€œํ„ฐ๋ธ”๋Ÿฐ์Šค๋Š” ์•„์ง ๋ชจ๋‘์˜ ๋งŒ์กฑ์„ ์œ„ํ•ด ํšจ์œจ์ ์ธ ์ ˆ์ฐจ
๋ชจ๋ธ๋กœ ํ•ด์•ผ ํ• ๊ฒƒ์ด ์žˆ์œผ๋ฉฐ multifractals๋ฅผ ์ดํ•ดํ•˜๊ณ 
        ์ปดํ“จํ„ฐ ๊ทธ๋ž˜ํ”ฝ์— ์ ์šฉโ€

        โ€œ์ตœ๊ณ ์˜ ํ†ตํ•ฉ ์ด๋ฏธ์ง€๋ฅผ
"์ง์ ‘์ ์œผ๋กœ ์šฐ๋ฆฌ์˜ ๊นŠ์ด์˜ ๋ฌด์–ธ๊ฐ€๋ฅผ ๋ฐ˜์˜ํ•˜๋Š” ๊ฒƒ์ธ
        ์ง€?" "๋ฌด์—‡์ด ๋ถ€์กฑํ•œ์ง€"
 โ€œ์šฐ๋ฆฌ๊ฐ€ ์‚ฌ๋Š” ์ด ์„ธ์ƒ์„ ์ดํ•ดํ•˜๋Š”๋ฐ ์‚ฌ์šฉ๋˜๋Š”์ง€"
              ์ƒ๊ฐโ€
                                 98
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120119 ch 7_time_and_temporal_manipulations
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120106 ch 5_basic image compositing_re
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111118 ch 4_basic image manipulation_web
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111028 ch 3_the digital representation of visual information
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20120824

  • 1. Texturering & Modeling a Procedual Approach ๊น€์ •๊ทผ
  • 3. Agenda Advantages Of Point Evaluation The Height Field Homogeneous fbm Terrain Models Fractal Dimension Visual Effects Of The Basis Function Heterogeneous Terrain Models Statistics By Altitude A Hybrid Multifractal Multiplicative Multifractal Terrains Conclusion 3
  • 4. ๋“ค์–ด๊ฐ€๊ธฐ ์ „์— โ€œ14์ฑ•ํ„ฐ์—์„œ ๋‹ค๋ฃจ์—ˆ๋“ฏ์ด ๋˜‘๊ฐ™์€ ์ ˆ์ฐจ์  ๊ตฌ์ถ•์„ ํ†ตํ•ด ํ…์Šค์ณ์ฒ˜๋Ÿผ ์‚ฌ์šฉํ•˜๋Š” ๊ฒƒ์ฒ˜๋Ÿผ ํ„ฐ๋ ˆ์ธ์„ ์ƒ์„ฑํ•  ๋•Œ๋„ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค.โ€ โ€œ์ฐจ์ด์ ์€ Function์ด Color ํ˜น์€ Surface ์†์„ฑ์„ Return ํ•œ ๊ฒƒ์„ ํ•ด์„ํ•˜๋Š” ๋Œ€์‹  altitude(๊ณ ๋„)๋ฅผ Return ํ•˜๊ณ  ๊ทธ๊ฒƒ์„ ํ•ด์„ํ•œ๋‹ค๋Š” ๊ฒƒโ€ 4
  • 7. ์ปดํ“จํ„ฐ๊ทธ๋ž˜ํ”ฝ๊ณผ์˜ ๊ด€๊ณ„ ์ดˆ๊ธฐ์ž‘์—…์˜ ์˜ˆ hexagon subdivision The Science of Fractal Image ์˜ appendix ์— ์žˆ๋Š” ๋งŒ๋ธ๋ธŒ๋กœ(Mandelbrot)๊ฐ€ ๋ฐœํ‘œ ํด๋ฆฌ๊ณค ๋ถ„ํ•  ํ„ฐ๋ ˆ์ธ์€ ์‚์ฃฝ์‚์ฃฝํ•œ ํŠน์„ฑ์„ ๊ฐ€์ง€๊ณ  ์žˆ๊ณ , (homogeneous ํ”„๋ž™ํƒˆ ์ฐจ์›์œผ๋กœ ์–ด๋””์—์„œ๋‚˜ ํ•ญ์ƒ ๋˜‘๊ฐ™์€ ๊ฑฐ์น ์€ ํŠน์„ฑ์„ ๊ฐ€์ง Erosion ํŠน์„ฑ์„ ์œ„ํ•œ ์ž‘์—… ํ„ฐ๋ ˆ์ธ์˜ ์ง€์—ญ ํŠน์„ฑ์„ ๋‹ค์–‘ํ•˜๊ธฐ ์œ„ํ•ด ๋ช‡๊ฐ€์ง€ ๊ฐ€์ •์„ ํ•˜์˜€๊ณ  ๋‘๊ฐœ์˜ ๋ฉ€ํ‹ฐ ํ”„๋ž™ํƒˆ์„ ๊ตฌ์ถ•ํ•จ (additive / multiplicative) 7
  • 8. 8
  • 9. ๋ฉ€ํ‹ฐํ”„๋ž™ํƒˆ ํ„ฐ๋ ˆ์ธ ํ•˜๋‚˜์˜ ๋ฉ€ํ‹ฐ ํ”„๋ž™ํƒˆ ๋ชจ๋ธ ํ‰์ง€, ์–ธ๋•, ์‚ฐ ์ด ๋ชจ๋‘ ํ•œ ๊ฐœ์˜ ํ”„๋ž™ํƒˆ ๋ชจ๋ธ๋กœ ๋งŒ๋“ค์–ด์ง„ ๊ฒƒ โ€œ์ด๋ฏธ ์กด์žฌํ•˜๋Š” ๋ฉ€ํ‹ฐํ”„๋ž™ํƒˆ์„ ๋‹ค์‹œ ๋งŒ๋“œ๋Š๋ผ ์‹œ๊ฐ„์„ ๋‚ญ๋น„ํ•จ์„ ๋Š๋‚Œโ€ Perlin ๋…ธ์ด์ฆˆ ๊ธฐ๋ฐ˜ ์ ˆ์ฐจ์  ํ”„๋ž™ํƒˆ ๊ตฌ์ถ•๋ฐฉ๋ฒ• ๊ทœ๋ชจ๋ฅผ ๋ฆฌ์Šค์ผ€์ผํ•˜๊ณ  ๋”ํ•œ๊ฒƒ(Saupe 1989) 9
  • 10. Point Evaluation ํ•˜๋‚˜์˜ Point์—์„œ ํ‰๊ฐ€ ํ”„๋ž™ํƒˆ ํ„ฐ๋ ˆ์ธ ์ƒ์„ฑ ์•Œ๊ณ ๋ฆฌ์ฆ˜์—์„œ ๊ฝค๋‚˜ ํฐ์ฐจ์ด์ (ํŠน์„ฑ) ํด๋ฆฌ๊ณค ๋ถ„ํ• ์—์„œ๋Š” ์ฃผ์–ด์ง„ ๊ณ ๋„๋Š” ์ฃผํŒŒ์ˆ˜๊ฐ€๊ฐ€ ๋” ๋‚ฎ์€ ์ง€์  ์ด์›ƒ๊ทผ๋ฐฉ์ง€์ ๋“ค ์‚ฌ์ด์—์„œ ๋ณด๊ฐ„์„ ํ†ตํ•ด ๊ฒฐ์ • ํ‘ธ๋ฆฌ์— ํ†ตํ•ฉ์—์„œ ์ „์ฒด ํ„ฐ๋ ˆ์ธ Patch๋Š” ํ•œ๋ฒˆ์— ์ƒ์„ฑ๋˜์–ด์•ผ ํ•˜๋ฉฐ ์–ด๋–ค ์ƒ˜ํ”Œ๋„ ํ˜ผ์ž์„œ ๊ณ ๋ฆฝ๋˜์–ด ๊ณ„์‚ฐ ํ• ์ˆ˜ ์—†์Œ ์ ˆ์ฐจ์  ๋ฐฉ๋ฒ•์—์„œ๋Š” ์ฃผ์–ด์ง„ Point๋กœ๋ถ€ํ„ฐ ์ด์›ƒ๋“ค์„ ์ฐธ์กฐํ•˜์ง€ ์•Š๊ณ  Point ํ‰๊ฐ€๋ฅผ ํ•˜๋Š” ๋…๋ฆฝ์„ฑ์„ ๊ฐ€์ง 10
  • 11. 11
  • 12. Point Evaluation ๋˜๋‹ค๋ฅธ ์ฐจ์ด์ (Noise Function) ์–ธ๋•์ด๋‚˜ ์˜ค๋ž˜๋œ ์‚ฐ๊ณผ ๊ฐ™์ด ๋ถ€๋“œ๋Ÿฝ๊ฒŒ ์นจ์‹๋œ ๋ชจ์–‘์„ ํ‘œํ˜„ํ• ์ˆ˜ ์žˆ์Œ ํด๋ฆฌ๊ณค ๋ถ„ํ• ์—์„œ ํ‘œํ˜„ํ• ๋ ค๋ฉด Lewis ์˜ "ํ™•๋ฅ ๋ก ์ ์œผ๋กœ ์ƒ์„ฑ๋˜๋Š” ๋ถ„ํ• โ€ ๊ณผ ๊ฐ™์€ ์Šคํ‚ค๋งˆ์˜ ๋ณต์žก์„ฑ์ •๋„์— ๊ธฐ๋Œ€ 12
  • 13. ์ ˆ์ฐจ์  ์ ‘๊ทผ์˜ ๋˜ ๋‹ค๋ฅธ ์ฐจ๋ณ„์ ์ธ ํŠน์ง• โ€œQAEB ๋ Œ๋”๋ง ๋‚ด์—์„œ ์ ์šฉํ• ๋งŒํ•œ LOD๋กœ ๋ Œ๋”๋ง์— ํ•„์š”ํ•œ ํ„ฐ๋ ˆ์ธ Geometry๋‚ด์—์„œ ์ ์šฉํ• ๋งŒํ•œ ๋Œ€์—ญ์ด ์ œํ•œ๋œ ์ฃผํŒŒ์ˆ˜๋ฅผ ์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ ์ˆ˜์šฉํ•œ๋‹ค๋Š” ์ .โ€ 13
  • 14. 14
  • 15. 15
  • 16. 16
  • 17. 17
  • 18. 18
  • 19. 19
  • 20. 20
  • 21. 21
  • 22. 22
  • 24. Height Field โ€œ์ปดํ“จํ„ฐ ๊ทธ๋ž˜ํ”ฝ์Šค์—์„œ ํ„ฐ๋ ˆ์ธ ๋ชจ๋ธ๋“ค์€ ์ผ๋ฐ˜์ ์œผ๋กœ Height Field ํ˜•ํƒœ๋ฅผ ๊ฐ€์งโ€ 24
  • 25. Height Field ์ผ์ •ํ•œ ๊ฐ„๊ฒฉ๋งˆ๋‹ค ๊ณ ๋„๊ฐ’์ด ์ €์žฅ(2์ฐจ์›๋ฐฐ์—ด) ์„ ๋“ค์ด ๊ต์ฐจํ•˜๋Š” ๋ชจ๋“  ์ง€์ ์— ๋Œ€ํ•œ ๊ณ ๋„๊ฐ’์ด ์ €์žฅ๋˜์–ด ์žˆ๋Š” ๊ทธ๋ž˜ํ”„ ์ข…์ด์˜ ์ผ๋ถ€๋ถ„ ๊ฐ™์€ ๊ฒƒ Grid Point์—์„œ ๊ณ ๋„๊ฐ’์ด 1:1๋กœ ๋งค์นญ height field ์—์„œ๋Š” ๋™๊ตด์ด๋‚˜ overhangs ์™€ ๊ฐ™์€ ๊ฒƒ์€ ์กด์žฌํ•  ์ˆ˜ ์—†๋‹ค โ€œ์ด๋Ÿฌํ•œ ์ œ์•ฝ์€ ์‰ฝ๊ฒŒ ๊ทน๋ณตํ• ์ˆ˜ ์žˆ๋Š” ๊ฒƒ์ฒ˜๋Ÿผ ๋ณด์ด์ง€๋งŒ ๊ทธ๋ ‡์ง€ ์•Š๋‹ค.โ€ 25
  • 26. 26
  • 27. Non Height Field QAEB-trace ๋ฅผ ์‚ฌ์šฉ ๋ฌผ๋ณด๋ผ์™€ ๋ฌผ๊ฑฐํ’ˆ์€ QAEB hyper texture ๋ Œ๋”๋ง ์‹œ๊ฐ„์€ ํ•˜๋ฃจ 27
  • 28. Height Field์˜ ๊ท ๋“ฑํ•œ ์ƒ˜ํ”Œ(๊ฐ„๊ฒฉ) ํšจ์œจ์ ์ธ ray-tracing ๋“ค์„ ์ˆ˜์šฉ grid tracing (Musgrave 1988) ๊ณผ quad tree (์ฟผ๋“œํŠธ๋ฆฌ)(Kajiya 1983) ๊ณต๊ฐ„๋ถ„ํ•  28
  • 29. ํ„ฐ๋ ˆ์ธ ๋ Œ๋”๋Ÿฌ Ray-Trace๋ฅผ ์‚ฌ์šฉํ•˜์ง€ ์•Š๋Š” ๋ Œ๋”๋Ÿฌ VistaPro Ray-Trace๋ฅผ ์‚ฌ์šฉํ•˜๋Š” World Tool Set MetaCreations ์˜ Bryce, Animatek World Builder, ๋ Œ๋”๋Ÿฌ Grid Tracing Craig Kolb ์˜ public domain ์ธ Rayshade ray tracer ๊ณ„์ธต์ ์ธ ์ ‘๊ทผ๋ฐฉ์‹์œผ๋กœ Grid Tracing์˜ ๊ฒฝ์šฐ ๋ฉ”๋ชจ๋ฆฌ ์˜ค๋ฒ„ํ—ค๋“œ๊ฐ€ ์  ๊ณ  ์ฟผ๋“œํŠธ๋ฆฌ ๋ฐฉ์‹์ด ์†๋„๋ฉด์—์„œ ์ด์ ์ด ์žˆ์Œ PPT ์•Œ๊ณ ๋ฆฌ์ฆ˜(Paglieroni 1994) ๋‚ ๊ณ ์žˆ๋Š” ์ƒํƒœ์™€ ๊ฐ™์€ ๊ฒฝ์šฐ,static ํ•œ height field ๋ฅผ ์—ฌ๋Ÿฌ๋ฒˆ ๋ Œ๋”๋ง ํ•˜๋Š” ๊ฒฝ์šฐ์— ๊ฐ€์žฅ ๋น  ๋ฅด๊ฒŒ ๋ Œ๋”๋ง ํ• ์ˆ˜ ์žˆ์Œ 29
  • 30. Height Field ํŒŒ์ผํฌ๋งท DEM (digital elevation map) Geological Survey (USGS) height field ๋ฏธ๊ตญ ์ „์ฒด๋ฅผ ํฌํ•จํ•˜๋Š” USGS ๋กœ๋ถ€ํ„ฐ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋Š” "quad" ์ง€ํ˜• ๋งต์— ๋Œ€์‘๋˜๋Š” ์ธก์ • ๋œ ๋ฐ์ดํ„ฐ๋“ค์„ ํฌํ•จ DTED (digital terrain elevation data) ๋ฏธ๊ตญ ๊ตฐ์‚ฌ๊ธฐ๊ด€, ๋ฏธ๊ตญ ๋ฐ”๊นฅ์˜ ํ„ฐ๋ ˆ์ธ๊ณผ ์˜ํ† ๋“ค๋„ ํ•จ๊ป˜ ํฌํ•จ 30
  • 31. Synthesist ์ž…์žฅ์—์„œ โ€œ์‰ฝ๊ฒŒ ์ด๋Ÿฐ ๋ฐ์ดํ„ฐ๋ฅผ ์ด์šฉํ•ด์„œ ๋ Œ๋”๋งํ•  ์ˆ˜ ์žˆ์ง€๋งŒ ์‹ค์ œ ๋ฐ์ดํ„ฐ๋ฅผ Cheating ํ•ด์„œ ์‚ฌ์šฉํ•  ๊ฒƒ์„ ๊ณ ๋ คโ€ โ€œ๊ธ์–ด์˜จ ๋ฐ์ดํ„ฐ๋“ค๋กœ๋ถ€ํ„ฐ ์‹ค์ œ์ฒ˜๋Ÿผ ์นœ์ˆ™ํ•ด ๋ณด์ด๋„๋ก ํ†ตํ•ฉํ•˜๋Š” ๊ฒƒ์ด ๋ชฉ์ โ€ 31
  • 32. Kenton์˜ ํฌ๋งท ์‚ฌ์ง„๋“ค์„ ์ฐ์€ ๊ฒƒ์œผ๋กœ๋ถ€ํ„ฐ ๋ฐ์ดํ„ฐ๋ฅผ ์–ป์Œ ์•„๋ž˜์˜ ์ •๋ณด๋ฅผ ํฌํ•จ integer ( 4 bytes) ์—๋Š” height field ์˜ ๊ฐ€๋กœ์„ธ๋กœ ํฌ๊ธฐ๋ฅผ ๊ธฐ์ˆ  ๊ทธ๋‹ค์Œ n^2 floats (๊ฐ๊ฐ 4 bytes) ์—์„œ n ์€ leading integer ๊ฐ’ ์ €์žฅํ•˜๊ณ  ์‹ถ์€ ์ถ”๊ฐ€์ ์ธ ๋ฐ์ดํ„ฐ- height field ์˜ ์ตœ๋Œ€ ์ตœ์†Œ๊ฐ’๊ณผ ๊ฐ™์€๊ฒƒ๊ณผ random number ์ƒ์„ฑ์— ํ•„์š”ํ•œ seed ๊ฐ™์€๊ฒƒ๋“ค์„ ์ถ”๊ฐ€๋กœ ํฌํ•จ ๋˜‘๊ฐ™์€ ๋ฐ์ดํ„ฐ์— ์•„์Šคํ‚ค ํฌ๋งท๋ณด๋‹ค๋Š” ๋ณด๋‹ค ์••์ถ•๋œ ๊ฒƒ๋“ค์„ ์‚ฌ์šฉํ•  ์ˆ˜๋„ ์žˆ์„ ๊ฒƒ์ด๊ณ , ์ด๋Ÿฐ ์ด์œ ๋“ค๋กœ ์ €์žฅ๊ณต๊ฐ„ ๊ตฌ์กฐ์— ์ผ๋ถ€ ๋น„ํšจ์œจ์ ์ธ ๋ถ€๋ถ„๋“ค์ด ์กด์žฌ 32
  • 33. Matt Pharr์˜ ๊ฐœ์„ ๋œ ํฌ๋งท 600 byte ํ—ค๋”๋ธ”๋Ÿญ์ด ์กด์žฌ ๊ณ ๋„ ๋ฐ์ดํ„ฐ๋Š” short(2 byte) ๋กœ ์ €์žฅ ์ด ๋ฐ์ดํ„ฐ๋“ค์€ normalize ๋˜๊ณ  0 ๋ถ€ํ„ฐ 2^16 -1 ๋ฒ”์œ„์˜ ์ •์ˆ˜๋กœ ์–‘์žํ™” ๊ณ ๋„์˜ ์ตœ์†Œ, ์ตœ๋Œ€๊ฐ’๋„ ์ €์žฅ ๊ณ ๋„๊ฐ’์€ ๋ Œ๋”๋งํƒ€์ž„๋•Œ ๋ณ€ํ™˜์— ์˜ํ•ด ๋ถ€๋™์†Œ์ˆ˜์ ์œผ๋กœ ๋‹ค์‹œ ์ €์žฅ๋ ์ˆ˜ ์žˆ๊ธฐ๋•Œ๋ฌธ 33
  • 34. Matt Pharr์˜ ๊ฐœ์„ ๋œ ํฌ๋งท a ๊ฐ€ quantized(์–‘์žํ™”)๋˜๊ณ , ํ•ด๋ฐœ๊ณ ๋„ ๊ฐ’์„ scale z ๋Š” ๋ถ€๋™ ์†Œ์ˆ˜์  ๊ฐ’์œผ๋กœ ๋””์ฝ”๋”ฉ zmin ๊ณผ zmax ๋Š” height field ์˜ min/max ๊ฐ’ 34
  • 35. Matt Pharr์˜ ๊ฐœ์„ ๋œ ํฌ๋งท big-endian/little-endian byte-order ๋ฌธ์ œ๋„ ์ œ๊ฑฐ 32-bit ์™€ 64-bit ์šด์˜์ฒด์ œ๊ฐ„์—๋„ ๋ฐ”์ด๋„ˆ๋ฆฌํŒŒ์ผ์„ ์•Œ์•„์„œ ์ž๋™์œผ๋กœ ์ „์†กํ•  ์ˆ˜ ์žˆ์„๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, ์„œ๋กœ ๋‹ค๋ฅธ ์ปดํ“จํ„ฐ๊ฐ„์— ๋ฐ”์ด๋„ˆ๋ฆฌ ํŒŒ์ผ๋“ค์„ ์ „์†กํ•  ๋•Œ์—๋„ ๋ฌธ์ œ์—†์Œ 35
  • 37. 37
  • 38. ์ปดํ“จํ„ฐ ๊ทธ๋ž˜ํ”ฝ์Šค์—์„œ ํ”„๋ž™ํƒˆ ์‚ฐ์˜ ๊ธฐ์›์€ ๋งŒ๋ธ๋ธŒ๋กœ๊ฐ€ fBm์„ ๊ฐ€์ง€๊ณ  1์ฐจ์›(ํ˜น์€ 1.xx ์ฐจ์›์—์„œ) ๊ฐ™์ด ์ž‘์—…ํ•œ ๊ฒƒ์—์„œ ์‹œ์ž‘ 38
  • 39. HOMOGENEOUS fBm TERRAIN MODELS โ€œ์ด ํ•จ์ˆ˜์˜ Trace๋Š” ๋พฐ์กฑํ•œ ์‚ฐ์— ๋งž๋‹ฟ์€ Skyline ๋‹ฎ์•˜๋‹ค๋Š” ๊ฒƒ์— ์ฃผ๋ชฉโ€ 39
  • 40. HOMOGENEOUS fBm TERRAIN MODELS โ€œ์œ ์ „์ž ๋ชจ๋ธ๋ง์— ๋น„์ถ”์–ด๋ณด์ž๋ฉด, ๋งŒ์•ฝ ์ด function์ด 2์ฐจ์›์œผ๋กœ ํ™•์žฅ๋๋‹ค๋ฉด, ๊ทธ ๊ฒฐ๊ณผ surface๋Š” ์‚ฐ๊ณผ ๋‹ฎ์„๊ฒƒ ์ž„์ด ํ‹€๋ฆผ์—†๋‹ค๊ณ  ์œ ์ถ”โ€ 40
  • 41. 41
  • 42. 42
  • 43. ์ปดํ“จํ„ฐ ๊ทธ๋ž˜ํ”ฝ์Šค์—์„œ ํ”„๋ž™ํƒˆ ์‚ฐ์˜ ๊ธฐ์›์€ ๋งŒ๋ธ๋ธŒ๋กœ๊ฐ€ fBm์„ ๊ฐ€์ง€๊ณ  1์ฐจ์›(ํ˜น์€ 1.xx ์ฐจ์›์—์„œ) ๊ฐ™์ด ์ž‘์—…ํ•œ ๊ฒƒ์—์„œ ์‹œ์ž‘ 43
  • 44. โ€œ์‹ค์ œ ์‚ฐ์˜ ๋ชจ์–‘์ด ํ”„๋ž™ํƒˆ ํ•จ์ˆ˜์˜ ํ˜•ํƒœ ์‚ฌ์ด์— ์•Œ๋ ค์ง„ ์ธ๊ณผ ๊ด€๊ณ„๋ฅผ ์—†๋‹ค.โ€ โ€œ๊ทธ ํ•จ์ˆ˜๊ฐ€ ๋งˆ์นจ ์‚ฐ๋“ค๊ณผ ๋‹จ์ˆœํžˆ ๋‹ฎ์•„๋ณด์ธ๊ฑฐ๊ณ  ํ•ด๋ณด๋‹ˆ ์‹ค์ œ๋กœ๋„ ๋„ˆ๋ฌด ๋งŽ์ด ๋‹ฎ์€๊ฒƒโ€ โ€œ๋ฌผ๋ก  ์‹ค์ œ ์‚ฐ๋“ค์„ ๋ณด๋ฉด, ๋งŽ์€ ํŠน์ง•๋“ค์ด ์กด์žฌ ํ•˜์ง€๋งŒ (ํ•˜๊ณ„๋ง์ด๋ผ๋“ ์ง€ ์นจ์‹๋œ ๋ถ€๋ถ„๋“ค์— ๋Œ€ํ•œ ํŠน์ง•) ์ด ์ฒซ๋ฒˆ์งธ ๋ชจ๋ธ์—๋Š” ๊ทธ๋Ÿฌํ•œ ํŠน์ง•์€ ์กด์žฌํ•˜์ง€ ์•Š์Œโ€œ 44
  • 45. Fractal Demension โ€œํ”„๋ž™ํƒˆ ์ฐจ์›์€ ํ‘œ๋ฉด์˜ ๊ฑฐ์น ๊ธฐ์˜ ์ฒ™๋„๋กœ ์ƒ๊ฐ ๋  ์ˆ˜ ์žˆ๋‹คโ€ โ€œํ”„๋ž™ํƒˆ ์ฐจ์›์ด ์ปค์งˆ์ˆ˜๋ก, ์„œํ”ผ์Šค(ํ‘œ๋ฉด)๋„ ๋” ๊ฑฐ์น ์–ด์ง„๋‹คโ€ 45
  • 46. 46
  • 47. ์ด ํŒจ์น˜์— ๋Œ€ํ•ด ๊ฐ€์žฅ ํฅ๋ฏธ๋กœ์šด ๊ฒƒ์€, ์™ผ์ชฝ์€ ์™„๋งŒํ•ด๋ณด์—ฌ๋„ ์™„์ „ํžˆ ํ‰ํ‰ํ•˜์ง€๋Š” ์•Š๊ณ  ์˜ค๋ฅธ์ชฝ์€ ๋นผ๊ณกํ•ด๋ณด์—ฌ๋„ ๊ณต๊ฐ„์„ ์™„์ „ํžˆ ๋‹ค ์ฑ„์šฐ๊ณ  ์žˆ์ง€๋Š” ์•Š๋‹ค๋Š” ๊ฒƒ 47
  • 48. ๊ทธ๋ž˜์„œ ์šฐ๋ฆฌ๋Š” fBm ์— ๋Œ€ํ•œ ํ”„๋ž™ํƒˆ ์ฐจ์›์˜ ๊ณต์‹์ ์ธ ์ •์˜๋Š” ๊ตฌ์กฐ์—์„œ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ์œ ์šฉํ•œ ํ”„๋ž™ํƒˆ ํ–‰๋™์„ ๋ชจ๋‘ ์บก์ฒ˜ ํ•˜์ง€ ์•Š๋Š” ๋‹ค๋Š” ๊ฒƒ์„ ์•Œ์ˆ˜ ์žˆ๋‹ค ์ด patch ์˜ ์™ผ์ชฝ ๋์— ํ‘œํ˜„๋˜๋Š” ์™„๋งŒํ•œ ํ‰์›๊ณผ ๊ฐ™์€ ์ข…๋ฅ˜๋“ค์€ ์‚ฌ์‹ค ์ž๊ธฐ์œ ์‚ฌ์„ฑ์„ ๊ฐ€์ง€๊ธฐ ๋•Œ๋ฌธ์—, ์šฐ๋ฆฌ์˜ ์ฒดํ—˜์ ์ธ "ํ”„๋ž™ํƒˆ" ์ •์˜์— ๋“ค์–ด๋งž๋Š”๋‹ค. 48
  • 49. ๊ทธ๋Ÿฌ๋‚˜ ์ด ํ‰์›๋“ค์€ ํ”„๋ž™ํƒˆ ์ฐจ์›์˜ ๊ณต์‹ ์ˆ˜ํ•™ ์ •์˜์—๋Š” ๋“ค์–ด๋งž์ง€ ์•Š๋Š”๋‹ค. 49
  • 50. ์ •์˜๋‚ด๋ฆฌ๊ธฐ ํž˜๋“  โ€œํ”„๋ž™ ํƒˆโ€ โ€œ์ด๊ฒƒ์€ ํ”„๋ž™ํƒˆ๋“ค์ด ์ •ํ™•ํ•œ ์ •์˜๋ฅผ ๋‚ด๋ฆฌ๊ธฐ๊ฐ€ ์–ผ๋งˆ๋‚˜ ํž˜๋“ ๊ฒƒ์ธ์ง€๋ฅผ ๋ณด์—ฌ์ฃผ๋Š” ์ข‹์€ ์˜ˆโ€ โ€œ์šฐ๋ฆฌ๋Š” ๋•Œ๋•Œ๋กœ ์•„์ฃผ ํญ์ด ๋„“์€ ๋ธŒ๋Ÿฌ์‰ฌ๋ฅผ ๊ฐ€์ง€๊ณ  ํŽ˜์ธํŠธํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ์™œ๋ƒํ•˜๋ฉด ๊ตณ์ด ๊ด€๋ จ๋œ ํ˜„์ƒ์„ ์ œ์™ธ์‹œํ‚ฌ ํ•„์š”๊ฐ€ ์—†๊ธฐ๋•Œ๋ฌธโ€ 50
  • 51. ์ •์˜๋‚ด๋ฆฌ๊ธฐ ํž˜๋“  โ€œํ”„๋ž™ ํƒˆโ€ โ€œ์ปดํ“จํ„ฐ ๊ทธ๋ž˜ํ”ฝ์Šค์˜ ๋งŽ์€ ์—ฐ๊ตฌ๊ฐ€๋“ค๊ณผ ๋‹ค๋ฅธ ๋ถ„์•ผ์‚ฌ๋žŒ๋“ค์€ "fractal" ์— ๋Œ€ํ•˜์—ฌ "stochastic"(ํ™•๋ฅ ์ ์ธ) ๊ณผ "self-similar"(์ž๊ธฐ์œ ์‚ฌ์„ฑ) ๊ณผ ๊ฐ™์€ ์šฉ์–ด๋“ค๋กœ ๋Œ€์ฒดํ•˜์—ฌ ์‚ฌ์šฉโ€ โ€œ๊ณต์‹ ์ •์˜๊ฐ€ ๋“ค์–ด๋งž๋Š”๊ฒŒ ์—†๊ธฐ๋•Œ๋ฌธ์ธ๋ฐ, ์ด๊ฒƒ๋„ ๊ทธ๋ ‡๊ฒŒ ์ ๋‹นํ•œ ์šฉ์–ด๋Š” ์•„๋‹๊ฒƒโ€ 51
  • 52. ์ •์˜๋‚ด๋ฆฌ๊ธฐ ํž˜๋“  โ€œํ”„๋ž™ ํƒˆโ€ โ€œ๋น„์ฅฌ์–ผ์ ์œผ๋กœ ์ž์—ฐ ํ˜„์ƒ์— ๋Œ€ํ•œ ์œ ์šฉํ•œ srochastic(ํ™•๋ฅ ) ๋ชจ๋ธ๋“ค ์ค‘์—์„œ ์ž๊ธฐ ์œ ์‚ฌ์„ฑ ํŠน์ง•์ด ์—†๋Š” ๋ชจ๋ธ๋“ค์€ ๊ฑฐ์˜ ์—†๊ณ  ์ž๊ธฐ์œ ์‚ฌ์„ฑ ๋ชจ๋ธ๋“ค์€ ์ง€์†์ ์ด์ง„ ์•Š๋”๋ผ๋„ ๊ณต์‹์ ์œผ๋กœ ํ™•์‹คํ•œ, ํ”„๋ž™ํƒˆ๋กœ์จ ๊ฐ€์žฅ ๊ทธ ์„ฑ์งˆ์„ ์ž˜ ํ‘œํ˜„ํ•œ ๋ชจ๋ธโ€ 52
  • 53. Visual Effects of the Basis Function โ€œ์ปดํ“จํ„ฐ ๊ทธ๋ž˜ํ”ฝ์— ๋Œ€ํ•œ ์ž„์˜์˜ ํ”„๋ž™ํƒˆ์„ ๋งŒ๋“ค ๋•Œ ์‚ฌ์šฉํ•œ ์ž‘์€ ์ŠคํŽ™ํŠธ๋Ÿผ ํ•ฉ๊ณ„๋Š” Basis Function์˜ ํŠน์„ฑ์ด ๊ฒฐ๊ณผ์— ๋ช…ํ™•ํ•˜๊ฒŒ ํ‘œ์‹œํ•  ์ˆ˜ ์žˆ์Œโ€ โ€œ๋ณดํ†ต, ๊ธฐ์ € ํ•จ์ˆ˜์˜ ์„ ํƒ์€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์—์„œ ์ž„ํ”Œ๋ฆฌ์‹œํŠธโ€ 53
  • 54. Visual Effects of the Basis Function โ€œํ‘ธ๋ฆฌ์— ํ†ตํ•ฉ์„ ์œ„ํ•œ ์‚ฌ์ธ ๊ณก์„ , ํด๋ฆฌ๊ณค ์„œ๋ธŒ๋””๋น„์ ผ์˜ ํ•˜๋‚˜์˜ ํ†ฑ๋‹ˆ๋ฐ”ํ€ด ๊ณก์„  ๊ทธ๋ฆฌ๊ณ  Noise์˜ Cubic Hermite Spline ๊ธฐ๋ฐ˜์˜ ์ ˆ์ฐจ์  fBMโ€ 54
  • 55. Visual Effects of the Basis Function Walsh ๋ณ€ํ™˜ Basis Function์œผ๋กœ square waves๋ฅผ ์–ป์„์ˆ˜ ์žˆ์Œ 55
  • 56. Visual Effects of the Basis Function Wavelets (Ruskai 1992) ์œ ํ•œํ•œ Basis Function์œผ๋กœ ๊ฐ•๋ ฅํ•œ ์ƒˆ๋กœ์šด ์„ธํŠธ๋ฅผ ์ œ๊ณต 56
  • 57. Visual Effects of the Basis Function sparse convolution (Lewis 1989) fractal sum of pulses (Lovejoy and Mandelbrot 1985) Basis Function ์„ ํƒ์— ํฐ ์œ ์—ฐ์„ฑ์„ ์ œ๊ณต โ€œ์ด๋Ÿฌํ•œ ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜๋ฉด ์ง€ํ˜•์˜ sinkholes๋กœ ์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ ์ด์–ด์ง€๋Š”, Basis Function๋กœ ๊ทน๋‹จ์ ์œผ๋กœ ์‚ฌ์‹ค์ ์ธ ํ”„๋กœํ•„์„ ์‚ฌ์šฉโ€ 57
  • 58. Visual Effects of the Basis Function Gavin Miller (1986) subdivision ์˜ ๋” ์ผ๋ฐ˜์ ์ธ ํ˜•์‹์˜ ํ„ฐ๋ ˆ์ธ ๊ตฌ์กฐ์—์„œ ์ฃผ๋ฆ„์ด ์žกํžŒ ๊ฒƒ์„ ๋ณด์—ฌ์คŒ ์„œ๋ธŒ๋””๋น„์ ผ ์•Œ๊ณ ๋ฆฌ์ฆ˜์•ˆ์— implicit ๋ณด๊ฐ„๋ฒ•์˜ ์•„ํ‹ฐํŒฉํŠธ์˜€์Œ โ€œํ•˜์ง€๋งŒ Basis Function์˜ ํŠน์„ฑ๊ณผ fBm๊ตฌ์กฐ์•ˆ์˜ Implicit ํ•œ Basis Function์ด๋ผ๋Š” ๊ฒƒ์„ ๋Œ€๋ถ€๋ถ„ ๊ฐ„๊ณผํ•œ๊ฒƒโ€ โ€œํŠน์ • ๋ฏธ์ ํšจ๊ณผ๋ฅผ ์–ป์„๋•Œ ์ด๋Ÿฐ ์ธ์‹(์•„ํ‹ฐํŒฉํŠธ๋ฅผ ์ด์šฉํ•˜๋Š”)๋ฅผ ์‚ฌ์šฉํ• ์ˆ˜ ์žˆ์Œโ€ 58
  • 59. Visual Effects of the Basis Function Hermite spline ๋ณด๊ฐ„ ๋…ธ์ด์ฆˆ ํ•จ์ˆ˜์—์„œ Hermite spline ๋ณด๊ฐ„๋ฒ•์˜ ๋ถ€๋“œ๋Ÿฌ์›€์€ ์šฐ๋ฆฌ๊ฐ€ ๋” ๋งŽ์ด ์ด์ „์— ์ปดํ“จํ„ฐ ๊ทธ๋ž˜ํ”ฝ์—์„œ ๋ณผ ์ˆ˜์žˆ๋Š” ๊ฒƒ๋ณด๋‹ค ์„ธ๋ จ๋œ ์ง€ํ˜•์„ ์ƒ์„ฑํ• ์ˆ˜ ์žˆ๊ฒŒ ํ•จ 59
  • 60. 60
  • 61. 61
  • 62. 62
  • 63. Visual Effects of the Basis Function Ridged Basis Function ๊ฐ€ํŒŒ๋ฅธ ์‚ฐ๋“ฑ์„ฑ์ด์˜ ๋Šฅ์„ ์™€ ์ง€ํ˜•์„ ์ƒ์„ฑํ•˜๋Š” ๋ฐ ์‚ฌ์šฉ 63
  • 64. 64
  • 65. 65
  • 66. 66
  • 67. 67
  • 68. 68
  • 69. 69
  • 70. 70
  • 72. Heterogeneous Terrain Models Even Vossโ€™s heterogeneous terrains (Voss 1988) ๊ท ๋“ฑํ•œ ํ”„๋ž™ํƒˆ ์ฐจ์›์˜ ํ•˜๋‚˜์˜ ์„œํ”ผ์Šค์˜ ๊ฐ„๋‹จํ•œ ์ง€์ˆ˜ํ•จ์ˆ˜์ ์ธ ์Šค์ผ€์ผ๋ง์„ ํ‘œํ˜„ ์‹ค์ œ ๋žœ๋“œ์„ธ์ดํ”„ ์•„์ฃผ heterogeneous ํŠนํžˆ ์ง€๋‚˜์น˜๊ฒŒ ํฐ ์Šค์ผ€์ผ(์˜ˆ๋ฅผ ๋“ค์–ด ํ‚ฌ๋กœ๋ฏธํ„ฐ) ์ƒ๋Œ€์ ์œผ๋กœ ํ”Œ๋žซํ•œ ํ‰์›์—์„œ ๋กํ‚ค์‚ฐ์˜ ๋“œ๋ผ๋งˆํ‹ฑํ•œ ์ƒ์Šน์„ ๋ณผ์ˆ˜ ์žˆ์Œ ๋กœํ‚ค ์‚ฐ๋งฅ, Sierras, ๊ทธ๋ฆฌ๊ณ  ์•Œํ”„์Šค์™€ ๊ฐ™์€ ๋†’์€ ๋ฒ”์œ„๋Š” ์ผ๋ฐ˜์ ์œผ๋กœ ๋น™ํ•˜๋กœ ์•Œ๋ ค์ง„ ๊ฑฐ ๋Œ€ํ•œ ์ง€๊ตฌ์˜ ์›€์ง์ž„์— ์˜ํ•ด ์ฃผ๋กœ ํ˜•์„ฑ๋œ ๋กค๋ง ํ’‹ํž์„ ๊ฐ€์ง„๋‹ค. ๋ชจ๋“  ์ž์—ฐ์ ์ธ ์ง€ํ˜•์€ ์•„๋งˆ๋„ ์ตœ๊ทผ์˜ ํ™”์‚ฐ ๊ฒƒ๋“ค์„ ์ œ์™ธํ•˜๊ณ , ์นจ์‹ ํ˜„์ƒ์ด ์žˆ์„๊ฒƒ 72
  • 73. 73
  • 74. Heterogeneous Terrain Models โ€œ์นจ์‹ ๊ทธ๋ฆฌ๊ณ  ๊ตฌ์กฐ ์ง€์งˆํ•™์€ ๋˜๋‹ค๋ฅธ ํ™”์‚ฐ ์ž‘์šฉ์— ์˜ํ•œ ํŠน์„ฑ, ์ถฉ๋Œ ๋ถ„ํ™”๊ตฌ, ๊ทธ๋ฆฌ๊ณ  ์ƒ๋ฌผ ๊ต๋ž€(์ธ๊ฐ„์„ ํฌํ•จํ•œ)์— ์˜ํ•œ ๋‹ค์–‘ํ•œ ํŠน์„ฑ ๋“ค๋ณด๋‹ค ์šฐ๋ฆฌ ํ–‰์„ฑ์˜ ๊ฑฐ์˜ ๋ชจ๋“  ์ง€ํ˜•ํ•™์˜ ํŠน์ง•์— ์ฑ…์ž„์ด ์žˆ์Œโ€ 74
  • 75. Heterogeneous Terrain Models โ€œ์นจ์‹ ๊ธฐ๋Šฅ์€ ๋ชจ๋ธ๋กœ ํ‘œํ˜„ํ•˜๊ธฐ ์šฉ์ดํ•˜๋‹คโ€ 75
  • 76. 76
  • 77. Heterogeneous Terrain Models โ€œ์ด ์žฅ์˜ ๋‚˜๋จธ์ง€๋Š” ์ง€๋‚˜์น˜๊ฒŒ ์›๋ž˜ fBm ๋ชจ๋ธ์˜ ์šฐ์•„ํ•จ๊ณผ ์ „์‚ฐ ํšจ์œจ์„ฑ์„ ์†์ƒ์‹œํ‚ค์ง€ ์•Š๊ณ , ํŠน์ • ์นจ์‹ ๊ธฐ๋Šฅ์˜ ์ฒซ ๋ฒˆ์งธ ๊ทผ์‚ฌ์น˜๋ฅผ ์–ป์„ ์ˆ˜ ์žˆ๋„๋ก ๊ณ ์•ˆ๋œ ํŠน์ • ontogenetic ๋ชจ๋ธ์„ ์„ค๋ช…โ€ 77
  • 78. Statistics by Altitude โ€œ์ €์ง€๋Œ€๋Š” ํ™๋”๋ฏธ์™€ ํ•จ๊ป˜ ์ฑ„์›Œ์ ธ ์ง€ํ˜•์ ์œผ๋กœ ๋ถ€๋“œ๋Ÿฌ์›Œ์ง€๋Š” ๊ฒฝํ–ฅ์ด ์žˆ์œผ๋ฉฐ ๋ฐ˜๋ฉด ๋ถ€์‹ ๊ณผ์ •์€ ๋†’์€ ์ง€์—ญ๋ณด๋‹ค ๋“ค์ญ‰๋‚ ์ญ‰ํ•จ์„ ์œ ์ง€ํ•˜๋Š” ๊ฒฝํ–ฅ์ด ์žˆ๋‹คโ€ 78
  • 79. 79
  • 80. 80
  • 81. 81
  • 82. A Hybrid Multifractal โ€œ๊ณ„๊ณก๋“ค์ด ๋ชจ๋“  ๊ณ ๋„์—์„œ ๋ถ€๋“œ๋Ÿฌ์šด ๋ฐ”๋‹ฅ์„ ๊ฐ€์ง€๊ณ  ์žˆ์–ด์•ผ ํ• ๊ฒƒ์ด๋ผ๊ณ  ํ–ˆ๋‹ค. ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ํ•ด์ €๋ ˆ๋ฒจ๋„ ์ดํ•ฉ์— ์ด์ „ ์ฃผํŒŒ์ˆ˜์˜ ๋กœ์ปฌ ๊ฐ’์œผ๋กœ ์Šค์ผ€์ผ๋งํ•˜์—ฌ ๋งŒ๋“  ๋” ๋†’์€ ์ฃผํŒŒ์ˆ˜๋กœ ์™„์„ฑ๋  ์ˆ˜ ์žˆ์„๊ฑฐ๋ผ ์ƒ๊ฐํ–ˆ๋‹คโ€ 82
  • 83. 83
  • 84. 84
  • 85. 85
  • 86. 86
  • 87. 87
  • 88. 88
  • 89. 89
  • 90. 90
  • 91. 91
  • 92. ์งˆ์ ์œผ๋กœ ํ„ฐ๋ ˆ์ธ ํŒจ์น˜๋Š” ํ†ต๊ณ„ ํŒจ์น˜์™€ ๋งค์šฐ ์œ ์‚ฌํ•œ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚จ 92
  • 93. Multiplicative Multifractal Terrains โ€œ์šฐ๋ฆฌ์˜ ๊ณต์‹? ์ฆ‰, ์˜ˆ์ˆ ์ด๋ผ๊ธฐ ๋ณด๋‹ค๋Š” ์ˆ˜ํ•™์ ์ธ? ์ด๋Ÿฌํ•œ multifractal ์ง€ํ˜• ๋ชจ๋ธ์˜ ์ˆ˜ํ•™์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ๋งค์šฐ ์ดˆ๊ธฐ์ด๋ฉฐ, ๊ทธ๋ž˜์„œ ๋ณด๊ณ ํ•˜๊ธฐ ์œ„ํ•ด ์‚ฌ์šฉํ•˜๋Š” ๊ฒƒ์ด ์กฐ๊ธˆ ๋ฐ–์— ์—†๋‹คโ€ 93
  • 94. Multiplicative Multifractal Terrains โ€œmultifractal construction๋Š” ์ผ๋ถ€ ํ˜ธ๊ธฐ์‹ฌ์„ ๋„๋Š” ์†์„ฑ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋Š”๋ฐ ์Šค์ผ€์ผ์˜ ๊ฐ’ ์ฒ˜๋Ÿผ ์ œ๋กœ๋ถ€ํ„ฐ ๋ฌดํ•œ์œผ๋กœ ๊ฐ€๊ณ  ์žˆ๊ณ , ํ•จ์ˆ˜๋Š” highly heterogeneous (0)๋กœ๋ถ€ํ„ฐ flat(๋ฌดํ•œ๋Œ€ ๋กœ ๋ถ„๊ธฐํ•˜๋Š”)์œผ๋กœ ๊ฐ€๊ณ  ์žˆ๋‹คโ€ 94
  • 95. ๋‹น๋ถ„๊ฐ„์€ โ€œ์ˆœ์ˆ˜ํ•œ multifractal ํ•จ์ˆ˜๋ฅผ ์‚ฌ์šฉํ•˜๋ ค๊ณ  ์‹œ๋„ํ•˜๋Š” ๊ฒƒ ๋ณด๋‹ค๋Š” ์ด ์žฅ์—์„œ ์ œ์‹œ๋œ ๋‘ ํ•˜์ด๋ธŒ๋ฆฌ๋“œ additive / multiplicative multifractal ๊ตฌ์กฐ์™€ ํ•จ๊ป˜ ์žˆ์–ด์•ผ ์ตœ์ƒ์˜ ๊ฒƒโ€ 95
  • 96. ๊ฒฐ๋ก  โ€œํ”„๋ž™ํƒˆ์€ ์šฐ๋ฆฌ๊ฐ€ ์‚ฌ๋Š” ์„ธ๊ณ„๋ฅผ ๋ฌ˜์‚ฌ์˜ ๋งˆ์ง€๋ง‰ ๋‹จ์–ด ์ผ์ˆ˜๋Š” ์—†์ง€๋งŒ, ๊ทธ๋“ค์€ ํ†ตํ•ฉ ์ด๋ฏธ์ง€์— ๋Œ€ํ•œ ์‹œ๊ฐ์  ๋ณต์žก์„ฑ์˜ ์šฐ์•„ํ•œ ์†Œ์Šค์„ ์ œ๊ณตํ•  ์ˆ˜ ์žˆ๋‹คโ€ 96
  • 97. ๊ฒฐ๋ก  โ€œ์ž์—ฐ ํ˜„์ƒ์˜ ํ”„๋ž™ํƒˆ ๋ชจ๋ธ์˜ ์ •ํ™•๋„๋Š” ๋‹ค์†Œ ๋ฌผ๋ฆฌ์ , ํŠน์„ฑ ๋ณด๋‹ค๋Š” ontogeneticํ•จ ๊ฝค ์ž˜ ํ˜•ํƒœํ•™์  ๋ฐ˜์˜์ด์ง€๋งŒ ์šฐ๋ฆฌ๊ฐ€ ์•„๋Š” ํ•œ, ๋ฌผ๋ฆฌ์  ๋ฒ•์น™์˜ ์ฒซ ๋ฒˆ์งธ ์›์น™์—์„œ ๋ฐœํ–‰ํ•˜์ง€ ์•Š์Œโ€ 97
  • 98. ์•ž์œผ๋กœ โ€œํ„ฐ๋ธ”๋Ÿฐ์Šค๋Š” ์•„์ง ๋ชจ๋‘์˜ ๋งŒ์กฑ์„ ์œ„ํ•ด ํšจ์œจ์ ์ธ ์ ˆ์ฐจ ๋ชจ๋ธ๋กœ ํ•ด์•ผ ํ• ๊ฒƒ์ด ์žˆ์œผ๋ฉฐ multifractals๋ฅผ ์ดํ•ดํ•˜๊ณ  ์ปดํ“จํ„ฐ ๊ทธ๋ž˜ํ”ฝ์— ์ ์šฉโ€ โ€œ์ตœ๊ณ ์˜ ํ†ตํ•ฉ ์ด๋ฏธ์ง€๋ฅผ "์ง์ ‘์ ์œผ๋กœ ์šฐ๋ฆฌ์˜ ๊นŠ์ด์˜ ๋ฌด์–ธ๊ฐ€๋ฅผ ๋ฐ˜์˜ํ•˜๋Š” ๊ฒƒ์ธ ์ง€?" "๋ฌด์—‡์ด ๋ถ€์กฑํ•œ์ง€" โ€œ์šฐ๋ฆฌ๊ฐ€ ์‚ฌ๋Š” ์ด ์„ธ์ƒ์„ ์ดํ•ดํ•˜๋Š”๋ฐ ์‚ฌ์šฉ๋˜๋Š”์ง€" ์ƒ๊ฐโ€ 98
  • 99. Q&A