motivation (2) halflife 2 microsoft flighsimulator real-time … · 2019-04-03 · pipeline slides...
TRANSCRIPT
1
Rea
l-Tim
eR
end
ering
Pro
ject Go
tha
mR
acin
g 2 (X
bo
x)
Micro
soft Flig
hsim
ula
tor
Ha
lflife2
Re
al-tim
e R
end
ering 2
Mo
tivatio
n (1)
•M
an
y gra
ph
ics ap
plica
tion
s are
dyn
am
ic
–Sim
ula
tors (su
rgery, flig
ht sim
ula
tors,…
)
–3D
com
pu
ter ga
me
s
–3D
wa
lk thro
ug
h’s
•M
inim
al fra
me
rate
s
–10
Hz: p
erceived
as a
nim
atio
n
–6
0 H
z: flicker fre
e
Rea
l-time R
end
ering 3
Mo
tivatio
n (2)
•G
eo
me
try (verte
x co
un
t)
Elevatio
n m
od
el of Sw
itzerlan
d(En
do
xon
AG
)B
roo
ks Ho
use
(UN
C W
alkth
ruG
rou
p)
Do
ub
le Ea
gle T
an
ker
(UN
C W
alkth
ruG
rou
p)
200
Millio
n0
.14 M
illion
83 M
illion
•D
ispla
y reso
lutio
n (p
ixel co
un
t)
–e
.g. 20
00
x 10
00
= 2 M
illion
Re
al-tim
e R
end
ering 4
Mo
tivatio
n (3)
•T
od
ay’s b
est
pe
ak
perfo
rma
nce
–6
00
M vertices/s →
10 M
vertices/fra
me
!
–6
G fra
gm
ents/s →
100
M fra
gm
en
ts/fram
e!
•R
eality
–La
test ha
rdw
are n
ot a
vaila
ble o
n a
ll system
s
–Slo
wd
ow
n b
y com
plex
sha
der
pro
gra
ms
–M
ulti p
ass ren
de
ring
–N
on
pe
rfect lo
ad
ba
lan
cing
→S
pe
ed
up
sha
din
g &
redu
ce g
eo
me
try
2
Rea
l-time R
end
ering 5
Overview
•Sh
ad
ing
Acce
lera
tion
–O
ptim
izatio
n o
f Gra
ph
ics Pip
eline
•G
eo
me
try Re
du
ction
–S
pa
tial D
ata
Stru
cture
s
–C
ullin
g T
ech
niq
ues
–Levels o
f De
tail
Re
al-tim
e R
end
ering 6
Fram
eB
uffer
Frag
men
t P
rocesso
r
Tex
ture
Sto
rag
e+
Filtering
Ra
sterizerG
eom
etry P
rocesso
rG
eom
etry S
tora
ge
CP
U
Ve
rtices
Pix
els
Simp
lified G
rap
hics P
ipelin
e
Pip
eline slid
es co
urtesy Jo
hn
Sp
itzer -N
VID
IA
Rea
l-time R
end
ering 7
Fram
eB
uffer
Frag
me
nt
Pro
cesso
r
Tex
ture
Sto
rag
e+
Filterin
g
Ra
sterizerG
eo
me
try
Pro
cesso
rG
eo
me
try
Sto
rag
eC
PU
CP
Utra
nsfe
rtra
nsfo
rmra
ster
textu
refra
gm
ent
fram
eb
uffer
Ve
rtex
Bo
un
dP
ixe
l Bo
un
d
Po
ssible P
ipelin
e Bo
ttlenecks
CP
U/B
us
Bo
un
d
Re
al-tim
e R
end
ering 8
Ba
ttle Pla
n fo
r Better
Perfo
rma
nce
•Lo
cate
the
bo
ttlen
eck(s)
•E
limin
ate
the
bo
ttlen
eck
(if po
ssible)
–D
ecrea
se wo
rkloa
d o
fth
e b
ottle
ne
cked
stag
e
•O
the
rwise
, ba
lan
ce th
e
pip
elin
e–
Incre
ase w
orklo
ad
of th
e n
on
-bo
ttlenecked
stag
es
3
Rea
l-time R
end
ering 9
Bo
ttleneck Id
entifica
tion
Ru
n A
pp
Va
ryFra
me
Bu
ffer
FPS
varies?
Fram
eB
uffe
rlim
ited
Va
ry te
xtu
resize/filterin
gFP
Sva
ries?
Va
ry reso
lutio
nFP
Sva
ries?
Tex
ture
limite
d
Va
ry
frag
me
nt
instru
ction
s
FPS
varie
s?
Va
ry v
erte
xin
structio
ns
FPS
varies?
Tra
nsfo
rmlim
ited
Va
ry v
erte
x size
/A
GP
rate
FPS
varies?
Tra
nsfe
rlim
ited
Frag
men
tlim
ited
Ra
sterlim
ited
CP
Ulim
ited
Ye
s
No
No
No
No
No
No
Ye
s
Ye
s
Ye
s
Ye
s
Ye
s
Re
al-tim
e R
end
ering 10
Fram
eB
uffer
Frag
men
t P
rocesso
r
Tex
ture
Sto
rag
e +
Filtering
Ra
sterizerG
eom
etry P
rocesso
rG
eom
etry S
tora
ge
CP
U
CP
Utra
nsfer
tran
sform
raste
rtex
ture
frag
men
tfra
me
bu
ffer
Ve
rtex
Bo
un
dP
ixe
l Bo
un
d
CP
U B
ottlen
ecks
CP
U/B
us
Bo
un
d
Rea
l-time R
end
ering 11
CP
U B
ottlen
ecks
•A
pp
licatio
n lim
ited
–sim
ula
tion
, disc I/O
slow
er th
at ren
de
ring
→tu
ne a
pp
licatio
n (p
rofilin
g to
ols)
•D
river o
r AP
I limite
d
–to
o m
an
y state ch
an
ges
–to
o m
an
y AP
I calls
→u
se a
rrays, d
ispla
y lists
Re
al-tim
e R
end
ering 12
Fram
eB
uffer
Frag
men
t P
rocesso
r
Tex
ture
Sto
rag
e+
Filtering
Ra
sterizerG
eom
etry P
rocesso
rG
eom
etry S
tora
ge
CP
U
CP
Utra
nsfer
tran
sform
raste
rtex
ture
frag
men
tfra
me
bu
ffer
Ve
rtex
Bo
un
dP
ixe
l Bo
un
d
Geo
metry T
ran
sfer B
ottlen
ecks
CP
U/B
us
Bo
un
d
4
Rea
l-time R
end
ering 13
•V
erte
x d
ata
pro
ble
ms
–size issu
es (ju
st un
der o
r ove
r 32 bytes)
–n
on
-na
tive types (e.g
. do
ub
le, pa
cked b
yte no
rma
ls)
•U
sing
the
wro
ng
AP
I calls
–im
me
dia
te mo
de, n
on
-accelera
ted ve
rtex
arra
ys
→u
se vertex a
rrays +
ind
exed
prim
itives(sh
ared
vertices tra
nsfe
rred o
nly o
nce)
→a
ccess vertice
s in se
qu
entia
l (cach
e friend
ly) pa
ttern
•A
GP
misco
nfig
ured
(rare)
Geo
metry T
ran
sfer B
ottlen
ecks
Re
al-tim
e R
end
ering 14
Fram
eB
uffer
Frag
men
t P
rocesso
r
Tex
ture
Sto
rag
e+
Filtering
Ra
sterizerG
eom
etry P
rocesso
rG
eom
etry S
tora
ge
CP
U
CP
Utra
nsfer
tran
sform
raste
rtex
ture
frag
men
tfra
me
bu
ffer
Ve
rtex
Bo
un
dP
ixe
l Bo
un
d
Geo
metry T
ran
sform
B
ottlen
ecks
CP
U/B
us
Bo
un
d
Rea
l-time R
end
ering 15
Geo
metry T
ran
sform
B
ottlen
ecks •
To
o m
an
y vertices
–U
se levels of d
etail (b
ut b
ewa
re of C
PU
overh
ead
)
–U
se bu
mp
ma
ps to
fake g
eo
me
tric deta
il
•T
oo
mu
ch co
mp
uta
tion
per vertex
–M
ove
pe
r-ob
ject calcu
latio
ns to
CP
U,
save
resu
lts as co
nsta
nts
–M
ake u
se of fu
ll spectru
m o
f instru
ction
set (LIT
, DS
T, S
IN,...)
–M
ake
use
of sw
izzle a
nd
ma
sk op
era
tors
to m
inim
ize MO
Vs
Re
al-tim
e R
end
ering 16
Fram
eb
uffer
Frag
men
t P
rocesso
r
Tex
ture
Sto
rag
e+
Filtering
Ra
sterizerG
eom
etry P
rocesso
rG
eom
etry S
tora
ge
CP
U
CP
Utra
nsfer
tran
sform
raste
rtex
ture
frag
men
tfra
me
bu
ffer
Ve
rtex
Bo
un
dP
ixe
l Bo
un
d
Ra
sterizatio
nB
ottlen
ecks
CP
U/B
us
Bo
un
d
5
Rea
l-time R
end
ering 17
Ra
sterizatio
nB
ottlen
ecks
•R
are
ly the
bo
ttlen
eck
•Sp
eed
influ
enced
by n
um
be
r of vertex
a
ttribu
tes to
be
inte
rpo
late
d
•B
e su
re to
ma
xim
ize d
ep
th cu
lling
efficiency
–C
lea
r dep
th a
t the b
egin
nin
g o
f each
fram
e
–C
oa
rsely so
rt ob
jects fron
t to b
ack
–C
on
strain
nea
r an
d fa
r pla
ne
s to g
eom
etry visib
le in
fram
e
Re
al-tim
e R
end
ering 18
Fram
eB
uffer
Frag
men
t P
rocesso
r
Tex
ture
Sto
rag
e+
Filtering
Ra
sterizerG
eom
etry P
rocesso
rG
eom
etry S
tora
ge
CP
U
CP
Utra
nsfer
tran
sform
raste
rtex
ture
frag
men
tfra
me
bu
ffer
Ve
rtex
Bo
un
dP
ixe
l Bo
un
d
Tex
ture B
ottlen
ecks
CP
U/B
us
Bo
un
d
Rea
l-time R
end
ering 19
Tex
ture B
ottlen
ecks
•R
un
nin
g o
ut o
f tex
ture
me
mo
ry
–T
ex
ture reso
lutio
ns o
nly a
s big
as n
ee
de
d
–A
void
exp
ensive in
tern
al fo
rma
ts (floa
ting
po
int)
–C
om
pre
ss textu
res (16
-bit co
lor d
ep
th fo
r e
nviro
nm
en
t ma
ps a
nd
sha
do
w m
ap
s)
•P
oo
r tex
ture
cach
e u
tilizatio
n
–Lo
calize
textu
re a
ccesse
s
•U
se e
xp
en
sive filtering
(trilinea
r, an
isotro
pic)
on
ly wh
en n
ee
ded
Re
al-tim
e R
end
ering
20
Fram
eB
uffer
Frag
men
t P
rocesso
r
Tex
ture
Sto
rag
e+
Filtering
Ra
sterizerG
eom
etry P
rocesso
rG
eom
etry S
tora
ge
CP
U
CP
Utra
nsfer
tran
sform
raste
rtex
ture
frag
men
tfra
me
bu
ffer
Ve
rtex
Bo
un
dP
ixe
l Bo
un
d
Frag
men
t Bo
ttlenecks
CP
U/B
us
Bo
un
d
6
Rea
l-time R
end
ering 21
Frag
men
t Bo
ttlenecks
•T
oo
ma
ny fra
gm
ents
–C
on
sider u
sing
a d
epth
-on
ly first pa
ss
–sh
ad
e on
ly the
visible
frag
men
ts in su
bse
qu
ent
pa
ss(es)
•T
oo
mu
ch co
mp
uta
tion
per fra
gm
ent
–U
se a m
ix o
f tex
ture
an
d m
ath
instru
ction
s (th
ey often
run
in p
ara
llel)
–M
ove co
nsta
nt p
er-tria
ng
le calcu
latio
ns to
vertex
p
rog
ram
, send
da
ta a
s textu
re coo
rdin
ates
–D
o sim
ilar w
ith va
lue
s tha
t can
be lin
ea
r in
terpo
lated
Re
al-tim
e R
end
ering 22
Fram
eB
uffer
Frag
men
t P
rocesso
r
Tex
ture
Sto
rag
e+
Filtering
Ra
sterizerG
eom
etry P
rocesso
rG
eom
etry S
tora
ge
CP
U
CP
Utra
nsfer
tran
sform
raste
rtex
ture
frag
men
tfra
me
bu
ffer
Ve
rtex
Bo
un
dP
ixe
l Bo
un
d
Fram
ebu
fferB
ottlen
ecks
CP
U/B
us
Bo
un
d
Rea
l-time R
end
ering 23
Min
imizin
g
Fram
ebu
fferTra
ffic
•R
ed
uce
nu
mb
er of p
asse
s with
lon
ge
r sha
de
rs
•Flo
atin
g p
oin
t fram
e b
uffers o
nly w
hen
nee
ded
•U
se 16
-bit Z
-de
pth
if you
can
ge
t aw
ay w
ith it
Re
al-tim
e R
end
ering 24
Overview
•Sh
ad
ing
Accele
ratio
n
–O
ptim
izatio
n o
f Gra
ph
ics Pip
eline
•G
eom
etry R
edu
ction
–Sp
atia
l Da
ta S
tructu
res
–C
ullin
g T
ech
niq
ues
–Levels o
f De
tail
7
Rea
l-time R
end
ering 25
Spa
tial D
ata
Structu
res
•K
ey to
ge
om
etry re
du
ction
:
–B
ou
nd
ing
Vo
lum
e H
ierarch
ies
–Sp
ace
Pa
rtition
ing
Tre
es
–Scen
e Gra
ph
s
Re
al-tim
e R
end
ering 26
Bo
un
din
g V
olu
mes
•B
ou
nd
ing
Vo
lum
e (B
V)
–V
olu
me
tha
t en
close
s a se
t of o
bje
cts
–Sh
ou
ld b
e mu
ch sim
ple
r ge
om
etrica
l sha
pe
tha
n
con
tain
ed
ob
jects
Sph
ereA
BB
OB
B6
-DO
PC
on
vex h
ull
qu
ality
simp
licity
Rea
l-time R
end
ering 27
Bo
un
din
g V
olu
me
Hiera
rchies
•T
ree
structu
re
•Lea
f no
des co
nta
in g
eom
etry + b
ou
nd
ing
volu
me
•In
terna
l no
des co
nta
in b
ou
nd
ing
volu
me o
f sub
tree
roo
t
Intern
al
no
de
Re
al-tim
e R
end
ering 28
Spa
ce Pa
rtition
ing
Trees
Un
iform
sub
divisio
nQ
ua
dtree
k-dtree
BSP
tree
8
Rea
l-time R
end
ering 29
Bin
ary Sp
ace P
artitio
nin
g
(BSP
) Trees•
Cre
atio
n
–U
se pla
ne
to d
ivide th
e sp
ace
in tw
o su
bsp
ace
s
–So
rt geo
metry in
to th
ese two
sub
spa
ces
–D
ivide su
bsp
ace
s recursive
ly
•T
ype
s
–P
lan
e ax
is or p
olyg
on
alig
ned
•U
sed
in m
an
y ga
me
en
gin
es:
–Q
ua
ke I/II/III, Do
om
I/II, Un
real, S
in, H
alf-Life etc.
Re
al-tim
e R
end
ering
30
Ax
is-Alig
ned
BSP
Trees
0
1a1b
AC E
DBp
lan
e 1a
pla
ne
1b
plane 2
plane 0
AB
C2
DE
•k-d
trees: cycle
thro
ug
h a
xe
s
•A
pp
licatio
ns:
–R
ou
gh
fron
t-to-b
ack so
rting
–O
cclusio
n cu
lling
Rea
l-time R
end
ering 31
Po
lygo
n-A
lign
ed B
SP Trees
•A
po
lygo
n is ch
osen
as th
e divid
er
A
BC
GF
ED
A
D
E
B
V
splitting plane
C
G
F
•C
rea
tion
time co
nsu
min
g•
Ap
plica
tion
s:–
Specia
l type in
pa
inter’s a
lgo
rithm
(no
Z-b
uffer n
eed
ed)
–In
tersection
testing
–C
ollisio
n d
etectio
nR
ea
l-time
Ren
derin
g 32
Octrees
•B
ox
is split sim
ulta
neo
usly a
lon
g a
ll thre
e a
xe
s
–Sp
lit po
int m
ust b
e cen
ter o
f bo
x
–C
rea
tes e
igh
t new
bo
xe
s, hen
ce th
e n
am
e
•In
2D: Q
ua
dtre
es
•R
egu
lar stru
cture
, thu
s efficien
t
•A
pp
licatio
ns:
–V
iew fru
stum
cullin
g
–O
cclusio
n cu
lling
–E
fficient ra
y inte
rsection
s
9
Rea
l-time R
end
ering 33
Scene G
rap
hs
•H
igh
er level d
ata
structu
re
–G
eom
etry, au
gm
ented
with
textu
res, tran
sform
s, le
vels-o
f-deta
il, ligh
t sou
rces
•T
ree
Rep
rese
nta
tion
–D
ep
th-first tra
versa
l for ren
de
ring
–R
oo
t no
de o
ften h
as a
BV
–Le
af sto
res g
eo
me
try
–Severa
l no
des m
ay p
oin
t to sa
me ch
ild n
od
e, thu
s th
e tre
e stru
cture
is a D
irecte
d A
cyclic Gra
ph
(DA
G)
Re
al-tim
e R
end
ering 34
Overview
•Sh
ad
ing
Accele
ratio
n
–O
ptim
izatio
n o
f Gra
ph
ics Pip
eline
•G
eom
etry R
edu
ction
–Sp
atia
l Da
ta S
tructu
res
–C
ullin
g T
ech
niq
ues
–Levels o
f De
tail
Rea
l-time R
end
ering 35
Cu
lling
Tech
niq
ues
•D
on
’t ren
de
r wh
at ca
n’t b
e se
en
–O
ff-scree
n: view-frustum
culling–
Occlu
de
d b
y oth
er o
bje
cts: occlusion culling
•W
hy in
softw
are
?
–O
ff-scree
n g
eo
me
try: solved by clipping–
Occlu
de
d g
eo
me
try: solved by Z-bufferR
ea
l-time
Ren
derin
g 36
Cu
lling
Tech
niq
ues
•Sa
ve ren
derin
g tim
e
–C
lipp
ing
an
d Z
-bu
ffering
take tim
e line
ar to
the
nu
mb
er o
f prim
itives
•C
ullin
g in
softw
are
:
–P
re-cullin
g o
f larg
e nu
mb
er o
f prim
itives
–A
pp
rox
ima
tive
–C
on
servative! K
eep
hid
den
prim
itive OK
, re
mo
ve visible p
rimitive n
ot O
K
–R
ema
ind
er is potentially visible set
or P
VS
•D
o clip
pin
g a
nd
Z-b
ufferin
g o
nly w
ith P
VS
10
Rea
l-time R
end
ering 37
Visib
ility From
a P
oin
t
viewp
oin
t
occlu
de
r
occlu
de
e
sha
do
w vo
lum
e =u
mb
ra
•T
erm
ino
log
y
Re
al-tim
e R
end
ering 38
Occlu
der
Fusio
n
•O
cclud
er
Fusio
nco
mb
ine
s effe
cts of m
ultip
le o
cclud
ers
–V
isible w
itho
ut o
cclud
er
fusio
n
–In
visible w
ith o
cclud
er
fusio
n
viewp
oin
t
2 occlu
de
rs
larg
er sh
ad
ow
vo
lum
e
Rea
l-time R
end
ering 39
Visib
ility From
a R
egio
n
viewin
g
reg
ion
1 occlu
de
r
sha
do
w vo
lum
e
2 occlu
de
rs
XX
X
Re
al-tim
e R
end
ering
40
sha
do
w vo
lum
e
viewin
g
reg
ion
2 occlu
de
rs
sha
do
w vo
lum
e
Visib
ility From
a R
egio
n (2)
XX
X
11
Rea
l-time R
end
ering 41
viewin
g
reg
ion
2 occlu
de
rs
Visib
ility From
a R
egio
n (3)
XX
Xsh
ad
ow
v
olu
me
•A
rea
XX
X is a
lwa
ys occlu
ded
-> com
plete sh
ad
ow
volu
me
is mo
re tha
n
the
un
ion
of in
divid
ua
l sha
do
w vo
lum
es
Re
al-tim
e R
end
ering 42
•S
ep
ara
ting
pla
ne
so
f two
con
vex
po
lyhed
raa
re pla
ne
s fo
rme
d b
y an
ed
ge
of o
ne
po
lyhed
ron
an
d a
verte
x o
f th
e o
the
r such
tha
t the
po
lyhe
dra
lie on
op
po
site sides
of th
e p
lan
e•
Bo
un
din
g p
lan
es
are
simila
r, ex
cept th
at b
oth
p
olyh
ed
ralie o
n th
e sam
e side o
f the p
lan
e
Bo
un
din
g P
lan
es an
d
Sepa
ratin
g P
lan
es
viewin
g
reg
ion
occlu
de
r
bo
un
din
g p
lan
es
sep
ara
ting
pla
ne
s
Rea
l-time R
end
ering 43
•T
he a
rea (vo
lum
e) in fu
ll sha
do
w is th
e um
bra
–S
imp
le in/o
ut cla
ssificatio
n
•T
he a
rea n
ot in
full sh
ad
ow
(gre
y) is the p
en
um
bra
–A
dd
ition
ally en
cod
es wh
ich p
art o
f the
view
ing
reg
ion
is visible
Um
bra
an
d P
enu
mb
ra
viewin
g
reg
ion
occlu
de
r um
bra
pe
nu
mb
ra
pe
nu
mb
ra
Re
al-tim
e R
end
ering 44
Occlu
sion
Cu
lling
From
a R
egio
n
•W
e w
an
t to ca
lcula
te th
e co
mp
lete um
bra
=
all re
gio
ns th
at a
re fu
lly occlu
de
d•
Um
bra
=
um
bra
for e
ach
ind
ividu
al o
cclud
er
+
all reg
ion
s wh
ere p
enu
mb
ra m
erg
es to
um
bra
viewin
g
reg
ion
occlu
de
r um
bra
pe
nu
mb
ra
pe
nu
mb
ra
12
Rea
l-time R
end
ering 45
Exa
mp
le Cu
lling
Meth
od
s
•B
ackfa
cecu
lling
•V
iew fru
stum
cullin
g
•P
orta
l cullin
g
•O
cclusio
n cu
lling
Re
al-tim
e R
end
ering
46
Ba
ckface
Cu
lling
•O
n th
e su
rface
of a
close
d m
an
ifold
, p
olyg
on
s wh
ose n
orm
als
po
int a
wa
y fro
m th
e cam
era
are a
lwa
ys occlu
de
d
Vie
we
r
No
te: b
ackfa
cecu
lling
alo
ne
do
esn
’t solve
the
hid
de
n-su
rface
pro
ble
m!
Rea
l-time R
end
ering 47
Clu
stered B
ackfa
ceC
ullin
g
•G
rou
p o
bje
cts with
simila
r n
orm
al
•C
lassifica
tion
of o
bje
cts:–
All fa
ces fron
t-facin
g->
rend
er all
–A
ll faces b
ack-fa
cing
-> ren
der n
on
e
–So
me
fron
t-facin
g,
som
e b
ack-fa
cing
-> ren
der a
ll
•D
on
e w
ith n
orm
al co
ne
s
α
Co
llected
no
rma
lsfro
m
a set o
f po
lygo
ns
Min
ima
l no
rma
l con
e d
efined
by o
ne n
orm
al
na
nd
a h
alf-a
ng
le α
n
Re
al-tim
e R
end
ering 48
Hiera
rchica
lV
iew Fru
stum
Cu
lling
intersect
intersect
insid
e
insid
e
ou
tside
insid
ein
side
insid
ein
side
Bo
un
din
g V
olu
me
Hie
rarch
y
13
Rea
l-time R
end
ering
49
Po
rtal C
ullin
g
•G
oa
l: wa
lk thro
ug
h a
rchitectu
ral m
od
els (b
uild
ing
s, cities, ca
taco
mb
s)
•T
he
se d
ivide
na
tura
lly into
cells
–R
oo
ms, a
lcove
s, corrid
ors…
•T
ran
spa
ren
t po
rtals
con
nect cells
–D
oo
rwa
ys, entra
nce
s, win
do
ws…
Re
al-tim
e R
end
ering
50
Po
rtal C
ullin
g (2)
•E
xa
mp
le
viewer
Rea
l-time R
end
ering 51
Po
rtal C
ullin
g (3)
•Id
ea
: –
Ce
lls form
the
ba
sic un
it of P
VS
–C
reate a
n a
dja
cency g
rap
ho
f cells
–S
tartin
g w
ith ce
ll con
tain
ing
eyepo
int,
traverse
gra
ph
, ren
derin
g visib
le ce
lls
–A
cell is on
ly visible if it ca
n b
e seen
thro
ug
h
a se
qu
en
ce of p
orta
ls•
So ce
ll visibility red
uces to
testing
po
rtal
seq
ue
nces fo
r a line of sight…
Re
al-tim
e R
end
ering 52
Occlu
sion
Cu
lling
•T
hre
e m
ain
alg
orith
ms:
–Lo
ose
fron
t-to-b
ack so
rting
•U
sing
an
octre
eo
r BS
P tre
e
•S
ortin
g ce
ntro
ids
or n
ea
r po
ints
of b
ou
nd
ing
volu
mes
–H
iera
rchica
l Z-b
uffer
•N
ed
Gre
ene
, SIG
GR
AP
H 9
3
–H
iera
rchica
l occlu
sion
ma
ps
•H
an
son
gZ
ha
ng
, SIG
GR
AP
H 9
7, no
t discu
ssed
14
Rea
l-time R
end
ering 53
Hiera
rchica
l Z-b
ufferin
g
•R
ep
lace
Z-b
uffe
r with
a Z-pyram
id–
Low
est leve
l: full-re
solu
tion
Z-b
uffer
–H
igh
er levels: e
ach
pix
el rep
rese
nts th
e
ma
xim
um
dep
tho
f the
fou
r pix
els “u
nd
ernea
th”
it
•B
asic id
ea: h
iera
rchica
l rasteriza
tion
of
the p
olyg
on
, with
early term
ina
tion
w
he
re p
olyg
on
is occlu
de
d
Re
al-tim
e R
end
ering 54
Hiera
rchica
l Z-b
ufferin
g (2)
•Id
ea: te
st po
lygo
n a
ga
inst h
igh
est leve
l first–
If po
lygo
n is fu
rther th
an
dista
nce
record
ed
in
pix
el, sto
p –
it’s occlu
ded
–If p
olyg
on
is close
r, recu
rsively ch
eck a
ga
inst
nex
t low
er level
–If p
olyg
on
is visible a
t low
est leve
l, set n
ew
d
istan
ce valu
e a
nd
pro
pa
ga
te up
•U
nfo
rtun
ate
ly: no
ha
rdw
are
sup
po
rt
Rea
l-time R
end
ering 55
Overview
•Sh
ad
ing
Acce
lera
tion
–O
ptim
izatio
n o
f Gra
ph
ics Pip
eline
•G
eo
me
try Re
du
ction
–S
pa
tial D
ata
Stru
cture
s
–C
ullin
g T
ech
niq
ues
–Levels o
f De
tail
Re
al-tim
e R
end
ering 56
Level of D
etail
•W
ha
t for?
–E
ven
afte
r visibility, m
od
el ma
y con
tain
too
m
an
y po
lygo
ns
•Id
ea
: redu
ce com
ple
xity fu
rther b
y m
an
ag
ing
level o
f de
tail (LO
D)
–M
ulti-re
solu
tion
mo
de
ling
–P
olyg
on
al sim
plifica
tion
15
Rea
l-time R
end
ering 57
Level of D
etail
Tra
ditio
na
l LOD
•C
rea
te levels of detail(LODs) o
f ob
jects
Co
urte
sy Sta
nfo
rd 3D
Sca
nn
ing
Rep
osito
ry
69
,451 p
olys
2,502 p
olys
251 po
lys76
po
lys
Re
al-tim
e R
end
ering 58
Level of D
etail
Tra
ditio
na
l LOD
•D
istan
t ob
jects use co
arser LO
Ds
•C
ha
ng
es nu
mb
er of ve
rtices
•N
ot n
um
be
r of fra
gm
ents
Rea
l-time R
end
ering 59
Level of D
etail
Issues
•LO
D g
en
era
tion
–Lo
ts of m
eth
od
s
•LO
D se
lectio
n–
Static (ra
ng
e/pro
jected a
rea
-ba
sed)
–R
ea
ctive (re
act to
last fra
me
s ren
de
ring
time)
–P
redictive (co
st/ben
efit mo
del)
•LO
D sw
itchin
g–
Ha
rd sw
itchin
g (p
op
pin
g a
rtifacts!)
–B
lend
ing
(ill-defin
ed b
ecau
se of Z
-bu
ffer!)
–G
eom
orp
h(fo
r con
tinu
ou
s level o
f deta
il)
Re
al-tim
e R
end
ering
60
Level of D
etail
LOD
Gen
eratio
n
•Lo
ts of m
eth
od
s
–D
ecima
tion
–V
ertex M
ergin
g (ed
ge-co
llap
sing
)
–A
da
ptive
sub
divisio
n
•In
tere
sting
: Diffe
ren
t erro
r me
trics!
16
Rea
l-time R
end
ering 61
Level of D
etail
LOD
Selection
•R
an
ge
-ba
sed
–A
ssocia
te d
iffere
nt LO
Ds
with
diffe
ren
t ra
ng
es
r1
r2
r3
LOD
0LO
D1
LOD
2LO
D3
Re
al-tim
e R
end
ering 62
Level of D
etail
LOD
Selection
•P
roje
cted
are
a-b
ase
d
–P
roje
cted
are
a o
f the
bo
un
din
g vo
lum
e
–E
stima
tion
of scre
en
spa
ce co
vera
ge
–E
.g. sp
here
s:
–A
rea
of p
roje
ction
is thu
s
–H
igh
er valu
e selects a
mo
re deta
iled LO
D
)(
vc
d−
⋅=
nrp
2pπ
vdc
r
n
Rea
l-time R
end
ering 63
Level of D
etail
LOD
Switch
ing
•D
iscrete
Ge
om
etry LO
Ds
–C
reate LO
Ds
for ea
ch o
bje
ct sepa
rately
in a
pre
pro
cess
–A
t run
-time
, pick e
ach
ob
ject’s LO
D
acco
rdin
g to
the
ob
ject’s dista
nce
(or
simila
r criterio
n)
–S
ince LO
Ds
are
crea
ted
offlin
e a
t fixe
d
resolu
tion
s, we
call th
is discrete LOD–
De
cou
ple
s simp
lificatio
n a
nd
rend
erin
gR
ea
l-time
Ren
derin
g
64
Level of D
etail
LOD
Switch
ing
(2)
•C
on
tinu
ou
s LOD
s–
Crea
te d
ata
structu
re fro
m w
hich
a d
esired
level o
f de
tail ca
n b
e ex
tracted
at ru
n tim
e.
–B
etter g
ran
ula
rity b
etter fid
elity•
LOD
is spe
cified e
xa
ctly, no
t cho
sen
from
a fe
w
pre-cre
ate
d o
ptio
ns
•T
hu
s ob
jects u
se no
mo
re po
lygo
ns th
an
n
ecessa
ry, wh
ich fre
es up
po
lygo
ns fo
r oth
er o
bje
cts
•N
et result: b
etter resou
rce utiliza
tion
, le
ad
ing
to b
etter o
vera
ll fide
lity/po
lygo
n
17
Rea
l-time R
end
ering 65
Level of D
etail
LOD
Switch
ing
(3)
•C
on
tinu
ou
s LOD
s
–B
etter gra
nu
larity ->
smo
oth
er tra
nsitio
ns
•Sw
itchin
g b
etween
trad
ition
al LO
Ds
can
in
trod
uce
visua
l “po
pp
ing
”effe
ct
•C
on
tinu
ou
s LOD
can
ad
just d
etail g
rad
ua
lly an
d
incre
men
tally, red
ucin
g visu
al p
op
s
•C
an
even g
eom
orp
hth
e fine-g
rain
ed
simp
lificatio
n o
pe
ratio
ns o
ver severa
l fram
es to
elim
ina
te p
op
s [Ho
pp
e 9
6, 9
8]
Re
al-tim
e R
end
ering
66
Level of D
etail
LOD
Switch
ing
(4)
•C
on
tinu
ou
s LOD
s(2)
–Su
pp
orts p
rog
ressive
tran
smissio
n•
Pro
gre
ssive Mesh
es [Ho
pp
e 97]
•P
rog
ressive Fo
rest Sp
lit Co
mp
ressio
n [T
au
bin
98
]
–Lea
ds to
vie
w-d
ep
en
de
nt LO
D
•U
se curre
nt vie
w p
ara
mete
rs to select b
est
rep
rese
nta
tion
for the current view•
Sing
le ob
jects ma
y thu
s spa
n se
veral leve
ls of
deta
il
Rea
l-time R
end
ering 67
Level of D
etail
Co
nclu
sion
s
•G
oo
d b
oo
k an
d w
eb
pa
ge
: lod
bo
ok.co
m
10k
20k
60
k20
0k
200
0k
Mich
ela
ng
elo
’s Da
vid
at d
ifferen
t leve
ls of d
eta
il [Pa
uly 0
2]
In p
oin
ts of th
e orig
ina
l mo
de
l, rend
ered
with
a p
oin
t spla
tter.
Re
al-tim
e R
end
ering
68
Accelera
tion
Alg
orith
ms
Co
nclu
sion
•M
ost visib
ility alg
orith
ms w
ork o
n th
e d
ata
b
efore
it ge
ts do
wn
the
gra
ph
ics pip
elin
e
–Id
ea
l op
timiza
tion
s
–P
rob
lema
tic if CP
U is b
ottlen
eck
•M
ost o
f the
wo
rk in LO
Ds
can
be
do
ne in
a p
re-p
roce
ssing
ph
ase