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EECS 274 Computer Vision. Sources, Shadows, and Shading. Surface brightness. Depends on local surface properties (albedo), surface shape (normal), and illumination Shading model: a model of how brightness of a surface is obtained Can interpret pixel values to reconstruct its shape and albedo - PowerPoint PPT Presentation
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EECS 274 Computer Vision
Sources, Shadows, and Shading
Surface brightness
• Depends on local surface properties (albedo), surface shape (normal), and illumination
• Shading model: a model of how brightness of a surface is obtained
• Can interpret pixel values to reconstruct its shape and albedo
• Reading: FP Chapter5, H Chapter 11
Radiometric properties of sources• How bright (or what color)
are objects?• One more definition:
Exitance of a light source is– the internally generated
power (not reflected) radiated per unit area on the radiating surface
• Similar to radiosity: a source can have both– radiosity, because it
reflects– exitance, because it emits
• Independent of its exit angle
• Internally generated energy radiated per unit time, per unit area
• But what aspects of the incoming radiance will we model?– Point, line, area source– Simple geometry
dPLPE oe 00 cos),,()(
Radiosity due to a point sources
• small, distant sphere radius e and exitance E, which is far away subtends solid angle
2
r
Typo in figure, d r
Radiosity due to a point source
id
id
Er
PB
cos
cos termExitanceangle solid2
• As r is increased, the rays leaving the surface patch and striking the sphere move closer evenly, and the collection changes only slightly, i.e., diffusive reflectance, or albedo
• Radiosity due to source
Nearby point source model
• The angle term can be written in terms of N and S• N is the surface normal• ρd is diffuse albedo• S is source vector - a vector from P to the source, whose
length is the intensity term, ε2E– works because a dot-product is basically a cosine
2
2
cosPr
PSPNPE
rPB did
Point source at infinity• Issue: nearby point source
gets bigger if one gets closer– the sun doesn’t for any
reasonable binding of closer
• Assume that all points in the model are close to each other with respect to the distance to the source
• Then the source vector doesn’t vary much, and the distance doesn’t vary much either, and we can roll the constants together to get:
SPNPPB d )(
2)(
Pr
PSPNPPB d
SN
r
SN
Prr
PSSN
Pr
PSPN
20
0
20
02 ))((
))((
Line sources
radiosity due to line source varies with inverse distance, if the source is long enough
Area sources• Examples: diffuser boxes,
white walls.• The radiosity at a point
due to an area source is obtained by adding up the contribution over the section of view hemisphere subtended by the source – change variables and add
up over the source
Radiosity due to an area source
• ρd is albedo• E is exitance• r is distance
between points Q and P
• Q is a coordinate on the source
source
usi
d
usi
source
d
id
ied
iid
dAr
QEP
r
dAQEp
dQE
P
dQPPLP
dQPPLPPB
2
2
coscos
coscos
cos
cos,
cos,
Shading models• Local shading model
– Surface has radiosity due only to sources visible at each point
– Advantages:• often easy to
manipulate, expressions easy
• supports quite simple theories of how shape information can be extracted from shading
• Global shading model– Surface radiosity is due to
radiance reflected from other surfaces as well as from surfaces
– Advantages:• usually very accurate
– Disadvantage:• extremely difficult to
infer anything from shading values
Local shading models
• For point sources at infinity:
• For point sources not at infinity
Pssd
Pss
SPNP
PBPB
from visiblesources
from visiblesources
)()(
)()(
Ps sd Pr
PSPNPPB
from visiblesources2)(
)()()()(
Shadows cast by a point source• A point that can’t see the
source is in shadow (self cast shadow)
• For point sources, the geometry is simple
Area source shadows
Are sources do not produce darkshadows with crisp boundaries
1. Out of shadow2. Penumbra (“almost shadow”)3. Umbra (“shadow”)
Photometric stereo
• Assume:– A local shading model– A set of point sources that are infinitely
distant– A set of pictures of an object, obtained
in exactly the same camera/object configuration but using different sources
– A Lambertian object (or the specular component has been identified and removed)
Projection model for surface recovery - Monge patch
In computer vision, it is often known as height map , depth map, or dense depth map
Monge patch
Image model• For each point source, we
know the source vector (by assumption)
• We assume we know the scaling constant of the linear camera (i.e., intensity value is linear in the surface radiosity)
• Fold the normal and the reflectance into one vector g, and the scaling constant and source vector into another Vj
• Out of shadow:
• g(x,y): describes the surface
• Vj: property of the illumination and of the camera
• In shadow:
j
j
j
j
Vyxg
kSyxNyx
SyxNyxk
yxkByxI
),(
)(),(),(
)),()(,(
),(),(
0),( yxI j
From many views
• From n sources, for each of which Vi is known
• For each image point, stack the measurements
• Solve least squares problem to obtain g
Tn
T
T
V
V
V
V
2
1
Tn yxIyxIyxIyxi )),(,),,(),,((),( 21
),(
),(
),(
),(
2
1
2
1
yxg
V
V
V
yxI
yxI
yxI
Tn
T
T
n
),(),( yxVgyxi
One linear system per point
Dealing with shadows
Known Known Known Unknown
),(
),(00
0
),(0
00),(
),(
),(
),(
2
1
2
1
2
22
21
yxg
V
V
V
yxI
yxI
yxI
yxI
yxI
yxI
Tn
T
T
nn
),(
),(
),(
),(
2
1
2
1
yxg
V
V
V
yxI
yxI
yxI
Tn
T
T
n
),(),( yxVgyxi
Recovering normal and reflectance
• Given sufficient sources, we can solve the previous equation (e.g., least squares solution) for g(x, y)
• Recall that g(x, y) = r (x,y) N(x, y) , and N(x, y) is the unit normal
• This means that (r x,y) =|g(x, y)|• This yields a check
– If the magnitude of g(x, y) is greater than 1, there’s a problem
• And N(x, y) = g(x, y) / (r x,y)
Five synthetic images
Recovered reflectance
|g(x,y)|=ρ(x,y): the value should be in the range of 0 and 1
Recovered normal field
Recovering a surface from normals• Recall the surface is
written as
• Parametric surface
• This means the normal has the form:
• If we write the known vector g as
• Then we obtain values for the partial derivatives of the surface:
)),(,,( yxfyx
11
1),(
22 y
x
yx
f
f
ffyxN
),(
),(
),(
),(
3
2
1
yxg
yxg
yxg
yxg
),(/),(),(
),(/),(),(
32
31
yxgyxgyxf
yxgyxgyxf
y
x
yx
yx
rrN
ky
fjrk
x
fir
,
Recovering a surface from normals (cont’d)• Recall that mixed second partials are equal --- this gives us a
check. We must have:
(or they should be similar, at least)• We can now recover the surface height at any point by
integration along some path, e.g.
x
yxgyxg
y
yxgyxg
)),(/),(()),(/),(( 3231
v u
xy cdxvxfdyyfvuf0 0
),(),0(),(
Recovered surface by integration
v u
xy cdxvxfdyyfvuf0 0
),(),0(),(
The Illumination Cone
N-dimensional Image Space
x1
x2
What is the set of n-pixel images of an object under all possible lighting conditions (at fixed pose)? (Belhuemuer and Kriegman IJCV 99)
xn
Single light source image
The Illumination Cone
N-dimensional Image Space
x1
x2
What is the set of n-pixel images of an object under all possible lighting conditions (but fixed pose)?
Illumination Cone
Proposition: Due to the superposition of images, the set of images is a convex polyhedral cone in the image space.
xn
Single light source images:Extreme rays of cone
2-light source image
For Lambertian surfaces, the illumination cone is determined by the 3D linear subspace B(x,y), where
When no shadows, then Use least-squares to find 3D linear subspace, subject to the
constraint fxy=fyx (Georghiades, Belhumeur, Kriegman, PAMI, June, 2001)
Original (Training) Images
a(x,y) fx(x,y) fy(x,y) albedo (surface normals) Surface. f (x,y) (albedo
textured mapped on surface)
3D linear subspace
Generating the Illumination Cone
i
ii
i syxBsyxnyxyxI )0,),(max()0,),(),(max(),(
i
isyxByxI ),(),(
Single Light Source Face Movie
Image-based rendering: Cast Shadows
Yale face database B
• 10 Individuals• 64 Lighting Conditions• 9 Poses => 5,760 Images
Variable lighting
Curious experimental fact
• Prepare two rooms, one with white walls and white objects, one with black walls and black objects
• Illuminate the black room with bright light, the white room with dim light
• People can tell which is which (due to Gilchrist)
• Why? (a local shading model predicts they can’t).
Global shading models
A view of a black room with black objects. We see a cross-section of the image intensity corresponding to the line drawn on the image.
A view of a white room with white objects. We see a cross-section of the image intensity corresponding to the line drawn on the image.
Can adjust so that local shading model predicts these pictures will be indistinguishable
What’s going on here?
• Local shading model is a poor description of physical processes that give rise to images– because surfaces reflect light onto one another
• This is a major nuisance; the distribution of light (in principle) depends on the configuration of every radiator; big distant ones are as important as small nearby ones (solid angle)
• The effects are easy to model• It appears to be hard to extract
information from these models
Interreflections - a global shading model• Other surfaces are now area sources
- this yields:
• Vis(x, u) is 1 if they can see each other, 0 if they can’t
usi
d dAuxVisuxr
B(u)xxExB ),(),(
coscos)()()(
surfacesother todueRadiosity Exitance surface aat Radiosity
2
sourcesother all
What do we do about this?
• Attempt to build approximations– Ambient illumination
• Study qualitative effects– reflexes– decreased dynamic range– smoothing
• Try to use other information to control errors
Ambient illumination
• Two forms– Add a constant to the radiosity at every point
in the scene to account for brighter shadows than predicted by point source model• Advantages: simple, easily managed (e.g. how would
you change photometric stereo?)• Disadvantages: poor approximation (compare black
and white rooms– Add a term at each point that depends on the
size of the clear viewing hemisphere at each point• Advantages: appears to be quite a good
approximation, but jury is out• Disadvantages: difficult to work with