Analysis of Lighting Effects
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Analysis of Lighting Effects
Outline: The problem Lighting models Shape from shading Photometric stereo Harmonic analysis of lighting
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Applications
Modeling the effect of lighting can be used for Recognition – particularly face recognition Shape reconstruction Motion estimation Re-rendering …
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Lighting is Complex
Lighting can come from any direction and at any strength
Infinite degree of freedom
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Issues in Lighting
Single light source (point, extended) vs. multiple light sources
Far light vs. near light Matt surfaces vs. specular surfaces Cast shadows Inter-reflections
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Lighting
From a source – travels in straight lines Energy decreases with r2 (r – distance from
source) When light rays reach an object
Part of the energy is absorbed Part is reflected (possibly different amounts
in different directions) Part may continue traveling into the object,
if object is transparent / translucent
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Specular Reflectance
When a surface is smooth light reflects in the opposite direction of the surface normal
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Specular Reflectance
When a surface is slightly rough the reflected light will fall off around the specular direction
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Lambertian Reflectance
When the surface is very rough light may be reflected equally in all directions
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Lambertian Reflectance
When the surface is very rough light may be reflected equally in all directions
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Lambertian Reflectance
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Lambert Law
n̂
cos ( 90 !)ˆ ˆ( , )
o
T
I E
I l n l E l n n
or
l̂
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BRDF
A general description of how opaque objects reflect light is given by the Bidirectional Reflectance Distribution Function (BRDF)
BRDF specifies for a unit of incoming light in a direction (θi,Φi) how much light will be reflected in a direction (θe,Φe) . BRDF is a function of 4 variables f(θi,Φi;θe,Φe).
(0,0) denotes the direction of the surface normal. Most surfaces are isotropic, i.e., reflectance in any
direction depends on the relative direction with respect to the incoming direction (leaving 3 parameters)
n̂
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Why BRDF is Needed?
Light from front Light from back
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Most Existing Algorithms
Assume a single, distant point source All normals visible to the source (θ<90°) Plus, maybe, ambient light (constant lighting
from all directions)
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Shape from Shading
Input: a single image Output: 3D shape Problem is ill-posed, many different shapes can
give rise to same image Common assumptions:
Lighting is known Reflectance properties are completely known –
For Lambertian surfaces albedo is known (usually uniform)
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convex
concave
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convex
concave
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HVS Assumes Light from Above
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HVS Assumes Light from Above
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Lambertian Shape from Shading (SFS) Image irradiance equation
Image intensity depends on surface orientation It also depends on lighting and albedo, but those
assumed to be known
ˆ( , ) ( ( , ))I x y R n x y
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Surface Normal
A surface z(x,y) A point on the surface: (x,y,z(x,y))T
Tangent directions tx=(1,0,p)T, ty=(0,1,q)T with p=zx, q=zy
2 2
1ˆ ( , ,1). 1
x y Tt tn p q
p q
ˆ ˆ( , , ) , 1Tx y zn n n n n
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Lambertian SFS
We obtain
Proportionality – because albedo is known up to scale For each point one differential equation in two unknowns, p and q
But both come from an integrable surface z(x,y) Thus, py= qx (zxy=zyx). Therefore, one differential equation in one unknowns
2 2ˆ( , ) ( , )
1x y zT l p l q l
I x y R p q l np q
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Lambertian SFS
cos 0.5
60
I
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SFS with Fast Marching
Suppose lighting coincides with viewing direction l=(0,0,1)T, then
Therefore
For general l we can rotate the camera
2 2
1
1I
p q
2 2 1x y zl p l q l
Ip q
2 22
1 1p q zI
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Distance Transform
is called Eikonal equation Consider d(x) s.t. |dx|=1 Assume x0=0
2
1 1zI
x0
d
x
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Distance Transform
is called Eikonal equation Consider d(x) s.t. |dx|=1 Assume x0=0 and x0=1
2
1 1zI
x0
d
xx1
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SFS with Fast Marching
- Some places are more difficult to walk than others
Solution to Eikonal equations –using a variation of Dijkstra’s algorithm
Initial condition: we need to know z at extrema Starting from lowest points, we propagate a
wave front, where we gradually compute new values of z from old ones
( , )z F x y
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Results
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Photometric Stereo
Fewer assumptions are needed if we have several images of the same object under different lightings
In this case we can solve for both lighting, albedo, and shape
This can be done by Factorization Recall that
Ignore the case θ>90°
s.t. 0T TI l n l n
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Photometric Stereo - FactorizationGoal: given M, find L and S
M L S
1 1 111 1
1 2
1 2
1 2
3
1 3
.... ..... ..... ....
p x y z
x x
y y
z z pf f f
f fp x y zf p f
I I l l ln nn nn n
I I l l l
What should rank(M) be?
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Photometric Stereo - Factorization Use SVD to find a rank 3 approximation
Define So Factorization is not unique, since
, A invertible
To reduce ambiguity we impose integrability
, 3 3TM U V
, TL U S V
M L S
1 ˆˆ( ) ( )M LA AS L S 1 ˆL̂ LA S AS
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Reducing Ambiguity
Assume We want to enforce integrability Notice that
Denote by the three rows of A, then
From which we obtain
n An
y xp q
, yx
z z
nnp qn n
1 2
3 3
,a n a np qa n a n
1 2
3 3
a n a ny a n x a n
1 2 3, ,a a a
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Reducing Ambiguity Linear transformations of a surface
It can be shown that this is the only transformation that maintains integrability
Such transformations are called “generalized bas relief transformations” (GBR)
Thus, by imposing integrability the surface is reconstructed up to GBR
( , ) ( , )
x
y
y y x x
z x y x y z x yp z pq z q
p p q q
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Relief Sculptures
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Illumination Cone Due to additivity, the set of images of an object
under different lighting forms a convex cone in RN
This characterization is generic, holds also with specularities, shadows and inter-reflections
Unfortunately, representing the cone is complicated (infinite degree of freedom)
= 0.5* +0.2* +0.3*
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Eigenfaces
Photobook/Eigenfaces (MIT Media Lab)
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Recognition with PCA
Amano, Hiura, Yamaguti, and Inokuchi; Atick and Redlich; Bakry, Abo-Elsoud, and Kamel; Belhumeur, Hespanha, and Kriegman; Bhatnagar, Shaw, and Williams; Black and Jepson; Brennan and Principe; Campbell and Flynn; Casasent, Sipe and Talukder; Chan, Nasrabadi and Torrieri; Chung, Kee and Kim; Cootes, Taylor, Cooper and Graham; Covell; Cui and Weng; Daily and Cottrell; Demir, Akarun, and Alpaydin; Duta, Jain and Dubuisson-Jolly; Hallinan; Han and Tewfik; Jebara and Pentland; Kagesawa, Ueno, Kasushi, and Kashiwagi; King and Xu; Kalocsai, Zhao, and Elagin; Lee, Jung, Kwon and Hong; Liu and Wechsler; Menser and Muller; Moghaddam; Moon and Philips; Murase and Nayar; Nishino, Sato, and Ikeuchi; Novak, and Owirka; Nishino, Sato, and Ikeuchi; Ohta, Kohtaro and Ikeuchi; Ong and Gong; Penev and Atick; Penev and Sirivitch; Lorente and Torres; Pentland, Moghaddam, and Starner; Ramanathan, Sum, and Soon; Reiter and Matas; Romdhani, Gong and Psarrou; Shan, Gao, Chen, and Ma; Shen, Fu, Xu, Hsu, Chang, and Meng; Sirivitch and Kirby; Song, Chang, and Shaowei; Torres, Reutter, and Lorente; Turk and Pentland; Watta, Gandhi, and Lakshmanan; Weng and Chen; Yuela, Dai, and Feng; Yuille, Snow, Epstein, and Belhumeur; Zhao, Chellappa, and Krishnaswamy; Zhao and Yang…
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Ball Face Phone Parrot
#1 48.2 53.7 67.9 42.8
#2 84.4 75.2 83.2 69.7
#3 94.4 90.2 88.2 76.3
#4 96.5 92.1 92.0 81.5
#5 97.9 93.5 94.1 84.7
#6 98.9 94.5 95.2 87.2
#7 99.1 95.3 96.3 88.5
#8 99.3 95.8 96.8 89.7
#9 99.5 96.3 97.2 90.7
#10 99.6 96.6 97.5 91.7
Empirical Study
(Yuille et al.)
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0 1 2 30
0.5
1
0 1 2 30
0.5
1
1.5
2
lighting
reflectance
Intuition
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(Light → Reflectance) = Convolution
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(Light → Reflectance) = Convolution
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Spherical Harmonics
Z YX
23 1Z XZ YZ22 YX XY
2 2 2 1X Y Z Positive valuesNegative values
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Harmonic Transform of Kernel
1.023
0.495
-0.111
0.05
-0.029
0.886
-0.5
0
0.5
1
1.5
0 1 2 3 4 5 6 7 8
00
( ) max(cos ,0) n nn
k k Y
nk
n
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37.5
87.599.22 99.81 99.93 99.97
0
20
40
60
80
100
0 1 2 3 4 5 6 7 8
Cumulative Energy
N
1
N
nn
E
(percents)
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Second Order Approximation
21 1 15( ) cos cos 264 2 64
k
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Reflectance Near 9D
0
4 ( )2 1
n
n nm nmn m n
r k k l Yn
Yields 9D linear subspace.
4D approximation (first order) can also be used = point source + ambient
0
2 4 ( )2 1
n
n nm nmn m n
k l Yn
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Harmonic Representations
2(3 1)zn 2 2( )x yn n x yn n x zn n y zn n
zn xn yn
Positive valuesNegative values( , , )x y zn n n n
ρ Albedon Surface normal
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Photometric Stereo
LMS
Image n
:
Image 1
Light n
:
Light 1
*
SVD recovers L and S up to an ambiguity
nz
nz
ny
nz2-1)
nx2-ny
2)
nxny
nxnz
nynz
r r
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Photometric Stereo
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Photometric Stereo
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Summary
Lighting effects are complex Algorithms for SFS and photometric stereo for
Lambertian object illuminated by a single light source
Harmonic analysis extends this to multiple light sources
Handling specularities, shadows, and inter-reflections is difficult