Biswarup Choudhury
Indian Institute of Technology-Bombay
Sharat Chandran
Indian Institute of Technology-Bombay
Image based Relighting, Survey, Image based Techniques.
Image-based Relighting (IBRL) has recently attracted a lot of research interest for its ability to relight real
objects or scenes, from novel illuminations captured in natural/synthetic environments. Complex lighting ef-
fects such as subsurface scattering, interreflection, shadowing, mesostructural self-occlusion, refraction and
other relevant phenomena can be generated using IBRL. The main advantage of Image-based graphics is that
the rendering time is independent of scene complexity as the rendering is actually a process of manipulating
image pixels, instead of simulating light transport. The goal of this paper is to provide a complete and sys-
tematic overview of the research in Image-based Relighting. We observe that essentially all IBRL techniques
can be broadly classified into three categories, based on how the scene/illumination information is captured:
Reflectance function based, Basis function based, and Plenoptic function based. We discuss the characteristics
of each of these categories and their representative methods. We also discuss about sampling density and types
of light source, relevant issues of IBRL.
Image-based Modeling and Rendering (IBMR) syn-
thesizes realistic images from pre-recorded images
without a complex and long rendering process as in
traditional geometry-based computer graphics. The
major drawback of IBMR is its inherent rigidity. Most
IBMR techniques assume that the static illumina-
tion condition. Obviously, these assumptions cannot
fully satisfy the computer graphics needs since illu-
mination modification is a key operation in computer
The ability to control illumination of the modeled
scene, enhances the three-dimensional illusion, which
in turn improves viewers’ understanding of the envi-
ronment (Fig. 1). If the illumination can be modified
by relighting the images, instead of rendering the geo-
metric models, the time for image synthesis will be
independent of the scene complexity. This also saves
the artist/designer enormous amount of time in fine
tuning the illumination conditions to achieve realistic
atmospheres. Applications range from global illumi-
nation and lighting design to augmented and mixed
reality, where real and virtual objects are combined
with consistent illumination. Two major motivations
for IBRL are :
Allows the user to vary illuminance of the whole
(or only interesting portions of the) scene improv-
ing recognition and satisfaction.
Brings us a step closer to realizing the use of
image-based entities as the basic rendering primi-
For didactic purposes, we classify image-based re-
lighting techniques into three categories, namely:
Reflectance-based, Basis Function-based, Plenoptic
Function-based. These categories should be actually
viewed as a continuum rather absolute discrete ones,
since there are techniques that defy these strict cate-
Reflectance Function-based Relighting tech-
niques explicitly estimate the reflectance function at
each visible point of the object or scene. This is
also known as the Anisotropic Reflection Model (Ka-
jiya, 1985) or the Bidirectional Surface Scattering
Reflectance Distribution Function (BSSRDF) (Jensen
et al., 2001). It is defined as the ratio of the outgoing
to the incoming radiance. Reflectance estimation can
be achieved with calibrated light setup, which provide
full control of the incident illumination. A reflectance
function is modeled with the data of the scene, cap-
Choudhury B. and Chandran S. (2006).
In Proceedings of the First International Conference on Computer Graphics Theory and Applications, pages 176-183
DOI: 10.5220/0001359201760183
(a) (b) (c)
Figure 1: IBRL: The skull is relighted with the illumination of different environments. Notice that the lighted and dark parts
of the skull correctly corresponds to the lighting in the scene.
tured under varying illumination and view direction.
Techniques then apply novel illumination and use the
reflectance function calculated, for generating novel
illumination effects in the scene.
Basis Function-based Relighting techniques take
advantage of the linearity of the rendering operator
with respect to illumination, for a fixed scene. Re-
rendering is accomplished via linear combination of a
set of pre-rendered “basis” images. These techniques,
for the purpose of computing a solution determine a
time-independent basis - a small number of “genera-
tive” global solutions - that suffice to simulate the set
of images under varying illumination and viewpoint.
Plenoptic Function-based Relighting techniques
are based on the computational model, the Plenoptic
Function (Adelson and Bergen, 1991). The original
plenoptic function aggregates all the illumination and
the scene changing factors in a single “time” parame-
ter. So most research concentrates on view interpo-
lation and leaves the time parameter untouched (illu-
mination and scene static). The plenoptic function-
based relighting techniques extract out the illumina-
tion component from the aggregate time parameter,
and facilitate relighting of scenes.
The remainder of the paper is organized as follows:
Section 2, Section 3 and Section 4 discuss each of
the three relighting categories, along with their repre-
sentative methods. In Section 5, we discuss some of
the other relevant issues of relighting. We then pro-
vide some directions of future research in Section 6.
Finally, we provide our concluding remarks in Sec-
tion 7.
A reflectance function is the measurement of how ma-
terials reflect light, or more specifically, how they
transform incident illumination into radiant illumi-
nation. The Bidirectional Reflectance Distribution
Function (BRDF) (Nicodemus et al., 1977) is a gen-
eral form of representing surface reflectivity. A bet-
ter representation is the Bidirectional Surface Scat-
tering Reflectance Distribution Function (BSSRDF)
(Jensen et al., 2001), which model effects such as
color bleeding, translucency and diffusion of light
across shadow boundaries, otherwise impossible with
a BRDF model. As introduced by (Debevec et al.,
2000), the reflectance function R, an 8D function, de-
termines the light transfer between light entering a
bounding volume at a direction and position ψ
and leaving at ψ
R = R(ψ
This calculated reflectance function can be used to
compute relit images of the objects, lit with novel il-
lumination. The computation for each relit pixel is
reduced to multiplying corresponding coefficients of
the reflectance function and the incident illumination.
where, is the space of all light directions over a
hemisphere centered around the object to be illumi-
nated (ω ). For every viewing direction, each
pixel in an image stores its appearance under all il-
lumination directions. Thus each pixel in an image is
a sample of the reflectance function.
We classify the estimation of reflectance functions
into three different categories: Forward, Inverse and
Pre-computed radiance transport.
2.1 Forward
The forward methods of estimating reflectance func-
tions sample these functions exhaustively and tabu-
late the results. For each incident illumination, they
store the reflectance function weights for a fixed ob-
served direction. The forward method of estimating
reflectance functions can further be divided into two
categories, on the basis of illumination information
provided, Known and Unknown.
The techniques with known illumination incorpo-
rate the information in their setup. The user is pro-
vided full control over the direction, position and
(a) Illumination 1 (b) Relit face using Illumi-
nation 1 of Fig 2(a)
(c) Illumination 2 (d) Relit face using Illumi-
nation 2 of Fig 2(c)
Figure 2: Mirrored ball, representing illumination of an environment, used for relighting faces(Debevec et al., 2000).
type of incident illumination. This information is di-
rectly used for finding the reflectance properties of the
scene. (Debevec et al., 2000) use the highest reso-
lution incident illumination with roughly 2000 direc-
tions and construct a reflectance function for each ob-
served image pixel from its values over the space of il-
lumination directions (Fig. 2). (Masselus et al., 2004)
sample the reflectance functions from real objects
by illuminating the object from a set of directions
while recording the photographs. They reconstruct a
smooth and continuous reflectance function, from the
sampled reflectance functions, using the multilevel B-
spline technique. (Masselus et al., 2003) exploit the
richness in the angular and spatial variation of the
incident illumination, and measure six-dimensional
slices of the eight-dimensional reflectance field, for
a fixed viewpoint. On the other hand, (Malzbender
et al., 2001) store the coefficients of a biquadratic
polynomial for each texel, thereby improving upon
the compactness of the representation, and uses it to
reconstruct the surface color under varied illumina-
tion conditions.
(Wong et al., 1997), (Wong et al., 2001) propose a
concept of apparent-BRDF to represent the outgoing
radiance distribution passing through the pixel win-
dow on the image plane. By treating each image
as an ordinary surface element, the radiance distrib-
ution of the pixel under various illumination condi-
tions is recorded in a table. (Koudelka et al., 2001)
samples the surface’s incident field to reconstruct a
non-parametric apparent BRDF at each visible point
on the surface. (Boivin and Gagalowicz, 2001) itera-
tively produces an approximation of the reflectance
model of diffuse, specular, isotropic or anisotropic
textured objects using a single image and the 3D geo-
metric model of the scene.
Techniques with unknown incident illumination
information estimate it. (Nishino and Nayar, 2004)
use eyes of a human subject and compute a large field
of view of the illumination distribution of the envi-
ronment surrounding a person, using the characteris-
tics of the imaging system formed by the cornea of an
eye and a camera viewing it. Their assumption of a
human subject in the scene, at all times, may not be
practical though. (Lensch et al., 2003) used six steel
spheres to recover the light source positions. They fit
an average BRDF function to the different materials
of the objects in the scene. Some other techniques
(Fuchs et al., 2005),(Tchou et al., 2004) indirectly
compute the incident illumination information by us-
ing black snooker ball/non-metallic sphere. A very
early work of IBRL, Inverse Rendering (Marschner,
1998), solves for unknown lighting and reflectance
properties of a scene, for relighting purposes.
2.2 Inverse
The inverse problem of estimation of reflectance func-
tions can be be stated as follows: Given an observa-
tion, what are the weights and parameters of the basis
functions that best explain the observation?
Inverse methods observe an output and compute the
probability that it came from a particular region in the
incident illumination domain. The incident illumi-
nation is typically represented by a bounded region,
such as an environment map, which is modeled as a
sum of basis functions [rectangular (Zongker et al.,
1999) or Gaussian kernels (Chuang et al., 2000)].
They capture an environment matte, which in addition
to capturing the foreground object and its traditional
matte, also describes how the object refracts and re-
flects light. This can then be placed in a new environ-
ment, where it will refract and reflect light from that
scene. Techniques (Matusik et al., 2004), (Peers and
Dutre, 2003) have been proposed which progressively
refine the approximation of the reflectance function
with an increasing number of samples.
For a more accurate reflectance estimation, (Ma-
tusik et al., 2002) combine a forward method (De-
bevec et al., 2000) for the low-frequency surface re-
flectance function and an inverse method, environ-
(a) View 1: Relit Scene 1 (b) View 1: Relit Scene 2 (c) View 2: Relit Scene 1 (d) View 2: Relit Scene 2
Figure 3: Pre-computed radiance transport based IBRL, depicting all-frequency shadows, reflections and highlights along
with view variation (Ng et al., 2004).
ment matting (Chuang et al., 2000), for the high-
frequency surface reflectance function. This is used
for capturing all the complex lighting effects, like
high-frequency reflections and refractions.
2.3 Pre-computed Radiance
A global transport simulator creates functions over
the object’s surface, representing transfer of ar-
bitrary incident lighting, into transferred radiance
which includes global effects like shadows, self-
interreflections, occlusion and scattering effects.
When the actual lighting condition is substituted at
run-time, the resulting model provides global illumi-
nation effects.
The radiance transport is pre-computed using a de-
tailed model of the scene (Sloan et al., 2002). To im-
prove upon the rendering performance, the incident
illumination can be represented using spherical har-
monics (Kautz et al., 2002), (Ramamoorthi and Han-
rahan, 2001), (Sloan et al., 2002) or wavelets (Ng
et al., 2003). The reflectance field, stored per vertex
as a transfer matrix, can be compressed using PCA
(Sloan et al., 2003) or wavelets (Ng et al., 2003).
(Ng et al., 2004) focuses on relighting for chang-
ing illumination and viewpoint, while including all-
frequency shadows, reflections and lighting (Fig. 3).
They propose a novel triple product integrals based
technique of factorizing the visibility and the mate-
rial properties. Recently, (Wang et al., 2005) pre-
sented a method of relighting translucent objects un-
der all-frequency lighting. They apply a two-pass hi-
erarchical technique for computing non-linearly ap-
proximated transport vectors due to diffuse multiple
Basis Function based techniques decompose the lumi-
nous intensity distributions into a series of basis func-
tions, and illuminances are obtained by simply sum-
ming luminance from each light source whose lumi-
nous intensity distribution obey each basis function.
Assuming multiple light sources, luminance at a cer-
tain point is obtained by calculating the luminance
from each light source and summing them. In gen-
eral, luminance calculation obeys the two following
rules of superposition:
1. The image resulting from an additive combination
of two illuminants is just the sum of the images
resulting from each of the illuminations indepen-
2. Multiplying the intensity of the illumination
sources by a factor of α results in a rendered im-
age that is multiplied by the same factor α.
These techniques calculate luminance in the case of
alterations in the luminous distributions and the di-
rection of light sources. The luminous intensity dis-
tribution of a point light source is expressed as the
sum of a series of basis distributions. Luminance due
to light source, whose luminance intensity distribu-
tion corresponds to one of the basis distributions, is
calculated in advance and stored as basis luminance.
Using the aforementioned property 1, the luminance
due to the light source with luminous intensity dis-
tribution, is calculated by summing the pre-calculated
basis luminances corresponding to each individual ba-
sis distribution. Using property 2, the luminance due
to a light source, whose luminous intensity distribu-
tion can be expressed as the weighted sum of the ba-
sis distributions, is obtained by multiplying each basis
luminance with corresponding weights and summing
them. Thus, once the basis luminance is calculated
in the pre-process, the resulting luminance can be ob-
tained quickly by calculating the weighted sum of the
basis luminances. Some desirable properties of a ba-
sis set of illumination functions are:
1. The basis functions should be general enough to
form any light source, one desires.
2. The number of basis functions should be small,
since this corresponds to the number of basis im-
ages we must actually store and render.
We classify the type of basis functions (used in Re-
lighting) into five categories and provide their corre-
sponding representative methods:
1. Steerable Functions(Nimeroff et al., 1994).
2. Spherical Harmonics Function (Dobashi et al.,
3. Singular Value Decomposition (Principal Compo-
nent Analysis) (Georghiades et al., 2001), (Osad-
chy and Keren, 2001), (Hawkins et al., 2004).
4. N-mode SVD: Multilinear Algebra of higher-order
Tensors (Vasilescu and Terzopoulos, 2004), (Fu-
rukawa et al., 2002), (Suykens et al., 2003), (Tong
et al., 2002).
5. Sampling Illumination Space (Masselus et al.,
2002), (Wenger et al., 2005), (Georghiades, 2003).
The appearance of the world can be thought of
as the dense array of light rays filling the space,
which can be observed by posing eyes or cameras in
space. These light rays can be represented through the
plenoptic function (from plenus, complete or full; and
optics) (Adelson and Bergen, 1991). The plenoptic
function is a 7D function that models a 3D dynamic
environment by recording the light rays at every space
location (V
), towards every possible direc-
tion (θ, φ), over any range of wavelengths (λ) and at
any time (t), i.e.,
P = P
An image of a scene with a pinhole camera records
the light rays passing through the camera’s center-of-
projection. They can also be considered as samples
of the plenoptic function. Basically, the function tells
us how the environment looks when our eye is posi-
tioned at V =(V
). The time parameter t actu-
ally models all the other unmentioned factors such as
the change of illumination and the scene.
Plenoptic Function-based relighting techniques
propose new formulations of the plenoptic function,
which explicitly specify the illumination component.
Using these formulations, one can generate complex
lighting effects. One can simulate various lighting
configurations such as multiple light sources, light
sources with different colors and also arbitrary types
of light sources (Section 5.1).
4.1 Representative Techniques
(Wong and Heng, 2004) discuss a new formulation of
the plenoptic function, Plenoptic Illumination Func-
tion, which explicitly specifies the illumination com-
ponent. They propose a local illumination model,
which utilizes the rules of superposition for relight-
ing under various lighting configurations. (Lin et al.,
2002) on the other hand, propose a representation
of the plenoptic function, the reflected irradiance
field. The reflected irradiance field stores the reflec-
tion of surface irradiance as an illuminating point light
source moves on a plane. With the reflected irradiance
field, the relit object/scene can be synthesized simply
by interpolating and superimposing appropriate sam-
ple reflections.
In this section, we discuss some of the relevant issues
involving IBRL.
5.1 Light Source Type
Illumination is a complex and high-dimensional func-
tion of computer graphics. To reduce the dimension-
ality and to analyze their complexity and practical-
ity, it is necessary to assume a specific type of light
source. Two types of light sources most commonly
used are:
1. Directional Light Source (DLS): A DLS emits
parallel rays which do not diverge or become dim-
mer with distance. It is parametrized using only
two variables (θ, φ), which denotes the direction of
the light vector. For planar surfaces lighted by a
DLS, the degree of shading will be the same right
across the surface. The computations required for
directional lights are therefore considerably less.
Using a DLS is also more meaningful, because the
captured pixel value in an image tells us what the
surface elements behind the pixel window actually
look like, when all surface elements are illuminated
by parallel rays in the direction of the viewing
point. DLS serves well with synthetic object/scene
where it is used to approximate the light coming
from an extremely distant light source. But it poses
practical difficulties for capturing real and large ob-
ject/scene. They can be approximated with strong
spotlights at a distance which greatly exceeds the
size of the object/scene.
2. Point Light Source (PLS): A PLS shines uni-
formly in all directions. Its intensity decreases with
the distance to the light source. A PLS is para-
metrized using three variables (P
), which
denote the 3D position of the PLS in space. As
a result, the angle between the light source and
the normals of the various affected surfaces can
change dramatically from one surface to the next.
In the presence of multiple light sources, this means
that for every vertex, one has to determine the
direction of the light vector corresponding to a
light source. This requires determination of the
depth map of the images using computer vision al-
gorithms, which though provide good approxima-
tions, make the lighting calculations computation-
ally intensive. Point light source are usually close
to the observer and so more practical for real and
large objects/scenes.
5.2 Sampling
Sampling is one of the key issues of image-based
graphics. It is a non-trivial problem because it in-
volves the complex relationship among three ele-
ments: the depth and texture of the scene, the number
of sample images, and the rendering solution. One
needs to determine the minimum sampling rate for
anti-aliased image-based rendering. Comparatively,
very little research (Chai et al., 2000), (Shum and
Kang, 2000), (Zhang and Chen, 2004), (Zhang and
Chen, 2001), (Zhang and Chen, 2003) has gone into
trying to tackle this problem.
In the context of IBRL, sampling deals with the il-
lumination component for efficient and realistic re-
lighting (Wong and Heng, 2004). (Lin et al., 2002)
prove that there exists a geometry-independent bound
of the sampling interval, which is analytically bound
to the BRDF of the scene. It ensures that the intensity
error in the relit image is smaller than a user-specified
tolerance, thus eliminating noticeable artifacts.
A lot of research remains to be done in IBRL. Some
ideas are:
1. Efficient Representation: BRDF function-based
IBRL techniques require huge number of samples
to accurately estimate a reflectance function. Most
techniques, for practical purposes, consider low-
frequency components, which compromises with
the visual quality of the rendered image. Almost
all Basis function-based techniques also require a
number of basis images for relighting. Thus, a lot
of research is required to find accurate and efficient
representations of a scene, which capture all the
complex phenomenas of lighting and reflectance
functions. A related area which deserves consid-
erable investigation, is IBRL for real and large en-
2. Sampling: Most techniques do not deal with the
minimum sampling density required for anti-aliased
IBRL. (Lin et al., 2002) discuss about a geometry-
independent sampling density based on radiomet-
ric tolerance. Though this serves our purpose of ef-
ficient sampling of certain scenes, what we need is
a photometric tolerance, which takes into account
the response function of human vision. (Dumont
et al., 2005) discuss the importance of psychophys-
ical quality scale for realistic IBRL of glossy sur-
3. Compression: No matter how much the storage
and memory increase in the future, compression
is always useful to keep the IBRL data at a man-
ageable size. A high compression ratio in IBRL
relies heavily on how good the images can be pre-
dicted. The sampled images for IBL, usually have a
strong inter-pixel and intra-pixel correlation, which
needs to be harnessed for efficient compression.
Currently, techniques such as spherical harmonics,
vector quantization, direct cosine transform and
spherical wavelets are used for compressing the
datasets of IBRL, but all of these have their own
inherent disadvantages.
4. Dynamics: Most IBRL techniques deal with sta-
tic environments, in terms of change in geome-
try of the scene/object. With the development of
high-end graphics processors, it is conceivable that
IBRL can be applied to dynamic environments.
We have surveyed the field of Image-based Relight-
ing. In particular, we observe that IBRL techniques
can be classified into three categories based on how
they capture the scene/illumination information: Re-
flectance Function-based, Basis Function-based and
Plenoptic Function-based. We have presented each
of the categories along with their corresponding rep-
resentative methods. Relevant issues of IBRL like
type of light source and sampling have also been dis-
It is interesting to note the trade-off between geom-
etry and images, needed for anti-aliased image-based
rendering. Efficient representation, realistic render-
ing, limitations of computer vision algorithms and
computational costs should motivate researchers to
invent efficient Image-based Relighting techniques in
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