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Volume 25 (2006), number 1 pp.


Classification of Illumination Methods for Mixed Reality

Katrien Jacobs and Celine Loscos

Department of Computer Science, University College London, UK


A mixed reality (MR) represents an environment composed both by real and virtual objects. MR applications
are used more and more, for instance in surgery, architecture, cultural heritage, entertainment, etc. For some of
these applications it is important to merge the real and virtual elements using consistent illumination. This paper
proposes a classification of illumination methods for MR applications that aim at generating a merged environment
in which illumination and shadows are consistent. Three different illumination methods can be identified: common
illumination, relighting and methods based on inverse illumination. In this paper a classification of the illumination
methods for MR is given based on their input requirements: the amount of geometry and radiance known of the
real environment. This led us to define four categories of methods that vary depending on the type of geometric
model used for representing the real scene, and the sdifferent radiance information available for each point of the
real scene. Various methods are described within their category.

The classification points out that in general the quality of the illumination interactions increases with the amount of
input information available. On the other hand, the accessibility of the method decreases since its pre-processing
time increases to gather the extra information. Recent developed techniques managed to compensate unknown
data with clever techniques using an iterative algorithm, hardware illumination or recent progress in stereovision.
Finally, a review of illumination techniques for MR is given with a discussion on important properties such as the
possibility of interactivity or the amount of complexity in the simulated illumination.
Keywords: Augmented Reality, common illumination, relighting, inverse illumination
ACM CCS: 1.3.6. Computer Graphics: Methodologies and Techniques 1.3.7 Computer Graphics: Three-
Dimensional Graphics and Realism 1.3.9 Computer Graphics: Miscellaneous: Common illumination, relighting,
inverse Illumination 1.4.8 Image Processing and Computer Vision: Scene Analysis

1. Introduction
of virtual elements; it will be referred to as reality and it lies
To understand the concept mixed reality it is necessary to on the left end of the RV continuum. In the second world, vir-
classify the different types of environments that can be gen- tual elements are added to a real scene. This world is referred
erated with a computer. Milgram and Kishino [1,2] present to with the term augmented reality (AR) [35]. In an opposite
such classification based on the amount and type of virtual scenario, the world consists of a virtual environment, aug-
and real elements that constitute the resulting world. In their mented with real elements. This world is consequently called
classification, all possible environments form one continuum an augmented virtuality (AV). The last and fourth world does
called realityvirtuality continuum (RV), see Figure 1. In this not contain any real elements and is therefore labeled as a vir-
continuum, four worlds can be identified that have an out- tual environment (VE); it lies on the right end of the RV con-
spoken character. These four worlds lie next to each other in tinuum. The term mixed reality (MR) refers to those worlds
the RV continuum and might even overlap. The first and most that are a mix of virtual and real elements, or, MR spans the
straightforward of these is the real world without any addition RV continuum. In general, methods that are developed for AR

c 2006 The Authors
Journal compilation 
c 2006 The Eurographics Association and
Blackwell Publishing Ltd. Published by Blackwell Publishing, Submitted November 2004
9600 Garsington Road, Oxford 2DQ, UK and 350 Main Street, Revised August 2005
Malden, MA 02148, USA. 29 Accepted October 2005
30 K. Jacobs and C. Loscos/Classification of Illumination Methods for Mixed Reality

local light interaction between real and virtual objects is in-

corporated in the MR scene. Simulating such effects results
in common illumination. An example of an application that
uses common illumination to improve the MR can be found in
the movie industry. Special effects in movies make an effort
to mix lighting effects and reflections as realistic as possible,
resulting in brilliant graphical effects in recent movies such as
Jurassic Park, Harry Potter and The Lord of the Rings trilogy.
Figure 1: Simplified representation of a realityvirtuality In these movies, computer-generated effects are blended en-
continuum [1,2]. (Courtesy of Milgram and Kishino) tirely with the real footage; usually this is carried out by hand.

focus on real-time applications. Therefore they usually differ Some methods allow to change the original illumination,
from methods that are specifically designed for MR applica- hereby influencing the appearance of virtual and real objects.
tions whose focus can be on non-real-time applications. This An example of an application domain for this method is archi-
paper will discuss the various existing illumination methods tecture. Being able to virtually change the lighting conditions
for MR applications in general. in the real scene makes it possible to see the impact of a new
building in a street under different lighting conditions with-
Two different classes of AR exist; they differ in the re- out the need of recording the real environment under all these
alization of the AR [1]. The first class groups the meth- different conditions. Another application area is crime inves-
ods for semi-transparent or see-through displays, examples tigation [9]: a recording of a scene at a certain time can be
are [6,7]. This first class contains two different see-through changed to the illumination at a different daytime, making
display methods. The first method (optical AR method [3]) it possible to visualize the perception of the criminal at the
projects the virtual objects on a transparent background, most time of the crime. Techniques that focus on virtually chang-
likely the glasses of goggles. The second method (video AR ing the illumination of an existing scene are simply known
method [3]) uses a head-mounted display: a head-mounted as relighting techniques.
camera records the environment and this image is projected
inside the display together with the virtual objects. The sec- The techniques brought together in a third category are
ond class of AR replaces the expensive see-through devices based on more complex estimations of the reflectances in
with a nonimmersive display; it is usually called a computer- the environment in order to provide more accurate results.
augmented reality (CAR). The quality of the immersion is The process of estimating the reflectances (bidirectional re-
higher for the first class than for the second. Nevertheless, flectance distribution function or BRDFs) from an existing
see-through devices are not always required by the applica- lighting system is called inverse illumination. It was origi-
tion: urban planning, architecture and some applications in nally developed to give more realism in computer graphics.
the entertainment industry are satisfied with the second type Reflectance properties of objects were estimated in order to
of CAR display methods. In earlier approaches of AR, virtual reproduce a more realistic simulation of virtual scenes. In
objects were positioned on top of a real environment. Cali- the context of MR, inverse illumination techniques aim at
bration and registration are difficult processes and for long making a correct estimate of the photometric properties of
the focus lied upon taking into account the possible occlu- the objects in the scene. While other techniques search for
sion and collision effects, while no further adaptations on acceptable solutions for the new illumination problem, in-
real and virtual objects were carried out. In other words, after verse illumination makes it possible to produce relit images
the inclusion, no resulting shadows were generated and no that aim to be an exact replica of the real conditions. A full
lighting changes were put through. An AR system of such discussion of the current state of the art of inverse illumi-
kind does not yield a high level of realism, consistency be- nation techniques can be found in [10], while Ramamoorthi
tween objects is restricted to geometric aspects. Nowadays, and Marschner [11] present a tutorial on some of the leading
three illumination techniques can be identified that attempt research work in this area.
to raise the quality of AR and in general MR: common illumi-
nation, relighting and inverse illumination for relighting or At the moment, fairly good techniques exist that can re-
common illumination. These techniques vary in the quality light an augmented scene with a different illumination. It is
of the illumination and in the consistency obtained between getting more difficult to differentiate between virtual objects
the illumination of the real and virtual objects. and real objects. The main limitation of most techniques is the
tedious pre-processing time and the slow update rate, which
The most straightforward method of these three results in excludes real-time applications. When a geometric model of
the addition of shadows in the MR environments. Generating the scene is required, the user will have to create one, usually
shadows is just as important as taking into account occlu- in a semi-manual and error-prone manner. The scene update
sions, since they help situating the objects in the scene and rate is often too slow to allow real-time user interaction, even
give information about the distance between different objects with the current progress in computer hardware and soft-
[8]. A higher level of realism can also be obtained when the ware. The research focus is moving toward using hardware

c 2006 The Authors
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c 2006 The Eurographics Association and Blackwell Publishing Ltd.
K. Jacobs and C. Loscos/Classification of Illumination Methods for Mixed Reality 31

for the calculation instead of software to accelerate compu- The virtual object(s) must look natural. A cartoon-
tation. Early results are promising, but more research needs like virtual object is easily detectable and therefore
to be carried out in this area. This paper does not review all efforts have been made to model objects that look real-
existing work. Instead, it concentrates on illumination tech- istic. One successful technique is image-based modeling,
niques in MR that are meant for large environments. When in which objects are rendered with textures based on real
optimized and extended, these techniques will be widely ap- images.
plicable in real-time applications, for instance see-through The illumination of the virtual object(s) needs to
display in AR. Several techniques exist for relighting human resemble the illumination of the real objects. There are
faces [12,13], or that focus on local objects or simple scenes two possible methodologies to achieve this requirement.
[14,15]. These techniques are classified mainly in the domain Either the illumination pattern of the real scene is known,
of inverse illumination as the emphasis was placed on this as- which in turn is used to illuminate the virtual object or all
pect in the referenced papers. Although these techniques are material properties of all objects in the scene are known
designed for small objects they can be used to build extremely or estimated, which allows the entire scene to be relighted
useful and strong methods for illumination in MR but they with a consistent known illumination pattern.
will not be further discussed in this paper.
If the user can interact with the MR environment, it is
This paper discusses the state of the art of those techniques clearly important that all update computations occur in
that strive to solve the problem of illumination in MR envi- real time. Any delay in the interaction will remind the user
ronments and gives an objective evaluation of their quality. of the fact that what is seen is unreal [16]. The requirement
Section 2 describes in more detail the context of this review of a real-time system is one of the most difficult to achieve,
and the assessment of the criteria on which the classification especially when no pre-processing time is allowed.
is based. Section 3 gives a structured overview of all the il-
lumination methods that were developed for MR. A further 2.2. Assessment of existing techniques
qualitative evaluation and discussion of the different tech-
The ultimate objective of the aforementioned techniques is
niques is given in Section 4. Finally Section 5 gives conclu-
defined by the amount of realism perceived by the user. This
sions and presents the necessary future work for illumination
inherent subjectivity complicates an objective assessment of
methods for MR.
the various techniques. In this section, a few quality criteria
are listed that will be used in Section 4 to assess the presented
2. Problem Assessment methods:
2.1. Objective and difficulties
2.2.1. Amount of realism
The classes described above are not necessarily designed
to lure the users into believing that what they see is real. In some cases, it is impossible to evaluate the amount of
For instance, VR often aims at trying to create the percep- realism without using a statistical measure. For instance, a
tion of a real world, without necessarily using convincing test audience can evaluate the technique, if the test group is
real imagery. Some AR systems merely add data displays large enough, a statistical value can be derived from the group
to real scenes, making no attempt to mix the two seam- evaluation. Alternatively, if the inserted virtual object is an
lessly. This paper considers MR scenes that do try to con- exact replica of an existing real object, it is possible to give an
vince the users of believing that a real world is surrounding exact value of the amount of realism in the produced scene.
them and will use this as a measure to assess the quality of the It suffices to compare the generated scene with an image of
method. the real object in the same scene. The difference between the
two gives a measure of the level of realism.
An MR is convincingly real when it is impossible to sepa-
rate the virtual elements from the real elements in the result- 2.2.2. Input requirements
ing environment. Four critical success factors were identified
that need to be present in the MR in order to be convincingly It is expected that the more input data are available, the higher
real: the quality of the end result will be. On the other hand, the
usability of the system reduces with the complexity of the
input data. Possible input data are: the geometry, the light
After including the virtual object(s), the resulting scene position, the illumination pattern and the material properties.
needs to have a consistent shadow configuration [8]. This report gives a classification of the various techniques
The main difficulty to obeye this requirement is to find based on their input requirement.
the correct appearance of the new shadows: their position
in the scene, shape and colour. Sometimes these are esti- 2.2.3. Processing time
mated, but they can be calculated exactly if the geometry of
the scene, the illumination characteristics and the material The time needed to create the end result is another impor-
properties of all objects in the scene are known. tant characteristic of the method. To offer the user a highly

c 2006 The Authors
Journal compilation 
c 2006 The Eurographics Association and Blackwell Publishing Ltd.
32 K. Jacobs and C. Loscos/Classification of Illumination Methods for Mixed Reality

Figure 2: Results for Sato et al. [17]. The top row shows results for an indoor scene, the bottom row for an outdoor scene. The
images on the left are the input images, the images on the right illustrate the resulting MR. Soft shadows are produced using
local common illumination. (Courtesy of Sato et al.)

realistic interactive environment, the computations need to problem. They were already listed in the introduction and are
be done in real time. Unfortunately, this is very hard to further discussed in this section:
achieve. If geometric and material properties of a scene need
to be known, it is unavoidable that some pre-processing time 2.3.1. Common illumination
needs to be incorporated. In general, the usability of the pro-
posed techniques depends on the amount of pre-processing To this category belong all methods that provide a certain
time needed and the computation speed of the illumination level of illumination blending, like the addition of shadows
technique. projected from real objects on virtual objects and shadows
cast by virtual objects on real objects. These techniques do
not allow any modification of the current illumination of the
2.2.4. Level of automation
scene. Two different types of common illumination can be
If the technique under consideration requires a considerable considered: local and global common illumination, refer-
amount of manual interaction while processing the input data, ring to the type of illumination simulated. For local common
the technique is less interesting than one that is automated. illumination, there is usually no requirement of any BRDF
information. For global illumination, it is often important to
have an estimate of the material properties of the real ob-
2.2.5. Level of interaction
jects. The accuracy of this type of techniques depends on the
A technique can be judged based on its dynamic character: accuracy of the known geometric model of the real scene.
the possibility to change the viewpoint of the camera, or the In Figure 2, an example is given of a rendering using global
possibility to let the user interact with the environment for common illumination [17].
instance by moving around objects. A higher degree of inter-
action gives a greater usability of the method. 2.3.2. Relighting after light removal

Relighting techniques make it possible to change the illu-

2.3. Methodology mination of the scene in two steps. Firstly, the current illu-
mination effects of the real scene are analysed and possibly
The various existing techniques can be grouped into three removed. Secondly, new illumination effects (shadows, inten-
different classes, based on the methodology used to solve the sity changes, addition of a new light, indirect lighting effects,

c 2006 The Authors
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K. Jacobs and C. Loscos/Classification of Illumination Methods for Mixed Reality 33

Figure 3: Results for Loscos et al. [18]. The image on the left-hand side represents the real scene. The image on the right-hand
side shows the relighted synthetic scene for which real light sources have been virtually turned off and a virtual light source is
inserted. Global common illumination updates are performed at interactive rates using an adapted version of [21]. (Courtesy
of Loscos et al.)

Figure 4: Results for Yu et al. [20]. Left: the original input scene. Right: the result of illuminating the original scene using a
different illumination pattern. The specular and diffuse parameters of the real objects are calculated. (Courtesy of Yu et al.)

etc.) are generated based on a new illumination pattern. These 3. Classification of Illumination Methods
methods do not necessarily require an exact knowledge of the for Mixed Reality
BRDF values of the real scene objects as for some methods
the focus lies on generating a scene that looks realistic. These MR brings together those applications that create a new envi-
techniques require in general a detailed geometric model of ronment, around or in front of a user, containing both real and
the real scene. An example of a relighted scene using global virtual elements. Sections 2.1 and 2.2 formulated the objec-
illumination techniques [18] is given in Figure 3. tives, the difficulties encountered and the assessment criteria
of the MR systems. One of these criteria, the type of input
2.3.3. Physically-based illumination requirements, regulates the accessibility and accuracy of the
technique. This criterion will be used to classify the differ-
This last category encloses those methods that make an at- ent methods. An overview of the input information needed
tempt to retrieve the photometric properties of all objects in to calculate a common illumination, relighting or inverse
the scene often referred to by the term inverse illumination. illumination solution is given in Figure 5.
These methods estimate BRDF values as correctly as possible
as well as the emittance and positions of the light sources in The classification put forward firstly takes into account
the real scene. The BRDF values can be estimated using a go- the required geometric model of the real scene, starting with
niometer [19] or can be calculated based on the photometric the techniques that require no geometric model and ending
equilibrium equations [12,15]. The BRDF information can with techniques that require a precise geometric model. In
be used for both common illumination or relighting meth- this paper, a geometric model is defined as a reconstruction
ods. The accurate BRDF estimation often permits to perform of a part of the (or the entire) real scene with significant de-
a complete and realistic relighting, which takes both reflec- tail. The pre-processing workload for techniques that extract
tions and global illumination techniques into account. Patow a basic geometric model, e.g. the depth at a low resolution,
and Pueyo [10] give an in-depth overview of inverse illumi- is significantly lower than those methods that do require a
nation techniques. An example of inverse global illumination high-level geometric model. Therefore techniques using ba-
[20] is illustrated in Figure 4. sic geometric information are classified under that group of

c 2006 The Authors
Journal compilation 
c 2006 The Eurographics Association and Blackwell Publishing Ltd.
34 K. Jacobs and C. Loscos/Classification of Illumination Methods for Mixed Reality

Figure 5: An overview of the dataflow in illumination calculation for MR. Three input sources are considered: the scene
geometry, the scene radiance and information about the to be inserted object(s). These are used to calculate the illumination
solution. The accuracy of the integration depends on the amount of input information available.

methods that do not require a geometric model, as this will difficult to model accurately. Instead of using a scanning de-
give a better indication of the amount of pre-processing time vice, modeling techniques based on stereovision can be used
required for each different class. to reconstruct the geometry of a scene. Most methods de-
scribed in this survey requiring a 3D model of the scene
Two different approaches exist to reconstruct a geometric make use of this low cost solution. In general, the 3D re-
model of the real scene. Either the scene is scanned with a construction requires at least two images from two different
scanning device [2224] or it is reconstructed using stere- viewpoints. However, the entire geometry of a scene cannot
ovision [2528]. The first option of using a scanning de- be captured with one image pair only, this would create gaps
vice gives a precise geometric model but is expensive and in the known geometry. Usually more than one image pair is
tedious. Often the model will capture too much detail, which required for a complete geometric reconstruction. The ease
is not always necessary and is difficult to manage for real- at which this reconstruction can take place depends on the
time applications. Objects such as trees and objects with a amount of information that is available for the camera(s) used.
highly specular surface are for some scanning techniques If the internal and external camera parameters are known, the

c 2006 The Authors
Journal compilation 
c 2006 The Eurographics Association and Blackwell Publishing Ltd.
K. Jacobs and C. Loscos/Classification of Illumination Methods for Mixed Reality 35

reconstruction is easier. Internal parameters can be estimated 2. Model of the real scene known, one image known
in a relatively simple way. Recording the external parameters (Section 3.2): a geometric model of the real scene is
is more difficult and involves a precise and tedious capture available. Any necessary radiance information is ex-
process. Fortunately, the fundamental matrix of the stereovi- tracted from one image only.
sion system can be estimated based on the internal parameters 3. Model of the real scene known, few images known
only, if at least eight corresponding points are known [25,26]. (Section 3.3): again a geometric model of the scene is
This was reduced to six known points [27,28] for a calibra- required. For a certain point in the scene, radiance in-
tion relative to a scale factor, which is often sufficient. Having formation is available from a few different images.
the fundamental matrix can ease the reconstruction but does
4. Model of the real scene known, many images known
not make it trivial. Research led to different types of sys-
(Section 3.4): this class groups those techniques that
tems: nonconstraint systems [15, 29] and constraint systems
require both a detailed geometric model of the real scene
[3033]. Good commercial reconstruction software [3437]
and radiance information from a large set of different
exists, but most of them lack the option of reconstructing
complex shapes and large environments. In general, we can
conclude that systems requiring geometric information de-
mand a long pre-processing time, and are not guaranteed to The rest of this section lists the most significant meth-
get an accurate geometric model. It is really important to rec- ods based on the above mentioned classification and briefly
ognize the geometric acquisition as a difficult problem, that discusses their methodology. A discussion of the techniques
still requires much more research efforts. based on the assessment criteria mentioned in Section 2.2 is
given in Section 4.
Our classification of methods is, parallel with the classifi-
cation based on the geometric information, also based on this
amount of image data needed to reconstruct a MR environ-
ment. Hereby excluding the image data needed for retrieving 3.1. Model of real scene unknown, one image known
geometric information. More precisely, the classification will To this challenging category, in terms of output quality, be-
use the amount of different input images used for the render- long those methods that require very little relevant informa-
ing or calculation process. These processes might use more tion about the real scene. Since no geometric model of the
than one image (or texture containing radiance information) scene is available it might be necessary to calculate depth
for the same point in the real scene, for instance for the BRDF information of the scene, to allow a correct inclusion of the
estimation. virtual objects, or some lighting information. For this group,
Some projects adopted the concepts of high dynamic range all radiance information is extracted from one single image.
images (HDR images) [38] which can be computed using Nakamae et al. [41] were the first to propose a method
techniques such as [39,40]. Each HDR image is generated for composing photographs with virtual elements. Input pho-
based on a set of images taken from the same viewpoint of tographs are calibrated and a very simple geometric model of
the same scene, but with a different exposure. The end result the real scene is extracted. The viewpoints of the photograph
is one image containing radiance values instead of ordinary and the virtual scene are aligned to ensure an appropriate
RGB values. In other words, radiance values are not clamped registration of the virtual objects within the photographed el-
in the RGB space. It may be argued that methods using HDR ements. The sun is positioned within the system according
images should be classified under that class with methods that to the time and date when the picture was taken. The sun
use more than one image for each point of the scene. However, intensity and an ambient term are estimated from two poly-
this paper considers that a HDR image provides one radiance gons in the image. The illumination on the virtual elements
value per point, and methods that use only one HDR image for is estimated and adjusted to satisfy the illumination in the
a certain point of the scene, are therefore classified as methods original photograph. The composition is done pixel by pixel
requiring only one input image. Similarly, techniques that and at that stage it is possible to add fog. All parameters are
require a few or many HDR images are classified as methods very inaccurate and therefore the results are limited in ac-
using respectively a few or many images. curacy. However, they were one of the first to mention the
The following classification is used throughout the rest of importance of using a radiometric model to improve the im-
this section: age composition. Figure 6 gives an example of the obtained

1. Model of the real scene unknown, one image known Techniques exist in computer graphics that use environ-
(Section 3.1): this category lists those techniques that ment maps to render objects in a scene. They were introduced
do not require any model of the real scene, except for to approximate reflections for interactive rendering [4244].
some low-level geometry like depth information. Any These techniques can also be used to assist the rendering of
necessary radiance information of a certain point in the glossy reflections [4547] by pre-filtering a map with a fixed
real scene is extracted from one single image. reflection model or a BRDF. At this moment, graphics cards

c 2006 The Authors
Journal compilation 
c 2006 The Eurographics Association and Blackwell Publishing Ltd.
36 K. Jacobs and C. Loscos/Classification of Illumination Methods for Mixed Reality

Figure 6: Results for Nakamae et al. [40]. The first image depicts the background scene, the second image shows the augmented
scene where the illumination of the augmented objects are matched to their surroundings and the shadows are cast accordingly.
(Courtesy of Nakamae et al.)

extensions support the real-time use of environment maps,

this encourages its use even more. Graphics cards now sup-
port cube maps [48]; ATI [49] presented at SIGGRAPH 2003
a demonstration of a real-time application for high resolution.
Environment maps can be used to represent the real scene in
a MR environment as a panorama and the information from
these images can be used to simulate reflections on a vertical
object positioned at the center of the environment map [50].
Agusanto et al. [14] exploited the idea of environment
maps to provide reflections in AR. They use HDR images
of the environment captured by a light probe to create the
environment map. These maps are filtered off-line to decom-
pose the diffuse from the glossy components. The rendering
is then performed with a multipass rendering algorithm that
exploits hardware capabilities. After some pre-processing, Figure 7: Results for Agusanto et al. [14]. The virtual ob-
like the inclusion of shadows, they present results for MR jects are rendered with skin textures. The left object is blended
environments rendered on a desktop. An impressive aspect with a diffuse map and no soft shadows. The right objects is
of their work is that the method also works for real-time AR. blended with a glossy map and with soft shadows. (Courtesy
The implementation of their method works with ARToolKit of Agusanto et al.)
[51] and the results show interactive reflections from the real
scene on virtual objects at interactive frame rate. An example
of such a projection is given in Figure 7. Although it should
be feasible, they have not yet provided a shadow algorithm with varying shutter speed. From these images, a radiance
for the AR application. distribution is calculated, which in turn is mapped onto the
geometry. To calculate the shadows and the local illumina-
Sato et al. [17] adopt a technique that extends the use of tion a ray casting technique is adopted. The radiance values
environment maps to perform common illumination. In their of the virtual objects are calculated using the information
method, it is assumed that no geometry is known a priori. known about the light sources, the radiance values of the real
However, at least a few images are known from different but scene, the geometry and the BRDF values of the virtual ob-
very restricted and known viewpoints, which can be used to jects. To simulate the shadows cast by virtual objects on real
estimate a very simple geometry of the scene and the po- objects, the radiance values of those points in the scene that
sition of the light sources. The obtained geometry does not lie in shadow are scaled. The simulated soft shadows look
offer a reliable occlusion detection and the positions of the realistic, see Figure 2. Their geometric estimate is poor and
virtual object are therefore restricted to lie in front of all real therefore usability of the method is limited and the positions
objects in the real scene. After this low-level geometric re- of the virtual objects are restricted. Nevertheless, the method
construction, a set of omnidirectional images are captured produces convincing local common illumination.

c 2006 The Authors
Journal compilation 
c 2006 The Eurographics Association and Blackwell Publishing Ltd.
K. Jacobs and C. Loscos/Classification of Illumination Methods for Mixed Reality 37

3.2. Model of real scene known, one image known objective is to add virtual animated objects in a calibrated
video sequence. The final video is a composition of the origi-
Most of the existing illumination methods assume that a geo- nal video sequence with a virtual video sequence that contains
metric model of the scene is available. The more detailed the both virtual objects and a representation of the real objects.
geometric model is, the more reliable the occlusion detec- The geometry of the real object is reconstructed a priori so
tion will be. Although not all techniques explain where this that for each frame in the video the geometry is known. The
model should come from, it is doubtful that a perfect geomet- rendering in the virtual sequence is performed using ray trac-
ric model can ever be acquired and this should be taken into ing. It is possible to modify the reflectance properties of real
account when evaluating a specific method. In this section, objects. Shadows are simulated in the virtual sequence, the
a discussion is given of those methods that take a certain 3D impact of the shadows in the final video is acquired by mod-
geometric model of the real scene as input and extract radi- ifying the original video with an attenuation factor. An oc-
ance information from one single image. All methods that clusion mask is created to reflect occlusion between virtual
belong to this category are further divided into three groups and real objects. This method came quite early in the his-
based on the type of illumination they produce: tory of common illumination and video composition. Even
though it is not applicable for real-time applications, it al-
local illumination for AR applications: Section 3.2.2 lows local common illumination and virtual modification of
common illumination: Section 3.2.1 the reflectance properties of real objects. The images on the
left in Figure 8 illustrate the original scene, the images on the
relighting: Section 3.2.3
right illustrate the composition.

Gibson and Murta [57] present a common illumination

3.2.1. Local illumination for AR
method, using images taken from one viewpoint that suc-
As mentioned before, AR has long been an area wherein peo- ceeds in producing MR images at interactive rates, by us-
ple focused on registration and calibration as these are still ing hardware accelerated rendering techniques. Apart from
difficult problems to solve in that area. However, a few papers constructing the geometry of the scene, the pre-processing
tried to introduce shadows in their systems, to show how well involves creating a set of radiance maps based on an omni
the registration was done and to improve the rendering qual- directional HDR image of the entire scene. New virtual ob-
ity. Recent improvements in graphics hardware for rendering jects are rendered via a spherical mapping algorithm, that
shadows made it possible to perform real-time rendering of maps the combination of these radiance maps onto the vir-
shadows on well-registered systems where the geometry is tual object under consideration. Later shadows are added us-
known. Early work was presented by State et al. [52] in which ing a two-step algorithm. To simulate the shadows, a set of
virtual objects are inserted in the see-through real scene. A M-light sources are identified, which imitate the true, un-
real light source is moved around and tracked, and shadows known illumination in the scene. Each light source is as-
of the virtual object due to this real light source are virtually signed a position and two parameters i and Ii , which define
cast onto real objects by using the shadow mapping technique the colour of the shadow. For each light source, a shadow map
[53]. In this case, the light source is assumed to be a point light is calculated using efficient hardware calculations (z-buffer).
source. It was very promising that some researchers in AR Shadow mapping is an intensive technique supported by the
were interested in using local common illumination in their graphics hardware that helps to create shadows in a fast and
systems, but it was followed by a long period in which no in- efficient way. The shadows created with shadow maps are
novative material emerged. Only recently, additional work of in nature hard shadows and therefore unsuitable for realis-
Haller et al. [54] and Jacobs et al. [55] was carried out to add tic shadow generation. Gibson and Murta combine the M-
shadows from a virtual object onto real objects. Both methods shadow maps in a specific way, using the above-mentioned
use shadow volumes, and in order to get good quality results parameters and now the system succeeds in simulating soft
knowledge about the scene geometry is essential. However, shadows, looking almost identical to the solutions obtained
Jacobs et al. start from a rough approximation of the geom- with a more computational and traditional ray-casting algo-
etry and use image processing techniques to compensate for rithm, see Figure 9. The system of M-light sources needs to
approximation errors. Other methods exist [7] that we will be defined so that it represents a close replica to the current
not discuss here, since they will not be applicable in general illumination system, an increase in number of light sources af-
MR systems because these systems would require the capture fects the rendering time. To demonstrate their method, Gibson
of a large environment. and Murta used eight light sources to simulate an indoor envi-
ronment. The position and the parameters of the light sources
are defined via an optimization algorithm, which needs to be
3.2.2. Common illumination executed only once for each different scene.
Jancene et al. [56] use a different approach to illuminate the Debevec [59] presents a more advanced common illumi-
virtual objects, they base their method, called RES (reality nation technique that estimates the BRDF values for a small
enriched by synthesis), on the principle of composition. The part of the scene. It is argued that if a virtual object is inserted

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38 K. Jacobs and C. Loscos/Classification of Illumination Methods for Mixed Reality

Figure 8: Results for Jancene et al. [56]. The images on the left-hand side show the original scene and the registration of the
cardboard box within this scene. The images on the right-hand side show two screen shots from the video sequence in which a
virtual dynamic green ball and static pink cube have been added to the original scene. The reflection of the green ball is visible
on the board behind it. (Courtesy of Jancene et al.)

Figure 9: Results for Gibson and Murta [57]. Comparison of a ray-traced (left) and a hardware generated image (right). The
ray-traced image was generated using RADIANCE [58], the hardware-generated image made use of the rendering method
described in [57]. The generation of the ray-traced image took approximately 2 hours, the generation of the hardware-rendered
image took place at nearly 10 frames-per-second. (Courtesy of Gibson and Murta)

into the scene, only a small fraction of the scene experiences straightforward, since only diffuse BRDF values are consid-
an influence from that inclusion. Relighting techniques using ered. A differential rendering technique was developed to
inverse illumination therefore only require the BRDF values reduce the possible inconsistencies in the geometric model
of those points that lie in this fraction. For most applications and the (specular) error on the BRDF estimates to an ac-
it is possible to know the position of the virtual objects and ceptable level. The rendering is a two-pass mechanism. First,
Debevec uses this position to divide the entire scene into two the augmented scene is rendered using a global illumination
parts: the local scene and the distant scene. The local scene is technique, the result is denoted by LSobj . Next the scene is ren-
that fraction of the scene whose appearance might alter after dered using the same global illumination technique, without
inclusion and the BRDF of the materials in that part need to including the virtual objects, denoted by LSnoobj . If the input
be estimated. On the other hand, the distant scene is that part scene is represented by LSb , than the difference between LSb
of the scene that undergoes no physical alteration after inclu- and LSnoobj is exactly the error that results from an incor-
sion. A schematic overview of the division in local and dis- rect BRDF estimation. The differential rendering therefore
tant scene and their corresponding influences is presented in calculates the final output rendering LSfinal as:
Figure 10. The local scene is restricted to be diffuse only; the
distant scene has no restrictions. An omni directional HDR L S f inal = L Sb + (L Sobj L Snoobj )
image is captured using a mirrored ball. The resulting light
probe image is used to present the illumination in the real This differential rendering technique removes most of the
scene. Based on the geometric model, the light probe im- inaccuracies and in a certain way it is similar to the one
age and the division into local and distant scene, the BRDF of Jancene et al. [56] presented above. The results of this
values in the local scene are estimated. The calculations are technique are promising, see Figure 10, but it still suffers

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K. Jacobs and C. Loscos/Classification of Illumination Methods for Mixed Reality 39

Figure 10: Debevec [59]. Left: a diagram illustrating the relation between the different components presented in [59]. The real
scene is divided into a local scene and a distant scene. The illumination from the distant scene influences the local scene and
the virtual objects. The virtual objects influence the local scene. The local scene and the virtual objects do not have an influence
on the distant scene. Middle: an image of the real scene. Right: an example of the differential rendering technique for an indoor
scene after inserting virtual objects. Diffuse effects are simulated. (Courtesy of Debevec.)

from a few deficiencies. Firstly, only diffuse parameters of the given of synthetic scenes rendered using the above-described
local scene are estimated, this introduces an error that should method. Renderings of the same synthetic scenes using a ray
be compensated by the differential rendering. Secondly, the tracing method are given in the middle column. The images
viewpoint can be altered but the technique is too slow to work in the last column are photographic reference images. An-
at interactive rates. If the rendering could be accelerated using other set of methods were built to exploit the structure of
low cost graphics hardware, it could be possible to achieve a radiosity method. Fournier et al. made pioneering work
interactive update rates for the MR. in this direction [61]. When this method was developed, fa-
cilities for modeling a geometric model from a real scene
Gibson et al. [60] developed a method to create soft shad- were not available. To overcome this issue, Fournier et al.
ows using a set of shadow maps. They created a rapid shadow decided to replace the geometry of the objects in the real
generation algorithm to calculate and visualize the shadows scene by their bounding box, and an image of the object was
in a scene after the material properties of the scene are calcu- applied on each of the faces of the box. An example of such
lated. A proper estimate of both the geometry and the radiance a model is shown in Figure 12. To set up the scene for global
information in the real scene needs to be available. It is as- common illumination computation, faces of the boxes repre-
sumed that the BRDF for all materials is diffuse. This diffuse senting the real objects are divided into patches. Using the
BRDF is estimated using geometry and radiance information information contained in the radiance textures, a diffuse lo-
(one radiance image per 3D point). In their method, the scene cal reflectance is computed by averaging pixels covered by
is divided into two parts: one part contains all patches in a each patch. Light source exitances are estimated and the ra-
scene that are visible from the camera, called the receiver diosity of the patches are set as an average of the per pixel
patches and another part contains those patches in the scene radiance covered by each patch. After insertion of the virtual
that have a significant radiance, called the source patches. objects and the virtual light sources in the model of the real
Then they organize these patches to build a shaft hierarchy scene, new radiosity values are computed for the elements in
between the receiver patches and the source patches. The the scene using progressive radiosity [62]. The rendering is
shaft hierarchy contains information on which patches block carried out by modifying the intensity of each patch with the
receiver patches from other source patches. Next they ren- ratio obtained by dividing the new radiosity by the original
der the scene from a certain viewpoint. This rendering is one. In Figure 12, an illustration of the result of this method
a two-pass mechanism. In a first pass, they go through the is given. The results of this technique look promising but it
shaft hierarchy to see which source patches partially or com- suffers from the lack of a detailed geometry. This leads to mis-
pletely illuminate a receiver patch. Once these source patches aligned shadows and other types of mismatching between real
are identified, they set the radiance of each receiver patch to and virtual objects. The technique is slow and will not allow
the sum of all irradiance coming from these source patches, real-time interaction. Nevertheless, this pioneering method
without taking occlusions into account. The second render- has influenced subsequent research work, e.g. Drettakis et al.
ing pass, takes the shadows in consideration. To calculate [63] and Loscos et al. [18] as presented in the remainder of
the portion of blocked light, they use the shadow mapping this section.
technique. In fact, they create a shadow map for each source
patch. At each receiver patch, these maps are then combined Drettakis et al. [63] present a method that builds on
and subtracted from the radiance value that was rendered Fournier et al. [61], but uses a finer model of the real scene.
in the first pass. This technique is capable of producing soft The same equations are used to estimate the light sources
shadows in a fast and efficient way. In Figure 11, examples are emittance, the reflectance of the patches and the original

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40 K. Jacobs and C. Loscos/Classification of Illumination Methods for Mixed Reality

Figure 11: Results for Gibson et al. [60]. A comparison of the rendering quality for three different scenes. The images in the
left column are produced using the system presented in [60]. The images in the middle column are rendered using ray tracing.
The images in the right column are photographic reference images. (Courtesy of Gibson et al.)

radiosity. Drettakis et al. make use of the more recent hi- either the current illumination or the current viewpoint. In
erarchical radiosity method hierarchical [64] accelerated by Figure 13, a screen shot is given of the 3D reconstruction and
using clustering [6567]. Based on [21] a hierarchy of shafts an example of the resulting MR using the above-explained
is built from the real scene model, which allows a local under- method.
standing when virtual objects are added. This permits an easy
identification of all patches that need to undergo a radiosity
alteration due to the influence of the newly added object. The
3.2.3. Relighting
advantage of this shaft hierarchy is that it permits interactive
updates of the illumination in the augmented scene when vir- In Loscos et al. [18], relighting is made possible, while keep-
tual objects move. The final display is made similarly to the ing the framework set by Fournier et al. [61] and Drettakis
method of Fournier et al. [61]: the intensity of the patches et al. [63]. The scene parameters are extracted in the same way
is modified with the ratio defined by the modified radiosity but all calculations are extended to the use of HDR images
divided by the original radiosity. This type of rendering is [68] as well. Since this technique focuses on relighting, a spe-
fast, compared to a ray tracing method, as it uses the hard- cific subdivision of the real scene is made to detect as much
ware capability to render textured polygons. This method direct shadows as possible. The radiosity of each element is
provides global common illumination with possible interac- modified to simulate nonblocked radiosity, in other words,
tion. Unfortunately, the technique does not allow changing to erase the shadows from the textures. A factor is computed

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K. Jacobs and C. Loscos/Classification of Illumination Methods for Mixed Reality 41

Figure 12: Results for Fournier et al. [61]. Left: wire-frame image, all objects in the scene are represented by a box, that
narrowly fits the object. Middle: Image information is mapped on the boxes (note that for the ball, a more complex shape was
used). Right: an example of an MR, the book on top of another book, lying on a table is virtual. Also a virtual light source is
added. The global common illumination effects are generated with an adaptive progressive radiosity algorithm. (Courtesy of
Fournier et al.)

Figure 13: Results for Drettakis et al. [63]. In the left image a screen shot is given of the 3D reconstruction of the real scene. The
right image gives an example of the MR, the floating box is the virtual object. The virtual objects can be moved at interactive rate
while keeping the global illumination effects. This is carried out by using an adaptation of hierarchical shafts for hierarchical
radiosity [21]. (Courtesy of Drettakis et al.)

using the radiosity method without taking the visibility in [18], it is possible to virtually modify the intensity of real
consideration. Then the new radiosity value is used to update light sources, to insert virtual objects that can be dynami-
the texture. Approximations of the estimation and of the in- cally moved and to insert virtual light sources. The problem
put data lead to an inexact modification of the texture. In a that comes with inserting new lights or increasing light source
second step, another factor is applied to automatically correct intensity is that the value of the factor computed between the
the imprecisions. This is done by using a reference patch that new radiosity value, divided by the original radiosity, may be
reflects the desired result. Once this is done, the new textures greater than one. In that case, multipass rendering is used to
are used instead of the original ones, and reflectance and orig- enable the visualization of brighter illumination. This method
inal radiosity values are updated accordingly. Shadows can allows interactivity and is fairly rapid in the pre-processing
be simulated using the factor of the newly computed radiosity computation. However, the obtained results are inaccurate as
solution divided by the original radiosity (without shadows). the illumination of the real scene is not fully estimated. Firstly,
This technique also extends the method presented in [21] for because lit areas are not altered at all, and secondly, because
the insertion of virtual lights. In the system of Loscos et al. it concentrates on the diffuse component only. An example

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42 K. Jacobs and C. Loscos/Classification of Illumination Methods for Mixed Reality

of the results is shown in Figure 3 using HDR images as an of the light sources is changed, extra BRDF information is
input. captured as well. In this section, the methods are discussed
that make use of this extra information.
Although it does not seem feasible to estimate specular
components of the BRDF from one single image, Boivin and Loscos et al. [70] developed a system that allows relight-
Gagalowicz [69] present a technique that re-renders diffuse ing, as well as virtual light source insertion, dynamic virtual
and specular effects based on radiance information from one objects inclusion and real object removal. They identified
single image and a full geometric model of the scene, includ- that it is difficult to estimate reflectance values in shadow re-
ing the light source positioning and the camera properties. gions due to saturation and because this estimate depends on
With a hierarchical and iterative technique they estimate the the quality of the indirect light estimation. This is compen-
reflectance parameters in the scene. In this method, the re- sated for by adding extra photographs captured under differ-
flectance model of Ward [19] is used, which presents the ent lighting. The geometry of the real scene is modeled from
entire BRDF with either three (isotropic materials) or five photographs. This geometric model is textured using one of
(anisotropic materials) different parameters. The BRDF es- the images, taken from the different viewpoints. A set of pic-
timation process starts by assuming that the BRDF values tures is then taken from this chosen viewpoint while a light
are all diffuse. A synthetic scene is rendered using the ge- source is moved around the scene to modify the illumination.
ometry, the current BRDF estimate and global illumination These pictures can be HDR images as used in [68]. Loscos
techniques. If the difference between the real scene and the et al. decided to mix a ray-casting approach to compute the
synthetic scene is too large, the BRDF values are re-estimated local illumination and a radiosity approach to compute the
using a more complex BRDF model. First specular effects indirect lighting. Two sets of reflectances are thus computed.
are added and a roughness factor is estimated using an time- First diffuse reflectance values are computed for each pixel
consuming optimization process. Later anisotropic effects are of the viewing window. This is done with a weighted average
introduced and the optimization continues until a reasonable of the reflectance evaluated with each input image differently
synthetic scene is acquired. This is very similar to the way lit. The applied weight is based on whether the 3D point as-
parameters are estimated in [20]. However, in this case, only sociated to the pixel is in shadow relative to the light source
one input image is used, and anisotropic parameters are esti- position, and also whether the radiance value captured is sat-
mated as well. The method of Boivin and Gagalowicz relies urated. The reflectance values are then used to initialize a
on one single image to capture all photometric information. radiosity system similar to those in [63,18]. This reflectance
The advantage of such an approach is that the image cap- can be refined by an iterative algorithm [70]. With this re-
turing is relatively easy; the disadvantage is that only partial flectance, Loscos et al. are able to relight the scene using
geometric information is available: there is no information global illumination. Pixel values are updated by adding the
for those surfaces that are not visible in the image. Neverthe- local illumination value, computed by ray casting, to the in-
less, the proposed technique allows changing the viewpoint. direct illumination value, computed by hierarchical radiosity
If a sufficiently large portion of a certain object is visible in using a rough subdivision of the scene. Local modifications
the image, the reflectance properties of the missing parts of are made after the insertion or moving of virtual objects by
the object are calculated based on this portion. Grouping ob- selecting the area of the window where local illumination
jects with similar reflectance properties makes this process will be affected. Indirect illumination is modified by adapt-
more robust. On the other hand, this requires that not only the ing the technique of [21]. Similarly, virtual light sources can
geometry needs to be known, but also a partitioning of the be added, and intensity of real light sources can be modified.
scene into objects with similar reflectance properties, which A very interesting application of this method is the removal
greatly compromises the operatability of this technique. Al- of real objects. The unknown information previously masked
though optimized, the rendering algorithm is computationally by the object is filled using automatic texture synthesis of
expensive and therefore only a nonreal-time solution can be a sample of the image of the reflectance values of the pre-
obtained. In Figure 14, an illustration is given of the output viously hidden object. The results show that the relighting
results of the described method. and the interaction with virtual objects can be achieved in an
interactive time. Image examples of the results are shown in
Figure 15. The produced results are good but could be im-
3.3. Model of real scene known, few images known proved by considering specular effects. Due to the nature of
the image capture process, it would be very difficult to apply
If more information about the radiance of the points in the this technique on real outdoor scenes.
scene is available, a better BRDF estimate can be acquired.
The radiance perceived at a certain point depends on the view- A different approach taken by Gibson et al. [71] results
ing angle, on the angle of incident light and the BRDF. Hence, in another relighting method, in which the reflectance of the
it is possible to gain more information about the BRDF of a material is roughly estimated based on a restricted amount of
certain point in the scene if radiance information is available geometry and radiance information of the scene. In theory,
from images captured from a different viewing angle. Alter- only geometry and radiance information is needed for those
natively, if the viewpoint is kept the same but the position parts of the scene that will be visible in the final relighted MR.

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K. Jacobs and C. Loscos/Classification of Illumination Methods for Mixed Reality 43

Figure 14: Results for Boivin and Gagalowicz [69]. The top left image illustrates the original scene. The top right image is
a relighted synthetic image. Diffuse and specular effects are simulated using an optimization algorithm. The bottom left image
illustrates the possibility of changing the viewpoint by grouping objects with similar properties. The bottom right image illustrates
the relighting of the original scene with a different illumination pattern. (Courtesy of Boivin and Gagalowicz.)

Figure 15: Results for Loscos et al. [71]. The left image is one of the input images of the real scene. The middle image is a
relighted image of the real scene, using the calculated BRDF values. The left image illustrates the removal of an object (the
door), the insertion of a new virtual object (the chair) and the insertion of a virtual light source. All manipulations are carried
out at interactive update rates. The illumination is updated locally with ray casting. The consistency of the indirect illumination
is kept using an adaptation of [21]. (Courtesy of Loscos et al.)

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for MR. The first inverse illumination method [20] focuses

on the BRDF estimation, using many HDR images from dif-
ferent viewpoints. The second [72] allows to relight outdoor
scenes. The remainder of this section briefly discusses these
two techniques.

Yu et al. [20] use a low parametric reflectance model, which

allows the diffuse reflectance to vary arbitrarily across the sur-
face while nondiffuse characteristics remain constant across a
certain region. The input to their system is the geometry of the
scene, a set of HDR images and the position of the direct light
sources. An inverse radiosity method is applied to recover the
diffuse albedo. The other two parameters in the reflectance
model of Ward [19], the roughness and the specular compo-
nent, are estimated through a nonlinear optimization. For the
Figure 16: Technique Gibson et al. [71]. The real illumina- estimation of the specular BRDF, it is assumed that many
tion is approximated by a illumination surface. This illumi- HDR images are available from a different set of viewpoints.
nation surface is covered by a set of virtual light sources. The The estimation makes use of the position of the light sources
parameters of these virtual light sources are estimated such and the possible highlights they may produce on a surface
that its effect resembles the real illumination. (Courtesy of due to specular effects. It is therefore helpful to capture im-
Gibson et al.) ages of the scene with a various number of light sources,
since this might increase the number of specular highlights.
This precise estimate of the BRDF values in the scene allows
In their approach a photometric reconstruction algorithm is to remove all illumination in the scene and a new illumina-
put forward, that is capable of estimating reflectance and tion pattern can be applied. To render the scene they make
illumination for a scene if only incomplete information is use of Wards radiance system [58]. No further steps were
available. To achieve this the direct illumination is modeled taken to speed up the rendering process. Figure 4 illustrates
as coming from unknown light sources using virtual light the results obtained for augmented images compared to pho-
sources, see Figure 16. The aim is not to produce an accurate tographs of the real scene. This technique is interesting for
illumination model, but rather a model that produces a similar MR because it provides an algorithm to estimate an accu-
illumination as in the original scene. The model used is a rate complex BRDF of a complex real scene, resulting in an
spherical illumination surface: a set of small area light sources accurate representation of the illumination.
that surrounds the known geometry. The parameters of this
Yu and Malik [72] present a technique that allows relight-
surface, the position and emission of the light sources, are
ing for outdoor scenes based on inverse illumination. As it is
estimated using an iterative minimization algorithm. Based
impossible to retrieve the geometry of the entire scene, they
on this model, the reflectance of the materials in the scene
separate the scene into four parts: the local model, the sun,
are estimated. The MR scene is rendered using a ray tracing
the sky and the surrounding environment. The illumination
algorithm. User interaction is impossible at real-time update
sources are the sun, the sky and the surrounding environ-
rate but nevertheless the method illustrates the possibility of
ment. Luminance due to the sun and the sky are estimated
getting fairly realistic mixed realities, without limiting input
based on a set of input images. At least two photographs per
requirements. This method is original, interesting and very
surface of the local model are captured, which should show
practical to adapt to many situations where information on a
two different lighting conditions (directly and not directly lit
real scene is partially known. Imprecisions and ambiguities
by the sun). The local model is subdivided into small sur-
are compensated for, resulting in a more accurate simulation
faces. Based on these two photographs, two pseudo-BRDF
of the existing illumination. An example of a relighted and
values are estimated per surface. One relates to the illumina-
augmented scene is given in Figure 17.
tion from the sun, the other relates to the illumination from the
integrated environment (sky plus surrounding environment).
3.4. Model of real scene known, many images known A least square solution is then used to approximate the spec-
ular term for each surface and for each lighting conditions
This category collects those techniques that require the most (from the integrated environment and from the sun). This
input information. Not only the geometry is known but also approach uses an approximation of the inverse illumination
radiance information under many different geometric setups. equation. It illustrates the difficulty of setting up a param-
Two significant methods could be identified that belong to this eterized MR system for outdoor scenes. At rendering time,
category of MR methods. They were selected from a broad different positions of the sun are simulated. After extracting
set of techniques on inverse illumination because they pro- the sun and the local model from the background, sky regions
vide a solution for a large group of objects, which is essential are identified and they are mapped on a mesh supported by

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K. Jacobs and C. Loscos/Classification of Illumination Methods for Mixed Reality 45

Figure 17: Results for Gibson et al. [71]. The left images illustrates the reconstructed scene from a novel viewpoint. The image
in the middle is a synthetic image illuminated with virtual light sources. The right image illustrates the addition of virtual objects.
Both specular and diffuse effects are simulated. (Courtesy of Gibson et al.)

Figure 18: Results for Yu and Malik [72]. The top row images illustrates the original tower from different viewpoints. The
bottom row are synthetic images of the tower from approximately the same viewpoint. The real images were not used to generate
the synthetic images, nevertheless the synthetic and real images look very similar. (Courtesy of Yu and Malik.)

a hemisphere. Three parameters control the sky intensity. A

4. Discussion
first scale factor is applied when simulating sunrise and sun-
set; it is constant otherwise. The second parameter adjusts the In Section 2.2, we pointed out that the assessment of the
intensity of the sky depending on the position of the mesh various illumination techniques for MR comes with a cer-
on the dome. The last parameter controls the sky intensity tain degree of subjectivity. Fortunately, there are some as-
depending on the suns position. Next, the radiance values pects that can be evaluated in a rather objective way. Some
and the pseudo-BRDFs are used to reproduce the global il- of these measures will be used in this section to assess the
lumination on the local scene. This method is the first to methods from Section 3. Section 4.1 discusses the amount
present the possibility of relighting outdoor scenes. Results of of pre-processing required. In Section 4.2 an evaluation of
these relighted scenes and a comparison image are shown in the degree of interactivity is given and in Section 4.3, the
Figure 18. Although it is difficult to evaluate the quality of methods will be evaluated based on the quality of the results.
the relighting from the images provided by the authors, the Section 4.4 explains which methods are suitable for outdoor
images resemble the real conditions, and this can satisfy most scenes. Finally an overview of the discussed methods is given
of the MR applications for outdoor environments. in Section 4.5.

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4.1. Pre-processing time Only a few of the methods operate in true real time (RT)
[14,52,60], others are near real time (near RT) [18,71,63] but
The term pre-processes refers to those steps, carried out once, most of them are nonreal time (NRT). However, it should be
that are required by the method before the merging of real noted that some methods were developed years ago, when
and virtual objects takes place. The geometry reconstruction, computer hardware and software were much slower than
image capturing and BRDF estimation, are considered as pre- nowadays. Also, it should be pointed out that some meth-
processing steps. ods did not make a special attempt in producing interactive
A few methods do not require a full geometric model of the systems. With a few modifications, it should be possible to
real scene: Sato et al. [17], Nakamae et al. [41] and Haller speed up most of the described systems.
et al. [54]. All other methods require a geometric model. Some methods that specifically tried to speed up the com-
Some of these methods do not explain how this model can be putations are worth mentioning. Agusanto et al. [14] ex-
constructed, others assume that it is constructed using semi- ploited the idea of environment mapping while State et al.
manual 3D reconstruction software, examples of such soft- [52] used shadow mapping and Haller et al. [54] shadow vol-
ware were given in Section 3. Using reconstruction software umes. Gibson and Murta [57] developed a new technique to
usually results in a low resolution model and is in general simulate soft shadows at interactive rates and Drettakis et al.
error prone, this is due to the fact that no automatic, accurate [63], Loscos et al. [18] and Loscos et al. [71] made use of a
3D reconstruction software is yet commercially available. hierarchical radiosity algorithm, that decreased the computa-
Scanning devices give a better resolution, but these devices tion time to interactive rates as well. Gibson et al. [60] used
are expensive and while the scanning of a small object might shadow volumes.
be straightforward, the scanning of a larger scene is tedious.
As a summary we can say that a perfect geometric model is Most methods that calculate the BRDF values are in prin-
difficult to acquire and that reconstruction is always a tedious ciple capable of changing the BRDF values into something
work. new. This can be used to modify the appearances of real ob-
jects in the scene. Relighting methods can use this BRDF
Some methods require radiance information captured from information to relight a scene using a different illumination
several viewpoints [21,71] or under different types of illumi- pattern. Table 1 gives an overview of the various different
nation [70,72]. Taking several HDR images from different types of illumination the discussed methods allow.
viewpoints and under different illumination delays the image
capture time.
4.3. Evaluation of the quality
Many methods calculate a BRDF estimate, some use a
diffuse model, some allow a more complex model. Often Some of the described methods evaluate the quality of
the calculation of the BRDF needs to be carried out off- their method using one or more of the following evaluation
line, due to timing issues and is therefore considered as methods:
pre-processing work. Methods that calculate a diffuse-only
BRDF are: [59,61,63,18,70], methods that allow specular
a comparison is made between a photograph reference of
components are: [71,20,72,69].
the real scene and a synthetic version of the same scene,
the BRDF is measured using a device and these results are
4.2. Level of interactivity compared with the calculated BRDF values.

Interactivity means: Gibson et al. [60] compare their shadow rendering tech-
nique with a ray traced rendering and an image of the real
the possibility of navigating objects or viewpoints in the scene, see Figure 11. They are capable of producing realistic
scene, and similar shadows as in the real image and at a faster time
than the ray traced rendering. In [57] the presented extended
the effort made to get an interactive rendering,
shadow mapping is compared with a ray-traced version using
the possibility to modify reflectance properties of real ob- the same input parameters, see Figure 9. There are some dif-
jects in the scene, ferences between the two synthetic scenes, but the generated
the possibility to modify the illumination sources in the shadows look realistic.
real scene.
Boivin and Gagalowicz [70] extract a full BRDF model
and compare their rendering with an original image of the
A few methods allow to navigate the virtual objects or real scene, see Figure 14. In [72] the diffuse and specu-
the viewpoints. These techniques have either enough BRDF lar components are calculated; the resulting rendering is
information [69,20,61], enough geometry and illumination compared with an original image of the real scene. Simi-
information [17,72] or use a different approach [14,52,56]. larly, Loscos et al. [18,70] estimate the diffuse BRDF and

c 2006 The Authors
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c 2006 The Eurographics Association and Blackwell Publishing Ltd.
K. Jacobs and C. Loscos/Classification of Illumination Methods for Mixed Reality 47
Table 1: Overview of illumination methods for mixed reality.

Geometric of Computation
model images Methodology Rendering time Section

[14] No One Global common illumination Environment maps, multipass rendering RT 3.1
[41] No One Local common illumination Ray casting NRT 3.1
[17] No One Global common illumination Ray casting NRT 3.1
[52] Yes One Local common illumination Shadow mapping RT 3.2
[54] Yes One Local common illumination Shadow volumes RT 3.2
[56] Yes One Local common illumination Ray tracing NRT 3.2
[60] Yes One Global common illumination Shadow mapping RT 3.2
[59] Yes One Global common illumination Differential rendering + ray tracing NRT 3.2
[57] Yes One Global common illumination Extended shadow mapping Near RT 3.2
[61] Yes One Global common illumination Radiosity + ray casting NRT 3.2
[63] Yes One Relighting using global illumination Hierarchical radiosity algorithm Near RT 3.2
[18] Yes One Relighting using global illumination Hierarchical radiosity algorithm Near RT 3.2
[69] Yes One Inverse global illumination Ray tracing NRT 3.2
[70] Yes Few Relighting using global illumination Hierarchical radiosity algorithm + ray casting Near RT 3.3
[71] Yes Few Relighting using global illumination Ray tracing NRT 3.3
[20] Yes Many Inverse global illumination Ray tracing NRT 3.4
[72] Yes Many Inverse global illumination Ray tracing NRT 3.4

compare a synthetic rendering with a original image of the methods that use environment maps are capable of capturing
real scene (see Figures 3 and 15). In both methods, the ren- the outdoor illumination. But some caution is in place when
dering occurs at interactive update rates. interpreting this statement [73]. If HDR images are used to
capture the environment map, which is in general the case,
Similarly, Gibson et al. [71], see Figure 17, compare an one needs to bare two things in mind. Firstly, the intensity of
original and synthetic image and find that the error between the sun is in general too bright to be captured in a HDR image
the two images decreases drastically in the first three itera- without saturation, even at very fast shutter speeds. Secondly,
tions. Both diffuse and specular reflectances are modelled. if the sky is clouded and the clouds drift in the sky, there will
Yu et al. [20], see Figure 4, estimate diffuse and specular inevitable be some movement in the low dynamic images
BRDF values and compare these with measured BRDF values used to compile the HDR image, making them worthless. It
of objects in the scene. The estimates and the true values are should be clear, that the extension from indoor to outdoor
similar. scenery is not straightforward. The current state of the art of
MR shows no good solutions for the outdoor scenes.
We can also compare methods that use both specular and
diffuse BRDF values for the rendering with those that have a 4.5. Overview
more restrictive understanding of the BRDF. It is understood
that systems based on a more complete BRDF model result Table 1 gives an overview of all methods discussed in Sec-
in an MR of a higher quality than those based on diffuse tion 3. For each method, the overview discusses the following
BRDF values only or those that do not estimate BRDF values aspects:
at all. For some methods, only a subjective user perceptive
assessment can be made. Geometric model of the scene: whether or not the method
requires a geometric model of the scene.
4.4. Usability on indoor and outdoor scenes Number of different images: the number of different im-
ages needed per point in the scene, to calculate the MR.
The reader may have noticed that most techniques were tested
on indoor scenes. Outdoor scenes are more complex than in- Methodology: the methodology used to create the MR. In
door scenes. Not only is the geometry more difficult to model, Section 2.3 three different approaches were discussed:
the illumination is difficult to extract as well. Outdoor illumi- 1. common illumination,
nation is time and weather dependent and difficult to model 2. relighting,
and simulate. Only three methods from Section 3 explicitly
used an outdoor scene to test their method [59,60,17,72] but 3. inverse illumination.
this does not imply that the other methods are not suitable Further to this division, a distinction is made between
for outdoor scenes. For instance, one might argue that all local and global illumination techniques.

c 2006 The Authors
Journal compilation 
c 2006 The Eurographics Association and Blackwell Publishing Ltd.
48 K. Jacobs and C. Loscos/Classification of Illumination Methods for Mixed Reality

Rendering: the rendering method used to compose the should be a real-time and automatic method with no pre-
MR. Possible answers are: ray-casting, ray-tracing, radios- processing requirements. It would allow any type of virtual
ity, etc. interaction: modification of lighting, removal of real objects,
Computation time: the update time of the method is real modification of material properties of real objects and ad-
time (RT), non real time (NRT) or near real time (near RT). dition of virtual elements. And the rendering quality would
perfectly match with the real one. Research is heading toward
this, and it is likely that this technology will become more ac-
5. Conclusions and Future Work cessible in future years. Progress in stereovision techniques,
in automatic calibration, registration and in computer graph-
In the past few years, research has been motivated to con- ics will help in the progression in illumination for mixed
sider a new type of simulation: the simulation of a new real- reality. More automatic reconstruction methods of the geom-
ity called mixed reality that refers to the concept of mixing a etry are needed, that will also model more complex details.
real scene with virtual elements. Mixed reality has now be- Progress in augmented reality is heading toward systems be-
come very important for various applications ranging from ing able to recognize shape and depth without markers. Com-
entertainment with movie post-production and games, archi- puter graphics research needs to provide more precise de-
tecture, cultural heritage, education and training, etc. Several scription of reflection models and rendering software needs
problems arise when composing reality with virtual elements. to be adapted to these more complex materials. Little work has
A first problem is how to automatically calibrate the relative been done in modeling the behavior of light sources, which
position and the occlusion of virtual objects with virtual ones. are often assumed diffuse. It will be important for future work
As mentioned in this paper, this has been addressed success- to consider more complex lighting in order to find a better
fully in two different ways. One can use a scanning device estimate for the illumination in mixed reality. Finally, most
or one can use real-time stereovision to extract depth and of the methods have been designed for indoor environments
shape information. A second problem is how to illuminate that are easier to control. Outdoor environments present a real
the virtual objects consistently with the original light condi- challenge, both in the capture and in the simulation. It is ex-
tions in the real scene. Research papers appeared already in pected that more work for outdoor environments will appear
the late 1980 to answer this last need of the industry, but it is in the near future.
only recently, within the last 10 years, that the international
community made a more significant effort to provide more
automated solutions for computing the illumination in mixed
We would like to thank Simon Gibson, Greg Ward, Anthony
Although it is tempting to compare techniques relatively to
Steed, Mel Slater, Erik De Witte, Hila Ritter Widerfield and
the quality of the results achieved, this paper classifies them
Bernhard Spanlang for reviewing this paper. This work was
depending on the context and the goal of the method. Firstly,
partly funded by CREATE (IST-2001-34231), a 3-year RTD
it is of course easier to compute illumination for mixed reality
project funded by the 5th Framework Information Society
if a 3D model is available. Secondly, it may be that only a few
Technologies (IST) Programme of the European Union and
images of the real scene are available from different view-
a UCL graduate school research scholarship.
points, and some available with different lighting conditions.
The more images are available for the illumination extrac-
tion, the easier the computation procedure becomes. On the
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