TOWARDS PROBE-LESS AUGMENTED REALITY

A Position Paper

Claus B. Madsen and Michael Nielsen

Computer Vision and Media Technology Lab, Aalborg University, Aalborg, Denmark

Keywords:

Augmented Reality, illumination estimation, rendering, shadow detection, shadow rendering.

Abstract:

The main problem area for Augmented Reality is ensuring that the illumination of the virtual objects is contin-

uously consistent with the illumination in the real scene. State of the art in the area typically requires the real

scene illumination conditions to be captured as a High Dynamic Range environment map. The environment

map is then used for shading and shadowing. Handling the real and the virtual shadows and their interaction is

the single most difﬁcult aspect. This paper presents a completely different approach to determining the illumi-

nation conditions in the real scene. Based on an assumption that the scene is outdoor we automatically detect

shadows in the image and use this information to determine the ratio of sky irradiance to sun irradiance. We

then present how to convert this information into radiance levels for both the sky and the sun. When combined

with a computation of the Sun’s position based on date, time and information about position on the Earth, we

arrive at a full illumination model applicable for rendering virtual objects into real scenes.

1 INTRODUCTION

Without doubt Augmented Reality (AR) will become

a widespread technology within few years. The pro-

liferation of computer-based, portable imaging de-

vices such as cell phones and PDAs makes it very

attractive to develop AR techniques that can enable

augmentation of images with credible renderings of

virtual geometry,- for entertainment, education, and

information purposes. To augment the real world

with real-time renderings of monsters to combat in

the street, to augment the real world with images of

how ancient architecture looked or to augment the real

world with route ﬁnding information, etc.

There are three technical problems areas to solve

in order to accomplish photo-realistic AR: 1) camera

registration, 2) occlusion handling, and 3) estimat-

ing the real world illumination. We conjecture that

the ﬁrst two problems will have a feasible technolog-

ical solution within relatively few years. Regarding

registration GPS combined with image-based track-

ing of features will allow for knowing the position

and orientation of the camera in real time. Regard-

ing occlusion handling, that is, determining whether

a virtual object occludes a real object or vice versa,

will also eventually be solved through combinations

of laser range ﬁnding and multi-view 3D reconstruc-

tion. The third problem, illumination, still holds

many complicated challenges, unless certain assump-

tions/restrictions are made.

In this paper we present an approach to estimat-

ing the illumination conditions in a real scene for use

in AR applications. Our approach is quite different

from main stream work in this area, which involve

acquiring a complete High Dynamic Range omni-

directional environment map of the scene, e.g., ac-

quired as a light probe using a reﬂective sphere. Our

approach is to determine the illumination conditions

directly from an image of the scene.

In general estimating illumination from an un-

known scene is ill-posed and may never be completely

solved. The things that make our approach work are:

1) we assume information about date, time and posi-

tion on Earth is available for the image, 2) we assume

the image is of an outdoor scene with only natural

(sky and sun) illumination, and 3) we assume there

is a predominant occurrence of approximately diffuse

surfaces in the scene. The ﬁrst assumption is very

reasonable, since cameras in the near future will in-

clude GPS information into the image header, as well

as date and time. The second assumption is reason-

able, since there is a lot of outdoor world to photo-

graph. The third assumption is also reasonable since

urban scenes have a lot of road, pavement, brick, and

concrete surfaces, which all are approximately Lam-

bertian. Moreover, we conjecture that the diffuse sur-

face assumption can be relaxed greatly in the future

due to further research.

255

B. Madsen C. and Nielsen M. (2008).

TOWARDS PROBE-LESS AUGMENTED REALITY - A Position Paper.

In Proceedings of the Third Inter national Conference on Computer Graphics Theory and Applications, pages 255-261

DOI: 10.5220/0001099502550261

 SciTePress

Figure 1: Illustration of idea in this work with images. Left: input image acquired outside in sunshine. Center: real shadows

automatically detected and removed to illustrate performance of detection. Right: information from shadow detection step

has been used to estimate Sun and Sky illumination conditions and virtual objects have been rendered into the scene with

credible shading and shadowing.

The general idea in the work presented here is that

the outdoor (daylight) illumination conditions can be

modeled quite accurately by a distant disk light source

(the Sun) in combination with a Sky dome. We as-

sume the radiance of the Sky dome is the same over

the entire hemi-sphere. The respective radiances of

the Sky and the Sun are estimated based on informa-

tion from a automated shadowdetection process. That

is, the shadows already present in the image provide

the only source of information for the parameters of

the illumination model (a part from the position of the

Sun, which is found procedurally from date, time and

Earth location information). Figure 1 shows an exam-

ple of application of the techniques presented here.

The bold statement in this paper is that photo-

realistic Augmented Reality using no explicit illumi-

nation calibration, such as light probes, will be possi-

ble sometime in the not so distant future. The position

taken in the paper is that the scene itself and images

of it contain enough information to reconstruct the il-

lumination conditions, and we present a speciﬁc tech-

nique which achieves exactly that on images within a

well deﬁned sub-set of outdoor scenery.

2 RELATED WORK

The most widely applied technique for obtaining il-

lumination information from a real scene is environ-

ment maps, also sometimes referredto as light probes.

Light probes are images taken of a reﬂective sphere

placed in the scene. This image can be remapped to

different omni-directional mappings which can be ap-

plied for shading virtual objects, either in a non-real-

time or a real-time rendering system, (Debevec, 2005;

Madsen et al., 2003; Havran et al., 2005; Barsi et al.,

2005; Jensen et al., 2006; Madsen and Laursen, 2007;

Debevec, 1998; Debevec, 2002).

This technique provides very precise information

about the illumination conditions in terms of radiance

and incoming direction, but there are several draw-

back. First of all the light probe image has to be in

a High Dynamic Range (HDR) format, which as of

yet requires multiple exposures of the same (static)

scene. It is though fair to assume that consumer cam-

eras in the future will provide HDR information in

a single exposure. Secondly, the coordinate system

of the probe has to be calibrated to the scene coor-

dinate system, as well as the camera. Furthermore,

the information contained in the light probe is only

valid at the precise point where the probe image was

acquired, so generally a single probe acquisition can-

not be used to model the illuminations across a larger

scene (the distant scene assumption). The latter prob-

lem can though be alleviated given a coarse 3D model

of the scene onto which the light probe image can be

back-projected, as in (Gibson et al., 2003). The single

most annoying thing about the light probe approach,

though, is that the probe information becomes invalid

in dynamic scenes, with changing illumination condi-

tions. For example in outdoor scenes. It is simply not

realistic to place a reﬂective sphere in the scene every

once in a while to capture the illumination conditions.

While the light probe approach is pre-dominant in

the literature, there are a few other approaches. For

a recent review of approaches to estimating illumi-

nation in Augmented Reality see (Jacobs and Loscos,

2004). The most promising frameworks employ some

form of inverse rendering, where illumination and re-

ﬂection properties of the surfaces of the scene are it-

eratively determined based on one or more images

of the scene, (Yu et al., 1999; Boivin and Gagalow-

icz, 2001; Boivin and Gagalowicz, 2002). The draw-

back of these approaches is that they require perfectly

modeled, complete 3D scene geometry and complete

knowledge about light sources (position, radiance, ra-

diation characteristics, etc.).

The ﬁnal category of related work go in a direction

toward trying to understand the illumination condition

based on single images of the scene with as little as

possible prior information and/or assumptions. Some

work is based on a single image of a geometrically

GRAPP 2008 - International Conference on Computer Graphics Theory and Applications

256

known 3D object casting shadow on a planar surface

of known reﬂectance, (Sato et al., 1999). Although el-

egant this technique’s primary shortcoming is the re-

quirement that a 3D model is available of the shadow

casting object, which makes the technique less suit-

able for general, automated real applications. Other

work, (Cao et al., 2005), extracts quantitative illumi-

nation information from images of general shadows,

in a manner related to what is presented in this pa-

per, but Cao’s work does not provide the illumination

information required for shading a virtual object cor-

rectly, only for determining the color characteristics

of virtual shadows.

The following issues have been a priority for the

work presented in this paper: we aspire to develop

techniques which have a potential for real-time per-

formance on dynamic scenes responding to constantly

changing illumination condition; we also wish for the

technique to be based on information obtained di-

rectly from images without requiring the presence of

special purpose objects for illumination estimation;

we aim toward techniques which assume as little as

possible concerning materials in the scene.

3 OVERVIEW OF APPROACH

The presented technique assumes the availability of

the 3D geometry of the areas in the scene which must

receive shadows from virtual objects. For most exam-

ples in this paper this simply means a ground plane.

We strongly envisage that real-time dense stereo re-

construction in the future will provide this rough ge-

ometry. In this work we manually calibrate the cam-

era position and orientation to the ground plane co-

ordinate system, and estimate camera focal length,

based on a small number (at least four) of known

points in the scene. Similar information could in a

real-world application be supplied by built-in iner-

tia sensors in the camera, combined with automatic

multi-view calibration and reconstruction. Addition-

ally we assume knowledge of how the ground coordi-

nate system is oriented relative to magnetic North. Fi-

nally, the approach requires that it is possible to com-

pute the direction vector to the Sun, which requires

knowledge of time of day, date, as well as the lati-

tude and longitude of the position where the image

is taken. A built-in solid state magnetic compass, a

clock and a GPS receiver can provide this informa-

tion for a consumer camera.

The technique is based on ﬁrst running a shadow

detection process in the image with no prior infor-

mation other than that the image is taken under out-

door daylight illumination conditions. Figure 1 shows

the performance of the shadow detection process by

its ability to remove shadow effects, although the

removal of shadows naturally is not the objective.

Based on the shadow detection process we get an es-

timate of the ratio between total irradiance in areas in

direct Sun and the total irradiance in areas in Shadow.

From this ratio, combined with a simple white bal-

ancing assumption, this paper shows how it is possi-

ble to determine the values of all parameters of a com-

plete illumination model consisting of a Sky dome of

a certain radiance, and Sun disk light source of a cer-

tain radiance. Both the Sky and the Sun part of the

model have scene consistent color balance.

Based on this illumination model, with its deter-

mined parameters, it is possible to render virtual ob-

jects with scene consistent shading and shadows, as

illustrated through a number of examples in the pa-

per. Here a software-based path tracing framework

has been employed, but it is entirely straight forward

to perform similar quality rendering in real-time.

4 SHADOW DETECTION

It is beyond the scope of the present paper to

fully describe the applied shadow detection approach,

which is documented in (Nielsen and Madsen, 2007b;

Nielsen and Madsen, 2007a). In general terms the

technique is based on using pixel statistics in the chro-

maticity plane to estimate the RGB values of an over-

lay, which when alpha blended on pixels in regions

in direct light change these regions into shadow re-

gions. Using a graph cut algorithm the method then

determines the correct alpha values for all pixels in

the image, such that an alpha value of 0 corresponds

to full direct light, and a value of 1 corresponds to full

shadow. Figure 2 illustrates the performance showing

the technique’s ability to deal with soft shadows.

The shadow detection is based on a general out-

door illumination model. The Achilles heel of the

technique is the initialization where pixel statistics

are used to determine the color of the shadow over-

lay. The technique is quite successful at initializing

itself, but not ﬂawless. We postulate that fully au-

tomated shadow detection will perform adequately at

some point, giventhe research interests in the area and

the current performance of known techniques. Addi-

tionally, it will be much simpler to achieve robust ini-

tialization in video streams of dynamic scenes, where

e.g. moving people and vehicles, as well as the move-

ment of shadows caused by the movement of the Sun,

will make it much easier to hypothesize what regions

in an image are shadow regions.

TOWARDS PROBE-LESS AUGMENTED REALITY - A Position Paper

257

(a) Input image

(b) Detected shadow levels

Figure 2: Top: input image. Bottom: estimated levels of

shadow at various regions in the image. Please refer to ﬁg-

ure 1 to see shadows removed from this image.

5 ILLUMINATION ESTIMATION

The shadow detection overlay described in the pre-

ceding section actually has a concrete physical inter-

pretation for diffuse surfaces. We use this information

to drive the computation of all parameters of the scene

illumination model.

As described previously the proposed illumina-

tion model consists of a Sky dome covering an entire

hemisphere above the scene, combined with a distant

disk source to model the Sun. The Sun subtends a

0.53 degree diameter disk viewed from Earth. The po-

sition of the Sun relative to the scene can be quite eas-

ily computed given the information listed in section 3

(time, date, latitude and longitude), see e.g. (Schlyter,

2007). Let~s denote the direction vector in scene coor-

dinates to the Sun at a given time, for a given location

on Earth. The information concerning the illumina-

tion model we do not have are the respectiveradiances

of the Sky dome and the Sun disk.

Subsequently, whenever a radiometric quantity is

being used it is to be implicitly understood that the

quantity has values for each color channel, i.e., has

3 components, Red, Green and Blue. For each color

channel we therefore have two unknowns: the Sky

radiance and the Sun radiance. The process of deter-

mining these values starts with the overlay produced

by the shadow detection process. Let the color of the

overlay be denoted by C

. It is easy to prove that for

diffuse surfaces the overlay value corresponds to the

ratio of the irradiance in shadow to the irradiance in

direct light for a given surface normal:

+ E

(1)

where E

is the irradiance due to the Sun for a

given surface normal, and E

(a for atmosphere) is

the irradiance due to the Sky. If somebody wishes to

employ the ideas of this paper, but do not have access

to a shadow detection system this quantity is easily

found manually in images by taking the average pixel

values in a shadow region and component-wise divide

them by the average pixel values of the same surface

in direct Sun light.

The overlay values, C

, provide one constraint on

the two unknowns since it constrains the relative val-

ues of the Sun and the Sky radiances, although we

shall stick with irradiances until at the very end. Let

⊥

denote the irradiance, due to the Sun, for a nor-

mal pointing straight into the Sun. Furthermore, letV

determine the fraction of the Sky dome visible for a

given point in the scene. Then the fraction of shadow

to direct light irradiance for a point in the scene can

be expressed as:

· E

+ E

⊥

· (

N ·~s)

⊥

= E

· (1−C

)

(

N ·~s) ·C

(2)

where

N is the surface normal of the point. Now

the Sun’s head-on irradiance is expressed in terms of

the Sky irradiance times properties from the image

and from partial 3D knowledge of the scene. If the

only scene model consists of a ground plane (as in

most of our examples)V

is simply set to 1. This is the

most information we can get from the overlay color,

i.e., a relative constraint.

The next constraint, which enables us to deter-

mine the relative strengths of the RGB components,

is based on an assumption that the camera has been

white-balanced to the scene and the illumination con-

ditions. If the camera is white-balanced to areas in

direct sunlight the combined Sun and Sky irradiance

at a white-balanced point is a constant for all color

channels:

k = E

⊥

· (

′

·~s) + E

·V

′

(3)

GRAPP 2008 - International Conference on Computer Graphics Theory and Applications

258

Here we use

′

and V

′

to indicate that the white-

balance direction and its associated Sky dome visi-

bility may be different than the direction for which

the overlay is tuned to provide full shadow. For the

examples given in the paper we have assumed that

the camera is white-balanced for the ground plane. In

practice we have in reality let the camera perform au-

tomatic white-balancing, but since the ground plane

dominates in the example views this is roughly the

same as groundplane white-balance. Note that we are

talking about an illumination white-balancing which

results in white paper lying on the ground appearing

white (has balanced RGB values); we are not talking

about assuming that the ground plane is grey/white!

Combining Eqs. 2 and 3 yields:

′

· ((

′

·~s)/(

N ·~s)) · (1/C

− 1)

(4)

Now the Sky irradiance is expressed solely in

scene and image components, so E

is computed ﬁrst

using Eq. 4, after which E

⊥

is computed by insert-

ing into Eq. 2. Now the color balance and relative

strengths of the Sky and the Sun irradiances are de-

termined, and we only lack to determine the absolute

levels. By arbitrarily setting the albedo, ρ, of some

point in the scene to 1/3 (Earth’s average albedo) we

get a ﬁnal constraint allowing us to set the absolute

values of the irradiances so as to be suitable for illu-

minating virtual objects into the image, in which the

pixel values (scene radiances) are naturally subject to

some unknown camera scale/gain factor. Let a pixel

value for a surface with normal

be L

. Then the

unknown scaling factor, S, for the Sun and Sky irradi-

ances can be found from:

= 1/(2π) · S· (E

·V

+ E

⊥

· (

·~s)) · ρ

S =

2π · L

·V

+ E

⊥

· (

·~s)) · ρ

(5)

Both E

and E

must be scaled by S as determined

by Eq. 5, setting irradiance values that are appropriate

for a scene given the input image camera exposure.

Now, all that remains is to convert from irradiance to

radiance for the Sky dome and the Sun disk, respec-

tively. The irradiance for a normal pointing straight

into a hemi-spherical sky dome of radiance L is π · L,

therefore the Sky radiance is set to L

= E

/π. The ir-

radiance produced on a normal pointing straight into

a disk source of radius r (in radians) and radiance L

is 2π · (1− cos(r)) · L (for small disks), so the Sun ra-

diance is set to L

= E

⊥

/(2π · (1 − cos(r))), which

for a 0.53 degree diameter Sun disk corresponds to a

scalefactor of 14880.

Figure 3 visually illustrates the illumination esti-

mation for a given scene. Notice how the color of the

Sky dome corresponds to the actual Sky color in the

image, although this information has not taken part

at all. The only information used is the overlay color

resulting from ﬁnding the intensity ratio between a re-

gion in direct light and a region in shadow.

(a) Input image

(b) Estimated sky radiance

Figure 3: Top: input image. Bottom: rendering showing

local scene, sky and sun. The sky’s and sun’s radiances

have been estimated directly from the shadow information

in the input image. Figure 1 shows ﬁnal composite of local

scene into input image.

6 RENDERING PROCESS

In terms of actually rendering augmentation into the

images there is a choice between two different overall

approaches: 1) differential rendering as in (Debevec,

1998) or 2) a relighting approach similar to the one

described in (Madsen and Laursen, 2007). We have

chosen the latter as it is much easier to apply, due to

the fact that it is in practice impossible to provide cor-

rect albedos for the so called local scene, i.e., that part

of the real scene for which a 3D model exists.

We have used the free ray tracing package Radi-

ance by Greg Ward for the renderings in this paper.

Figure 4 shows a few more rendering examples. The

TOWARDS PROBE-LESS AUGMENTED REALITY - A Position Paper

259

(a) Example 1 (b) Example 2 (c) Example 3

Figure 4: Different rendering examples based on images taken within a brief hour of sunlight during an otherwise very rainy

and gray month of November 2007, in Denmark.

rendering process can be listed as follows (unfortu-

nately we do not have space to illustrate with images):

1. render irradiance values of local scene without

augmentation objects, e.g. just ground plane

2. render irradiance values of local scene including

augmentation objects

3. render radiance values of local scene including

augmentation objects, i.e., render the local scene

with the estimated illumination conditions con-

sisting of a Sky dome and a Sun disk

4. render augmentation mask (binary image, zero at

pixels that correspond to augmentation objects,

one elsewhere)

5. multiply input image with augmentation mask

6. divide masked input image from step 5 by irradi-

ance image from step 1

7. multiply image from step 6 with irradiance image

from step 2

8. multiply local scene rendering from step 3 with

inverse of the augmentation mask from step 4

9. add relit image from step 7 with masked augmen-

tation from step 8

7 DISCUSSIONS AND FUTURE

WORK

There are naturally unresolved issues. One deals

with how to avoid effectively creating double shad-

ows when rendering a virtual shadow on top of a real

one, resulting in a much too dark shadow. This we

have a solution for, which is not described in this pa-

per. Figure 5 shows that it is possible to render virtual

shadows across real shadow without creating double

shadow by simply using the shadow level mask cre-

ated by the shadow detection module. Unfortunately

overlapping shadows will not occur unless the virtual

object casts a shadow also on the real object creating

the real shadow, or vice versa. We have ideas for par-

tially handling these interactions, but this is left for

future research.

It would also be interesting to measure the perfor-

mance of the technique presented in this paper against

light probe images to establish the degree of absolute

accuracy.

Figure 5: Shadow manually drawn into image. The pixel

values inside the artiﬁcial shadow is automatically deter-

mined. Using this approach we intend to solve the shadow

protection problem in order to avoid double shadows.

8 CONCLUSIONS

We have presented a method for determining all pa-

rameters of a complete outdoor illumination model

based entirely on simple image measures from im-

ages with shadow. The illumination model consists

of a Sky dome and a Sun disk. The main contribution

is that the presented technique can continuously es-

timate the changing illumination conditions in a real

outdoor scene bypassing the need for special purpose

objects in the scene such as reﬂective spheres for light

probe/environment map acquisition or known 3D ob-

jects in the scene for illumination estimation.

We have demonstrated on real images that we can

render credible augmentations into the images includ-

ing global illumination effects such as contact shad-

ows and color bleeding from virtual objects into real

objects.

GRAPP 2008 - International Conference on Computer Graphics Theory and Applications

260

The technique assumes the availability of time,

date, compass heading and Earth location informa-

tion, all of which represent information which it is

quite feasible can be produced automatically in con-

sumer cameras.

ACKNOWLEDGEMENTS

This research is funded by the CoSPE project (26-04-

0171) under the Danish Research Agency. This sup-

port is gratefully acknowledged.

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