A Practical Framework for Virtual Viewing and Relighting

Conference Paper (PDF Available) · January 2007with8 Reads
DOI: 10.1007/978-3-540-74873-1_34 · Source: DBLP
Conference: Entertainment Computing - ICEC 2007, 6th International Conference, Shanghai, China, September 15-17, 2007, Proceedings
Abstract
Recently many practical applications have concerned with observing objects that have specular reflection properties. They intend to know how the specular reflections and other details vary according to different lighting conditions and view positions. In this paper, we propose an innovative framework combining the novel view synthesis algorithm and the relighting algorithm to fulfill these requirements. Another important feature of this framework is that all the algorithms are based on image without acquiring any D model which may contain some high confidential secrets. Meanwhile an image measurement criterion is proposed to verify the feasibility and effectiveness of the framework.

Figures

A practical framework for virtual viewing and
relighting
Qi Duan, Jianjun Yu, Xubo Yang, Shuangjiu Xiao
Digital Art Laboratory,
School of Software
Shanghai Jiao Tong University
{duanqi1983,jianjun-yu,yangxubo,xsjiu99}@sjtu.edu.cn
http://dalab.se.sjtu.edu.cn
Abstract. Recently many practical applications have concerned with
observing objects that often have specula reflection properties. They in-
tend to know how the specula reflections and details on the surface of
objects vary according to different light conditions and view positions
without calculating any 3D information of the objects. In this paper,
we propose an innovative framework combining the novel view synthesis
algorithm and the relighting algorithm to fulfill these practical require-
ments. This framework can observe objects under different light condi-
tions and in arbitrary view positions, with specula reflection properties.
Another important feature of this framework is that all the algorithms
are based on image without acquiring any 3D model since any kind of
mo del may contain some high confidential secrets. Meanwhile an image
measurement criterion is proposed to verify the feasibility and effective-
ness of the framework.
Key words: Novel View Synthesis, Image-based Relighting, Image Mea-
surement
1 Introduction
Recently, the technique that can render images of product under arbitrary illu-
minations or view points became increasingly important in Factory Automation
application. It is also of great value in movie, museums, and electronic commerce
applications. For example, in machine vision applications, rendering method is
used to simulate and decide the optimal lightning or viewpoint of the camera. In
order to enhance the exhibition effect of object in some special circumstances,
various special lights will be placed in certain positions to augment the effects
of details on product surface; meanwhile the observation position and angle also
influence the display result. The position of viewpoints and the condition of lu-
minance are two key factors that influence the effects. To determine the best
luminance condition and view point position, it is very helpful to design a tool
that can precisely observe the surface reflection effect of target under various
light conditions, such as different colors and types of LED light sources, and
2 A practical framework for virtual viewing and relighting
also can observe objects in some arbitrary view positions. Considering that the
product may contain some high confidential secrets, those requirements can be
summarized as follows:
A method to simulate the result that the object is placed under various light
conditions, for example, different numbers, sizes, types, colors of light re-
sources and different distances between objects and light. Considering practi-
cal application requirements, we need to get the best result with the minimal
cost.
A technique to synthesize novel images of the objects. Considering the ob-
jects may be some industrial products relating to commercial commercial
confidential secrets, the less 3D information of the target we calculate, the
better.
However, currently all the related work solves only one of the two requirements.
Thus, we design this new practical framework to handle this problem.
1.1 Related work
Two main algorithms employed in this framework are the image-based relighting
and the novel view synthesis.
Image-based Relighting(IBL) attracts a lot of attention recently, because
it does not require precise physical models. Image-based relighting methods
can be classified into three categories, named reflectance-based relighting, ba-
sis function-based relighting and plenoptic function-based relighting. Reflectance
Function-based Relighting techniques explicitly estimate the reflectance function
at each visible point of the object or scene, which is known as the Anisotropic
Reflection Model[1] or the Bidirectional Surface Scattering Reflectance Distribu-
tion Function (BSSRDF)[2]. Lin et al.[3] researched the dual of the Lumigraph,
in which the camera is fixed and a point light source is moved. Basis Function-
based Relighting techniques take advantage of the linearity of the rendering op-
erator with respect to illumination. Relighting process is accomplished via linear
combination of a set of pre-rendered ”basis” images. Nimeroff et al. [4] used a
technique of combining images to relight a scene. Debevec et al.[5] describes a
Light Stage in which an object can be placed. Plenoptic Function-based Relight-
ing techniques are based on the computational model, the Plenoptic Function[6].
So the plenoptic function-based relighting techniques extract out the illumina-
tion component from the aggregate time parameter, and facilitate relighting of
scenes.
Novel image synthesis has been studied in the last decade. The work in this
area can be divided into two main classes roughly: image-based modeling and
rendering(IBMR) and image-based morphing and rendering. The first class is
designed to reconstruct 3D models from photographs. Debevec et al.[7] have
A practical framework for virtual viewing and relighting 3
shown a method of modeling and rendering architecture from a small number
of photos. Criminisi et al.[8] create 3D texture-mapped models relying solely
on one single image for its input. Byong Mok Oh et al.[9] take a single photo
as input and model the scene. After modeling the scene, we can change from
different viewpoints; modify the shape, color and illumination of the scene. The
second class does not calculate any 3D information of the scene. It use various
methods to synthesis the novel view image such as image warping[10], image in-
terpolation[11], image extrapolation and so on. For example, Seitz and Dyer[12]
interpolate along the base line of image pairs to obtain correct images. Levoy and
Hanrahan[13] and Gortler et al.[14] interpolate between a dense set of several
thousand-example images to obtain novel view images. Peleg and Herman[15]
relax the fixed camera constraint to synthesis results. Avidan and Shashua[18]
introduce the concept of trilinear tensor space and use trilinear tensor to syn-
thesize the novel view images.
However, the image-base relighting approach can only help us to view the tar-
get under various light conditions and the novel view synthesis method can only
generate the novel image under arbitrary viewpoints. Our framework integrates
the image based relighting approach and the novel view synthesis method, which
can render images of product under arbitrary illuminations and view points.
The following sections describe the framework, the experiment result and the
conclusion. In section 2 we introduce the method to simulate the object under
various light conditions and the process to synthesize a novel view image of object
in our framework. Image measurement criterion and framework performance are
described in section 3. Finally, we conclude and present the idea for future work
in section 4.
2 Algorithms
2.1 Framework architecture
The most significant feature of the framework is the ability to observe the tar-
get under arbitrary illumination and novel view positions, and all this results
are totally based on images without acquiring any 3D information. Therefore,
the framework integrates two important algorithms above as two modules. The
structure is shown in Figure 1. Firstly, in image-based relighting module, the
camera position is fixed and the relighting process is performed, then the result
image is obtained under arbitrary luminance situation. This image is named
the source image. Secondly, the camera position and orientation is moved in a
little range and a picture of the target object is captured as reference image.
Using source image and reference image as the input of novel view synthesis
module, the final novel image can be synthesized. The relationship between the
source image and the reference image is simply related to the two different cam-
era positions and calculated only once in the process procedure. Once the light
environment changes, the relighting result is recalculated and the source image
is updated, leaving reference image unmodified. Then the novel image is syn-
4 A practical framework for virtual viewing and relighting
thesized again. Employing this framework, we can observe the target of various
luminance situations under arbitrary viewpoints.
The basis function-based relighting and the trilinear tensor novel view synthe-
sis are selected in this framework instead of reflectance-based relighting, plenop-
tic function-based relighting and other novel view algorithms since these two
image-based method are sup erior in keeping confidential secret. The two impor-
tant methods will be introduced in the following sections of this chapter.
Fig. 1. Framework Architecture.The architecture contains two main algorithm
mo dules. The source image is the output of relighting module, and the input of the
second module, as well as the reference image. The novel image is the final result as
the output of the novel module.
2.2 Image-Based Relighting
Image-based Relighting (IBL) can synthesize realistic images without a complex
and long rendering process as in traditional geometry-based computer graphics.
The algorithm detail is divided into four parts and will b e introduced in the
following.
Basis image acquisition. A light stage is designed to capture the basis images,
which is shown in Figure 2. This stage is a spherical structure attached with
prepared light sources. A point light is used as the ”standard” light source and
the target is placed at the center of the stage. The system captures images
of the target while only illuminated by the ”standard” light. The light moves
around the surface of sphere and for each light position and a corresponding basis
image is captured. This process is repeated until the entire sphere of incident
illumination is sampled at a pre-defined resolution. Since the distance between
the light and the target is much bigger than the target’s size, the p oint light
source can be considered as ”directional light source”.
A practical framework for virtual viewing and relighting 5
Fig. 2. Light Stage and Result Images. The target on the platform is illuminated
by ”standard” light. For each light direction, a corresponding image is captured. The
right image shows the capture results under our spherical sample methods.
Luminance mapping. When doing the renderings, it will be convenient to
have a table that maps from the image number to a direction on the sphere.
Thus, the sphere map is extended to latitude-longitude map, which is shown in
Figure 3.
Fig. 3. Sphere map to Latitude-Longitude map. According to the Light Stage,
we split the sphere surface that light source covered in different regions. Then these
regions are projected into a latitude-longitude map, each grid corresponding to a region
in sphere surface.
From the Figure 3 we notice that different section on the sphere has different
area. To obtain an accurate result, the impact for each region of samples is
scaled according to the area of the section on the sphere. The scaling factors in
latitude-longitude map can be determined by the following equation 1:
A
i
=
1
4π
Z
(
φ
i
+
∆φ
2
)
(
φ
i
∆φ
2
)
Z
(
φ
i
+
∆θ
2
)
(
φ
i
∆θ
2
)
sin(θ)
6 A practical framework for virtual viewing and relighting
=
1
4π
(∆φ)
µ
cos
µ
θ
i
∆θ
2
cos
µ
θ
i
+
∆θ
2
¶¶
(1)
Relighting process. To render the target under arbitrary illumination, the
virtual light illumination is mapped onto the sphere map firstly. Then the sphere
map is converted to the Longitude-Latitude map. So the relighting problem can
be solved by linear combination of the basis images. Relighting of the sequence
of images I
i
can be described by the following equation 2:
I =
N
X
i
I
i
Z
(
φ
i
+
∆θ
2
)
(
φ
i
∆θ
2
)
E(φ, θ) sin(θ) (2)
Here the equation 2 is approximated by a linear combination equation 3 of
the images I
i
and the mean of the lighting environment E
i
over the solid angle
scaled by the relative area of the solid angle projected onto the unit sphere.
I
N
X
i
I
i
A
i
C
i
E
i
=
N
X
i
I
i
A
i
E
i
· I
D
/I
R
(3)
Where C
i
is the difference between arbitrary light intensity and standard light
intensity.I
D
and I
R
are the intensity of arbitrary light and standard light re-
spectively.
With the images set, latitude-longitude maps and the weights, the image
under novel illuminations can be generated.
Fig. 4. Virtual Ring Light Relighting. A virtual ring light is simulated using the
combination method of spatial arrays of point light sources and all the point light
sources are mapped to the latitude-longitude map. The relighting image under virtual
ring light source is shown in the left.
2.3 Novel View Synthesis
The novel view synthesis algorithm is employed to synthesize the image under
arbitrary view position. This technique is based on two or three sample images
A practical framework for virtual viewing and relighting 7
and the primary process is the tirlinear tensor operation, which is quite fit to
our application.
Feature point detection. During the novel process, the relationship between
the sample images is crucial to this approach. To get a more accurate result, the
matching feature points are automatically detected in the sample images. The
Harris corner points are firstly detected and then the putative matches[16] of
the feature points in images are calculated. In the end, the RANSAC iteration
algorithm is employed to detect the feature points accurately and robustly.
Trilinear tensor operation. At the b eginning of the process, a tensor is calcu-
lated to be the basis tensor. In the projection theory, given three sample images,
there are three different projection matrixes [I, 0], [A, V
0
], [B, V
00
]. The trilinear
tensor is a 3×3×3 matrix described by a bilinear function of the camera matrices
A, B:
T
jk
i
= v
0j
b
k
i
v
00k
a
j
i
(4)
where a
j
i
, b
k
i
, v
0j
, v
00k
represent the elements of the homographies A,B and
vector v
0
,v respectively.
In the implement, to accelerate and simplify our process, we use only two
sample images, which means a
j
i
= b
k
i
. The fundamental matrix of these two im-
ages is calculated and the elements of the fundamental matrix are rearranged[17]
into the tensor; the basis tensor can also be obtained in this way.
To acquire novel image of the object, a 3 × 3 homography R and a 3 × 1
vector t should be specified, which represent the rotation component and trans-
lation component from sample image to novel image. The new tensor G
jk
i
will
be calculated in equation 5[18]:
G
jk
i
= R
k
l
T
jl
i
+ t
k
a
j
i
(5)
The G
jk
i
is just the new trilinear tensor used to re-project the first two sample
images onto the desired novel view image.
Re-projection process. After obtaining the new trilinear tensor and two sam-
ple image, the final procedure is to re-project the two sample image onto the
desired new novel image. The final novel image can be generated from the source
image using the re-projection equation 6[18].
p
i
s
u
j
T
jk
i
= p
00k
(6)
Where p is the matching point in the source image, s
u
j
represents the eipolar
line intersecting at the corresponding point of p in reference image, p
00
is the point
in the novel image. Using the new tensor, two sample images and re-projection
equation, the new novel image can be re-projected directly.
8 A practical framework for virtual viewing and relighting
3 Experiments
In this section, some experiments are designed to verify the accuracy and ef-
ficiency of this framework. Regarding to the two different algorithms, diverse
verification experiments are carried out separately according to different mod-
ules.
Fig. 5. Relighting Comparison. The left image is the generated relighting image
and the right image is the captured real image. The region in red square is the specula
reflection area on the surface.
As we see from Figure 5, it is very difficult to distinguish the difference be-
tween the synthesized relighting image and the real image. To verify the accuracy
of this relighting algorithm, the comparison between the synthesized image and
the real photo is essential. Generally speaking, taking difference pixel by pixel
is the most accurate way to measure the difference of two images. Most image
measurement methods before concentrate on the effect of contrast and sharpness
of an image on human perception. They are all used to compute the perceivable
difference of one image pair, such as the CIELAB color model[19] and CIE2000
color difference formula[20]. Those methods are not suitable for our verification
because they focus on human perception while our goal is to measure the quality
and effectiveness of the relighting algorithm. Under such circumstances, a new
appropriate approach is designed to evaluate the result of this framework.
Taking the aim of this module into account, the luminance property of the
image is quite important. So the histograms of the R, G, B channel as well as
the luminance of the two images are compared in this approach. In addition to
the histogram, the mean value and variance of the two images are calculated as
well, which are two widely used statistical parameters. Here we show the analysis
results of the relighting result and the real photo in Table 1 and Figure 6.
As we can see from the analysis results and Figure 5, the position and in-
tensity of specula reflection and the self-occlusion shadow on the target surface
under arbitrary luminance conditions are simulated precisely, and the range of
error between image pair is limited to a very small fraction. This experiment
proves the method used for image-based relighting is extremely accurate and
efficient.
A practical framework for virtual viewing and relighting 9
Fig. 6. Histogram Analysis. Histogram comparison of the image pair in red channel
(left above), green channel (right above), blue channel (left below) and luminance (right
b elow).
Considering the interpolation and extrapolation characteristic of novel view
algorithm, it is not suitable to measure the efficiency of our framework in quan-
titative methods. Therefore, the criterion of measurement is that the result sat-
isfies p eople’s vision effect and the physical distortions of the target object occur
in the acceptable range. Meanwhile the intensity and specula reflection property
as well as shadow details can be preserved in the novel image. Figure 7 shows
the experiment result of this module, which is also the final novel result of this
framework.
The efficiency of this framework is also what we concern about. The spec-
ification of the computer is Dell Dimension(TM) E520,Intel(R) Pentium(R) D
915 with Dual Core Processing,1GB Dual Channel DDR2 Memory and 160 GB
SATA HDD. This process time of this framework lies on the resolution of basis
images. In this exp eriment, the resolution of image is fixed at 512 × 342. During
the relighting process, the process time also depend on the condition of arbitrary
light situation such as type, color, shape, distance of light source. In the worst
situation, all the 36× 17 basis images should be handled, at this time the process
10 A practical framework for virtual viewing and relighting
Channel Mean Value Variance Error
Our Method Real Photo Our Method Real Photo
Red 127 128 89.16 93.29 0.03%
Green 115 115 89.08 94.54 0.03%
Blue 120 122 90.81 95.77 0.02%
Luminance 119 119 81.94 88.04 0.02%
Table 1. Statistical result of the image pair.
Fig. 7. Novel View Results. The left above image is the reference image and the
left below image is the generated source image. The other images are the synthesized
novel view images that we specify the camera move around the Z-axis of the target
from left to right. In the right blow image, which means the camera position changes
in a large range, the distortion of the target is obvious and unacceptable.
time is more than 70 seconds. But in the practical application, only parts of the
basis images are required to simulate under the arbitrary light situation, take
the virtual ring light source shown in Figure 4 for example, the process time
is nearly 12 seconds. During the novel view process, we only need to re-project
and calculate the result image per pixel once. So the process time is less than 1
second. We can nearly synthesize the real-time novel result.
4 Conclusion and Future Work
In this paper we present a framework for observing the target to satisfy some
practical requirements. We choose the image-based methods to avoid calculating
3D information or model of objects and still can synthesize the result under ar-
bitrary luminance conditions and view positions. Under various light condition
A practical framework for virtual viewing and relighting 11
and viewpoint, we can observe different details on the object’s surface clearly
especially the specula and shadow details of the target. The accuracy and effi-
ciency of this framework is acceptable. In future, we will still do some researches
in these aspects: accelerating the process of relighting, improving the accuracy
of novel view methods, extending the novel view algorithm that can observe the
target globally.
Acknowledgments. This research is sponsored by 863 National High Technol-
ogy R&D Program of China (No. 2006AA01Z307) and National Natural Science
Foundation of China (No. 60403044)
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  • [Show abstract] [Hide abstract] ABSTRACT: This paper introduces a simple model for subsurface light transport in translucent materials. The model enables efficient simulation of effects that BRDF models cannot capture, such as color bleeding within materials and diffusion of light across shadow boundaries. The technique is efficient even for anisotropic, highly scattering media that are expensive to simulate using existing methods. The model combines an exact solution for single scattering with a dipole point source diffusion approximation for multiple scattering. We also have designed a new, rapid image-based measurement technique for determining the optical properties of translucent materials. We validate the model by comparing predicted and measured values and show how the technique can be used to recover the optical properties of a variety of materials, including milk, marble, and skin. Finally, we describe sampling techniques that allow the model to be used within a conventional ray tracer.
    Article · Sep 2002 · International Journal of Computer Vision
  • [Show abstract] [Hide abstract] ABSTRACT: Image-based relighting (IBL) is a technique to change the illumination of an image-based object/scene. In this paper, we define a representation called the reflected irradiance field which records the light reflected from a scene as viewed at a fixed viewpoint as a result of moving a point light source on a plane. It synthesizes a novel image under a different illumination by interpolating and superimposing appropriate recorded samples. Furthermore, we study the minimum sampling problem of the reflected irradiance field, i.e., how many light source positions are needed. We find that there exists a geometry-independent bound for the sampling interval whenever the second-order derivatives of the surface BRDF and the minimum depth of the scene are bounded. This bound ensures that when the novel light source is on the plane, the error in the reconstructed image is controlled by a given tolerance, regardless of the geometry. We also analyze the bound of depth error so that the extra reconstruction error can also be governed when the novel light source is off-plane. Experiments on both synthetic and real surfaces are conducted to verify our analysis.
    Full-text · Article · Sep 2002
  • [Show abstract] [Hide abstract] ABSTRACT: We present a method to acquire the reflectance field of a human face and use these measurements to render the face under arbitrary changes in lighting and viewpoint. We first acquire images of the face from a small set of viewpoints under a dense sampling of incident illumination directions using a light stage. We then construct a reflectance function image for each observed image pixel from its values over the space of illumination directions. From the reflectance functions, we can directly generate images of the face from the original viewpoints in any form of sampled or computed illumination. To change the viewpoint, we use a model of skin reflectance to estimate the appearance of the reflectance functions for novel viewpoints. We demonstrate the technique with synthetic renderings of a person's face under novel illumination and viewpoints.
    Full-text · Conference Paper · Jul 2000 · International Journal of Computer Vision
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