3D Body Scanning in a Mirror Cabinet

DAGM 2008, LNCS 5096, pp. 284–293
3D Body Scanning in a Mirror Cabinet
Sven Molkenstruck, Simon Winkelbach, and Friedrich M. Wahl
Institute for Robotics and Process Control, Technical University of Braunschweig,
Mühlenpfordtstr. 23, D-38106 Braunschweig, Germany
{S.Molkenstruck, S.Winkelbach, F.Wahl}@tu-bs.de
Abstract. Body scanners offer significant potential for use in many applications
like clothing industry, orthopedy, surgery, healthcare, monument
conservation, art, as well as film- and computer game industry.
In order to avoid distortions due to body movements (e.g. breathing or
balance control), it is necessary to scan the entire surface in one pass
within a few seconds. Most body scanners commercially available nowadays
require several scans to obtain all sides, or they consist of several
sensors making them unnecessarily expensive. We propose a new body
scanning system, which is able to fully scan a person’s body or an object
from three sides at the same time. By taking advantage of two mirrors,
we only require a single grayscale camera and at least one standard line
laser, making the system far more affordable than previously possible.
Our experimental results prove efficiency and usability of the proposed
setup.
1 Introduction
The first systems for three-dimensional body scanning have been developed more
than ten years ago (see e.g. [1], [2]), but an exploitation of all promising applications
is still in the very early stages. Modern body scanners can capture the
whole shape of a human in a few seconds. Such systems offer significant potential
for use in e.g. clothing industry, orthopedy, surgery, healthcare, monument conservation,
art, as well as film- and computer game industry. Recently, Treleaven
and Wells published an article that thoroughly emphasizes the expected “major
impact on medical research and practice” [3]. They exemplify the high capability
of 3D body scanners to improve e.g. scoliosis treatment, prosthetics, drug
dosage and cosmetic surgery. Most publications dealing with body scanners are
motivated by the increasing importance of fast and automatic body measuring
systems for the clothing industry (see e.g. [1], [4], [5]), since such systems may
enable the industry to produce mass customized clothing.
In the last years a few approaches for contact-free 3D body scanning have
been proposed and some commercial products are already available. Most systems
rely on well-known acquisition techniques like coded light, phase shift,
structured light, laser triangulation, and time-of-flight (see [6] for a review of
different range sensors). Surface acquisition techniques are state-of-the-art (see
e.g. [7], [8], [9]), but when they are applied to body scanning, there are still

Fig. 1. Proposed body scanner setup consisting of two mirrors, at least one camera,
and multiple line laser modules which are mounted on a linear slide.
some restrictive conditions that have to be taken into consideration. In order
to avoid problems with body movement (e.g. due to breathing or balance control),
it is necessary to scan the entire surface in one pass within a few seconds.
Whole-body scanners must capture the surface from multiple viewing directions
to obtain all sides of a person. Therefore most of them consist of several sensors,
which make them very expensive.
Alternative low-cost solutions are in great demand. We propose a new body
scanner system that is able to scan a person’s body or an object from all sides
at the same time. By making intelligent use of two mirrors, we require only one
single grayscale camera and at least one standard line laser, making the system
far more affordable than was previously possible.
2 System Setup
Our proposed body scanner setup is illustrated in Fig. 1. It consists of two
mirrors, at least one camera, and multiple line laser modules mounted on a linear
slide. Surface acquisition is based on standard triangulation (i.e. intersection of
camera rays and laser planes, see e.g. [7], [10]). The key point of our setup are two
mirrors behind the person, having an angle of about 120 ◦ between them. This
‘mirror cabinet’ not only provides that each camera can capture three viewing
directions of the subject (see Fig. 2), but additionally reflects each laser plane
into itself, yielding a coplanar illumination from three directions and a visible
‘laser ring’ at the subject’s surface. This enables a simultaneous measurement of
multiple sides of the person with minimal hardware requirements.
Details, possible variations, and recommendations about the setup are discussed
in Section 4.

Fig. 2. View of the upper camera. Each camera captures three viewing directions of
the subject: two mirrored views in the left and right side of the image, and one direct
view in the middle.
2.1 Camera/Mirror Calibration
A precise camera calibration is an essential precondition for accurate body scanning.
To estimate the intrinsic and extrinsic camera parameters, the calibration
process requires points in 3D world coordinates and corresponding points in 2D
pixel coordinates. These points should preferably span the entire working space
(i.e. scan volume). Therefore, we built a calibration target with visible markers
at three sides as shown in Fig. 3. Since each camera in our mirror setup can capture
images in three viewing directions, we derive three camera models for each
camera: One model for the center view and two for the left and right reflected
views. In this way we can act as if we used three separate cameras that can see
three different sides of the body. Our enhanced calibration procedure consists of
three steps:
1. Separate calibration of reflected left, reflected right, and unreflected center
camera.
2. Coarse pose estimation of the left and right mirror plane.
3. Combined fine calibration of the overall mirror-camera setup.
The first step applies three times the standard camera calibration approach of
Tsai [11], except that the reflected cameras require to reflect the x-component
whenever we map from image to world coordinates or vice versa. The results are
three calibrated camera models representing three viewing directions of one real
camera.
The second step estimates an initial pose of each mirror using the fact that
each of them lies in a midplane of two camera focal points. For example, the left
mirror plane is given by plane equation
(f c − f l ) · (x − (f l + f c )/2) = 0; x ∈ R 3 (1)

Fig. 3. Calibration target consisting of three white panels mounted at a precise angle
of 120 ◦ and having a total of 48 markers: (Left) schematic layout; (right) shot of the
upper camera.
with focal point f c of the unreflected center camera and focal point f l of the
reflected left camera.
Theoretically those two calibration steps are sufficient for the following triangulation
approach. However, a well-known weakness of fixed camera calibration
is the close relationship between focal length f and camera-object distance T z .
The effects on the image of small changes in f compared to small changes in
T z are very similar, which leads to measurement inaccuracy. This fact is the
main reason for slight differences in the focal length of all three camera models
(which should be equal). Therefore, the calibration is refined in the third step by
performing a numerical optimization of both mirror poses and all center camera
model parameters, incorporating all 48 markers. Finally, optimized left and right
camera models can be derived from the optimized center camera by reflection
on the left and right mirror plane. A comparison of scans with and without this
last optimization step is given in the last section (Fig. 5).
3 3D Scanning
Computation of surface data consists of three steps: (i) Extraction of laser line(s)
from camera images, (ii) determination of the laser slide position for each camera
frame, and (iii) triangulation of surface points and filtering. We suggest to
perform only the first step during scanning; all further processing steps can be
carried out afterwards when time is no longer critical.

3.1 Laser Line Extraction
From each camera image, the laser line(s) need to be extracted for later processing.
In our case an efficient column-wise line detection algorithm can be applied,
since the laser lines run roughly horizontally (see Fig. 2). In order to obtain the
peak position of a laser line to subpixel precision, we use a conventional centerof-mass
estimation: For each image row x, the subpixel precise y coordinate of a
laser line is calculated as the average y coordinate of all “bright” pixels within a
moving window, weighted with its corresponding pixel intensities. (See [12] for
a comparison of different subpixel peak detection methods.) The resulting peak
coordinates per image column are collected for each camera frame and are used
in the following steps.
3.2 Laser Calibration
There are several possible ways to obtain the laser slide position (i.e. height
of the laser light planes) for each camera frame: (i) Synchronized camera(s)
and servo drive system for laser movement, (ii) stereo vision using synchronized
cameras, or (iii) a laser calibration target (a diffusely reflective object/surface)
at a precisely known location, such that at least one laser line is always visible
on it.
The latter method has been implemented in our experimental setup, as it has
the advantage of requiring neither any hardware synchronization nor expensive
servo drive systems. In our setup we simply use the two flat panels behind
the mirrors as laser calibration target, since these panels run parallel to the
previously calibrated mirror planes. 3D points along the laser lines that lie on
the panels are obtained by ray-plane intersection. To be robust with respect to
occlusions and noise, we presume that the laser moves at constant speed. This
allows us to estimate a linear function that represents the laser height over time,
e.g. by using the Hough Transform for lines [13], [14]. From this processing step,
the position (height) of the laser sledge is precisely known for each camera frame,
allowing triangulation of 3D surface points described in the following.
3.3 Triangulation and Filtering
Triangulation of 3D surface points from calibrated cameras and known laser
planes is simple if we use a single laser plane. But since we want to use several
laser planes simultaneously, we have to solve the pixel-to-plane correspondence
problem (i.e. identify the laser plane number that corresponds to a given illuminated
pixel). Here we propose two heuristics, which can be combined:
Bounding Volume Constraint. When the scanned subject is a human body
and its approximate position in the scene is known, its surface can be roughly
approximated by a bounding volume, e.g. by a surrounding vertical cylinder c
with a certain height and radius. Triangulation with all n possible laser planes

Fig. 4. Intersection of a camera ray with the light planes of laser 1 and laser 2 produces
two possible surface points p 1 and p 2. However, only the correct point p 2 lies inside the
bounding cylinder.
yields n different surface points, which lie on the corresponding light ray to the
camera. In a reasonable setup, only one of them lies inside c (see Fig. 4)
The maximum radius of c, i.e. the scanning region, depends on the distance
between the laser planes and the triangulation angle between laser planes and
camera view. Obviously these parameters need to be adjusted to the scanned
subject’s size.
Column-Wise Laser Counting. Another possible way of determining the
correct laser for each illuminated surface point can be integrated into the laser
line extraction algorithm (Section 3.1): If the number of detected laser peaks in
an image column is equal to the number of lasers n, their respective laser plane
index i can be easily determined by simple counting.
However, this is not possible in all columns due to occlusion, reflections,
image noise, etc. Therefore, we suggest to use a specialized labeling algorithm
that assigns the same label to illuminated points that are direct neighbors in
space or time (i.e. frame number). Thus all points with the same label most
probably have been illuminated by the same laser line. The index of this laser
line can be determined by a majority vote of all available indexes i.
Note: The same principle of majority vote can be used to decide whether
points with a specific label have been seen directly, via the left mirror, or via the
right mirror. This can be necessary if some directly visible parts of the subject
(e.g. the arms, see Fig. 2) reach into the left and/or right image region, occluding
the mirrors.
3.4 Postprocessing
Finally the resulting surface fragments are merged to a closed triangle mesh using
Poisson Reconstruction [15]. If they do not fit perfectly, e.g. due to imprecisions

in setup or calibration, surface registration techniques like ICP [16], [17] can be
used to improve their alignment.
4 Experiments and Conclusion
4.1 Experimental Setup
The approach described in this paper allows several setup variations. One parameter
is the number of parallel laser planes. Increasing this number has the
advantage of decreasing the duration of a whole surface scan, but makes the
pixel-to-plane correspondence problem (discussed in Section 3.3) more difficult.
In our experiments, we have used one to three lasers (650 nm, 16 mW ) with a
distance of 20 cm. The lasers are mounted on a linear slide, which is driven at constant
speed. Images are captured by two Firewire grayscale cameras (1024 × 768
pixels, 30 fps), set up as depicted in Fig. 1. In case of three lasers we can capture
up to 90 body slices per second, which should be sufficient for most applications.
Depending on the scanned object, a single camera may be sufficient; but if
some surface areas are occluded, a second camera may be useful to cover them.
Since only laser lines will be extracted from the camera images during scanning,
depending on the environmental light conditions, it may be useful to attach a
corresponding wavelength bandpass filter to the cameras.
In our prototypical setup, we use two usual hall mirrors from a do-it-yourself
store (62 cm × 170 cm each). Although those mirrors are not perfectly flat (especially
the one on the right), we obtained satisfying results. However, industrial
front surface mirrors should be preferred in a professional setup.
4.2 Results and Conclusion
To verify the absolute precision of our setup, we have scanned a precisely known
object: a cuboid of 227.7 mm × 316.8 mm × 206.6 mm, mounted on a tripod at
about chest height (Fig. 5). The obtained scan has an absolute error of approx.
1 mm in size. However, the influence of our slightly distorted right mirror can be
seen at the right side of Fig. 5(b): The right edge of the cuboid is not precisely
parallel to the left one.
Furthermore, we have analyzed the surface noise of the measured cuboid
faces (i.e. the captured surface point distances to the corresponding planes). The
scan data of the front side, which is directly visible in the camera image, have
lowest noise: The 17025 captured surface points exhibit a standard deviation of
0.63 mm; their maximum distance is less than 2.2 mm. As the other three faces
are not directly visible (only as reflections in the mirrors), their pixel resolution
is lower, and noise is slightly stronger: We obtained 5993 to 8558 surface points
per side, having a standard deviation of 0.82 mm to 0.93 mm and a maximum
outlier distance of 3.9 mm.
Some qualitative results from our experiments can be seen in Fig. 7 and
Fig. 8. Fig. 6 shows a corresponding camera view directly before scanning and

(a) (b) (c)
Fig. 5. Unfiltered scan of a 206.6 mm × 316.8 mm × 227.7 mm cuboid. (a) Horizontal
slice (seen from above) after performing a standard camera calibration. (b) Same horizontal
slice after performing our refined calibration (see step 3 in Section 2.1). The
divergence at the right side is caused by our slightly distorting right mirror. (c) 3D
rendering of the result.
Fig. 6. Shots of the upper camera: (Left) Person in a mirror cabinet; (right) overlaid
laser lines of every 10th camera frame.
an overlay of multiple camera images during scanning. The 3D data have only
been filtered with a 3 × 3 averaging filter. On closer inspection of the surface one
can see some small horizontal waves appearing at the surface that are caused by
slight body motion, which is almost impossible to avoid. In future work, we will
try to eliminate this waviness by adjusting the balance point of each body slice.
Gaps appear in the 360 ◦ view on too specular or dark surface parts (e.g. hair),
or in regions which are occluded from the lasers or cameras by other body parts.
Our results prove usability and efficiency of the suggested body scanner setup.
Its precision is sufficient for many applications in health care or clothing industries,
and the use of mirrors and related algorithms makes our setup very
cost-efficient compared to previous techniques, because each mirror saves costs
of at least one camera, one laser, and one linear slide.

(a) (b) (c)
Fig. 7. Scan results of the person in Fig. 6: (a) Front and rear view of a scan using the
lower camera only; (b) merged data of lower and upper camera, front view; (c) rear
view.
(a) (b) (c) (d)
Fig. 8. Further results: (a) Front view of a female; (b) rear view. (c) Front view of a
male; (c) rear view.

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