平成 20～22 年度 大阪学院大学・大阪学院短期大学 研究助成費 研究概要報告大阪学院大学 企業情報学部教授 石倉弘樹

‘DAVID’ 3-D Digitizing Software to Scan Men's Suit Fabrics
Abstract
It used to be difficult to measure the 3D shape of dark color cloth precisely with low cost. An
example to measure the 3D shape of a dark color cloth is shown by using 3D scanning software
called DAVID.
1. Introduction
The designing and processing of three-dimensional computer graphics (3dcg) have become
familiar operations by improving computer hardware and software because the cost for 3dcg has
been decreasing rapidly for several years. Nowadays the modeling of an object and its texture
mapping are easy work. Recently, a $600 3d camera was put on the market. However, the 360
degree scanning of an object is still costly work compared to the designing. In the search of thisauthor, at least 15,000 US dollars are necessary for getting a system to scan an object in which
all sides are less than 20cm. Prices of scanning systems for larger objects are more than tens ofthousands of dollars. Therefore, 3d shape scanning has been done only by larger facilities; onthe other hand 3dcg is widely done including personal use for hobbies. In the studies of apparelproducts, the measurement of 3d shapes is needed, because appearance is one of the important
points of evaluation for apparel products, and measuring the 3d shape accurately is the first stepto evaluate appearance. However, it is difficult on a low budget. For such situations, 3d object
scanning digitalize software was developed in 2007 by Sven Molkenstruck and Simon
Winkelbach in Germany[1]. The DAVID LASER SCANNER is a low-cost system for contact-freescanning of 3d objects. The software is 350 US dollars and the hardware requirements are ahand-held laser, a web camera and a Windows PC. In this system the output of the laser can bechosen freely therefore the shape of a black cloth which is absorptive of laser beams can bescanned. However, it is not enough to use just the DAVID manual, because the setting of acamera and a laser are uncertain. The example of how to use this measurement system for 3d
shape of the apparel product or the cloth is described.

- 1 -2. Devices
2.1 PC
OS: Widows XP Media Center Edition CPU: Intel core 2Duo E6400 2.13GHz
Main memory: 2.75GB
Graphic Board: EN8800GTS 320MB DDR3
2.2 Web camera
Logicool 30fps

2.3 Laser
BDL210Stype1 Pop Rivets and Fasteners Ltd

2.4 Softwear
3D digitizer: DAVID Ver. 2.4.2[2]Class 2, Weave length 633nm-670nm

3DCG: Shade 9.0 Basic

3. Experiments
3.1 Digitizing(2) Open DAVID-Laserscanner.exe at desktop.

Select [system default] and Click
Fig1 & 2)

to start an experiment at first- 2 -window (Cf.

Fig. 1 The first window for ‘DAVID’Fig. 2 The bottom part of the first window(3) Select [Logicool Qcam Orbit/Sphere AF] at [1.Choose a camera and adjust exposure andbrightness] (Cf. Fig.3). The distance between the web camera and the board is 59cm.
(4) Click(5) Clickto set output size 640×480 and frame ratio 30.000.

toadjust

all reference points enter the image by thefollowing buttons.

- 3 -Fig.3 The window for camera calibration(6) Enter an appropriate value to the box of ‘Scale (mm)’ in Fig. 3.

A4 form: 60.00.

A3 form: 83.00

Fig. 4 The window for ‘Device setting ’- 4 -(7) Set four above-mentioned items Brightness 6020, Contrast 1255, Chroma 1255 andPurity 8784. Check White balance and Focus for Automatic operation as shown in Fig.
4.
(8) Check 'Automatic gain control' of 'Details tab' in Fig. 5.

Fig.5 The window for ‘Details’(6) Click ‘Calibrate Camera!’ (Cf. Fig.6) and if all red × signs are displayed in 25 blackround signs as shown in Fig. 7, the calibration is successful.

Fig.6 The window for ‘Calibrate camera’- 5 -Fig.7The image that succeeds in the calibration(10) Click ‘Eraser’ and paint out by using the mouse excluding black points as shown in Fig. 9and 10.

Fig.9 The window to erase causes of calibration failure.

Fig. 10 Image from which causes of calibration failure are erased- 6 -（11）Check all red × signs are displayed in 25 black round signs as same as Fig.7 and click.

(12) Clickto obtain an angle 3D data. 11 (Cf. Fig. 11) and irradiate the laser

to the cloth by hand .
(13) Clickafter necessary 3D data for the angle are obtained.

Fig. 11 The window to take 3D data
(14) Click ‘Interpolate’ and ‘Smooth Average’ at ‘3.Interpolation and Filtering’ in Fig. 11.
(15) Click ‘Save’ at ‘4.View and save results’ in Fig.11 for saving data..
(16) Clickandfor the next angle.

(17) Click ‘Add’ to open the obj file which was stored in (15) at ‘1.Specify list of input scans’ inFig.12 .

- 7 -Fig. 12 The window for ‘3D Shape Fusion’(18) Repeat the above operation about all the data of the different angles(19) Clickto display the files in turn.

Fig. 13 The bottom part of ‘3D Shape Fusion’(20) Clickto open Fig. 14Fig. 14 The window for surface registration
3D shape data of a cloth are obtained by operation of the above (1) - (20).

- 8 -Fig. 15 Four 3D data different by 90°

Fig. 16 shows the example of composition. The perspective of the drape is obtained from 4
different angles data.

Fig. 16 The composition of 4 data
3.2 Analysis and texture mapping
The final purpose of this study is to estimate the texture of a cloth by the 3D shape and make
3DCG by using the mechanical properties of a cloth.
Fig. 17 shows the front elevation, top view and side view of the sample. The 3DCG of thesample is also shown in Fig. 18. It was given texture mapping by 3DCG software Shadebecause DAVID can obtain only 3D data.

- 9 -Fig. 17 The front elevation, top view and side view of the sample.

2003

Fig 18 The Rendering image of the sample
4. Conclusions
It used to be difficult to measure the 3D shapes of dark color cloth precisely with low cost. An
example to measure 3D shape of a dark color cloth is shown by using 3D scanning software
DAVID.
References
1) http://www.david-laserscanner.com/
2) S. Winkelbach, S. Molkenstruck and F. Wahl, Low-Cost Laser Range Scanner and Fast
Surface Regstration Approach, DAGM 2006, LNCS 4174, pp.718-728,2006, Springer
Berlin Heidelberg 2006.

- 10 -

Study of the Objective Measurement for Woven Fabric Wrinkles-Relationships between the Subjective Measurements of Experts and 3-D Shapes-

1. Introduction
How to make wrinkles unnoticeable is one of the important problems in the apparel and textile
industries. Cloth structures and chemical processing have been researched to reduce wrinkles
and a lot of new products have been developed. But the problem has not been settled. In thestudies, the objective evaluation of wrinkles is the issue which should be solved first, because if
the evaluation is unreliable, textile designers cannot set a clear numerical target. Wrinkles arestill evaluated and ranked by engineers and/or experts in the industries. In this study it isexamined whether analyzed 3-D digitizer data can replace the subjective evaluations or not.
The relationships between appearances (distribution of height direction positional data) andsubjective evaluations of 8 fabric samples for men’s suits are analyzed.
2. Experiments
2.1 Sample material
Samples used in this study are shown in table 1
Table1 Sample materials
NoMaterials

Color

1

Wool 100％

White

2

Wool 100％

Black3Wool 100％

Black

4

Wool100％

Gray

5

Wool 91％

Black

Polyester Filaments 9%(Whitestripe)

6

Wool 91％

Gray

Polyester Filaments 9%78

Wool 30％

Navy

Polyester staple fibers 70％

Blue

Wool 30％

Charcoal

Polyester staple fibers70％

Grey- 11 -2.2 Sun-ray wrinkle test

Figure 1 Sun-ray test sample
As shown in Figure 1, at first a fabric is cut into doughnut shape. Next the cut fabric is folded
into 16 layers and a weight is put on the fabric. After the weight is removed the grade of awrinkle is evaluated. In this study, standard times for weighting and recovery are ignored,
because only the relationships between sample shapes and evaluations are focused on in thisstudy.
2.3 Devices(1) 3-D digitizer

Figure 2 VIVID 900 by MINOLTA
Vivid 900 is used as a scanner. 640×480 3-d data are taken. The accuracy is ±20μm.

(2) Software
3-D CG software Shade 8.5 by e frontier is used.

- 12 -(3) SystemScanPCSample
Figure 3 The outline of system
2.4 Subjective evaluation(1) Samples are evaluated with the naked eye by two engineers.
(2) A value is chosen from grade 1 to 5. The average value is 2.5 and grade 1 is given for themost prominent wrinkle.
3. Results
Figure 4 shows the top view, front view, side view and perspective view obtained by 3-D data.

Figure 4 The Processed 3-D data of sample 1.
The rendering made from 3-D data is shown in the following figure. The wrinkles arereproduced in detail.

- 13 -

Figure 5 The rendering of Sample 1In the next table basic statistics are shown.
Table 2 Basic statistics
SampleMeanStandardMax.

Experts

Heightdeviationheightevaluation(mm)height(mm)(grade)

(mm)

1

4.322

1.476

10.330

3.00

2

4.662

1.659

11.350

2.50

3

4.195

0.849

7.230

4.25

4

5.782

0.845

11.1303.75

53.259

0.844

6.900

4.50

6

4.372

0.803

8.140

4.50

7

3.726

1.937

13.1902.5082.956

0.844

12.1003.625

The height of a point = the maximum distance on a sample from the sensor －the distance ofthe point from the sensor.

Figure 6 Relationships between subjective evaluations and average heights.
- 14 -

As shown in Figure 6, no correlation can be found between subjective evaluations and averageheights.

Figure 7 Relationships between subjective evaluations and standard deviations of heights.
The smaller standard deviations are, the better subjective evaluations become, as shown inFigure 7. It can also be said that the lower maximum heights are, the better subjectiveevaluations become, by the following figure.

Figure 8 Relationships between subjective evaluations and maximum heights.

Figure 9 below shows the relationships between standard deviations of heights and maximum
heights. There is no strong correlation between them. So it cannot be said that both properties
are not independent.
- 15 -

Figure 9 Relationships between standard deviations of heights and maximum heights.
Each sample’s frequency distribution of heights is shown in Figures 10 to 17.

Figure 10 Frequency distribution of heights of sample- 16 -

Figure 11 Frequency distribution of heights of Sample 2.

Figure 12 Frequency distribution of heights of sample 3.

- 17 -

Figure 13 Frequency distribution of heights of sample 4.

Figure 14 Frequency distribution of heights of sample 5.

Figure 15 Frequency distribution of heights of sample 6.
- 18 -

Figure 16 Frequency distribution of heights of sample 7.

Figure 17 Frequency distribution of heights of sample 8.
From those 8 figures, it is confirmed that the dispersion of height data and the maximum valueof heights are deeply related to subjective evaluations. As the result, we try to obtain a multiple
regression equation. In the equation, the standard deviations of heights and the maximum heights
are used as explaining variables and the subjective evaluation is the bound variable. In themultiple regression analysis, the following equation is derived.
y = 6.425-1.1414 x1 + 0.14953x2
Where,
y: Experts’ subjective evaluation (grade)x1: Standard deviation of height (mm)x2: Maximum height (mm).
In the results, the multiple correlation coefficient R is 0.972017 and F=0.003045 under significancelevel 5％.
4. Discussion
Directions of normal vectors for polygons are presented in the figures below. The red and blue
express deviations of the normal vector from the vertical.
- 19 -

Grade 3, Standard deviation of height 1.476 mm,Max. height 10.33 mm

Figure 18 Directions of normal vectors for polygons on sample 1

Grade 3.75, Standard deviation of heights 0.845 mm, Max. height 11.13 mm
Figure 19 Directions of normal vectors for polygons on sample 4

The maximum height of sample 1 is smaller than sample 4, but wrinkle ridge lines of sample 1 areclearer than sample 4. It is possible that low wrinkles are continuing. Therefore, it is assumed thatthe standard deviation of heights is larger than sample 4, and the subjective evaluation is also lower
than sample 4. Sample 6, which was evaluated very high by experts, does not have a clear line asshown in Figure 20.

- 20 -

Grade 4.5, Standard deviation of heights, 0.803 mm, Max. height 8.140 mm
Figure 20 Directions of normal vectors for polygons on sample 6

Analysis of normal vectors may be useful to evaluate wrinkles more precisely by using numericaldata.

5. Conclusion
It becomes clear that subjective evaluations of JIS Sun-Ray wrinkle tests can be estimated by basic
3-D shape data. The subjective evaluations were given by experienced textile engineers. Themaximum height and the standard deviation of heights are used as basic 3-D shape data.
References[1] Zhifeng SU , Zhifeng HAN, Jian, YANG and Mitsuo MATSUDAIRA, Sen’I Gakaishi, Japan,
Vol. 59, No. 10 (2003)[2] Zhifeng SU, Jian, YANG, Zhifeng HAN and Mitsuo MATSUDAIRA, Sen’I Gakaishi, Japan,
Vol. 60, No. 4 (2004)研究発表〔雑誌論文〕
石倉弘樹 大阪学院大学『企業情報学研究』 8 巻，1 号 平成 20 年 ｢サンレイテスト法による布のシワの客観評価に関する研究－専門家の主観評価と 3 次元形状の基本統計デー
タの関係－｣ 61－72 頁
Hiroki Ishikura 大阪学院大学『企業情報学研究』 9 巻，2 号 平成 21 年 ｢Settingconditions for “DAVID”3-d digitizing software to measure the 3-d shape of darkcolor men’s suit fabric｣ 105－107 頁- 21 -