During the decommissioning of nuclear facilities, 3D digital model that precisely describes the work environment can expedite the accomplishment of the work. Thus, the workers’ exposure to radiation is minimized and the safety risk to the workers is reduced, while precluding inadvertent effects on the environment. However, it is common that the 3D model does not exist for legacy nuclear facilities as most of the initial design drawings are 2D drawings and even some of the 2D drawings are missing. Even in the case that all of the 2D drawings are intact, these initial design drawings need to be updated using asbuilt data because facilities get modified through years of operation. In those cases, 3D scanning can be a good option to quickly and accurately generate a structure’s actual 3D geometric information. 3D scanning is a technique used to capture the shape of an object in the form of point cloud. Point cloud is a collection of large number of points on the external surfaces of objects measured by 3D scanners. The conversion of point cloud to 3D digital model is a labor-intensive process as a human worker needs to recognize objects in the point cloud and convert the objects into 3D model, even though some of the conversion process can be automated by using commercial software packages. With the aim of full automation of scan-to-3D-model process, deep learning techniques that take point cloud as input and generate corresponding 3D model have been studies recently. This paper introduces an efficient scan simulation method. The simulator generates synthetic point cloud data used to train deep learning models for classifying reactor parts in robotic nuclear decommissioning system. The simulator is built by implementing a ray-casting mechanism using a python library called ‘Pycaster’. In order to improve the speed of simulation, multiprocessing is applied. This paper describes the ray casting simulation mechanism and compares the in-house scan simulator with an open source sensor simulation package called Blensor.

VR은 사용자가 가상 공간에 완전히 몰입하고 상호 작용할 수 있는 가상 세계이다. 본 연구는 스톱모션 애니 메이션을 VR 애니메이션으로 제작할 시 효과적인 객체 표현 방법에 대한 내용을 제시하고자 한다. 실험 결 과 데이터는 VR 스톱모션 애니메이션 및 일반 스톱모션 애니메이션, VR게임 등 다양한 콘텐츠에 적용이 가 능하다. 3D 오프젝트의 변화되는 형태를 앵글별로 3D 스캔한 후 게임엔진에서 다양하게 활용할 수 있도록 데이터화 한 뒤 콘텐츠에 적용하여 구현한다. 기존 스톱모션 애니메이션에서 느낄 수 있는 수작업 느낌을 살리되 디지털적인 이펙트를 추가적으로 이용할 수 있는 장점이 있다. 기존의 스톱 모션 애니메이터들이 접 근하기 힘든 영역을 작업하는데 필요한 세가지 모듈을 구현하여 실험하였다. VR 콘텐츠 제작에서 오브젝트 의 표현과 관련된 분야의 발전에 기여하게 될 것이며 새로운 방향을 제시할 것이다. 향후 연구에서는 각 캐 릭터 관절의 움직임에 따른 3D 스캔 데이터 관련 연구를 수행할 예정이다.

The apparel industry has recently been recognizing the important target market of middle-aged women. The aim of this study was to examine the anthropometric characteristics of US women of 46 to 65 years of age and identify distinctive body shape characteristics of US middle-aged women. A total of 1915 middle-aged women whose ages ranged from 46 to 65 were selected from the SizeUSA database. The age range was divided into two groups: 46-55 and 56-65. Twenty-four body measurements important for apparel development were chosen. Four factors—Girth Factor, Height Factor, Hip Drop Factor, and Bust Drop Factor—accounted for the US middle-aged women’s body measurements. The body shapes were classified into four body shapes, which were Y-Shape in the overweight range, S-Shape in the overweight range, H-Shape in the overweight range, and the A-Shape in obese range. H-Shape, which was the least-defined waist in relation to the bust and hips with a short height, existed more in older middle-aged women than in younger middle-aged women. Y-Shape, S-Shape, and A-Shape existed more in the group of younger middle-aged women than in the group of older middle-aged women. In addition, compared with the younger middle-aged women, older middle-aged women had narrower shoulders, a larger waist, thinner legs, and a longer distance between side neck to bust point. The findings from the current study may be applied in the apparel industry for developing clothing sizing systems for US middle-aged women.

Measuring body size with a 3D scanner can reduce inter-measurer variability and provide better accuracy compared to using a traditional methods of measurement (Park, Nam, & Park, 2009). Many size measurement projects (or studies) that measure body size established a size measurement method prior to the development of a 3D scanner and automatic size measurement programs that produce 3D virtual body size measurements (Park, &Nam, 2012). Size data measured through an automatic size measurement program are more accurate and have a lower variability that is more appropriate for body measurements (Han, & Nam, 2004; Nam, Choi, Jung, & Yun, 2004). However, this method is limited to healthy subjects who can maintain a correct posture in a 3D scanner. It is difficult for the elderly to maintain the correct posture for body measurements in ‘Basic Human Body Measurements for Technological Design’ of ISO 7250(1997). Body measurement definitions are based on vertical and horizontal directions consequently, it is hard to measure those with a bent body type even if they stand in a correct posture. Most body measurement items are automatically measured in vertical and horizontal directions because current automatic size measurement programs utilize algorithms based on typical body measurement definitions. The size measurement method based on a vertical and horizontal directions tends to have a problem for elderly individuals with a bent body type who have difficulty maintaining a correct posture for 3D scanning as defined in ISO 7250(1997)(Ashdown, & Na, 2008).This study analyzes the problem of present auto-measurement programs that use elderly’s 3D body scan data. We conducted a comparative analysis of elderly’s body sizes using an auto-measurement program from virtual 3D body scan data and direct measurement with traditional measurement methods. We establish 34 typical body size measurements for the use of data from 464 males and 472 females (total 936) between the ages of 70 to 85. For error analysis, data separated to normal values and outliers compared with ISO 20655(2003). ISO 20685 defines the accuracy of extracted measurements by classification and measurement type (segment lengths, body height/breaths/depth, large/small circumferences, and head/hand/foot dimensions). The majority of outliers for the male and female body height type was “height”. Total number of persons with outliers for Height’s data was 603; consequently, 64.4% of subjects (elderly group of 70-85 yrs.) could not maintain a correct posture when scanning. Other data also had many errors from inaccurate measurement postures. A total of 72.3% of males and 70% of females have incorrect values in small circumferences. The segment lengths’ error data was 76.5% of males and 75% of females; in addition, the head dimension’ outliers were 87% for both male and female subjects. Especially 57.46% of males had incorrect data, while 74.67% of females had a type of large circumference. Female chest circumference had significant errors due to sagging breasts. The differences identify with a correlation between type of large circumference (chest, hip, under bust, waist, waist of omphalion) and gender. There were several correlations between the many measurement errors because values over 70% of data have outliers; however, each measurement type has properties in regards to correlation. A substantial positive correlation was found between all measurements (except hip circumference) in the type of large circumference; in addition, one-way ANOVA indicated that the measurements influenced height and were statistically significant. Outliers found in height data for the elderly’s were more likely to have errors in the type of large circumference. The type of body height indicated a strong correlation and statistical significance between the axilla height and other measurements (height, waist, crotch, lateral malleolus). Axilla height with more outliers indicated that other type of body height measurements had a higher potential for errors. The posture for body measurement was standardized as standing erect; however, this study indicated that many measurement errors were possible between using an auto-measurement program and direct measurement. The value of outlier about a particular measurement item can expect increased errors about any group (height: large circumference group/ axilla height: body height group). We have to study the relation in measurements in these types ‘large circumference’ because ‘head dimensions’ types correlate between measurements in each group. We need a more detailed analysis about outliers to find the major factors for measurement errors in regards to the elderly as well as discuss the possibility of ISO measurement-standard’s application for the elderly.

This research aims at developing the dress form for the aged women based on their body shapes using the three-dimensional body scan data with the body shape categorization(according to the previous research). To accomplish this goal, the sample group of representative body shape of the 50% of median was selected by using the high frequency proportion range of each type of body shape of the aged women, and the sample group of representative body shape of each type was averaged in a three-dimensional way by using the morphing method of a three-dimension reverse-engineered software. RP in the form of torso was produced based on the shape data of the final model and the data was formed into an actual object, by which an aged women’s dress form model was drawn out. The differences of the girth of the bust, hip and waist between the developed dress form model and the existing dress form model were examined. The result showed that the developed dress form had a bigger size of waist girth than that of bust and hip girth, compared to the existing dress form, which shows that it reflects the aged women’s tendency of abdomen obesity, so it’s expected to be more proper for the human bodies of the targeted age group than the existing dress form. These research results may help design the clothing suitable for the body shape of the aged women so that their demand for the clothing of good fit will be satisfied in the future.

The purpose of this study is to classify body shapes of aged women by using 3D body scan data. For the body shape analysis and classification, 3D body scan data of 270 aged women were used, and 16 main measurements consisting of a human body were used to conduct factor analysis, cluster analysis and discriminant analysis. The analysis were performed on all ‘the method using the absolute value’, ‘the method using index of height and weight’, and ‘the method using index of height,’ and according to the classification results, the method which categorizes body shapes best in terms of their shapes was adopted. As the factor analysis result using the numerical value of height to categorize the body shapes of the aged women, factor 1 was the thickness and width for the height, factor 2 was the height of the upper part of the body for the height, factor 3 was the height of hips for the height, and factor 4 was the height of belly for the height. When the body shapes were categorized with the deducted factors as variables, they were divided into two types. Type 1 was a short and fat body shape(▅ type) and 55.6% of the subjects were of this type. Type 2 was for the body shape whose vertical height, including weight, was long but all kinds of width and thickness were small, that is, tall and thin body shape(▋ type), and 44.4% of the aged women were in this case.

3D Photo Scan is the science of making measurements from photographs, especially for recovering the exact positions of surface points. 3D Photo Scan may employ high-speed imaging and remote sensing in order to detect, measure and record complex 2D and 3D fields. It will be used as basic data on the facility maintenace through BIM based of RC measurements using advanced 3D photo scan.