Evaluating drape shape in woven fabrics

Gallissà, Enric Carrera; Juan, Francisco Javier Capdevila; Morell, Josep Valldeperas

doi:10.1080/00405000.2016.1166804

Cited by 19 publications

(22 citation statements)

References 15 publications

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“…Fabric drape is evaluated objectively with 'drape coefficient' (DC) [3], however, equivalent DC is possible for fabrics having different bending rigidities. Therefore, DC is unable to characterize the fabric 3D drape [4]. Traditionally, fabric and garment designers assess its drape subjectively in an informal manner.…”

Section: Introductionmentioning

confidence: 99%

Optimization of fabric drape measurement based on 3D model

Hussain¹,

Naveed²,

IQBAL³

et al. 2019

Ind. Tex.

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Optimization of fabric drape measurement based on 3D model Drapability is an important characteristic of fabric appearance, mainly correlates to its stiffness. It is evaluated objectively with a dominant indicator, drape coefficient (DC). Drape coefficient is based on the two-dimensional projection-area of three-dimensional (3D) draped sample. In this study, three dimensional (3D) drape-model is evaluated and a new basis of the area is revealed with slice function. The proposed area was calculated using a horizontal plane that cut the 3D drape-model (slice curve) just above the highest point of actual boundary-curve. Modified drape coefficient (MDC) was compared with DC against bending stiffness. The results demonstrate that the bending stiffness has better conformance with MDC than that of DC. This implies that MDC is more effective in evaluating fabric drapability.

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Section: Introductionmentioning

confidence: 99%

Optimization of fabric drape measurement based on 3D model

Hussain¹,

Naveed²,

IQBAL³

et al. 2019

Ind. Tex.

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show abstract

“…Cusick's drape is mainly observed after being projected as a 2D image onto a horizontal plane. For various analysis methods for Cusick's drape, we refer to [9]. Numerous studies have demonstrated that Cusick's drape is highly correlated with most KES measurements [10]- [13].…”

Section: A Hanging Drape Vs Cusick's Drapementioning

confidence: 99%

Estimating Cloth Simulation Parameters From a Static Drape Using Neural Networks

Ju¹,

Choi

2020

IEEE Access

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We present a neural network learning approach for estimating a set of cloth simulation parameters from a static drape of a given fabric. We use a variant of Cusick's drape, which is used in the fashion textile industry to classify fabric according to mechanical properties. In order to produce a large enough set of reliable training data, we first randomly sample simulation parameters using a Gaussian mixture model that is fitted with 400 sets of primary simulation parameters derived from real fabrics. Then, we simulate our modified Cusick's drape for each sample parameter set. To learn the training data, we propose a two-stream fully connected neural network model. We prove the suitability of our neural network model through comparisons of the learning errors and accuracy with other similar neural network and linear regression models. Additionally, to demonstrate the practicality of our method, we reproduce the drape shapes of real fabrics using the simulation parameters estimated from the trained neural networks.

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“…This deformation caused by the displacement is one of the manifestations of the comprehensive performance of the fabric. So far, the analysis of drape configuration has remained at the number of drape nodes, the unevenness of angles between drape peaks, the unevenness of the wavelength, etc [13]. However, in the computer graphics field, the shape analysis of 3D models has emerged a variety of shape descriptors, among which are well-known WKS, heat kernel signature (HKS) [14] and 3D scale-invariant feature transform (SIFT) features.…”

Section: Encoding Wksmentioning

confidence: 99%

“…The drape coefficient (DR) of the fabric is an indicator to characterize the ability of a fabric to drape. The number of fabric drape nodes (N ) can characterize the morphology of the draped fabric, as well as the solid rate (R s ) of fabric drape [13]. Therefore, the contrast experiment uses a vector consisting of the three drape indicators: the drape coefficient, the number of fabric drape nodes, and the solid value of the drape fabric.…”

Section: Matching Fabrics With a Vector Consisting Of Drape Indicatorsmentioning

confidence: 99%

Matching Fabric Based on Wavelet Kernel Signature (WKS) and Drape Indicators

Yu¹,

Zhong²,

Xie³

2019

JFBI

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The drape performance of the fabric is an important part of the fabric style, as well as a special expression of the structural parameters and mechanical properties. The comprehensive drape performance of fabric can support fabric matching. This study was conducted to match fabric based on 3D triangular meshes of draped fabrics. Firstly, the three-dimensional (3D) point cloud of draped fabric was scanned with a self-built 3D scanning device followed with triangulation. Secondly, three drape indicators, i.e., drape coefficient, the number of fabric drape nodes, and the solid rate of a draped fabric was extracted, as well as the wavelet kernel signatures (WKS) of triangular meshes. The WKS of vertices on the 3D boundary was selected and clustered into different classes with K-Means. With the cluster centers, the WKS of vertices on the boundary was encoded into a vector with a fixed length. Finally, two methods for matching fabrics were proposed based on WKS+DR and a vector consisting of three drape indicators separately. The results show that WKS+DR outperforms the vector consisting of drape indicators in characterizing the drape configuration details of a draped fabric. The accuracy of the method based on WKS+DR could reach 89.6%. The fabrics obtained with this method are close to the matching fabrics in both drape-ability and details of a triangle surface.

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Evaluating drape shape in woven fabrics

Cited by 19 publications

References 15 publications

Optimization of fabric drape measurement based on 3D model

Optimization of fabric drape measurement based on 3D model

Estimating Cloth Simulation Parameters From a Static Drape Using Neural Networks

Matching Fabric Based on Wavelet Kernel Signature (WKS) and Drape Indicators

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