Abstract:In this paper, a system with six depth cameras was built to scan both feet simultaneously. An improved calibration method based on a T-shaped checkerboard was used to calculate the extrinsic parameters of the cameras. T-shaped virtual checkerboards were introduced to further fine-tune the accuracy of calibration based on the iterative closest point algorithm. Based on the proposed foot scanner, a complete procedure was introduced to measure the foot automatically by locating the anatomical landmarks without ma… Show more
“…To reconstruct the 3D drape models, fabric samples were placed on drape-meter (figure 1) and scanned at different locations by multiple depth cameras using a method developed in our earlier research [21][22]. We made a modification on this method with four depth cameras instead of six.…”
Section: Slice Area Reconstruction Of 3d Drape-modelmentioning
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.
“…To reconstruct the 3D drape models, fabric samples were placed on drape-meter (figure 1) and scanned at different locations by multiple depth cameras using a method developed in our earlier research [21][22]. We made a modification on this method with four depth cameras instead of six.…”
Section: Slice Area Reconstruction Of 3d Drape-modelmentioning
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.
“…15 and Wu et al. 16 The scanning device is shown in Figure 1b. Figure 1.Scanning fabric drape workflow: (a) calibration, (b) scanning, (c) four patches, (d) completed 3D points cloud, and (e) 3D triangulated mesh.…”
Section: Methodsmentioning
confidence: 99%
“…The angle between the two adjacent cameras was 90 . To achieve an accurate scanning result, a T-shaped checkerboard shown in Figure 1a was used to calibrate the extrinsic parameters for the four cameras based on the algorithm proposed by Zhang et al 15 and Wu et al 16 The scanning device is shown in Figure 1b. Figure 1c illustrates the four point-cloud patches of the draped fabric captured by each camera.…”
To evaluate the ability of woven fabrics to drape in a more accurate way, a three-dimensional point cloud of a draped woven fabric was captured via an in-house drape-scanner. A new indicator, total drape angle (TDA), was proposed based on the three-dimensional fabric drape to characterize the ability of a woven fabric to drape. The relationship between TDA and the drape coefficient (DC) was analyzed to validate the performance of TDA. The result indicated that TDA is more stable and representative than the traditional DC in characterizing the ability of a woven fabric to drape. In addition, the drape angle distribution function (DADF) of the triangular mesh was employed to describe fabric drape, as well as to bridge the gap between drape configuration and the warp bending rigidity of woven fabric. The results showed that the correlation coefficient between the real warp bending rigidity value and what was predicted warp based on DADF and fabric weight was 0.952.
“…The scanner was based on six depth-cameras and its accuracy was less than 2 mm. 24 As shown in Figure 1, the scan result included the upper surfaces and the soles of the feet. 80 pairs of scans were randomly selected as the training set to generate the regressive model.…”
Section: Foot Scanningmentioning
confidence: 99%
“…23 Since this method requires the information on the foot sole, it can only be used for the foot in non-weight-bearing postures. Wu et al 24 described a multi-camera foot scanning system using a structured light technique, which changed the aspect of the foot surface by projecting infrared light patterns on it. Wang et al 25 presented a multi-camera foot reconstruction system, which used principle component analysis (PCA) to fit their reconstructed data into a generic model obtained from a foot shape database.…”
In this paper, a new method was proposed to establish the relationship between three-dimensional (3D) foot shapes and their two-dimensional (2D) foot silhouettes, through which a complete 3D foot shape can be predicted by simply inputting its two 2D silhouettes. 3D foot scans of 80 participants were randomly selected as the training set, and those of another 20 participants were used as the testing set. Elliptical Fourier analysis (EFA) and principle component analysis (PCA) were adopted to parameterize the 3D foot shapes. A linear regressive model was then developed to predict the 3D foot shape with the foot silhouettes. Experiment results indicated individual 3D foot shape can be predicted with a mean error between 1.21 and 1.27 mm, which can provide enough accuracy for the fit evaluation of footwear.
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