Charlie C. L. Wang

Parameterization and parametric design of mannequins

Abstract This paper presents a novel feature based parameterization approach of human bodies from the unorganized cloud points and the parametric design method for generating new models based on the parameterization. The parameterization consists of two phases. First, the semantic feature extraction technique is applied to construct the feature wireframe of a human body from laser scanned 3D unorganized points. Secondly, the symmetric detail mesh surface of the human body is modeled. Gregory patches are utilized to generate G1 continuous mesh surface interpolating the curves on feature wireframe. After that, a voxel-based algorithm adds details on the smooth G1 continuous surface by the cloud points. Finally, the mesh surface is adjusted to become symmetric. Compared to other template fitting based approaches, the parameterization approach introduced in this paper is more efficient. The parametric design approach synthesizes parameterized sample models to a new human body according to user input sizing dimensions. It is based on a numerical optimization process. The strategy of choosing samples for synthesis is also introduced. Human bodies according to a wide range of dimensions can be generated by our approach. Different from the mathematical interpolation function based human body synthesis methods, the models generated in our method have the approximation errors minimized. All mannequins constructed by our approach have consistent feature patches, which benefits the design automation of customized clothes around human bodies a lot.

Design automation for customized apparel products

This paper presents solution techniques for a three-dimensional Automatic Made-to-Measure scheme for apparel products. Freeform surface is adopted to represent the complex geometry models of apparel products. When designing the complex surface of an apparel product, abstractions are stored in conjunction with the models using a non-manifold data structure. Apparel products are essentially designed with reference to human body features, and thus share a common set of features as the human model. Therefore, the parametric feature-based modeling enables the automatic generation of fitted garments on differing body shapes. In our approach, different apparel products are each represented by a specific feature template preserving its individual characteristics and styling. When the specific feature template is encoded as the equivalent human body feature template, it automates the generation of made-to-measure apparel products. The encoding process is performed in 3D, which fundamentally solves the fitting problems of the 2D tailoring and pattern-making process. This paper gives an integrated solution scheme all above problems. In detail, a non-manifold data structure, a constructive design method, four freeform modification tools, and a detail template encoding/decoding method are developed for the design automation of customized apparel products.

Drawing on Air: Input Techniques for Controlled 3D Line Illustration

Stretch-free surface flattening has been requested by a variety of applications. At present, the most difficult problem is how to segment a given model into nearly developable atlases so that a nearly stretch-free flattening can be computed. The criterion for segmentation is needed to evaluate the possibility of flattening a given surface patch, which should be fast computed. In this paper, we present a method to compute the length-preserved free boundary (LPFB) of a mesh patch, which speeds up the mesh parameterization. The distortion on parameterization can then be employed as the criterion in a trial-and-error algorithm for segmenting a given model into nearly developable atlases. The computation of LPFB is formulated as a numerical optimization problem in the angle space, where we are trying to optimize the angle excesses on the boundary while preserving the constraints derived from the closed-path theorem and the length preservation.

Automatic polycube-maps

We propose an automatic PolyCube-Maps construction scheme. Firstly, input mesh is decomposed into a set of feature regions, and further split into patches. Then, each region is approximated by a simple basic polycube primitive with each patch mapped to a rectangular sub-surface of the basic polycube primitive which can be parameterized independently. After that, an iterative procedure is performed to improve the parameterization quality globally. By these steps, we can obtain the polycubic parameterization result efficiently.

Technical Section: Gradient based image completion by solving the Poisson equation

This paper presents a novel gradient-based image completion algorithm for removing significant objects from natural images or photographs. Our method reconstructs the region of removal in two phases. Firstly, the gradient maps in the removed area are completed through a patch-based filling algorithm. After that, the image is reconstructed from the gradient maps by solving a Poisson equation. A new patch-matching criterion is developed in our approach to govern the completion of gradient maps. Both the gradient and the color information are incorporated in this new criterion, so a better image completion result is obtained. Several examples and comparisons are given at the end of the paper to demonstrate the performance of our gradient-based image completion approach.

Freeform surface flattening based on fitting a woven mesh model

This paper presents a robust and efficient surface flattening approach based on fitting a woven-like mesh model on a 3D freeform surface. The fitting algorithm is based on tendon node mapping (TNM) and diagonal node mapping (DNM), where TNM determines the position of a new node on the surface along the warp or weft direction and DNM locates a node along the diagonal direction. During the 3D fitting process, strain energy of the woven model is released by a diffusion process that minimizes the deformation between the resultant 2D pattern and the given surface. Nodes mapping and movement in the proposed approach are based on the discrete geodesic curve generation algorithm, so no parametric surface or pre-parameterization is required. After fitting the woven model onto the given surface, a continuous planar coordinate mapping is established between the 3D surface and its counterpart in the plane, based on the idea of geodesic interpolation of the mappings of the nodes in the woven model. The proposed approach accommodates surfaces with darts, which are commonly utilized in clothing industry to reduce the stretch of surface forming and flattening. Both isotropic and anisotropic materials are supported.

Automatic design of conformal cooling circuits for rapid tooling

Abstract This paper presents an automatic method for designing conformal cooling circuits, which is an essential component that directly affects the quality and timing for products fabricated by rapid tooling. To reduce the time of cooling and to control the uniformity of temperature and volumetric shrinkage, industry expects to have cooling channels that are conformal to the shape of the products. We achieve the goal of automatically designing such conformal cooling circuits in a twofold manner. First, the relationship between the conformal cooling and the geometry shape of cooling circuit is formulated. Based on that, we investigate a geometric modeling algorithm to design a cooling circuit approaching conformal cooling. Simulations have been made to verify the advantage of the cooling circuit generated by our algorithm.

Fast Query for Exemplar-Based Image Completion

In this paper, we present a fast algorithm for filling unknown regions in an image using the strategy of exemplar-matching. Unlike the original exemplar-based method using exhaustive search, we decompose exemplars into the frequency coefficients and select fewer coefficients which are the most significant to evaluate the matching score. We have also developed a local gradient-based algorithm to fill the unknown pixels in a query image block. These two techniques bring the ability of input with varied dimensions to the fast query of similar image exemplars. The fast query is based upon a search-array data structure, and can be conducted very efficiently. Moreover, the evaluation of search-arrays runs in parallel maps well on the modern graphics hardware with graphics processing units (GPU). The functionality of the approach has been demonstrated by experimental results on real photographs.

Support slimming for single material based additive manufacturing

Abstract In layer-based additive manufacturing (AM), supporting structures need to be inserted to support the overhanging regions. The adding of supporting structures slows down the speed of fabrication and introduces artifacts onto the finished surface. We present an orientation-driven shape optimizer to slim down the supporting structures used in single material-based AM. The optimizer can be employed as a tool to help designers to optimize the original model to achieve a more self-supported shape, which can be used as a reference for their further design. The model to be optimized is first enclosed in a volumetric mesh, which is employed as the domain of computation. The optimizer is driven by the operations of reorientation taken on tetrahedra with ‘facing-down’ surface facets. We formulate the demand on minimizing shape variation as global rigidity energy. The local optimization problem for determining a minimal rotation is analyzed on the Gauss sphere, which leads to a closed-form solution. Moreover, we also extend our approach to create the functions of controlling the deformation and searching for optimal printing directions.

Solid modeling of polyhedral objects by Layered Depth-Normal Images on the GPU

We introduce a novel solid modeling framework taking advantage of the architecture of parallel computing on modern graphics hardware. Solid models in this framework are represented by an extension of the ray representation - Layered Depth-Normal Images (LDNI), which inherits the good properties of Boolean simplicity, localization and domain decoupling. The defect of ray representation in computational intensity has been overcome by the newly developed parallel algorithms running on the graphics hardware equipped with Graphics Processing Unit (GPU). The LDNI for a solid model whose boundary is represented by a closed polygonal mesh can be generated efficiently with the help of hardware accelerated sampling. The parallel algorithm for computing Boolean operations on two LDNI solids runs well on modern graphics hardware. A parallel algorithm is also introduced in this paper to convert LDNI solids to sharp-feature preserved polygonal mesh surfaces, which can be used in downstream applications (e.g., finite element analysis). Different from those GPU-based techniques for rendering CSG-tree of solid models Hable and Rossignac (2007, 2005) [1,2], we compute and store the shape of objects in solid modeling completely on graphics hardware. This greatly eliminates the communication bottleneck between the graphics memory and the main memory.