Chensheng Wang

Shape Knowledge Indexing Using Invariant Shape Code

Shape knowledge indexing is crucial in both design reuse and knowledge engineering, in which the pivot issue is to establish the unique representation of the invariant shape properties. Treating the shape of the region of interest as a surface signal, in this paper, a local shape-indexing scheme is developed by applying the affine invariant nature of the Fourier spectrum of the spatial shape distribution. The shape-coding scheme is theoretically proven being strictly invariant under affine transformations. A framework applying the invariant shape code in shape knowledge indexing is presented. Associated examples and the quantity analysis results are provided to justify the robustness, simplicity, and adaptability of the proposed shape knowledge-indexing scheme. Further, the proposed approach could be regarded as an alternative choice to represent local shape knowledge, especially for that of freeform features.Copyright

Dynamic Shape Typing

Automatic processing of shape information requires the selection of a representation form of shape. Shape modeling is based on a choice of shape type, which is the joint specification of representation form and a set of operations. In shape applications, such as shape design and shape optimization, it is not sufficient to maintain a static shape type. Depending on the specific needs during the application, i.e. depending on the modeling context, the appropriate shape type might be continuously varying. Programmed systems can handle static shape types relatively well. However, to support dynamic shape typing a number of fundamental problems need to be understood and solved. An approach to dynamic typing of freeform shapes is presented. Theoretical issues will be described and some concrete examples and initial experimental results will be presented.Copyright

Freeform Feature Retrieval by Signal Processing

Feature retrieval is of great importance in shape modelling, in terms of supporting design reuse by obtaining reusable geometric entities. However, conventional techniques for feature retrieval are generally limited to the extraction of feature lines, curve segments, or surfaces, and the feature distortion imposed by feature interaction remains unconsidered. This paper investigates approaches for freeform feature retrieval by means of signal processing techniques. By treating features or regions of interest as surface signals, we employ digital filters to separate the feature signal from that of the domain surface, retrieving the “pure” feature from an existing shape model. Strategies for different model types are elaborated, for instance, the exact feature retrieval method designed for shape models with explicit data structure, such as B-Rep, or other accessible representations; and the signal filtering method for models with structured or unstructured data sets, such as that in mesh or point cloud models. Specifically, in the signal filtering method feature retrieval is implemented by the convolving operator in the frequency domain. By transforming the problem of shape decomposition from geometric extraction in the spatial domain to computation in the frequency domain, the proposed methods not only brings in significant computational efficiency, but also reduces the complexity of problem solving for feature retrieval. Provided examples show that the proposed approaches can achieve satisfactory results for simple geometries, whereas for sophisticated shapes guidelines for the design of dedicated filters are elaborated.Copyright

A method to chart the structure of designers' clay modelling processes

During ideation, designers often prefer physical modelling tools over virtual ones. If we want to make CAD more appropriate for shape ideation, it is important to know what happens during a successful ideation process. A method was developed to observe actual shape ideation processes. The method consists of video recording the process, coding the recordings, aggregating the coded data into patterns, sequences and parts, and finally analysing the aggregated data. A test case was performed to verify the method. The test case contained three clay modelling assignments. The activities of the clay modeller were identified and grouped into patterns. The majority of the patterns were performed repeatedly. Sequences of repeated patterns could be grouped into parts that produced a particular shape element and seem to reflect the shape intents of the test subject.

Identifying and Tracking Features in Freeform Shape

Building a smooth and well structured surface to fit unstructured 3-D data is always an interesting topic in Computer-Aided Design (CAD). In this paper, a method of approximating complex freeform shapes with parameterized freeform feature templates is proposed. To achieve this, a portion of a digitized 3-Dimensional (3-D) shape should be matched, or fitted, to a deformable shape feature template, where the deformation is a function of intrinsic feature parameters. 3-D shape matching to, possibly sparse, inaccurate or otherwise degraded, freeform surface data is known to be hard. Using a variant of the directed Hausdorff distance measure of shapes, it is shown that convergence towards a shape match is feasible. Based on sensitivity analyses of the shape distance measures, it is determined that adjusting coefficients of the optimization function in different stages of optimizations can accelerate the optimization procedure. By the matching results, a standard deviation-like function is proposed to achieve automatic feature recognition. With the proposed extendable concept, complex freeform shapes are tracked and fitted automatically. Based on a defined interference ratio, interfered feature can also be identified. Numerical experiments were conducted in order to verify the proposed method and to find the maximal degree of feature interference for which matching is successful. It is also described how the presented technique can be applied in shape modeling applications.Copyright

Extracting Freeform Shape Information by Template Fitting

Finding effective and interactive tools for extracting freeform shape information continues to be a challenging problem in reverse engineering. Given a freeform shape, it may be constructed by adding one shape, named a pattern, to another. In this paper, an approach of extracting the pattern by template fitting is proposed. By similarity analysis, a user-defined region of interest in the shape can be matched, or fitted, to a shape template. According to the different methods in constructing the shape, several different kinds of R 3 to R 3 functions are defined. With those functions, the original shape is mapped to the fitted shape template, thus the template can be used as a “ruler” to measure the region of interest in the shape. The measuring results, e.g., the extracted pattern, can be generated through an inverse mapping, thus it can be used in the future design. Several implementations were conducted based on ACIS® and OpenGL® in order to verify the proposed method. It is also described how the proposed technique can be applied in practical shape modeling applications.Copyright

Towards new transitions among shape representations

Four classes of shape representation are dominating nowadays in computer-supported design and modeling of products, (1) point clouds, (2) surface meshes, (3) solid/surface models and (4) design/styling models. To support applications such as high-level shape design, feature-based design, shape modeling, shape analysis, rapid prototyping, feature recognition and shape presentation, it is required that transitions among and within the four representation classes take place. Transitions from a “lower” representation class to “higher” class are far from trivial, and at the same time highly demanded for reverse design purposes. New methods and algorithms are needed to accomplish new transitions. A characterization of the four classes is presented, the most relevant transitions are reviewed and a relatively new transition, from point cloud directly to design/styling model is proposed and experimented. The importance of this transition for new methods of shape reuse and redesign is pointed out and demonstrated.Copyright