4,206 research outputs found

    Supporting reinterpretation in computer-aided conceptual design

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    This paper presents research that aims to inform the development of computational tools that better support design exploration and idea transformation - key objectives in conceptual design. Analyses of experimental data from two fields - product design and architecture - suggest that the interactions of designers with their sketches can be formalised according to a finite number of generalised shape rules defined within a shape grammar. Such rules can provide a basis for the generation of alternative design concepts and they have informed the development of a prototype shape synthesis system that supports dynamic reinterpretation of shapes in design activity. The notion of 'sub-shapes' is introduced and the significance of these to perception, recognition and the development of emergent structures is discussed. The paper concludes with some speculation on how such a system might find application in a range of design fields

    Shape exploration in design : formalising and supporting a transformational process

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    The process of sketching can support the sort of transformational thinking that is seen as essential for the interpretation and reinterpretation of ideas in innovative design. Such transformational thinking, however, is not yet well supported by computer-aided design systems. In this paper, outcomes of experimental investigations into the mechanics of sketching are described, in particular those employed by practising architects and industrial designers as they responded to a series of conceptual design tasks,. Analyses of the experimental data suggest that the interactions of designers with their sketches can be formalised according to a finite number of generalised shape rules. A set of shape rules, formalising the reinterpretation and transformations of shapes, e.g. through deformation or restructuring, are presented. These rules are suggestive of the manipulations that need to be afforded in computational tools intended to support designers in design exploration. Accordingly, the results of the experimental investigations informed the development of a prototype shape synthesis system, and a discussion is presented in which the future requirements of such systems are explored

    Universal attraction force-inspired freeform surface modeling for 3D sketching

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    This paper presents a novel freeform surface modeling method to construct a freeform surface from 3D sketch. The approach is inspired by Newton’s universal attraction force law to construct a surface model from rough boundary curves and unorganized interior characteristic curves which may cross the boundary curves or not. Based on these unorganized curves, an initial surface can be obtained for conceptual design and it can be improved later in a commercial package. The approach has been tested with examples and it is capable of dealing with unorganized design curves for surface modeling

    3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks

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    We propose a method for reconstructing 3D shapes from 2D sketches in the form of line drawings. Our method takes as input a single sketch, or multiple sketches, and outputs a dense point cloud representing a 3D reconstruction of the input sketch(es). The point cloud is then converted into a polygon mesh. At the heart of our method lies a deep, encoder-decoder network. The encoder converts the sketch into a compact representation encoding shape information. The decoder converts this representation into depth and normal maps capturing the underlying surface from several output viewpoints. The multi-view maps are then consolidated into a 3D point cloud by solving an optimization problem that fuses depth and normals across all viewpoints. Based on our experiments, compared to other methods, such as volumetric networks, our architecture offers several advantages, including more faithful reconstruction, higher output surface resolution, better preservation of topology and shape structure.Comment: 3DV 2017 (oral
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