Interactive Visualization for Design Dialog

Mohiuddin, Arefin; Woodbury, Robert

doi:10.1007/978-3-030-90625-2_29

Cited by 3 publications

(6 citation statements)

References 27 publications

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“…These can be generated by the Cartesian product of two sets of parameter values, which can be obtained from the interpolation of parametric ranges. This strategy is found in several design space explorers, such as Dream Lens, 2 Design Gallery, 5 and Autodesk Refinery, 7 allowing users to perceive the meaning of a parameter and how it affects the design.…”

Section: White-box Explorationmentioning

confidence: 99%

“…4 Other tools are more oriented towards navigating GDSs, such as design space explorers, although these are more focused on the visualization and interaction aspects of the exploration process. 2,5 White-box methods can be dispersedly found in specific GDSs. 1,6 In this paper, we propose the Navigator tool, which gathers and clarifies some of the most well-known white-box exploration mechanisms, combining human judgement with fast automated generation.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Navigating Design Spaces: Finding Designs, Design Collections, and Design Subspaces

Garcia

Leitão

2022

International Journal of Architectural Computing

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Generative design systems can generate a wide panoply of solutions, from which designers search for those that best suit their interests. However, without guidance, this search can be highly inefficient, and many interesting solutions may remain unexplored. This problem is mitigated with automated exploration methods. Still, the ones typically provided by generative design tools are mostly based on black-box methods that drastically reduce the role of the designer, while more straightforward white-box mechanisms are dispersedly found in specific applications. This paper proposes the Navigator tool, which gathers a set of white-box mechanisms that automate the generation of default, random, similar and hybrid designs and design subspaces, while also supporting the generation of design collections. The proposed mechanisms were tested with two generative systems that create, respectively, tower and chair designs. We expect that, by providing understandable mechanisms for navigating design spaces, designers can become more engaged in the search process.

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Section: White-box Explorationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Navigating Design Spaces: Finding Designs, Design Collections, and Design Subspaces

Garcia

Leitão

2022

International Journal of Architectural Computing

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“…In this section, we analyze nine AD-based DSEIs: Autodesk's Dialog Control Language (DCL), (Ambrosius, 2015); Design Explorer (Thornton Tomasetti and CORE studio, 2015); Design Gallery (Mohiuddin and Woodbury, 2022); Autodesk's Dream Lens (Matejka et al, 2018); Human UI (Heumann, 2016); Navigator (Garcia and Leitão, 2022); Autodesk's Refinery -an evolution of Project Fractal (Autodesk, 2016); ShapeDiver (Schiftner, Höbinger and Huard, 2015); and MakerBot's Customizer (Williams, 2014). Apart from DCL, which was launched in 1990, the remaining tools have been developed in the past eight years.…”

Section: Design Space Exploration Interfacesmentioning

confidence: 99%

“…In short, DSEIs make GDSs more accessible and more interactive. Nevertheless, DSEIs face two main challenges: first, the search for feasible design alternatives can be time-consuming and inefficient, since design spaces are frequently very large and filled with incomplete or meaningless solutions; second, the navigation and visualization of multidimensional design spaces is challenging (Mohiuddin and Woodbury, 2022).…”

Section: Introductionmentioning

confidence: 99%

Interfaces for Design Space Exploration

Garcia¹,

Leitão²

2022

Proceedings of the 40th International Conference on Education and Research in Computer Aided Architectural Design in Europe (eC

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A Generative Design System (GDS) allows for the generation and exploration of a wide number of design alternatives and for the automation of analysis and optimization processes. Algorithmic Design (AD) tools effectively support the development of GDSs, but they tend to be less appealing for the usage of such systems, as they rely on complex algorithmic descriptions of the design that quickly become overwhelming for less experienced programmers. The usage of GDSs is facilitated by Design Space Exploration Interfaces (DSEIs), which allows users to iteratively explore, visualize, and select design alternatives among the multidimensional design space defined by the GDS. DSEIs promote the collaboration between designers, clients, and end-users, making GDSs more interactive and more accessible. DSEIs rely on graphical user interfaces that relieve users from the burden of dealing with AD programs. The creation of such interfaces can be automated, so that they can be quickly created and modified. Although AD-based DSEIs exist for at least three decades, they have been more intensively researched and commercialized over the past eight years. In this article, existing AD-based DSEIs are reviewed, characterized, and compared according to several criteria, such as: navigation, visualization, search, ranking, grouping, filtering, and selection techniques. From this comparative study, a set of guidelines for the development of DSEIs is proposed.

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How Do We Know if a Design Space Representation Is Useful? Insights from the DS-Viz Method

Paravizo,

Crilly

2024

Design Computing and Cognition’24

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Interactive Visualization for Design Dialog

Cited by 3 publications

References 27 publications

Navigating Design Spaces: Finding Designs, Design Collections, and Design Subspaces

Navigating Design Spaces: Finding Designs, Design Collections, and Design Subspaces

Interfaces for Design Space Exploration

How Do We Know if a Design Space Representation Is Useful? Insights from the DS-Viz Method

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