Interactive Furniture Layout Using Interior Design Guidelines
Frniture |
We present an interactive furniture layout system that assists users by suggesting furniture arrangements that are based on interior de-sign guidelines. Our system incorporates the layout guidelines as terms in a density function and generates layout suggestions by rapidly sampling the density function using a hardware-accelerated. Monte Carlo sampler. Our results demonstrate that the suggestion generation functionality measurably increases the quality of furni- ture arrangements produced by participants with no prior training in interior design
most comfortable and visually pleasing setting for your home?
Furniture placement is challenging because it requires jointly opti mizing a variety of functional and visual criteria. Skilled interior designers follow numerous high-level guidelines in producing fur-
niture layouts [Lyons 2008; Ward 1999]. In a living room for example, the furniture should support comfortable conversation, align with prominent features of the space, and collectively form a vi-
sually balanced composition. In practice these guidelines are often imprecise and sometimes contradictory. Experienced designers learn to balance the tradeoffs between the guidelines through an iterative trial-and-error process.
Yet most people responsible for furnishing a new home have no training in interior design. They may not be aware of interior design guidelines and they are unlikely to have the tacit knowledge
and experience required to optimally balance the tradeoffs. Instead such amateur designers rely on intuitive rules such as pushing large furniture items against the walls. These intuitive rules often
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In this paper, we identify a set of interior design guidelines for furniture layout and develop an interactive system based on these guidelines. In our system, the user begins by specifying the shape
of a room and the set of furniture that must be arranged within it. The user then interactively moves furniture pieces. In response, the system suggests a small set of furniture layouts that follow the interior design guidelines. The user can interactively select a suggestion and move any piece of furniture to modify the layout. Thus, the user and computer work together to iteratively evolve the design(Figure 1).
Our approach represents the furniture layout guidelines as terms in a density function and treats manual placement of pieces as subspace constraints. Since the resulting function is highly multimodal, we employ a Markov chain Monte Carlo sampler to suggest optimized layouts. To deal with the substantial computational requirements of stochastic sampling, we use graphics hardware to
enable interactive performance.
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ing furniture arrangements based on these guidelines. Our results demonstrate that the suggestion generation functionality of our system measurably increases the quality of furniture arrangements produced by users with no prior training in interior design.
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