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- W2786712773 abstract "During early design phases, the architect's task is to develop a floor plan layout from a high level description. This process is usually conducted manually nowadays in an iterative manner. In order to assist the architect with repetitive tasks during the individual design steps, we trained a recurrent neural network to mimic the architect's behavior. Our approach is based on sequences that recreate the user's behavior and that we generated from simple floor plans. By utilizing a dedicated inferencing mechanism, we are able to implement the generation of different design steps and tasks using a single LSTM model. We compare two different types of sequencing approaches by calculating their errors on a test set for a selected design step and evaluating the results qualitatively. While the current performance still needs to be improved for productive use, our dedicated inference mechanism shows a functional behavior." @default.
- W2786712773 created "2018-02-23" @default.
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- W2786712773 date "2017-11-01" @default.
- W2786712773 modified "2023-09-26" @default.
- W2786712773 title "Floor Plan Generation and Auto Completion Based on Recurrent Neural Networks" @default.
- W2786712773 cites W1810943226 @default.
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- W2786712773 doi "https://doi.org/10.1109/icdar.2017.272" @default.
- W2786712773 hasPublicationYear "2017" @default.
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