In this paper, we present a system that automatically populates indoor virtual scenes with furniture objects and optimizes their positions and orientations with respect to aesthetic, ergonomic and functional rules called interior design guidelines. These guidelines are represented as mathematical expressions which form the cost function. Our system optimizes the set of multiple interior designs by minimizing the cost function using a genetic algorithm. Moreover, we extend the optimization to transdimensional space by enabling automatic selection of furniture objects. Finally, we optimize the assignment of materials to the furniture objects to achieve a unified design and harmonious color distribution. We investigate the capability of our system to generate sensible and livable interior designs in a perceptual study.
P. Kán, H. Kaufmann: "Automated Interior Design Using a Genetic Algorithm"; in: "Proceedings of the 23rd ACM Symposium on Virtual Reality Software and Technology", IEEE Computer Society, 2017, 1 - 10.
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