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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 mul- timodal, we employ a Markov chain Monte Carlo sampler to sug- gest optimized layouts. To deal with the substantial computational requirements of stochastic sampling, we use graphics hardware to enable interactive performance. In summary, our work makes two main contributions. First, we identify and operationalize a set of design guidelines for furni- ture layout. Second, we develop an interactive system for creat- ing furniture arrangements based on these guidelines. Our results demonstrate that the suggestion generation functionality of our sys- tem measurably increases. | Furniture MARKET OPPORTUNITIES FASTEST GROWING FREE MARKET DEMOCRACY INDIA BRAND EQUITY FOUNDATION www.ibef.in Furniture MARKET OPPORTUNITIES CONTENTS Introduction 2 Indian Furniture Industry 3 Appendix 11 A report by KPMG for .
