Flexible indoor scene synthesis based on multi-object particle swarm intelligence optimization and user intentions with 3D gesture

Abstract

Indoor scene synthesis is the generation of an indoor scene with proper furniture layout, which has been a hot topic in recent years because it is widely used in games, virtual reality, and augmented reality. The problem can be transformed into “put what in where”, to select the proper furniture and optimize the location of the furniture. We think that arranging selected furniture in the proper place is a problem of complex non-convex optimization, a multi-objective problem, which has conflicting relationships among all objects. First, we formulate the optimization problem with five objective energy functions, namely, overlap, pairwise, wall, aisle and angle, while considering the design criterion, aesthetic criterion and movement criterion. Then, we introduce the multi-objective particle swarm optimization algorithm (MOPSO) with the Pareto principle and the adaptive grid method to solve this optimization problem, which can balance these five energy functions. Last, we propose 3D gestures as the interaction to solve the problem of how to select proper furniture, together with enhancing the users’ interests in the furniture locations. Compared with previous work that used linear weighted energy functions, our method can effectively solve the conflicts between the energy functions without using heuristic weights decision in the decision. Therefore, the energy functions would converge to a smaller value simultaneously in our method compared with the single-objective optimization method. The efficiency of each energy function and 3D gestures is also validated by a series of experiments. The method can be easily extended to related works and can generate a series of indoor scenes.