Flexible Indoor Scene Synthesis via a Multi-object Particle Swarm Intelligence Optimization Algorithm and User Intentions

Abstract

Flexible indoor scene synthesis is a popular topic in computer graphics and virtual reality research due to its wide-ranging applications in home design, games and automated robotics training. We propose a novel approach to automatic and flexible indoor scene synthesis using an energy-based method. We regard indoor scene synthesis as a multiple-object optimization problem with furniture location and orientation according to the user's intention, as a constraint on the energy of the optimization problem. Based on the relationship of objects, the embedded aesthetic criterion, the design criterion for proper placement and human movement in a scene, we design five energy functions, the overlap constraint, pairwise constraint, wall constraint, aisle constraint, angle constraint and penalty item, are proposed. We use a multi-object particle swarm intelligence optimization method with a Markov chain Monte Carlo algorithm to solve this optimization problem and obtain a Pareto-optimal solution. 3D gestures are used as the medium of interaction between the user and the system. Our method significantly enhances the existing weighted energy optimization method by allowing a joint optimization of various energy functions. The experiments confirm that all the energy functions can converge at the same time and that the proposed method obtains results superior to those of the weighted methods. The proposed method is general which can be used to obtain layouts for various kind of rooms with different furniture.