Automatic Indoor Lighting Generation Driven by Human Activity Learned from Virtual Experience.

A good indoor lighting solution should fit with people’s habitual activity and have a low energy cost. However, it’s challenging to capture and model human activity in reality due to its high complexity, let alone incorporating it into lighting planning. As a result, indoor lighting designing still relies on professional’s hands, which is laborious and inefficient. To solve this problem, we propose a novel framework for automatic indoor lighting generation driven by human activity learned from virtual experience. We first harnesses Virtual Reality to simulate and model the user’s daily activities within an indoor scene, and then devises a robust objective function which encompasses multiple activity-driven cost terms for lighting layout optimization. With the objective function and the collected user behavioral data, such as trajectory and head pose, an optimization algorithm is applied to search for the optimal solution. Experiments under different indoor scenes demonstrate that the proposed method can generate lighting solutions that meet personalized behavioral needs in an energy-economic way, which are competitive against those designed by professionals.