Position-Dependent Importance Sampling of Light Field Luminaires

The possibility to use real world light sources (aka luminaires) for synthesizing images greatly contributes to their physical realism. Among existing models, the ones based on light fields are attractive due to their ability to represent faithfully the near-field and due to their possibility of being directly acquired. In this paper, we introduce a dynamic sampling strategy for complex light field luminaires with the corresponding unbiased estimator. The sampling strategy is adapted, for each 3D scene position and each frame, by restricting the sampling domain dynamically and by balancing the number of samples between the different components of the representation. This is achieved efficiently by simple position-dependent affine transformations and restrictions of Cumulative Distributive Functions that ensure that every generated sample conveys energy and contributes to the final result. Therefore, our approach only requires a low number of samples to achieve almost converged results. We demonstrate the efficiency of our approach on modern hardware by introducing a GPU-based implementation. Combined with a fast shadow algorithm, our solution exhibits interactive frame rates for direct lighting for large measured luminaires.