Adaptive acquisition of lumigraphs from synthetic scenes

Light fields and Lwnigraphs are capable of rendering scenes of arbitrary geometrical or illumination complexity in real time. They are thus interesting ways of interacting with both recorded real-world and high-quality synthetic scenes. Unfortunately, both light fields and Lumigraph rely on a dense sampling of the illumination to provide a good rendering quality. This induces high costs both in terms of storage requirements and computational resources for the image acquisition. Techniques for acquiring adaptive light field and Lumigraph representations are thus mandatory for practical applications. In this paper we present a method for the adaptive acquisition of images for Lwnigraphs from synthetic scenes. Using image warping to predict the potential improvement in image quality when adding a certain view, we decide which new views of the scene should be rendered and added to the light field. This a-priori error estimator accounts for both visibility problems and illumination effects such as specular highlights.