title: A Machine-Learning-Driven Sky Model.
abstract: Sky illumination is responsible for much of the lighting in a virtual environment. A machine-learning-based approach can compactly represent sky illumination from both existing analytic sky models and from captured environment maps. The proposed approach can approximate the captured lighting at a significantly reduced memory cost and enable smooth transitions of sky lighting to be created from a small set of environment maps captured at discrete times of day. The author's results demonstrate accuracy close to the ground truth for both analytical and capture-based methods. The approach has a low runtime overhead, so it can be used as a generic approach for both offline and real-time applications.
authors.forename: Pynar
authors.initials: P
authors.lastname: Satylmys
authors.forename: Thomas
authors.initials: T
authors.lastname: Bashford-Rogers
authors.forename: Alan
authors.initials: A
authors.lastname: Chalmers
authors.forename: Kurt
authors.initials: K
authors.lastname: Debattista