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Title: High fidelity sky models
Author: Satilmis, Pinar
ISNI:       0000 0004 6348 5869
Awarding Body: University of Warwick
Current Institution: University of Warwick
Date of Award: 2016
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Light sources are an important part of physically-based rendering when accurate imagery is required. High-fidelity models of sky illumination are essential when virtual environments are illuminated via the sky as is commonplace in most outdoor scenarios. The complex nature of sky lighting makes it difficult to accurately model real life skies. The current solutions to sky illumination can be analytically based and are computationally expensive for complex models, or based on captured data. Such captured data is impractical to capture and difficult to use due to temporally inconsistencies in the captured content. This thesis enhances the state-of-the-art in sky lighting by addressing these problems via novel sky illumination methods that are accurate, practical and flexible. This thesis presents two novel sky illumination methods where; the first of which focuses on clear sky lighting and the second one deals with illumination from cloudy skies. The first approach compactly and efficiently represents sky illumination from both existing analytic sky models and from captured environment maps. For analytic models, the approach leads to a low, constant runtime cost for evaluating lighting. When applied to environment maps, this approach approximates the captured lighting at a significantly reduced memory cost, and enables smooth transitions of sky lighting to be created from a small set of environment maps captured at discrete times of day. This makes capture and rendering of real world sky illumination a practical proposition. Results demonstrate less than 4% loss of accuracy compared to ground truth data. The straightforward implementation makes it possible to compute skies at sub milliseconds times on modest GPUs. The second approach focuses on modelling of clouds from whole sky HDR images by using classification and optimisation techniques. This method pre-classifies the input image according to the cloud types of the pixels which improves both the duration and accuracy of the optimisation. The classification process itself compares well with similar processes from meteorological science and classifies whole images with 97% accuracy and individual pixels with an 80% accuracy. The method can be applied to any cloud type as soon as the optical properties are known. When combined with artificial sky lighting models consisting of arbitrary sun position to relight the extracted cloud model any day time simulations can be obtained based on the original single capture. Results for this method demonstrate a performance of 90% accuracy for fully digitally generated environment maps constructed from a single captured environment map when compared with the original capture.
Supervisor: Not available Sponsor: Yükseköğretim Kurulu (Turkey) ; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: T Technology (General)