The slow render times of global illumination algorithms make them impractical in most commercial and academic settings. We propose a novel framework for calculating the computational complexity of global illumination algorithms and show that no other recent improvements have reduced this complexity. We further show that many algorithms use a tree as their rendering paradigm. We propose a new rendering algorithm, pipe casting, which calculates light paths using a graph instead of a tree. Pipe casting significantly reduces both computational complexity and actual render time of rendering. Using an L2 pixel-wise error comparison, on average our algorithm can render a variety of scenes at the same error as traditional algorithms but in about 50% of the time.

The field of computer graphics has made significant progress over the years, transforming from simple, pixelated images to highly realistic visuals used across various industries including entertainment, fashion, and video gaming. However, the traditional process of rendering images remains complex and time-consuming, requiring a deep understanding of geometry, materials, and textures. This thesis introduces a simpler approach through a machine learning model, specifically using Cycle-Consistent Adversarial Networks (CycleGAN), to generate realistic images and estimate global illumination in real-time, significantly reducing the need for extensive expertise and time investment. Our experiments on the Blender and Portal datasets demonstrate the model's ability to efficiently generate high-quality, globally illuminated scenes, while a comparative study with the Pix2Pix model highlights our approach's strengths in preserving fine visual details. Despite these advancements, we acknowledge the limitations posed by hardware constraints and dataset diversity, pointing towards areas for future improvement and exploration. This work aims to simplify the complex world of computer graphics, making it more accessible and user-friendly, while maintaining high standards of visual realism. / Master of Science / Creating realistic images on a computer is a crucial part of making video games and movies more immersive and lifelike. Traditionally, this has been a complex and time-consuming task, requiring a deep understanding of how light interacts with objects to create shadows and highlights. This study introduces a simpler and quicker method using a type of smart computer program that learns from examples. This program, known as Cycle-Consistent Adversarial Networks (CycleGAN), is designed to understand the complex play of light in virtual scenes and recreate it in a way that makes the image look real. In testing this new method on different types of images, from simpler scenes to more complex ones, the results were impressive. The program was not only able to significantly cut down the time needed to render an image, but it also maintained the fine details that bring an image to life. While there were challenges, such as working with limited computer power and needing a wider variety of images for the program to learn from, the study shows great promise. It represents a big step forward in making the creation of high-quality, realistic computer graphics more accessible and achievable for a wider range of applications.

This study used Michelson's (1976) Intersystems Congruence Model and Hall's (1966) theories of proxemic zones to guide in the investigation of illumination level's influence on proxemic behavior. This study attempted to determine potential influence of the effect of bright or dim lighting conditions on the personal space requirements within the behavioral setting of waiting/reception areas. Both a quantitative approach and qualitative approach were taken with this investigation. To control for various cultural, social, environmental and situational variables as possible, illuminated scale-models were constructed of three waiting/reception area scenarios and a homogenous sample of Americans participated in the study. Subjects interacted with these models by placing scale-figures within them, and answered a series of both quantitative and open-ended questions. Proxemic recordings of scale-figure placements were performed and statistically analyzed. The quantitative results showed that under general ambient bright and dim lighting conditions, there were no significant differences in personal space requirements in the waiting/receptions area behavioral setting. Thus, lighting does not appear to be a determining factor in achieving adequate personal space. The qualitative analysis agreed with these results, noting other factors as being more important, such as, the proximity to others, presence of tables (assuming they hold reading materials), and a view of the entire space. This phenomenon means ambient illumination level does not play a large role in determining proxemic distances between individuals in waiting area settings. / Master of Science

Cette thèse introduit un cadre théorique pour l'étude de différents schémas d'échantillonnage dans un domaine sphérique, et de leurs effets sur le calcul d'intégrales pour l'illumination globale. Le calcul de l'illumination (du transport lumineux) est un composant majeur de la synthèse d'images réalistes, qui se traduit par l'évaluation d'intégrales multidimensionnelles. Les schémas d'intégration numériques de type Monte-Carlo sont utilisés intensivement pour le calcul de telles intégrales. L'un des aspects majeurs de tout schéma d'intégration numérique est l'échantillonnage. En effet, la façon dont les échantillons sont distribués dans le domaine d'intégration peut fortement affecter le résultat final. Par exemple, pour la synthèse d'images, les effets liés aux différents schémas d'échantillonnage apparaissent sous la forme d'artéfacts structurés ou, au contrire, de bruit non structuré. Dans de nombreuses situations, des résultats complètement faux (biaisés) peuvent être obtenus à cause du schéma d'échantillonnage utilisé pour réaliser l'intégration. La distribution d'un échantillonnage peut être caractérisée à l'aide de son spectre de Fourier. Des schémas d'échantillonnage peuvent être générés à partir d'un spectre de puissance dans le domaine de Fourier. Cette technique peut être utilisée pour améliorer l'erreur d'intégration, car un tel contrôle spectral permet d'adapter le schéma d'échantillonnage au spectre de Fourier de l'intégrande. Il n'existe cependant pas de relation directe entre l'erreur dans l'intégration par méthode de Monte-Carlo et le spectre de puissance de la distribution des échantillons. Dans ces travaux, nous proposons une formulation de la variance qui établit un lien direct entre la variance d'une méthode de Monte-Carlo, les spectres de puissance du schéma d'échantillonnage ainsi que de l'intégrande. Pour obtenir notre formulation de la variance, nous utilisons la notion d'homogénéité de la distribution des échantillons qui permet d'exprimer l'erreur de l'intégration par une méthode de Monte-Carlo uniquement sous forme de variance. À partir de cette formulation de la variance, nous développons un outil d'analyse pouvant être utilisé pour déterminer le taux de convergence théorique de la variance de différents schémas d'échantillonnage proposés dans la littérature. Notre analyse fournit un éclairage sur les bonnes pratiques à mettre en œuvre dans la définition de nouveaux schémas d'échantillonnage basés sur l'intégrande / This dissertation introduces a theoretical framework to study different sampling patterns in the spherical domain and their effects in the evaluation of global illumination integrals. Evaluating illumination (light transport) is one of the most essential aspect in image synthesis to achieve realism which involves solving multi-dimensional space integrals. Monte Carlo based numerical integration schemes are heavily employed to solve these high dimensional integrals. One of the most important aspect of any numerical integration method is sampling. The way samples are distributed on an integration domain can greatly affect the final result. For example, in images, the effects of various sampling patterns appear in the form of either structural artifacts or completely unstructured noise. In many cases, we may get completely false (biased) results due to the sampling pattern used in integration. The distribution of sampling patterns can be characterized using their Fourier power spectra. It is also possible to use the Fourier power spectrum as input, to generate the corresponding sample distribution. This further allows spectral control over the sample distributions. Since this spectral control allows tailoring new sampling patterns directly from the input Fourier power spectrum, it can be used to improve error in integration. However, a direct relation between the error in Monte Carlo integration and the sampling power spectrum is missing. In this work, we propose a variance formulation, that establishes a direct link between the variance in Monte Carlo integration and the power spectra of both the sampling pattern and the integrand involved. To derive our closed-form variance formulation, we use the notion of homogeneous sample distributions that allows expression of error in Monte Carlo integration, only in the form of variance. Based on our variance formulation, we develop an analysis tool that can be used to derive theoretical variance convergence rates of various state-of-the-art sampling patterns. Our analysis gives insights to design principles that can be used to tailor new sampling patterns based on the integrand

Background subtraction is the technique of segmenting moving foreground objects from stationary or dynamic background scenes. Background subtraction is a critical step in many computer vision applications including video surveillance, tracking, gesture recognition etc. This thesis addresses the challenges associated with the background subtraction systems due to the sudden illumination changes happening in an indoor environment. Most of the existing techniques adapt to gradual illumination changes, but fail to cope with the sudden illumination changes. Here, we introduce a Global change reactive background subtraction to model these changes as a regression function of spatial image coordinates. The regression model is learned from highly probable background regions and the background model is compensated for the illumination changes by the model parameters estimated. Experiments were performed in the indoor environment to show the effectiveness of our approach in modeling the sudden illumination changes by a higher order regression polynomial. The results of non-linear SVM regression were also presented to show the robustness of our regression model.

Thesis (Ph.D.)--Ohio University, June, 2004. / Includes bibliographical references (leaves [426]-446)

Thesis--Leipzig. / Vita. Bibliography: p. [3].

Thesis (Ph. D.)--Bryn Mawr College, 1983. / "A Garland series." Includes bibliographical references (p. xxxiv-xxxv) and index.

Thesis--Leipzig. / Vita. Includes bibliographical references (p. [3]).

Thesis--Leipzig. / Vita.