Voxel cone tracing (VCT) is a rendering method designed to approximate global illumination in a fast and efficient way. Global illumination means to render not only the direct lighting of a scene but also light from indirect sources, simulating how light in the real-world tend to bounce around and illuminate even the areas that are occluded from a direct light source. Rendering accurate global illumination in real-time has for a long time been a challenge in the field of computer graphics. This effect is most accurately simulated through expensive algorithms such as path tracing, where individual rays of light are traced from the pixels of a camera as they bounce around the scene and sample the environment. More efficient methods tend to rely on static image-based approaches, where global illumination is pre-rendered and baked into textures. VCT presents itself as a middle ground of the two, trading some of the accuracy for improved performance and the ability to work in a fully dynamic environment where objects and light sources may be moved around. VCT computes global illumination by volumetrically sampling a lower resolution voxel-based representation of the rendered scene by tracing cones.   The aim of this thesis is to further investigate the performance and image quality of VCT through an implementation and evaluation method. A VCT algorithm has been implemented that is capable of real-time global illumination in a dynamic setting, utilizing physically based rendering for improved image quality and a sparse 3D texture for efficient voxel storage. Performance was then measured in terms of rendering speed and memory usage. Image quality was evaluated through comparison with accurate path traced reference images. The results show that VCT is a promising rendering method for achieving a real-time approximation of global illumination, but that it also suffers from some issues regarding the image quality.

Ray tracing is widely used technique for realistic rendering of computer scenes. Its major drawback is time needed to compute the image, therefore it's usually parallelized. This thesis describes parallelization and ray tracing in general. It explains the possibility of how can be ray tracing parallelized as well as it defines the problems which may occur during the process. The result is parallel rendering application which uses selected ray tracing software and measurement of how successful this application is.

This thesis brings an overview about scene rendering using the ray tracing method. It describes aspects used when creating the application which uses this method such as intersection computation, lighting and shading models etc. It also describes the basic algorithm used for rendering one frame. Each class of the object oriented design is described. There is also detail explanation what is the purpose of the each class in the model and what are the most important used methods. The work also contains demonstration application showing the usage of model in practice.

Goal of this diploma thesis is to get knowledge about history and evolution of computer graphic in area of realistic image synthesis, get knowledge about rendering system mental ray and about writing shader for it and write several shader. Create manual about writing  shaders for mental ray. Get knowledge about program Maya and create a tutorial hot to get the shader into it.

京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第19504号 / 理博第4164号 / 新制||理||1598(附属図書館) / 32540 / 京都大学大学院理学研究科地球惑星科学専攻 / (主査)教授 田口 聡, 教授 家森 俊彦, 教授 余田 成男 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM

京都大学 / 0048 / 新制・課程博士 / 博士(地球環境学) / 甲第20538号 / 地環博第159号 / 新制||地環||32(附属図書館) / 京都大学大学院地球環境学舎地球環境学専攻 / (主査)准教授 須崎 純一, 准教授 西前 出, 教授 柴田 昌三 / 学位規則第4条第1項該当 / Doctor of Global Environmental Studies / Kyoto University / DFAM

No description available.

No description available.

No description available.

The study aims to identify the role of artificial intelligence and big data that can help us to confront the COVID-19. The study adopted the literature review methodology by reading and analyzing academic studies. This study was divided into two parts: a theoretical part that deals with the basic concepts of artificial intelligence and big data, and an analytical section that deals with reviewing and analyzing artificial intelligence and big data applications to confront COVID-19, including Contact Tracing Apps, then find the weaknesses and develop a recommendation list. The paper concludes that artificial intelligence and big data applications and apps could help to confrontCOVID-19 to some extent. However, artificial intelligence and big data are in the first steps. Moreover, they have not yet had a significant impact on controlling the COVID-19 since some issues and challenges hamper the use of these technologies like accuracy and public trust, etc. Hard work from governments is required in order to overcome these challenges in the first place. It is doubtful that these challenges will be addressed during the COVID-19. However, it is a great learning experience and an opportunity to develop our technologies to overcome future pandemics.