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Photorealistic rendering focuses on creating images with a computer that imitates pictures of reallife scenes as faithfully as possible. To achieve this, rendering algorithms require incorporating accurate modeling of how light interacts with various types of matter. For most objects, this model needs to account for the scattering of the light rays. However, this model falls short when rendering objects of sizes smaller or comparable to the wavelength of the incident light. In this case, new phenomena such as diffraction or interference are observed and have been characterized in optics. Digital rendering of those phenomena involve different light representations than the approximate light ray optics properties traditionally used in rendering. A first part of this work has been dedicated to creating analytical models to account for appearance phenomena which occur when light is interacting with small objects, namely, hair fibers, thin film coatings and quantum dots. A second part of this work focuses on measured material appearance models and how to find a parametrization over the appearance which can be used for editing.

Various biomedical technologies like CT, MRI and PET scanners provide detailed cross-sectional views of the human anatomy. The image information obtained from these scanning devices is typically represented as large data sets whose sizes vary from several hundred megabytes to about one hundred gigabytes. As these data sets cannot be stored on one's local hard drive, SDSC provides a large data repository to store such data sets. These data sets need to be accessed by researchers around the world to collaborate in their research. But the size of these data sets make them difficult to be transmitted over the current network. This thesis presents a 3-D Haar wavelet algorithm which enables these data sets to be transformed into smaller hierarchical representations. These transformed data sets are transmitted over the network and reconstructed to a 3-D volume on the client's side through progressive refinement of the images and 3-D texture mapping techniques.

This collection of videos were created by Jonathan Taylor for THEA 4540 (Theatre Drawing and Rendering Techniques) and THEA 3330 (Scenic Design). The majority of the videos provide tutorials on using Vectorworks. / https://dc.etsu.edu/etsu-oer/1011/thumbnail.jpg

Remote visualization has emerged as a necessary tool in the analysis of big data. High-performance computing clusters can provide several benefits in scaling to larger data sizes, from parallel file systems to larger RAM profiles to parallel computation among many CPUs and GPUs. For scalable data visualization, remote visualization tools and infrastructure is critical where only pixels and interaction events are sent over the network instead of the data. In this paper, we present our pipeline using VirtualGL, TurboVNC, and ParaView to render over 40 million points using remote HPC clusters and project over 26 million pixels in a CAVE-style system. We benchmark the system by varying the video stream compression parameters supported by TurboVNC and establish some best practices for typical usage scenarios. This work will help research scientists and academicians in scaling their big data visualizations for real time interaction. / Master of Science / With advancements made in the technology sector, there are now improved and more scientific ways to see the data. 10 years ago, nobody would have thought what a 3D movie is or how it would feel to watch a movie in 3D. Some may even have questioned if it is possible. But watching 3D cinema is typical now and we do not care much about what goes behind the scenes to make this experience possible. Similarly, is it possible to see and interact with 3D data in the same way Tony Stark does in the movie Iron Man? The answer is yes, it is possible with several tools available now and one of these tools is called ParaView, which is mostly used for scientific visualization of data like climate research, computational fluid dynamics, astronomy among other things. You can either visualize this data on a 2D screen or in a 3D environment where a user will feel a sense of immersion as if they are within the scene looking and interacting with the data. But where is this data actually drawn? And how much time does it take to draw if we are dealing with large datasets? Do we want to draw all this 3D data on a local machine or can we make use of powerful remote machines that do the drawing part and send the final image through a network to the client? In most cases, drawing on a remote machine is a better solution when dealing with big data and the biggest bottleneck is how fast can data be sent to and received from the remote machines. In this work, we seek to understand the best practices of drawing big data on remote machines using ParaView and visualizing it in a 3D projection room like a CAVE (see section 2.2 for details on what is a CAVE).

GPU computing can significantly improve performance by taking advantage of massive parallelism of GPUs for data parallel applications. Computation in visualization applications is suitable for parallelization on the GPU, which can improve performance and interactivity in these applications. If used effectively, multiple GPUs can lead to a significant speedup over a single GPU. However, the use of multiple GPUs requires memory management, scheduling, and load balancing to ensure that a program takes full advantage of available processors. This work presents methods for data-driven and dynamic multi-GPU load balancing using a pipelined approach and a framework for use with different applications. Data-driven load balancing can improve utilization for applications by taking into account past performance for different combinations of input parameters. The dynamic load balancing method based on buffer fullness can adjust to workload changes at runtime to gain an additional performance improvement. This work provides a framework for load balancing to account for differing characteristics of applications. Implementation of a multi-GPU data structure allows for use of these load balancing methods in the framework. The effectiveness of the framework is demonstrated with performance results from interactive visualization that shows a significant speedup due to load balancing. / Master of Science