586 research outputs found

    Doctor of Philosophy

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    dissertationConfocal microscopy has become a popular imaging technique in biology research in recent years. It is often used to study three-dimensional (3D) structures of biological samples. Confocal data are commonly multichannel, with each channel resulting from a different fluorescent staining. This technique also results in finely detailed structures in 3D, such as neuron fibers. Despite the plethora of volume rendering techniques that have been available for many years, there is a demand from biologists for a flexible tool that allows interactive visualization and analysis of multichannel confocal data. Together with biologists, we have designed and developed FluoRender. It incorporates volume rendering techniques such as a two-dimensional (2D) transfer function and multichannel intermixing. Rendering results can be enhanced through tone-mappings and overlays. To facilitate analyses of confocal data, FluoRender provides interactive operations for extracting complex structures. Furthermore, we developed the Synthetic Brainbow technique, which takes advantage of the asynchronous behavior in Graphics Processing Unit (GPU) framebuffer loops and generates random colorizations for different structures in single-channel confocal data. The results from our Synthetic Brainbows, when applied to a sequence of developing cells, can then be used for tracking the movements of these cells. Finally, we present an application of FluoRender in the workflow of constructing anatomical atlases

    Towards a filmic look and feel in real time computer graphics

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    Film footage has a distinct look and feel that audience can instantly recognize, making its replication desirable for computer generated graphics. This thesis presents methods capable of replicating significant portions of the film look and feel while being able to fit within the constraints imposed by real-time computer generated graphics on consumer hardware

    High-Level GPU Programming: Domain-Specific Optimization and Inference

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    When writing computer software one is often forced to balance the need for high run-time performance with high programmer productivity. By using a high-level language it is often possible to cut development times, but this typically comes at the cost of reduced run-time performance. Using a lower-level language, programs can be made very efficient but at the cost of increased development time. Real-time computer graphics is an area where there are very high demands on both performance and visual quality. Typically, large portions of such applications are written in lower-level languages and also rely on dedicated hardware, in the form of programmable graphics processing units (GPUs), for handling computationally demanding rendering algorithms. These GPUs are parallel stream processors, specialized towards computer graphics, that have computational performance more than a magnitude higher than corresponding CPUs. This has revolutionized computer graphics and also led to GPUs being used to solve more general numerical problems, such as fluid and physics simulation, protein folding, image processing, and databases. Unfortunately, the highly specialized nature of GPUs has also made them difficult to program. In this dissertation we show that GPUs can be programmed at a higher level, while maintaining performance, compared to current lower-level languages. By constructing a domain-specific language (DSL), which provides appropriate domain-specific abstractions and user-annotations, it is possible to write programs in a more abstract and modular manner. Using knowledge of the domain it is possible for the DSL compiler to generate very efficient code. We show that, by experiment, the performance of our DSLs is equal to that of GPU programs written by hand using current low-level languages. Also, control over the trade-offs between visual quality and performance is retained. In the papers included in this dissertation, we present domain-specific languages targeted at numerical processing and computer graphics, respectively. These DSL have been implemented as embedded languages in Python, a dynamic programming language that provide a rich set of high-level features. In this dissertation we show how these features can be used to facilitate the construction of embedded languages

    ์ง์ ‘ ๋ณผ๋ฅจ ๋ Œ๋”๋ง์—์„œ ์ ์ง„์  ๋ Œ์ฆˆ ์ƒ˜ํ”Œ๋ง์„ ์‚ฌ์šฉํ•œ ํ”ผ์‚ฌ๊ณ„ ์‹ฌ๋„ ๋ Œ๋”๋ง

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2021. 2. ์‹ ์˜๊ธธ.Direct volume rendering is a widely used technique for extracting information from 3D scalar fields acquired by measurement or numerical simulation. To visualize the structure inside the volume, the voxels scalar value is often represented by a translucent color. This translucency of direct volume rendering makes it difficult to perceive the depth between the nested structures. Various volume rendering techniques to improve depth perception are mainly based on illustrative rendering techniques, and physically based rendering techniques such as depth of field effects are difficult to apply due to long computation time. With the development of immersive systems such as virtual and augmented reality and the growing interest in perceptually motivated medical visualization, it is necessary to implement depth of field in direct volume rendering. This study proposes a novel method for applying depth of field effects to volume ray casting to improve the depth perception. By performing ray casting using multiple rays per pixel, objects at a distance in focus are sharply rendered and objects at an out-of-focus distance are blurred. To achieve these effects, a thin lens camera model is used to simulate rays passing through different parts of the lens. And an effective lens sampling method is used to generate an aliasing-free image with a minimum number of lens samples that directly affect performance. The proposed method is implemented without preprocessing based on the GPU-based volume ray casting pipeline. Therefore, all acceleration techniques of volume ray casting can be applied without restrictions. We also propose multi-pass rendering using progressive lens sampling as an acceleration technique. More lens samples are progressively used for ray generation over multiple render passes. Each pixel has a different final render pass depending on the predicted maximum blurring size based on the circle of confusion. This technique makes it possible to apply a different number of lens samples for each pixel, depending on the degree of blurring of the depth of field effects over distance. This acceleration method reduces unnecessary lens sampling and increases the cache hit rate of the GPU, allowing us to generate the depth of field effects at interactive frame rates in direct volume rendering. In the experiments using various data, the proposed method generated realistic depth of field effects in real time. These results demonstrate that our method produces depth of field effects with similar quality to the offline image synthesis method and is up to 12 times faster than the existing depth of field method in direct volume rendering.์ง์ ‘ ๋ณผ๋ฅจ ๋ Œ๋”๋ง(direct volume rendering, DVR)์€ ์ธก์ • ๋˜๋Š” ์ˆ˜์น˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์œผ๋กœ ์–ป์€ 3์ฐจ์› ๊ณต๊ฐ„์˜ ์Šค์นผ๋ผ ํ•„๋“œ(3D scalar fields) ๋ฐ์ดํ„ฐ์—์„œ ์ •๋ณด๋ฅผ ์ถ”์ถœํ•˜๋Š”๋ฐ ๋„๋ฆฌ ์‚ฌ์šฉ๋˜๋Š” ๊ธฐ์ˆ ์ด๋‹ค. ๋ณผ๋ฅจ ๋‚ด๋ถ€์˜ ๊ตฌ์กฐ๋ฅผ ๊ฐ€์‹œํ™”ํ•˜๊ธฐ ์œ„ํ•ด ๋ณต์…€(voxel)์˜ ์Šค์นผ๋ผ ๊ฐ’์€ ์ข…์ข… ๋ฐ˜ํˆฌ๋ช…์˜ ์ƒ‰์ƒ์œผ๋กœ ํ‘œํ˜„๋œ๋‹ค. ์ด๋Ÿฌํ•œ ์ง์ ‘ ๋ณผ๋ฅจ ๋ Œ๋”๋ง์˜ ๋ฐ˜ํˆฌ๋ช…์„ฑ์€ ์ค‘์ฒฉ๋œ ๊ตฌ์กฐ ๊ฐ„ ๊นŠ์ด ์ธ์‹์„ ์–ด๋ ต๊ฒŒ ํ•œ๋‹ค. ๊นŠ์ด ์ธ์‹์„ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•œ ๋‹ค์–‘ํ•œ ๋ณผ๋ฅจ ๋ Œ๋”๋ง ๊ธฐ๋ฒ•๋“ค์€ ์ฃผ๋กœ ์‚ฝํ™”ํ’ ๋ Œ๋”๋ง(illustrative rendering)์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋ฉฐ, ํ”ผ์‚ฌ๊ณ„ ์‹ฌ๋„(depth of field, DoF) ํšจ๊ณผ์™€ ๊ฐ™์€ ๋ฌผ๋ฆฌ ๊ธฐ๋ฐ˜ ๋ Œ๋”๋ง(physically based rendering) ๊ธฐ๋ฒ•๋“ค์€ ๊ณ„์‚ฐ ์‹œ๊ฐ„์ด ์˜ค๋ž˜ ๊ฑธ๋ฆฌ๊ธฐ ๋•Œ๋ฌธ์— ์ ์šฉ์ด ์–ด๋ ต๋‹ค. ๊ฐ€์ƒ ๋ฐ ์ฆ๊ฐ• ํ˜„์‹ค๊ณผ ๊ฐ™์€ ๋ชฐ์ž…ํ˜• ์‹œ์Šคํ…œ์˜ ๋ฐœ์ „๊ณผ ์ธ๊ฐ„์˜ ์ง€๊ฐ์— ๊ธฐ๋ฐ˜ํ•œ ์˜๋ฃŒ์˜์ƒ ์‹œ๊ฐํ™”์— ๋Œ€ํ•œ ๊ด€์‹ฌ์ด ์ฆ๊ฐ€ํ•จ์— ๋”ฐ๋ผ ์ง์ ‘ ๋ณผ๋ฅจ ๋ Œ๋”๋ง์—์„œ ํ”ผ์‚ฌ๊ณ„ ์‹ฌ๋„๋ฅผ ๊ตฌํ˜„ํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ง์ ‘ ๋ณผ๋ฅจ ๋ Œ๋”๋ง์˜ ๊นŠ์ด ์ธ์‹์„ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•ด ๋ณผ๋ฅจ ๊ด‘์„ ํˆฌ์‚ฌ๋ฒ•์— ํ”ผ์‚ฌ๊ณ„ ์‹ฌ๋„ ํšจ๊ณผ๋ฅผ ์ ์šฉํ•˜๋Š” ์ƒˆ๋กœ์šด ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ํ”ฝ์…€ ๋‹น ์—ฌ๋Ÿฌ ๊ฐœ์˜ ๊ด‘์„ ์„ ์‚ฌ์šฉํ•œ ๊ด‘์„ ํˆฌ์‚ฌ๋ฒ•(ray casting)์„ ์ˆ˜ํ–‰ํ•˜์—ฌ ์ดˆ์ ์ด ๋งž๋Š” ๊ฑฐ๋ฆฌ์— ์žˆ๋Š” ๋ฌผ์ฒด๋Š” ์„ ๋ช…ํ•˜๊ฒŒ ํ‘œํ˜„๋˜๊ณ  ์ดˆ์ ์ด ๋งž์ง€ ์•Š๋Š” ๊ฑฐ๋ฆฌ์— ์žˆ๋Š” ๋ฌผ์ฒด๋Š” ํ๋ฆฌ๊ฒŒ ํ‘œํ˜„๋œ๋‹ค. ์ด๋Ÿฌํ•œ ํšจ๊ณผ๋ฅผ ์–ป๊ธฐ ์œ„ํ•˜์—ฌ ๋ Œ์ฆˆ์˜ ์„œ๋กœ ๋‹ค๋ฅธ ๋ถ€๋ถ„์„ ํ†ต๊ณผํ•˜๋Š” ๊ด‘์„ ๋“ค์„ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ํ•˜๋Š” ์–‡์€ ๋ Œ์ฆˆ ์นด๋ฉ”๋ผ ๋ชจ๋ธ(thin lens camera model)์ด ์‚ฌ์šฉ๋˜์—ˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์„ฑ๋Šฅ์— ์ง์ ‘์ ์œผ๋กœ ์˜ํ–ฅ์„ ๋ผ์น˜๋Š” ๋ Œ์ฆˆ ์ƒ˜ํ”Œ์€ ์ตœ์ ์˜ ๋ Œ์ฆˆ ์ƒ˜ํ”Œ๋ง ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜์—ฌ ์ตœ์†Œํ•œ์˜ ๊ฐœ์ˆ˜๋ฅผ ๊ฐ€์ง€๊ณ  ์•จ๋ฆฌ์–ด์‹ฑ(aliasing)์ด ์—†๋Š” ์ด๋ฏธ์ง€๋ฅผ ์ƒ์„ฑํ•˜์˜€๋‹ค. ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•์€ ๊ธฐ์กด์˜ GPU ๊ธฐ๋ฐ˜ ๋ณผ๋ฅจ ๊ด‘์„ ํˆฌ์‚ฌ๋ฒ• ํŒŒ์ดํ”„๋ผ์ธ ๋‚ด์—์„œ ์ „์ฒ˜๋ฆฌ ์—†์ด ๊ตฌํ˜„๋œ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณผ๋ฅจ ๊ด‘์„ ํˆฌ์‚ฌ๋ฒ•์˜ ๋ชจ๋“  ๊ฐ€์†ํ™” ๊ธฐ๋ฒ•์„ ์ œํ•œ์—†์ด ์ ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ ๊ฐ€์† ๊ธฐ์ˆ ๋กœ ๋ˆ„์ง„ ๋ Œ์ฆˆ ์ƒ˜ํ”Œ๋ง(progressive lens sampling)์„ ์‚ฌ์šฉํ•˜๋Š” ๋‹ค์ค‘ ํŒจ์Šค ๋ Œ๋”๋ง(multi-pass rendering)์„ ์ œ์•ˆํ•œ๋‹ค. ๋” ๋งŽ์€ ๋ Œ์ฆˆ ์ƒ˜ํ”Œ๋“ค์ด ์—ฌ๋Ÿฌ ๋ Œ๋” ํŒจ์Šค๋“ค์„ ๊ฑฐ์น˜๋ฉด์„œ ์ ์ง„์ ์œผ๋กœ ์‚ฌ์šฉ๋œ๋‹ค. ๊ฐ ํ”ฝ์…€์€ ์ฐฉ๋ž€์›(circle of confusion)์„ ๊ธฐ๋ฐ˜์œผ๋กœ ์˜ˆ์ธก๋œ ์ตœ๋Œ€ ํ๋ฆผ ์ •๋„์— ๋”ฐ๋ผ ๋‹ค๋ฅธ ์ตœ์ข… ๋ Œ๋”๋ง ํŒจ์Šค๋ฅผ ๊ฐ–๋Š”๋‹ค. ์ด ๊ธฐ๋ฒ•์€ ๊ฑฐ๋ฆฌ์— ๋”ฐ๋ฅธ ํ”ผ์‚ฌ๊ณ„ ์‹ฌ๋„ ํšจ๊ณผ์˜ ํ๋ฆผ ์ •๋„์— ๋”ฐ๋ผ ๊ฐ ํ”ฝ์…€์— ๋‹ค๋ฅธ ๊ฐœ์ˆ˜์˜ ๋ Œ์ฆˆ ์ƒ˜ํ”Œ์„ ์ ์šฉํ•  ์ˆ˜ ์žˆ๊ฒŒ ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฐ€์†ํ™” ๋ฐฉ๋ฒ•์€ ๋ถˆํ•„์š”ํ•œ ๋ Œ์ฆˆ ์ƒ˜ํ”Œ๋ง์„ ์ค„์ด๊ณ  GPU์˜ ์บ์‹œ(cache) ์ ์ค‘๋ฅ ์„ ๋†’์—ฌ ์ง์ ‘ ๋ณผ๋ฅจ ๋ Œ๋”๋ง์—์„œ ์ƒํ˜ธ์ž‘์šฉ์ด ๊ฐ€๋Šฅํ•œ ํ”„๋ ˆ์ž„ ์†๋„๋กœ ํ”ผ์‚ฌ๊ณ„ ์‹ฌ๋„ ํšจ๊ณผ๋ฅผ ๋ Œ๋”๋ง ํ•  ์ˆ˜ ์žˆ๊ฒŒ ํ•œ๋‹ค. ๋‹ค์–‘ํ•œ ๋ฐ์ดํ„ฐ๋ฅผ ์‚ฌ์šฉํ•œ ์‹คํ—˜์—์„œ ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•์€ ์‹ค์‹œ๊ฐ„์œผ๋กœ ์‚ฌ์‹ค์ ์ธ ํ”ผ์‚ฌ๊ณ„ ์‹ฌ๋„ ํšจ๊ณผ๋ฅผ ์ƒ์„ฑํ–ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋Š” ์šฐ๋ฆฌ์˜ ๋ฐฉ๋ฒ•์ด ์˜คํ”„๋ผ์ธ ์ด๋ฏธ์ง€ ํ•ฉ์„ฑ ๋ฐฉ๋ฒ•๊ณผ ์œ ์‚ฌํ•œ ํ’ˆ์งˆ์˜ ํ”ผ์‚ฌ๊ณ„ ์‹ฌ๋„ ํšจ๊ณผ๋ฅผ ์ƒ์„ฑํ•˜๋ฉด์„œ ์ง์ ‘ ๋ณผ๋ฅจ ๋ Œ๋”๋ง์˜ ๊ธฐ์กด ํ”ผ์‚ฌ๊ณ„ ์‹ฌ๋„ ๋ Œ๋”๋ง ๋ฐฉ๋ฒ•๋ณด๋‹ค ์ตœ๋Œ€ 12๋ฐฐ๊นŒ์ง€ ๋น ๋ฅด๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์—ฌ์ค€๋‹ค.CHAPTER 1 INTRODUCTION 1 1.1 Motivation 1 1.2 Dissertation Goals 5 1.3 Main Contributions 6 1.4 Organization of Dissertation 8 CHAPTER 2 RELATED WORK 9 2.1 Depth of Field on Surface Rendering 10 2.1.1 Object-Space Approaches 11 2.1.2 Image-Space Approaches 15 2.2 Depth of Field on Volume Rendering 26 2.2.1 Blur Filtering on Slice-Based Volume Rendering 28 2.2.2 Stochastic Sampling on Volume Ray Casting 30 CHAPTER 3 DEPTH OF FIELD VOLUME RAY CASTING 33 3.1 Fundamentals 33 3.1.1 Depth of Field 34 3.1.2 Camera Models 36 3.1.3 Direct Volume Rendering 42 3.2 Geometry Setup 48 3.3 Lens Sampling Strategy 53 3.3.1 Sampling Techniques 53 3.3.2 Disk Mapping 57 3.4 CoC-Based Multi-Pass Rendering 60 3.4.1 Progressive Lens Sample Sequence 60 3.4.2 Final Render Pass Determination 62 CHAPTER 4 GPU IMPLEMENTATION 66 4.1 Overview 66 4.2 Rendering Pipeline 67 4.3 Focal Plane Transformation 74 4.4 Lens Sample Transformation 76 CHAPTER 5 EXPERIMENTAL RESULTS 78 5.1 Number of Lens Samples 79 5.2 Number of Render Passes 82 5.3 Render Pass Parameter 84 5.4 Comparison with Previous Methods 87 CHAPTER 6 CONCLUSION 97 Bibliography 101 Appendix 111Docto

    Dynamic Volume Rendering of Functional Medical Data on Dissimilar Hardware Platforms

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    In the last 30 years, medical imaging has become one of the most used diagnostic tools in the medical profession. Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) technologies have become widely adopted because of their ability to capture the human body in a non-invasive manner. A volumetric dataset is a series of orthogonal 2D slices captured at a regular interval, typically along the axis of the body from the head to the feet. Volume rendering is a computer graphics technique that allows volumetric data to be visualized and manipulated as a single 3D object. Iso-surface rendering, image splatting, shear warp, texture slicing, and raycasting are volume rendering methods, each with associated advantages and disadvantages. Raycasting is widely regarded as the highest quality renderer of these methods. Originally, CT and MRI hardware was limited to providing a single 3D scan of the human body. The technology has improved to allow a set of scans capable of capturing anatomical movements like a beating heart. The capturing of anatomical data over time is referred to as functional imaging. Functional MRI (fMRI) is used to capture changes in the human body over time. While fMRIโ€™s can be used to capture any anatomical data over time, one of the more common uses of fMRI is to capture brain activity. The fMRI scanning process is typically broken up into a time consuming high resolution anatomical scan and a series of quick low resolution scans capturing activity. The low resolution activity data is mapped onto the high resolution anatomical data to show changes over time. Academic research has advanced volume rendering and specifically fMRI volume rendering. Unfortunately, academic research is typically a one-off solution to a singular medical case or set of data, causing any advances to be problem specific as opposed to a general capability. Additionally, academic volume renderers are often designed to work on a specific device and operating system under controlled conditions. This prevents volume rendering from being used across the ever expanding number of different computing devices, such as desktops, laptops, immersive virtual reality systems, and mobile computers like phones or tablets. This research will investigate the feasibility of creating a generic software capability to perform real-time 4D volume rendering, via raycasting, on desktop, mobile, and immersive virtual reality platforms. Implementing a GPU-based 4D volume raycasting method for mobile devices will harness the power of the increasing number of mobile computational devices being used by medical professionals. Developing support for immersive virtual reality can enhance medical professionalsโ€™ interpretation of 3D physiology with the additional depth information provided by stereoscopic 3D. The results of this research will help expand the use of 4D volume rendering beyond the traditional desktop computer in the medical field. Developing the same 4D volume rendering capabilities across dissimilar platforms has many challenges. Each platform relies on their own coding languages, libraries, and hardware support. There are tradeoffs between using languages and libraries native to each platform and using a generic cross-platform system, such as a game engine. Native libraries will generally be more efficient during application run-time, but they require different coding implementations for each platform. The decision was made to use platform native languages and libraries in this research, whenever practical, in an attempt to achieve the best possible frame rates. 4D volume raycasting provides unique challenges independent of the platform. Specifically, fMRI data loading, volume animation, and multiple volume rendering. Additionally, real-time raycasting has never been successfully performed on a mobile device. Previous research relied on less computationally expensive methods, such as orthogonal texture slicing, to achieve real-time frame rates. These challenges will be addressed as the contributions of this research. The first contribution was exploring the feasibility of generic functional data input across desktop, mobile, and immersive virtual reality. To visualize 4D fMRI data it was necessary to build in the capability to read Neuroimaging Informatics Technology Initiative (NIfTI) files. The NIfTI format was designed to overcome limitations of 3D file formats like DICOM and store functional imagery with a single high-resolution anatomical scan and a set of low-resolution anatomical scans. Allowing input of the NIfTI binary data required creating custom C++ routines, as no object oriented APIs freely available for use existed. The NIfTI input code was built using C++ and the C++ Standard Library to be both light weight and cross-platform. Multi-volume rendering is another challenge of fMRI data visualization and a contribution of this work. fMRI data is typically broken into a single high-resolution anatomical volume and a series of low-resolution volumes that capture anatomical changes. Visualizing two volumes at the same time is known as multi-volume visualization. Therefore, the ability to correctly align and scale the volumes relative to each other was necessary. It was also necessary to develop a compositing method to combine data from both volumes into a single cohesive representation. Three prototype applications were built for the different platforms to test the feasibility of 4D volume raycasting. One each for desktop, mobile, and virtual reality. Although the backend implementations were required to be different between the three platforms, the raycasting functionality and features were identical. Therefore, the same fMRI dataset resulted in the same 3D visualization independent of the platform itself. Each platform uses the same NIfTI data loader and provides support for dataset coloring and windowing (tissue density manipulation). The fMRI data can be viewed changing over time by either animation through the time steps, like a movie, or using an interface slider to โ€œscrubโ€ through the different time steps of the data. The prototype applications data load times and frame rates were tested to determine if they achieved the real-time interaction goal. Real-time interaction was defined by achieving 10 frames per second (fps) or better, based on the work of Miller [1]. The desktop version was evaluated on a 2013 MacBook Pro running OS X 10.12 with a 2.6 GHz Intel Core i7 processor, 16 GB of RAM, and a NVIDIA GeForce GT 750M graphics card. The immersive application was tested in the C6 CAVEโ„ข, a 96 graphics node computer cluster comprised of NVIDIA Quadro 6000 graphics cards running Red Hat Enterprise Linux. The mobile application was evaluated on a 2016 9.7โ€ iPad Pro running iOS 9.3.4. The iPad had a 64-bit Apple A9X dual core processor with 2 GB of built in memory. Two different fMRI brain activity datasets with different voxel resolutions were used as test datasets. Datasets were tested using both the 3D structural data, the 4D functional data, and a combination of the two. Frame rates for the desktop implementation were consistently above 10 fps, indicating that real-time 4D volume raycasting is possible on desktop hardware. The mobile and virtual reality platforms were able to perform real-time 3D volume raycasting consistently. This is a marked improvement for 3D mobile volume raycasting that was previously only able to achieve under one frame per second [2]. Both VR and mobile platforms were able to raycast the 4D only data at real-time frame rates, but did not consistently meet 10 fps when rendering both the 3D structural and 4D functional data simultaneously. However, 7 frames per second was the lowest frame rate recorded, indicating that hardware advances will allow consistent real-time raycasting of 4D fMRI data in the near future

    Compression, Modeling, and Real-Time Rendering of Realistic Materials and Objects

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    The realism of a scene basically depends on the quality of the geometry, the illumination and the materials that are used. Whereas many sources for the creation of three-dimensional geometry exist and numerous algorithms for the approximation of global illumination were presented, the acquisition and rendering of realistic materials remains a challenging problem. Realistic materials are very important in computer graphics, because they describe the reflectance properties of surfaces, which are based on the interaction of light and matter. In the real world, an enormous diversity of materials can be found, comprising very different properties. One important objective in computer graphics is to understand these processes, to formalize them and to finally simulate them. For this purpose various analytical models do already exist, but their parameterization remains difficult as the number of parameters is usually very high. Also, they fail for very complex materials that occur in the real world. Measured materials, on the other hand, are prone to long acquisition time and to huge input data size. Although very efficient statistical compression algorithms were presented, most of them do not allow for editability, such as altering the diffuse color or mesostructure. In this thesis, a material representation is introduced that makes it possible to edit these features. This makes it possible to re-use the acquisition results in order to easily and quickly create deviations of the original material. These deviations may be subtle, but also substantial, allowing for a wide spectrum of material appearances. The approach presented in this thesis is not based on compression, but on a decomposition of the surface into several materials with different reflection properties. Based on a microfacette model, the light-matter interaction is represented by a function that can be stored in an ordinary two-dimensional texture. Additionally, depth information, local rotations, and the diffuse color are stored in these textures. As a result of the decomposition, some of the original information is inevitably lost, therefore an algorithm for the efficient simulation of subsurface scattering is presented as well. Another contribution of this work is a novel perception-based simplification metric that includes the material of an object. This metric comprises features of the human visual system, for example trichromatic color perception or reduced resolution. The proposed metric allows for a more aggressive simplification in regions where geometric metrics do not simplif

    Beyond high-resolution geometry in 3D Cultural Heritage: enhancing visualization realism in interactive contexts

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    La tesi, nellโ€™ambito della computer graphics 3D interattiva, descrive la definizione e sviluppo di algoritmi per un migliore realismo nella visualizzazione di modelli tridimensionali di grandi dimensioni, con particolare attenzione alle applicazioni di queste tecnologie di visualizzazione 3D ai beni culturali
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