Volume ray casting techniques and applications using general purpose computations on graphics processing units

Traditional 3D computer graphics focus on rendering the exterior of objects. Volume rendering is a technique used to visualize information corresponding to the interior of an object, commonly used in medical imaging and other fields. Visualization of such data may be accomplished by ray casting; an embarrassingly parallel algorithm also commonly used in ray tracing. There has been growing interest in performing general purpose computations on graphics processing units (GPGPU), which are capable exploiting parallel applications and yielding far greater performance than sequential implementations on CPUs. Modern GPUs allow for rapid acceleration of volume rendering applications, offering affordable high performance visualization systems. This thesis explores volume ray casting performance and visual quality enhancements using the NVIDIA CUDA platform, and demonstrates how high quality volume renderings can be produced with interactive and real time frame rates on modern commodity graphics hardware. A number of techniques are employed in this effort, including early ray termination, super sampling and texture filtering. In a performance comparison of a sequential versus CUDA implementation on high-end hardware, the latter is capable of rendering 60 frames per second with an impressive price-performance ratio heavily favoring GPUs. A number of unique volume rendering applications are explored including multiple volume rendering capable of arbitrary placement and rigid volume registration, hypertexturing and stereoscopic anaglyphs, each greatly enhanced by the real time interaction of volume data. The techniques and applications discussed in this thesis may prove to be invaluable tools in fields such as medical and molecular imaging, flow and scientific visualization, engineering drawing and many others

On the design of a real-time volume rendering engine

An architecture for a Real-Time Volume Rendering Engine (RT-VRE) is given, capable of computing 750 × 750 × 512 samples from a 3D dataset at a rate of 25 images per second. The RT-VRE uses for this purpose 64 dedicated rendering chips, cooperating with 16 RISC-processors. A plane interpolator circuit and a composition circuit, both capable to operate at very high speeds, have been designed for a 1.6 micron VLSI process. Both the interpolator and composition circuit are back from production. They have been tested and both complied with our specifications

Evaluation of 3D Voxel Rendering Algorithms for Real-Time Interaction on a SIMD Graphics Processor

The display of three-dimensional medical data is becoming more common, but current hardware and image rendering algorithms do not generally allow real-time interaction with the image by the user. Real-time interactions, such as image rotation, utilize the motion processing capabilities of the human visual system, allowing a better understanding of the structures being imaged. Recent advances in general purpose graphics display equipment could make real-time interaction feasible in clinical setting. We have evaluated the capabilities of one type of advanced display architecture, the PIXAR Imaging Computer, for real-time interaction while displaying three-dimensional medical data as two-dimensional projections. It was discovered during this investigation that most suitable algorithms for implementation were based on the rendering of voxel rather than surface data. Two voxel-based techniques, back-to-front and front-to-back rendering produced acceptable, but not real-time performance. The quality of the images produced was not high, but allowed the determination of an image orientation which could then be used by a later high-quality rendering technique. Two conclusions were reached: first, the current performance of display hardware may allow acceptable interactive performance and produce high-quality images if a scheme of adaptive refinement is used wherein successively higher quality images are generated for the user. Second, the correct algorithm to use for fast rendering of volume data is highly dependent upon the architecture of the display processor, and in particular upon the ability of the processor to randomly access image data. If the processor is constrained to sequential or near sequential access to the voxel data, the choice of algorithms and the utilization of parallel processing is severely limited

Hardware-accelerated interactive data visualization for neuroscience in Python.

Large datasets are becoming more and more common in science, particularly in neuroscience where experimental techniques are rapidly evolving. Obtaining interpretable results from raw data can sometimes be done automatically; however, there are numerous situations where there is a need, at all processing stages, to visualize the data in an interactive way. This enables the scientist to gain intuition, discover unexpected patterns, and find guidance about subsequent analysis steps. Existing visualization tools mostly focus on static publication-quality figures and do not support interactive visualization of large datasets. While working on Python software for visualization of neurophysiological data, we developed techniques to leverage the computational power of modern graphics cards for high-performance interactive data visualization. We were able to achieve very high performance despite the interpreted and dynamic nature of Python, by using state-of-the-art, fast libraries such as NumPy, PyOpenGL, and PyTables. We present applications of these methods to visualization of neurophysiological data. We believe our tools will be useful in a broad range of domains, in neuroscience and beyond, where there is an increasing need for scalable and fast interactive visualization

Inviwo -- A Visualization System with Usage Abstraction Levels

The complexity of today's visualization applications demands specific visualization systems tailored for the development of these applications. Frequently, such systems utilize levels of abstraction to improve the application development process, for instance by providing a data flow network editor. Unfortunately, these abstractions result in several issues, which need to be circumvented through an abstraction-centered system design. Often, a high level of abstraction hides low level details, which makes it difficult to directly access the underlying computing platform, which would be important to achieve an optimal performance. Therefore, we propose a layer structure developed for modern and sustainable visualization systems allowing developers to interact with all contained abstraction levels. We refer to this interaction capabilities as usage abstraction levels, since we target application developers with various levels of experience. We formulate the requirements for such a system, derive the desired architecture, and present how the concepts have been exemplary realized within the Inviwo visualization system. Furthermore, we address several specific challenges that arise during the realization of such a layered architecture, such as communication between different computing platforms, performance centered encapsulation, as well as layer-independent development by supporting cross layer documentation and debugging capabilities

Interactive Visualization of the Largest Radioastronomy Cubes

3D visualization is an important data analysis and knowledge discovery tool, however, interactive visualization of large 3D astronomical datasets poses a challenge for many existing data visualization packages. We present a solution to interactively visualize larger-than-memory 3D astronomical data cubes by utilizing a heterogeneous cluster of CPUs and GPUs. The system partitions the data volume into smaller sub-volumes that are distributed over the rendering workstations. A GPU-based ray casting volume rendering is performed to generate images for each sub-volume, which are composited to generate the whole volume output, and returned to the user. Datasets including the HI Parkes All Sky Survey (HIPASS - 12 GB) southern sky and the Galactic All Sky Survey (GASS - 26 GB) data cubes were used to demonstrate our framework's performance. The framework can render the GASS data cube with a maximum render time < 0.3 second with 1024 x 1024 pixels output resolution using 3 rendering workstations and 8 GPUs. Our framework will scale to visualize larger datasets, even of Terabyte order, if proper hardware infrastructure is available.Comment: 15 pages, 12 figures, Accepted New Astronomy July 201

Understanding Next-Generation VR: Classifying Commodity Clusters for Immersive Virtual Reality

Commodity clusters offer the ability to deliver higher performance computer graphics at lower prices than traditional graphics supercomputers. Immersive virtual reality systems demand notoriously high computational requirements to deliver adequate real-time graphics, leading to the emergence of commodity clusters for immersive virtual reality. Such clusters deliver the graphics power needed by leveraging the combined power of several computers to meet the demands of real-time interactive immersive computer graphics.However, the field of commodity cluster-based virtual reality is still in early stages of development and the field is currently adhoc in nature and lacks order. There is no accepted means for comparing approaches and implementers are left with instinctual or trial-and-error means for selecting an approach.This paper provides a classification system that facilitates understanding not only of the nature of different clustering systems but also the interrelations between them. The system is built from a new model for generalized computer graphics applications, which is based on the flow of data through a sequence of operations over the entire context of the application. Prior models and classification systems have been too focused in context and application whereas the system described here provides a unified means for comparison of works within the field