EWA Splatting

In this paper, we present a framework for high quality splatting based on elliptical Gaussian kernels. To avoid aliasing artifacts, we introduce the concept of a resampling filter, combining a reconstruction kernel with a low-pass filter. Because of the similarity to Heckbert's EWA (elliptical weighted average) filter for texture mapping, we call our technique EWA splatting. Our framework allows us to derive EWA splat primitives for volume data and for point-sampled surface data. It provides high image quality without aliasing artifacts or excessive blurring for volume data and, additionally, features anisotropic texture filtering for point-sampled surfaces. It also handles nonspherical volume kernels efficiently; hence, it is suitable for regular, rectilinear, and irregular volume datasets. Moreover, our framework introduces a novel approach to compute the footprint function, facilitating efficient perspective projection of arbitrary elliptical kernels at very little additional cost. Finally, we show that EWA volume reconstruction kernels can be reduced to surface reconstruction kernels. This makes our splat primitive universal in rendering surface and volume data.Engineering and Applied Science

A Multi-Resolution Interactive Previewer for Volumetric Data on Arbitary Meshes

In this paper we describe a rendering method suitable for interactive previewing of large-scale arbitary-mesh volume data sets. A data set to be visualized is represented by a ''point cloud,'' i. e., a set of points and associated data values without known connectivity between the points. The method uses a multi-resolution approach to achieve interactive rendering rates of several frames per second for arbitrarily large data sets. Lower-resolution approximations of an original data set are created by iteratively applying a point- decimation operation to higher-resolution levels. The goal of this method is to provide the user with an interactive navigation and exploration tool to determine good viewpoints and transfer functions to pass on to a high-quality volume renderer that uses a standard algorithm

Rzsweep: A New Volume-Rendering Technique for Uniform Rectilinear Datasets

A great challenge in the volume-rendering field is to achieve high-quality images in an acceptable amount of time. In the area of volume rendering, there is always a trade-off between speed and quality. Applications where only high-quality images are acceptable often use the ray-casting algorithm, but this method is computationally expensive and typically achieves low frame rates. The work presented here is RZSweep, a new volume-rendering algorithm for uniform rectilinear datasets, that gives high-quality images in a reasonable amount of time. In this algorithm a plane sweeps the vertices of the implicit grid of regular datasets in depth order, projecting all the implicit faces incident on each vertex. This algorithm uses the inherent properties of a rectilinear datasets. RZSweep is an object-order, back-toront, direct volume rendering, face projection algorithm for rectilinear datasets using the cell approach. It is a single processor serial algorithm. The simplicity of the algorithm allows the use of the graphics pipeline for hardware-assisted projection, and also, with minimum modification, a version of the algorithm that is graphics-hardware independent. Lighting, color and various opacity transfer functions are implemented for giving realism to the final resulting images. Finally, an image comparison is done between RZSweep and a 3D texture-based method for volume rendering using standard image metrics like Euclidian and geometric differences

New techniques for the scientific visualization of three-dimensional multi-variate and vector fields

Volume rendering allows us to represent a density cloud with ideal properties (single scattering, no self-shadowing, etc.). Scientific visualization utilizes this technique by mapping an abstract variable or property in a computer simulation to a synthetic density cloud. This thesis extends volume rendering from its limitation of isotropic density clouds to anisotropic and/or noisy density clouds. Design aspects of these techniques are discussed that aid in the comprehension of scientific information. Anisotropic volume rendering is used to represent vector based quantities in scientific visualization. Velocity and vorticity in a fluid flow, electric and magnetic waves in an electromagnetic simulation, and blood flow within the body are examples of vector based information within a computer simulation or gathered from instrumentation. Understand these fields can be crucial to understanding the overall physics or physiology. Three techniques for representing three-dimensional vector fields are presented: Line Bundles, Textured Splats and Hair Splats. These techniques are aimed at providing a high-level (qualitative) overview of the flows, offering the user a substantial amount of information with a single image or animation. Non-homogenous volume rendering is used to represent multiple variables. Computer simulations can typically have over thirty variables, which describe properties whose understanding are useful to the scientist. Trying to understand each of these separately can be time consuming. Trying to understand any cause and effect relationships between different variables can be impossible. NoiseSplats is introduced to represent two or more properties in a single volume rendering of the data. This technique is also aimed at providing a qualitative overview of the flows

Real-time voxel rendering algorithm based on screen space billboard voxel buffer with sparse lookup textures

In this paper, we present a novel approach to efficient real-time rendering of numerous high-resolution voxelized objects. We present a voxel rendering algorithm based on triangle rasterization pipeline with screen space rendering computational complexity. In order to limit the number of vertex shader invocations, voxel filtering algorithm with fixed size voxel data buffer was developed. Voxelized objects are represented by sparse voxel octree (SVO) structure. Using sparse texture available in modern graphics APIs, we create a 3D lookup table for voxel ids. Voxel filtering algorithm is based on 3D sparse texture ray marching approach. Screen Space Billboard Voxel Buffer is filled by voxels from visible voxels point cloud. Thanks to using 3D sparse textures, we are able to store high-resolution objects in VRAM memory. Moreover, sparse texture mipmaps can be used to control object level of detail (LOD). The geometry of a voxelized object is represented by a collection of points extracted from object SVO. Each point is defined by position, normal vector and texture coordinates. We also show how to take advantage of programmable geometry shaders in order to store voxel objects with extremely low memory requirements and to perform real-time visualization. Moreover, geometry shaders are used to generate billboard quads from the point cloud and to perform fast face culling. As a result, we obtained comparable or even better performance results in comparison to SVO ray tracing approach. The number of rendered voxels is limited to defined Screen Space Billboard Voxel Buffer resolution. Last but not least, thanks to graphics card adapter support, developed algorithm can be easily integrated with any graphics engine using triangle rasterization pipeline

Dynamic Volume Rendering of Functional Medical Data on Dissimilar Hardware Platforms

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 fMRI’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 professionals’ 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 “scrub” 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 CAVE™, 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 9.7” 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

Volume MLS Ray Casting

The method of Moving Least Squares (MLS) is a popular framework for reconstructing continuous functions from scattered data due to its rich mathematical properties and well-understood theoretical foundations. This paper applies MLS to volume rendering, providing a unified mathematical framework for ray casting of scalar data stored over regular as well as irregular grids. We use the MLS reconstruction to render smooth isosurfaces and to compute accurate derivatives for high-quality shading effects. We also present a novel, adaptive preintegration scheme to improve the efficiency of the ray casting algorithm by reducing the overall number of function evaluations, and an efficient implementation of our framework exploiting modern graphics hardware. The resulting system enables high-quality volume integration and shaded isosurface rendering for regular and irregular volume data.Engineering and Applied Science

Visuelle Analyse großer Partikeldaten

Partikelsimulationen sind eine bewährte und weit verbreitete numerische Methode in der Forschung und Technik. Beispielsweise werden Partikelsimulationen zur Erforschung der Kraftstoffzerstäubung in Flugzeugturbinen eingesetzt. Auch die Entstehung des Universums wird durch die Simulation von dunkler Materiepartikeln untersucht. Die hierbei produzierten Datenmengen sind immens. So enthalten aktuelle Simulationen Billionen von Partikeln, die sich über die Zeit bewegen und miteinander interagieren. Die Visualisierung bietet ein großes Potenzial zur Exploration, Validation und Analyse wissenschaftlicher Datensätze sowie der zugrundeliegenden Modelle. Allerdings liegt der Fokus meist auf strukturierten Daten mit einer regulären Topologie. Im Gegensatz hierzu bewegen sich Partikel frei durch Raum und Zeit. Diese Betrachtungsweise ist aus der Physik als das lagrange Bezugssystem bekannt. Zwar können Partikel aus dem lagrangen in ein reguläres eulersches Bezugssystem, wie beispielsweise in ein uniformes Gitter, konvertiert werden. Dies ist bei einer großen Menge an Partikeln jedoch mit einem erheblichen Aufwand verbunden. Darüber hinaus führt diese Konversion meist zu einem Verlust der Präzision bei gleichzeitig erhöhtem Speicherverbrauch. Im Rahmen dieser Dissertation werde ich neue Visualisierungstechniken erforschen, welche speziell auf der lagrangen Sichtweise basieren. Diese ermöglichen eine effiziente und effektive visuelle Analyse großer Partikeldaten