An image-based approach to interactive crease extraction and rendering

AbstractRidge and valley manifolds are receiving a growing attention in visualization research due to their ability to reveal the shapes of salient structures in numerical datasets across scientific, engineering, and medical applications. However, the methods proposed to date for their extraction in the visualization and image analysis literature are computationally expensive and typically applied in an offline setting. This setup does not properly support a userdriven exploration, which often requires control over various parameters tuned to filter false positives and spurious artifacts and highlight the most significant structures. This paper presents a GPU-based adaptive technique for crease extraction and visualization across scales. Our method combines a scale-space analysis of the data in pre-processing with a ray casting approach supporting a robust and efficient one-dimensional numerical search, and an image-based rendering strategy. This general framework achieves high-quality crease surface representations at interactive frame rates. Results are proposed for analytical, medical, and computational datasets

Interactive isosurface ray tracing of large octree volumes

Journal ArticleWe present a technique for ray tracing isosurfaces of large compressed structured volumes. Data is first converted into a losslesscompression octree representation that occupies a fraction of the original memory footprint. An isosurface is then dynamically rendered by tracing rays through a min/max hierarchy inside interior octree nodes. By embedding the acceleration tree and scalar data in a single structure and employing optimized octree hash schemes, we achieve competitive frame rates on common multicore architectures, and render large time-variant data that could not otherwise be accomodated

Doctor of Philosophy

dissertationDataflow pipeline models are widely used in visualization systems. Despite recent advancements in parallel architecture, most systems still support only a single CPU or a small collection of CPUs such as a SMP workstation. Even for systems that are specifically tuned towards parallel visualization, their execution models only provide support for data-parallelism while ignoring taskparallelism and pipeline-parallelism. With the recent popularization of machines equipped with multicore CPUs and multi-GPU units, these visualization systems are undoubtedly falling further behind in reaching maximum efficiency. On the other hand, there exist several libraries that can schedule program executions on multiple CPUs and/or multiple GPUs. However, due to differences in executing a task graph and a pipeline along with their APIs being considerably low-level, it still remains a challenge to integrate these run-time libraries into current visualization systems. Thus, there is a need for a redesigned dataflow architecture to fully support and exploit the power of highly parallel machines in large-scale visualization. The new design must be able to schedule executions on heterogeneous platforms while at the same time supporting arbitrarily large datasets through the use of streaming data structures. The primary goal of this dissertation work is to develop a parallel dataflow architecture for streaming large-scale visualizations. The framework includes supports for platforms ranging from multicore processors to clusters consisting of thousands CPUs and GPUs. We achieve this in our system by introducing the notion of Virtual Processing Elements and Task-Oriented Modules along with a highly customizable scheduler that controls the assignment of tasks to elements dynamically. This creates an intuitive way to maintain multiple CPU/GPU kernels yet still provide coherency and synchronization across module executions. We have implemented these techniques into HyperFlow which is made of an API with all basic dataflow constructs described in the dissertation, and a distributed run-time library that can be used to deploy those pipelines on multicore, multi-GPU and cluster-based platforms

Doctor of Philosophy

dissertationRay tracing presents an efficient rendering algorithm for scientific visualization using common visualization tools and scales with increasingly large geometry counts while allowing for accurate physically-based visualization and analysis, which enables enhanced rendering and new visualization techniques. Interactivity is of great importance for data exploration and analysis in order to gain insight into large-scale data. Increasingly large data sizes are pushing the limits of brute-force rasterization algorithms present in the most widely-used visualization software. Interactive ray tracing presents an alternative rendering solution which scales well on multicore shared memory machines and multinode distributed systems while scaling with increasing geometry counts through logarithmic acceleration structure traversals. Ray tracing within existing tools also provides enhanced rendering options over current implementations, giving users additional insight from better depth cues while also enabling publication-quality rendering and new models of visualization such as replicating photographic visualization techniques

New Algorithmic Techniques for Large Scale Volumetric Data Visualization on Parallel Architectures

Volume visualization is widely used as an effective approach for the visual exploration, computational analysis, and manipulation of volumetric datasets. Due to the dramatic advances in imaging instruments and computing technologies, such datasets are now appearing at a very fast rate with increasingly larger sizes in many engineering, science and medical applications. Isosurface and direct volume rendering(DVR) are two of the most widely used techniques to render such datasets. This dissertation introduces novel techniques for rendering isosurfaces and volumes, and extends these techniques to multiprocessor architectures. We first focus on cluster-based techniques for isosurface extraction and rendering using polygonal approximation. We present a new simple indexing scheme and data layout approach, which enable scalable and efficient isosurface generation. This algorithm is the first known parallel algorithm to achieve provable load balancing on multiprocessor systems. We also develop an algorithm to generate isosurfaces using ray-casting on multi-core processors. Our method is based on a hybrid strategy that begins with an object order traversal of the data followed by ray-casting on ordered sets of an adaptive number of subcubes, one set for each small group of pixels on the image. We develop a multithreaded implementation, which uses new dynamic load balancing techniques that start with an image partitioning for the initial stage and then perform dynamic allocation of groups of ray-casting tasks among the different threads. The strategy ensures almost equal loads among the cores while maintaining spatial data locality. This scheme is extended to perform direct volume rendering and is shown to achieve similar improvements in terms of overall performance, load balancing, and scalability. We conduct a large number of tests for all our algorithms on the University of Maryland Visualization Cluster and on the 8-core Clovertown platform using a wide variety of datasets such as Richtmyer-Meshkov Instability dataset (7.5GB for each time step) and Visible Human dataset (~1GB). We obtain results that consistently validate the efficiency and the scalability of our algorithms. In particular, the overall performance of our hybrid ray-casting scheme achieves an interactive rendering rate on high resolution (1024x1024) screens for all the datasets tested

Efficient rendering of large 3-D and 4-D scalar fields

Rendering volumetric data, as a compute/communication intensive and highly parallel application, represents the characteristics of future workloads for desktop computers. Interactively rendering volumetric data has been a challenging problem due to its high computational and communication requirements. With the consistent trend toward high resolution data, it has remained a difficult problem despite the continuous increase in processing power, because of the increasing performance gap between computation and communication. On the other hand, the new multi-core architecture trend in computational units in PC, which can be characterized by parallelism and heterogeneity, provides both opportunities and challenges. While the new on-chip parallel architectures offer opportunities for extremely high performance, widespread use of those parallel processors requires extensive changes in previous algorithms to take advantage of the new architectures. In this dissertation, we develop new methods and techniques to support interactive rendering of large volumetric data. In particular, we present a novel method to layout data on disk for efficiently performing an out-of-core axis-aligned slicing of large multidimensional scalar fields. We also present a new method to efficiently build an out-of-core indexing structure for n-dimensional volumetric data. Then, we describe a streaming model for efficiently implementing volume ray casting on a heterogeneous compute resource environment. We describe how we implement the model on SONY/TOSHIBA/IBM Cell Broadband Engine and on NVIDIA CUDA architecture. Our results show that our out-of-core techniques significantly reduce the communication bandwidth requirements and that our streaming model very effectively makes use of the strengths of those heterogeneous parallel compute resource environment for volume rendering. In all cases, we achieve scalability and load balancing, while hiding memory latency

Ray tracing techniques for computer games and isosurface visualization

Ray tracing is a powerful image synthesis technique, that has been used for high-quality offline rendering since decades. In recent years, this technique has become more important for realtime applications, but still plays only a minor role in many areas. Some of the reasons are that ray tracing is compute intensive and has to rely on preprocessed data structures to achieve fast performance. This dissertation investigates methods to broaden the applicability of ray tracing and is divided into two parts. The first part explores the opportunities offered by ray tracing based game technology in the context of current and expected future performance levels. In this regard, novel methods are developed to efficiently support certain kinds of dynamic scenes, while avoiding the burden to fully recompute the required data structures. Furthermore, todays ray tracing performance levels are below what is needed for 3D games. Therefore, the multi-core CPU of the Playstation 3 is investigated, and an optimized ray tracing architecture presented to take steps towards the required performance. In part two, the focus shifts to isosurface raytracing. Isosurfaces are particularly important to understand the distribution of certain values in volumetric data. Since the structure of volumetric data sets is diverse, op- timized algorithms and data structures are developed for rectilinear as well as unstructured data sets which allow for realtime rendering of isosurfaces including advanced shading and visualization effects. This also includes tech- niques for out-of-core and time-varying data sets.Ray-tracing ist ein flexibles Bildgebungsverfahren, das schon seit Jahrzehnten für hoch qualitative, aber langsame Bilderzeugung genutzt wird. In den letzten Jahren wurde Ray-tracing auch für Echtzeitanwendungen immer interessanter, spielt aber in vielen Anwendungsbereichen noch immer eine untergeordnete Rolle. Einige der Gründe sind die Rechenintensität von Ray-tracing sowie die Abhängigkeit von vorberechneten Datenstrukturen um hohe Geschwindigkeiten zu erreichen. Diese Dissertation untersucht Methoden um die Anwendbarkeit von Ray-tracing in zwei verschiedenen Bereichen zu erhöhen. Im ersten Teil dieser Dissertation werden die Möglichkeiten, die Ray- tracing basierte Spieletechnologie bietet, im Kontext mit aktueller sowie zukünftig erwarteten Geschwindigkeiten untersucht. Darüber hinaus werden in diesem Zusammenhang Methoden entwickelt um bestimmte zeitveränderliche Szenen darstellen zu können ohne die dafür benötigen Datenstrukturen von Grund auf neu erstellen zu müssen. Da die Geschwindigkeit von Ray-tracing für Spiele bisher nicht ausreichend ist, wird die Mehrkern- CPU der Playstation 3 untersucht, und ein optimiertes Ray-tracing System beschrieben, das Ray-tracing näher an die benötigte Geschwindigkeit heranbringt. Der zweite Teil beschäftigt sich mit der Darstellung von Isoflächen mittels Ray-tracing. Isoflächen sind insbesonders wichtig um die Verteilung einzelner Werte in volumetrischen Datensätzen zu verstehen. Da diese Datensätze verschieden strukturiert sein können, werden für gitterförmige und unstrukturierte Datensätze optimierte Algorithmen und Datenstrukturen entwickelt, die die Echtzeitdarstellung von Isoflächen erlauben. Dies beinhaltet auch Erweiterungen für extrem große und zeitveränderliche Datensätze

Surface Reconstruction from Constructive Solid Geometry for Interactive Visualization

A method is presented for constructing a set of triangles that closely approximates the surface of a constructive solid geometry model. The method subdivides an initial triangulation of the model’s primitives into triangles that can be classified accurately as either on or off of the surface of the whole model, and then recombines these small triangles into larger ones that are still either entirely on or entirely off the surface. Subdivision and recombination can be done in a preprocessing step, allowing later rendering of the triangles on the surface (i.e., the triangles visible from outside the model) to proceed at interactive rates. Performance measurements confirm that this method achieves interactive rendering speeds. This approach has been used with good results in an interactive scientific visualization program

Interactive High Performance Volume Rendering

This thesis is about Direct Volume Rendering on high performance computing systems. As direct rendering methods do not create a lower-dimensional geometric representation, the whole scientific dataset must be kept in memory. Thus, this family of algorithms has a tremendous resource demand. Direct Volume Rendering algorithms in general are well suited to be implemented for dedicated graphics hardware. Nevertheless, high performance computing systems often do not provide resources for hardware accelerated rendering, so that the visualization algorithm must be implemented for the available general-purpose hardware. Ever growing datasets that imply copying large amounts of data from the compute system to the workstation of the scientist, and the need to review intermediate simulation results, make porting Direct Volume Rendering to high performance computing systems highly relevant. The contribution of this thesis is twofold. As part of the first contribution, after devising a software architecture for general implementations of Direct Volume Rendering on highly parallel platforms, parallelization issues and implementation details for various modern architectures are discussed. The contribution results in a highly parallel implementation that tackles several platforms. The second contribution is concerned with the display phase of the “Distributed Volume Rendering Pipeline”. Rendering on a high performance computing system typically implies displaying the rendered result at a remote location. This thesis presents a remote rendering technique that is capable of hiding latency and can thus be used in an interactive environment