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

A Distributed GPU-based Framework for real-time 3D Volume Rendering of Large Astronomical Data Cubes

We present a framework to interactively volume-render three-dimensional data cubes using distributed ray-casting and volume bricking over a cluster of workstations powered by one or more graphics processing units (GPUs) and a multi-core CPU. The main design target for this framework is to provide an in-core visualization solution able to provide three-dimensional interactive views of terabyte-sized data cubes. We tested the presented framework using a computing cluster comprising 64 nodes with a total of 128 GPUs. The framework proved to be scalable to render a 204 GB data cube with an average of 30 frames per second. Our performance analyses also compare between using NVIDIA Tesla 1060 and 2050 GPU architectures and the effect of increasing the visualization output resolution on the rendering performance. Although our initial focus, and the examples presented in this work, is volume rendering of spectral data cubes from radio astronomy, we contend that our approach has applicability to other disciplines where close to real-time volume rendering of terabyte-order 3D data sets is a requirement.Comment: 13 Pages, 7 figures, has been accepted for publication in Publications of the Astronomical Society of Australi

Relief mapping on cubic cell complexes

In this paper we present an algorithm for parameterizing arbitrary surfaces onto a quadrilateral domain defined by a collection of cubic cells. The parameterization inside each cell is implicit and thus requires storing no texture coordinates. Based upon this parameterization, we propose a unified representation of geometric and appearance information of complex models. The representation consists of a set of cubic cells (providing a coarse representation of the object) together with a collection of distance maps (encoding fine geometric detail inside each cell). Our new representation has similar uses than geometry images, but it requires storing a single distance value per texel instead of full vertex coordinates. When combined with color and normal maps, our representation can be used to render an approximation of the model through an output-sensitive relief mapping algorithm, thus being specially amenable for GPU raytracing.Postprint (author’s final draft

Performance and quality analysis of convolution-based volume illumination

Convolution-based techniques for volume rendering are among the fastest in the on-the-fly volumetric illumination category. Such methods, however, are still considerably slower than conventional local illumination techniques. In this paper we describe how to adapt two commonly used strategies for reducing aliasing artifacts, namely pre-integration and supersampling, to such techniques. These strategies can help reduce the sampling rate of the lighting information (thus the number of convolutions), bringing considerable performance benefits. We present a comparative analysis of their effectiveness in offering performance improvements. We also analyze the (negligible) differences they introduce when comparing their output to the reference method. These strategies can be highly beneficial in setups where direct volume rendering of continuously streaming data is desired and continuous recomputation of full lighting information is too expensive, or where memory constraints make it preferable not to keep additional precomputed volumetric data in memory. In such situations these strategies make single pass, convolution-based volumetric illumination models viable for a broader range of applications, and this paper provides practical guidelines for using and tuning such strategies to specific use cases

Survey of texture mapping techniques for representing and rendering volumetric mesostructure

Representation and rendering of volumetric mesostructure using texture mapping can potentially allow the display of highly detailed, animated surfaces at a low performance cost. Given the need for consistently more detailed and dynamic worlds rendered in real-time, volumetric texture mapping now becomes an area of great importance.In this survey, we review the developments of algorithms and techniques for representing volumetric mesostructure as texture-mapped detail. Our goal is to provide researchers with an overview of novel contributions to volumetric texture mapping as a starting point for further research and developers with a comparative review of techniques, giving insight into which methods would be fitting for particular tasks.We start by defining the scope of our domain and provide background information regarding mesostructure and volumetric texture mapping. Existing techniques are assessed in terms of content representation and storage as well as quality and performance of parameterization and rendering. Finally, we provide insights to the field and opportunities for research directions in terms of real-time volumetric texture-mapped surfaces under deformation