Han-Wei Shen

A fast volume rendering algorithm for time-varying fields using a time-space partitioning (TSP) tree

We present a fast volume rendering algorithm for time-varying fields. We propose a new data structure, called time-space partitioning (TSP) tree, that can effectively capture both the spatial and the temporal coherence from a time-varying field. Using the proposed data structure, the rendering speed is substantially improved. In addition, our data structure helps to maintain the memory access locality and to provide the sparse data traversal so that our algorithm becomes suitable for large-scale out-of-core applications. Finally, our algorithm allows flexible error control for both the temporal and the spatial coherence so that a trade-off between image quality and rendering speed is possible. We demonstrate the utility and speed of our algorithm with data from several time-varying CFD simulations. Our rendering algorithm can achieve substantial speedup while the storage space overhead for the TSP tree is kept at a minimum.

Isosurfacing in span space with utmost efficiency (ISSUE)

We present efficient sequential and parallel algorithms for isosurface extraction. Based on the Span Space data representation, new data subdivision and searching methods are described. We also present a parallel implementation with an emphasis on load balancing. The performance of our sequential algorithm to locate the cell elements intersected by isosurfaces is faster than the Kd tree searching method originally used for the Span Space algorithm. The parallel algorithm can achieve high load balancing for massively parallel machines with distributed memory architectures.

A new line integral convolution algorithm for visualizing time-varying flow fields

New challenges on vector field visualization emerge as time dependent numerical simulations become ubiquitous in the field of computational fluid dynamics (CFD). To visualize data generated from these simulations, traditional techniques, such as displaying particle traces, can only reveal flow phenomena in preselected local regions and thus, are unable to track the evolution of global flow features over time. The paper presents an algorithm, called UFLIC (Unsteady Flow LIC), to visualize vector data in unsteady flow fields. Our algorithm extends a texture synthesis technique, called Line Integral Convolution (LIC), by devising a new convolution algorithm that uses a time-accurate value scattering scheme to model the texture advection. In addition, our algorithm maintains the coherence of the flow animation by successively updating the convolution results over time. Furthermore, we propose a parallel UFLIC algorithm that can achieve high load balancing for multiprocessor computers with shared memory architecture. We demonstrate the effectiveness of our new algorithm by presenting image snapshots from several CFD case studies.

UFLIC: a line integral convolution algorithm for visualizing unsteady flows

The paper presents an algorithm, UFLIC (Unsteady Flow LIC), to visualize vector data in unsteady flow fields. Using line integral convolution (LIC) as the underlying method, a new convolution algorithm is proposed that can effectively trace the flows global features over time. The new algorithm consists of a time-accurate value depositing scheme and a successive feedforward method. The value depositing scheme accurately models the flow advection, and the successive feedforward method maintains the coherence between animation frames. The new algorithm can produce time-accurate, highly coherent flow animations to highlight global features in unsteady flow fields. CFD scientists, for the first time, are able to visualize unsteady surface flows using the algorithm.

Differential volume rendering: a fast volume visualization technique for flow animation

We present a direct volume rendering algorithm to speed up volume animation for flow visualizations. Data coherency between consecutive simulation time steps is used to avoid casting rays from those pixels retaining color values assigned to the previous image. The algorithm calculates the differential information among a sequence of 3D volumetric simulation data. At each time step the differential information is used to compute the locations of pixels that need updating and a ray-casting method as utilized to produce the updated image. We illustrate the utility and speed of the differential volume rendering algorithm with simulation data from computational bioelectric and fluid dynamics applications. We can achieve considerable disk-space savings and nearly real-time rendering of 3D flows using low-cost, single processor workstations for models which contain hundreds of thousands of data points.<<ETX>>

An Information-Theoretic Framework for Flow Visualization

The process of visualization can be seen as a visual communication channel where the input to the channel is the raw data, and the output is the result of a visualization algorithm. From this point of view, we can evaluate the effectiveness of visualization by measuring how much information in the original data is being communicated through the visual communication channel. In this paper, we present an information-theoretic framework for flow visualization with a special focus on streamline generation. In our framework, a vector field is modeled as a distribution of directions from which Shannons entropy is used to measure the information content in the field. The effectiveness of the streamlines displayed in visualization can be measured by first constructing a new distribution of vectors derived from the existing streamlines, and then comparing this distribution with that of the original data set using the conditional entropy. The conditional entropy between these two distributions indicates how much information in the original data remains hidden after the selected streamlines are displayed. The quality of the visualization can be improved by progressively introducing new streamlines until the conditional entropy converges to a small value. We describe the key components of our framework with detailed analysis, and show that the framework can effectively visualize 2D and 3D flow data.

Sweeping simplices: a fast iso-surface extraction algorithm for unstructured grids

Presents an algorithm that accelerates the extraction of iso-surfaces from unstructured grids by avoiding the traversal of the entire set of cells in the volume. The algorithm consists of a sweep algorithm and a data decomposition scheme. The sweep algorithm incrementally locates intersected elements, and the data decomposition scheme restricts the algorithms worst-case performance. For data sets consisting of hundreds of thousands of elements, our algorithm can reduce the cell traversal time by more than 90% over the naive iso-surface extraction algorithm, thus facilitating interactive probing of scalar fields for large-scale problems on unstructured three-dimensional grids.

High dimensional direct rendering of time-varying volumetric data

We present an alternative method for viewing time-varying volumetric data. We consider such data as a four-dimensional data field, rather than considering space and time as separate entities. If we treat the data in this manner, we can apply high dimensional slicing and projection techniques to generate an image hyperplane. The user is provided with an intuitive user interface to specify arbitrary hyperplanes in 4D, which can be displayed with standard volume rendering techniques. From the volume specification, we are able to extract arbitrary hyperslices, combine slices together into a hyperprojection volume, or apply a 4D raycasting method to generate the same results. In combination with appropriate integration operators and transfer functions, we are able to extract and present different space-time features to the user.

The Top 10 Challenges in Extreme-Scale Visual Analytics

A team of scientists and researchers discusses the top 10 challenges in extreme-scale visual analytics (VA). The discussion covers applying VA technologies to both scientific and nonscientific data, evaluating the problems and challenges from both technical and social perspectives.

Multi-variate, Time Varying, and Comparative Visualization with Contextual Cues

Time-varying, multi-variate, and comparative data sets are not easily visualized due to the amount of data that is presented to the user at once. By combining several volumes together with different operators into one visualized volume, the user is able to compare values from different data sets in space over time, run, or field without having to mentally switch between different renderings of individual data sets. In this paper, we propose using a volume shader where the user is given the ability to easily select and operate on many data volumes to create comparison relationships. The user specifies an expression with set and numerical operations and her data to see relationships between data fields. Furthermore, we render the contextual information of the volume shader by converting it to a volume tree. We visualize the different levels and nodes of the volume tree so that the user can see the results of suboperations. This gives the user a deeper understanding of the final visualization, by seeing how the parts of the whole are operationally constructed