IEEE VIS Publication Dataset

This paper presents an algorithm combining view-dependent rendering and conservative occlusion culling for interactive display of complex environments. A vertex hierarchy of the entire scene is decomposed into a cluster hierarchy through a novel clustering and partitioning algorithm. The cluster hierarchy is then used for view-frustum and occlusion culling. Using hardware accelerated occlusion queries and frame-to-frame coherence, a potentially visible set of clusters is computed. An active vertex front and face list is computed from the visible clusters and rendered using vertex arrays. The integrated algorithm has been implemented on a Pentium IV PC with a NVIDIA GeForce 4 graphics card and applied in two complex environments composed of millions of triangles. The resulting system can render these environments at interactive rates with little loss in image quality and minimal popping artifacts.

In this paper we show how out-of-core mesh processing techniques can be adapted to perform their computations based on the new processing sequence paradigm (Isenburg, et al., 2003), using mesh simplification as an example. We believe that this processing concept will also prove useful for other tasks, such a parameterization, remeshing, or smoothing, for which currently only in-core solutions exist. A processing sequence represents a mesh as a particular interleaved ordering of indexed triangles and vertices. This representation allows streaming very large meshes through main memory while maintaining information about the visitation status of edges and vertices. At any time, only a small portion of the mesh is kept in-core, with the bulk of the mesh data residing on disk. Mesh access is restricted to a fixed traversal order, but full connectivity and geometry information is available for the active elements of the traversal. This provides seamless and highly efficient out-of-core access to very large meshes for algorithms that can adapt their computations to this fixed ordering. The two abstractions that are naturally supported by this representation are boundary-based and buffer-based processing. We illustrate both abstractions by adapting two different simplification methods to perform their computation using a prototype of our mesh processing sequence API. Both algorithms benefit from using processing sequences in terms of improved quality, more efficient execution, and smaller memory footprints.

LightKit is a system for lighting three-dimensional synthetic scenes. LightKit simplifies the task of producing visually pleasing, easily interpretable images for visualization while making it harder to produce results where the scene illumination distracts from the visualization process. LightKit is based on lighting designs developed by artists and photographers and shown in previous studies to enhance shape perception. A key light provides natural overhead illumination of the scene, augmented by fill, head, and back lights. By default, lights are attached to a normalized, subject-centric, camera-relative coordinate frame to ensure consistent lighting independent of camera location or orientation. This system allows all lights to be positioned by specifying just six parameters. The intensity of each light is specified as a ratio to the key light intensity, allowing the scene's brightness to be adjusted using a single parameter. The color of each light is specified by a single normalized color parameter called warmth that is based on color temperature of natural sources. LightKit's default values for light position, intensity, and color are chosen to produce good results for a variety of scenes. LightKit is designed to work with both hardware graphics systems and, potentially, higher quality off-line rendering systems. We provide examples of images created using a LightKit implementation within the VTK visualization toolkit software framework.

We describe a modification of the widely used marching cubes method that leads to the useful property that the resulting isosurfaces are locally single valued functions. This implies that conventional interpolation and approximation methods can be used to locally represent the surface. These representations can be used for computing approximations for local surface properties. We utilize this possibility in order to develop algorithms for locally approximating Gaussian and mean curvature, methods for constrained smoothing of isosurface, and techniques for the parameterization of isosurfaces.

2D and 3D views are used together in many visualization domains, such as medical imaging, flow visualization, oceanographic visualization, and computer aided design (CAD). Combining these views into one display can be done by: (1) orientation icon (i.e., separate windows), (2) in-place methods (e.g., clip and cutting planes), and (3) a new method called ExoVis. How 2D and 3D views are displayed affects ease of mental registration (understanding the spatial relationship between views), an important factor influencing user performance. This paper compares the above methods in terms of their ability to support mental registration. Empirical results show that mental registration is significantly easier with in-place displays than with ExoVis, and significantly easier with ExoVis than with orientation icons. Different mental transformation strategies can explain this result. The results suggest that ExoVis may be a better alternative to orientation icons when in-place displays are not appropriate (e.g., when in-place methods hide data or cut the 3D view into several pieces).

In this paper a novel volume-rendering technique based on Monte Carlo integration is presented. As a result of a preprocessing, a point cloud of random samples is generated using a normalized continuous reconstruction of the volume as a probability density function. This point cloud is projected onto the image plane, and to each pixel an intensity value is assigned which is proportional to the number of samples projected onto the corresponding pixel area. In such a way a simulated X-ray image of the volume can be obtained. Theoretically, for a fixed image resolution, there exists an M number of samples such that the average standard deviation of the estimated pixel intensities us under the level of quantization error regardless of the number of voxels. Therefore Monte Carlo Volume Rendering (MCVR) is mainly proposed to efficiently visualize large volume data sets. Furthermore, network applications are also supported, since the trade-off between image quality and interactivity can be adapted to the bandwidth of the client/server connection by using progressive refinement.

In this paper, we propose a novel out-of-core isosurface extraction technique for large time-varying fields over irregular grids. We employ our meta-cell technique to explore the spatial coherence of the data, and our time tree algorithm to consider the temporal coherence as well. Our one-time preprocessing phase first partitions the dataset into meta-cells that cluster spatially neighboring cells together and are stored in disk. We then build a time tree to index the meta-cells for fast isosurface extraction. The time tree takes advantage of the temporal coherence among the scalar values at different time steps, and uses BBIO trees as secondary structures, which are stored in disk and support I/O-optimal interval searches. The time tree algorithm employs a novel meta-interval collapsing scheme and the buffer technique, to take care of the temporal coherence in an I/O-efficient way. We further make the time tree cache-oblivious, so that searching on it automatically performs optimal number of block transfers between any two consecutive levels of memory hierarchy (such as between cache and main memory and between main memory and disk) simultaneously. At run-time, we perform optimal cache-oblivious searches in the time tree, together with I/O-optimal searches in the BBIO trees, to read the active meta-cells from disk and generate the queried isosurface efficiently. The experiments demonstrate the effectiveness of our new technique. In particular, compared with the query-optimal main-memory algorithm by Cignoni et al. (1997) (extended for time-varying fields) when there is not enough main memory, our technique can speed up the isosurface queries from more than 18 hours to less than 4 minutes.

This paper addresses the problem of surface reconstruction of highly noisy point clouds. The surfaces to be reconstructed are assumed to be 2-manifolds of piecewise C1 continuity, with isolated small irregular regions of high curvature, sophisticated local topology or abrupt burst of noise. At each sample point, a quadric field is locally fitted via a modified moving least squares method. These locally fitted quadric fields are then blended together to produce a pseudo-signed distance field using Shepard's method. We introduce a prioritized front growing scheme in the process of local quadrics fitting. Flatter surface areas tend to grow faster. The already fitted regions will subsequently guide the fitting of those irregular regions in their neighborhood.

We describe an efficient technique for out-of-core management and interactive rendering of planet sized textured terrain surfaces. The technique, called planet-sized batched dynamic adaptive meshes (P-BDAM), extends the BDAM approach by using as basic primitive a general triangulation of points on a displaced triangle. The proposed framework introduces several advances with respect to the state of the art: thanks to a batched host-to-graphics communication model, we outperform current adaptive tessellation solutions in terms of rendering speed; we guarantee overall geometric continuity, exploiting programmable graphics hardware to cope with the accuracy issues introduced by single precision floating points; we exploit a compressed out of core representation and speculative prefetching for hiding disk latency during rendering of out-of-core data; we efficiently construct high quality simplified representations with a novel distributed out of core simplification algorithm working on a standard PC network.

This paper describes the work of a team of researchers in computer graphics, geometric computing, and civil engineering to produce a visualization of the September 2001 attack on the Pentagon. The immediate motivation for the project was to understand the behavior of the building under the impact. The longer term motivation was to establish a path for producing high-quality visualizations of large scale simulations. The first challenge was managing the enormous complexity of the scene to fit within the limits of state-of-the art simulation software systems and supercomputing resources. The second challenge was to integrate the simulation results into a high-quality visualization. To meet this challenge, we implemented a custom importer that simplifies and loads the massive simulation data in a commercial animation system. The surrounding scene is modeled using image-based techniques and is also imported in the animation system where the visualization is produced. A specific issue for us was to federate the simulation and the animation systems, both commercial systems not under our control and following internally different conceptualizations of geometry and animation. This had to be done such that scalability was achieved. The reusable link created between the two systems allows communicating the results to non-specialists and the public at large, as well as facilitating communication in teams with members having diverse technical backgrounds.

A new method was developed to increase the saliency of changing variables in a cardiovascular visualization for use by anesthesiologists in the operating room (OR). Clinically meaningful changes in patient physiology were identified and then mapped to the inherent psychophysical properties of the visualization. A long history of psychophysical research has provided an understanding of the parameters within which the human information processing system is able to detect changes in the size, shape and color of visual objects (Gescheider, 1976, Spence, 1990, and Baird, 1970). These detection thresholds are known as just noticeable differences (JNDs) which characterize the amount of change in an object's attribute that is recognizable 50% of the time. A prototype version of the display has been demonstrated to facilitate anesthesiologist's performance while reducing cognitive workload during simulated cardiac events (Agutter et al., 2002). In order to further improve the utility of the new cardiovascular visualization, the clinically relevant changes in cardiovascular variables are mapped to noticeable perceptual changes in the representational elements of the display. The results of the method described in this paper are used to merge information from the psychophysical properties of the cardiovascular visualization, with clinically relevant changes in the patient's cardiovascular physiology as measured by the clinical meaningfulness questionnaire. The result of this combination will create a visualization that is sensitive to changes in the cardiovascular health of the patient and communicates this information to the user in a meaningful, salient and intuitive manner.

In this paper, we propose a quasi-static approximation (QSA) approach to simulate the movement of the movable object in 6-degrees-of-freedom (DOF) haptic rendering. In our QSA approach, we solve for static equilibrium during each haptic time step, ignoring any dynamical properties such as inertia. The major contribution of this approach is to overcome the computational instability problem in overly stiff systems arising from numerical integration of second-order differential equations in previous dynamic models. Our primary experimental results on both simulated aircraft geometry and a large-scale real-world aircraft engine showed that our QSA approach was capable of maintaining the 1000Hz haptic refresh rate with more robust collision avoidance and more reliable force and torque feedback.

In this paper we present a generic method for incremental mesh adaptation based on hierarchy of semi-regular meshes. Our method supports any refinement rule mapping vertices onto vertices such as 1-to-4 split or 3-subdivision. Resulting adaptive mesh has subdivision connectivity and hence good aspect ratio of triangles. Hierarchic representation of the mesh allows incremental local refinement and simplification operations exploiting frame-to-frame coherence. We also present an out-of-core storage layout scheme designed for semi-regular meshes of arbitrary subdivision connectivity. It provides high cache coherency in the data retrieval and relies on the interleaved storage of resolution levels and maintaining good geometrical proximity within each level. The efficiency of the proposed method is demonstrated with applications in physically-based cloth simulation, real-time terrain visualization and procedural modeling.

One of the reasons that topological methods have a limited popularity for the visualization of complex 3D flow fields is the fact that such topological structures contain a number of separating stream surfaces. Since these stream surfaces tend to hide each other as well as other topological features, for complex 3D topologies the visualizations become cluttered and hardly interpretable. This paper proposes to use particular stream lines called saddle connectors instead of separating stream surfaces and to depict single surfaces only on user demand. We discuss properties and computational issues of saddle connectors and apply these methods to complex flow data. We show that the use of saddle connectors makes topological skeletons available as a valuable visualization tool even for topologically complex 3D flow data.

We present a new algorithm for simplifying the shape of 3D objects by manipulating their medial axis transform (MAT). From an unorganized set of boundary points, our algorithm computes the MAT, decomposes the axis into parts, then selectively removes a subset of these parts in order to reduce the complexity of the overall shape. The result is simplified MAT that can be used for a variety of shape operations. In addition, a polygonal surface of the resulting shape can be directly generated from the filtered MAT using a robust surface reconstruction method. The algorithm presented is shown to have a number of advantages over other existing approaches.

This paper presents a signed distance transform algorithm using graphics hardware, which computes the scalar valued function of the Euclidean distance to a given manifold of co-dimension one. If the manifold is closed and orientable, the distance has a negative sign on one side of the manifold and a positive sign on the other. Triangle meshes are considered for the representation of a two-dimensional manifold and the distance function is sampled on a regular Cartesian grid. In order to achieve linear complexity in the number of grid points, to each primitive we assign a simple polyhedron enclosing its Voronoi cell. Voronoi cells are known to contain exactly all points that lay closest to its corresponding primitive. Thus, the distance to the primitive only has to be computed for grid points inside its polyhedron. Although Voronoi cells partition space, the polyhedrons enclosing these cells do overlap. In regions where these overlaps occur, the minimum of all computed distances is assigned to a grid point. In order to speed up computations, points inside each polyhedron are determined by scan conversion of grid slices using graphics hardware. For this task, a fragment program is used to perform the nonlinear interpolation and minimization of distance values.

In this paper, we present a space efficient algorithm for speeding up isosurface extraction. Even though there exist algorithms that can achieve optimal search performance to identify isosurface cells, they prove impractical for large datasets due to a high storage overhead. With the dual goals of achieving fast isosurface extraction and simultaneously reducing the space requirement, we introduce an algorithm based on transform coding to compress the interval information of the cells in a dataset. Compression is achieved by first transforming the cell intervals (minima, maxima) into a form which allows more efficient compaction. It is followed by a dataset optimized non-uniform quantization stage. The compressed data is stored in a data structure that allows fast searches in the compression domain, thus eliminating the need to retrieve the original representation of the intervals at run-time. The space requirement of our search data structure is the mandatory cost of storing every cell ID once, plus an overhead for quantization information. The overhead is typically in the order of a few hundredths of the dataset size.

Many traditional techniques for "looking inside" volumetric data involve removing portions of the data, for example using various cutting tools, to reveal the interior. This allows the user to see hidden parts of the data, but has the disadvantage of removing potentially important surrounding contextual information. We explore an alternate strategy for browsing that uses deformations, where the user can cut into and open up, spread apart, or peel away parts of the volume in real time, making the interior visible while still retaining surrounding context. We consider various deformation strategies and present a number of interaction techniques based on different metaphors. Our designs pay special attention to the semantic layers that might compose a volume (e.g. the skin, muscle, bone in a scan of a human). Users can apply deformations to only selected layers, or apply a given deformation to a different degree to each layer, making browsing more flexible and facilitating the visualization of relationships between layers. Our interaction techniques are controlled with direct, "in place" manipulation, using pop-up menus and 3D widgets, to avoid the divided attention and awkwardness that would come with panels of traditional widgets. Initial user feedback indicates that our techniques are valuable, especially for showing portions of the data spatially situated in context with surrounding data.