Accelerating Volume Raycasting using Proxy Spheres

In this paper, we propose an efficient solution that addresses the performance problems of current single-pass GPU raycasting algorithms. Our paper provides more control over the rendering process by introducing tighter ray segments for raycasting, while at the same time avoiding the introduction of any new rendering artefacts. We achieve this by dynamically generating, on the GPU, a coarsely fitted proxy geometry, composed of spheres, for the active blocks. The spheres are then rasterised into two z-buffers by a single rendering pass. The resulting two z-buffers are used as the first-hit and last-hit points for the subsequent raycaster. With this approach, only the valid ray segments between the two z-buffers need to be sampled during raycasting. This also provides more coherent parallelism on the GPU due to more consistent ray length and avoidance of the overheads and dynamic branching of performing checks on a per-sample basis during the raycasting pass.
Our technique is ideal for dynamic data exploration in which both the transfer function and view parameters need to be changed frequently at runtime. The rendering results of our algorithm are identical to the general cube-based proxy geometry algorithm, but the performance can be up to 15.7 times faster. Furthermore, the approach can be adopted by any existing raycasting system in a straightforward way.     

Fully Automatic Visualisation of Overlapping Sets

Visualisation of taxonomies and sets has recently become an active area of research. Many application fields now require more than a strict classification of elements into a hierarchy tree. Euler diagrams, one of the most natural ways of depicting intersecting sets, may provide a solution to these problems.
In this paper, we present an approach for the automatic generation of Euler-like diagrams. This algorithm differs from previous approaches in that it has no undrawable instances of input, allowing it to be used in systems where the output is always required. We also improve the readability of Euler diagrams through the use of Bézier curves and transparent coloured textures. Our approach has been implemented using the Tulip platform. Both the source and executable program used to generate the results are freely available.     

Color correction for tone mapping

Tone mapping algorithms offer sophisticated methods for mapping a real-world luminance range to the luminance range of the output medium but they often cause changes in color appearance. In this work we conduct a series of subjective appearance matching experiments to measure the change in image colorfulness after contrast compression and enhancement. The results indicate that the relation between contrast compression and the color saturation correction that matches color appearance is non-linear and smaller color correction is required for small change of contrast. We demonstrate that the relation cannot be fully explained by color appearance models. We propose color correction formulas that can be used with existing tone mapping algorithms. We extend existing global and local tone mapping operators and show that the proposed color correction formulas can preserve original image colors after tone scale manipulation.     

Frame Sequential Interpolation for Discrete Level‐of‐Detail Rendering

In this paper we present a method for automatic interpolation between adjacent discrete levels of detail to achieve smooth LOD changes in image space. We achieve this by breaking the problem into two passes: We render the two LOD levels individually and combine them in a separate pass afterwards. The interpolation is formulated in a way that only one level has to be updated per frame and the other can be reused from the previous frame, thereby causing roughly the same render cost as with simple non interpolated discrete LOD rendering, only incurring the slight overhead of the final combination pass. Additionally we describe customized interpolation schemes using visibility textures. The method was designed with the ease of integration into existing engines in mind. It requires neither sorting nor blending of objects, nor does it introduce any constrains in the LOD used. The LODs can be coplanar, alpha masked, animated, impostors, and intersecting, while still interpolating smoothly.     

MultiClusterTree: Interactive Visual Exploration of Hierarchical Clusters in Multidimensional Multivariate Data

Visual analytics of multidimensional multivariate data is a challenging task because of the difficulty in understanding metrics in attribute spaces with more than three dimensions. Frequently, the analysis goal is not to look into individual records but to understand the distribution of the records at large and to find clusters of records with similar attribute values. A large number of (typically hierarchical) clustering algorithms have been developed to group individual records to clusters of statistical significance. However, only few visualization techniques exist for further exploring and understanding the clustering results. We propose visualization and interaction methods for analyzing individual clusters as well as cluster distribution within and across levels in the cluster hierarchy. We also provide a clustering method that operates on density rather than individual records. To not restrict our search for clusters, we compute density in the given multidimensional multivariate space. Clusters are formed by areas of high density. We present an approach that automatically computes a hierarchical tree of high density clusters. To visually represent the cluster hierarchy, we present a 2D radial layout that supports an intuitive understanding of the distribution structure of the multidimensional multivariate data set. Individual clusters can be explored interactively using parallel coordinates when being selected in the cluster tree. Furthermore, we integrate circular parallel coordinates into the radial hierarchical cluster tree layout, which allows for the analysis of the overall cluster distribution. This visual representation supports the comprehension of the relations between clusters and the original attributes. The combination of the 2D radial layout and the circular parallel coordinates is used to overcome the overplotting problem of parallel coordinates when looking into data sets with many records. We apply an automatic coloring scheme based on the 2D radial layout of the hierarchical cluster tree encoding hue, saturation, and value of the HSV color space. The colors support linking the 2D radial layout to other views such as the standard parallel coordinates or, in case data is obtained from multidimensional spatial data, the distribution in object space.     

Visualizing the Positional and Geometrical Variability of Isosurfaces in Uncertain Scalar Fields

We present a novel approach for visualizing the positional and geometrical variability of isosurfaces in uncertain 3D scalar fields. Our approach extends recent work by Pöthkow and Hege [ [PH10] ] in that it accounts for correlations in the data to determine more reliable isosurface crossing probabilities. We introduce an incremental update‐scheme that allows integrating the probability computation into front‐to‐back volume ray‐casting efficiently. Our method accounts for homogeneous and anisotropic correlations, and it determines for each sampling interval along a ray the probability of crossing an isosurface for the first time. To visualize the positional and geometrical uncertainty even under viewing directions parallel to the surface normal, we propose a new color mapping scheme based on the approximate spatial deviation of possible surface points from the mean surface. The additional use of saturation enables to distinguish between areas of high and low statistical dependence. Experimental results confirm the effectiveness of our approach for the visualization of uncertainty related to position and shape of convex and concave isosurface structures.     

Instant Volume Visualization using Maximum Intensity Difference Accumulation

It has long been recognized that transfer function setup for Direct Volume Rendering (DVR) is crucial to its usability. However, the task of finding an appropriate transfer function is complex and time-consuming even for experts. Thus, in many practical applications simpler techniques which do not rely on complex transfer functions are employed. One common example is Maximum Intensity Projection (MIP) which depicts the maximum value along each viewing ray. In this paper, we introduce Maximum Intensity Difference Accumulation (MIDA), a new approach which combines the advantages of DVR and MIP. Like MIP, MIDA exploits common data characteristics and hence does not require complex transfer functions to generate good visualization results. It does, however, feature occlusion and shape cues similar to DVR. Furthermore, we show that MIDA – in addition to being a useful technique in its own right – can be used to smoothly transition between DVR and MIP in an intuitive manner. MIDA can be easily implemented using volume raycasting and achieves real-time performance on current graphics hardware.     

Motion based Painterly Rendering

Previous painterly rendering techniques normally use image gradients for deciding stroke orientations. Image gradients are good for expressing object shapes, but difficult to express the flow or movements of objects. In real painting, the use of brush strokes corresponding to the actual movement of objects allows viewers to recognize objects' motion better and thus to have an impression of the dynamic. In this paper, we propose a novel painterly rendering algorithm to express dynamic objects based on their motion information. We first extract motion information (magnitude, direction, standard deviation) of a scene from a set of consecutive image sequences from the same view. Then the motion directions are used for determining stroke orientations in the regions with significant motions, and image gradients determine stroke orientations where little motion is observed. Our algorithm is useful for realistically and dynamically representing moving objects. We have applied our algorithm for rendering landscapes. We could segment a scene into dynamic and static regions, and express the actual movement of dynamic objects using motion based strokes.     

Energy-Based Image Deformation

We present a general approach to shape deformation based on energy minimization, and applications of this approach to the problems of image resizing and 2D shape deformation. Our deformation energy generalizes that found in the prior art, while still admitting an efficient algorithm for its optimization. The key advantage of our energy function is the flexibility with which the set of "legal transformations" may be expressed; these transformations are the ones which are not considered to be distorting. This flexibility allows us to pose the problems of image resizing and 2D shape deformation in a natural way and generate minimally distorted results. It also allows us to strongly reduce undesirable foldovers or self-intersections. Results of both algorithms demonstrate the effectiveness of our approach.     

Isotropic Remeshing with Fast and Exact Computation of Restricted Voronoi Diagram

We propose a new isotropic remeshing method, based on Centroidal Voronoi Tessellation (CVT) . Constructing CVT requires to repeatedly compute Restricted Voronoi Diagram (RVD) , defined as the intersection between a 3D Voronoi diagram and an input mesh surface. Existing methods use some approximations of RVD. In this paper, we introduce an efficient algorithm that computes RVD exactly and robustly. As a consequence, we achieve better remeshing quality than approximation-based approaches, without sacrificing efficiency. Our method for RVD computation uses a simple procedure and a kd -tree to quickly identify and compute the intersection of each triangle face with its incident Voronoi cells. Its time complexity is O ( m log n ), where n is the number of seed points and m is the number of triangles of the input mesh. Fast convergence of CVT is achieved using a quasi-Newton method, which proved much faster than Lloyd's iteration. Examples are presented to demonstrate the better quality of remeshing results with our method than with the state-of-art approaches.     

Object-Space Visibility Ordering for Point-Based and Volume Rendering

This paper presents a method to accelerate algorithms that need a correct and complete visibility ordering of their data for rendering. The technique works by pre‐sorting primitives in object‐space using three lists (one for each axis: X, Y and Z), and then combining the lists using graphics hardware by rendering each list to a texture and merging the textures in the end. We validate our algorithm by applying it to the splatting technique using several types of rendering, including point‐based rendering and volume rendering. We also detail our hardware implementation for volume rendering using point sprites.     

Structure-Preserving Reshape for Textured Architectural Scenes

Modeling large architectural environments is a difficult task due to the intricate nature of these models and the complex dependencies between the structures represented. Moreover, textures are an essential part of architectural models. While the number of geometric primitives is usually relatively low (i.e., many walls are at surfaces), textures actually contain many detailed architectural elements.
We present an approach for modeling architectural scenes by reshaping and combining existing textured models, where the manipulation of the geometry and texture are tightly coupled. For geometry, preserving angles such as oor orientation or vertical walls is of key importance. We thus allow the user to interactively modify lengths of edges, while constraining angles. Our texture reshaping solution introduces a measure of directional autosimilarity to focus stretching in areas of stochastic content and to preserve details in such areas.
We show results on several challenging models, and show two applications: Building complex road structures from simple initial pieces and creating complex game-levels from an existing game based on pre-existing model pieces.     

Efficient and Adaptive Rendering of 2-D Continuous Scatterplots

We extend the rendering technique for continuous scatterplots to allow for a broad class of interpolation methods within the spatial grid instead of only linear interpolation. To do this, we propose an approach that projects the image of a cell from the spatial domain to the scatterplot domain. We approximate this image using either the convex hull or an axis-aligned rectangle that forms a tight fit of the projected points. In both cases, the approach relies on subdivision in the spatial domain to control the approximation error introduced in the scatterplot domain. Acceleration of this algorithm in homogeneous regions of the spatial domain is achieved using an octree hierarchy. The algorithm is scalable and adaptive since it allows us to balance computation time and scatterplot quality. We evaluate and discuss the results with respect to accuracy and computational speed. Our methods are applied to examples of 2-D transfer function design.     

Virtual Klingler Dissection: Putting Fibers into Context

Fiber tracking is a standard tool to estimate the course of major white matter tracts from diffusion tensor magnetic resonance imaging (DT‐MRI) data. In this work, we aim at supporting the visual analysis of classical streamlines from fiber tracking by integrating context from anatomical data, acquired by a T₁‐weighted MRI measurement. To this end, we suggest a novel visualization metaphor, which is based on data‐driven deformation of geometry and has been inspired by a technique for anatomical fiber preparation known as Klingler dissection. We demonstrate that our method conveys the relation between streamlines and surrounding anatomical features more effectively than standard techniques like slice images and direct volume rendering. The method works automatically, but its GPU‐based implementation allows for additional, intuitive interaction.     

Real‐Time Depth‐of‐Field Rendering Using Point Splatting on Per‐Pixel Layers

We present a real‐time method for rendering a depth‐of‐field effect based on the per‐pixel layered splatting where source pixels are scattered on one of the three layers of a destination pixel. In addition, the missing information behind foreground objects is filled with an additional image of the areas occluded by nearer objects. The method creates high‐quality depth‐of‐field results even in the presence of partial occlusion, without major artifacts often present in the previous real‐time methods. The method can also be applied to simulating defocused highlights. The entire framework is accelerated by GPU, enabling real‐time post‐processing for both off‐line and interactive applications.     

Precomputed Radiance Transfer Field for Rendering Interreflections in Dynamic Scenes

In this paper, we introduce a new representation – radiance transfer fields (RTF) – for rendering interreflections in dynamic scenes under low frequency illumination. The RTF describes the radiance transferred by an individual object to its surrounding space as a function of the incident radiance. An important property of RTF is its independence of the scene configuration, enabling interreflection computation in dynamic scenes. Secondly, RTFs naturally fit in with the rendering framework of precomputed shadow fields, incurring negligible cost to add interreflection effects. In addition, RTFs can be used to compute interreflections for both diffuse and glossy objects. We also show that RTF data can be highly compressed by clustered principal component analysis (CPCA), which not only reduces the memory cost but also accelerates rendering. Finally, we present some experimental results demonstrating our techniques.     

Visualization of Neural Network Predictions for Weather Forecasting

Recurrent neural networks are prime candidates for learning evolutions in multi‐dimensional time series data. The performance of such a network is judged by the loss function, which is aggregated into a scalar value that decreases during training. Observing only this number hides the variation that occurs within the typically large training and testing data sets. Understanding these variations is of highest importance to adjust network hyper‐parameters, such as the number of neurons, number of layers or to adjust the training set to include more representative examples. In this paper, we design a comprehensive and interactive system that allows users to study the output of recurrent neural networks on both the complete training data and testing data. We follow a coarse‐to‐fine strategy, providing overviews of annual, monthly and daily patterns in the time series and directly support a comparison of different hyper‐parameter settings. We applied our method to a recurrent convolutional neural network that was trained and tested on 25 years of climate data to forecast meteorological attributes, such as temperature, pressure and wind velocity. We further visualize the quality of the forecasting models, when applied to various locations on the Earth and we examine the combination of several forecasting models.     

Packet-based Hierarchal Soft Shadow Mapping

Recent soft shadow mapping techniques based on back-projection can render high quality soft shadows in real time. However, real time high quality rendering of large penumbrae is still challenging, especially when multilayer shadow maps are used to reduce single light sample silhouette artifact. In this paper, we present an efficient algorithm to attack this problem. We first present a GPU-friendly packet-based approach rendering a packet of neighboring pixels together to amortize the cost of computing visibility factors. Then, we propose a hierarchical technique to quickly locate the contour edges, further reducing the computation cost. At last, we suggest a multi-view shadow map approach to reduce the single light sample artifact. We also demonstrate its higher image quality and higher efficiency compared to the existing depth peeling approaches.