Fig. 1. Sample rendering of a plume vector field dataset using our streamline selection technique. Our technique is able to depict the interesting data features in a view-dependent fashion while avoiding self-occlusion from the streamlines, and does not require any user intervention.Abstract-This paper introduces a new streamline placement and selection algorithm for 3D vector fields. Instead of considering the problem as a simple feature search in data space, we base our work on the observation that most streamline fields generate a lot of self-occlusion which prevents proper visualization. In order to avoid this issue, we approach the problem in a view-dependent fashion and dynamically determine a set of streamlines which contributes to data understanding without cluttering the view. Since our technique couples flow characteristic criteria and view-dependent streamline selection we are able achieve the best of both worlds: relevant flow description and intelligible, uncluttered pictures. We detail an efficient GPU implementation of our algorithm, show comprehensive visual results on multiple datasets and compare our method with existing flow depiction techniques. Our results show that our technique greatly improves the readability of streamline visualizations on different datasets without requiring user intervention.

Fig. 1. Close-up of an AMR dataset showing a meteorite falling into the sea rendered using our system.Abstract-This paper presents a pipeline for high quality volume rendering of adaptive mesh refinement (AMR) datasets. We introduce a new method allowing high quality visualization of hexahedral cells in this context; this method avoids artifacts like discontinuities in the isosurfaces. To achieve this, we choose the number and placement of sampling points over the cast rays according to the analytical properties of the reconstructed signal inside each cell. We extend our method to handle volume shading of such cells. We propose an interpolation scheme that guarantees continuity between adjacent cells of different AMR levels. We introduce an efficient hybrid CPU-GPU mesh traversal technique. We present an implementation of our AMR visualization method on current graphics hardware, and show results demonstrating both the quality and performance of our method.

In the field of Volume Rendering, pre-integration techniques for arbitrary transfer functions has certainly led to the most significant and convincing results both quality and performance wise on standard PC consumer graphics. By showing that the ideal scalar signal along the cast rays is better approximated by a succession of polynomial curves as opposed to linear segments, we propose a new method for pre-integrated volume rendering. This method is based on a second order polynomial interpolation of the scalar values, allowing it to converge more rapidly towards the integration of a volume reconstructed by a trilinear filter. This approach manages to capture the smoothness of the volume's details without the need of further ray resampling, and consequently succeeds in reducing the visual artefacts in comparison to previous techniques. Futhermore, we adapt an existing technique to compute our pre-integration tables using the GPU, thus making our approach suitable for transfer function manipulations.

Classical direct volume rendering techniques accumulate color and opacity contributions using the standard volume rendering equation approximated by alpha blending. However, such standard rendering techniques, often also aiming at visual realism, are not always adequate for efficient data exploration, especially when large opaque areas are present in a data set, since such areas can occlude important features and make them invisible. On the other hand, the use of highly transparent transfer functions allows viewing all the features at once, but often makes these features barely visible. In order to enhance feature visibility, we present in this paper a straightforward rendering technique that consists of modifying the traditional volume rendering equation. Our approach does not require an opacity transfer function, and instead is based on a function quantifying the relative importance of each voxel in the final rendering called relevance function. This function is subsequently used to dynamically adjust the opacity of the contributions per pixel. We conduct experiments with a number of possible relevance functions in order to show the influence of this parameter. As will be shown by our comparative study, our rendering method is much more suitable than standard volume rendering for interactive data exploration at a low extra cost. Thereby, our method avoids feature visibility restrictions without relying on a transfer function and yet maintains a visual similarity with standard volume rendering.