Pre-integrated volume rendering with non-linear gradient interpolation.

Shading is an important feature for the comprehension of volume datasets, but is difficult to implement accurately. Current techniques based on pre-integrated direct volume rendering approximate the volume rendering integral by ignoring non-linear gradient variations between front and back samples, which might result in cumulated shading errors when gradient variations are important and / or when the illumination function features high frequencies. In this paper, we explore a simple approach for pre-integrated volume rendering with non-linear gradient interpolation between front and back samples. We consider that the gradient smoothly varies along a quadratic curve instead of a segment in-between consecutive samples. This not only allows us to compute more accurate shaded pre-integrated look-up tables, but also allows us to more efficiently process shading amplifying effects, based on gradient filtering. An interesting property is that the pre-integration tables we use remain two-dimensional as for usual pre-integrated classification. We conduct experiments using a full hardware approach with the Blinn-Phong illumination model as well as with a non-photorealistic illumination model.

View-dependent streamlines for 3D vector fields.

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.

Per-pixel opacity modulation for feature enhancement in volume rendering.

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.

High-quality, semi-analytical volume rendering for AMR data.

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.