Abstract—In this paper, we present a framework for high quality splatting based on elliptical Gaussian kernels. To avoid aliasing artifacts, we introduce the concept of a resampling filter, combining a reconstruction kernel with a low-pass filter. Because of the similarity to Heckbert’s EWA (elliptical weighted average) filter for texture mapping, we call our technique EWA splatting. Our framework allows us to derive EWA splat primitives for volume data and for point-sampled surface data. It provides high image quality without aliasing artifacts or excessive blurring for volume data and, additionally, features anisotropic texture filtering for point-sampled surfaces. It also handles nonspherical volume kernels efficiently; hence, it is suitable for regular, rectilinear, and irregular volume datasets. Moreover, our framework introduces a novel approach to compute the footprint function, facilitating efficient perspective projection of arbitrary elliptical kernels at very little additional cost. Finally, we show that EWA volume reconstruction kernels can be reduced to surface reconstruction kernels. This makes our splat primitive universal in rendering surface and volume data. Index Terms—Rendering systems, volume rendering, texture mapping, splatting, antialiasing. 1

In this paper we present a novel framework for direct volume rendering using a splatting approach based on elliptical Gaussian kernels. To avoid aliasing artifacts, we introduce the concept of a resampling filter combining a reconstruction with a low-pass kernel. Because of the similarity to Heckbert’s EWA (elliptical weighted average) filter for texture mapping we call our technique EWA volume splatting. It provides high image quality without aliasing artifacts or excessive blurring even with non-spherical kernels. Hence it is suitable for regular, rectilinear, and irregular volume data sets. Moreover, our framework introduces a novel approach to compute the footprint function. It facilitates efficient perspective projection of arbitrary elliptical kernels at very little additional cost. Finally, we show that EWA volume reconstruction kernels can be reduced to surface reconstruction kernels. This makes our splat primitive universal in reconstructing surface and volume data.

We present a new technique which enables direct volume rendering based on 3D texture mapping hardware, enabling shading as well as classification of the interpolated data. Our technique supports accurate lighting for a one directional light source, semi-transparent classification, and correct blending. To circumvent the limitations of one general classification, we introduce multiple classification spaces which are very valuable to understand the visualized data, and even mandatory to comprehensively grasp the 3D relationship of different materials present in the volumetric data. Furthermore, we illustrate how multiple classification spaces can be realized using existing graphics hardware. In contrast to previously reported algorithms, our technique is capable of performing all the above mentioned tasks within the graphics pipeline. Therefore, it is very efficient: The three dimensional texture needs to be stored only once and no load is put onto the CPU. Besides using standard OpenGL...

This paper evaluates and compares four volume rendering algorithms that have become rather popular for rendering datasets described on uniform rectilinear grids: raycasting, splatting, shear-warp, and hardware-assisted 3D texture-mapping. In order to assess both the strengths and the weaknesses of these algorithms in a wide variety of scenarios, a set of real-life benchmark datasets with different characteristics was carefully selected. In the rendering, all algorithm-independent image synthesis parameters, such as viewing matrix, transfer functions, and optical model, were kept constant to enable a fair comparison of the rendering results. Both image quality and computational complexity were evaluated and compared, with the aim of providing both researchers and practitioners with guidelines on which algorithm is most suited in which scenario. Our analysis also indicates the current weaknesses in each algorithm’s pipeline, and possible solutions to these as well as pointers for future research are offered.

Many large scale physics-based simulations which take place on PC clusters or supercomputers produce huge amounts of data including vector fields. While these vector data such as electromagnetic fields, fluid flow fields, or particle paths can be represented by lines, the sheer number of the lines overwhelms the memory and computation capability of a high-end PC used for visualization. Further, very dense or intertwined lines, rendered with traditional visualization techniques, can produce unintelligible results with unclear depth relationships between the lines and no sense of global structure. Our approach is to apply a lighting model to the lines and sample them into an anisotropic voxel representation based on spherical harmonics as a preprocessing step. Then we evaluate and render these voxels for a given view using traditional volume rendering. For extremely large line based datasets, conversion to anisotropic voxels reduces the overall storage and rendering for O(n) lines to O(1) with a large constant that is still small enough to allow meaningful visualization of the entire dataset at nearly interactive rates on a single commodity PC.

In this paper we investigate the effects of function composition in the form g ( f(x)) = h(x) by means of a spectral analysis of h. We decompose the spectral description of h(x) into a scalar product of the spectral description of g(x) and a term that solely depends on f(x) and that is independent of g(x). We then use the method of stationary phase to derive the essential maximum frequency of g ( f(x)) bounding the main portion of the energy of its spectrum. This limit is the product of the maximum frequency of g(x) and the maximum derivative of f(x). This leads to a proper sampling of the composition h of the two functions g and f. We apply our theoretical results to a fundamental open problem in volume rendering—the proper sampling of the rendering integral after the application of a transfer function. In particular, we demonstrate how the sampling criterion can be incorporated in adaptive ray integration, visualization with multi-dimensional transfer functions, and pre-integrated volume rendering.

Volume visualization has become more and more important in modern world due to its wide applicability. Numerous techniques have been developed to render data sets in the form of regular grids (voxel data) and irregular grids. As the volume data sets grow bigger and bigger, data compression algorithms are required to reduce the disk storage size, and potentially the memory size during rendering as well. This paper surveys several techniques of volume visualization and volume compression, together with their integration or interaction. In general the strategies include: decompression the whole data set before rendering, on-the-fly rendering during decompression, on-the-fly decompression during rendering, and rendering in the compression domain.

For applications of volume visualization in medicine, it is important to assure that the 3D images show the true anatomical situation, or at least to know about their limitations. In this paper, various methods for evaluation of image quality are reviewed. They are classified based on the fundamental terms of intelligibility and fidelity, and discussed with respect to the question what clues they provide on how to choose parameters, or improve imaging and visualization procedures.

Shading and classification are among the most powerful and important techniques used in volume rendering. Unfortunately, for hardware accelerated volume rendering based on OpenGL, direct classification is only supported on SGI platforms and shading could previously only be approximated inaccurately, resulting in shading artifacts mostly visible in darkening artifacts. So far, the combination of classification and shading either required multi-pass rendering or two volumetric textures.

We developed a direct volume rendering technique, that supports low latency real time visual feedback in parallel with physical simulation on commodity graphics platforms. In our approach, a fast approximation of the diffuse shading equation is computed on the fly by the graphics pipe-line directly from the scalar data. We do this by exploiting the possibilities offered by multi-texturing with the register combiner OpenGL extension, that provides a configurable means to determine per-pixel fragment coloring. The effectiveness of our approach, that supports a full decoupling of simulation and rendering, is demonstrated in a training system for temporal bone surgery.