We present a parallel visualization algorithm for the illustrative rendering of depth-dependent stylized dense tube data at interactive frame rates. While this computation could be efficiently performed on a GPU device, we target a parallel framework to enable it to be efficiently running on an ordinary multi-core CPU platform which is much more available than GPUs for common users. Our approach is to map the depth information in each tube onto each of the visual dimensions of shape, color, texture, value, and size on the basis of Bertin's semiology theory. The purpose is to enable more legible displays in the dense tube environments. A major contribution of our work is an efficient and effective parallel depthordering algorithm that makes use of the message passing interface (MPI) with VTK. We evaluated our framework with visualizations of depth-stylized tubes derived from 3D diffusion tensor MRI data by comparing its efficiency with several other alternative parallelization platforms running the same computations. As our results show, the parallelization framework we proposed can efficiently render highly dense 3D data sets like the tube data and thus is useful as a complement to parallel visualization environments that rely on GPUs.
Even direct volume rendering techniques often suffer from poor depth cues because the data sets commonly have a large number of overlapping structures. With MIP (maximum intensity projection) rendering @cite_24 , however, only few effort is required to create a good understanding of the structures represented by high signal intensities. This algorithm adds two different visual cues, occlusion revealing and depth based color. In the first one, they modify the MIP color in the presence of occluding objects with the same materials than the one at the point of maximum intensity while in other the actual position of the shaded fragment is used to change its color using a supporting spherical map. In this paper, we explore depth enhancement in dense geometry visualizations by encoding depth information with various visual variables.
@cite_24
1591049691 15713842 2158707602 2166103081
The two most common methods for the visualization of volumetric data are Direct Volume Rendering (DVR) and Maximum Intensity Projection (MIP). Direct Volume Rendering is superior to MIP in providing a larger amount of properly shaded details, because it employs a more complex shading model together with the use of user-defined transfer functions. However, the generation of adequate transfer functions is a laborious and time costly task, even for expert users. As a consequence, medical doctors often use MIP because it does not require the definition of complex transfer functions and because it gives good results on contrasted images. Unfortunately, MIP does not allow to perceive depth ordering and therefore spatial context is lost. In this paper we present a new approach to MIP rendering that uses depth and simple color blending to disambiguate the ordering of internal structures, while maintaining most of the details visible through MIP. It is usually faster than DVR and only requires the transfer function used by MIP rendering. In this paper, we propose to conduct inpainting and upsampling for defective depth maps when aligned color images are given. These tasks are referred to as the guided depth enhancement problem. We formulate the problem based on the heat diffusion framework. The pixels with known depth values are treated as the heat sources and the depth enhancement is performed via diffusing the depth from these sources to unknown regions. The diffusion conductivity is designed in terms of the guidance color image so that a linear anisotropic diffusion problem is formed. We further cast the steady state problem of this diffusion into the famous random walk model, by which the enhancement is achieved efficiently by solving a sparse linear system. The proposed algorithm is quantitatively evaluated on the Middlebury stereo dataset and is applied to inpaint Kinect data and upsample Lidar's range data. Comparisons to the commonly used bilateral filter and Markov Random Field based methods are also presented, showing that our algorithm is competent. Volumetric data commonly has high depth complexity which makes it difficult to judge spatial relationships accurately. There are many different ways to enhance depth perception, such as shading, contours, and shadows. Artists and illustrators frequently employ halos for this purpose. In this technique, regions surrounding the edges of certain structures are darkened or brightened which makes it easier to judge occlusion. Based on this concept, we present a flexible method for enhancing and highlighting structures of interest using GPU-based direct volume rendering. Our approach uses an interactively defined halo transfer function to classify structures of interest based on data value, direction, and position. A feature-preserving spreading algorithm is applied to distribute seed values to neighboring locations, generating a controllably smooth field of halo intensities. These halo intensities are then mapped to colors and opacities using a halo profile function. Our method can be used to annotate features at interactive frame rates. We propose a new approach to efficiently render large volumetric data sets. The system achieves interactive to real-time rendering performance for several billion voxels. Our solution is based on an adaptive data representation depending on the current view and occlusion information, coupled to an efficient ray-casting rendering algorithm. One key element of our method is to guide data production and streaming directly based on information extracted during rendering. Our data structure exploits the fact that in CG scenes, details are often concentrated on the interface between free space and clusters of density and shows that volumetric models might become a valuable alternative as a rendering primitive for real-time applications. In this spirit, we allow a quality performance trade-off and exploit temporal coherence. We also introduce a mipmapping-like process that allows for an increased display rate and better quality through high quality filtering. To further enrich the data set, we create additional details through a variety of procedural methods. We demonstrate our approach in several scenarios, like the exploration of a 3D scan (81923 resolution), of hypertextured meshes (163843 virtual resolution), or of a fractal (theoretically infinite resolution). All examples are rendered on current generation hardware at 20--90 fps and respect the limited GPU memory budget.