Accelerated isosurface extraction in time-varying fields

Journal ArticleFor large time-varying data sets, memory and disk limitations can lower the performance of visualization applications. Algorithms and data structures must be explicitly designed to handle these data sets in order to achieve more interactive rates. The Temporal Branch-on-Need Octree (T-BON) extends the three-dimensional branch-on-need octree for time-varying isosurface extraction. This data structure minimizes the impact of the I/O bottleneck by reading from disk only those portions of the search structure and data necessary to construct the current isosurface

A Novel Information-Aware Octree for the Visualization of Large Scale Time-Varying Data

Large scale scientific simulations are increasingly generating very large data sets that present substantial challenges to current visualization systems. In this paper, we develop a new scalable and efficient scheme for the visual exploration of 4-D isosurfaces of time varying data by rendering the 3-D isosurfaces obtained through an arbitrary axis-parallel hyperplane cut. The new scheme is based on: (i) a new 4-D hierarchical indexing structure, called Information-Aware Octree; (ii) a controllable delayed fetching technique; and (iii) an optimized data layout. Together, these techniques enable efficient and scalable out-of-core visualization of large scale time varying data sets. We introduce an entropy-based dimension integration technique by which the relative resolutions of the spatial and temporal dimensions are established, and use this information to design a compact size 4-D hierarchical indexing structure. We also present scalable and efficient techniques for out-of-core rendering. Compared with previous algorithms for constructing 4-D isosurfaces, our scheme is substantially faster and requires much less memory. Compared to the Temporal Branch-On-Need octree (T-BON), which can only handle a subset of our queries, our indexing structure is an order of magnitude smaller and is at least as effective in dealing with the queries that the T-BON can handle. We have tested our scheme on two large time-varying data sets and obtained very good performance for a wide range of isosurface extraction queries using an order of magnitude smaller indexing structures than previous techniques. In particular, we can generate isosurfaces at intermediate time steps very quickly

Frame-to-frame coherent image-aligned sheet-buffered splatting

Splatting is a classical volume rendering technique that has recently gained in popularity for the visualization of point-based suface models. Up to now, there has been few publications on its adaptation to time-varying data. In this paper, we propose a novel frame-to-frame coherent view-aligned sheet-buffer splatting of time-varying data, that tries to reduce as much as possible the memory load and the rendering computations taking into account the similarity in the data and in the images at successive instants of time. The results presented in the paper are encouraging and show that the proposed technique may be useful to explore data through time.Postprint (published version

Time-varying volume visualization

Volume rendering is a very active research field in Computer Graphics because of its wide range of applications in various sciences, from medicine to flow mechanics. In this report, we survey a state-of-the-art on time-varying volume rendering. We state several basic concepts and then we establish several criteria to classify the studied works: IVR versus DVR, 4D versus 3D+time, compression techniques, involved architectures, use of parallelism and image-space versus object-space coherence. We also address other related problems as transfer functions and 2D cross-sections computation of time-varying volume data. All the papers reviewed are classified into several tables based on the mentioned classification and, finally, several conclusions are presented.Preprin

Synergistic Visualization And Quantitative Analysis Of Volumetric Medical Images

The medical diagnosis process starts with an interview with the patient, and continues with the physical exam. In practice, the medical professional may require additional screenings to precisely diagnose. Medical imaging is one of the most frequently used non-invasive screening methods to acquire insight of human body. Medical imaging is not only essential for accurate diagnosis, but also it can enable early prevention. Medical data visualization refers to projecting the medical data into a human understandable format at mediums such as 2D or head-mounted displays without causing any interpretation which may lead to clinical intervention. In contrast to the medical visualization, quantification refers to extracting the information in the medical scan to enable the clinicians to make fast and accurate decisions. Despite the extraordinary process both in medical visualization and quantitative radiology, efforts to improve these two complementary fields are often performed independently and synergistic combination is under-studied. Existing image-based software platforms mostly fail to be used in routine clinics due to lack of a unified strategy that guides clinicians both visually and quan- titatively. Hence, there is an urgent need for a bridge connecting the medical visualization and automatic quantification algorithms in the same software platform. In this thesis, we aim to fill this research gap by visualizing medical images interactively from anywhere, and performing a fast, accurate and fully-automatic quantification of the medical imaging data. To end this, we propose several innovative and novel methods. Specifically, we solve the following sub-problems of the ul- timate goal: (1) direct web-based out-of-core volume rendering, (2) robust, accurate, and efficient learning based algorithms to segment highly pathological medical data, (3) automatic landmark- ing for aiding diagnosis and surgical planning and (4) novel artificial intelligence algorithms to determine the sufficient and necessary data to derive large-scale problems

Extracting Iso-valued Features in 4-dimensional Scalar Fields

Isosurfaces are an important tool for finding features in 3D scalar data. This paper describes how recursive contour meshing is applied to extract similar features in 4-dimensional space. In the case of time-varying isosurfaces f(x, y,,z, t) = c, the technique constructs a solid mesh for the isosurface that sweeps a volume in space-time. An instance of an isosurface at a particular time results from applying a second constraint against this volume. The envelope defined by the time-varying isosurface can be captured in a similar way: when a time-varying isosurface f=c reaches is maximum extent, the function’s partial derivative with respect to time must be zero. This second constraint and produces a surface containing the extrema of the isosurfaces. Multi-resolution models and inter-penetrating blobby objects and can also be extracted from 4-dimensional representations.

Efficient rendering of large 3-D and 4-D scalar fields

Rendering volumetric data, as a compute/communication intensive and highly parallel application, represents the characteristics of future workloads for desktop computers. Interactively rendering volumetric data has been a challenging problem due to its high computational and communication requirements. With the consistent trend toward high resolution data, it has remained a difficult problem despite the continuous increase in processing power, because of the increasing performance gap between computation and communication. On the other hand, the new multi-core architecture trend in computational units in PC, which can be characterized by parallelism and heterogeneity, provides both opportunities and challenges. While the new on-chip parallel architectures offer opportunities for extremely high performance, widespread use of those parallel processors requires extensive changes in previous algorithms to take advantage of the new architectures. In this dissertation, we develop new methods and techniques to support interactive rendering of large volumetric data. In particular, we present a novel method to layout data on disk for efficiently performing an out-of-core axis-aligned slicing of large multidimensional scalar fields. We also present a new method to efficiently build an out-of-core indexing structure for n-dimensional volumetric data. Then, we describe a streaming model for efficiently implementing volume ray casting on a heterogeneous compute resource environment. We describe how we implement the model on SONY/TOSHIBA/IBM Cell Broadband Engine and on NVIDIA CUDA architecture. Our results show that our out-of-core techniques significantly reduce the communication bandwidth requirements and that our streaming model very effectively makes use of the strengths of those heterogeneous parallel compute resource environment for volume rendering. In all cases, we achieve scalability and load balancing, while hiding memory latency