Residency Octree: A Hybrid Approach for Scalable Web-Based Multi-Volume Rendering.

We present a hybrid multi-volume rendering approach based on a novel Residency Octree that combines the advantages of out-of-core volume rendering using page tables with those of standard octrees. Octree approaches work by performing hierarchical tree traversal. However, in octree volume rendering, tree traversal and the selection of data resolution are intrinsically coupled.

Vortex Lens: Interactive Vortex Core Line Extraction using Observed Line Integral Convolution.

This paper describes a novel method for detecting and visualizing vortex structures in unsteady 2D fluid flows. The method is based on an interactive local reference frame estimation that minimizes the observed time derivative of the input flow field v(x, t). A locally optimal reference frame w(x, t) assists the user in the identification of physically observable vortex structures in Observed Line Integral Convolution (LIC) visualizations.

Ultraliser: a framework for creating multiscale, high-fidelity and geometrically realistic 3D models for in silico neuroscience.

Unlabelled: Ultraliser is a neuroscience-specific software framework capable of creating accurate and biologically realistic 3D models of complex neuroscientific structures at intracellular (e.g. mitochondria and endoplasmic reticula), cellular (e.

Multivariate Probabilistic Range Queries for Scalable Interactive 3D Visualization.

Large-scale scientific data, such as weather and climate simulations, often comprise a large number of attributes for each data sample, like temperature, pressure, humidity, and many more. Interactive visualization and analysis require filtering according to any desired combination of attributes, in particular logical AND operations, which is challenging for large data and many attributes. Many general data structures for this problem are built for and scale with a fixed number of attributes, and scalability of joint queries with arbitrary attribute subsets remains a significant problem.

Real-Time Visualization of Large-Scale Geological Models With Nonlinear Feature-Preserving Levels of Detail.

The rapidly growing size and complexity of 3D geological models has increased the need for level-of-detail techniques and compact encodings to facilitate interactive visualization. For large-scale hexahedral meshes, state-of-the-art approaches often employ wavelet schemes for level of detail as well as for data compression. Here, wavelet transforms serve two purposes: (1) they achieve substantial compression for data reduction; and (2) the multiresolution encoding provides levels of detail for visualization.

Interactive Exploration of Physically-Observable Objective Vortices in Unsteady 2D Flow.

State-of-the-art computation and visualization of vortices in unsteady fluid flow employ objective vortex criteria, which makes them independent of reference frames or observers. However, objectivity by itself, although crucial, is not sufficient to guarantee that one can identify physically-realizable observers that would perceive or detect the same vortices. Moreover, a significant challenge is that a single reference frame is often not sufficient to accurately observe multiple vortices that follow different motions.

Probabilistic Occlusion Culling using Confidence Maps for High-Quality Rendering of Large Particle Data.

Achieving high rendering quality in the visualization of large particle data, for example from large-scale molecular dynamics simulations, requires a significant amount of sub-pixel super-sampling, due to very high numbers of particles per pixel. Although it is impossible to super-sample all particles of large-scale data at interactive rates, efficient occlusion culling can decouple the overall data size from a high effective sampling rate of visible particles. However, while the latter is essential for domain scientists to be able to see important data features, performing occlusion culling by sampling or sorting the data is usually slow or error-prone due to visibility estimates of insufficient quality.

Objective Observer-Relative Flow Visualization in Curved Spaces for Unsteady 2D Geophysical Flows.

Computing and visualizing features in fluid flow often depends on the observer, or reference frame, relative to which the input velocity field is given. A desired property of feature detectors is therefore that they are objective, meaning independent of the input reference frame. However, the standard definition of objectivity is only given for Euclidean domains and cannot be applied in curved spaces.

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells.

Serial sectioning and subsequent high-resolution imaging of biological tissue using electron microscopy (EM) allow for the segmentation and reconstruction of high-resolution imaged stacks to reveal ultrastructural patterns that could not be resolved using 2D images. Indeed, the latter might lead to a misinterpretation of morphologies, like in the case of mitochondria; the use of 3D models is, therefore, more and more common and applied to the formulation of morphology-based functional hypotheses. To date, the use of 3D models generated from light or electron image stacks makes qualitative, visual assessments, as well as quantification, more convenient to be performed directly in 3D.

3D cellular reconstruction of cortical glia and parenchymal morphometric analysis from Serial Block-Face Electron Microscopy of juvenile rat.

With the rapid evolution in the automation of serial electron microscopy in life sciences, the acquisition of terabyte-sized datasets is becoming increasingly common. High resolution serial block-face imaging (SBEM) of biological tissues offers the opportunity to segment and reconstruct nanoscale structures to reveal spatial features previously inaccessible with simple, single section, two-dimensional images. In particular, we focussed here on glial cells, whose reconstruction efforts in literature are still limited, compared to neurons.

A Process for Digitizing and Simulating Biologically Realistic Oligocellular Networks Demonstrated for the Neuro-Glio-Vascular Ensemble.

One will not understand the brain without an integrated exploration of structure and function, these attributes being two sides of the same coin: together they form the currency of biological computation. Accordingly, biologically realistic models require the re-creation of the architecture of the cellular components in which biochemical reactions are contained. We describe here a process of reconstructing a functional oligocellular assembly that is responsible for energy supply management in the brain and creating a computational model of the associated biochemical and biophysical processes.

Culling for Extreme-Scale Segmentation Volumes: A Hybrid Deterministic and Probabilistic Approach.

With the rapid increase in raw volume data sizes, such as terabyte-sized microscopy volumes, the corresponding segmentation label volumes have become extremely large as well. We focus on integer label data, whose efficient representation in memory, as well as fast random data access, pose an even greater challenge than the raw image data. Often, it is crucial to be able to rapidly identify which segments are located where, whether for empty space skipping for fast rendering, or for spatial proximity queries.

Time-Dependent Flow seen through Approximate Observer Killing Fields.

Flow fields are usually visualized relative to a global observer, i.e., a single frame of reference.

Abstractocyte: A Visual Tool for Exploring Nanoscale Astroglial Cells.

This paper presents Abstractocyte, a system for the visual analysis of astrocytes and their relation to neurons, in nanoscale volumes of brain tissue. Astrocytes are glial cells, i.e.

SparseLeap: Efficient Empty Space Skipping for Large-Scale Volume Rendering.

Recent advances in data acquisition produce volume data of very high resolution and large size, such as terabyte-sized microscopy volumes. These data often contain many fine and intricate structures, which pose huge challenges for volume rendering, and make it particularly important to efficiently skip empty space. This paper addresses two major challenges: (1) The complexity of large volumes containing fine structures often leads to highly fragmented space subdivisions that make empty regions hard to skip efficiently.

Screen-Space Normal Distribution Function Caching for Consistent Multi-Resolution Rendering of Large Particle Data.

Molecular dynamics (MD) simulations are crucial to investigating important processes in physics and thermodynamics. The simulated atoms are usually visualized as hard spheres with Phong shading, where individual particles and their local density can be perceived well in close-up views. However, for large-scale simulations with 10 million particles or more, the visualization of large fields-of-view usually suffers from strong aliasing artifacts, because the mismatch between data size and output resolution leads to severe under-sampling of the geometry.

JiTTree: A Just-in-Time Compiled Sparse GPU Volume Data Structure.

Sparse volume data structures enable the efficient representation of large but sparse volumes in GPU memory for computation and visualization. However, the choice of a specific data structure for a given data set depends on several factors, such as the memory budget, the sparsity of the data, and data access patterns. In general, there is no single optimal sparse data structure, but a set of several candidates with individual strengths and drawbacks.

NeuroBlocks--Visual Tracking of Segmentation and Proofreading for Large Connectomics Projects.

In the field of connectomics, neuroscientists acquire electron microscopy volumes at nanometer resolution in order to reconstruct a detailed wiring diagram of the neurons in the brain. The resulting image volumes, which often are hundreds of terabytes in size, need to be segmented to identify cell boundaries, synapses, and important cell organelles. However, the segmentation process of a single volume is very complex, time-intensive, and usually performed using a diverse set of tools and many users.

Ovis: A Framework for Visual Analysis of Ocean Forecast Ensembles.

We present a novel integrated visualization system that enables interactive visual analysis of ensemble simulations of the sea surface height that is used in ocean forecasting. The position of eddies can be derived directly from the sea surface height and our visualization approach enables their interactive exploration and analysis.The behavior of eddies is important in different application settings of which we present two in this paper.

ViSlang: A System for Interpreted Domain-Specific Languages for Scientific Visualization.

Researchers from many domains use scientific visualization in their daily practice. Existing implementations of algorithms usually come with a graphical user interface (high-level interface), or as software library or source code (low-level interface). In this paper we present a system that integrates domain-specific languages (DSLs) and facilitates the creation of new DSLs.

NeuroLines: A Subway Map Metaphor for Visualizing Nanoscale Neuronal Connectivity.

We present NeuroLines, a novel visualization technique designed for scalable detailed analysis of neuronal connectivity at the nanoscale level. The topology of 3D brain tissue data is abstracted into a multi-scale, relative distance-preserving subway map visualization that allows domain scientists to conduct an interactive analysis of neurons and their connectivity. Nanoscale connectomics aims at reverse-engineering the wiring of the brain.

Sparse PDF Volumes for Consistent Multi-Resolution Volume Rendering.

This paper presents a new multi-resolution volume representation called sparse pdf volumes, which enables consistent multi-resolution volume rendering based on probability density functions (pdfs) of voxel neighborhoods. These pdfs are defined in the 4D domain jointly comprising the 3D volume and its 1D intensity range. Crucially, the computation of sparse pdf volumes exploits data coherence in 4D, resulting in a sparse representation with surprisingly low storage requirements.

Exploring the connectome: petascale volume visualization of microscopy data streams.

Recent advances in high-resolution microscopy let neuroscientists acquire neural-tissue volume data of extremely large sizes. However, the tremendous resolution and the high complexity of neural structures present big challenges to storage, processing, and visualization at interactive rates. A proposed system provides interactive exploration of petascale (petavoxel) volumes resulting from high-throughput electron microscopy data streams.

ConnectomeExplorer: query-guided visual analysis of large volumetric neuroscience data.

This paper presents ConnectomeExplorer, an application for the interactive exploration and query-guided visual analysis of large volumetric electron microscopy (EM) data sets in connectomics research. Our system incorporates a knowledge-based query algebra that supports the interactive specification of dynamically evaluated queries, which enable neuroscientists to pose and answer domain-specific questions in an intuitive manner. Queries are built step by step in a visual query builder, building more complex queries from combinations of simpler queries.

Interactive volume visualization of general polyhedral grids.

This paper presents a novel framework for visualizing volumetric data specified on complex polyhedral grids, without the need to perform any kind of a priori tetrahedralization. These grids are composed of polyhedra that often are non-convex and have an arbitrary number of faces, where the faces can be non-planar with an arbitrary number of vertices. The importance of such grids in state-of-the-art simulation packages is increasing rapidly.