A Multi-Resolution Interactive Previewer for Volumetric Data on Arbitary Meshes

In this paper we describe a rendering method suitable for interactive previewing of large-scale arbitary-mesh volume data sets. A data set to be visualized is represented by a ''point cloud,'' i. e., a set of points and associated data values without known connectivity between the points. The method uses a multi-resolution approach to achieve interactive rendering rates of several frames per second for arbitrarily large data sets. Lower-resolution approximations of an original data set are created by iteratively applying a point- decimation operation to higher-resolution levels. The goal of this method is to provide the user with an interactive navigation and exploration tool to determine good viewpoints and transfer functions to pass on to a high-quality volume renderer that uses a standard algorithm

Real-time Terrain Mapping

We present an interactive, real-time mapping system for digital elevation maps (DEMs), which allows Earth scientists to map and therefore understand the deformation of the continental crust at length scales of 10m to 1000km. Our system visualizes the surface of the Earth as a 3D~surface generated from a DEM, with a color texture generated from a registered multispectral image and vector-based mapping elements draped over it. We use a quadtree-based multiresolution method to be able to render high-resolution terrain mapping data sets of large spatial regions in real time. The main strength of our system is the combination of interactive rendering and interactive mapping directly onto the 3D~surface, with the ability to navigate the terrain and to change viewpoints arbitrarily during mapping. User studies and comparisons with commercially available mapping software show that our system improves mapping accuracy and efficiency, and also enables qualitatively different observations that are not possible to make with existing systems

Deploying Web-based Visual Exploration Tools on the Grid

We discuss a web-based portal for the exploration, encapsulation, and dissemination of visualization results over the Grid. This portal integrates three components: an interface client for structured visualization exploration, a visualization web application to manage the generation and capture of the visualization results, and a centralized portal application server to access and manage grid resources. Our approach uses standard web technologies to make the system accessible with minimal user setup. We demonstrate the usefulness of the developed system using an example for Adaptive Mesh Refinement (AMR) data visualization

Understanding Microbialite Morphology Using a Comprehensive Suite of Three-Dimensional Analysis Tools

Abstract Microbialites can have complex morphologies that preserve clues to ancient microbial ecology. However, extracting and interpreting these clues is challenging due to both the complexity of microbial structures and the difficulties of connecting morphology to microbial processes. Fenestrate microbialites from the 2521 -3 Ma Gamohaan Formation, South Africa, have intricate structures composed of three distinct microbial structures: steeply dipping supports (surfaces defined by organic inclusions), more shallowly dipping supports with diffuse organic inclusions below them, and draping laminae. In polished slabs, shallowly dipping supports with diffuse organic inclusions show apparent dips from 27°to 60°, and supports without associated zones of diffuse inclusions dip 75°to 88°, which suggests a distinction between support types based on orientation. However, dips exposed in polished slabs are apparent dips, and three-dimensional analysis is required for analysis of true dips. Through the Keck Center for Active Visualization in Earth Sciences (KeckCAVES), we used locally developed software that controls a three-dimensional environment with head and hand tracking (an ''immersive environment'') to visualize and interpret virtual microbialite data sets. Immersive environments have not penetrated into standard scientific work processes (''workflows'') due to their high costs, steep learning curves, and low productivity for users. By contrast, our suite of software tools allowed us to develop a personalized scientific workflow that provides a complete path from initial ideas to characterization of fenestrate microbialites' features. Results of three-dimensional analysis of fenestrate microbialites show that supports with inclusions dip 65°to 75°, whereas supports without inclusions dip 85°to 90°. These results demonstrate that all supports have very steep dips, and a 10°dip gap exists between supports with and without inclusions, which suggests they grew in fundamentally different ways. Results also emphasize how valuable three-dimensional analysis is when combined with a comprehensive workflow for understanding intricate structures such as fenestrate microbialites

The Decentralized Visualization Facility: Local and Remote Collaboration Using Commodity VR Hardware

What, then, will be the reason for continued support of costly visualization facilities? We believe that the future of central facilities lies in the concept of “visualization as a service:” in addition to, or instead of, offering users a central place where they can use shared equipment to work with their own data, facilities will make their expertise, software, and support available to remote users who then work with their own data on their own equipment. In addition, facilities will provide central services such as shared data repositories and persistent shared virtual spaces where multiple users can collaboratively analyze shared data. For example, a visualization facility might contain a single CAVE or display wall. One or more users in that central system may then work with one or more remote users in their own respective commodity VR systems. Or, a visualization facility might consist of a “VR room” with multiple independent VR headsets, and let multiple users collaborate in a space that is shared both physically and virtually. Or, users in visualization facilities of heterogeneous types at multiple institutions may work together, or any combination of the above. To support these use cases, KeckCAVES has been developing a foundational software framework, the Vrui Collaboration Infrastructure (VCI), for local and/or remote collaboration between multiple independent VR systems, including centralized high-end systems such as CAVEs or display walls and shared or individually owned and operated commodity head-mounted VR systems. The core principle of VCI is the creation of shared virtual spaces where users are represented as animated avatars or through live 2D/3D video, and can interact with other users via text chat, spatial annotations, and 3D spatial audio. Unlike many other shared virtual spaces, VCI is not a closed application, meaning its underlying collaboration protocol can be seamlessly extended via custom plugin protocols providing application-specific functionality. This not only makes it possible to develop new and highly interactive collaborative visualization applications, but also to extend existing single-user applications to multiple users. Examples include a shared application to view and analyze ultra-high resolution 3D scanning data; a shared application to analyze 3D volumetric data such as 3D medical images; a shared application to view and analyze results from large-scale molecular dynamics simulations; and a shared 3D network viewer

On Simulated Annealing and the Construction of Linear Spline Approximations for Scattered Data

We describe a method to create optimal linear spline approximations to arbitrary functions of one or two variables, given as scattered data without known connectivity. We start with an initial approximation consisting of a fixed number of vertices and improve this approximation by choosing different vertices, governed by a simulated annealing algorithm. In the case of one variable, the approximation is defined by line segments; in the case of two variables, the vertices are connected to define a Delaunay triangulation of the selected subset of sites in the plane. In a second version of this algorithm, specifically designed for the bivariate case, we choose vertex sets and also change the triangulation to achieve both optimal vertex placement and optimal triangulation. We then create a hierarchy of linear spline approximations, each one being a superset of all lower-resolution ones

Data Structures for optimizing linear spline Approximations

We describe the algorithms and data structures used for optimizing linear spline approximations of bivariate functions. Our method creates a random initial triangulation of a given data set and then employs a simulated annealing algorithm to improve this initial approximation. In every iteration step, the current approximation is changed in a random but local way, and the distance measure between it and the data is re-calculated. Depending on the difference between the old and new distance measures, an iteration step is either accepted or rejected.We discuss the basic operations and data structures of our optimization technique. We present a variant of the half-edge data structure and associated algorithms