Adaptive transfer functions: improved multiresolution visualization of medical models

The final publication is available at Springer via http://dx.doi.org/10.1007/s00371-016-1253-9Medical datasets are continuously increasing in size. Although larger models may be available for certain research purposes, in the common clinical practice the models are usually of up to 512x512x2000 voxels. These resolutions exceed the capabilities of conventional GPUs, the ones usually found in the medical doctors’ desktop PCs. Commercial solutions typically reduce the data by downsampling the dataset iteratively until it fits the available target specifications. The data loss reduces the visualization quality and this is not commonly compensated with other actions that might alleviate its effects. In this paper, we propose adaptive transfer functions, an algorithm that improves the transfer function in downsampled multiresolution models so that the quality of renderings is highly improved. The technique is simple and lightweight, and it is suitable, not only to visualize huge models that would not fit in a GPU, but also to render not-so-large models in mobile GPUs, which are less capable than their desktop counterparts. Moreover, it can also be used to accelerate rendering frame rates using lower levels of the multiresolution hierarchy while still maintaining high-quality results in a focus and context approach. We also show an evaluation of these results based on perceptual metrics.Peer ReviewedPostprint (author's final draft

Volume visualization of time-varying data using parallel, multiresolution and adaptive-resolution techniques

This paper presents a parallel rendering approach that allows high-quality visualization of large time-varying volume datasets. Multiresolution and adaptive-resolution techniques are also incorporated to improve the efficiency of the rendering. Three basic steps are needed to implement this kind of an application. First we divide the task through decomposition of data. This decomposition can be either temporal or spatial or a mix of both. After data has been divided, each of the data portions is rendered by a separate processor to create sub-images or frames. Finally these sub-images or frames are assembled together into a final image or animation. After developing this application, several experiments were performed to show that this approach indeed saves time when a reasonable number of processors are used. Also, we conclude that the optimal number of processors is dependent on the size of the dataset used

Interactive inspection of complex multi-object industrial assemblies

The final publication is available at Springer via http://dx.doi.org/10.1016/j.cad.2016.06.005The use of virtual prototypes and digital models containing thousands of individual objects is commonplace in complex industrial applications like the cooperative design of huge ships. Designers are interested in selecting and editing specific sets of objects during the interactive inspection sessions. This is however not supported by standard visualization systems for huge models. In this paper we discuss in detail the concept of rendering front in multiresolution trees, their properties and the algorithms that construct the hierarchy and efficiently render it, applied to very complex CAD models, so that the model structure and the identities of objects are preserved. We also propose an algorithm for the interactive inspection of huge models which uses a rendering budget and supports selection of individual objects and sets of objects, displacement of the selected objects and real-time collision detection during these displacements. Our solution–based on the analysis of several existing view-dependent visualization schemes–uses a Hybrid Multiresolution Tree that mixes layers of exact geometry, simplified models and impostors, together with a time-critical, view-dependent algorithm and a Constrained Front. The algorithm has been successfully tested in real industrial environments; the models involved are presented and discussed in the paper.Peer ReviewedPostprint (author's final draft

Locally adaptive image denoising by a statistical multiresolution criterion

We demonstrate how one can choose the smoothing parameter in image denoising by a statistical multiresolution criterion, both globally and locally. Using inhomogeneous diffusion and total variation regularization as examples for localized regularization schemes, we present an efficient method for locally adaptive image denoising. As expected, the smoothing parameter serves as an edge detector in this framework. Numerical examples illustrate the usefulness of our approach. We also present an application in confocal microscopy

VisIVO - Integrated Tools and Services for Large-Scale Astrophysical Visualization

VisIVO is an integrated suite of tools and services specifically designed for the Virtual Observatory. This suite constitutes a software framework for effective visual discovery in currently available (and next-generation) very large-scale astrophysical datasets. VisIVO consists of VisiVO Desktop - a stand alone application for interactive visualization on standard PCs, VisIVO Server - a grid-enabled platform for high performance visualization and VisIVO Web - a custom designed web portal supporting services based on the VisIVO Server functionality. The main characteristic of VisIVO is support for high-performance, multidimensional visualization of very large-scale astrophysical datasets. Users can obtain meaningful visualizations rapidly while preserving full and intuitive control of the relevant visualization parameters. This paper focuses on newly developed integrated tools in VisIVO Server allowing intuitive visual discovery with 3D views being created from data tables. VisIVO Server can be installed easily on any web server with a database repository. We discuss briefly aspects of our implementation of VisiVO Server on a computational grid and also outline the functionality of the services offered by VisIVO Web. Finally we conclude with a summary of our work and pointers to future developments

Estimation of a 3D motion field from a multi-camera array using a multiresolution Gaussian mixture model

The problem of modelling geometry for video based rendering has been much studied in recent years, due to the growing interest in 'free viewpoint' video and similar applications. Common approaches fall into two categories: those which approximate surfaces from dense depth maps obtained by generalisations of stereopsis and those which employ an explicit geometric representation such as a mesh. While the former have generality with respect to geometry, they are limited in terms of viewpoint; the latter, on the other hand, sacrifice generality of geometry for freedom to pick an arbitary viewpoint. The purpose of the work reported here is to bridge this gap in object representation, by employing a stochastic model of object structure: a multiresolution Gaussian mixture. Estimation of the model and tracking it through time from multiple cameras is achieved by a multiresolution stochastic simulation. After a brief outline of the method, its use in modelling human motion using data from local and other sources is presented to illustrate its effectiveness compared to the current state of the art

TetSplat: Real-time Rendering and Volume Clipping of Large Unstructured Tetrahedral Meshes

We present a novel approach to interactive visualization and exploration of large unstructured tetrahedral meshes. These massive 3D meshes are used in mission-critical CFD and structural mechanics simulations, and typically sample multiple field values on several millions of unstructured grid points. Our method relies on the pre-processing of the tetrahedral mesh to partition it into non-convex boundaries and internal fragments that are subsequently encoded into compressed multi-resolution data representations. These compact hierarchical data structures are then adaptively rendered and probed in real-time on a commodity PC. Our point-based rendering algorithm, which is inspired by QSplat, employs a simple but highly efficient splatting technique that guarantees interactive frame-rates regardless of the size of the input mesh and the available rendering hardware. It furthermore allows for real-time probing of the volumetric data-set through constructive solid geometry operations as well as interactive editing of color transfer functions for an arbitrary number of field values. Thus, the presented visualization technique allows end-users for the first time to interactively render and explore very large unstructured tetrahedral meshes on relatively inexpensive hardware

Multiple dataset visualization (MDV) framework for scalar volume data

Many applications require comparative analysis of multiple datasets representing different samples, conditions, time instants, or views in order to develop a better understanding of the scientific problem/system under consideration. One effective approach for such analysis is visualization of the data. In this PhD thesis, we propose an innovative multiple dataset visualization (MDV) approach in which two or more datasets of a given type are rendered concurrently in the same visualization. MDV is an important concept for the cases where it is not possible to make an inference based on one dataset, and comparisons between many datasets are required to reveal cross-correlations among them. The proposed MDV framework, which deals with some fundamental issues that arise when several datasets are visualized together, follows a multithreaded architecture consisting of three core components, data preparation/loading, visualization and rendering. The visualization module - the major focus of this study, currently deals with isosurface extraction and texture-based rendering techniques. For isosurface extraction, our all-in-memory approach keeps datasets under consideration and the corresponding geometric data in the memory. Alternatively, the only-polygons- or points-in-memory only keeps the geometric data in memory. To address the issues related to storage and computation, we develop adaptive data coherency and multiresolution schemes. The inter-dataset coherency scheme exploits the similarities among datasets to approximate the portions of isosurfaces of datasets using the isosurface of one or more reference datasets whereas the intra/inter-dataset multiresolution scheme processes the selected portions of each data volume at varying levels of resolution. The graphics hardware-accelerated approaches adopted for MDV include volume clipping, isosurface extraction and volume rendering, which use 3D textures and advanced per fragment operations. With appropriate user-defined threshold criteria, we find that various MDV techniques maintain a linear time-N relationship, improve the geometry generation and rendering time, and increase the maximum N that can be handled (N: number of datasets). Finally, we justify the effectiveness and usefulness of the proposed MDV by visualizing 3D scalar data (representing electron density distributions in magnesium oxide and magnesium silicate) from parallel quantum mechanical simulation