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

Deep Hierarchical Super-Resolution for Scientific Data Reduction and Visualization

We present an approach for hierarchical super resolution (SR) using neural networks on an octree data representation. We train a hierarchy of neural networks, each capable of 2x upscaling in each spatial dimension between two levels of detail, and use these networks in tandem to facilitate large scale factor super resolution, scaling with the number of trained networks. We utilize these networks in a hierarchical super resolution algorithm that upscales multiresolution data to a uniform high resolution without introducing seam artifacts on octree node boundaries. We evaluate application of this algorithm in a data reduction framework by dynamically downscaling input data to an octree-based data structure to represent the multiresolution data before compressing for additional storage reduction. We demonstrate that our approach avoids seam artifacts common to multiresolution data formats, and show how neural network super resolution assisted data reduction can preserve global features better than compressors alone at the same compression ratios

Error-driven adaptive resolutions for large scientific data sets

The process of making observations and drawing conclusions from large data sets is an essential part of modern scientific research. However, the size of these data sets can easily exceed the available resources of a typical workstation, making visualization and analysis a formidable challenge. Many solutions, including multiresolution and adaptive resolution representations, have been proposed and implemented to address these problems. This thesis describes an error model for calculating and representing localized error from data reduction and a process for constructing error-driven adaptive resolutions from this data, allowing fully-renderable error driven adaptive resolutions to be constructed from a single, high-resolution data set. We evaluated the performance of these adaptive resolutions generated with various parameters compared to the original data set. We found that adaptive resolutions generated with reasonable subdomain sizes and error tolerances show improved performance daring visualization

Hierarchical Large-scale Volume Representation with 3rd-root-of-2 Subdivision and Trivariate B-spline Wavelets

Multiresolution methods provide a means for representing data at multiple levels of detail. They are typically based on a hierarchical data organization scheme and update rules needed for data value computation. We use a data organization that is based on what we call $\sqrt[n]{2}$ subdivision. The main advantage of $\sqrt[n]{2}$ subdivision, compared to quadtree (n=2) or octree (n=3) organizations, is that the number of vertices is only doubled in each subdivision step instead of multiplied by a factor of four or eight, respectively. To update data values we use n-variate B-spline wavelets, which yield better approximations for each level of detail. We develop a lifting scheme for n=2 and n=3 based on the $\sqrt[n]{2}$-subdivision scheme. We obtain narrow masks that provide a basis for out-of-core techniques as well as view-dependent visualization and adaptive, localized refinement

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

Techniques and algorithms for immersive and interactive visualization of large datasets

Advances in computing power have made it possible for scientists to perform atomistic simulations of material systems that range in size, from a few hundred thousand atoms to one billion atoms. An immersive and interactive walkthrough of such datasets is an ideal method for exploring and understanding the complex material processes in these simulations. However rendering such large datasets at interactive frame rates is a major challenge. A scalable visualization platform is developed that is scalable and allows interactive exploration in an immersive, virtual environment. The system uses an octree based data management system that forms the core of the application. This reduces the amount of data sent to the pipeline without a per-atom analysis. Secondary algorithms and techniques such as modified occlusion culling, multiresolution rendering and distributed computing are employed to further speed up the rendering process. The resulting system is highly scalable and is capable of visualizing large molecular systems at interactive frame rates on dual processor SGI Onyx2 with an InfinteReality2 graphics pipeline

AMM: Adaptive Multilinear Meshes

We present Adaptive Multilinear Meshes (AMM), a new framework that significantly reduces the memory footprint compared to existing data structures. AMM uses a hierarchy of cuboidal cells to create continuous, piecewise multilinear representation of uniformly sampled data. Furthermore, AMM can selectively relax or enforce constraints on conformity, continuity, and coverage, creating a highly adaptive and flexible representation to support a wide range of use cases. AMM supports incremental updates in both spatial resolution and numerical precision establishing the first practical data structure that can seamlessly explore the tradeoff between resolution and precision. We use tensor products of linear B-spline wavelets to create an adaptive representation and illustrate the advantages of our framework. AMM provides a simple interface for evaluating the function defined on the adaptive mesh, efficiently traversing the mesh, and manipulating the mesh, including incremental, partial updates. Our framework is easy to adopt for standard visualization and analysis tasks. As an example, we provide a VTK interface, through efficient on-demand conversion, which can be used directly by corresponding tools, such as VisIt, disseminating the advantages of faster processing and a smaller memory footprint to a wider audience. We demonstrate the advantages of our approach for simplifying scalar-valued data for commonly used visualization and analysis tasks using incremental construction, according to mixed resolution and precision data streams

Recent results in rendering massive models on horizontal parallax-only light field displays

In this contribution, we report on specialized out-of-core multiresolution real-time rendering systems able to render massive surface and volume models on a special class of horizontal parallax-only light field displays. The displays are based on a specially arranged array of projectors emitting light beams onto a holographic screen, which then makes the necessary optical transformation to compose these beams into a continuous 3D view. The rendering methods employ state-of-the-art out-of-core multiresolution techniques able to correctly project geometries onto the display and to dynamically adapt model resolution by taking into account the particular spatial accuracy characteristics of the display. The programmability of latest generation graphics architectures is exploited to achieve interactive performance. As a result, multiple freely moving naked-eye viewers can inspect and manipulate virtual 3D objects that appear to them floating at fixed physical locations. The approach provides rapid visual understanding of complex multi-gigabyte surface models and volumetric data sets.304-30