Multiscale Tensor Approximation for Volume Data (original) (raw)
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Application of Tensor Approximation to Multiscale Volume Feature Representations
Advanced 3D microstructural analysis in natural sciences and engineering depends ever more on modern data acquisition and imaging technologies such as micro-computed or synchrotron tomography and interactive visualization. The acquired volume data sets are not only of high-resolution but in particular exhibit complex spatial structures at different levels of scale (e.g. variable spatial expression of multiscale periodic growth structures in tooth enamel). Such highly structured volume data sets represent a tough challenge to be analyzed and explored by means of interactive visualization due to the amount of raw volume data to be processed and filtered for the desired features. As an approach to address this bottleneck by multiscale feature preserving data reduction, we propose higher-order tensor approximations (TAs). We demonstrate the power of TA to represent, and highlight the structural features in volume data. We visually and quantitatively show that TA yields high data reducti...
Interactive Multiscale Tensor Reconstruction for Multiresolution Volume Visualization
IEEE Transactions on Visualization and Computer Graphics, 2000
Large scale and structurally complex volume datasets from high-resolution 3D imaging devices or computational simulations pose a number of technical challenges for interactive visual analysis. In this paper, we present the first integration of a multiscale volume representation based on tensor approximation within a GPU-accelerated out-of-core multiresolution rendering framework. Specific contributions include (a) a hierarchical brick-tensor decomposition approach for pre-processing large volume data, (b) a GPU accelerated tensor reconstruction implementation exploiting CUDA capabilities, and (c) an effective tensor-specific quantization strategy for reducing data transfer bandwidth and out-of-core memory footprint. Our multiscale representation allows for the extraction, analysis and display of structural features at variable spatial scales, while adaptive level-of-detail rendering methods make it possible to interactively explore large datasets within a constrained memory footprint. The quality and performance of our prototype system is evaluated on large structurally complex datasets, including gigabyte-sized micro-tomographic volumes.
Multiresolution tetrahedral framework for visualizing regular volume data
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We present a multiresolution framework, called Multi-Tetra framework, that approximates volume data with different levelsof-detail tetrahedra. The framework is generated through a recursive subdivision of the volume data and is represented by binary trees. Instead of using a certain level of the Multi-Tetra framework for approximation, an error-based model (EBM) is generated by recursively fusing a sequence of tetrahedra from different levels of the Multi-Tetra framework. The EBM significantly reduces the number of voxels required to model an object, while preserving the original topology. Our approach provides continuous distribution of rendered intensity or generated isosurfaces along boundaries of different levels-of-detail, thus solving the crack problem. Our model supports typical rendering approaches, such as Marching Cubes, direct volume projection, and splatting. Experimental results demonstrate the strengths of our approach.
TTHRESH: Tensor Compression for Multidimensional Visual Data
IEEE Transactions on Visualization and Computer Graphics, 2019
Senior Member, IEEE (a) Original (512MB) (b) 10:1 compression (51.2MB) (c) 300:1 compression (1.71MB) Fig. 1. (a) a 512 3 isotropic turbulence volume [1]; (b) visually identical compression result; (c) result after extreme compression.
GPU-Based Volume Visualization from High-Order Finite Element Fields
IEEE Transactions on Visualization and Computer Graphics, 2014
This paper describes a new volume rendering system for spectral/hp finite-element methods that has as its goal to be both accurate and interactive. Even though high-order finite element methods are commonly used by scientists and engineers, there are few visualization methods designed to display this data directly. Consequently, visualizations of high-order data are generally created by first sampling the high-order field onto a regular grid and then generating the visualization via traditional methods based on linear interpolation. This approach, however, introduces error into the visualization pipeline and requires the user to balance image quality, interactivity, and resource consumption. We first show that evaluation of the volume rendering integral, when applied to the composition of piecewise-smooth transfer functions with the high-order scalar field, typically exhibits second-order convergence for a wide range of high-order quadrature schemes, and has worst case first-order convergence. This result provides bounds on the ability to achieve high-order convergence to the volume rendering integral. We then develop an algorithm for optimized evaluation of the volume rendering integral, based on the categorization of each ray according to the local behavior of the field and transfer function. We demonstrate the effectiveness of our system by running performance benchmarks on several high-order fluid-flow simulations.
Physically based methods for tensor field visualization
IEEE Visualization 2004, 2004
The physical interpretation of mathematical features of tensor fields is highly application-specific. Existing visualization methods for tensor fields only cover a fraction of the broad application areas. We present a visualization method tailored specifically to the class of tensor field exhibiting properties similar to stress and strain tensors, which are commonly encountered in geomechanics. Our technique is a global method that represents the physical meaning of these tensor fields with their central features: regions of compression or expansion. The method is based on two steps: first, we define a positive definite metric, with the same topological structure as the tensor field; second, we visualize the resulting metric. The eigenvector fields are represented using a texture-based approach resembling line integral convolution (LIC) methods. The eigenvalues of the metric are encoded in free parameters of the texture definition. Our method supports an intuitive distinction between positive and negative eigenvalues. We have applied our method to synthetic and some standard data sets, and "real" data from Earth science and mechanical engineering application.
Fast and Memory Efficient GPU-based Rendering of Tensor Data
Graphics hardware is advancing very fast and offers new possibilities to programmers. The new features can be used in scientific visualization to move calculations from the CPU to the graphics processing unit (GPU). This is useful especially when mixing CPU intense calculations with on the fly visualization of intermediate results. We present a method to display a large amount of superquadric glyphs and demonstrate its use for visualization of measured second--order tensor data in diffusion tensor imaging (DTI) and to stress and strain tensors of computational fluid dynamic and material simulations.
Proceedings of GRAPP, 2008
Direct Volume Rendering is a popular method for displaying volumetric data sets without generating intermediate representations. The technique is most frequently applied to scalar data and few specialized techniques exist for visualizing higher-order data, such as tensor fields, directly. This is a serious limitation because progress in medical imaging, satellite technology and numerical simulations has made higher-order and multifield data sets a common entity in medicine, science and engineering. In this paper we present a framework for the interactive exploration of complex data sets using direct volume rendering. This is achieved by applying sophisticated Software Engineering (SE) to modularize the direct volume rendering pipeline and by exploiting the latest advances in graphics hardware and shading languages to modify rendering effects and to compute derived data sets at runtime. We discuss how the framework can be used to mimic the latest specialized direct volume rendering algorithms and to interactively explore and gain new insight into high-order and multifield data sets. The capabilities of the framework are demonstrated by three case studies and the efficiency and effectiveness of the framework is evaluated.