Browsing by Author "Wu, Qi"

Now showing 1 - 3 of 3
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    A Flexible Data Streaming Design for Interactive Visualization of Large-Scale Volume Data
    (The Eurographics Association, 2022) Wu, Qi; Doyle, Michael J.; Ma, Kwan-Liu; Bujack, Roxana; Tierny, Julien; Sadlo, Filip
    Modern simulations and experiments can produce massive amounts of high-fidelity data that are challenging to transport and visualize interactively. We have designed a data streaming system to support interactive visualization of large volume data. Our streaming system design is unique in its flexibility to support diverse data organizations and its coupling with a highly efficient CPU-based ray-tracing renderer. In this paper, we present our streaming and rendering design and demonstrate the efficacy of our system with progressive rendering of streaming tree-based AMR (TAMR) volume data and radial basis function (RBF) particle volume data. With our system, interactive visualization can be achieved using only a mid-range workstation with a single CPU and a modest quantity of RAM.
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    Memory-Efficient GPU Volume Path Tracing of AMR Data Using the Dual Mesh
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Zellmann, Stefan; Wu, Qi; Ma, Kwan-Liu; Wald, Ingo; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
    A common way to render cell-centric adaptive mesh refinement (AMR) data is to compute the dual mesh and visualize that with a standard unstructured element renderer. While the dual mesh provides a high-quality interpolator, the memory requirements of the dual mesh data structure are significantly higher than those of the original grid, which prevents rendering very large data sets. We introduce a GPU-friendly data structure and a clustering algorithm that allow for efficient AMR dual mesh rendering with a competitive memory footprint. Fundamentally, any off-the-shelf unstructured element renderer running on GPUs could be extended to support our data structure just by adding a gridlet element type in addition to the standard tetrahedra, pyramids, wedges, and hexahedra supported by default. We integrated the data structure into a volumetric path tracer to compare it to various state-of-the-art unstructured element sampling methods. We show that our data structure easily competes with these methods in terms of rendering performance, but is much more memory-efficient.
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    Ray Tracing Generalized Tube Primitives: Method and Applications
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Han, Mengjiao; Wald, Ingo; Usher, Will; Wu, Qi; Wang, Feng; Pascucci, Valerio; Hansen, Charles D.; Johnson, Chris R.; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    We present a general high-performance technique for ray tracing generalized tube primitives. Our technique efficiently supports tube primitives with fixed and varying radii, general acyclic graph structures with bifurcations, and correct transparency with interior surface removal. Such tube primitives are widely used in scientific visualization to represent diffusion tensor imaging tractographies, neuron morphologies, and scalar or vector fields of 3D flow. We implement our approach within the OSPRay ray tracing framework, and evaluate it on a range of interactive visualization use cases of fixed- and varying-radius streamlines, pathlines, complex neuron morphologies, and brain tractographies. Our proposed approach provides interactive, high-quality rendering, with low memory overhead.