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Parallel Rendering and Large Data Visualization
We are living in the big data age: An ever increasing amount of data is ...
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Real-Time Visualization in Non-Isotropic Geometries
Non-isotropic geometries are of interest to low-dimensional topologists,...
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LOCALIS: Locally-adaptive Line Simplification for GPU-based Geographic Vector Data Visualization
Vector data is abundant in many fields such as geography and cartography...
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Cinema Darkroom: A Deferred Rendering Framework for Large-Scale Datasets
This paper presents a framework that fully leverages the advantages of a...
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The Hiperwall Visualization Platform for Big Data Research
In the era of Big Data, with the increasing use of large-scale data-driv...
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Towards realistic HPC models of the neuromuscular system
Realistic simulations of detailed, biophysics-based, multi-scale models ...
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BigDataViewer: Interactive Visualization and Image Processing for Terabyte Data Sets
The increasingly popular light sheet microscopy techniques generate very...
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HiVision: Rapid Visualization of Large-Scale Spatial Vector Data
Rapid visualization of large-scale spatial vector data is a long-standing challenge in Geographic Information Science. In existing methods, the computation overheads grow rapidly with data volumes, leading to the incapability of providing real-time visualization for large-scale spatial vector data, even with parallel acceleration technologies. To fill the gap, we present HiVision, a display-driven visualization model for large-scale spatial vector data. Different from traditional data-driven methods, the computing units in HiVision are pixels rather than spatial objects to achieve real-time performance, and efficient spatial-index-based strategies are introduced to estimate the topological relationships between pixels and spatial objects. HiVision can maintain exceedingly good performance regardless of the data volume due to the stable pixel number for display. In addition, an optimized parallel computing architecture is proposed in HiVision to ensure the ability of real-time visualization. Experiments show that our approach outperforms traditional methods in rendering speed and visual effects while dealing with large-scale spatial vector data, and can provide interactive visualization of datasets with billion-scale points/segments/edges in real-time with flexible rendering styles. The HiVision code is open-sourced at https://github.com/MemoryMmy/HiVision with an online demonstration.
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