论文标题
Hivision：大规模空间矢量数据的快速可视化
HiVision: Rapid Visualization of Large-Scale Spatial Vector Data
论文作者
论文摘要
大规模空间矢量数据的快速可视化是地理信息科学中的长期挑战。在现有方法中，计算开销随数据量迅速增长，从而导致无效的大规模空间矢量数据的实时可视化，即使使用并行加速技术也是如此。为了填补空白，我们提出了Hivision，这是大规模空间矢量数据的显示驱动的可视化模型。与传统数据驱动的方法不同，Hivision中的计算单元是像素，而不是空间对象来实现实时性能，并引入了有效的基于空间索引的策略来估计像素和空间对象之间的拓扑关系。由于显示稳定的像素编号，Hivision可以保持极佳的性能。此外，在Hivision中提出了优化的并行计算体系结构，以确保实时可视化的能力。实验表明，我们的方法在处理大规模的空间矢量数据时，在渲染速度和视觉效果方面优于传统方法，并且可以实时使用具有十亿个尺度的点/段/边缘的数据集进行交互式可视化，并具有灵活的渲染样式。 Hivision代码在https://github.com/memorymymy/hivision通过在线演示进行开源。
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.
