| 引用本文: | 王华维,何柳,曹轶,等.大规模科学数据体绘制技术综述.[J].国防科技大学学报,2020,42(2):1-12.[点击复制] |
| WANG Huawei,HE Liu,CAO Yi,et al.A survey of the techniques of volume rendering for large-scale scientific data[J].Journal of National University of Defense Technology,2020,42(2):1-12[点击复制] |
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| 大规模科学数据体绘制技术综述 |
| 王华维1,2,何柳1,曹轶1,2,肖丽1,2 |
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(1. 北京应用物理与计算数学研究所 计算物理重点实验室, 北京 100088;2. 中物院高性能数值模拟软件中心, 北京 100088)
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| 摘要: |
| 体绘制是刻画大规模科学数据中复杂物理特征的有效途径,然而,数据量极大、特征难以捕捉等问题依然是目前体绘制研究的主要挑战。为此,研究者们从三个方面对体绘制算法进行了深入研究,以提高大规模数据体绘制的效率和效果。一方面,依托硬件通过多处理器核来分担计算,降低单处理器核所要完成的计算量,是提高体绘制效率的一个有效途径。另一方面,充分发掘数据场内在特性对三维数据场进行约简,大幅减少绘制处理数据量从而降低算法开销,也是提高体绘制效率的一个有效途径。同时,在体绘制算法中融入特征分析和特征增强方法,让复杂物理特征从数据场中突显出来,以实现对科学数据的高质量绘制。本文对国内外体绘制技术相关研究进展进行了调研、综述,并分析了不同的研究方法,最后展望了未来体绘制技术研究的可能发展方向,包括应用驱动的特征体绘制、基于特征的约简体绘制、适应硬件的体绘制多级加速以及原位智能化体绘制等。 |
| 关键词: 体绘制 并行加速 数据约简 效果增强 特征可视化 |
| DOI：10.11887/j.cn.202002001 |
| 投稿日期：2019-09-15 |
| 基金项目:国家重点研发计划资助项目(2017YFB0202203)；国防基础科研计划资助项目(C1520110002)；计算物理重点实验室基金资助项目(9140C690504150C69305) |
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| A survey of the techniques of volume rendering for large-scale scientific data |
| WANG Huawei 1,2, HE Liu1, CAO Yi1,2, XIAO Li 1,2 |
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(1. Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Beijing 100088, China;2. CAEP Software Center for High Performance Numerical Simulation, Beijing 100088, China)
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| Abstract: |
| Volume rendering is an effective method to visualize complex physical features in large-scale scientific data with high expressiveness, the difficulties in processing huge amount of data and capturing complex features, however, are still a great challenge to volume rendering. To address the challenges and improve efficiency and effect of volume rendering, researchers conducted in-depth research on volume rendering algorithms from three aspects. On the one hand, it is an effective way to improve the efficiency of volume rendering by sharing computation with lots of processor cores so as to reduce the computational amount of one processor core. On the other hand, by fully exploring the intrinsic characteristics of three-dimensional data fields, data reduction methods can greatly decrease the amount of data in the rendering process and thus reduce the overhead of a volume rendering algorithm. In addition, feature analysis and enhancement techniques can also be integrated into volume rendering algorithms, thus complex physical features are highlighted from the data fields and high-quality rendering of scientific data is achieved. A survey of recent advances on volume rendering techniques was presented and various research methods were analyzed. In the end, this paper makes prospection for future research directions on volume rendering of large-scale scientific data, including application-driven feature volume rendering, feature-based data reduction in volume rendering, hardware-adapted multi-level acceleration of volume rendering and in-situ intelligent volume rendering. |
| Keywords: volume rendering parallel acceleration data reduction effect enhancement feature visualization |
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