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Multi-scale stream reduction for volume rendering on GPUs

机译:多尺度流缩减以在GPU上进行体积渲染

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摘要

In this paper, we present a uniform acceleration framework for GPU-based interactive visualization of regular scalar fields. Firstly, in order to exploit the coherence of volume fields in both the object space and the image space, we propose a general bi-space rendering proxy (BSRP) to represent volume fields. These BSRP are organized into pointerless tree structures which can index voxels in a multi-scale manner. Based on BSRP, we present a novel multi-scale stream reduction (MSSR) algorithm to rapidly process BSRP-indexed valid voxels (i.e., active voxels in marching cubes or nonempty space in volume raycasting). In the object space, MSSR utilizes pre-computed tree structure to rapidly get rid of invalid voxels using multi-scale BSRP with minimal overhead, and thus can noticeably reduce the complexity of classification, scan and compaction for valid voxels. In the image space, given view parameters, the BSRP containing valid voxels are rasterized in a coarse-scale. Then, MSSR expands them as lossless ray segments for volume raycasting, where both the exterior and interior empty space are skipped. Our framework addresses the acceleration problem by decomposing volume rendering algorithm into several data-parallel stages processing multi-scale stream, which are mapped efficiently to the massively parallel architecture of modern GPUs. Thanks to the proposed MSSR algorithm, our framework is immune to the changes of iso-value, transfer function and view parameters, which is especially efficient in scenarios requiring frequently interactions. Experimental results demonstrate that the performance of our framework outperforms state-of-the-art algorithms. (C) 2016 Elsevier B.V. All rights reserved.
机译:在本文中,我们提出了一个统一的加速框架,用于基于GPU的常规标量字段的交互式可视化。首先,为了在对象空间和图像空间中利用体积场的一致性,我们提出了一种通用的双空间渲染代理(BSRP)来表示体积场。这些BSRP被组织成无指针的树状结构,可以以多尺度的方式索引体素。基于BSRP,我们提出了一种新颖的多尺度流缩减(MSSR)算法,可快速处理BSRP索引的有效体素(即行进立方体中的活动体素或体射线广播中的非空空间)。在对象空间中,MSSR利用预先计算的树结构以最小的开销使用多尺度BSRP快速消除无效体素,从而可以显着降低有效体素的分类,扫描和压缩的复杂性。在图像空间中,在给定视图参数的情况下,将对包含有效体素的BSRP进行粗光栅化。然后,MSSR将它们扩展为无损射线段,以进行体积射线广播,同时跳过外部和内部空白空间。我们的框架通过将体积渲染算法分解为几个处理多尺度流的数据并行阶段来解决加速问题,这些数据并行阶段被有效映射到现代GPU的大规模并行体系结构。多亏了提出的MSSR算法,我们的框架不受等值,传递函数和视图参数变化的影响,在需要频繁交互的情况下特别有效。实验结果表明,我们框架的性能优于最先进的算法。 (C)2016 Elsevier B.V.保留所有权利。

著录项

  • 来源
    《Microprocessors and microsystems》 |2016年第11期|133-141|共9页
  • 作者单位

    Shandong Univ, Weihai, Peoples R China;

    Sungkyul Univ, Dept Multimedia, Anyang, South Korea;

    Informat & Commun Co Hunan EPC, Changsha, Hunan, Peoples R China;

    Harbin Inst Technol, Sch Comp Sci, Harbin, Peoples R China;

    Shandong Univ, Dept Comp, Weihai, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    GPU; Marching cubes; Volume raycasting;

    机译:GPU;行进立方体;大量光线投射;

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