Multi-scale stream reduction for volume rendering on GPUs

被引:2
|
作者
Jiang, Yatong [1 ]
Rho, Seungmin [3 ]
Zhang, Yingping [4 ]
Jiang, Feng [5 ]
Yin, Jian [2 ]
机构
[1] Shandong Univ, Weihai, Peoples R China
[2] Shandong Univ, Dept Comp, Weihai, Peoples R China
[3] Sungkyul Univ, Dept Multimedia, Anyang, South Korea
[4] Informat & Commun Co Hunan EPC, Changsha, Hunan, Peoples R China
[5] Harbin Inst Technol, Sch Comp Sci, Harbin, Peoples R China
关键词
GPU; Marching cubes; Volume raycasting;
D O I
10.1016/j.micpro.2016.04.004
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
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.
引用
收藏
页码:133 / 141
页数:9
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