X. Zhang, C. Bajaj, and W. Blanke. Scalable isosurface visualization of massive datasets on COTS clusters. In Proc. of Parallel and Large-Data Vis. and Graphics Symposium'01, pages 51--58, 2001.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....geometry of the curvilinear grid, which may or may not require large 339 space depending on the complexity of the grid geometry. A further extension to unstructured volume data is beyond the scope of our method, though. A new isosurface compression method has just been proposed by Zhang et al. in [16]. The method works by encoding two 3D bitmaps and a list of voxel values. The first bitmap distinguishes those grid points that are end points of cell edges that intersect the isosurface. For each of these grid points the corresponding voxel value is entered in a list, which is encoded using ....

X. Zhang, C. Bajaj, Q. Blanke, D. Fussell. Scalable isosurface visualization of massive datasets on COTS clusters. In Proceedings IEEE Symp. Parallel and Large-Data Visualization and Graphics. San Diego, Oct. 2001. 340

....rich set of viewport mappings that include multi resolution support, anti aliasing, and translations in addition to optional per pixel depth compositing. Some of the architectural principles of the MetaBuffer design have been demonstrated in an application to large scale iso surface visualization [29]. At large scales both Lightning 2 and MetaBuffer suffer from mesh scaling. A mesh configuration with 1000 computers and 100 displays requires roughly 100 times as many components as a corresponding Sepia configuration. In configurations where the number of displays is proportional to the number ....

Xiaoyu Zhang, Chandrajit Bajaj, William Blanke, and Donald Fussell. Scalable isosurface visualization of massive datasets on COTS clusters. In Proceedings of the IEEE Symposium on Parallel and Large-Data Visualization and Graphics, 2001.

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X. Zhang, C. Bajaj, and W. Blanke. Scalable isosurface visualization of massive datasets on cots clusters. In Proceedings of the IEEE Symposium on Parallel and Large-Data Visualization and Graphics, 2001.

....changing viewpoints, frame rate is no longer a concern. At this point polygons are be redistributed in order to once again form completely high resolution viewports. The example data set used in this paper to test the progressive image com position technique is an isosurface generated by Zhang[17] from the visible human model. This data set consists of 9,128,798 polygons split into ten partitions of 912,880 polygons each using a greedy polygon to viewport allocation algorithm[16] Fig. 8. Sample frames from the progressive image composition movie using the isosurface from the visible ....

X. Zhang, C. Bajaj, W. Blanke, Scalable isosurface visualization of massive datasets on cots clusters, in: Proceedings of IEEE 2001.

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ZHANG, X., BAJAJ, C., AND BLANKE, W. Scalable isosurface visualization of massive datasets on cots clusters. In Proceedings of IEEE 2001 Symposium on Parallel and Large-Data Visualization and Graphics (2001), IEEE Computer Society Press, pp. 51--58. The Eurographics Association 2002. Frame 119 Frame 255 Frame 352 Frame 360

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ZHANG, X., BAJAJ, C., AND BLANKE, W. Scalable isosurface visualization of massive datasets on cots clusters. In Proceedings of IEEE Parallel Visualization and Graphics Symposium (October 2001). c  The Eurographics Association 2002. (a) 14.7% (b) 24.4% (c) 55.3% (d) 5.3% (e) 13.2% (f) 41.7%

....an implementation of out of core isocontouring using the I O optimal external interval tree on a single processor. Bajaj et al. 2] use range partition to reduce the size of data that are loaded for given isocontour queries and balance the load within a range partition. More recently Zhang et al. [35] propose a scalable isosurface visualization framework for massive datasets on commodity off the shelf clusters by combining the parallel and out of core isocontouring techniques. Chiang et al. 7] also try to combine parallel and out of core techniques for isosurface and volume rendering for ....

....data partition method. As demonstrated later, such a static data partition can achieve good load balance for the whole range of isovalues. The parallel and out of core view dependent isocontouring is based on the same framework for scalable isocontouring on commodity off the shelf workstations[35]. In this framework volume datasets are divided into blocks of the same order as disk blocks and statically partitioned onto multiple processors and their local disks without duplication. An external interval tree [1] is then built for the data partition on each local disk in order to load only ....

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ZHANG, X., BAJAJ, C., AND BLANKE, W. Scalable isosurface visualization of massive datasets on cots clusters. In Proceedings of IEEE Parallel Visualization and Graphics Symposium (October 2001). (a) 14.7% (b) 24.4% (c) 55.3% (d) 5.3% (e) 13.2% (f) 41.7%

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X. Zhang, C. Bajaj, and W. Blanke. Scalable isosurface visualization of massive datasets on COTS clusters. In Proc. of Parallel and Large-Data Vis. and Graphics Symposium'01, pages 51--58, 2001.

No context found.

X. Zhang, C. Bajaj, W. Blanke, and D. Fussell. Scalable isosurface visualization of massive datasets on COTS clusters. In Proceedings of the IEEE Symposium on Parallel and Large-Data Visualization and Graphics, pages 51--58, 2001. The Eurographics Association 2002.

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