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Main Memory (main + memory)
Selected AbstractsReduceM: Interactive and Memory Efficient Ray Tracing of Large ModelsCOMPUTER GRAPHICS FORUM, Issue 4 2008Christian Lauterbach We present a novel representation and algorithm, ReduceM, for memory efficient ray tracing of large scenes. ReduceM exploits the connectivity between triangles in a mesh and decomposes the model into triangle strips. We also describe a new stripification algorithm, Strip-RT, that can generate long strips with high spatial coherence. Our approach uses a two-level traversal algorithm for ray-primitive intersection. In practice, ReduceM can significantly reduce the storage overhead and ray trace massive models with hundreds of millions of triangles at interactive rates on desktop PCs with 4-8GB of main memory. [source] GPU-Based Nonlinear Ray TracingCOMPUTER GRAPHICS FORUM, Issue 3 2004Daniel Weiskopf In this paper, we present a mapping of nonlinear ray tracing to the GPU which avoids any data transfer back to main memory. The rendering process consists of the following parts: ray setup according to the camera parameters, ray integration, ray-object intersection, and local illumination. Bent rays are approximated by polygonal lines that are represented by textures. Ray integration is based on an iterative numerical solution of ordinary differential equations whose initial values are determined during ray setup. To improve the rendering performance, we propose acceleration techniques such as early ray termination and adaptive ray integration. Finally, we discuss a variety of applications that range from the visualization of dynamical systems to the general relativistic visualization in astrophysics and the rendering of the continuous refraction in media with varying density. Categories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: Picture/Image Generation I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism [source] An Adaptive Sampling Scheme for Out-of-Core SimplificationCOMPUTER GRAPHICS FORUM, Issue 2 2002Guangzheng Fei Current out-of-core simplification algorithms can efficiently simplify large models that are too complex to be loaded in to the main memory at one time. However, these algorithms do not preserve surface details well since adaptive sampling, a typical strategy for detail preservation, remains to be an open issue for out-of-core simplification. In this paper, we present an adaptive sampling scheme, called the balanced retriangulation (BR), for out-of-core simplification. A key idea behind BR is that we can use Garland's quadric error matrix to analyze the global distribution of surface details. Based on this analysis, a local retriangulation achieves adaptive sampling by restoring detailed areas with cell split operations while further simplifying smooth areas with edge collapse operations. For a given triangle budget, BR preserves surface details significantly better than uniform sampling algorithms such as uniform clustering. Like uniform clustering, our algorithm has linear running time and small memory requirement. [source] Block merging for off-line compressionJOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY, Issue 1 2007Raymond Wan To bound memory consumption, most compression systems provide a facility that controls the amount of data that may be processed at once,usually as a block size, but sometimes as a direct megabyte limit. In this work we consider the Re-Pair mechanism of Larsson and Moffat (2000), which processes large messages as disjoint blocks to limit memory consumption. We show that the blocks emitted by Re-Pair can be postprocessed to yield further savings, and describe techniques that allow files of 500 MB or more to be compressed in a holistic manner using less than that much main memory. The block merging process we describe has the additional advantage of allowing new text to be appended to the end of the compressed file. [source] | ||||||||||