The Experts below are selected from a list of 17346 Experts worldwide ranked by ideXlab platform
Rüdiger Westermann - One of the best experts on this subject based on the ideXlab platform.
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A Voxel-Based Rendering Pipeline for Large 3D Line Sets
IEEE Transactions on Visualization and Computer Graphics, 2019Co-Authors: Mathias Kanzler, Marc Rautenhaus, Rüdiger WestermannAbstract:We present a voxel-based rendering pipeline for large 3D line sets that employs GPU Ray-Casting to achieve scalable rendering including transparency and global illumination effects. Even for opaque lines we demonstrate superior rendering performance compared to GPU rasterization of lines, and when transparency is used we can interactively render amounts of lines that are infeasible to be rendered via rasterization. We propose a direction-preserving encoding of lines into a regular voxel grid, along with the quantization of directions using face-to-face connectivity in this grid. On the regular grid structure, parallel GPU Ray-Casting is used to determine visible fragments in correct visibility order. To enable interactive rendering of global illumination effects like low-frequency shadows and ambient occlusions, illumination simulation is performed during Ray-Casting on a level-of-detail (LoD) line representation that considers the number of lines and their lengths per voxel. In this way we can render effects which are very difficult to render via GPU rasterization. A detailed performance and quality evaluation compares our approach to rasterization-based rendering of lines.
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A Voxel-based Rendering Pipeline for Large 3D Line Sets
arXiv: Graphics, 2018Co-Authors: Mathias Kanzler, Marc Rautenhaus, Rüdiger WestermannAbstract:We present a voxel-based rendering pipeline for large 3D line sets that employs GPU Ray-Casting to achieve scalable rendering including transparency and global illumination effects that cannot be achieved with GPU rasterization. Even for opaque lines we demonstrate superior rendering performance compared to GPU rasterization of lines, and when transparency is used we can interactively render large amounts of lines that are infeasible to be rendered via rasterization. To achieve this, we propose a direction-preserving encoding of lines into a regular voxel grid, along with the quantization of directions using face-to-face connectivity in this grid. On the regular grid structure, parallel GPU Ray-Casting is used to determine visible fragments in correct visibility order. To enable interactive rendering of global illumination effects like low-frequency shadows and ambient occlusions, illumination simulation is performed during Ray-Casting on a level-of-detail (LoD) line representation that considers the number of lines and their lengths per voxel. In this way we can render effects which are very difficult to render via GPU rasterization. A detailed performance and quality evaluation compares our approach to rasterization-based rendering of lines.
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accelerated volume Ray Casting using texture mapping
IEEE Visualization, 2001Co-Authors: Rüdiger Westermann, Bernd SevenichAbstract:Acceleration techniques for volume Ray-Casting are primarily based on pre-computed data structures that allow one to efficiently traverse empty or homogeneous regions. In order to display volume data that successively undergoes color lookups, however, the data structures have to be re-built continuously. In this paper we propose a technique that circumvents this drawback using hardware accelerated texture mapping. In a first rendering pass we employ graphics hardware to interactively determine for each Ray where the material is hit. In a second pass Ray-Casting is performed, but Ray traversal starts right in front of the previously determined regions. The algorithm enables interactive classification and it considerably accelerates the view dependent display of selected materials and surfaces from volume data. In contrast to other techniques that are solely based on texture mapping our approach requires less memory and accurately performs the composition of material contributions along the Ray.
Hanspeter Pfister - One of the best experts on this subject based on the ideXlab platform.
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sparseleap efficient empty space skipping for large scale volume rendering
IEEE Transactions on Visualization and Computer Graphics, 2018Co-Authors: Markus Hadwiger, Ali K Alawami, Johanna Beyer, Marco Agus, Hanspeter PfisterAbstract:Recent advances in data acquisition produce volume data of very high resolution and large size, such as terabyte-sized microscopy volumes. These data often contain many fine and intricate structures, which pose huge challenges for volume rendering, and make it particularly important to efficiently skip empty space. This paper addresses two major challenges: (1) The complexity of large volumes containing fine structures often leads to highly fragmented space subdivisions that make empty regions hard to skip efficiently. (2) The classification of space into empty and non-empty regions changes frequently, because the user or the evaluation of an interactive query activate a different set of objects, which makes it unfeasible to pre-compute a well-adapted space subdivision. We describe the novel SparseLeap method for efficient empty space skipping in very large volumes, even around fine structures. The main performance characteristic of SparseLeap is that it moves the major cost of empty space skipping out of the Ray-Casting stage. We achieve this via a hybrid strategy that balances the computational load between determining empty Ray segments in a rasterization (object-order) stage, and sampling non-empty volume data in the Ray-Casting (image-order) stage. Before Ray-Casting, we exploit the fast hardware rasterization of GPUs to create a Ray segment list for each pixel, which identifies non-empty regions along the Ray. The Ray-Casting stage then leaps over empty space without hierarchy traversal. Ray segment lists are created by rasterizing a set of fine-grained, view-independent bounding boxes. Frame coherence is exploited by re-using the same bounding boxes unless the set of active objects changes. We show that SparseLeap scales better to large, sparse data than standard octree empty space skipping.
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Volume MLS Ray Casting
IEEE Transactions on Visualization and Computer Graphics, 2008Co-Authors: Christian Ledergerber, Gael Guennebaud, Miriah Meyer, Moritz Bacher, Hanspeter PfisterAbstract:The method of Moving Least Squares (MLS) is a popular framework for reconstructing continuous functions from scattered data due to its rich mathematical properties and well-understood theoretical foundations. This paper applies MLS to volume rendering, providing a unified mathematical framework for Ray Casting of scalar data stored over regular as well as irregular grids. We use the MLS reconstruction to render smooth isosurfaces and to compute accurate derivatives for high-quality shading effects. We also present a novel, adaptive preintegration scheme to improve the efficiency of the Ray Casting algorithm by reducing the overall number of function evaluations, and an efficient implementation of our framework exploiting modern graphics hardware. The resulting system enables high-quality volume integration and shaded isosurface rendering for regular and irregular volume data.
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Ray Casting architectures for volume visualization
IEEE Transactions on Visualization and Computer Graphics, 1999Co-Authors: Harvey Ray, Hanspeter Pfister, Deborah Silver, T A CookAbstract:Real-time visualization of large volume data sets demands high-performance computation, pushing the storage, processing and data communication requirements to the limits of current technology. General-purpose parallel processors have been used to visualize moderate-size data sets at interactive frame rates; however, the cost and size of these supercomputers inhibits the widespread use for real-time visualization. This paper surveys several special-purpose architectures that seek to render volumes at interactive rates. These specialized visualization accelerators have cost, performance and size advantages over parallel processors. All architectures implement Ray Casting using parallel and pipelined hardware. We introduce a new metric that normalizes performance to compare these architectures. The architectures included in this survey are VOGUE, VIRIM, ArRay-Based Ray Casting, EM-Cube and VIZARD II. We also discuss future applications of special-purpose accelerators.
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the volumepro real time Ray Casting system
International Conference on Computer Graphics and Interactive Techniques, 1999Co-Authors: Hanspeter Pfister, Hugh C Lauer, Jan C Hardenbergh, Jim Knittel, Larry D SeilerAbstract:This paper describes VolumePro, the world’s first single-chip realtime volume rendering system for consumer PCs. VolumePro implements Ray-Casting with parallel slice-by-slice processing. Our discussion of the architecture focuses mainly on the rendering pipeline and the memory organization. VolumePro has hardware for gradient estimation, classification, and per-sample Phong illumination. The system does not perform any pre-processing and makes parameter adjustments and changes to the volume data immediately visible. We describe several advanced features of VolumePro, such as gradient magnitude modulation of opacity and illumination, supersampling, cropping and cut planes. The system renders 500 million interpolated, Phong illuminated, composited samples per second. This is sufficient to render volumes with up to 16 million voxels (e.g., 256) at 30 frames per second. CR Categories: B.4.2 [Hardware]: Input/Output and Data Communications—Input/Output DevicesImage display; C.3 [Computer Systems Organization]: Special-Purpose and ApplicationBased Systems—Real-time and embedded systems; I.3.1 [Computer Graphics]: Hardware Architecture—Graphics processor;
Joseph Jaja - One of the best experts on this subject based on the ideXlab platform.
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streaming model based volume Ray Casting implementation for cell broadband engine
IEEE International Conference on High Performance Computing Data and Analytics, 2009Co-Authors: Jusub Kim, Joseph JajaAbstract:Interactive high quality volume rendering is becoming increasingly more important as the amount of more complex volumetric data steadily grows. While a number of volumetric rendering techniques have been widely used, Ray Casting has been recognized as an effective approach for generating high quality visualization. However, for most users, the use of Ray Casting has been limited to datasets that are very small because of its high demands on computational power and memory bandwidth. However the recent introduction of the Cell Broadband Engine (Cell B.E.) processor, which consists of 9 heterogeneous cores designed to handle extremely demanding computations with large streams of data, provides an opportunity to put the Ray Casting into practical use. In this paper, we introduce an efficient parallel implementation of volume Ray Casting on the Cell B.E. The implementation is designed to take full advantage of the computational power and memory bandwidth of the Cell B.E. using an intricate orchestration of the Ray Casting computation on the available heterogeneous resources. Specifically, we introduce streaming model based schemes and techniques to efficiently implement acceleration techniques for Ray Casting on Cell B.E. In addition to ensuring effective SIMD utilization, our method provides two key benefits: there is no cost for empty space skipping and there is no memory bottleneck on moving volumetric data for processing. Our experimental results show that we can interactively render practical datasets on a single Cell B.E. processor.
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interactive high resolution isosurface Ray Casting on multicore processors
IEEE Transactions on Visualization and Computer Graphics, 2008Co-Authors: Qin Wang, Joseph JajaAbstract:We present a new method for the interactive rendering of isosurfaces using Ray Casting on multicore processors. This method consists of a combination of an object-order traversal that coarsely identifies possible candidate three-dimensional (3D) data blocks for each small set of contiguous pixels and an isosurface Ray Casting strategy tailored for the resulting limited-size lists of candidate 3D data blocks. Our implementation scheme results in a compact indexing structure and makes careful use of multithreading and memory management environments commonly present in multicore processors. Although static screen partitioning is widely used in the literature, our scheme starts with an image partitioning for the initial stage and then performs dynamic allocation of groups of Ray Casting tasks among the different threads to ensure almost equal loads among the different cores while maintaining spatial locality. We also pay a particular attention to the overhead incurred by moving the data across the different levels of the memory hierarchy. We test our system on a two-processor Clovertown platform, each consisting of a Quad-Core 1.86-GHz Intel Xeon Processor and present detailed experimental results for a number of widely different benchmarks. We show that our system is efficient and scalable and achieves high cache performance and excellent load balancing, resulting in an overall performance that is superior to any of the previous algorithms. In fact, we achieve interactive isosurface rendering on a screen with 1.0242 resolution for all the data sets tested up to the maximum size that can fit in the main memory of our platform.
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streaming model based volume Ray Casting implementation for cell broadband engine
Eurographics Workshop on Parallel Graphics and Visualization, 2008Co-Authors: Jusub Kim, Joseph JajaAbstract:Interactive high quality volume rendering is becoming increasingly more important as the amount of more complex volumetric data steadily grows. While a number of volumetric rendering techniques have been widely used, Ray Casting has been recognized as one of the techniques which can generate high quality rendering. However, for most users, the use of Ray Casting has been limited to datasets that are very small because of its high demands on computational power and memory bandwidth. However the recent introduction of the Cell Broadband Engine (Cell B.E.) processor, which consists of 9 heterogeneous cores designed to handle extremely demanding computations with large streams of data, provides an opportunity to put the Ray Casting into practical use. In this paper, we introduce an efficient parallel implementation of volume Ray Casting on the Cell B.E. The implementation is designed to take full advantage of the computational power and memory bandwidth of the Cell B.E. using an intricate orchestration of the Ray Casting computation on the available heterogeneous resources. Specifically, we introduce streaming model based schemes and techniques to efficiently implement acceleration techniques for Ray Casting on Cell B.E. In addition to better SIMD utilization, our method provides two key benefits: there is no cost for empty space skipping and there is no memory bottleneck on moving volumetric data for processing. Our experimental results show that we can interactively Ray cast practical datasets of moderate size with one Cell B.E processor 3.2GHz.
Arie E. Kaufman - One of the best experts on this subject based on the ideXlab platform.
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gpu based object order Ray Casting for large datasets
Eurographics, 2005Co-Authors: Wei Hong, Feng Qiu, Arie E. KaufmanAbstract:We propose a GPU-based object-order Ray-Casting algorithm for the rendering of large volumetric datasets, such as the Visible Human CT datasets. A volumetric dataset is decomposed into small sub-volumes, which are then organized using a min-max octree structure. The small sub-volumes are stored in the leaf nodes of the min-max octree, which are also called cells. The cells are classified using a transfer function, and the visible cells are then loaded into the video memory or the AGP memory. The cells are sorted and projected onto the image plane front to back. The cell projection is implemented using a volumetric Ray-Casting algorithm on the GPU. In order to make the cell projection more efficient, we devise a propagation method to sort cells into layers. The cells within the same layer are projected at the same time. We demonstrate the efficiency of our algorithm using the visible human datasets and a segmented photographic brain dataset on commodity PCs.
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high performance presence accelerated Ray Casting
IEEE Visualization, 1999Co-Authors: Ming Wan, Arie E. Kaufman, Steve BrysonAbstract:We present a novel presence acceleration for volumetric Ray Casting. A highly accurate estimation for object presence is obtained by projecting all grid cells associated with the object boundary on the image plane. Memory space and access time are reduced by run-length encoding of the boundary cells, while boundary cell projection time is reduced by exploiting projection templates and multiresolution volumes. Efforts have also been made towards a fast perspective projection as well as interactive classification. We further present task partitioning schemes for effective parallelization of both boundary cell projection and Ray traversal procedures. Good load balancing has been reached by taking full advantage of both the optimizations in the serial rendering algorithm and shared-memory architecture. Our experimental results on a 16-processor SGI Power Challenge have shown interactive rendering rates for 2563 volumetric data sets at 10 - 30 Hz. This paper describes the theory and implementation of our algorithm, and shows its superiority over the shear-warp factorization approach.
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fast projection based Ray Casting algorithm for rendering curvilinear volumes
IEEE Transactions on Visualization and Computer Graphics, 1999Co-Authors: Lichan Hong, Arie E. KaufmanAbstract:We present an efficient and robust Ray-Casting algorithm for directly rendering a curvilinear volume of arbitrarily-shaped cells. By projecting cell-faces onto the image plane, we have effectively addressed three critical steps of the Ray-Casting process, namely finding the entry cell-faces for a Ray, traversing along the Ray from one cell to another, and reconstructing data values at the Ray/cell-face intersections. Our algorithm significantly reduces rendering time, alleviates memory space consumption, and overcomes the conventional limitation requiring cells to be convex. Application of this algorithm to several commonly used curvilinear data sets has produced a favorable performance when compared with recently reported algorithms.
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accelerated Ray Casting for curvilinear volumes
IEEE Visualization, 1998Co-Authors: Lichan Hong, Arie E. KaufmanAbstract:We present an efficient and robust Ray-Casting algorithm for directly rendering a curvilinear volume of arbitrarily-shaped cells. We designed the algorithm to alleviate the consumption of CPU power and memory space. By incorporating the essence of the projection paradigm into the Ray-Casting process, we have successfully accelerated the Ray traversal through the grid and data interpolations at sample points. Our algorithm also overcomes the conventional limitation requiring the cells to be convex. Application of this algorithm to several commonly-used curvilinear data sets has produced a favorable performance when compared with recently reported algorithms.
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Boundary cell-based acceleration for volume Ray Casting
Computers & Graphics, 1998Co-Authors: Ming Wan, Steve Bryson, Arie E. KaufmanAbstract:Abstract Several effective acceleration techniques for volume rendering offer efficient means to skip over empty space, providing significant speedup without affecting image quality. The effectiveness of such an approach depends on its ability to accurately estimate the object boundary inside a volume with minimal computational overhead. We propose a novel boundary cell-based acceleration technique for Ray Casting which skips over empty space by accurately calculating the intersection distance for each Ray. Very short distance estimation time is achieved by exploiting a projection template to calculate the parallel-projection values of each boundary cell and the coherency of adjacent cells. Since no hardware acceleration is used, the projection procedure can also be efficiently parallelized. Experimental results are provided to demonstrate the performance of our new algorithm.
Jeremy Sweezy - One of the best experts on this subject based on the ideXlab platform.
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a monte carlo volumetric Ray Casting estimator for global fluence tallies on gpus
Journal of Computational Physics, 2018Co-Authors: Jeremy SweezyAbstract:Abstract A Monte Carlo fluence estimator has been designed to take advantage of the computational power of graphical processing units (GPUs). This new estimator, termed the volumetric-Ray-Casting estimator, is an extension of the expectation estimator. It can be used as a replacement of the track-length estimator for the estimation of global fluence. Calculations for this estimator are performed on the GPU while the Monte Carlo random walk is performed on the central processing unit (CPU). This method lowers the implementation cost for GPU acceleration of existing Monte Carlo particle transport codes as there is little modification of the particle history logic flow. Three test problems have been evaluated to assess the performance of the volumetric-Ray-Casting estimator for neutron transport on GPU hardware in comparison to the standard track-length estimator on CPU hardware. Evaluation of neutron transport through air in a criticality accident scenario showed that the volumetric-Ray-Casting estimator achieved 23 times the performance of the track-length estimator using a single core CPU paired with a GPU and 15 times the performance of the track-length estimator using an eight core CPU paired with a GPU. Simulation of a pressurized water reactor fuel assembly showed that the performance improvement was 6 times within the fuel and 7 times within the control rods using an eight core CPU paired with a single GPU.
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a monte carlo volumetric Ray Casting estimator for global fluence tallies on gpus
Journal of Computational Physics, 2018Co-Authors: Jeremy SweezyAbstract:Abstract A Monte Carlo fluence estimator has been designed to take advantage of the computational power of graphical processing units (GPUs). This new estimator, termed the volumetric-Ray-Casting estimator, is an extension of the expectation estimator. It can be used as a replacement of the track-length estimator for the estimation of global fluence. Calculations for this estimator are performed on the GPU while the Monte Carlo random walk is performed on the central processing unit (CPU). This method lowers the implementation cost for GPU acceleration of existing Monte Carlo particle transport codes as there is little modification of the particle history logic flow. Three test problems have been evaluated to assess the performance of the volumetric-Ray-Casting estimator for neutron transport on GPU hardware in comparison to the standard track-length estimator on CPU hardware. Evaluation of neutron transport through air in a criticality accident scenario showed that the volumetric-Ray-Casting estimator achieved 23 times the performance of the track-length estimator using a single core CPU paired with a GPU and 15 times the performance of the track-length estimator using an eight core CPU paired with a GPU. Simulation of a pressurized water reactor fuel assembly showed that the performance improvement was 6 times within the fuel and 7 times within the control rods using an eight core CPU paired with a single GPU.
