Real-time volume graphics

Engel, Klaus; Hadwiger, Markus; Kniss, Joe; Rezk‐Salama, Christof

doi:10.1145/1103900.1103929

Cited by 171 publications

(204 citation statements)

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“…The available GPU computing power together with advantages in volume ray-casting made DVR the technique of choice for interactive 3D volume visualization [11]. However, huge amounts of volume data need to be processed for each frame to display the final aggregated 2D image on screen.…”

Section: Compact Volume Representationmentioning

confidence: 99%

Analysis of tensor approximation for compression-domain volume visualization

Ballester-Ripoll

Suter

Pajarola

2015

Computers & Graphics

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As modern high-resolution imaging devices allow to acquire increasingly large and complex volume data sets, their effective and compact representation for visualization becomes a challenging task. The Tucker decomposition has already confirmed higher-order tensor approximation (TA) as a viable technique for compressed volume representation; however, alternative decomposition approaches exist. In this work, we review the main TA models proposed in the literature on multiway data analysis and study their application in a visualization context, where reconstruction performance is emphasized along with reduced data representation costs. Progressive and selective detail reconstruction is a main goal for such representations and can efficiently be achieved by truncating an existing decomposition. To this end, we explore alternative incremental variations of the CANDECOMP/PARAFAC and Tucker models. We give theoretical time and space complexity estimates for every discussed approach and variant. Addi-tionally, their empirical decomposition and reconstruction times and approximation quality are tested in both C++ and MATLAB implementations. Several scanned real-life exemplar volumes are used varying data sizes, initialization methods, degree of com-pression and truncation. As a result of this, we demonstrate the superiority of the Tucker model for most visualization purposes, while canonical-based models offer benefits only in limited situations. AbstractAs modern high-resolution imaging devices allow to acquire increasingly large and complex volume data sets, their effective and compact representation for visualization becomes a challenging task. The Tucker decomposition has already confirmed higherorder tensor approximation (TA) as a viable technique for compressed volume representation; however, alternative decomposition approaches exist. In this work, we review the main TA models proposed in the literature on multiway data analysis and study their application in a visualization context, where reconstruction performance is emphasized along with reduced data representation costs. Progressive and selective detail reconstruction is a main goal for such representations and can efficiently be achieved by truncating an existing decomposition. To this end, we explore alternative incremental variations of the CANDECOMP/PARAFAC and Tucker models. We give theoretical time and space complexity estimates for every discussed approach and variant. Additionally, their empirical decomposition and reconstruction times and approximation quality are tested in both C++ and MATLAB implementations. Several scanned real-life exemplar volumes are used varying data sizes, initialization methods, degree of compression and truncation. As a result of this, we demonstrate the superiority of the Tucker model for most visualization purposes, while canonical-based models offer benefits only in limited situations.

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Section: Compact Volume Representationmentioning

confidence: 99%

Analysis of tensor approximation for compression-domain volume visualization

Ballester-Ripoll

Suter

Pajarola

2015

Computers & Graphics

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“…Engel et al [2] present techniques used to render large data on the GPU while Silva et al [3] present a survey of external memory techniques. In this section we summarize work that is most closely related to ours.…”

Section: Related Workmentioning

confidence: 99%

“…One pattern is implemented in an arbitrary direction slicer application that reads slices from a volume of data and composes them to build a maximum intensity projection (MIP) [2] representation of the volume. A ray casting application supports three different access patterns: it reads individual voxels, blocks of size 2 3 voxels and blocks of size 4 3 voxels along rays and builds either a MIP representation or a volume rendering [17] representation of the volume.…”

Section: Dynamic Chunking Validationmentioning

confidence: 99%

Dynamic Chunking for Out-of-Core Volume Visualization Applications

Lipsa

Sparr

Laramee

2009

Advances in Visual Computing

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Abstract. Given the size of today's data, out-of-core visualization techniques are increasingly important in many domains of scientific research. In earlier work a technique called dynamic chunking [1] was proposed that can provide significant performance improvements for an out-of-core, arbitrary direction slicer application. In this work we validate dynamic chunking for several common data access patterns used in volume visualization applications. We propose optimizations that take advantage of extra knowledge about how data is accessed or knowledge about the behavior of previous iterations and can significantly improve performance. We present experimental results that show that dynamic chunking has performance close to regular chunking but has the added advantage that no reorganization of data is required. Dynamic chunking with the proposed optimizations can be significantly faster on average than chunking for certain common data access patterns.

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“…In the field of atmospheric science and geosciences, volume rendering [1] is a popular method for data presentation and analysis. The method has been incorporated into scientific research tools like IDV [2], VAPOR [3], Met.3D [4], and ParaView [5].…”

Section: Introductionmentioning

confidence: 99%

Volume Rendering of Planet-scaled 3D Atmospheric Data on Virtual Globes

Yu¹

2018

dtcse

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Although texture slicing based volume rendering method has been widely used in data visualization, applying it to render atmospheric data that span entire globe has always been a challenging task. The difficulties are threefold. First, conventional volume rendering methods are used to process data referenced in a Cartesian coordinate system, whereas atmospheric data are georeferenced to a geodetic coordinate system. This difference brings about complications in texture sampling. Second, planet-scaled atmospheric data, especially those in high resolution, exacerbate performance issue in rendering time complexity. Third, spherical shaped bounding volume of Earth's atmosphere makes the calculation of proxy geometry difficult. In this paper, a new method is proposed to tackle the challenges aforementioned. The coordinates of volume vertices and textures are represented in spherical coordinate system to avoid the accuracy loss due to conversion between Cartesian and geodetic coordinate system. The calculation of proxy geometry is worked out. The proposed method is tested by being implemented into a rendering engine visualizing atmospheric data set.

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Real-time volume graphics

Cited by 171 publications

References 0 publications

Analysis of tensor approximation for compression-domain volume visualization

Analysis of tensor approximation for compression-domain volume visualization

Dynamic Chunking for Out-of-Core Volume Visualization Applications

Volume Rendering of Planet-scaled 3D Atmospheric Data on Virtual Globes

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