A refined data addressing and processing scheme to accelerate volume raycasting

Grimm, Sören; Brückner, Stefan; Kanitsar, Armin; Gröller, M. Eduard

doi:10.1016/j.cag.2004.06.010

Cited by 24 publications

(13 citation statements)

References 10 publications

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“…A class of techniques exist, where spectral data properties are studied with the goal to estimate the optimal sampling step [Danskin and Hanrahan 1992;Stander and Hart 1994]. In others, hierarchical encoding [Levoy 1990a;Levoy 1990b;Ke and Chang 1993;Fung and Heng 1998;Feng Dong et al 2000], spectral representation [Totsuka and Levoy 1993;Malzbender 1993] or specialized data structures [Udupa and Odhner 1993;Lacroute and Levoy 1994;Parker et al 1998;Parker et al 1999;Grimm et al 2004] are used. Other techniques take advantage from segmentation of the volume in background and foreground.…”

Section: Dvr By Ray Castingmentioning

confidence: 99%

20 years of volume rendering

Šrámek

2006

Proceedings of the 22nd Spring Conference on Computer Graphics

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In the year 2007 we celebrate the 20-th anniversary of coining the notion of volume rendering, followed by its first "official" usage in the papers by Levoy and by Drebin, Carpenter and Hanrahan in 1988. The new and revolutionary introduced by these papers was the possibility to visualize objects represented by discrete threedimensional scalar fields-usually tomographic scans-without the necessity to fit any surface models to the objects represented therein. This new approach conceptually simplified sequence of the required rendering operations-the rendering pipeline, and enabled to keep the important connection between the original three-dimensional input data and the final renditions. Thus, a possibility of high quality rendering of complex structures emerged, not limited just to objects with well defined surfaces but also to those with diffused, blurred and weakly defined boundaries in general.In this overview a brief sketch of the volume rendering history is given, with an emphasis on the most important developments in the areas of the basic algorithms and their accelerated modifications, object and tissue specification techniques by transfer functions and segmentation and rendering of complex multiobject scenes.

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Section: Dvr By Ray Castingmentioning

confidence: 99%

20 years of volume rendering

Šrámek

2006

Proceedings of the 22nd Spring Conference on Computer Graphics

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“…Bricking on the CPU was used to improve cache coherency by Grimm et al [9]. Kähler et al [13] use a CPU based kd-tree on top of a bricking approach to render adaptive mesh refinement data.…”

Section: Related Workmentioning

confidence: 99%

JiTTree: A Just-in-Time Compiled Sparse GPU Volume Data Structure

Labschutz

Brückner

Gröller

et al. 2016

IEEE Trans. Visual. Comput. Graphics

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“…This transfer is very time consuming, thus interactive feedback becomes problematic. In contrast, within a pure CPU based solution this transfer is unnecessary allowing more efficient processing of data (Grimm et al, 2004).…”

Section: Volume Rendering On the Cpumentioning

confidence: 99%

Medical Imaging in a Cloud Computing Environment

Parsonson¹,

Bai²,

Bourn³

et al. 2011

Proceedings of the 1st International Conference on Cloud Computing and Services Science

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Abstract:In this paper we present a cloud computing environment for medical imaging which deals with the issues of scaling a traditionally single-user solution to a software-as-a-service solution. We will first introduce volume rendering for medical imaging, and the issues with volume rendering of medical images on the cloud. We will then describe our method for accelerating CPU based volume rendering on the cloud and for scaling the system to a software-as-a-service solution.

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A refined data addressing and processing scheme to accelerate volume raycasting

Cited by 24 publications

References 10 publications

20 years of volume rendering

20 years of volume rendering

JiTTree: A Just-in-Time Compiled Sparse GPU Volume Data Structure

Medical Imaging in a Cloud Computing Environment

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