GAMVOLVIS

GPU-Assisted Massive Volume Data Visualisation

Coordinatore	UNIVERSITY OF BEDFORDSHIRE    more  Organization address address: PARK SQUARE city: LUTON postcode: LU1 3JU contact info Titolo: Prof. Nome: Gordon Cognome: Clapworthy Email: send email Telefono: -745034 Fax: -490750
Nazionalità Coordinatore	United Kingdom [UK]
Totale costo	0 €
EC contributo	173˙185 €
Programma	FP7-PEOPLE Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
Code Call	FP7-PEOPLE-IIF-2008
Funding Scheme	MC-IIF
Anno di inizio	2009
Periodo (anno-mese-giorno)	2009-09-01   -   2011-08-31

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	UNIVERSITY OF BEDFORDSHIRE  Organization address address: PARK SQUARE city: LUTON postcode: LU1 3JU contact info Titolo: Prof. Nome: Gordon Cognome: Clapworthy Email: send email Telefono: -745034 Fax: -490750	UK (LUTON)	coordinator	173˙185.81

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 Obiettivo del progetto (Objective)

'High-resolution medical volume data (containing billions of voxels) is used in many clinical applications. However, interactive visualisation of gigabyte-sized volumes on a desktop PC is challenging, due to the heavy computation and the memory consumption. In recent years, the graphics processing unit (GPU) has evolved at an increasing pace, and tremendous improvements have been achieved in its capabilities. GPU performance now substantially exceeds that of CPUs in both the raw computational power provided and its speed of development. Thus, the GPU is now the ideal platform for efficient visualisation for large volume data. However, current methods for rendering large volume datasets are limited, either in performance, accuracy, flexibility or scalability. This project addresses this by presenting techniques to perform volume visualisation of large datasets on off-the-shelf hardware by harnessing the power of GPUs to provide high performance and produce high-quality images. We will achieve this by implementing a multi-resolution framework with two parts: data management on the CPU and real-time rendering on the GPU. We propose a new rendering pipeline by directly treating each volume brick as a single GPU rendering primitive, and efficient cache management to avoid cache thrashing of GPU memory. Forward mapping will allow arbitrariiy many primitives to be rendered on the GPU in a stream-like manner, making our system fully scalable to an arbitrarily large dataset. Further acceleration will be achieved by using a coarsely-fitted proxy geometry, and advanced illumination techniques will be introduced for better quality visualisation at high frame rates. We will also extend our applications from a single GPU to multiple GPUs. The resulting system will allow domain scientists to effectively visualise super-large-scale volume data on moderate PC platforms without being heavily restricted by the data size, which is a key advance from the current state of the art.'