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Parallel Methods for Projection on Strongly Curved Surfaces
Norwegian University of Science and Technology, Faculty of Information Technology, Mathematics and Electrical Engineering, Department of Computer and Information Science.
2011 (English)MasteroppgaveStudent thesis
Abstract [en]

Using the parallel architecture of the graphics processing unit for general purpose programming has become increasingly common in the recent years. The process of creating a mathematically correct transformation of a scene for curved stereoscopic projection is a very expensive task, which would greatly benefit from a massively parallel solution implemented on the GPU. In this thesis, we first investigate two different methods for obtaining a mathematically correct transformation of images intended for stereoscopic projection on strongly curved surfaces. One method revolves around transforming a pre-rendered image, pixel by pixel, while the other method applies the transformation to the projection of the vertices in the scene before they are rendered as an image. We then develop massively parallel solutions for both these methods on the GPU, striving to a reach a real-time rate for the stereoscopic projection of the transformed images. We test both methods for different problem areas, and compare the results to map their strengths and weaknesses. From the obtained results, we conclude that they are both useful in different areas. The vertex transformation performs poorly when the number of vertices in the scene is very high, but for a moderate number of vertices it achieves excellent results, even for exceptionally large image resolutions. The pixel transformation is far less affected by the number of vertices in the scene; however its performance declines rapidly as we increase the size of the image. Both methods were able to execute in real-time for relevant problem sizes.

Place, publisher, year, edition, pages
Institutt for datateknikk og informasjonsvitenskap , 2011. , 106 p.
Keyword [no]
ntnudaim:6190, MTDT datateknikk, Komplekse datasystemer
URN: urn:nbn:no:ntnu:diva-14979Local ID: ntnudaim:6190OAI: diva2:473469
Available from: 2012-01-06 Created: 2012-01-06

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