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Masters Degrees (Computer Engineering)

Permanent URI for this collectionhttps://hdl.handle.net/10413/6913

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Browsing Masters Degrees (Computer Engineering) by Subject "Central processing unit."

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    Parallel patch-based volumetric reconstruction from images.
    (2014) Jermy, Robert Sydney.; Naidoo, Bashan.; Tapamo, Jules-Raymond.
    Three Dimensional (3D) reconstruction relates to the creating of 3D computer models from sets of Two Dimensional (2D) images. 3D reconstruction algorithms tend to have long execution times, meaning they are ill suited to real time 3D reconstruction tasks. This is a significant limitation which this dissertation attempts to address. Modern Graphics Processing Units (GPUs) have become fully programmable and have spawned the field known as General Purpose GPU (GPGPU) processing. Using this technology it is possible to of- fload certain types of tasks from the Central Processing Unit (CPU) to the GPU. GPGPU processing is designed for problems that have data parallelism. This means that a particular task can be split into many smaller tasks that can run in parallel, the results of which and are not dependent upon the order in which the tasks are completed. Therefore to properly make use of both CPU parallelism and GPGPU processing a 3D reconstruction algorithm with data parallelism was required. The selected algorithm was the Patch-Based Multi-View Stereopsis (PMVS) method, proposed and implemented by Yasutaka Furukawa and Jean Ponce. This algorithm uses small oriented rectangular patches to model a surface and is broken into four major steps: Feature detection; feature matching, expansion and filtering. The reconstructed patches are independent and as such the algorithm is data parallel. Some segments of the PMVS algorithm were programmed for GPGPU and others for CPU parallelism. Results show that the feature detection stage runs 10 times faster on the GPU than the equivalent CPU implementation. The patch creation and expansion stages also benefited from GPU implementation. Which brought an improvement in the execution time of two times for large images, and equivalent execution times for small images, when compared to the CPU implementation. These results show that the use of GPGPU and CPU parallelism can indeed improve the performance of this 3D reconstruction algorithm.