Parallel patch-based volumetric reconstruction from images.
Loading...
Date
2014
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
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.
Description
M. Sc. Eng. University of KwaZulu-Natal, Durban 2014.
Keywords
Three-dimensional imaging., Image processing., Computer graphics., Graphics processing units., Theses--Computer engineering., 3-D reconstruction algorithm., Central processing unit.