GPU-Accelerated Real-Time Stereo Matching

dc.contributor.authorHillerström, Peter
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)sv
dc.contributor.departmentChalmers University of Technology / Department of Computer Science and Engineering (Chalmers)en
dc.description.abstractA problem in the field of computer vision is the correspondence problem, the problem of finding pixels which correspond to each other in different images. A stereo matching algorithm is used to solve this kind of problem, and typically produces a disparity map, or a depth map. Current approaches are often too slow to be used in real-time, leading to the question of which algorithm is best for such purposes. This thesis explores which approach to stereo matching is most appropriate for real-time purposes. In addition, it is also explored what optimizations and approximations can be applied in order to improve performance. This was accomplished by implementing an Adaptive Support Weights based stereo matching algorithm in CUDA, and exploring various approximations and performance optimizations related to it. It is shown that Adaptive Support Weights is a good method for real-time use. This thesis’ most significant contribution is the performance optimizations presented, which significantly improve upon the performance of the algorithm compared to previous work.
dc.subjectInformations- och kommunikationsteknik
dc.subjectData- och informationsvetenskap
dc.subjectInformation & Communication Technology
dc.subjectComputer and Information Science
dc.titleGPU-Accelerated Real-Time Stereo Matching
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
local.programmeComputer science – algorithms, languages and logic (MPALG), MSc
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