Research on CUDA-based image parallel dense matching

By analyzing the computer burden of image dense matching and the characteristic of Graphic Processor Unit(GPU) parallel computing mode, we designed a new solution of image parallel dense matching based on CUDA. We adopted the coarse-to-fine strategy, firstly implemented the pixel level normalized cr...

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Bibliographic Details
Published in2013 Chinese Automation Congress pp. 482 - 486
Main Authors Zhu Zunshang, Ge Zhen, Chen Shengyi, Sun Xiaoliang, Shang Yang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2013
Subjects
Computer architecture
CUDA
Equations
GPU
Gradient methods
Graphics processing units
image dense matching
Image matching
Instruction sets
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Summary:By analyzing the computer burden of image dense matching and the characteristic of Graphic Processor Unit(GPU) parallel computing mode, we designed a new solution of image parallel dense matching based on CUDA. We adopted the coarse-to-fine strategy, firstly implemented the pixel level normalized cross-correlation(NCC) matching method on CUDA; and then improved the matching precision by parallel affine least square matching(ALSM) under the GPU architecture. The proposed method implemented the dense matching in a full parallel mode, and took the advantage of the multi-threads supported by GPU, and finally obtained an obvious improvement in computational efficiency. The experiment results indicated that: the overall time consuming for the proposed dense matching method on GPU can achieve up to 25 times speedup over the version on CPU, which makes real-time 3D reconstruction become possible.
DOI:10.1109/CAC.2013.6775782