Analysis of the efficiency of dense stereovision algorithm implementation on different computer architectures

Algorithms of dense stereovision can be of great interest for designing computer vision systems of autonomous mobile objects. In the process of computing dense stereo, on the one hand, a lot of arithmetic operations are performed and a significant memory resource is required, and, on the other hand, the majority of dense stereovision algorithms can be parallelized. Different modern computer architectures available for processing data from stereo sensors can be named. While choosing certain architecture it is necessary to take into consideration energy efficiency, performance and dimensions of final algorithm implementation. The article gives justification to choose the algorithm of dense stereovision and analyzes its efficiency on different calculators. The analysis is performed on three different architectures: classic CPU, GPU and FPGA. The aim of the analysis is to find the best architecture to suit the task of using dense stereovision algorithms in mobile systems of computer vision.