Large-scale feature matching with distributed and heterogeneous computing

Feature matching is a fundamental problem in many computer vision tasks. As datasets become larger, and individual image resolution increases, this is becoming more and more computationally demanding work. While prior knowledge about the scene geometry can, in some cases, reduce the number of image pairs that need to be considered, the sheer volume of data means that parallel and distributed computing solutions must be considered. In this paper we examine the costs incurred in such solutions, and assess the way in which the problem scales with the number of cores within a single node, and the number of nodes in a distributed system. We also consider the role of heterogeneous systems, where nodes with different numbers and types of cores (including GPUs) are included in a distributed system. We show that distribution of this task across a cluster of machines has good (sometimes super-linear) scalability. However, scalability on many-core machines and GPU architectures is more limited, and is thus an important area for future research.