We discuss how to automatically obtain the metric calibration of an ad-hoc network of cameras with no centralized processor. We model the set of uncalibrated cameras as nodes in a communication network, and propose a distributed algorithm in which each camera performs a local, robust bundle adjustment over the camera parameters and scene points of its neighbors in an overlay "vision graph". We analyze the performance of the algorithm on both simulated and real data, and show that the distributed algorithm results in a fairer allocation of messages per node while achieving comparable calibration accuracy to centralized bundle adjustment.

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Abstract-Large high-resolution displays (LHRD) enable visualization of extremely large-scale data sets with high resolution, large physical size, scalable rendering performance, advanced interaction methods, and collaboration. Despite the advantages, applications for LHRD can be developed only by a select group of researchers and programmers, since its software implementation requires design and development paradigms different from typical desktop environments. It is critical for developers to understand and take advantage of appropriate software tools and methods for developing their LHRD applications. In this paper, we present a survey of the state-of-the-art software frameworks and applications for cluster-based LHRD, highlighting a three-aspect taxonomy. This survey can aid LHRD application and framework developers in choosing more suitable development techniques and software environments for new LHRD applications, and guide LHRD researchers to open needs in LHRD software frameworks.

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