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Periodic Patterns Recovery for Multicamera Calibration

机译：用于多摄像机校准的定期模式恢复

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摘要

Camera calibration is an essential step for most computer vision applications. This task usually requires the consistent detection of a 2D periodic pattern across multiple views and in practice one of the main difficulties is a correct localization of the pattern origin and its orientation in case of partial occlusion. To overcome this problem many calibration tools require a full visibility of the calibration pattern, which is not always possible, especially when a multicamera systems are used. This paper addresses the specific problem of consistent recovery of the calibration pattern, captured by a multicamera systems under the condition of partial occlusion of the calibration object in several (even all) calibration images. The proposed algorithm is structured in two sequential steps aimed at the removal of the rotational and the translational components of the pattern offset transformation, which is essential for a correct calibration. The paper focuses on two common calibration patterns, the checkerboard grid and the bundle of parallel lines; however, the technique can be easily rearranged in order to cope with other classes of periodic patterns. The algorithm effectiveness has been successfully proven on the simulated data and two real calibration datasets, captured using a fisheye stereo rig.
机译：相机校准是大多数计算机视觉应用的重要步骤。该任务通常需要跨多个视图的2D周期性模式的一致检测，并且在实践中，主要困难之一是在部分闭塞的情况下的模式来源的正确定位及其方向。为了克服这个问题，许多校准工具需要完全可见的校准模式，这并不总是可能的，特别是当使用多轨系统时。本文解决了在校准对象的校准对象的部分闭塞条件下由多色系统捕获的校准模式的一致恢复的具体问题。所提出的算法以两个顺序步骤来说，旨在去除图案偏移变换的旋转和平移组件，这对于正确的校准是必不可少的。本文侧重于两个常见的校准模式，棋盘网格和平行线束;然而，该技术可以很容易地重新排列，以便应对其他类周期性模式。在模拟数据和两个实际校准数据集上成功证明了算法的有效性，使用Fisheye立体声钻机捕获。

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