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Nonmetric calibration of wide-angle lenses and polycameras

机译:广角镜和多相机的非度量校准

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Images taken with wide-angle cameras tend to have severe distortions which pull points towards the optical center. This paper proposes a simple method for recovering the distortion parameters without the use of any calibration objects. Since distortions cause straight lines in the scene to appear as curves in the image, our algorithm seeks to find the distortion parameters that map the image curves to straight lines. The user selects a small set of points along the image curves. Recovery of the distortion parameters is formulated as the minimization of an objective function which is designed to explicitly account for noise in the selected image points. Experimental results are presented for synthetic data as well as real images. We also present the idea of a polycamera which is defined as a tightly packed camera cluster. Possible configurations are proposed to capture very large fields of view. Such camera clusters tend to have a nonsingle viewpoint. We therefore provide analysis of what we call the minimum working distance for such clusters. Finally, we present results for a polycamera consisting of four wide-angle sensors having a minimum working distance of about 4 m. On undistorting the acquired images using our proposed technique, we create real-time high resolution panoramas.
机译:用广角相机拍摄的图像往往会出现严重的畸变,将点拉向光学中心。本文提出了一种无需使用任何校准对象即可恢复失真参数的简单方法。由于失真会导致场景中的直线在图像中显示为曲线,因此我们的算法试图找到将图像曲线映射到直线的失真参数。用户沿着图像曲线选择一小组点。失真参数的恢复被公式化为目标函数的最小化,该目标函数旨在明确考虑所选图像点中的噪声。给出了合成数据以及真实图像的实验结果。我们还提出了将多相机定义为紧密包装的相机集群的想法。提出了可能的配置以捕获非常大的视野。这样的相机组倾向于具有非单一的观点。因此,我们对所谓的此类群集的最小工作距离进行了分析。最后,我们介绍了一个由四个最小工作距离约为4 m的广角传感器组成的多相照相机的结果。使用我们提出的技术使采集的图像不失真时,我们会创建实时高分辨率全景图。

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