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Accurate camera calibration for hand-held camera in multi-view stereo reconstruction

机译:多视角立体声重建中手持摄像机的精确摄像机校准

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A flexible and accurate camera calibration algorithm with circles and concentric circle patterns is proposed for hand-held camera for multiview stereo (MVS) application, which can avoid user interaction for feature extraction in traditional planar pattern based camera calibration method. In order to automatically identify the features in image, we encode the circular patterns according to the size of the patterns. The unique code can be used to match features in images and planar patterns. We use three methods to extract the circle centers, e.g. ellipse fitting, perspective correction and circle chord midpoint approximation methods. Both simulation and real image data are utilized to validate the effectiveness of the proposed method. We use this method in multiview stereo(MVS) to reconstruct three visual hull models from image sequences. The experiments demonstrate that the concentric circle patterns have most accurate results.
机译:提出了一种灵活,准确的带有圆和同心圆图案的摄像机标定算法,用于多视点立体(MVS)手持摄像机,可以避免用户交互传统基于平面图样的摄像机标定方法中的特征提取。为了自动识别图像中的特征,我们根据图案的大小对圆形图案进行编码。唯一的代码可用于匹配图像和平面图案中的特征。我们使用三种方法来提取圆心,例如椭圆拟合,透视校正和圆弦中点逼近方法。仿真和真实图像数据均被用来验证所提出方法的有效性。我们在多视图立体(MVS)中使用此方法从图像序列重建三个视觉船体模型。实验表明,同心圆图案具有最准确的结果。

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