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Free-viewpoint Image Generation from a Video Captured bya Handheld Camera

机译:从手持式摄像机捕获的视频的自由视图像生成

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In general, free-viewpoint image is generated by captured images by a camera array aligned on a straight line or circle. A camera array is able to capture synchronized dynamic scene. However, camera array is expensive and requires great care to be aligned exactly. In contrast to camera array, a handy camera is easily available and can capture a static scene easily. We propose a method that generates free-viewpoint images from a video captured by a handheld camera in a static scene. To generate free-viewpoint images, view images from several viewpoints and information of camera pose/positions of these viewpoints are needed. In a previous work, a checkerboard pattern has to be captured in every frame to calculate these parameters. And in another work, a pseudo perspective projection is assumed to estimate parameters. This assumption limits a camera movement. However, in this paper, we can calculate these parameters by "Structure From Motion". Additionally, we propose a selection method for reference images from many captured frames. And we propose a method that uses projective block matching and graph-cuts algorithm with reconstructed feature points to estimate a depth map of a virtual viewpoint.
机译:通常,通过在直线或圆圈上对齐的相机阵列由捕获的图像产生义视点图像。相机阵列能够捕获同步动态场景。但是,相机阵列昂贵,需要精心谨慎。与相机阵列相比,轻松的相机很容易可用,可以轻松捕捉静态场景。我们提出了一种方法,该方法从静态场景中由手持式相机捕获的视频生成自由视点图像。要生成义视点图像,需要从多个视点查看图像和这些观点的相机姿势/位置的信息。在以前的工作中,必须在每个帧中捕获棋盘模式以计算这些参数。在另一个工作中,假设假设伪透视投影来估计参数。此假设限制了相机运动。但是,在本文中,我们可以通过“来自运动”来计算这些参数。另外,我们提出了一种从许多捕获帧的参考图像的选择方法。并且我们提出了一种方法,该方法使用具有重建特征点的重建特征点来估计虚拟视点的深度图。

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