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Learning Object Appearance from Occlusions Using Structure and Motion Recovery

机译:使用结构和运动恢复从遮挡中学习对象的外观

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Visual effect creation as used in movie production often require structure and motion recovery and video segmentation. Both techniques are essential to integrate virtual objects between scene elements. In this paper, a new method for video segmentation is presented. It incorporates 3D scene information from the structure and motion recovery. By connecting and evaluating discontinued feature tracks, occlusion and reappearance information is obtained during sequential camera and scene estimation. The foreground is characterized as image regions which temporarily occlude the rigid scene structure. The scene structure is represented by reconstructed object points. Their projections onto the camera images provide the cues for regions classified as foreground or background. The knowledge of occluded parts of a connected feature track is used to feed the object segmentation which crops the foreground image regions automatically. Two applications are presented: the occlusion of integrated virtual objects and the blurred background effect. Several demonstrations on official and self-made data show very realistic results in augmented reality.
机译:电影制作中使用的视觉效果创建通常需要结构和动作恢复以及视频分割。这两种技术对于在场景元素之间集成虚拟对象都是必不可少的。本文提出了一种新的视频分割方法。它结合了来自结构和运动恢复的3D场景信息。通过连接和评估不连续的特征轨迹,可以在顺序相机和场景估计期间获得遮挡和重现信息。前景的特征是暂时遮盖了刚性场景结构的图像区域。场景结构由重建的对象点表示。它们在摄像机图像上的投影为分类为前景或背景的区域提供了提示。所连接要素轨迹的被遮挡部分的知识用于馈送对象分割,该对象分割会自动裁剪前景图像区域。提出了两个应用程序:集成虚拟对象的遮挡和模糊的背景效果。关于官方数据和自制数据的几次演示在增强现实中显示了非常现实的结果。

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