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Automatic 3D model acquisition and generation of new images from video sequences

机译:自动3D模型获取和从视频序列生成新图像

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We describe a method to completely automatically recover 3D scene structure together with 3D camera positions from a sequence of images acquired by an unknown camera undergoing unknown movement. Unlike "tuned" systems which use calibration objects or markers to recover this information, and are therefore often limited to a particular scale, the approach of this paper is more general and can be applied to a large class of scenes. It is demonstrated here for interior and exterior sequences using both controlled-motion and handheld cameras. The paper reviews Computer Vision research into structure and motion recovery, providing a tutorial introduction to the geometry of multiple views, estimation and correspondence in video streams. The core method, which simultaneously extracts the 3D scene structure and camera positions, is applied to the automated recovery of VRML 3D textured models from a video sequence.
机译:我们描述了一种方法,该方法可以完全自动地从由经历未知运动的未知摄像机获取的图像序列中恢复3D场景结构以及3D摄像机位置。与使用校准对象或标记来恢复此信息的“已调整”系统不同,因此通常被限制在特定的比例范围内,本文的方法更为通用,可应用于大量场景。此处演示了使用受控运动和手持摄像机的内部和外部序列。本文回顾了计算机视觉在结构和运动恢复方面的研究,并提供了有关视频流中多视图,估计和对应关系的几何结构的教程介绍。同时提取3D场景结构和摄像机位置的核心方法应用于从视频序列自动恢复VRML 3D纹理模型。

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