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Structure and Motion Recovery Using Two Step Sampling for 3D Match Move

机译:使用两步采样进行3D匹配移动的结构和运动恢复

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Camera pose and scene geometry estimation is a fundamental requirement for match move to insert synthetic 3D objects in real scenes. In order to automate this process, auto-calibration that estimates the camera motion without prior calibration information is needed. Most auto-calibration methods for multi-views contain bundle adjustment or non-linear minimization process that is complex and difficult problem. This paper presents two methods for recovering structure and motion from handheld image sequences: the one is key-frame selection, and the other is to reject the frame with large errors among key-frames in absolute quadric estimation by LMedS (Least Median of Square). The experimental results showed the proposed method can achieve precisely camera pose and scene geometry estimation without bundle adjustment.
机译:相机姿势和场景几何估计是匹配移动的基本要求,以在真实场景中插入合成3D对象。为了自动化此过程,需要自动校准,估计没有先前校准信息的相机运动。多视图的大多数自动校准方法包含捆绑调整或非线性最小化过程,这是复杂和难题的复杂性。本文介绍了从手持式图像序列中恢复结构和运动的两种方法:一个是键帧选择,另一个是抑制屏幕(最小二乘中位数)的键帧中具有大误差的帧。实验结果表明,该方法可以在没有束调节的情况下实现精确的相机姿势和场景几何估计。

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