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首页> 外文期刊>Journal of visual communication & image representation >3D information extraction using Region-based Deformable Net for monocular robot navigation
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3D information extraction using Region-based Deformable Net for monocular robot navigation

机译：使用基于区域的可变形网进行单眼机器人导航的3D信息提取

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摘要

This paper extends the Region-based Deformable Net (RbDN) technique described in [ 1 ] to extract the 3D information of all the objects in the scene from a single moving camera. The technique is used for seg menting real-time video sequences captured from a single moving camera. The deformation process tracks the changes in the location and the shape of the segments across the frames. These changes along with the camera displacement are used to estimate the 3D information. The algorithm is completely autonomous and does not require pre-knowledge, training, or assumption about the contents of the sequence. It can handle the difficult case where the motion of the camera is parallel to its optical axis. It can also estimate the distances to objects that are more than 100 m away as long as the camera dis placement is over 10% of the expected distance to the objects.
机译：本文扩展了[1]中描述的基于区域的可变形网络（RbDN）技术，以从单个移动摄像机中提取场景中所有对象的3D信息。该技术用于分割从单个移动摄像机捕获的实时视频序列。变形过程跟踪跨框架的片段的位置和形状的变化。这些变化与相机位移一起用于估计3D信息。该算法是完全自主的，不需要预先了解，训练或假设有关序列内容的情况。它可以处理相机运动平行于其光轴的困难情况。只要摄像机的位移超过到对象的预期距离的10％，它也可以估计到100m以上距离的对象的距离。

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