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Robust 3D Object Tracking from Monocular Images Using Stable Parts
IEEE Transactions on Pattern Analysis and Machine Intelligence ( IF 23.6 ) Pub Date : 2017-05-26 , DOI: 10.1109/tpami.2017.2708711
Alberto Crivellaro , Mahdi Rad , Yannick Verdie , Kwang Moo Yi , Pascal Fua , Vincent Lepetit

We present an algorithm for estimating the pose of a rigid object in real-time under challenging conditions. Our method effectively handles poorly textured objects in cluttered, changing environments, even when their appearance is corrupted by large occlusions, and it relies on grayscale images to handle metallic environments on which depth cameras would fail. As a result, our method is suitable for practical Augmented Reality applications including industrial environments. At the core of our approach is a novel representation for the 3D pose of object parts: We predict the 3D pose of each part in the form of the 2D projections of a few control points. The advantages of this representation is three-fold: We can predict the 3D pose of the object even when only one part is visible; when several parts are visible, we can easily combine them to compute a better pose of the object; the 3D pose we obtain is usually very accurate, even when only few parts are visible. We show how to use this representation in a robust 3D tracking framework. In addition to extensive comparisons with the state-of-the-art, we demonstrate our method on a practical Augmented Reality application for maintenance assistance in the ATLAS particle detector at CERN.

中文翻译:

使用稳定零件从单眼图像进行稳健的3D对象跟踪

我们提出了一种在挑战性条件下实时估算刚性物体姿态的算法。我们的方法可以有效地处理杂乱变化的环境中质地较差的对象,即使它们的外观因较大的遮挡而损坏,也可以依靠灰度图像来处理深度相机可能无法使用的金属环境。因此,我们的方法适用于包括工业环境在内的实际增强现实应用。我们方法的核心是对对象零件的3D姿态的新颖表示:我们以一些控制点的2D投影的形式预测每个零件的3D姿态。这种表示法的优点有三方面:即使只有一部分可见,我们也可以预测对象的3D姿势。当几个部分可见时,我们可以轻松地将它们组合起来以计算出更好的对象姿态;即使只有很少的部分可见,我们获得的3D姿态通常也非常准确。我们展示了如何在强大的3D跟踪框架中使用此表示形式。除了与最新技术进行大量比较之外,我们还在CERN的ATLAS粒子探测器中在实际的增强现实应用中展示了我们的方法,以用于维护协助。
更新日期:2018-05-05
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