Articulated and Restricted Motion Subspaces and Their Signatures

Bastien Jacquet, Roland Angst, M. Pollefeys
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引用次数: 18

Abstract

Articulated objects represent an important class of objects in our everyday environment. Automatic detection of the type of articulated or otherwise restricted motion and extraction of the corresponding motion parameters are therefore of high value, \eg in order to augment an otherwise static 3D reconstruction with dynamic semantics, such as rotation axes and allowable translation directions for certain rigid parts or objects. Hence, in this paper, a novel theory to analyse relative transformations between two motion-restricted parts will be presented. The analysis is based on linear subspaces spanned by relative transformations. Moreover, a signature for relative transformations will be introduced which uniquely specifies the type of restricted motion encoded in these relative transformations. This theoretic framework enables the derivation of novel algebraic constraints, such as low-rank constraints for subsequent rotations around two fixed axes for example. Lastly, given the type of restricted motion as predicted by the signature, the paper shows how to extract all the motion parameters with matrix manipulations from linear algebra. Our theory is verified on several real data sets, such as a rotating blackboard or a wheel rolling on the floor amongst others.
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铰接式和受限运动子空间及其特征
铰接对象代表了我们日常环境中一类重要的对象。因此,自动检测关节或其他受限运动的类型并提取相应的运动参数具有很高的价值,例如,为了增强具有动态语义的静态3D重建,例如某些刚性部件或对象的旋转轴和允许的平移方向。因此,本文将提出一种新的理论来分析两个运动受限部分之间的相对变换。该分析基于由相对变换张成的线性子空间。此外,将引入一个相对变换的签名,它唯一地指定在这些相对变换中编码的受限运动的类型。这一理论框架使得新的代数约束的推导成为可能,例如围绕两个固定轴的后续旋转的低秩约束。最后,根据特征预测的受限运动类型,给出了如何从线性代数中利用矩阵处理提取所有运动参数的方法。我们的理论在几个真实的数据集上得到了验证,例如旋转的黑板或在地板上滚动的轮子等等。
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