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Model-based force-driven nonrigid motion recovery from sequences of range images without point correspondences

机译:基于模型的力驱动非刚性运动从无点对应的距离图像序列中恢复

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In this article we propose a new method for accurate nonrigid motion analysis when point correspondence data is not available. Nonlinear finite element models are constructed by integrating range data and prior knowledge about an object's properties. The motion sequences is recovered given an initial alignment of the model with the first frame of the sequence. The main idea of the method is to find the forces that are responsible for the motion or shape deformation of the given object. The task is broken into subtasks of finding the forces for each frame. Both absolute values and direction of these forces are taken into consideration and iteratively varied not only for each frame, but also between the frames. Experimental results demonstrate the success of the proposed algorithm. The method is applied to man-made elastic materials and human hand modeling. It allows for recovery of single and multiple forces using restricted (elastic-articulated) and completely unrestricted (elastic) models. Our work demonstrates the possibility of accurate nonrigid motion analysis and force recovery from range image sequences containing nonrigid objects and large motion without interframe point correspondences.
机译:在本文中,我们提出了一种在点对应数据不可用时进行精确的非刚性运动分析的新方法。非线性有限元模型是通过整合范围数据和有关对象属性的先验知识而构建的。给定模型与序列的第一帧的初始比对,可恢复运动序列。该方法的主要思想是找到导致给定对象运动或形状变形的力。该任务分为为每个框架找到力的子任务。这些力的绝对值和方向都被考虑,并且不仅对于每个框架而且还在框架之间迭代地变化。实验结果证明了该算法的成功。该方法适用于人造弹性材料和人手建模。它允许使用限制(弹性铰接)和完全不受限制(弹性)模型来恢复单个和多个力。我们的工作证明了从包含非刚性物体的大范围图像序列中进行准确的非刚性运动分析和力恢复的可能性,并且无需帧间点对应即可进行大运动。

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