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3D non-rigid registration using color: Color Coherent Point Drift

机译:使用颜色的3D非刚性配准:颜色相干点漂移

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Research into object deformations using computer vision techniques has been under intense study in recent years. A widely used technique is 3D non-rigid registration to estimate the transformation between two instances of a deforming structure. Despite many previous developments on this topic, it remains a challenging problem. In this paper we propose a novel approach to non-rigid registration combining two data spaces in order to robustly calculate the correspondences and transformation between two data sets. In particular, we use point color as well as 3D location as these are the common outputs of RGB-D cameras. We have propose the Color Coherent Point Drift (CCPD) algorithm (an extension of the CPD method (Myronenko and Song, 2010)). Evaluation is performed using synthetic and real data. The synthetic data includes easy shapes that allow evaluation of the effect of noise, outliers and missing data. Moreover, an evaluation of realistic figures obtained using Blensor is carried out. Real data acquired using a general purpose Primesense Carmine sensor is used to validate the CCPD for real shapes. For all tests, the proposed method is compared to the original CPD showing better results in registration accuracy in most cases.
机译:近年来,使用计算机视觉技术对物体变形的研究一直在深入研究。广泛使用的技术是3D非刚性配准,以估计变形结构的两个实例之间的转换。尽管在此主题上已有许多发展,但它仍然是一个具有挑战性的问题。在本文中,我们提出了一种新的非刚性配准方法,该方法将两个数据空间组合在一起,以便稳健地计算两个数据集之间的对应关系和转换。特别是,我们使用点颜色以及3D位置,因为这些是RGB-D相机的常见输出。我们提出了颜色相干点漂移(CCPD)算法(CPD方法的扩展(Myronenko和Song,2010))。使用综合和真实数据进行评估。合成数据包括简单的形状,可以评估噪声,离群值和缺失数据的影响。此外,对使用Blensor获得的真实图形进行了评估。使用通用Primesense胭脂红传感器获取的真实数据用于验证CCPD的真实形状。对于所有测试,在大多数情况下,将建议的方法与原始CPD进行比较,显示出更好的配准精度结果。

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