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Registration of 3D Point Cloud of Human Body Based on the Range Images and RGB Images

机译:基于范围图像和RGB图像的人体3D点云注册

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The existing 3D reconstruction techniques rarely can be easily used in people's daily life, and the traditional registration algorithms have the drawback of massive calculation. In this paper it presented a registration algorithm of body point cloud based on RGB images and Range images. First, it used kinect to obtain the RGB images and Range images from different perspectives. Then it extracted the pairs of 2D feature points on RGB images using scale-invariant feature transform (SIFT) and random sample consensus (RANSAC) algorithm application for the coarse registration and used the improved iterative closest point (ICP) algorithm for the fine registration. Second, it eliminated the background information and the noise points of the model edges. Finally it completed the registration process. Experimental results show that the algorithm can accurately accomplish the body point clouds registration using the low-cost instrument and has a relatively high efficiency.
机译:现有的3D重建技术很少可以在人们的日常生活中容易地使用,传统的登记算法具有大规模计算的缺点。本文基于RGB图像和范围图像呈现了体点云的登记算法。首先,它使用Kinect从不同的角度获取RGB图像和范围图像。然后,它使用比例不变特征变换(SIFT)和随机样本共识(RANSAC)算法应用于粗略注册,并使用改进的迭代最接近点(ICP)算法来提取RGB图像对的2D特征点对RGB图像对。其次,它消除了模型边缘的背景信息和噪声点。最后它完成了注册过程。实验结果表明,该算法可以使用低成本仪器准确完成体点云注册,并具有相对高的效率。

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