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首页> 外文期刊>Journal of Advanced Computatioanl Intelligence and Intelligent Informatics >Local Character Tensors for 3D Registration Method on Free-View Datasets
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Local Character Tensors for 3D Registration Method on Free-View Datasets

机译:自由视图数据集上用于3D注册方法的本地字符张量

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摘要

A local character tensor is proposed for the automatic three-dimensional (3D) pair-wise registration based on free-view 3D datasets. In the proposed method, there are two characters, i.e., the optimal segmentation to realize the automatic processing and local character tensor to improve the matching probability. It is applied for solving the mismatching problem and large-scale 3D datasets, using non-structured datasets are tested in a PC with Intel Pentium M 1.50 GHz and 1.0 GB memory. Pair-wised experimental results show the proposed method increases average 12.6% matching probability and decreases average 18.9 seconds computational time compared to the conventional local character based registration method. This registration method can be further applied to 3D reconstruction from navigation, model based object recognition to accurate 3D geometric object model application.
机译:提出了一种局部字符张量,用于基于自由视图3D数据集的自动三维(3D)成对注册。在提出的方法中,存在两个字符,即实现自动处理的最佳分割和提高匹配概率的局部字符张量。它用于解决不匹配问题和大规模3D数据集,使用非结构化数据集在具有Intel Pentium M 1.50 GHz和1.0 GB内存的PC上进行了测试。成对的实验结果表明,与传统的基于本地字符的注册方法相比,该方法提高了平均12.6%的匹配概率,并减少了平均18.9秒的计算时间。该配准方法可以进一步应用于从导航,基于模型的对象识别到精确的3D几何对象模型应用的3D重建。

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