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首页> 外文期刊>International Journal of Innovative Computing Information and Control >A NOVEL CLASSIFICATION & REPAIRING FRAMEWORK FOR IRREGULAR MODEL RECONSTRUCTION USING MASS POINT CLOUDS BASED ON FUZZY INFERENCE
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A NOVEL CLASSIFICATION & REPAIRING FRAMEWORK FOR IRREGULAR MODEL RECONSTRUCTION USING MASS POINT CLOUDS BASED ON FUZZY INFERENCE

机译：基于模糊推理的质量点云，一种新型分类与修复框架，用于使用质量点云

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摘要

Mass point clouds of irregular model obtained from laser scanner are incomplete, which causes the problem that affects surface reconstruction and subsequent application. Due to the fact that geometry feature of irregular model local area has uncertainty, it is difficult to adopt one effective method to repair the lost data of model. In this paper, point clouds of irregular model are meshed firstly. According to the shape of the boundary, the hole is divided into different types by fuzzy inference system (FIS). Then the corresponding repairing method is presented for each type. For these repairing methods, public modules are introduced in detail. The repairing effect is improved greatly for corresponding methods are carried out to repair hole with different shapes. Experimental results show that the efficiency and accuracy of dental models are improved by the proposed classification and repairing framework effectively.
机译：从激光扫描仪获得的不规则模型的质量点云是不完整的，这导致影响表面重建和随后的应用的问题。由于不规则模型局域的几何特征存在不确定性，难以采用一种有效的方法来修复模型的丢失数据。在本文中，首先啮合不规则模型的点云。根据边界的形状，通过模糊推理系统（FIS）分成不同类型的孔。然后给出相应的修复方法为每种类型呈现。对于这些修复方法，详细介绍了公共模块。对于相应的方法来修复修复效果，用于修复具有不同形状的孔。实验结果表明，牙科型号的效率和准确性得到了有效的分类和修复框架。

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