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首页> 外文期刊>International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences >LARGE SCALE 3D POINT CLOUD MODELING FROM CAD DATABASE IN COMPLEX INDUSTRIAL ENVIRONMENTS
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LARGE SCALE 3D POINT CLOUD MODELING FROM CAD DATABASE IN COMPLEX INDUSTRIAL ENVIRONMENTS

机译：复杂工业环境中CAD数据库的大型3D点云建模

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摘要

In this paper, we present a method to build Computer Aided Design (CAD) representations of dense 3D point cloud scenes by queries in a large CAD model database. This method is applied to real world industrial scenes for infrastructure modeling. The proposed method firstly relies on a region growing algorithm based on novel edge detection method. This algorithm is able to produce geometrically coherent regions which can be agglomerated in order to extract the objects of interest of an industrial environment. Each segment is then processed to compute relevant keypoints and multi-scale features in order to be compared to all CAD models from the database. The best fitting model is estimated together with the rigid six degree of freedom (6 DOF) transformation for positioning the CAD model on the 3D scene. The proposed novel keypoints extractor achieves robust and repeatable results that captures both thin geometrical details and global shape of objects. Our new multi-scale descriptor stacks geometrical information around each keypoint at short and long range, allowing non-ambiguous matching for object recognition and positioning. We illustrate the efficiency of our method in a real-world application on 3D segmentation and modeling of electrical substations.
机译：在本文中，我们介绍了一种在大CAD模型数据库中通过查询构建计算机辅助设计（CAD）表示的计算机辅助设计（CAD）表示。这种方法适用于基础设施建模的现实世界工业场景。所提出的方法首先依赖于基于新颖边缘检测方法的区域生长算法。该算法能够产生可以凝聚的几何相干区域，以便提取工业环境的兴趣对象。然后处理每个段以计算相关的关键点和多尺度特征，以便与来自数据库的所有CAD模型进行比较。最佳拟合模型与刚性六度自由度（6 DOF）转换估计，用于将CAD模型定位在3D场景上。所提出的新型关键点提取器实现了稳健和可重复的结果，可捕获薄的几何细节和全局对象形状。我们的新多尺度描述符在短期和长距离围绕每个关键点周围堆叠几何信息，允许对对象识别和定位进行非模糊匹配。我们说明了我们在真实世界应用于3D分割和电气变电站建模中的方法中的方法。

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