New 3d segmentation approach for reverse engineering selective sampling acquisition
New 3d segmentation approach for reverse engineering selective sampling acquisition
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DOI：
10.1007/s00170-006-0772-3
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发表时间：
2008
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影响因子：
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通讯作者：
A. Courtial;Enrico Vezzetti
中科院分区：
文献类型：
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作者：
A. Courtial;Enrico Vezzetti
“分段”，即，在接近逆向工程循环时，在不同形态区域中的三维点云划分是必要的操作，因为它有助于操作者生成表面模型。该操作通常在采集和预处理阶段之后进行，并且它试图定义边界网格，随后的表面拟合操作将使用该边界网格来定义表面模型。许多方法应用远离3D扫描仪设备的分割方法。相反，本研究提出了一种迭代策略，该策略从第一个原始点采集开始，然后对对象表面进行分区，并识别出显示出显著形态特征（形状变化）的区域的边界。因此，它们将通过更深的扫描进行重新数字化，以获得更精确的形态信息。这种分割操作是由形态描述符，高斯曲率，给出局部表面形态复杂性的估计。此外，该算法采用三维扫描仪测量的不确定性，以定义一个“曲率变化阈值”，以确定这些区域显示显着的形态形状变化。
The “segmentation”, i.e., the three-dimensional point cloud partition in different morphological zones, is a necessary operation while approaching the reverse engineering cycle, because it helps the operator in generating the surface model. This operation is usually developed after the acquisition and the pre-processing phases, and it tries to define a boundary grid which the following surface fitting operation will employ for the surface model definition. Many approaches apply the segmentation methods far from the 3D scanner device. On the contrary, this research proposes an iterative strategy which starts from a first raw point acquisition and then partitions the object surface and identifies the boundary of those zones showing significant morphological features (shape-changes). As a consequence, they will be re-digitised with deeper scansions, in order to reach more precise morphological information. This partitioning operation is driven by a morphology descriptor, the gaussian curvature, giving an estimation of the local surface morphological complexity. Moreover the proposed algorithm employs the 3D scanner measuring uncertainty to define a “curvature variation threshold”, in order to identify those zones showing significant morphological shape-changes.