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首页> 外文会议>IEEE International Conference on Shape Modeling and Applications >Segmenting Point Sets
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Segmenting Point Sets

机译：分段点集

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Extracting features from point sets is becoming increasingly important for purposes like model classification, matching, and exploration. We introduce a technique for segmenting a point-sampled surface into distinct features without explicit construction of a mesh or other surface representation. Our approach achieves computational efficiency through a three-phase segmentation process. The first phase of the process uses a topological approach to define features and coarsens the input, resulting in a set of supernodes, each one representing a collection of input points. A graph cut is employed in the second phase to bisect the set of supernodes. Similarity between supernodes is computed as a weighted combination of geodesic distances and connectivity. Repeated application of the graph cut results in a hierarchical segmentation of the point input. In the last phase, a segmentation of the original point set is constructed by refining the segmentation of the supernodes based on their associated feature sizes.We apply our segmentation algorithm on laser-scanned models to evaluate its ability to capture geometric features in complex data sets.
机译：从点集中提取功能对于模型分类，匹配和探索等目的而言变得越来越重要。我们介绍一种将点采样表面分割成不同特征的技术，而无需明确构造网格或其他表面表示。我们的方法通过三相分割过程实现了计算效率。该过程的第一阶段使用拓扑方法来定义特征并造熟输入，从而产生一组超节点，每个超节点表示输入点的集合。在第二阶段采用图表切割以使一组超节点分解。超节点之间的相似性被计算为测地距离和连接的加权组合。重复应用图形切割导致点输入的分层分割。在最后一段中，通过基于它们的关联特征大小精制超大节点的分割来构造原始点集的分割。我们在激光扫描模型上应用我们的分段算法，以评估其在复杂数据集中捕获几何特征的能力。

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