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首页> 外文会议>2011 Eighth International Conference on Computer Graphics, Imaging Visualization >The DSO Feature Based Point Cloud Simplification
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The DSO Feature Based Point Cloud Simplification

机译：基于DSO功能的点云简化

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This study proposes an effective low-error point cloud simplification method to retain the physical features of the models. The value of Discrete Shape Operator (DSO) is adopted to extract the features points of the models, and those are postponed to simplify. The value of DSO is defined as the discrete sum over the directional curvature and torsion. The proposed method improves the Quadric Error Metric of vertex pair contraction, it not only effectively simplifies the point cloud model and keeps the features of object model, but also decreases the preprocessing time cost associated with feature analysis. This study also proposes a method to obtain unique simplified model for each model and the time cost involved in calculating DSO is about 17.29% of the execution time. The unique simplified model obtained by this study can significantly reduce the computation cost about 72.72% than mesh simplification which reconstruct original points first.
机译：这项研究提出了一种有效的低误差点云简化方法，以保留模型的物理特征。采用离散形状算子（Discrete Shape Operator，DSO）的值来提取模型的特征点，并将其推迟以简化。 DSO的值定义为方向曲率和扭转上的离散和。该方法改进了顶点对收缩的二次误差度量，不仅有效简化了点云模型，保留了对象模型的特征，而且减少了特征分析带来的预处理时间成本。这项研究还提出了一种为每种模型获得唯一的简化模型的方法，并且计算DSO所涉及的时间成本约为执行时间的17.29％。与首先重建原始点的网格简化相比，本研究获得的独特简化模型可以显着减少约72.72％的计算成本。

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