Point cloud decomposition by internal and external critical points

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Decomposing a complex object into simple components is a fundamental problem in geometry processing. Existing methods for decomposing point clouds rely on local or global features of an object, which leads to over-segmentation or unnatural component boundaries. In this paper, we propose a novel method for decomposing the point cloud by using internal and external critical points. First, we propose a novel shrinking strategy to build the global skeleton topology, from which we can extract internal critical points for locating components. External critical points are selected from the ridge and valley points for component segmentation. Then, we apply the constraint of internal and external critical points to decompose an object into semantic components by skeleton-based piecewise labeling. Experimental results demonstrate that our method is effective in decomposing 3D point clouds and is robust to limited noise and incomplete data.