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| To understand and to recognize 3D objects represented as point cloud data, we provide a shape semantic graph (SSG) representation method to describe 3D objects. Based on the decomposed components of the object, the boundary surface of different components and the topology of the skeleton, the SSG gives a semantic description that is consistent with human vision perception. The similarity measurement of SSG for different objects is also effective to distinguish the type of objects and find the most similar one. Experiments on the shape database show that SSG is valuable for capturing the components of the objects and the corresponding relations between them. Not only can it be suitable for the object without loop but also appropriate for the object with loop to represent the shape and the topology. Besides, the combination of two-step progressive similarity measurement strategy can effectively improve the recognition rate in the shape database containing point-sample data. |
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| Keywords:Computer science and technology, point cloud, shape semantic graph, boundary surface, similarity measurement |
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