A method of 3D CAD model retrieval based on spatial bag of words

In order to improve both the discriminative power for models’ local parts and the searching efficiency in 3D CAD model retrieval, a novel hierarchical feature descriptor for retrieval based on spatial bag of words is proposed in this paper. By extracting the essential information from Boundary Representation (B-Rep), 3D CAD models are transformed to Labelled Attribute Adjacency Graphs (LAAGs). Next, the models in training dataset are segmented into different regions according to their corresponding LAAG with an improved segmentation method. All collections of these local regions are described as local feature vectors with graph spectrum, and the codebook is created by clustering all these vectors. Each library model is then decomposed with the same methods mentioned above and globally represented as a spatial histogram of word pairs along with the adjacent relations of its regions, called Spatial Bags-of-Words (SBoWs),and then, the hierarchical feature descriptor(HFD) of each library model composed of global SBoWs and local graph spectrum is constructed. Finally, according to HFD, a two-level searching framework is presented for CAD model retrieval: the candidates are acquired by comparing the query with each target model based on their SBoWs vectors, and the remaining candidates are verified using optimal matching algorithm according to their local features. Experimental results show that the proposed methods promote both retrieval quality and efficiency significantly, so they can support the effective reuse of CAD models for enterprises.

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