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To effectively characterize the topology and geometric information of 3D CAD models, trees, graphs and other non-linear data structures have become the important descriptors of 3D CAD models. However, the matching based on tree or graph is still the main bottleneck of 3D CAD model similar evaluation. In this paper, a tree kernels based similar evaluation algorithm is proposed. With extended feature trees as the feature descriptors of local regions, the method uses convolution operation to break down the extended feature tree into sub-structure, and the final evaluation of extended feature trees is achieved by the matching between their sub-structures. The method ensures the polynomial matching time, improves the retrieval accuracy by refining the matching size. Experimental results demonstrate the effectiveness of the algorithm.

Similarity assessment between local regions of 3D CAD model based on tree kernel