Semantic Shape Context for the Registration of Multiple Partial 3D Views

Point-to-point matching is a crucial stage of 3D shape analysis. It is usually solved by using descriptors that summarize the most characteristic and discriminative properties of each point. Combining local and global context information in the point descriptor is a promising approach. We propose a new approach based on what we call semantic shape context to combine effectively local descriptors and global context information by exploiting the Bag of Words (BoW) paradigm for the representation of a single 3D point. Several local point descriptors are collected and quantized from the training set, by defining the visual vocabulary composed by a fixed number of visual words. Each point is then represented by a set of BoWs which encode the inter-relationship with all the other points of the object (i.e., the context). Experiments were carried out on several 3D models. The proposed approach makes fully automatic 3D registration of partial views possible, and generally outperforms stateof-the-art methods in terms of robustness and accuracy.

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