A Fragment Fracture Surface Segmentation Method Based on Learning of Local Geometric Features on Margins Used for Automatic Utensil Reassembly

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To achieve the automatic reassembly (piecing) of utensil fragments, a fracture surface extraction method based on the learning of local geometric features (core focus) and a utensil reassembly method (secondary focus) are presented in this paper. The steps of the methodological framework are as follows. First, based on obtained 3D models of utensil fragments, a triangle cell descriptor is proposed to describe the geometric features of spatial neighborhoods. Second, a set of feature mapping images (FMIs) is established as a training dataset. Third, after labeling of the ground-truth data, a convolutional neural network (CNN) is trained using the FMIs. Fourth, based on processing to eliminate mislabeled triangle cells, skeletons of the fracture surface margins can be generated. Fifth, a shortcut-based strategy is proposed to eliminate residual triangle cells to extract the fracture surfaces. Sixth, a control-point- and vector-based strategy is proposed to achieve the matching and prealignment of the fracture surfaces. Finally, a cyclic error iteration strategy is designed to assemble the fragments into a holonomic utensil. This learning-based framework is more effective at extracting the key geometric data (fracture surfaces) of utensil fragments than several classical methods. It may also enable a new strategy for 3D graph processing.