Paper Info
Title
A Fragment Fracture Surface Segmentation Method Based On Learning Of Local Geometric Features On Margins Used For Automatic Utensil Reassembly
Abstract
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. (C) 2020 Elsevier Ltd. All rights reserved.
Year
DOI
Venue
2021
10.1016/j.cad.2020.102963
COMPUTER-AIDED DESIGN
Keywords
DocType
Volume
Utensil reassembly, Local geometric feature descriptors, Fragment fracture surface extraction, CNN
Journal
132
ISSN
Citations 
PageRank 
0010-4485
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Bin Liu102.03
Mingzhe Wang202.03
Xiaolei Niu322.12
Shengfa Wang433.41
Song Zhang567.38
Jianxin Zhang6739.64