A Siamese network-based method for automatic stitching of artifact fragments

For the problem of fragmentation of cultural relic fragments caused by natural or man-made factors, this paper proposes a method of automatic splicing of cultural relic fragments based on the siamese network. First, the method employs an improved region growing segmentation algorithm to segment the fractured and non-fractured surfaces of the point cloud of artifact fragments. Second, a rigid-body mechanics simulation method is used to fragment virtual artifacts and establish a database of fragments for deep learning algorithm training. Then, point cloud similarity comparison using a neural network DGCNN-Siamese net to achieve matching of fracture surfaces of broken pieces. Third, the fracture surface point cloud registration is aligned by using Harris-3D feature point extraction, neighborhood point feature histogram (PFH) feature description, and iterative closest point (ICP) method. The experimental result shows that the overall matching accuracy of the method is 96.99%, the method is able to reduce the registration deviation and achieve more complete recovery of the fragmented artifacts through comparative analysis.