Developing a Reassembling Algorithm for Broken Objects

TL;DR: The proposed method can fuse multiple features of the fragments effectively and achieve an outstanding matching effect on the defected fragments, and that the proposed method is faster than the existing methods in literature.

Abstract: The research on reassembling broken objects has many important applications, such as cultural relics restoration, medical surgery and solving puzzle. Because of the complicated surfaces of the fractured object pieces, it is not easy to extract salient features from them. It becomes even more difficult and very time-consuming to reassemble broken objects when the fragments are severely corroded or some of them are lost. In order to improve the accuracy and speed of 3D fragment reassembling, an effective and efficient fragment reassembling algorithm based on point clouds is proposed this article. This method first extracts keypoints and their concavity and convexity according to the symbolic projection distance of the point cloud, and then uses the local neighborhood information of the keypoints to construct a multi-scale covariance matrix descriptor. Furthermore, by calculating the similarity of the covariance matrix descriptors, the initial pairs of match points are obtained. Finally, the geometric constraints are gradually added to optimize the sampling so as to find good hypotheses as quickly as possible. By doing so, the search space is narrowed continuously in each iteration of the process to speed up the hypothesis test. We have conducted extensive experiments. The results show that the proposed method can fuse multiple features of the fragments effectively and achieve an outstanding matching effect on the defected fragments, and that the proposed method is faster than the existing methods in literature.