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.
Highlights
At present, the fragment reassembling has been of great application value in the fields of cultural relics restoration[1][2][3], medical research[4][5][6][7] and object recognition[8]
Many methods have been proposed in the field of 3D fragment reassembling, which can be roughly divided into the following two types: 1) fragment reassembling based on contour lines; 2) fragment reassembling based on fracture surface
Random Sample Consensus (RANSAC) is a robust estimation method introduced by Fischler [22], which is divided into two processes: Hypothesis and Test
Summary
The fragment reassembling has been of great application value in the fields of cultural relics restoration[1][2][3], medical research[4][5][6][7] and object recognition[8]. In order to make the fragment reassembling method more versatile, a 3D fragment reassembling algorithm based on keypoints is proposed in this paper This algorithm does not need to extract the contour lines or the fracture surfaces of the fragments, but focuses on the keypoints and their features of the neighborhood points for restoration. A minimal subset is sampled from the initial matching point pairs and used to estimate and validate a hypothesis Such operations repeat until a satisfactory solution is obtained. In the final step of our algorithm, the combination of the geometric consistency and the above improved RANSAC is used to eliminate false matching from the initial matching and achieve an excellent reassembling result.
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