Research On Self-organizing Maps For Surface Reconstruction From Point Clouds

With the advance of modern computer technology, reverse engineering and additive manufacturing technology is more and more popular. Point clouds reconstruction is an important research direction, and it is widely used in automotive, aerospace, biomedical and other fields. In recent years, artificial intelligence technology has many advantages with strong learning ability, dealing with nonlinear, cooperative ability. It has a broad prospect in reverse engineering and additive manufacturing. It can apply to the research of point clouds reconstruction and has a broad propect. This paper uses artificial intelligence to implement the reconstruction for unorganized point clouds. Some related algorithms are proposed:(1) In order to improve the quality, rate of convergence and surface accuracy of point clouds reconstruction, dynamic growing self-organizing maps algorithm is proposed in this paper. Based on self-organizing map algorithm, we construct the spherical triangle mesh as map of the network. Then, we split the nodes and delete the unstable nodes to change the immobility of the network structure by learning for point clouds. In addition, we optimize the grid to make the nodes and discrete points keep closer together. Finally, it can get good reconstruction result.(2) On account of the curved surface of complex topology structure, higher genus surface, dynamic growing neural gas algorithm is proposed in this paper. According to the point clouds, the algorithm adjusts to the growth of the speed of reconstruction adaptively and maintains the convergence and coordination of geometric relationships and topological structure. It can insert a new neural node using the age threshold value and cumulative error threshold values and delete redundant boundary using age connection mechanism. The algorithm with surface reconstruction has high robustness, the topology of surface reconstruction has convergence, plus is that reconstructed mesh approximates the suface in high accuracy, the result of reconstruction is more ideal.(3) On account of holes repairing for triangular mesh model, dynamic growing gas algorithm is studued. Based on thought of the algorithm, it uses non-mainfold edge detection mechianism to delect redundant boundary and updates the information of triangular mesh. Every triangular mesh achieves convergence in the progress of algorithm learning. This paper uses the algorithm to fill hole defects of solid model, it will speed up the reconstruction of the triangular mesh and the accuracy of surface.Based on Matlab platform design, experiments demonstrate that this algorithm is effective.