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《Journal of Graphics》 2014-02
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Reconstruction of Triangle Mesh for Unorganized Point Cloud Data with High Approximation Precision

Zhang Wei;College of Mechanical and Electronic Engineering, China Jiliang University;  
An approach based on the self-organizing feature map(SOFM) neural network has been developed to reconstruct Delaunay triangle mesh for the unorganized measured point cloud. However the approach suffers from approximation and boundary problems. A triangle mesh model with high approximation precision is proposed in order to reduce the approximation error and boundary error. First all the neurons of the mesh model are trained directly over the unorganized point cloud. Next the neuron location weights of the mesh model are adjusted along the normal vectors of the mesh vertices. Last only the boundary neurons of the mesh model undergo training by the boundary points of the measured point cloud. As a result of applying the proposed mesh model, the boundary error is greatly reduced and the mesh is drawn toward the sampled object with higher precision comparing with the original SOFM training algorithm. The feasibility of the developed mesh model is demonstrated on two examples.
【Fund】: 浙江省自然科学基金资助项目(Y1091012);; 国家质检总局科技计划资助项目(2006QK65)
【CateGory Index】: TP391.41
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