Incomplete Points Cloud Data Surface Reconstruction Based on Neural Network

Neural network arithmetic was employed in incomplete points cloud data surface reconstruction, Radial basis function neural network and simulated annealing arithmetic was combined. The new arithmetic can approach any nonlinear function by arbitrary precision, and also keep the network from getting into local minimum. Global optimization feature of simulated annealing was employed to adjust the network weights. MATLAB program was compiled, experiments on incomplete points cloud data have been done employing this arithmetic, the result shows that this arithmetic can efficiently approach the surface with 10
^-4
mm error precision, and also the learning speed is quick and the reconstruction surface is smooth. Different methods have been employed to do surface reconstruction in comparison, the results illustrate the error employed algorithmic proposed in the paper is little and converge speed is quick.