While the reconstruction of 3D objects is increasingly used today, the simplification of 3D point cloud, however, becomes a\nsubstantial phase in this process of reconstruction. This is due to the huge amounts of dense 3D point cloud produced by 3D\nscanning devices. In this paper, a new approach is proposed to simplify 3D point cloud based on k-nearest neighbor (k-NN) and\nclustering algorithm. Initially, 3D point cloud is divided into clusters using k-means algorithm. Then, an entropy estimation is\nperformed for each cluster to remove the ones that have minimal entropy. In this paper, MATLAB is used to carry out the\nsimulation, and the performance of our method is testified by test dataset. Numerous experiments demonstrate the effectiveness of\nthe proposed simplification method of 3D point cloud.
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