Abstract
To further improve the performance of the point cloud simplification algorithm and reserve the feature information of parts point cloud, a new method based on modified fuzzy c-means (MFCM) clustering algorithm with feature information reserved is proposed. Firstly, the normal vector, angle entropy, curvature, and density information of point cloud are calculated by combining principal component analysis (PCA) and k-nearest neighbors (k-NN) algorithm, respectively; Secondly, gravitational search algorithm (GSA) is introduced to optimize the initial cluster center of fuzzy c-means (FCM) clustering algorithm. Thirdly, the point cloud data combined coordinates with its feature information are divided by the MFCM algorithm. Finally, the point cloud is simplified according to point cloud feature information and simplified parameters. The point cloud test data are simplified using the new algorithm and traditional algorithms; then, the results are compared and discussed. The results show that the new proposed algorithm can not only effectively improve the precision of point cloud simplification but also reserve the accuracy of part features.
Highlights
To further improve the performance of the point cloud simplification algorithm and reserve the feature information of parts point cloud, a new method based on modified fuzzy c-means (MFCM) clustering algorithm with feature information reserved is proposed
The normal vector, angle entropy, curvature, and density information of point cloud are calculated by combining principal component analysis (PCA) and k-nearest neighbors (k-NN) algorithm, respectively; Secondly, gravitational search algorithm (GSA) is introduced to optimize the initial cluster center of fuzzy c-means (FCM) clustering algorithm. irdly, the point cloud data combined coordinates with its feature information are divided by the MFCM algorithm
Chen et al proposed a point cloud simplification algorithm based on the normal vector included angle local entropy model, and the experimental results showed that the algorithm can achieve the optimal computational accuracy and efficiency [15]
Summary
For point cloud simplification algorithms, Lee et al implemented the point cloud simplification process based on geometric information and proved the advantages of the algorithm through experimental data [9]. Shi et al designed an adaptive point cloud simplification method based on k-mean clustering algorithm [11]. Chen et al proposed a point cloud simplification algorithm based on the normal vector included angle local entropy model, and the experimental results showed that the algorithm can achieve the optimal computational accuracy and efficiency [15]. Wang et al designed a point cloud simplification algorithm based on feature perception, which can reduce the simplification error while ensuring the original geometric accuracy of parts [23]. Erefore, based on the FCM clustering algorithm, this paper firstly optimized the initial clustering center through GSA, combined the point cloud coordinates with feature information to divide the point cloud, reserved strong feature information, and simplified the point cloud data in different regions. Four groups of commonly used point cloud data are applied to the final simulation experiment process
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