POINT CLOUD SIMPLIFICATION METHOD BASED ON IMPROVED FUZZY C-MEANS WITH AUTOMATIC NUMBER OF CLUSTERS

Abstract

The essence of the point cloud is to express geometric information about objects by getting discrete coordinates on their surfaces. However, with a million points, this data may record redundant details in which it is needless to be kept for the model’s analysis. In addition, there is also a limitation where the processors cannot process the large-size datasets. The point cloud simplification algorithm was developed to solve the stated obstacles in data processing. Numerous algorithms have been published to produce the best methods for data reduction process. Since the simplification process might eliminate essential features of the data, this study introduces the features preservation process to keep the important points before the simplification. This study employed the Fuzzy C-Means (FCM) algorithms for the simplification stage due to their simplicity and ability to generate an accurate result. Regardless, the FCM still suffers from drawbacks, where their initial cluster centres are prone to fall into local optima. This study improved the FCM by employing the Score and Minimum Distance (SMD) to determine the number of clusters and cluster centres. The SMD is enhanced by changing the Gaussian to Cosine kernel function to increase the accuracy. This new technique is named SMD(C)-IFCM. The method was then applied to the 3D point cloud of a box, cup, and Stanford bunny. The performance of the developed method was compared with the original SMD-FCM and SMD-IFCM for the percentage of the simplified data, error evaluation, and processing time. The result and analysis showed that the developed method had the best score, which was six (6) out of nine (9) measurements, compared to the other two methods with scores of one (1) and two (2) respectively. This score suggests that the developed algorithm successfully reduced the error evaluation and the processing time to generate the output.