Abstract
With the development of modern 3D measurement technologies, it becomes easy to capture dense point cloud datasets. To settle the problem of pruning the redundant points and fast reconstruction, simplification for point cloud is a necessary step during the processing. In this paper, a new method is proposed to simplify point cloud data. The kernel procedure of the method is to evaluate the importance of points based on local entropy of normal angle. After the estimation of normal vectors, the importance evaluation of points is derived based on normal angles and the theory of information entropy. The simplification proceeds and finishes by removing the least important points and updating the normal vectors and importance values progressively until user-specified reduction ratio is reached. To evaluate the accuracy of the simplification results quantitatively, an indicator is determined by calculating the mean entropy of the simplified point cloud. Furthermore, the performance of the proposed approach is illustrated with two sets of validation experiments where other three classical simplification methods are employed for contrast. The results show that the proposed method performs much better than other three methods for point cloud simplification.
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Acknowledgements
This work is supported by the National Natural Science Foundation of China (Grant No. 41674005, 41501502), the CRSRI Open Research Program (CKWV2015230/KY), the Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology (DLLJ201601), Jiangxi Natural Science Foundation of China (20171BAB203032), and the Open Foundation of Postdoctors Innovation and Practice Base of Wuhan Geomatics Institute (Grant No. WGF 2016002).
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Xuan, W., Hua, X., Chen, X. et al. A New Progressive Simplification Method for Point Cloud Using Local Entropy of Normal Angle. J Indian Soc Remote Sens 46, 581–589 (2018). https://doi.org/10.1007/s12524-017-0730-6
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DOI: https://doi.org/10.1007/s12524-017-0730-6