Abstract
To simplify the point cloud data of three-dimensional reconstruction of object surface, a point cloud simplification method based on K-means and Gauss sphere is proposed. Without changing the geometric features of the model and retaining the details of the object, the redundant data of the point cloud is removed and the point cloud data is greatly simplified. Firstly, the classical point cloud simplification algorithm is introduced, and its advantages and disadvantages are analyzed. Then, according to the characteristics of various algorithms, PCA method is used to estimate the normal vector features, K-means and Gauss sphere clustering are used to simplify the point cloud. Finally, the validity of the algorithm is validated by the point cloud data of Stanford Bunny model and Dragon model.
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Acknowledgments
This paper has been supported and guided by my tutor Professor Li Xiaobin. Here, thanks to my tutor. The rigorous scientific research spirit, profound knowledge and modest attitude of my tutor are the realms of life that I pursue. In addition, I would like to express my heartfelt thanks to all those who have helped me. Thank you!
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Ma, T., Li, X., Yu, T. (2020). Simplification of Gauss Spherical Point Cloud Based on K-Mean. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2019 Chinese Intelligent Systems Conference. CISC 2019. Lecture Notes in Electrical Engineering, vol 593. Springer, Singapore. https://doi.org/10.1007/978-981-32-9686-2_33
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DOI: https://doi.org/10.1007/978-981-32-9686-2_33
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