Abstract
As the original point cloud data reflecting the shape information of three-dimensional objects is often data too large which will adversely affect the point cloud registration, three-dimensional reconstruction, and other follow-up operations. Uniform grid method, curvature sampling method, and other methods are classical point cloud data reduction algorithms. This paper presents a new point cloud reduction algorithm based on SIFT3D feature points. Compared with the traditional uniform mesh method and curvature sampling method, it can be found that, on the premise of retaining the feature points, the 3D solid simplified by this method still obtains better results in the standard deviation and surface area.
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Zhang, H., Dong, J., Lu, J., Ling, Y., Ou, Y., Cai, Z. (2021). Point Cloud Data Reduction Algorithm Based on SIFT3D Features. In: Hassanien, A.E., et al. Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2021). AICV 2021. Advances in Intelligent Systems and Computing, vol 1377. Springer, Cham. https://doi.org/10.1007/978-3-030-76346-6_33
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DOI: https://doi.org/10.1007/978-3-030-76346-6_33
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