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Denoising Algorithm for Bilateral Filtered Point Cloud Based on Variance Threshold

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DOI: 10.23977/msmee.2018.72147


Linzhe Chen, Baohong Feng

Corresponding Author

Linzhe Chen


Based on the traditional K neighborhood search algorithm, a K neighborhood search algorithm based on space cell is proposed, which changes the construction of the k-d tree and improves the search speed of the nearest neighbor (k) neighbor. In view of the denoising of space scattered point clouds, a data point classification method based on variance threshold judgment is proposed, which is based on the improvement of bilateral filtering algorithm. The experiment shows that the algorithm is better than the bilateral filtering algorithm, and the defect of the bilateral filtering algorithm is improved to a certain extent under the condition that the processing speed is greatly improved, and it has strong practicability.


Points cloud denoising, bilateral filtering algorithm, k neighbor search, variance threshold

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