Bilateral filtering denoise algorithm for point cloud based on feature selection
In order to remove the noise mixed with the real points and retain characteristics, the noise points are divided into three categories, among which the ones mixed with the real points are called as the third category noise points.By using the improved bilateral filtering algorithm, this kind of points can be removed.First, the points are judged to be feature points or non-feature points according to adjacent points.Then, the bilateral filtering factors of the feature points and non-feature points are calculated according to the point cloud in different scale.Finally, bilateral filtering denoise for point cloud based on feature selection is realized.This algorithm is used to remove the noise in the point cloud data of a box and an industrial component obtained by a handheld three-dimensional laser scanner.The results show that this algorithm can effectively remove noise and reserve the char-acteristics of scanned objects.It can also prevent the excessive smoothing phenomenon caused by the reason that bilateral filtering does not take the characteristics of adjacent points into account.
point cloud denoisebilateral filteringfeature selectioncurvature
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