3D point cloud denoising method based on statistical outlier and adaptive bilateral hybrid filtering

Abstract

The invention discloses a 3D point cloud denoising method based on statistical outlier and adaptive bilateral hybrid filtering, and the method comprises the steps: firstly calculating the average distance from each point in a 3D point cloud to the nearest K neighborhood point, the expected value of the average distance, a standard deviation and a distance threshold value; secondly, if judging thatthe average distance is larger than a distance threshold value, filtering is carried out, otherwise, performing retaining and 3D point cloud data obtained after primary denoising is obtained; and finally, calculating a bilateral smooth filtering factor, and processing the 3D point cloud data subjected to primary denoising by using the smooth filter to obtain final denoised 3D point cloud data. Astatistical outlier elimination filter and a self-adaptive bilateral filter are effectively combined to carry out denoising processing on 3D point cloud data. Results show that the method not only removes outlier noise points, but also removes fluctuating noise points. Meanwhile, the boundary of the denoised 3D point cloud data is smoother, and a good foundation is laid for subsequent segmentationand feature extraction of the 3D point cloud data.

Description

technical field [0001] The invention belongs to the technical field of 3D point cloud data processing, in particular to a 3D point cloud denoising method based on statistical outliers and self-adaptive bilateral hybrid filtering. Background technique [0002] With the development of artificial intelligence, machine vision has gradually transitioned from two-dimensional images to three-dimensional images, and 3D point cloud, as one of the typical representatives of three-dimensional images, has gradually been widely used. Under normal circumstances, when using 3D point cloud data acquisition equipment to obtain 3D point clouds, due to the influence of factors such as surface ripples, surface roughness, equipment accuracy, ambient light, and human disturbances of the measured object, the obtained 3D point clouds are inevitably affected by noise. The existence of noise will not only seriously affect the preprocessing such as simplification and registration of subsequent 3D poi...

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Problems solved by technology

[0004] The purpose of the present invention is to provide a 3D point cloud denoising method based on statistical outliers and adaptive bilateral hybrid filtering, which solves the problem that the existing single statistical outlier filter only filters out outlier noise points, the boundary is not smooth and a single self Adapting to the problem of bilateral filters only filtering out fluctuation noise points

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