3D point cloud denoising method based on K nearest neighbor and absolute phase recovery

Jiale Long; Zihao Du; Jianmin Zhang; Yi Ding

doi:10.1117/12.2688747

6 November 2023 3D point cloud denoising method based on K nearest neighbor and absolute phase recovery

Jiale Long, Zihao Du, Jianmin Zhang, Yi Ding

Proceedings Volume 12921, Third International Computing Imaging Conference (CITA 2023); 129211W (2023) https://doi.org/10.1117/12.2688747
Event: Third International Computing Imaging Conference (CITA 2023), 2023, Sydney, Australia

Abstract

In processing the point cloud of three-dimensional (3D) reconstruction, various point cloud noises would generate, resulting in decreasing the accuracy of 3D point cloud. In order to improve the accuracy of 3D point cloud, this paper proposes a point cloud denoising method based on 3D point cloud segmentation. This method can be divided into four steps. Firstly, the K Nearest Neighbor (KNN) algorithm is used to construct a KNN look-up table for 3D point cloud. Secondly, the look-up table is used to segment the 3D point cloud to obtain the noise point cloud and the noise-free point cloud. Then, using the relationship between the noise point cloud and the noise point of the absolute phase, the reference noise-free phase is established to restore the absolute phase of the noise-free point. Finally, 3D reconstruction is performed according to the recovered absolute phase to obtain a noise-free 3D point cloud. This proposed method only calculates the KNN once for the initial 3D point cloud and only uses a KNN look-up table to segment the point cloud, which also accelerates the speed of 3D point cloud segmentation. The experimental results show that this method not only removes the noisy point cloud, but also restores part of the noisy point cloud into a noise-free 3D point cloud, improving the accuracy of the 3D point cloud.

Citation Download Citation

Jiale Long, Zihao Du, Jianmin Zhang, and Yi Ding "3D point cloud denoising method based on K nearest neighbor and absolute phase recovery", Proc. SPIE 12921, Third International Computing Imaging Conference (CITA 2023), 129211W (6 November 2023); https://doi.org/10.1117/12.2688747

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KEYWORDS

Point clouds

3D image processing

Image segmentation

3D modeling

Denoising

Image restoration

3D metrology

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