Point cloud denoising by robust PCA dimension reduction
Lidar sensors are often used to scan point cloud data, but the signals it sends are often negatively affected by atmospheric particles, light scattering and other influencing factors during transmission, resulting in noise in point cloud images. In this dissertation, advanced point cloud data den...
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sg-ntu-dr.10356-1787302024-07-05T15:43:12Z Point cloud denoising by robust PCA dimension reduction Guo, Yu Wen Bihan School of Electrical and Electronic Engineering bihan.wen@ntu.edu.sg Engineering Lidar sensors are often used to scan point cloud data, but the signals it sends are often negatively affected by atmospheric particles, light scattering and other influencing factors during transmission, resulting in noise in point cloud images. In this dissertation, advanced point cloud data denoising and dimensionality reduction technology is studied, which is a density-based spatial clustering of application with noise (DBSCAN) algorithm based on robust principal component analysis (RPCA), aiming to improve the accuracy and efficiency of high point cloud data processing. Different from directly processing 3D point clouds, we reduce the dimension of data to 2D point clouds through RPCA technology, which effectively reduces the impact of noise and outliers, and also increases robustness compared with traditional PCA, while retaining the characteristics of less information consumption. This is combined with an adaptive clustering mechanism that dynamically adjusts parameters based on local data features, which has been shown to significantly improve the preservation of basic data features while removing redundant and noisy data. Point cloud data will be restored to 3D space after clustering and noise reduction. Experimental results of simulated and real data sets show that the proposed method not only reduces noise levels, but also preserves key structural details more efficiently than traditional methods. The combination of RPCA and DBSCAN algorithms called robust PCA adaptive clustering (RPCAAC) was validated through extensive computational performance analysis, showing substantial improvements in processing efficiency and data fidelity. Master's degree 2024-07-04T01:02:39Z 2024-07-04T01:02:39Z 2024 Thesis-Master by Coursework Guo, Y. (2024). Point cloud denoising by robust PCA dimension reduction. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/178730 https://hdl.handle.net/10356/178730 en application/pdf Nanyang Technological University |
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Lidar sensors are often used to scan point cloud data, but the signals it sends
are often negatively affected by atmospheric particles, light scattering and other
influencing factors during transmission, resulting in noise in point cloud images.
In this dissertation, advanced point cloud data denoising and dimensionality
reduction technology is studied, which is a density-based spatial clustering
of application with noise (DBSCAN) algorithm based on robust principal
component analysis (RPCA), aiming to improve the accuracy and efficiency of
high point cloud data processing. Different from directly processing 3D point
clouds, we reduce the dimension of data to 2D point clouds through RPCA
technology, which effectively reduces the impact of noise and outliers, and also
increases robustness compared with traditional PCA, while retaining the characteristics
of less information consumption. This is combined with an adaptive
clustering mechanism that dynamically adjusts parameters based on local data
features, which has been shown to significantly improve the preservation of basic
data features while removing redundant and noisy data. Point cloud data
will be restored to 3D space after clustering and noise reduction. Experimental
results of simulated and real data sets show that the proposed method not only
reduces noise levels, but also preserves key structural details more efficiently
than traditional methods. The combination of RPCA and DBSCAN algorithms
called robust PCA adaptive clustering (RPCAAC) was validated through extensive
computational performance analysis, showing substantial improvements in
processing efficiency and data fidelity. |
author2 |
Wen Bihan |
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Wen Bihan Guo, Yu |
format |
Thesis-Master by Coursework |
author |
Guo, Yu |
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Guo, Yu |
title |
Point cloud denoising by robust PCA dimension reduction |
title_short |
Point cloud denoising by robust PCA dimension reduction |
title_full |
Point cloud denoising by robust PCA dimension reduction |
title_fullStr |
Point cloud denoising by robust PCA dimension reduction |
title_full_unstemmed |
Point cloud denoising by robust PCA dimension reduction |
title_sort |
point cloud denoising by robust pca dimension reduction |
publisher |
Nanyang Technological University |
publishDate |
2024 |
url |
https://hdl.handle.net/10356/178730 |
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1814047323406204928 |