Robust segmentation for multiple planar surface extraction in laser scanning 3D point cloud data
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This paper investigates the segmentation of multiple planar surfaces from 3D point clouds. A Principle Component Analysis (PCA) based covariance technique is used for segmentation which is one of the most popular approaches in point cloud processing. It is well known that PCA is very sensitive to outliers and does not give reliable estimates for segmentation. We propose a statistically robust segmentation algorithm using a fast-minimum covariance determinant based robust PCA approach to get the local covariance statistics. This results in more reliable, robust and accurate segmentation. The application of the proposed method to simulated and terrestrial laser scanning point cloud datasets gives good results for multiple planar surface extraction and shows significantly better performance than PCA based methods. The algorithm has the potential for non-planar complex surface reconstruction.
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Nurunnabi, A.; West, Geoff; Belton, D. (2015)Three dimensional point cloud data obtained from mobile laser scanning systems commonly contain outliers. In the presence of outliers most of the currently used methods such as principal component analysis for point cloud ...
Nurunnabi, Abdul; Belton, David; West, Geoffrey (2012)Segmentation is a most important intermediate step in point cloud data processing and understanding. Covariance statistics based local saliency features from Principal Component Analysis (PCA) are frequently used for point ...
Nurunnabi, A.; Belton, David; West, Geoff (2014)This paper proposes robust methods for local planar surface fitting in 3D laser scanning data. Searching through the literature revealed that many authors frequently used Least Squares (LS) and Principal Component Analysis ...