Effective Planar Cluster Detection in Point Clouds Using Histogram-Driven Kd-Like Partition and Shifted Mahalanobis Distance Based Regression
Point cloud segmentation for planar surface detection is a valid problem of automatic laser scans analysis. It is widely exploited for many industrial remote sensing tasks, such as LIDAR city scanning, creating inventories of buildings, or object reconstruction. Many current methods rely on robustly...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/11/21/2465 |
| _version_ | 1798019202721775616 |
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| author | Jakub Walczak Tadeusz Poreda Adam Wojciechowski |
| author_facet | Jakub Walczak Tadeusz Poreda Adam Wojciechowski |
| author_sort | Jakub Walczak |
| collection | DOAJ |
| description | Point cloud segmentation for planar surface detection is a valid problem of automatic laser scans analysis. It is widely exploited for many industrial remote sensing tasks, such as LIDAR city scanning, creating inventories of buildings, or object reconstruction. Many current methods rely on robustly calculated covariance and centroid for plane model estimation or global energy optimization. This is coupled with point cloud division strategies, based on uniform or regular space subdivision. These approaches result in many redundant divisions, plane maladjustments caused by outliers, and excessive number of processing iterations. In this paper, a new robust method of point clouds segmentation, based on histogram-driven hierarchical space division, inspired by kd-tree is presented. The proposed partition method produces results with a smaller oversegmentation rate. Moreover, state-of-the-art partitions often lead to nodes of low cardinality, which results in the rejection of many points. In the proposed method, the point rejection rate was reduced. Point cloud subdivision is followed by resilient plane estimation, using Mahalanobis distance with respect to seven cardinal points. These points were established based on eigenvectors of the covariance matrix of the considered point cluster. The proposed method shows high robustness and yields good quality metrics, much faster than a FAST-MCD approach. The overall results indicate improvements in terms of plane precision, plane recall, under-, and the over- segmentation rate with respect to the reference benchmark methods. Plane precision for the S3DIS dataset increased on average by 2.6pp and plane recall- by 3pp. Both over- and under- segmentation rates fell by 3.2pp and 4.3pp. |
| first_indexed | 2024-04-11T16:36:43Z |
| format | Article |
| id | doaj.art-345756d4dd0a42649875168d99a62f63 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-11T16:36:43Z |
| publishDate | 2019-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-345756d4dd0a42649875168d99a62f632022-12-22T04:13:48ZengMDPI AGRemote Sensing2072-42922019-10-011121246510.3390/rs11212465rs11212465Effective Planar Cluster Detection in Point Clouds Using Histogram-Driven Kd-Like Partition and Shifted Mahalanobis Distance Based RegressionJakub Walczak0Tadeusz Poreda1Adam Wojciechowski2Institute of Information Technology, Lodz University of Technology, 90-924 Łódź, PolandInstitute of Mathematics, Lodz University of Technology, 90-924 Łódź, PolandInstitute of Information Technology, Lodz University of Technology, 90-924 Łódź, PolandPoint cloud segmentation for planar surface detection is a valid problem of automatic laser scans analysis. It is widely exploited for many industrial remote sensing tasks, such as LIDAR city scanning, creating inventories of buildings, or object reconstruction. Many current methods rely on robustly calculated covariance and centroid for plane model estimation or global energy optimization. This is coupled with point cloud division strategies, based on uniform or regular space subdivision. These approaches result in many redundant divisions, plane maladjustments caused by outliers, and excessive number of processing iterations. In this paper, a new robust method of point clouds segmentation, based on histogram-driven hierarchical space division, inspired by kd-tree is presented. The proposed partition method produces results with a smaller oversegmentation rate. Moreover, state-of-the-art partitions often lead to nodes of low cardinality, which results in the rejection of many points. In the proposed method, the point rejection rate was reduced. Point cloud subdivision is followed by resilient plane estimation, using Mahalanobis distance with respect to seven cardinal points. These points were established based on eigenvectors of the covariance matrix of the considered point cluster. The proposed method shows high robustness and yields good quality metrics, much faster than a FAST-MCD approach. The overall results indicate improvements in terms of plane precision, plane recall, under-, and the over- segmentation rate with respect to the reference benchmark methods. Plane precision for the S3DIS dataset increased on average by 2.6pp and plane recall- by 3pp. Both over- and under- segmentation rates fell by 3.2pp and 4.3pp.https://www.mdpi.com/2072-4292/11/21/2465shifted mahalanobis distancehd-kd-treeplanes detectionpoint cloud segmentationmultivariate normal distribution |
| spellingShingle | Jakub Walczak Tadeusz Poreda Adam Wojciechowski Effective Planar Cluster Detection in Point Clouds Using Histogram-Driven Kd-Like Partition and Shifted Mahalanobis Distance Based Regression Remote Sensing shifted mahalanobis distance hd-kd-tree planes detection point cloud segmentation multivariate normal distribution |
| title | Effective Planar Cluster Detection in Point Clouds Using Histogram-Driven Kd-Like Partition and Shifted Mahalanobis Distance Based Regression |
| title_full | Effective Planar Cluster Detection in Point Clouds Using Histogram-Driven Kd-Like Partition and Shifted Mahalanobis Distance Based Regression |
| title_fullStr | Effective Planar Cluster Detection in Point Clouds Using Histogram-Driven Kd-Like Partition and Shifted Mahalanobis Distance Based Regression |
| title_full_unstemmed | Effective Planar Cluster Detection in Point Clouds Using Histogram-Driven Kd-Like Partition and Shifted Mahalanobis Distance Based Regression |
| title_short | Effective Planar Cluster Detection in Point Clouds Using Histogram-Driven Kd-Like Partition and Shifted Mahalanobis Distance Based Regression |
| title_sort | effective planar cluster detection in point clouds using histogram driven kd like partition and shifted mahalanobis distance based regression |
| topic | shifted mahalanobis distance hd-kd-tree planes detection point cloud segmentation multivariate normal distribution |
| url | https://www.mdpi.com/2072-4292/11/21/2465 |
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