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.been widely discussed in the literature. In this research, attention was paid to the area of modelfitting-based on plane detection methods for indoor scans, since this group of methods is currently the most successful [10][11][12][13]. Though thoroughly studied, model-based approaches still face generic problems related the difficulty in modelling the outlying points and performance in point clouds segmentation [14,15]. The attention was paid to indoor scans which similarly to all human-made structures may be usually reliably decomposed into geometrical primitives, particularly planes.This paper introduces a new model-fitting based method for indoor scans, relying on Mahalanobis distance (MD) and histogram-driven kd-like point cloud division. The hierarchical, well-balanced point cloud subdivision process enables a shallow yet sufficient partition, preventing the adjacent planar point sets from unintended splitting. The proposed strategy of adapting MD for determination of the set of core points, especially around corners and edges, results in a robust plane model fitting, ensuring higher resistance to outlying points and better than the state-of-the-art precision methods in detecting planar clusters.
Related Works
Space OrganizationEach point cloud segmentation method, regardless of the category it ...
