An Accurate and Robust Region-Growing Algorithm for Plane Segmentation of TLS Point Clouds Using a Multiscale Tensor Voting Method

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing 2019-10, Vol.12 (10), p.4160-4168
• Main Authors: Wu, Hao, Zhang, Xiang, Shi, Wenzhong, Song, Shaoxian, Cardenas-Tristan, Abraham, Li, Kui
• Format: Article
• Language: English
• Subjects: Algorithms; Buildings; Eigenvalues and eigenfunctions; Lasers; Mathematical analysis; Mathematical models; Multiscale analysis; Multiscale tensor voting method (MSTVM); Noise measurement; plane segmentation; Principal component analysis; Principal components analysis; region-growing algorithm; Robustness; Scale effect; seed point determination; Segmentation; semivariogram; Tensors; Terrestrial environments; terrestrial laser scanning (TLS); Three dimensional models; Three-dimensional displays; Voting
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2019.2936662

The accuracy and robustness of plane segmentation using a region-growing algorithm remains an important and challenging topic for terrestrial laser scanning point clouds. The plane segmentation of a region-growing algorithm depends heavily on the seed point, as there are currently no universally valid criteria. This article proposes a multiscale tensor voting method (MSTVM) to determine the appropriate seed point for the region-growing algorithm. A comprehensive plane strength indicator calculated by the semivariogram model has been established to assess whether a certain point is suitably considered as a seed point or not. A point cloud containing 17, 881 points in a 400-m 2 area was selected to validate the proposed algorithm. The results suggest that the scale range calculated by the semivariogram model can effectively mitigate the scale effect of the tensor voting method (TVM). The comprehensive plane strength of our proposed algorithm in seed point determination is shown to be more salient than the principal component analysis and the TVM. The findings further reveal that the utility of the MSTVM-based region-growing algorithm can achieve more accurate plane segmentation results and perform with better robustness in noisy point clouds. This allows our proposed method to be more widely applied to complex real situations.