Objectives  The accuracy 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.

  Methods  Firstly, we applied the tensor voting (including representation, communication, and decomposition) to calculate the plane strength of each input point. During the communication of the tensor voting, we should use varying numbers of votes to obtain plane strengths for different scales. Secondly, we set the threshold for plane feature strength and count the scale that is greater than the threshold. We then calculate the comprehensive plane strength and use it to determine seed points. Then, according to various criteria, including the angle threshold, the distance threshold, and the minimum region size, we used these seed points to perform the plane segmentation.

  Results  On the basis of the traditional tensor voting method, the probability of point cloud feature recognition under various scales is fully considered. A multi-scale tensor voting method is proposed to realize plane segmentation, which realizes more accurate recognition of facade features. Finally, results of contrast experiments with principal component analysis and tensor voting show that the multi-scale tensor voting method is superior in segmentation accuracy.

  Conclusions  For terrestrial laser scanned point clouds, the region-growing algorithm is a classic plane segmentation method and is seriously affected to a large extent by seed points. Although existing research results have shown that the tensor voting has the potential to detect distinct geometric structures, such as points, lines, and planes, determining seed points using tensor voting often is heavily disturbed by effects of scale(i.e., the voten umber). We propose multiscale tensor voting method to improve the selection of seed points in region growing for plane segmentation.