# Soft Segmentation of Terrestrial Laser Scanning Point Cloud of Forests
## 1 Introduction  
Both natural and planted forests are essential to the pursuit of harmony between man and nature. Therefore, they are worthy of attention and research, including tree counting, shape analysis, and the extracting of structural characteristics. In recent years, laser scanning has become a convenient technology for obtaining data on forests. Although the data consist of a large number of points, named point cloud, in three-dimensional (3D) space and those points do not directly contain adjacency information, all points have accurate 3D position information, which is helpful for forest analysis. Point cloud segmentation, for example, tree recognition and crown extraction, is the basis of forest analysis. Forest point cloud has become a hot topic in intelligent biological data processing in recent decades. Based on accurate segmentation, each tree crown is obtained and can be reconstructed into a 3D digital geometric model. This geometrical model can be used to extract and analyze geometric parameters of the tree and as the elements of virtual reality scenes (3D animation and movie, etc.). Regarding forest point cloud segmentation, existing representative methods related to our main contributions can be classified into two categories: individual tree identification and tree crown shape extraction. Individual tree identification is an important research topic for supporting the collection of automatic field inventory using Light Detection and Ranging (LiDAR) technology [1]. Common methods or ideas include layer stacking, mean shift or region growth, bottom-up or up-down, clustering, and characteristic analysis based on differential quantity. The layer stacking method for forest point cloud segmentation is used to slice the forest point cloud at fixed (0.5 m, for example) height intervals and detect trees in each layer, merging the results from all layers to build all tree profiles [2]. Similar to layerstacking, [PERSON] et al. [3] proposed a tree segmentation method for multi-story stands that stratifies the point cloud to canopy layers and segments individual tree crowns within each layer using a digital surface model-based tree segmentation method. Some of the literature adopted mean shift or region growth methods to segment forest point clouds. [PERSON] et al. [4] completed an experimental assessment of the mean shift algorithm for the segmentation of airborne LiDAR data. [PERSON] et al. [5] presented a two-stage method to detect individual trees from LiDAR data and adopted a region-growing algorithm to complete the initial segmentation. [PERSON] et al. [6] presented a trunk-growth method with normal vector directions for single tree point cloud segmentation and applied this method to the primeval forest scenes. [PERSON] et al. [7] proposed an automatic data-driven approach to extract individual trees from a large-area terrestrial point cloud based on the point cloud graph by pathfinding. The bottom-up or up-down methods are explored for forest point cloud segmentation. [PERSON] et al. [8] built a bottom-up algorithm using point cloud data's intensity and 3D structure to segment deciduous forests. In addition, hybrid clustering techniques are also used for forest segmentation. [PERSON] et al. [9] proposed a hybrid clustering technique by combining DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and K-means for segmenting individual trees from airborne LiDAR point cloud data. For terrestrial backpack LiDAR data, [PERSON] et al. [10] used an individual tree extraction method based on DBSCAN clustering and cylinder voxelization of the volume. To avoid under- and over-segmentation effects, [PERSON] et al. [1] presented an integrated single tree segmentation using a graph-cut clustering method that is supported by automatic stem detection. [PERSON] et al. [11] proposed a method for individual tree stem detection using a graph-cut clustering method.