# Point Cloud Segmentation from iPhone-Based LiDAR Sensors Using the Tensor Feature
## 2 Related Works  
Based on the way features are computed, point cloud segmentation methods can be categorized into two types:  
(1) Point cloud segmentation based on the geometric feature extraction. The geometric features of the point cloud can be computed directly from the unstructured, scattered data, using computation geometric theory, and geometric features [2; 3; 9], such as line features which represent the geometric frame line of the object structure, and flat features, which may be part of the continuous flat surfaces, are usually extracted. Since the edge is one of the most salient geometric features of the point cloud, different edge structures are usually computed. Then, the closed geometric features represented by the edges are detected, and the point cloud dataset is segmented based on the boundary of each segment [2; 9; 10]. It is simple and convenient for the computer to implement, but the data are usually over-segmented. Meanwhile, in regional growth-based methods [11; 12], point cloud segmentation is started with a seed, which can be computed with the minimum curvature. Then the process is iteratively computed based on the curvature threshold values, and different point segments are generated. Compared with the regional growth-based method, seeds points are not needed in feature clustering-based methods, and the point cloud data are usually segmented based on the clustering of point features, such as the curvature, angle difference, and discreteness. No structures are predefined, and the point cloud dataset is just segmented based on the closeness of features, and the cluster methods can be the K-means cluster, fuzzy cluster, and mean shift cluster [13; 14; 15]. However, in the incomplete sample scene, a single object can be segmented into different groups. However, in geometric model fitting-based methods, the predefined geometric models, such as the box, sphere, and cylinder, are applied to the point cloud. Hough Transformation [16], Random Sample Consensus (referred to as RANSAC) [17], or KD-tree-based space division [18] are usually used to obtain the optimal fitting segmentation result. In addition, a more complex model fitting process is needed for the continuous smooth geometric structures. Hence, in the principal structure-based methods, the geometric features are computed using the neighborhood, based on Principal Component Analysis (PCA), and the geometric structures are represented by the eigenvalues [19]. Then, different kinds of structures are split into different categories by a supervised or unsupervised method, such as Support Vector Machine (SVM) [20]. Other kinds of geometric feature computation methods, including the tensor voting algorithm [21; 22; 23], where the geometric information is computed based on the tensor decomposition from the scattered point, share a similar feature computation principle with the point's neighborhood. Geometric features are computed based on the spatial relations between the point and its neighborhood, without manual interactions of training operations. Due to the intuitive and direct representation of the geometric structures in the point cloud, the geometric feature-based point cloud segmentation method has been widely used for years. However, the computation result could be unstable and misjudged in situations where there are many noisy point samplings. (2) Point cloud segmentation based on the deep-learning neural network With the rapid development of graphics processing unit (GPU) parallel computing, deep-learning-based point cloud segmentation methods have been widely used and applied in many kinds of research. The point cloud dataset is separated into training, validation, and experimental datasets [4; 5]. To deal with point cloud segmentation based on the 3D convolution neural network (CNN), the intuitive method is used to transform the 3D scattered point cloud into 2D images.