# Point Cloud Segmentation from iPhone-Based LiDAR Sensors Using the Tensor Feature
## 2 Related Works
Hence, in the multi-view CNN (MVCNN) [24]-based point cloud segmentation method, the point cloud in the training set is projected onto the 2D planes in a surrounding circle, then the neural network is trained and applied to semantic segmentation work. Though applied in 3D, the work is actually done with 2D processing. Some other similar works, such as group-view CNN (GVCNN) [25] and SqueezeSeg [26], actually use a similar technique. Another similar CNN-based method is 3D voxel-based point cloud segmentation [27], where the point cloud data are converted to spatially regularized voxels, then voxels are taken as 3D cubes and the 3D CNN-based deep-learning neural network is applied. These methods are adapted from the CNN of image processing, not directly designed for the scattered points; moreover, in the training dataset and validation dataset, the point cloud is labeled manually, and large amounts of the dataset are processed and input into the training program, which takes a long time to obtain the desired accuracy. In the first mentioned cloud segmentation method, PointNet [28] just takes the points as the input, and extracts the features of the data based on multi-layer perceptron (MLP), which can deal with the unstructured point cloud. Improvements have been made by PointNet++ [29] and other similar methods, with the neighborhood information accounted for. Another kind of point cloud segmentation method is the graph convolution-based method, which combines convolution with the graph structure. In the superpoints graph (SPG) [30], the point clouds are segmented and used as the super points of the graph, then split into each semantic group based on the graph convolution. However, the point data are still unstructured, and it is difficult to compute the convolution. To address the issue, PointCNN [31] applies the x transformation to the point cloud, and some other similar methods are proposed. These methods focus on both the segmentation and semantic labeling of the point cloud, and take the neighborhood and context into account, such as the scale. Some other improvements consider the attention mechanism of the neural network, such as in RandLA [32], or the feature encoding rules [33; 34; 35]. With the automatic fitting of the feature extracting and representing model, the point cloud can be quickly separated into different segments, even with the labels of each category. Nevertheless, the deep-learning neural network-based point cloud segmentation method is actually a statistical method, whose accuracy is limited by the training dataset and the result is affected by the pre-trained parameters. Moreover, if the kind of features that are not included in the training dataset or there are noise points in the input dataset, the result will become unstable. To deal with the actual noisy dataset and acquire a stable geometric feature description from the input data, a point cloud segmentation method using the tensor feature is proposed in this paper, and the point cloud accessed on the iPhone-based LiDAR sensor is used. The proposed method belongs to geometric feature extraction-based point cloud segmentation, and several similar kinds of methods are experimented with and compared.