# Point Cloud Segmentation from iPhone-Based LiDAR Sensors Using the Tensor Feature
## 3 Methodology
### The Algorithm of the Point Cloud Segmentation Workflow
The average _recall_ for the result of [PERSON] [18] is 0.0654, which is higher than that of [PERSON] [9], [PERSON] [21], and [PERSON] [22], and lower than that of the proposed method. The average _precision_, average _recall_, and average \(F_{score}\) for the result of the proposed method are 0.8865, 0.1041, and 0.1470, which outperforms the result of other \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Different Methods** & **Average _Precision_** & **Average _Recall_** & **Average \(F_{score}\)** \\ \hline [PERSON] [18] & 0.7012 & 0.0654 & 0.0969 \\ [PERSON] [19] and [PERSON] [20] & 0.9467 & 0.0177 & 0.0245 \\ [PERSON] [9], [PERSON] [21] & & 0.9527 & 0.0369 & 0.0515 \\ and [PERSON] [22] & 0.8865 & 0.1041 & 0.1470 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistics of point cloud segmentation result for dataset 2. Figure 13: The \(F_{score}\) of each part in comparison with different segmentation methods. **(a)** The \(F_{score}\) of the segmentation result by [PERSON] [18]; **(b)** The \(F_{score}\) of the segmentation result by [PERSON] [19] and [PERSON] [20]; **(c)** The \(F_{score}\) of the segmentation result by [PERSON] [9], [PERSON] [21], and [PERSON] [22]; **(d)** The \(F_{score}\) of the segmentation result by the proposed method. methods, overall, and it is consistent with the result in Figure 13. From the comparison, the proposed methods perform better than other methods. ## 5 Conclusions

Point cloud segmentation is the fundamental procedure for advanced point cloud applications, such as indoor/outdoor scene understanding; however, work has become more difficult in noisy sampling situations, where point cloud data are acquired by consumer electronic devices. To obtain geometric features from a noisy point cloud, a tensor feature-based point cloud segmentation method is proposed, and experiments are conducted. The pipeline for point cloud segmentation is constructed from the following steps: normal vector computation, tensor aggregation, tensor feature decomposition, and point cloud clustering are conducted, based on the tensor features. To obtain normal information, geometric features in the unstructured, scattered data are first encoded as a ball tensor, then a new tensor is generated using normal vectors and further decomposed into different geometric structures. A point cloud with different geometric structures is clustered based on the regional growth method. The normal vectors, line features, and surface features are computed and extracted from the point cloud acquired from the iPhone-based LiDAR sensor. In addition, no pre-training procedure is needed for the point cloud segmentation framework, since the features are directly computed from the data based on tensor computation. Experiment results show that normal vectors can be computed based on initial tensor encoding, and be further refined by the normal information-encoded tensor. Moreover, scattered points are filtered out, line features are extracted and represented, and surface features are successfully segmented. The proposed point cloud segmentation method can be applied to data acquired with low accuracy and high noise. Future research can use the parallel computing technique to segment a point cloud in real time, which is a desperate need when dealing with vast amounts of data, such as automatic driving. Another aspect that needs to be further explored is improving segmentation completeness and accuracy of every object in more complex scenes. [PERSON] performed the theory analysis, methodology, and contributed to drafting the manuscript. [PERSON] analyzed the data, design, and coding. [PERSON] collected the data, and performed the experiments. [PERSON] performed the literature reviews, improved the writing. [PERSON] revised the paper, provided the background knowledge and funding. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (NSFC) (No. 61875088, No. 62005128). Not applicable.