Segmentation for point cloud based on local geometric feature enhancement

Deep neural networks designed for point cloud processing are challenging due to their regional irregularity and lack of order. Meanwhile, the core self-attention network of the Transformer has a very significant effect on natural language processing, and at the same time makes significant contributions to image analysis tasks such as image classification and object detection. The transformer illustrates great potential in image processing and also has inherent permutation invariance when processing a series of points. However, it has shortcomings in extracting the effective local features from input points which plays an important part of deep learning. To address the issue of disregarding local information, the process of extracting local features from the point cloud commences by solely focusing on the characteristics of points within the local neighborhood and gradually adding various relationships between points. Although the segmentation effect is enhanced to varying extents, it also leads to a considerable number of computations and neglects some contextual information at the local level. Given this, we propose to enhance the local feature input by incorporating the local geometric information of points, directly obtain relevant local feature information through mathematical calculation change, and no longer perform complex spatial change processing. The proposed local geometric feature enhancement network, as well as other popular approaches, were evaluated on a large public database S3DIS. The experimental result demonstrates that the proposed model achieves 77.4 and 70.0 under the mAcc and mIoU metrics that outperform other competing approaches by a significant margin.