IJCAS

In the field of point cloud scene segmentation with deep learning, the ability of the network to extract spatial structure information limits the performance of semantic segmentation. This work proposes a novel framework named PointMS, which handles the semantic segmentation of point cloud scene, to solve the problem of missing local feature information due to the lack of spatial structure information on the training stage. The structure of framework utilizes spatial structure information of point cloud and balances the extraction of global feature and subtle feature when processing point cloud data. Firstly, a multi-scale combination module (SIFT-MS) is used to extract local features of different scales for enhancing the perception of local structure information at each point. Secondly, the process of feature transmission often leads to the loss of information, so a feature supplement module (FSM) is proposed to complete the information lost after feature transformation through the effective combination of global feature and subtle feature. This module integrates the features of different locations to supplement the information lost in feature conversion. The experimental results demonstrate that the proposed framework is efficient for semantic segmentation of S3DIS dataset. SIFT-MS module and FSM module can effectively improve the performance of the semantic segmentation model of point cloud.

Keywords: Deep learning, point cloud, semantic segmentation, spatial structure information.